Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation
Open accessPublished version of a preprint
Abstract
Structural resolution of protein interactions enables mechanistic and functional studies as well as interpretation of disease variants. However, structural data is still missing for most protein interactions because we lack computational and experimental tools at scale. This is particularly true for interactions mediated by short linear motifs occurring in disordered regions of proteins. We find that AlphaFold-Multimer predicts with high sensitivity but limited specificity structures of domain-motif interactions when using small protein fragments as input. Sensitivity decreased substantially when using long protein fragments or full length proteins. We delineated a protein fragmentation strategy particularly suited for the prediction of domain-motif interfaces and applied it to interactions between human proteins associated with neurodevelopmental disorders. This enabled the prediction of highly confident and likely disease-related novel interfaces, which we further experimentally corroborated for FBXO23-STX1B, STX1B-VAMP2, ESRRG-PSMC5, PEX3-PEX19, PEX3-PEX16, and SNRPB-GIGYF1 providing novel molecular insights for diverse biological processes. Our work highlights exciting perspectives, but also reveals clear limitations and the need for future developments to maximize the power of Alphafold-Multimer for interface predictions.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Yeast Pol δ remains bound to the replisome over multiple cycles of Okazaki fragment synthesis, an effect that depends on Pol32, the yeast counterpart of POLD3.
“In agreement with this model, single molecule experiments demonstrate that yeast Pol δ remains bound to the replisome over multiple cycles of Okazaki fragment synthesis, an effect that depends on Pol32, the yeast counterpart of POLD372.”
From Predictomes, a classifier-curated database of AlphaFold-modeled protein-protein interactions (2025) · cites this paper as [72]
The Results passages on PEX3, PEX19, and PEX16 discuss protein-interaction interfaces, not DNA replication. Searches for Pol32, POLD3, yeast Pol δ, and Okazaki fragments returned only unrelated passages from the Methods and Results; our copy does not address the claimed single-molecule finding. (LLM, conf=0.76)
Experimental — accuracy not yet measuredAF-M predicted the existence of a ternary complex of pol δ, RFC, and pol α that promotes Okazaki fragment synthesis in a processive assembly line, consistent with single molecule experiments.
“For example, AF-M predicted the existence of a ternary complex of pol δ, RFC, and pol α that promotes Okazaki fragment synthesis in a processive assembly line, consistent with single molecule experiments72.”
From Predictomes, a classifier-curated database of AlphaFold-modeled protein-protein interactions (2025) · cites this paper as [72]
The Introduction describes benchmarking AlphaFold-Multimer for protein interaction interfaces, and the Discussion describes other predicted complexes, but neither addresses the claimed pol δ–RFC–pol α complex. Searches for Okazaki fragment synthesis and for the processive assembly-line and single-molecule-experiment claims found no relevant passage in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · cited 2× · see citing works
“We fi rst inspected the monomeric structural models from the AlphaFold database (Varadi et al, 2022; Jumper et al, 2021) of both interacting proteins to determine the boundaries of their ordered and coiled-coil regions, which were also treated as “ ordered ” .”
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · cited 1× · see citing works
“For every NDD-NDD PPI we used Interactome3D (Mosca et al, 2013) and PDB database searches (https://www.rcsb.org/ (Berman et al, 2000)) to identify whether a structure already existed for this PPI.”
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249 · cited 1× · see citing works
“We used the Python libraries pandas (McKinney, 2010) for data analysis, and Matplotlib (Hunter, 2007) and seaborn (Waskom, 2021) for data visualization.”
MethodSciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430 · cited 1× · see citing works
“Titration curves were fi tted using the leastsq function from the scipy.optimize python package (Virtanen et al, 2020) using the model BRET = ((A/D) * BRETmax)/(BRET50 + (A/D)) described in (Drinovec et al, 2012), which assumes a 1:1 binding mode, to obtain estimates for the BRETmax and BRET50.”
A simple method for displaying the hydropathic character of a protein
Kyte, J., & Doolittle, R. F. (1982) · Journal of Molecular Biology · doi:10.1016/0022-2836(82)90515-0 · Cited by 22,694 · cited 2× · see citing works
“In cases where the motif adopts partial secondary structure, such as loop-helix-loop or loop-strand-loop, they are treated as helical or strand, respectively.”
Data Structures for Statistical Computing in Python
McKinney, W. (2010) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-92bf1922-00a · Cited by 11,511 · cited 1× · see citing works
“We used the Python libraries pandas (McKinney, 2010) for data analysis, and Matplotlib (Hunter, 2007) and seaborn (Waskom, 2021) for data visualization.”
MethodScikit-learn: Machine Learning in Python
(2011) · Journal of Machine Learning Research · doi:10.5555/1953048.2078195 · Cited by 8,539 · cited 1× · see citing works
“ROC and PR statistics were calculated using the Python package sci-kit learn (Pedregosa et al , 2012).”
MethodVaradi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1061 · Cited by 7,850 · cited 1× · see citing works
“Copyright (c) Schrodinger, LLC., for the visualization and superimposition of AlphaFold models.”
Pfam: The protein families database in 2021
Mistry, J., Chuguransky, S., Williams, L., Qureshi, M., Salazar, G. A., Sonnhammer, E. L. L., Tosatto, S. C. E., Paladin, L., Raj, S., Richardson, L. J., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa913 · Cited by 7,452 · cited 2× · see citing works
“The minimal binding domains of the ELM instances were detected in the interaction partner using Pfam HMMs (Mistry et al , 2021).”
seaborn: statistical data visualization
Waskom, M. (2021) · Journal of Open Source Software · doi:10.21105/joss.03021 · Cited by 7,310 · cited 1× · see citing works
“We used the Python libraries, Biopython (Cock et al, 2009) for sequence similarity computation, pandas (McKin-ney, 2010) for data analysis, and Matplotlib (Hunter, 2007) and seaborn (Waskom, 2021) for data visualization.”
Biopython: freely available python tools for computational molecular biology and bioinformatics
Cock, P. J. A., Antao, T., Chang, J. T., Chapman, B. A., Cox, C. J., Dalke, A., Friedberg, I., Hamelryck, T., Kauff, F., Wilczynski, B., et al. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp163 · Cited by 6,080 · cited 1× · see citing works
“We used the Python libraries, Biopython (Cock et al, 2009) for sequence similarity computation, pandas (McKin-ney, 2010) for data analysis, and Matplotlib (Hunter, 2007) and seaborn (Waskom, 2021) for data visualization.”
Protein complex prediction with AlphaFold-Multimer
Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al. (2021) · doi:10.1101/2021.10.04.463034 · Cited by 3,808 · cited 4× · see citing works
“…(PR) curves using the positive and random datasets (Fig 1E, Fig S2A,B, Table S2) show that the AF-derived metrics model confidence (preprint:Evans et al , 2021), average interface residue pLDDT, average motif interface residue pLDDT, pDockQ (Bryant et al , 2022), and iPAE (Teufel et al ,…”
BackgroundMethodInfluentialSzende, P., Bagnera, S., & Cole, D. (2000) · Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · doi:10.1201/b16062-3 · Cited by 3,481
SMART: recent updates, new developments and status in 2020
Letunic, I., Khedkar, S., & Bork, P. (2020) · Nucleic Acids Res. · doi:10.1093/nar/gkaa937 · Cited by 2,121 · cited 1× · see citing works
“We searched for functional annotations and existing structures for the monomers using the PDB, ProViz (Jehl et al, 2016), SMART (Letunic et al, 2021), and the scienti fi c literature to identify enzymatic pockets or binding interfaces for DNA, RNA, or metal ions.”
Harnessing protein folding neural networks for peptide–protein docking
Tsaban, T., Varga, J. K., Avraham, O., Ben-Aharon, Z., Khramushin, A., & Schueler-Furman, O. (2022) · Nature Communications · doi:10.1038/s41467-021-27838-9 · Cited by 1,205 · see citing works
Self-citationHarnessing protein folding neural networks for peptide-protein docking
(2021) · doi:10.1101/2021.08.01.454656 · Cited by 1,203 · cited 5× · see citing works
“Using minimal interacting regions of curated DMI structures from the ELM DB, we obtained sensitivity estimates for AF of around 80%, which is similar compared to previously published work (Tsaban et al , 2022; Johansson-Åkhe et al , 2021).”
BackgroundResultInfluentialSelf-citationA reference map of the human binary protein interactome
Luck, K., Kim, D.-K., Lambourne, L., Spirohn, K., Begg, B. E., Bian, W., Brignall, R., Cafarelli, T., Campos-Laborie, F. J., Charloteaux, B., et al. (2020) · Nature · doi:10.1038/s41586-020-2188-x · Cited by 1,197 · cited 3× · see citing works
“Using yeast two-hybrid (Y2H) as primary screening method, more than 60,000 binary interactions between human proteins have been identified (hereafter referred to as the HuRI dataset, (Luck et al , 2020)).”
