Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants

Highly accurate protein structure prediction with AlphaFold

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ggplot2

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BLAST+: architecture and applications

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Cd-hit: Accelerated for clustering the next-generation sequencing data

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Complex heatmaps reveal patterns and correlations in multidimensional genomic data

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The Amber biomolecular simulation programs

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ColabFold - Making protein folding accessible to all

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AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

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Bootstrap Methods and their Application

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AlphaFold2 Predicts Whether Proteins Interact Amidst Confounding Structural Compatibility

Martin, J. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01805 · Cited by 20

Deep-learning-based prediction framework for protein-peptide interactions with structure generation pipeline

Ge, J., Jiang, D., Sun, H., Kang, Y., Pan, P., Deng, Y., Hsieh, C., & Hou, T. (2024) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2024.101980 · Cited by 17

TEMPRO: nanobody melting temperature estimation model using protein embeddings

Alvarez, J. A. E., & Dean, S. N. (2024) · Scientific Reports · doi:10.1038/s41598-024-70101-6 · Cited by 17

Non-Markovian Dynamic Models Identify Non-Canonical KRAS-VHL Encounter Complex Conformations for Novel PROTAC Design

Qiu, Y., Wiewiora, R., Izaguirre, J. A., Xu, H., Sherman, W., Tang, W., & Huang, X. (2024) · JACS Au · doi:10.1021/jacsau.4c00503 · Cited by 20

Assessing scoring metrics for AlphaFold2 and AlphaFold3 protein complex predictions

Genz, L. R., Nair, S., Nagar, N., & Topf, M. (2025) · Protein Science · doi:10.1002/pro.70327 · Cited by 17

Leveraging Cross-Linking Mass Spectrometry for Modeling Antibody–Antigen Complexes

Ianni, A. D., Ianni, A. D., Cowan, K. J., Barbero, L. M., & Sirtori, F. R. (2024) · Journal of Proteome Research · doi:10.1021/acs.jproteome.3c00816 · Cited by 16

Fast and accurate modeling and design of antibody-antigen complex using tFoldPreprint

Wu, F., Zhao, Y. S., Wu, J., Jiang, B., He, B., Huang, L.-K., Qin, C., Yang, F., Huang, N., Xiao, Y., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.05.578892 · Cited by 16

Phytopathogen Effector Biology in the Burgeoning AI Era

Jones, D., & Raffaele, S. (2025) · Annual Review of Phytopathology · doi:10.1146/annurev-phyto-121823-081033 · Cited by 17

Innovations and trends in antibody repertoire analysis

Townsend, D. R., Towers, D. M., Lavinder, J. J., & Ippolito, G. C. (2024) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2024.103082 · Cited by 17

An in-depth review of AI-powered advancements in cancer drug discovery

Le, M. H. N., Nguyen, P. K., Nguyen, T. P. T., Nguyen, H. Q., Tam, D. N. H., Huynh, H. H., Huynh, P. K., & Le, N. Q. K. (2025) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2025.167680 · Cited by 15

Learning the Language of Antibody HypervariabilityPreprint

Singh, R., Im, C., Qiu, Y., Mackness, B. C., Gupta, A., Sorenson, T., Sledzieski, S., Erlach, L., Wendt, M., Fomekong‐Nanfack, Y., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.26.538476 · Cited by 16

Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics

Esmaeilpour, D., Hamblin, M. R., Cheng, J., Khosravi, A., Liu, J., Zarepour, A., Zarrabi, A., Sillanpaa, M., Zare‬, E. N., Shen, J., et al. (2026) · Bioactive Materials · doi:10.1016/j.bioactmat.2026.01.036 · Cited by 18

Structural Modeling of Cytokine-Receptor-JAK2 Signaling Complexes Using AlphaFold Multimer

Pogozheva, I. D., Cherepanov, S., Park, S., Raghavan, M., Im, W., & Lomize, A. L. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00926 · Cited by 15

Structural determinants for activation of the Tau kinase CDK5 by the serotonin receptor 5-HT7R

Ackmann, J., Brüge, A., Gotina, L., Lim, S., Jahreis, K., Vollbrecht, A.-L., Kim, Y. K., Pae, A. N., Labus, J., & Ponimaskin, E. G. (2024) · Cell Communication and Signaling · doi:10.1186/s12964-024-01612-y · Cited by 14

Antimicrobial peptide class that forms discrete β-barrel stable pores anchored by transmembrane helices

Dickey, S. W., Burgin, D. J., Antwi, A. N., Villaruz, A. E., Galac, M. R., Cheung, G. Y. C., Rostovtseva, T. K., Worrall, L. J., Lazarski, A. C., Cino, E. A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62604-1 · Cited by 13

