SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots
Open accessAbstract
We present SpotOn, a web server to identify and classify interfacial residues as Hot-Spots (HS) and Null-Spots (NS). SpotON implements a robust algorithm with a demonstrated accuracy of 0.95 and sensitivity of 0.98 on an independent test set. The predictor was developed using an ensemble machine learning approach with up-sampling of the minor class. It was trained on 53 complexes using various features, based on both protein 3D structure and sequence. The SpotOn web interface is freely available at: http://milou.science.uu.nl/services/SPOTON/.
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PSSM has been proven to provide crucial information in related topics such as binding site predictions and hot-spot predictions.
“PSSM has been proven to provide crucial information in various related topics, such as binding site predictions37 and hot‐spot predictions.38”
From iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations (2019) · cites this paper as [38]
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Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,706 · see citing works
Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669 · see citing works
VMD: visual molecular dynamics
Humphrey, W., Dalke, A., & Schulten, K. (1996) · Journal of Molecular Graphics · doi:10.1016/0263-7855(96)00018-5 · Cited by 66,952 · see citing works
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
BLAST+: architecture and applications
Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K., & Madden, T. L. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-421 · Cited by 22,074 · see citing works
Building Predictive Models in R Using the caret Package
Kühn, M. (2008) · Journal of Statistical Software · doi:10.18637/jss.v028.i05 · Cited by 9,986 · see citing works
Protein structure prediction and analysis using the Robetta server
Kim, D. E., Chivian, D. C., & Baker, D. A. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh468 · Cited by 2,099 · see citing works
A Hot Spot of Binding Energy in a Hormone-Receptor Interface
Clackson, T., & Wells, J. A. (1995) · Science · doi:10.1126/science.7529940 · Cited by 1,987 · see citing works
Some remarks on protein attribute prediction and pseudo amino acid composition
Chou, K.-C. (2011) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2010.12.024 · Cited by 1,225 · see citing works
Hot spots—A review of the protein–protein interface determinant amino‐acid residues
Moreira, I. S., Fernandes, P. A., & Ramos, M. J. (2007) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.21396 · Cited by 760 · see citing works
Self-citationNGL Viewer: a web application for molecular visualization
Rose, A. S., & Hildebrand, P. W. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv402 · Cited by 462 · see citing works
Thorn, K. S., & Bogan, A. A. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.3.284 · Cited by 403 · see citing works
Xiao, N., Cao, D.-S., Zhu, M.-F., & Xu, Q.-S. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv042 · Cited by 334 · see citing works
CPORT: A Consensus Interface Predictor and Its Performance in Prediction-Driven Docking with HADDOCK
de Vries, S. J., & Bonvin, A. M. J. J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0017695 · Cited by 319 · see citing works
Self-citationEnsembles of Learning Machines
Valentini, G., & Masulli, F. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45808-5_1 · Cited by 289 · see citing works
Du, P.-F., Gu, S., & Jiao, Y.-S. (2014) · International Journal of Molecular Sciences · doi:10.3390/ijms15033495 · Cited by 282 · see citing works
Moal, I. H., & Fernández-Recio, J. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts489 · Cited by 281 · see citing works
Lin, H. (2008) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2008.02.004 · Cited by 251 · see citing works
Modulation of Protein–Protein Interactions for the Development of Novel Therapeutics
Πέττα, Ι., Lievens, S., Libert, C., Tavernier, J. H., & Bosscher, K. D. (2015) · Molecular Therapy · doi:10.1038/mt.2015.214 · Cited by 240 · see citing works
Ding, H., Deng, E.-Z., Yuan, L., Liu, L., Lin, H., Chen, W., & Chou, K. (2014) · BioMed Research International · doi:10.1155/2014/286419 · Cited by 225 · see citing works
Refined solution structure of the oligomerization domain of the tumour suppressor p53
Clore, G. M., Ernst, J. A., Clubb, R. T., Omichinski, J. G., Kennedy, W. P., Sakaguchi, K., Appella, E., & Gronenborn, A. M. (1995) · Nature Structural & Molecular Biology · doi:10.1038/nsb0495-321 · Cited by 224 · see citing works
Zhu, X., & Mitchell, J. C. (2011) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.23094 · Cited by 221 · see citing works
Lin, H., Ding, H., Guo, F., et al. (2008) · Protein and Peptide Letters · doi:10.2174/092986608785133681 · Cited by 193 · see citing works
Computational prediction of protein interfaces: A review of data driven methods
Xue, L. C., Dobbs, D. L., Bonvin, A. M. J. J., & Honavar, V. (2015) · FEBS Letters · doi:10.1016/j.febslet.2015.10.003 · Cited by 184 · see citing works
Self-citationIdentifying Sigma70 Promoters with Novel Pseudo Nucleotide Composition
Lin, H., Liang, Z.-Y., Tang, H., & Chen, W. (2017) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2017.2666141 · Cited by 180 · see citing works
van Westen, G. J. P., Wegner, J. K., IJzerman, A. P., van Vlijmen, H. W. T., & Bender, A. (2011) · MedChemComm · doi:10.1039/c0md00165a · Cited by 176 · see citing works