BackgroundMethodSelf-citationImproved prediction of protein-protein interactions using AlphaFold2
Bryant, P., Pozzati, G., & Elofsson, A. (2021) · doi:10.1101/2021.09.15.460468 · Cited by 1,025 · cited 7× · see citing works
“Sensitivities of around 70% were reported using benchmark datasets of structurally resolved protein interactions originally developed to evaluate docking methods (Akdel et al, 2022; Bryant et al, 2022; Johansson-Åkhe et al, 2021; preprint:Evans et al, 2021).”
InfluentialDual proteome-scale networks reveal cell-specific remodeling of the human interactome
Huttlin, E. L., Bruckner, R. J., Navarrete-Perea, J., Cannon, J. R., Baltier, K., Gebreab, F., Gygi, M. P., Thornock, A., Zarraga, G., Tam, S., et al. (2020) · doi:10.1101/2020.01.19.905109 · Cited by 960 · cited 1× · see citing works
“Mass spectrometry-based approaches involving affinity purification or co-fractionation have identified around 150,000 co-complex associations between human proteins (Drew et al , 2017; Huttlin et al , 2021).”
BackgroundA structural biology community assessment of AlphaFold2 applications
Akdel, M., Pires, D. E. V., Pardo, E. P., Jänes, J., Zalevsky, A. O., Mészáros, B., Bryant, P., Good, L. L., Laskowski, R. A., Pozzati, G., et al. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00849-w · Cited by 675 · see citing works
Attributes of short linear motifs
Davey, N. E., Roey, K. V., Weatheritt, R. J., Toedt, G., Uyar, B., Altenberg, B., Budd, A., Diella, F., Dinkel, H., & Gibson, T. J. (2011) · Molecular BioSystems · doi:10.1039/c1mb05231d · Cited by 626 · cited 3× · see citing works
“Motifs are short stretches of amino acids in disordered regions of proteins that usually adopt a more rigid structure upon binding to folded domains in interaction partners (Davey et al , 2012).”
BackgroundSelf-citationA Million Peptide Motifs for the Molecular Biologist
Tompa, P., Davey, N. E., Gibson, T. J., & Babu, M. M. (2014) · Molecular Cell · doi:10.1016/j.molcel.2014.05.032 · Cited by 515 · cited 2× · see citing works
“This is likely particularly true for motif-mediated PPIs, which are anticipated to number in the hundreds of thousands or millions (Tompa et al, 2014).”
Self-citationInteractome3D: adding structural details to protein networks
Mosca, R., Céol, A., & Aloy, P. (2013) · Nature Methods · doi:10.1038/nmeth.2289 · Cited by 479 · cited 2× · see citing works
“For every NDD-NDD PPI we used Interactome3D (Mosca et al, 2013) and PDB database searches (https://www.rcsb.org/ (Berman et al, 2000)) to identify whether a structure already existed for this PPI.”
Durocher, D., Taylor, I., Sarbassova, D., Haire, L., Westcott, S., Jackson, S., Smerdon, S., & Yaffe, M. (2000) · Molecules and Cells · doi:10.1016/s1097-2765(00)00114-3 · Cited by 475 · cited 1× · see citing works
“These short regions were predicted to bind to a pocket on the FHA domain that is known to bind phosphorylated threonines (Durocher et al , 2000), which led us to conclude that these predictions were likely wrong.”
BackgroundDockQ: A Quality Measure for Protein-Protein Docking Models
Basu, S., & Wallner, B. (2016) · PLOS ONE · doi:10.1371/journal.pone.0161879 · Cited by 460 · cited 1× · see citing works
“The calculation of DockQ scores of AlphaFold models was done in reference to their solved structures using the code available on the github repository of DockQ (https://github.com/bjornwallner/ DockQ, (Basu and Wallner, 2016).”
An experimentally derived confidence score for binary protein-protein interactions
Falter‐Braun, P., Tasan, M., Dreze, M., Barrios‐Rodiles, M., Lemmens, I. H., Yu, H., Sahalie, J. M., Murray, R. R., Roncari, L., de Smet, A.-S., et al. (2008) · Nature Methods · doi:10.1038/nmeth.1281 · Cited by 444 · see citing works
The IntAct database: efficient access to fine-grained molecular interaction data
del Toro, N., Shrivastava, A., Ragueneau, E., Meldal, B., Combe, C., Barrera, E., Perfetto, L., How, K., Ratan, P., Shirodkar, G., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1006 · Cited by 405 · see citing works
Can AlphaFold2 predict the impact of missense mutations on structure?
Buel, G. R., & Walters, K. J. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-021-00714-2 · Cited by 332 · cited 1× · see citing works
“This points to an important limitation in AF ’ s ability to predict binding speci fi cities and is in line with previous reports on AF ’ s inability to predict the effect of mutations (Buel and Walters, 2022).”
Two types of amino acid substitutions in protein evolution
Miyata, T., Miyazawa, S., & Yasunaga, T. (1979) · Journal of Molecular Evolution · doi:10.1007/bf01732340 · Cited by 321 · cited 1× · see citing works
“To mutate the key residues to the ones with opposite physico-chemical properties, we substituted one or two key residues with the ones that are of the largest Miyata distance (Miyata et al , 1979) (Table S2).”
Background3did: a catalog of domain-based interactions of known three-dimensional structure
Mosca, R., Ceol, A. G. M., Stein, A., Olivella, R., & Aloy, P. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt887 · Cited by 307 · cited 5× · see citing works
“Such interface type collections can be used to predict occurrences of known interface types in protein interactions (Weatheritt et al, 2012; Mosca et al, 2013).”
InfluentialJilani, A., Ramotar, D., Slack, C., Ong, C., Yang, X. M., Scherer, S. W., & Lasko, D. D. (1999) · Journal of Biological Chemistry · doi:10.1074/jbc.274.34.24176 · Cited by 293 · cited 1× · see citing works
“PNKP has dual phosphatase and kinase activity that it uses to modify broken DNA strands preparing them for repair (Aceytuno et al , 2017; Jilani et al , 1999).”
BackgroundThe peroxisome: an update on mysteries 2.0
Islinger, M., Völkl, A., Fahimi, H. D., & Schrader, M. (2018) · Histochemistry and Cell Biology · doi:10.1007/s00418-018-1722-5 · Cited by 287 · cited 1× · see citing works
“Peroxisomes mediate these functions via peroxisomal membrane proteins (PMPs) and proteins in the peroxisomal matrix (Islinger et al , 2018).”
BackgroundThe deubiquitinating enzyme BAP1 regulates cell growth via interaction with HCF-1
Machida, Y. J., Machida, Y., Vashisht, A. A., Wohlschlegel, J. A., & Dutta, A. (2009) · Journal of Biological Chemistry · doi:10.1074/jbc.m109.046755 · Cited by 274 · cited 1× · see citing works
“HCFC1 is a transcription factor that associates with other transcription factors (Lu et al , 1997), splice factors (Ajuh et al , 2002), and cell cycle regulators (Freiman & Herr, 1997; Machida et al , 2009).”
BackgroundTowards a structurally resolved human protein interaction network
Burke, D. F., Bryant, P., Barrio-Hernandez, I., Memon, D., Pozzati, G., Shenoy, A., Zhu, W., Dunham, A. S., Albanese, P., Keller, A., et al. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00910-8 · Cited by 269 · cited 6× · see citing works
“While in-cell XL-MS is a very elegant approach to obtain experimental information on PPI interfaces in unperturbed settings, it is still a method that is only accessible to few experts in the fi eld.”
InfluentialDana, J., Gutmanas, A., Tyagi, N., Qi, G., O'Donovan, C., Martin, M., & Velankar, S. (2018) · Nucleic Acids Res. · doi:10.1093/nar/gky1114 · Cited by 258 · cited 1× · see citing works
“To verify that they indeed do not have structural homologues in the PDB, we both used the SIFTS mapping (Dana et al , 2019) between the Pfam domain in ELM and the PDB and also looked at the ELM classes that were listed as homologs on the ELM website.”
The Eukaryotic Linear Motif resource: 2022 release
Kumar, M., Michael, S., Alvarado-Valverde, J., Mészáros, B., Sámano-Sánchez, H., Zeke, A., Dobson, L., Lazar, T., Örd, M., Nagpal, A., et al. (2021) · Nucleic Acids Res. · doi:10.1093/nar/gkab975 · Cited by 227 · cited 2× · see citing works
“As some domain types can bind to motifs from distinct ELM classes, we manually checked that the randomized pairings did not coincide with actual domain-motif interface types (Dataset EV2).”
Self-citationDrew, K., Lee, C., Huizar, R. L., Tu, F., Borgeson, B., McWhite, C. D., Ma, Y., Wallingford, J. B., & Marcotte, E. M. (2017) · Molecular Systems Biology · doi:10.15252/msb.20167490 · Cited by 191 · cited 1× · see citing works
“The last decade has seen tremendous progress in the systematic mapping of human protein interactions enabling gene function prediction and the study of genotype-to-phenotype relationships (Luck et al, 2020; Drew et al, 2017; Huttlin et al, 2021).”
Self-citationLu, R., Yang, P., O'Hare, P., & Misra, V. (1997) · Molecular and Cellular Biology · doi:10.1128/mcb.17.9.5117 · Cited by 182 · cited 1× · see citing works
“HCFC1 is a transcription factor that associates with other transcription factors (Lu et al , 1997), splice factors (Ajuh et al , 2002), and cell cycle regulators (Freiman & Herr, 1997; Machida et al , 2009).”