Acarbose May Function as a Competitive Exclusion Agent for the Producing Bacteria

Tanoeyadi, S., Tsunoda, T., Ito, T., Philmus, B. J., & Mahmud, T. (2023) · ACS Chemical Biology · doi:10.1021/acschembio.2c00795 · Cited by 13

Structural and physical features that distinguish tumor-controlling from inactive cancer neoepitopes

Custódio, J. M. F., Ayres, C. M., Rosales, T. J., Brambley, C. A., Arbuiso, A. G., Landau, L. M., Keller, G. L. J., Srivastava, P. K., & Baker, B. M. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2312057120 · Cited by 13

Integrating Large-Scale Protein Structure Prediction into Human Genetics Research

Marrero, M. C., Jänes, J., Baptista, D., & Beltrão, P. (2024) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-120622-020615 · Cited by 13

Accelerating protein–protein interaction screens with reduced AlphaFold-Multimer sampling

Bellinzona, G., Sassera, D., & Bonvin, A. M. J. J. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae153 · Cited by 13

TRAP: a contrastive learning-enhanced framework for robust TCR–pMHC binding prediction with improved generalizability

Ge, J., Wang, J., Ye, Q., Pan, L., Kang, Y., Shen, C., Deng, Y., Hsieh, C., & Hou, T. (2025) · Chemical Science · doi:10.1039/d4sc08141b · Cited by 12

Experimental NOE, Chemical Shift, and Proline Isomerization Data Provide Detailed Insights into Amelotin Oligomerization

Chiliveri, S. C., Shen, Y., Baber, J. L., Ying, J., Sagar, V., Wistow, G. J., Anfinrud, P., & Bax, A. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.3c05710 · Cited by 13

Family-wide analysis of integrin structures predicted by AlphaFold2

Zhang, H., Zhu, D. S., & Zhu, J. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.09.022 · Cited by 12

Predicting therapeutic and side effects from drug binding affinities to human proteome structures

Sawada, R., Sakajiri, Y., Shibata, T., & Yamanishi, Y. (2024) · iScience · doi:10.1016/j.isci.2024.110032 · Cited by 12

AI-augmented physics-based docking for antibody-antigen complex prediction

Gaudreault, F., Sulea, T. I., & Corbeil, C. R. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf129 · Cited by 15

APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

Park, H., Patel, P., Haas, R., & Huerta, E. A. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2311888121 · Cited by 14

Unraveling the Behavior of Intrinsically Disordered Protein c-Myc: A Study Utilizing Gaussian-Accelerated Molecular Dynamics

Gunasinghe, K. K. J., Rahman, T., & Wezen, X. C. (2023) · ACS Omega · doi:10.1021/acsomega.3c05822 · Cited by 11

Role of odorant binding protein C12 in the response of Tribolium castaneum to chemical agents

Wang, L., Hou, M., Liang, C., Xu, Q., Lu, Y., & Zhao, Z. (2024) · Pesticide Biochemistry and Physiology · doi:10.1016/j.pestbp.2024.105861 · Cited by 11

Multimodal Mass Spectrometry Identifies a Conserved Protective Epitope in S. pyogenes Streptolysin O

Tang, D., Gueto-Tettay, C. A., Hjortswang, E., Ströbaek, J., Ekström, S., Happonen, L., Malmström, L., & Malmström, J. A. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.4c00596 · Cited by 11

An agnostic analysis of the human AlphaFold2 proteome using local protein conformations

de Brevern, A. G. (2022) · Biochimie · doi:10.1016/j.biochi.2022.11.009 · Cited by 12

The Fgf/Erf/NCoR1/2 repressive axis controls trophoblast cell fate

Lackner, A., Müller, M., Gamperl, M., Stoeva, D., Langmann, O., Papúchová, H., Roitinger, E., Dürnberger, G., Imre, R., Mechtler, K., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38101-8 · Cited by 12

Interface refinement of low- to medium-resolution Cryo-EM complexes using HADDOCK2.4

Neijenhuis, T., van Keulen, S. C., & Bonvin, A. M. J. J. (2022) · Structure · doi:10.1016/j.str.2022.02.001 · Cited by 12

Novel Computational Methods for Cancer Drug Design

Talluri, S., Kamal, M. A., & Malla, R. R. (2023) · Current Medicinal Chemistry · doi:10.2174/0929867330666230403100008 · Cited by 10