BackgroundThe Deciphering Developmental Disorders (DDD) study
FIRTH, H. V., WRIGHT, C. F., & Study, F. T. D. (2011) · Developmental Medicine & Child Neurology · doi:10.1111/j.1469-8749.2011.04032.x · Cited by 180 · cited 1× · see citing works
“A list of NDD genes was assembled using whole exome and whole genome sequencing studies of cohorts of NDD patients from Gene4Denovo (Zhao et al, 2020) and Deciphering Developmental Disorders (DDD) study (Firth et al, 2011), respectively.”
TRIM37 is a new histone H2A ubiquitin ligase and breast cancer oncoprotein
Bhatnagar, S., Gazin, C., Chamberlain, L., Ou, J., Zhu, X., Tushir, J. S., Virbasius, C.-M. A., Lin, L., Zhu, L. J., Wajapeyee, N., et al. (2014) · Nature · doi:10.1038/nature13955 · Cited by 177 · cited 1× · see citing works
“TRIM37 is a RING domain-containing E3 ligase known to regulate centriole reduplication, transcriptional repression, and peroxisome import (Kallijärvi et al , 2005; Bhatnagar et al , 2014; Wang et al , 2017).”
BackgroundInteractions by disorder – A matter of context
Bugge, K., Brakti, I., Fernandes, C. B., Dreier, J. E., Lundsgaard, J. E., Olsen, J. G., Skriver, K., & Kragelund, B. B. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.00110 · Cited by 155 · cited 1× · see citing works
“Various studies have highlighted that fl anking sequences of motifs can in fl uence binding af fi nities and speci fi cities (Luck et al, 2012; Bugge et al, 2020).”
Matsuzaki, T., & Fujiki, Y. (2008) · Journal of Cell Biology · doi:10.1083/jcb.200806062 · Cited by 152 · cited 22× · see citing works
“The following proteins were also tested as C-terminal fusions: CSNK2B-NL, ESRRG-NL, CUL3-NL, PEX3-NL, PEX19-NL, PSMC5-NL, PEX3-mCit, PEX19-mCit, PEX16-mCit, RORB-mCit, ESRRG-mCit, PAX6-mCit, CSNK2B-mCit, PSMC5-mCit, KCTD7-mCit (Dataset EV12).”
InfluentialMotif switches: decision-making in cell regulation
Roey, K. V., Gibson, T. J., & Davey, N. E. (2012) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2012.03.004 · Cited by 150 · cited 1× · see citing works
“Motif-mediated interactions are of moderate binding af fi nity and thus, are particularly suited to mediate dynamic cell regulatory and signaling events (Van Roey et al, 2012).”
Self-citationImport of peroxisomal membrane proteins: the interplay of Pex3p- and Pex19p-mediated interactions.
Fujiki, Y., Matsuzono, Y., Matsuzaki, T., & Fransen, M. (2006) · Biochimica et Biophysica Acta · doi:10.1016/j.bbamcr.2006.09.030 · Cited by 150 · cited 18× · see citing works
“The following proteins were also tested as C-terminal fusions: CSNK2B-NL, ESRRG-NL, CUL3-NL, PEX3-NL, PEX19-NL, PSMC5-NL, PEX3-mCit, PEX19-mCit, PEX16-mCit, RORB-mCit, ESRRG-mCit, PAX6-mCit, CSNK2B-mCit, PSMC5-mCit, KCTD7-mCit (Dataset EV12).”
InfluentialProtein complexes in cells by AI‐assisted structural proteomics
(2023) · doi:10.15252/msb.202311544 · Cited by 147 · see citing works
Viral mimicry: common mode of association with HCF by VP16 and the cellular protein LZIP.Preprint
Freiman, R., & Herr, W. (1997) · Genes & Development · doi:10.1101/gad.11.23.3122 · Cited by 144 · cited 1× · see citing works
“HCFC1 is a transcription factor that associates with other transcription factors (Lu et al, 1997), splice factors (Ajuh et al, 2002), and cell cycle regulators (Freiman and Herr, 1997; Machida et al, 2009).”
Structure of the spliceosomal U4 snRNP core domain and its implication for snRNP biogenesis
Leung, A., Nagai, K., & Li, J. (2011) · Nature · doi:10.1038/nature09956 · Cited by 132 · cited 1× · see citing works
“A more detailed description of the curation procedure and information on the pairs will be soon published elsewhere (Geist et al, in preparation).”
Luck, K., Charbonnier, S., & Travé, G. (2012) · FEBS Letters · doi:10.1016/j.febslet.2012.03.056 · Cited by 127 · cited 1× · see citing works
“Various studies have highlighted that fl anking sequences of motifs can in fl uence binding af fi nities and speci fi cities (Luck et al, 2012; Bugge et al, 2020).”
Self-citationThe ORFeome Collaboration: a genome-scale human ORF-clone resource
Wiemann, S., Jöcker, A., Wellenreuther, R., Heil, O., Hotz‐Wagenblatt, A., Pennacchio, C., Hu, Y., Hunter, P. D., Park, J., Seiler, C., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3776 · Cited by 117 · cited 13× · see citing works
“For donor saturation experiments various donor DNA amounts (1, 2, 4 and 8 ng) encoding NL-fused proteins were co-transfected with increasing amounts of acceptor DNA (12.5, 25, 50, 100, 200 ng) encoding mCitrine-fused proteins.”
InfluentialKrystkowiak, I., & Davey, N. E. (2017) · Nucleic Acids Res. · doi:10.1093/nar/gkx238 · Cited by 116 · cited 1× · see citing works
“Key conserved residues of the motifs in the DMI benchmark dataset were identi fi ed computationally using the regular expression of the corresponding ELM class in the ELM DB and SLiMSearch (Krystkowiak and Davey, 2017).”
ClinVar Miner: Demonstrating Utility of a Web-Based Tool for Viewing and Filtering ClinVar Data
Henrie, A., Hemphill, S. E., Ruiz-Schultz, N., Cushman, B. J., DiStefano, M. T., Azzariti, D. R., Harrison, S., Rehm, H., & Eilbeck, K. (2018) · Human Mutation · doi:10.1002/humu.23555 · Cited by 111 · cited 5× · see citing works
“Unfortunately, no assertion criteria for the annotation of this mutation to be pathogenic is provided by ClinVar meaning that the mutation is either not pathogenic after all or its pathogenicity is mediated via another perturbed function not tested in this study.”
InfluentialFrom interaction networks to interfaces: Scanning intrinsically disordered regions using AlphaFold2
Bret, H., Andreani, J., & Guerois, R. (2023) · doi:10.1101/2023.05.25.542287 · Cited by 103 · cited 2× · see citing works
“However, longer disordered fragments or fragments containing ordered and large disordered regions generally decrease AF prediction accuracies as also reported in a recent preprint (preprint:Bret et al , 2023).”
BackgroundFrom interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2
Bret, H., Gao, J., Zea, D. J., Andreani, J., & Guerois, R. (2024) · Nature Communications · doi:10.1038/s41467-023-44288-7 · Cited by 103 · see citing works
TRIM37 defective in mulibrey nanism is a novel RING finger ubiquitin E3 ligase.
Kallijärvi, J., Lahtinen, U., Hämäläinen, R., Lipsanen-Nyman, M., Palvimo, J., & Lehesjoki, A. (2005) · Experimental Cell Research · doi:10.1016/j.yexcr.2005.04.001 · Cited by 103 · cited 1× · see citing works
“TRIM37 is a RING domain-containing E3 ligase known to regulate centriole reduplication, transcriptional repression, and peroxisome import (Kallijärvi et al , 2005; Bhatnagar et al , 2014; Wang et al , 2017).”
BackgroundRanking Peptide Binders by Affinity with AlphaFold**
Chang, L., & Perez, A. (2023) · Angewandte Chemie International Edition · doi:10.1002/anie.202213362 · Cited by 100 · see citing works
Ernst, J., & Brunger, A. (2003) · Journal of Biological Chemistry · doi:10.1074/jbc.m211889200 · Cited by 97 · cited 1× · see citing works
“The complex between STX1A, VAMP2, and SNAP25 has been solved structurally and shows a 4-helix bundle with a 1:1:2 stoichiometry, respectively (PDB:1N7S (Ernst & Brunger, 2003)).”
BackgroundZhu, Y., Wang, W. L., Yu, D., Ouyang, Q., Lu, Y., & Mao, Y. (2018) · Nature Communications · doi:10.1038/s41467-018-03785-w · Cited by 92 · cited 1× · see citing works
“These contacts are also observed in solved structures of the proteasome (i.e. PDB:5VFT (Zhu et al , 2018)).”