Non-standard proteins in the lens of AlphaFold 3: a case study of amyloids

Wojciechowska, A. W., Wojciechowski, J. W., Vriend, G., & Kotulska, M. (2026) · Scientific Reports · doi:10.1038/s41598-026-68041-4 · Cited by 11

AlphaFold2-Based Characterization of Apo and Holo Protein Structures and Conformational Ensembles Using Randomized Alanine Sequence Scanning Adaptation: Capturing Shared Signature Dynamics and Ligand-Induced Conformational Changes

Raisinghani, N., Parikh, V., Foley, B., & Verkhivker, G. M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252312968 · Cited by 10

RosettaHDX: Predicting antibody-antigen interaction from hydrogen-deuterium exchange mass spectrometry data

Tran, M. H., Martina, C. E., Moretti, R., Nagel, M., Schey, K. L., & Meiler, J. (2025) · Journal of Structural Biology · doi:10.1016/j.jsb.2025.108166 · Cited by 9

The Molecular Frequency, Conservation and Role of Reactive Cysteines in Plant Lipid Metabolism

Cannon, A. E., & Horn, P. J. (2023) · Plant and Cell Physiology · doi:10.1093/pcp/pcad163 · Cited by 10

Inhibitory effect of truncated isoforms on GPCR dimerization predicted by combinatorial computational strategy

Li, M., Qing, R., Tao, F., Xu, P., & Zhang, S. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.12.008 · Cited by 9

Computational methods for modeling protein–protein interactions in the AI era: Current status and future directions

Li, H., Nithin, C., Kmiecik, S., & Huang, S. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104382 · Cited by 9

Antibody-drug conjugate design and mechanisms of action for cancer treatment: state of the art and beyond

Cheung, A., Liu, Y., Chenoweth, A. M., Montaseri, H., Esapa, B., Chudasama, V., Baker, J. R., Thurston, D. E., & Karagiannis, S. N. (2026) · Physiological Reviews · doi:10.1152/physrev.00039.2025 · Cited by 12

A deep learning model for predicting optimal distance range in crosslinking mass spectrometry data

Cohen, S., & Schneidman‐Duhovny, D. (2023) · PROTEOMICS · doi:10.1002/pmic.202200341 · Cited by 9

Growing ecosystem of deep learning methods for modeling protein–protein interactions

Rogers, J. R., Nikolényi, G., & AlQuraishi, M. (2023) · Protein Engineering Design and Selection · doi:10.1093/protein/gzad023 · Cited by 12

pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen BindingPreprint

Han, Y., Yang, Y., Tian, Y., Fattah, F. J., von Itzstein, M. S., Hu, Y., Zhang, M., Kang, X., Yang, D. M., Liu, J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.01.569599 · Cited by 10

AlphaFold models of host-pathogen interactions elucidate the prevalence and structural modes of molecular mimicryPreprint

Baptista, D., Gomez-Lucas, L., Jänes, J., Krogan, N. J., Amorim, M. J., Ivarsson, Y., & Beltrão, P. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.04.657796 · Cited by 9

Strengths and limitations of web servers for the modeling of TCRpMHC complexes

Le, H. N., de Freitas, M. V., & Antunes, D. A. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.06.028 · Cited by 9

Evaluation of AlphaFold modeling for elucidation of nanobody–peptide epitope interactions

Sachdev, S., Roy, S., Saha, S. J., Zhao, G., Kumariya, R., Creemer, B. A., Yin, R., Pierce, B. G., Bewley, C. A., & Cheloha, R. W. (2025) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2025.110268 · Cited by 8

Grafted Coiled-Coil Peptides as Multivalent Scaffolds for Protein Recognition

Acevedo‐Jake, A. M., Mylemans, B., Kay, D. F., Zhang, P., Korona, B., Rhys, G. G., Leney, A. C., Huang, D. T. C., Edwards, T. A., Itzhaki, L. S., et al. (2025) · ACS Chemical Biology · doi:10.1021/acschembio.5c00137 · Cited by 9

Structure Modeling Protocols for Protein Multimer and RNA in CASP16 With Enhanced MSAs , Model Ranking, and Deep Learning

Kagaya, Y., Nakamura, T., Verburgt, J. C., Jain, A., Terashi, G., Punuru, P., Tugolukova, E., Park, J. H., Saha, A., Huang, D. Y. C., et al. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70033 · Cited by 8

Laboratory Evolution Reveals Transcriptional Mechanisms Underlying Thermal Adaptation of Escherichia coli