BackgroundDynamic interaction of CD2 with the GYF and the SH3 domain of compartmentalized effector molecules
Freund, C., Kühne, R., Yang, H.-L., Park, S., Reinherz, E., & Wagner, G. (2002) · EMBO Journal · doi:10.1093/emboj/cdf602 · Cited by 90 · cited 2× · see citing works
“Interestingly, this hydrophobic pocket does not exist in the previously solved structure of the GYF domain of CDBP2 binding to a proline-rich peptide that is fl anked by positively charged residues establishing important contacts with the domain (PDB:1L2Z, (Freund et al, 2002)).”
TRIM37, a novel E3 ligase for PEX5-mediated peroxisomal matrix protein import
Wang, W., Xia, Z.-J., Farré, J.-C., & Subramani, S. (2017) · Journal of Cell Biology · doi:10.1083/jcb.201611170 · Cited by 78 · cited 1× · see citing works
“TRIM37 is a RING domain-containing E3 ligase known to regulate centriole reduplication, transcriptional repression, and peroxisome import (Kallijärvi et al , 2005; Bhatnagar et al , 2014; Wang et al , 2017).”
BackgroundMaximizing binary interactome mapping with a minimal number of assays
Choi, S. G., Olivet, J., Cassonnet, P., Vidalain, P., Luck, K., Lambourne, L., Spirohn, K., Lemmens, I. H., Santos, M. D., Demeret, C., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11809-2 · Cited by 76 · cited 1× · see citing works
“This retest rate is actually higher compared to retest rates of gold standard PPI datasets used in the past to benchmark various binary PPI assays including this BRET assay, attesting the overall detectability of PPIs from HuRI (Braun et al, 2009; Trepte et al, 2018; Choi et al, 2019).”
Self-citationSystematic discovery of mutation-directed neo-protein-protein interactions in cancer
Mo, X., Niu, Q., Ivanov, A. A., Tsang, Y. H., Tang, C., Shu, C., Li, Q., Qian, K., Wahafu, A., Doyle, S. P., et al. (2022) · Cell · doi:10.1016/j.cell.2022.04.014 · Cited by 72 · see citing works
Jehl, P., Manguy, J., Shields, D. C., Higgins, D. G., & Davey, N. E. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw265 · Cited by 71 · cited 1× · see citing works
“We searched for functional annotations and existing structures for the monomers using the PDB, ProViz (Jehl et al , 2016) , SMART (Letunic et al , 2021), and the scientific literature to identify enzymatic pockets or binding interfaces for DNA, RNA, or metal ions.”
MethodGene4Denovo: an integrated database and analytic platform for de novo mutations in humans
Zhao, G., Li, K., Li, B., Wang, Z., Fang, Z., Wang, X., Zhang, Y., Luo, T., Zhou, Q., Wang, L., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz923 · Cited by 64 · cited 2× · see citing works
“From Gene4Denovo, we selected genes linked to autism-spectrum disorders (ASD), intellectual disability (ID), epilepsy (EE), undiagnosed developmental disorders (UDD) and NDDs in general.”
MethodSystematic discovery of mutation-directed neo-protein-protein interactions in cancer
Mo, X.-L., Niu, Q.-K., Ivanov, A., Tsang, Y., Tang, C., Shu, C., Li, Q.-J., Qian, K., Wahafu, A., Doyle, S. P., et al. (2021) · bioRxiv · doi:10.1101/2021.10.03.462422 · Cited by 63
Schmidt, F., Treiber, N., Zocher, G., Bjelić, S., Steinmetz, M., Kalbacher, H., Stehle, T., & Dodt, G. (2010) · Journal of Biological Chemistry · doi:10.1074/jbc.m110.138503 · Cited by 60 · cited 1× · see citing works
“…two distinct interfaces between PEX19 and PEX3, one of which, believed to be the dominant one, was crystallized and is described in the ELM DB as an interaction between an N-terminal motif in PEX19 that binds to the cytosolic alpha-helical domain of PEX3 (PDB:3MK4, (Schmidt et al , 2010)).”
BackgroundStructural and functional characterization of the PNKP–XRCC4–LigIV DNA repair complex
Aceytuno, R. D., Piett, C. G., Havali-Shahriari, Z., Edwards, R. A., Rey, M., Ye, R., Javed, F., Fang, S., Mani, R. S., Weinfeld, M., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx275 · Cited by 51 · cited 1× · see citing works
“PNKP has dual phosphatase and kinase activity that it uses to modify broken DNA strands preparing them for repair (Aceytuno et al , 2017; Jilani et al , 1999).”
BackgroundMolecular insights into peroxisome homeostasis and peroxisome biogenesis disorders
Fujiki, Y., Okumoto, K., Honsho, M., & Abe, Y. (2022) · Biochimica et Biophysica Acta (BBA) - Molecular Cell Research · doi:10.1016/j.bbamcr.2022.119330 · Cited by 47 · cited 1× · see citing works
“Mutations in any of the three PEX proteins are associated with severe developmental phenotypes referred to as peroxisome biogenesis disorders (Fujiki et al, 2022).”
Parsons, J. L., Khoronenkova, S. V., Dianova, I. I., Ternette, N., Kessler, B. M., Datta, P. K., & Dianov, G. L. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks909 · Cited by 45 · cited 1× · see citing works
“In particular, the motif ranging from 110-116 attracted our attention because phosphorylation of S114 has been reported to stabilize PNKP protein levels (Parsons et al , 2012).”
BackgroundEbersberger, S., Hipp, C., Mulorz, M. M., Buchbender, A., Hubrich, D., Kang, H.-S., Martínez‐Lumbreras, S., Kristofori, P., Sutandy, F. X. R., Allcca, L. L., et al. (2023) · Molecular Cell · doi:10.1016/j.molcel.2023.07.002 · Cited by 42 · cited 1× · see citing works
“The BRET assay used here has been shown in previous studies to be sensitive enough to quantify weakening of binding introduced by point mutations and to detect motif-mediated PPIs (Ebersberger et al , 2023; Trepte et al , 2018; Mo et al , 2022).”
MethodSelf-citationTrepte, P., Kruse, S., Kostova, S., Hoffmann, S., Buntru, A., Tempelmeier, A., Secker, C., Diez, L., Schulz, A., Klockmeier, K., et al. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178071 · Cited by 39 · cited 7× · see citing works
“This retest rate is actually higher compared to retest rates of gold standard PPI datasets used in the past to benchmark various binary PPI assays including this BRET assay, attesting the overall detectability of PPIs from HuRI (Braun et al, 2009; Trepte et al, 2018; Choi et al, 2019).”
InfluentialiELM—a web server to explore short linear motif-mediated interactions
Weatheritt, R. J., Jehl, P., Dinkel, H., & Gibson, T. (2012) · Nucleic Acids Res. · doi:10.1093/nar/gks444 · Cited by 38 · cited 1× · see citing works
“Such interface type collections can be used to predict occurrences of known interface types in protein interactions (Weatheritt et al, 2012; Mosca et al, 2013).”
Self-citationMourão, A., Bonnal, S., Soni, K., Warner, L., Bordonné, R., Valcárcel, J., & Sattler, M. (2016) · eLife · doi:10.7554/elife.14707 · Cited by 37 · cited 1× · see citing works
“This disordered region is particularly rich in prolines and glycines and has been shown previously to bind to the OCRE domain of RBM5 (Mourão et al , 2016).”
BackgroundInParanoiDB 9: Ortholog groups for protein domains and full-length proteins.
Persson, E., & Sonnhammer, E. L. L. (2023) · Journal of Molecular Biology · doi:10.1016/j.jmb.2023.168001 · Cited by 34 · cited 1× · see citing works
“To select a subset of these motif classes, where we can do experimental testing, we also used the InParanoid resource (Persson & Sonnhammer, 2023) to map ELM instances where both proteins are from mouse to their human orthologs.”
Drinovec, L., Kubale, V., Larsen, J. N., & Vrecl, M. (2012) · Front. Endocrin. · doi:10.3389/fendo.2012.00104 · Cited by 34 · cited 1× · see citing works
“Titration curves were fi tted using the leastsq function from the scipy.optimize python package (Virtanen et al, 2020) using the model BRET = ((A/D) * BRETmax)/(BRET50 + (A/D)) described in (Drinovec et al, 2012), which assumes a 1:1 binding mode, to obtain estimates for the BRETmax and BRET50.”
InterPepRank: Assessment of Docked Peptide Conformations by a Deep Graph Network
Johansson-Åkhe, I., Mirabello, C., & Wallner, B. (2021) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2021.763102 · Cited by 28 · cited 4× · see citing works
“Using minimal interacting regions for interface prediction we reached sensitivities of up to 80% similar to previously published work (Tsaban et al, 2022; Johansson-Åkhe et al, 2021).”
InfluentialDeorphanizing Peptides Using Structure Prediction
Teufel, F., Refsgaard, J. C., Kasimova, M. A., Deibler, K., Madsen, C. T., Stahlhut, C., Grønborg, M., Winther, O., & Madsen, D. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00378 · Cited by 27 · cited 3× · see citing works
“…fi dence (preprint:Evans et al, 2021), average interface residue pLDDT, average motif interface residue pLDDT, pDockQ (Bryant et al, 2022), and iPAE (Teufel et al, 2023) discriminated well between both reference datasets when rando-mizing domain-motif pairs or introducing two motif mutations (max…”
Limits and potential of combined folding and docking
Pozzati, G., Zhu, W., Bassot, C., Lamb, J., Kundrotas, P., & Elofsson, A. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab760 · Cited by 25 · cited 1× · see citing works
“We also evaluated whether the top 5 reported models by AF tend to be more similar to each other when corresponding to a correct structural model (Pozzati et al, 2022) and found that this feature has moderate predictive power (Appendix Fig.”