Rychel, K., Chen, K., Catoiu, E., Olson, C. A., Sandberg, T. E., Gao, Y., Xu, S., Hefner, Y., Szubin, R., Patel, A., et al. (2025) · Genome Biology and Evolution · doi:10.1093/gbe/evaf171 · Cited by 11

Structure-based discovery and definition of RiPP recognition elements

Bregman, M. H., Cogan, D. P., Shelton, K. E., Rice, A. J., Dommaraju, S. R., Nair, S. K., & Mitchell, D. A. (2025) · mSystems · doi:10.1128/msystems.01252-25 · Cited by 10

Deciphering cross-species reactivity of LAMP-1 antibodies using deep mutational epitope mapping and AlphaFold

Pruvost, T., Mathieu, M., DuBois, S. G., Maillère, B., Vigne, E., & Nozach, H. (2023) · mAbs · doi:10.1080/19420862.2023.2175311 · Cited by 9

An Innate Host Defense Protein β2-Microglobulin Keeps a Check on α-Synuclein amyloid Assembly: Implications in Parkinson's Disease

Rani, K., Pal, A., Gurnani, B., Agarwala, P., Sasmal, D. K., & Jain, N. (2023) · Journal of Molecular Biology · doi:10.1016/j.jmb.2023.168285 · Cited by 8

Predicting Mutation-Induced Allosteric Changes in Structures and Conformational Ensembles of the ABL Kinase Using AlphaFold2 Adaptations with Alanine Sequence Scanning

Raisinghani, N., Alshahrani, M., Gupta, G., & Verkhivker, G. M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms251810082 · Cited by 7

Learning with Privileged Knowledge Distillation for Improved Peptide–Protein Docking

Zhang, Z., Verburgt, J. C., Kagaya, Y., Christoffer, C. W., & Kihara, D. (2025) · ACS Omega · doi:10.1021/acsomega.5c00967 · Cited by 8

PointDE: Protein Docking Evaluation Using 3D Point Cloud Neural Network

Chen, Z. X., Liu, N., Huang, Y., Min, X., Zeng, X., Ge, S., Zhang, J., & Xia, N. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3279019 · Cited by 8

Reliable protein-protein docking with AlphaFold, Rosetta, and replica-exchangePreprint

Harmalkar, A., Lyskov, S., & Gray, J. J. (2024) · eLife · doi:10.7554/elife.94029.1 · Cited by 8

Comparative evaluation of the prediction accuracy of AlphaFold and ESMFold for monomeric and dimeric proteins

Mahtha, S. K., Sureshkumar, V., & Mohanty, D. (2026) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqag002 · Cited by 8

Structure-informed machine learning for drug discovery: a task-centric perspective

Li, Y., Zhan, R.-H., Rao, J., Liu, M., Sang, P., Zeng, X., Zheng, M., Li, X., & Yang, L. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag081 · Cited by 8

Protein Interactome Profiling of Stable Molecular Complexes in Biomaterial Lysate

Mezentsev, Y. V., Ershov, P., Yablokov, E. O., Kaluzhskiy, L. A., Kupriyanov, K., Gnedenko, O. V., & Ivanov, A. S. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232415697 · Cited by 6

Anammox Bacterial S-Adenosyl-l-Methionine Dependent Methyltransferase Crystal Structure and Its Interaction with Acyl Carrier Proteins

Uegaki, T., Takei, T., Yamaguchi, S., Fujiyama, K., Sato, Y., Hino, T., & Nagano, S. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24010744 · Cited by 7

Crystal Structure of a Chimeric Antigen Receptor (CAR) scFv Domain Rearrangement Forming a VL-VL Dimer

Cheung, J., Wazir, S., Bell, D. R., Kochenderfer, J. N., Hendrickson, W. A., & Youkharibache, P. (2023) · Crystals · doi:10.3390/cryst13040710 · Cited by 7

Co‐evolution at protein–protein interfaces guides inference of stoichiometry of oligomeric protein complexes by de novo structure prediction

Kilian, M., & Bischofs, I. B. (2023) · Molecular Microbiology · doi:10.1111/mmi.15169 · Cited by 8

Dynamic Dimerization of Chemokine Receptors and Potential Inhibitory Role of Their Truncated Isoforms Revealed through Combinatorial Prediction

Li, M., Qing, R., Tao, F., Xu, P., & Zhang, S. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms242216266 · Cited by 7

Structural insights into the formation of repulsive netrin guidance complexes

Priest, J. M., Nichols, E. L., Smock, R. G., Hopkins, J. B., Mendoza, J. L., Meijers, R., Shen, K., & Özkan, E. (2024) · Science Advances · doi:10.1126/sciadv.adj8083 · Cited by 8