Ferry, C., Gianni, M., Lalevée, S., Bruck, N., Plassat, J.-L., Raska, I., Garattini, E., & Rochette-Egly, C. (2009) · Journal of Biological Chemistry · doi:10.1074/jbc.m808815200 · Cited by 24 · cited 1× · see citing works
“It has been shown previously that PSMC5 is recruited to promoters of nucleic acid receptor target genes and to interact with the nucleic acid receptor RARA thereby regulating transcription (Ferry et al , 2009).”
BackgroundAjuh, P., Chusainow, J., Ryder, U., & Lamond, A. (2002) · EMBO Journal · doi:10.1093/emboj/cdf652 · Cited by 21 · cited 1× · see citing works
“HCFC1 is a transcription factor that associates with other transcription factors (Lu et al, 1997), splice factors (Ajuh et al, 2002), and cell cycle regulators (Freiman and Herr, 1997; Machida et al, 2009).”
Ranking Peptide Binders by Affinity with AlphaFold**
Chang, L., & Pérez, A. (2022) · Angewandte Chemie · doi:10.1002/ange.202213362 · Cited by 15 · see citing works
AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor
Trepte, P., Secker, C., Kostova, S., Maseko, S. B., Choi, S.-G., Blavier, J., Minia, I., Ramos, E. S., Cassonnet, P., Golusik, S., et al. (2023) · bioRxiv · doi:10.1101/2023.06.14.544560 · Cited by 10 · cited 2×
“Further increases in retest rate can be obtained by testing all possible combinations of N-and C-terminal fusions to the NanoLuc luciferase and mCitrine (preprint:Trepte et al , 2021; preprint:Trepte et al , 2023).”
BackgroundMolecular basis for GIGYF–TNRC6 complex assembly
Sobti, M., Mead, B. J., Stewart, A. G., Igreja, C., & Christie, M. (2023) · RNA · doi:10.1261/rna.079596.123 · Cited by 9 · see citing works
Bronkhorst, A. W., Lee, C. Y., Möckel, M. M., Ruegenberg, S., Domingues, A. M. D. J., Sadouki, S., Piccinno, R., Sumiyoshi, T., Siomi, M. C., Stelzl, L. S., et al. (2023) · The EMBO Journal · doi:10.15252/embj.2023114072 · Cited by 8 · see citing works
Self-citationMishra, M., Jiang, H., & Wei, Q. (2023) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2023.175873 · Cited by 5 · cited 1× · see citing works
“AF interface predictions for individual PPIs have occasionally been experimentally corroborated (Mishra et al, 2023; Bronkhorst et al, 2023).”
A quantitative mapping approach to identify direct interactions within complexomesPreprint
Trepte, P., Secker, C., Choi, S. G., Olivet, J., Ramos, E. S., Cassonnet, P., Golusik, S., Zenkner, M., Beetz, S., Sperling, M., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.25.457734 · Cited by 2 · cited 2× · see citing works
“Further increases in retest rate can be obtained by testing all possible combinations of N-and C-terminal fusions to the NanoLuc luciferase and mCitrine (preprint:Trepte et al , 2021; preprint:Trepte et al , 2023).”
Background
Flagged first, then by citations
16 of the 101 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
ELM—the Eukaryotic Linear Motif resource—2024 update
Kumar, M., Michael, S., Alvarado-Valverde, J., Zeke, A., Lázár, T., Glavina, J., Nagy‐Kanta, E., Donagh, J. M., Kálmán, Z. E., Pascarelli, S., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1058 · Cited by 185 · see citing works
Self-citationPredictomes: A classifier-curated database of AlphaFold-modeled protein-protein interactions
(2024) · doi:10.1101/2024.04.09.588596 · Cited by 121 · see citing works
“Many of these can probably be eliminated by increased sampling 67 or segmenting proteins 31,68 .”
BackgroundPredictomes, a classifier-curated database of AlphaFold-modeled protein-protein interactions
Schmid, E. W., & Walter, J. C. (2025) · Molecular Cell · doi:10.1016/j.molcel.2025.01.034 · Cited by 120 · see citing works
“Some of these can probably be eliminated by folding pairs in all 5 models, increased sampling 74 , or segmenting proteins 75,76 .”
Masters, M. R., Mahmoud, A. H., & Lill, M. A. (2025) · Nature Communications · doi:10.1038/s41467-025-63947-5 · Cited by 119 · see citing works
Rēs ipSAE loquuntur : What’s wrong with AlphaFold’s ipTM score and how to fix itPreprint
Dunbrack, R. L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.10.637595 · Cited by 98 · see citing works
“In a comprehensive study, Lee et al. found shorter fragments of peptides binding to protein domains often scored better than longer fragments or full-length proteins (15).”
From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2
Bret, H., Gao, J., Zea, D. J., Andreani, J., & Guerois, R. (2024) · Nature Communications · doi:10.1038/s41467-023-44288-7 · Cited by 103 · see citing works
“AF2sc values indicate the best AF2 con fi dencescore values obtained foreach complex. correctly delineate and cut the intrinsically disordered regions have also proven useful in increasing the success rate of predictions 46 .”
InfluentialOmidi, A., Møller, M. H., Malhis, N., Bui, J. M., & Gsponer, J. A. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2406407121 · Cited by 103 · see citing works
Orand, T., & Jensen, M. R. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102958 · Cited by 33 · see citing works
Analysing protein complexes in plant science: insights and limitation with AlphaFold 3
Lin, P.-Y., Huang, S.-C., Chen, K.-L., Huang, Y., Liao, C.-Y., Lin, G.-J., Lee, H., & Chen, P. (2025) · Botanical studies · doi:10.1186/s40529-025-00462-2 · Cited by 34 · see citing works
“This phenomenon has been reported in single-structure prediction, where small protein fragments obtain about 5% higher sensitivity and specificity than full-length proteins (Lee et al. 2024).”
Decoding the functional impact of the cancer genome through protein–protein interactions
Fu, H., Mo, X., & Ivanov, A. A. (2025) · Nature reviews. Cancer · doi:10.1038/s41568-024-00784-6 · Cited by 21 · see citing works
“The rapidly evolving AI tools may help to address this challenge 153,272 .”
actifpTM: a refined confidence metric of AlphaFold2 predictions involving flexible regions
Varga, J. K., Ovchinnikov, S., & Schueler‐Furman, O. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf107 · Cited by 56 · see citing works
“The peptides were predicted with three different lengths: only the motif region, adding flanking regions of same length as the motif, or adding flanking regions twice the motif length (Lee et al. 2024).”
InfluentialSelf-citationThe roles of intrinsically disordered proteins in neurodegeneration
Utami, K. H., Morimoto, S., Mitsukura, Y., & Okano, H. (2025) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2025.130772 · Cited by 34 · see citing works
Emerging frontiers in protein structure prediction following the AlphaFold revolution
Rennie, M. L., & Oliver, M. R. (2025) · Journal of The Royal Society Interface · doi:10.1098/rsif.2024.0886 · Cited by 27 · see citing works
Peptide design to control protein–protein interactions
Wier, S. P. V., & Beekman, A. M. (2025) · Chemical Society Reviews · doi:10.1039/d4cs00243a · Cited by 32 · see citing works
AlphaFold Database expands to proteome-scale quaternary structuresPreprint
Han, Y.-O., Tsenkov, M. I., Venanzi, N. A. E., Cha, S., Patel, N., Nair, S., Abbara, R., Bertoni, D., Chacon, A., Dietrich, N., et al. (2026) · bioRxiv · doi:10.64898/2026.03.27.714458 · Cited by 20 · see citing works
“Although complex prediction has not yet reached experimental accuracy and still exhibits biases 27,28 , these models can generate strong initial hypotheses; however, large-scale dry-lab structural characterisation of protein interactions remains intractable due to experimental cost and time.”
Multimeric protein interaction and complex prediction: Structure, dynamics and function
Lu, D., Yu, S., Huang, Y., & Gong, X. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.05.009 · Cited by 20 · see citing works
“In this study, researchers employed AlphaFold to generate predicted protein complex structures and integrated these predictions with bio-physical experiments, such as surface plasmon resonance and yeast two-hybrid, to validate the interactions [189].”
Simonetti, F., Zhong, W., Hutten, S., Uliana, F., Schifferer, M., Rezaei, A., Ramirez, L. M., Hochmair, J., Sankar, R., Gopalan, A. B., et al. (2025) · The EMBO Journal · doi:10.1038/s44318-025-00590-2 · Cited by 22 · see citing works
“AlphaFold prediction of crosslinking: AlphaFold2 multimer (version 2.3) (Evans et al, 2021; Jumper et al, 2021) to model the interaction between TDP-43 and TAU using a fragmented approach (Lee et al, 2024).”