A conserved inhibitory interdomain interaction regulates DNA-binding activities of hybrid two-component systems in Bacteroides

Gao, R., Wu, T., & Stock, A. (2024) · mBio · doi:10.1128/mbio.01220-24 · Cited by 7

Protein loop structure prediction by community-based deep learning and its application to antibody CDR H3 loop modeling

Woo, H., Kim, Y., & Seok, C. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012239 · Cited by 8

Interactive tools for functional annotation of bacterial genomes

Price, M. N., & Arkin, A. P. (2024) · Database · doi:10.1093/database/baae089 · Cited by 10

FTDMP : A Framework for Protein–Protein, Protein– DNA, and Protein– RNA Docking and Scoring

Olechnovič, K., Banciul, R., Dapkūnas, J., & Venclovas, Č. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26792 · Cited by 9

Model Quality Assessment for CASP16

Fadini, A., Studer, G., & Read, R. J. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70037 · Cited by 9

Hidden Glutathione Transferases in the Human Genome

Oakley, A. J. (2023) · Biomolecules · doi:10.3390/biom13081240 · Cited by 8

Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks

Ferreira, R. P., Ding, R. F., Zhang, F., Pu, H., Donnat, C., Chen, Y., & Chen, J. (2025) · Molecular Systems Design & Engineering · doi:10.1039/d4me00203b · Cited by 6

Disobind: A sequence-based, partner-dependent contact map and interface residue predictor for intrinsically disordered regions

Majila, K., Ullanat, V., & Viswanath, S. (2026) · Cell Systems · doi:10.1016/j.cels.2025.101486 · Cited by 8

Comprehensive evaluation of artificial intelligence-empowered approaches for protein–aptamer complex prediction

Zhao, J., Tram, K., Yan, H., & Li, Y. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag206 · Cited by 6

Random mutagenesis-based screening of the interface of phyllogen, a bacterial phyllody-inducing effector, for interaction with plant MADS-box proteins

Kitazawa, Y., Iwabuchi, N., Maejima, K., Matsumoto, O., Suzuki, M., Matsuyama, J., Koinuma, H., Oshima, K., NAMBA, S., & Yamaji, Y. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1058059 · Cited by 6

AlphaFold Accurately Predicts the Structure of Ribosomally Synthesized and Post-Translationally Modified Peptide Biosynthetic Enzymes

Gordon, C. H., Hendrix, E., He, Y. J., & Walker, M. C. (2023) · Biomolecules · doi:10.3390/biom13081243 · Cited by 6

Conservation of Hot Spots and Ligand Binding Sites in Protein Models by AlphaFold2

Bekar-Cesaretli, A. A., Khan, O., Nguyen, T., Kozakov, D., Joseph‐McCarthy, D., & Vajda, S. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01761 · Cited by 6

Computational analysis of propeptide‐containing proteins and prediction of their post‐cleavage conformation changes

Pei, J., Kinch, L. N., & Cong, Q. (2024) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26702 · Cited by 6

Biochemical and structural characterization of Rab3GAP reveals insights into Rab18 nucleotide exchange activity

Fairlie, G. M. J., Nguyen, K. M., Nam, S.-E., Shaw, A. L., Parson, M. A., Shariati, H. R., Wang, X., Jenkins, M. L., Gong, M., Burke, J. E., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-55828-8 · Cited by 6

A Web Server for GPCR-GPCR Interaction Pair Prediction

Nemoto, W., Yamanishi, Y., Limviphuvadh, V., Fujishiro, S., Shimamura, S., Fukushima, A., & Toh, H. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.825195 · Cited by 6

Application of radish pods in households and effect of their active components against different diseases: A review

Noreen, S., Khalid, W., Niazi, M. K., Javed, M., Aziz, A., Raza, A., Khalid, M. Z., & Chamba, M. V. M. (2023) · International Journal of Food Properties · doi:10.1080/10942912.2023.2241662 · Cited by 6

Predictive Modeling of Proteins Encoded by a Plant Virus Sheds a New Light on Their Structure and Inherent Multifunctionality

Roy, B. G., Choi, J., & Fuchs, M. (2024) · Biomolecules · doi:10.3390/biom14010062 · Cited by 6

Accuracy of AlphaFold models: Comparison with short N O contacts in atomic resolution protein crystal structures

Carugo, O. (2024) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2024.108069 · Cited by 6

Balancing the Affinity and Tumor Cell Binding of a Two-in-One Antibody Simultaneously Targeting EGFR and PD-L1

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Characterization of molecular interactions between HDAC7 and MEF2A

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