InfluentialBias in, bias out – AlphaFold-Multimer and the structural complexity of protein interfaces
Strom, J. M., & Luck, K. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103002 · Cited by 22 · see citing works
Self-citationHigh-throughput discovery of inhibitory protein fragments with AlphaFold
Savinov, A., Swanson, S., Keating, A. E., & Li, G.-W. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2322412122 · Cited by 18 · see citing works
“We analyzed the predicted structural features of C-terminal FtsZ fragment interactions with divisome proteins.”
InfluentialProspects for synthetic biology in 21st century agriculture
Ye, X., Qin, K., Fernie, A. R., & Zhang, Y. (2024) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2024.12.016 · Cited by 22 · see citing works
A Model-Centric Review of Deep Learning for Protein DesignPreprint
Kyro, G. W., Qiu, T., & Batista, V. S. (2025) · arXiv.org · doi:10.48550/arxiv.2502.19173 · Cited by 16 · see citing works
Mehra, C., Alvarado-Valverde, J., Matias, A. M. N., Torelli, F., Medeiros, T., Straub, J., Asaki, J. D., Bradley, P. J., Luck, K., Lawo, S., et al. (2025) · Nature Microbiology · doi:10.1038/s41564-025-02193-3 · Cited by 9 · see citing works
“Candidates with model confidence ≥ 0.7 and average motif pLDDT ≥ 70 were classified as high confidence 41 .”
InfluentialSelf-citationDo Deep Learning Models for Co-Folding Learn the Physics of Protein-Ligand Interactions?Preprint
Masters, M., Mahmoud, A., & Lill, M. A. (2024) · bioRxiv · doi:10.1101/2024.06.03.597219 · Cited by 11 · see citing works
“…spawning hundreds of subsequent papers including high-throughput structure prediction [3, 4, 5], ensemble sampling [6, 7, 8], molecular docking [9, 10, 11, 12], stability prediction [13, 14, 15, 16], further method development [17, 18, 19, 20], and a host of other applied studies [21, 22, 23, 24].”
BackgroundTraining bias and sequence alignments shape protein–peptide docking by AlphaFold and related methods
Guan, L., & Keating, A. E. (2025) · Protein Science · doi:10.1002/pro.70331 · Cited by 17 · see citing works
Sequence-Based Protein–Protein Interaction Prediction and Its Applications in Drug Discovery
Charih, F., Green, J. R., & Biggar, K. (2025) · Cells · doi:10.3390/cells14181449 · Cited by 10
“Finally, even the most accurate protein structure prediction models are also limited in their ability to model proteins whose conformation is dynamic, e.g., in response to a switch between the cofactor-free apo-and the cofactor-bound holo-states.”
Majila, K., Ullanat, V., & Viswanath, S. (2026) · Cell Systems · doi:10.1016/j.cels.2025.101486 · Cited by 8 · see citing works
Cutting-edge deep-learning based tools for metagenomic research
Karin, E. L., & Steinegger, M. (2025) · National Science Review · doi:10.1093/nsr/nwaf056 · Cited by 10 · see citing works
“In addition, us-ing AlphaFold-Multimer for a systematic screen of potential interactors faces challenges of multiple testing as its false positive rate reaches 20% in some cases [38 ].”
AlphaFold2 SLiM screen for LC3-LIR interactions in autophagy
Stuke, J. F. M., & Hummer, G. (2025) · Autophagy · doi:10.1080/15548627.2025.2493999 · Cited by 8 · see citing works
“Coming from a different angle, various new approaches have been developed to make use of AlphaFold2 Multimer’s ability in the prediction of interactions between proteins and peptides or protein fragments [34,35].”
Chakraborty, C., Bhattacharya, M., & Lee, S.-S. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1739303 · Cited by 10 · see citing works
“AF structures provide insight into protein–protein interaction networks and their interfaces (Lee et al., 2024).”
Kim, A., Huang, K., Johnson, J. L., Yaron, T. M., Wang, K., Cantley, L. C., Hu, Y., & Perrimon, N. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.10.681672 · Cited by 3 · see citing works
“Although AFM can model such transient complexes with high local accuracy, the presence of extensive non-interacting flexible segments can reduce overall confidence scores, pushing them below commonly accepted thresholds.”
From concept to application: Exploring the evolution and potential of DUBTAC technology
Wang, D., Min, W., Jiang, B., Sun, H., Sun, C., & Yang, P. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.11.022 · Cited by 5 · see citing works
“Beyond adopting PROTAC-derived machine learning models for ligand prediction, linker design, and ADMET profiling, emerging tools such as AlphaFold-Multimer show high accuracy in predicting protein — protein and multicomponent complex structures 187 — 189 .”
Veinstein, M., Stroobant, V., Wavreil, F., Michiels, T., & Sorgeloos, F. (2025) · PLoS Pathogens · doi:10.1371/journal.ppat.1013016 · Cited by 6 · see citing works
“To this end, peptides encompassing the DDVF motif were in silico-folded with a fragment from the human RSK2 N-terminal kinase domain [21].”
Fichó, E., Pancsa, R., Magyar, C., Kálmán, Z. E., Schád, É., Németh, B. Z., Simon, I., Dobson, L., & Tusnády, G. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae976 · Cited by 7 · see citing works
Regularly updated benchmark sets for statistically correct evaluations of AlphaFold applications
Dobson, L., Tusnády, G. E., & Tompa, P. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf104 · Cited by 6 · see citing works
“…and physicochemical features of proteins in specific areas, such as protein stability [4], conformational heterogeneity [5], protein function [6], structure, [7] or interfaces [8] of protein complexes, domain definitions [9], or disordered regions that do not fold into unique structures [10].”
TET dioxygenases localize at splicing speckles and promote RNA splicing
Hastert, F. D., Weber, J., Bauer, C., Zhadan, A., Singh, D. N. D., Dix, T. C., Arnold, R., Bessonov, S., Soller, M., Leonhardt, H., et al. (2025) · Nucleus · doi:10.1080/19491034.2025.2536902 · Cited by 5 · see citing works
“Previous studies reported significant decreases in the sensitivity of AlphaFold for complex structure predictions when using full-length sequences [78].”
Unifying Constraints Linking Protein Folding and Native Dynamics Decoded from AlphaFold
Zhang, Z., Ren, W., Xie, L., Zheng, Y., Guan, X., Wang, J., Li, W., & Tang, Q.-Y. (2026) · Physical Review Letters · doi:10.1103/7j2j-f8f7 · Cited by 5 · see citing works
Arroyo, M. D. C. G., Casas-Delucchi, C. S., Pabba, M. K., Prorok, P., Pradhan, S. K., Rausch, C., Lehmkuhl, A., Maiser, A., Buschbeck, M., Pasque, V., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae734 · Cited by 7 · see citing works
Self-citationIntegrative modeling meets deep learning: Recent advances in modeling protein assemblies
Shor, B., & Schneidman‐Duhovny, D. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102841 · Cited by 6 · see citing works
Unifying constraints linking protein folding and native dynamics decoded from AlphaFold
Zhang, Z., Ren, W., Xie, L., Zheng, Y., Guan, X., Wang, J., Li, W., & Tang, Q.-Y. (2026) · doi:10.64898/2026.01.08.698426 · Cited by 5 · see citing works
“Looking ahead, integrating this framework with models of sequence variation [32, 65], protein–protein interactions [66, 67], and functional constraints (e.g., allosteric transitions and ligand binding) [68–70] A. Entropic Measures —”
Peng, J., & Zhao, L. (2026) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1013899 · Cited by 5 · see citing works
AlphaCRV: a pipeline for identifying accurate binder topologies in mass-modeling with AlphaFold
Guzmán-Vega, F. J., & Arold, S. T. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae131 · Cited by 4 · see citing works
“…complex prediction by AlphaFold-Multimer may be enhanced, e.g. by testing different domains or fragments of the interacting proteins (Bret et al. 2024, Lee et al. 2024), or by enforcing a dropout at inference time to increase sampling of different conformations (Johansson-Åkhe and Wallner 2022).”
BackgroundMethodA CDK1 phospho-switch reprograms TRAIP to unload replisomes in mitosis
Can, G., Shyian, M., Krishnamoorthy, A., Ahmed, S., Lim, Y., Wu, A., Pavani, R., Zaher, M. S., Nussenzweig, A., Räschle, M., et al. (2026) · Science · doi:10.1126/science.aeh1834 · Cited by 13 · see citing works
Vancomycin resistance in gram-positive infections: evolutionary strategies of survival
Hu, T., & Wang, L. (2026) · Archives of Microbiology · doi:10.1007/s00203-025-04698-1 · Cited by 4 · see citing works
“…simulations enables in silico exploration of vancomycin scaffold modifi - cations and prediction of their impact on binding energetics, guiding the design of next-generation derivatives intended to overcome D-Ala-D-Lac–mediated resistance (Zhu et al. 2024; Talat and Khan 2023 ; Lee et al. 2024 ).”
A simple workflow to identify novel small linear motif (SLiM)-mediated interactions with AlphaFold
Veinstein, M., Janssens, V., Iorga, B. I., Helaers, R., Michiels, T., & Sorgeloos, F. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf501 · Cited by 3 · see citing works
“Subsequent benchmarking by Lee et al. provided a systematic comparison of different AAF-based metrics for SLiM–domain interactions [5].”
InfluentialPPI-ID: Streamlining protein-protein interaction prediction through domain and SLiM mapping
Goodwin, H. V., & Atkinson, N. S. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013062 · Cited by 3 · see citing works
“This information can be used to delimit the protein sequences for AlphaFold-Multimer modeling, which enhances the accuracy of the structure [7,8].”
InfluentialAlphaFold-based peptide structure prediction: Opportunities, limitations, and future directions
Zhang, B., Zhu, J., Chen, H.-F., & Liu, L. (2026) · Biotechnology Advances · doi:10.1016/j.biotechadv.2026.108977 · Cited by 3 · see citing works
Jiao, F., Braitbard, M., Yu, C., Shor, B., Wulff, B., Schneidman‐Duhovny, D., & Huang, L. (2025) · Nature Communications · doi:10.1038/s41467-025-66023-0 · Cited by 5 · see citing works
“Third, AFM ’ s success rate is lower when predicting full-length proteins compared to protein fragments, such as interacting domains 42 .”
Non-covalent SUMO interactions with ligases and effectors: SUMO-interacting motifs and beyond
Mishra, A., Cisse, E. H., & Suskiewicz, M. J. (2025) · Essays in Biochemistry · doi:10.1042/ebc20253039 · Cited by 2 · see citing works
Sakaguchi, T., Irifune, Y., Kamada, R., & Sakaguchi, K. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26199326 · Cited by 3 · see citing works
“This capability enables proteome-scale computational screening for PPIs [136,137], and its strength lies in synergy with experimental validation.”
AlphaFold opens the doors to deorphanizing secreted proteins
Viswanath, S. (2024) · Cell Systems · doi:10.1016/j.cels.2024.10.010 · Cited by 2 · see citing works
Structures of dynamic interactors at native proteasomes by PhIX-MS and cryo-electron microscopy
Lee, K., Negi, H., Chen, X., Atallah-Yunes, K., Truslow, S., Castelino, R. E., Guest, M. R., Ciancone, A. M., Lu, X., Tarasov, S. G., et al. (2026) · Molecular Cell · doi:10.1016/j.molcel.2026.06.032 · Cited by 6 · see citing works
Schumacher, L. A., Groves, N. R., Conway, D. E., & Meier, I. (2026) · Journal of Cell Science · doi:10.1242/jcs.264672 · Cited by 2
“Modeling the shortest region required for an interaction has been shown to be effective and give higher confidence models (Lee et al., 2024).”
Bailey, J., Spina, S. C., Hu, A., Phan, N., Getman, R. B., & Kimmel, B. R. (2026) · ACS Engineering Au · doi:10.1021/acsengineeringau.5c00099 · Cited by 3 · see citing works
Konstantinou, A., Córdova-Pérez, A., Varga, J. K., Madhu, P., Simonetti, L., Vieler, M., Ishimura, R., Lamoliatte, F., Schueler‐Furman, O., Davey, N. E., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-73047-7 · Cited by 3 · see citing works
“Modeling domains and peptide fragments rather than full-length proteins have been shown to improve prediction accuracy in AlphaFold-based approaches 58,59 .”
Self-citationEngineering Peptide Modulators for T-Cell Migration by Structural Scaffold Matching
Gattringer, J., Hasinger, S., Weidmann, A., Walczewska-Szewc, K., Jadhav, K. B., Zrzavy, T., Steinmaurer, A., Baeten, P., Perisic, M., Cochrane, W., et al. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5c00677 · Cited by 2 · see citing works
Barhoon, M., & Mahdiuni, H. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01029 · Cited by 3 · see citing works
Structural Analysis of a Metamorphic Protein using AlphaFold2Preprint
Tyree, S., & Kim, Y.-I. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.04.621945 · Cited by 2 · see citing works
“AlphaFold has been shown to be able to predict novel interface types which could have potential in extending definitions for existing interface types (Lee et al. 2024).”
BackgroundGeist, J. L., Lee, C. Y., Strom, J. M., Naveja, J. J., & Luck, K. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae482 · Cited by 4 · see citing works
“To identify highly confident structural models, we applied a model confidence cutoff of 0.8 for which it was previously estimated that the false and true positive rate equaled zero and 31%, respectively (Lee et al. 2024).”
BackgroundMethodResultInfluentialSelf-citationBrunello, F. G., Erra, L., Nicola, J., & Martí, M. A. (2025) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1012829 · Cited by 1 · see citing works
“…also been successfully applied to study domain–motif interactions in the context of neurodevelopmental disorders, although predictive accuracy was shown to decrease when full-length proteins containing the motif were used as input, highlighting the need for tailored fragmentation strategies [27].”
A functional landscape of human microproteinsPreprint
Slivak, M., Choteau, S. A., Spinelli, L., Souville, L., Pierre, P., Carvunis, A., Bornberg-Bauer, E., Zanzoni, A., & Brun, C. (2026) · bioRxiv · doi:10.1101/2024.06.10.598216 · Cited by 1 · see citing works
“Indeed, not only AlphaFold performs poorly on proteins lacking related sequences and on disordered regions, but AF-Multimer predictions of domain-motif interactions are not specific when using small sequences 72 .”
Esler, M. A., Werther, R. A., Doyle, L., Ubilla‐Rodriguez, N. C., Schwensen, J. S., Hallinan, J. P., Lambert, A. R., Young, J. C., Silverstein, M., & Stoddard, B. L. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag608 · Cited by 1 · see citing works
Singh, S., Singh, R., & Sharma, S. (2026) · BioData Mining · doi:10.1186/s13040-026-00577-7 · Cited by 1 · see citing works
“They require protein fragmentation strategies, as AF-Multimer largely fails to identify these interfaces when full-length sequences are used [81].”
Dall’Armellina, F., Urbé, S., & Rigden, D. J. (2026) · Protein Science · doi:10.1002/pro.70572 · Cited by 3 · see citing works
“While ipTM has been applied in docking studies involving peptide-protein complexes (Bret et al., 2024; Lee et al., 2024; Teufel et al., 2023), it was originally trained on structured PDB entries and is sensitive to sequence length, treating all residues equally regardless of their involvement in…”
Buzuk, A., Khan, O., Kang, S., Yim, L., Vajda, S., & Perlstein, D. L. (2026) · bioRxiv · doi:10.64898/2026.04.06.716496 · Cited by 1 · see citing works
“Because accurate prediction of novel peptide-protein interactions remains a challenging regime for structure prediction programs, including AlphaFold,(24, 25) we examined the Cia1-Cia2 structure to identify any overlooked features that could be serving as a structural template for the AlphaFold prediction.”
Kálmán, Z. E., Lázár, T., Dobson, L., & Pancsa, R. (2025) · Biomolecules · doi:10.3390/biom15121641
Akuh, O.-A., Maus, D., Blume, M., Saliba, K., & Seeber, F. (2025) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2025.110726 · Cited by 1 · see citing works
AlphaFold-Based Protein-Protein Interaction Prediction Methods Classification
Faisal, T., Alofairi, A. A., & Mohsen, . A. (2025) · International Conference on Emerging Smart Technologies and Applications (eSmarTA) · doi:10.1109/esmarta66764.2025.11132127 · Cited by 2 · see citing works
Walker, D. R., Fujimura, G., Vanegas, J. M., & Barbar, E. J. (2025) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2025.1531793 · Cited by 1 · see citing works
Tubulin acetylation regulates nuclear shape fluctuations and DNA repair
Wallis, S., Tse, W. Y., Donker, L., Baxter, A., Dave, N., Weber, A., Gillespie, E., Chen, B., Burden, J. M., Friskes, A., et al. (2026) · Nature Cell Biology · doi:10.1038/s41556-026-02096-3
Challenges in AI-based structural modeling of host–pathogen protein interactions
Gilep, K., & Kosiński, J. (2026) · Current Opinion in Virology · doi:10.1016/j.coviro.2026.101587
Gardner, T. P., Anastasia, P., Lee, C., Marcotte, E. M., & Wallingford, J. B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.05.756796
“…we note that the large region of intrinsic disorder is expected to reduce AlphaFold scores and interaction metrics (Banhos Danneskiold-Samsøe et al. , 2024; Bret et al. , 2024; Homma et al. , 2024; Lee et al. , 2024; Martin, 2024; Pándy-Szekeres et al. , 2024; Dunbrack, 2025; Mitic et al. , 2025).”
Self-citationProtein interactions with small molecules, peptides and proteins share residue cluster classes
Castillo, J. M. H., Macias, E. H. V., & del Rio, G. (2026) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2026.109614
Zhang, J., Zhang, J., Zheng, L., Ruan, H., Li, Y., Li, Y., & Luo, G. (2026) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2026.109610
Structural compatibility is not enough: Current challenges in protein-protein interaction prediction
Elofsson, A. (2026) · Molecular Cell · doi:10.1016/j.molcel.2026.08.030
Pathak, M. (2026) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2026.119442
The lysis mechanism of the haloarchaeal virus HFTV1
Kuiper, B. P., van Tilburg, M., Bäcker, L. E., Maity, S., Ariyanto, Y. S., Petters, J. G., Aguirre-Sourrouille, Z., Nijland, J. G., Karsens, H. A., Berrelkamp, G., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.118117
Meng, J., Chen, L., Zhou, D., Deng, S., Feng, H., Wang, N., Zhang, J., Zhao, M., Wei, L., GuiYing, Z., et al. (2026) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2026.154974
Wang, M., Shao, H., Wei, J., Wu, Y., Niu, M., Tan, P., Liu, H., Wu, J., Zhang, R., Hu, X., et al. (2026) · Plant Physiology and Biochemistry · doi:10.1016/j.plaphy.2026.111704
Scalable Extraction of Information on Protein-Protein Interactions using Topological Data AnalysisPreprint
Mukherjee, A., Park, B., Malmstrom, A., Cisewski-Kehe, J., Lehn, R. C. V., & Zavala, V. M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.06.743405
Variant characterization in the intrinsically disordered human proteome
Hubrich, D., Valverde, J. A., Lee, C. Y., Djokic, M., Welzel, M., Hintz, K., Strom, J. M., & Luck, K. (2026) · Nature Structural & Molecular Biology · doi:10.1038/s41594-026-01846-z
“Lee et al. 20 demonstrated that including flanking residues around motifs in AlphaFold predictions could improve the accuracy of predicted models.”
InfluentialSelf-citationStructural insights into the activation of phospholipase D1 by the small GTPase ARL11
Marr, D. J., Gao, S., Frohman, M. A., & Airola, M. V. (2026) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2026.113375
SPPIDER-seq: sequence-based partner-aware predictor of protein-protein interaction sites
Porollo, A., Jadhav, O., Alvarez, A., & Chen, J.-C. (2026) · Bioinform. · doi:10.1093/bioinformatics/btag388
“Such emergent specialization mirrors hierarchical binding principles observed in experimental PPI networks, where structured receptors recognize recurring motifs across multiple partners (Landgraf et al. 2004, Stein et al. 2009, Uversky 2016).”
In Silico Structure-Based Interactomic Analysis of the Scaffolding Protein DCAF7Preprint
Mezghrani, A., Reys, V., & Labesse, G. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.13.724911
“Nevertheless, successful strategies have been developed to increase the accuracy of AF for de novo identification of SLiMs (Bret et al. 2024; Lee et al. 2024; Veinstein et al. 2025).”
Neuronal calcium sensors emerge as novel adenylyl cyclase 8 regulatorsPreprint
Khanppnavar, B., Florea, R., Schuster, D., Lavriha, P., Kosturanova, A., Ruckstuhl, M. M., Kantarci, I., Vaithia, A., Shi, J., Picotti, P., et al. (2026) · bioRxiv · doi:10.64898/2026.04.19.719435
“Additionally, the interactions appear to be motif-driven, which has been reported to present challenges in AlphaFold-Multimer predictions [28] To overcome this limitation, we incorporated experimentally derived XL-MS restraints and performed integrative modeling using AlphaLink2, a method that…”
Valkonen, M., Girych, M., Havukainen, S., Sveholm, E., Luoto, J. C., Schmidt, G., Adlung, N., Aalto, A. P., Ollila, O. H. S., Mattila, H., et al. (2026) · bioRxiv · doi:10.64898/2026.05.05.722857
“To investigate how SLP2 target the AQ12 silk domain, we computationally mapped its putative cleavage sites within the AQ12 sequence using an AlphaFold2-based fragment docking approach evaluated with the actifpTM confidence metric (18, 19) (see Methods).”
SPPIDER-seq: Sequence-based partner-aware predictor of protein-protein interaction sitesPreprint
Porollo, A., Jadhav, O., Alvarez, A., & Chen, J.-C. (2026) · bioRxiv · doi:10.64898/2026.04.28.721359
“Similar behavior has been noted in comparative evaluations of deep-learning structure predictors versus disorder-centric binding models (Basu et al., 2023; Lee et al., 2024; Majila et al., 2026).”
Systematic Evaluation of AlphaFold2 and OpenFold3 on Protein–Peptide ComplexesPreprint
Fayetörbay, R., Timuçin, A. C., & Timuçin, E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.21.720042 · Cited by 1 · see citing works
“This limitation could be particularly critical for protein-peptide complexes where the peptide contains disordered regions [46, 47].”
Saha, A., Tang, C. L., Murray, D., & Honig, B. H. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2527957123
“For most tested cases, AF3Complex colocalized the isolated SLiM to its cognate PRD within the full - length receptor; however, consistent with previous work (26), this was not the case when the full - length SLiM - containing protein was used.”
Reliable prediction of short linear motifs in the human proteomePreprint
Pancsa, R., Fichó, E., Kálmán, Z. E., Gerdán, C., Reményi, I., Zeke, A., Tusnády, G., & Dobson, L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.04.709504
“In addition, we aligned M. musculus and R. norvegicus proteins with their H. sapiens orthologs (sharing the same gene name) and transferred the motif boundaries from orthologous sequences onto the human counterparts (STable 2).”
Discovery of a FANCD2-interacting protein motif (DIP-box) linking DNA Damage Response processesPreprint
Cao, Z.-B., Briola, G., Ionita, C., Streetley, J., Walden, H., & Rennie, M. (2026) · bioRxiv · doi:10.64898/2026.02.24.707417
“Protein fragmentation for AlphaFold predictions has been shown to improve sensitivity but it can also reduce specificity ( 32 ).”
Wang, M., Li, S., Zhu, X., Guo, M., Guo, C., & Shu, Y. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2026.1772992
Han, X.-M., Feng, J., Guan, H., Niu, F., Wang, J., Li, X., Guo, B., He, W., Wei, Y.-H., & Fu, Y. (2026) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2026.150973
Alfaifi, M., & Kamli, H. M. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.108940
A Survey of Predicted Protein–Protein Interactions Involving Disordered Regions in Humans
Pei, J., Zhang, J. J., & Cong, Q. (2026) · Journal of Molecular Biology · doi:10.1016/j.jmb.2026.169636 · Cited by 1 · see citing works
Challenges and future directions in protein structure prediction: lessons from AlphaFold’s evolution
Xie, L.-X. (2025) · Biomedical Informatics · doi:10.55092/bi20250005
“The experimental data were used as constraints to achieve the prediction of protein structure [52,53].”
Competition between binding partners of yeast Pex3 affects peroxisome biology
de Lange, E. M. F., Chojnowski, G., Bürgi, J., Vlijm, R., Wilmanns, M., & van der Klei, I. J. (2025) · FEBS Journal · doi:10.1111/febs.70229 · Cited by 1 · see citing works
CtGH76, a Glycoside Hydrolase 76 from Chaetomium thermophilum, with Elongated Glycan-Binding Canyon
Ruppenthal, S. R., Wang, P., Watad, M., Rosner, C. J., Vogt, M. S., Friedrich, M., Voigt, A.-L., Petz, A., Gnau, P., & Essen, L. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26146589
“Notably, AlphaFold 3’s multimer mode, when used with peptide fragments, has shown greater success in modeling intrinsically disordered regions of protein ligands compared to full polypeptide ligands [36].”
Protist biochemistry: Functional SUF system facilitated mitochondrial loss
Lawton, M., & Gawryluk, R. M. R. (2024) · Current Biology · doi:10.1016/j.cub.2024.07.054
- Authors
- Lee, C. Y., Hubrich, D., Varga, J. K., Schäfer, C., Welzel, M., Schumbera, E., Djokic, M., Strom, J. M., Schönfeld, J., Geist, J. L., Polat, F., Gibson, T. J., Keller Valsecchi, C. I., Kumar, M., Schueler-Furman, O., & Luck, K.
- Affiliations
- Institute of Molecular Biology (DE)
- Hebrew University of Jerusalem (IL)
- European Molecular Biology Laboratory (DE)
- Corresponding author
- Luck, Katja
- ORCID
- Venue
- Molecular Systems Biology
- Published
- 2024-01-15
- Volume
- 20
- Issue
- 2
- Pages
- 75-97
- ISSN
- 1744-4292 (ISSN-L)
- PMC
- PMC10883280
- OpenAlex
- W4390876228
- Cited by
- 105
- References
- 101
- Citations by year
- 2026: 36 · 2025: 46 · 2024: 20 · 2023: 3
- FWCI
- 13.89
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Molecular Systems Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- German Research Foundation (LU 2568/1-1, SFB1551 464588647)
- European Union Horizon 2020 UBIMOTIF programme (860517)
- Israel Science Foundation (301/2021)
- IMB collaborative research PhD stipend
- Ministry of Science and Health (MWG), Rhineland Palatinate (TB-Nr.:3658/19)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein-protein interfaces, AlphaFold-Multimer, intrinsically disordered regions, short linear motifs, neurodevelopmental disorders
- MeSH terms
- Carrier Proteins, Humans, Membrane Proteins, Proteins
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review