Zhang, C.-J., Tang, H., Li, W., Lin, H., Chen, W., & Chou, K. (2016) · Oncotarget · doi:10.18632/oncotarget.11975 · Cited by 176 · see citing works
The binding interface database (BID): a compilation of amino acid hot spots in protein interfaces
Fischer, T. B., Arunachalam, K. V., Bailey, D., Mangual, V., Bakhru, S., Russo, R., Huang, D. S., Paczkowski, M., Lalchandani, V., Ramachandra, C., et al. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg163 · Cited by 164 · see citing works
Prediction of Cell Wall Lytic Enzymes Using Chous Amphiphilic Pseudo Amino Acid Composition
Ding, H., Luo, L., & Lin, H. (2009) · Protein and Peptide Letters · doi:10.2174/092986609787848045 · Cited by 153 · see citing works
Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition
Lin, H., & Ding, H. (2010) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2010.10.019 · Cited by 151 · see citing works
Brender, J. R., & Zhang, Y. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004494 · Cited by 140 · see citing works
Identification of Secretory Proteins inMycobacterium tuberculosisUsing Pseudo Amino Acid Composition
Yang, H., Tang, H., Chen, X.-X., Zhang, C.-J., Zhu, P.-P., Ding, H., Chen, W., & Lin, H. (2016) · BioMed Research International · doi:10.1155/2016/5413903 · Cited by 127 · see citing works
AcalPred: A Sequence-Based Tool for Discriminating between Acidic and Alkaline Enzymes
Lin, H., Chen, W., & Ding, H. (2013) · PLoS ONE · doi:10.1371/journal.pone.0075726 · Cited by 122 · see citing works
PINT: Protein-protein Interactions Thermodynamic Database
Kumar, M. D. S. (2005) · Nucleic Acids Research · doi:10.1093/nar/gkj017 · Cited by 103 · see citing works
Identification of mitochondrial proteins of malaria parasite using analysis of variance
Ding, H., & Li, D. (2014) · Amino Acids · doi:10.1007/s00726-014-1862-4 · Cited by 101 · see citing works
Ding, H., Liu, L., Guo, F., et al. (2011) · Protein and Peptide Letters · doi:10.2174/092986611794328708 · Cited by 90 · see citing works
Eukaryotic and prokaryotic promoter prediction using hybrid approach
Lin, H., & Li, Q. (2010) · Theory in Biosciences · doi:10.1007/s12064-010-0114-8 · Cited by 63 · see citing works
A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces
Melo, R., Fieldhouse, R. J., Melo, A., Correia, J. D. G., Cordeiro, M. N. D. S., Gumus, Z. H., da Costa, J. P., Bonvin, A. M. J. J., & Moreira, I. S. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17081215 · Cited by 60 · see citing works
Self-citationRAMPred: identifying the N1-methyladenosine sites in eukaryotic transcriptomes
Chen, W., Feng, P., Tang, H., et al. (2016) · Scientific Reports · doi:10.1038/srep31080 · Cited by 60 · see citing works
Identifying RNA 5-methylcytosine sites via pseudo nucleotide compositions
Feng, P., Ding, H., Chen, W., & Lin, H. (2016) · Molecular BioSystems · doi:10.1039/c6mb00471g · Cited by 58 · see citing works
Prediction of phosphothreonine sites in human proteins by fusing different features
Zhao, Y.-W., Lai, H.-Y., Tang, H., Chen, W., & Lin, H. (2016) · Scientific Reports · doi:10.1038/srep34817 · Cited by 56 · see citing works
The prediction of protein structural class using averaged chemical shifts
Lin, H., Ding, C., Song, Q., et al. (2012) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2011.672628 · Cited by 54 · see citing works
Munteanu, C. R., Pimenta, A. C., Fernandez-Lozano, C., Melo, A., Cordeiro, M. N. D. S., & Moreira, I. S. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500760m · Cited by 54 · see citing works
Self-citationProtein function in precision medicine: deep understanding with machine learning
Rost, B., Predrag Radivojac, A., & Bromberg, Y. (2016) · FEBS Letters · doi:10.1002/1873-3468.12307 · Cited by 51 · see citing works
Solvent‐accessible surface area: How well can be applied to hot‐spot detection?
Martins, J. M. D. S., Ramos, R. M., Pimenta, A. C., & Moreira, I. S. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24413 · Cited by 48 · see citing works
Self-citationRamos, R. M., & Moreira, I. S. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400387r · Cited by 39 · see citing works
Self-citationPAI: Predicting adenosine to inosine editing sites by using pseudo nucleotide compositions
Chen, W., Feng, P., Ding, H., et al. (2016) · Scientific Reports · doi:10.1038/srep35123 · Cited by 36 · see citing works
Definition by Means of Indefiniteness
Zhuravlev, Y. N. (2012) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/073911012010525015 · Cited by 27 · see citing works
The Role of Water Occlusion for the Definition of a Protein Binding Hot-Spot
Moreira, I. S. (2015) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026615666150519103733 · Cited by 18 · see citing works
Self-citationSimulation of biological evolution and the NFL theorems
Meester, R. W. J. (2008) · Biology & Philosophy · doi:10.1007/s10539-008-9134-x · Cited by 7 · see citing works
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Research works printed but not linked: 1
- [22]Valentini, G. & Masulli, F. In Neural Nets: 13th Italian Workshop on Neural Nets, WIRN VIETRI 2002 Vietri sul Mare, Italy, May 30 – June 1, 2002 Revised Papers (eds Maria Marinaro & Roberto Tagliaferri) 3–20 (Springer Berlin Heidelberg, 2002).
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Geng, C., Vangone, A., Folkers, G. E., Xue, L. C., & Bonvin, A. M. J. J. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25630 · Cited by 82 · see citing works
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- Authors
- Moreira, I. S., Koukos, P. I., Melo, R., Almeida, J. G., Preto, A. J., Schaarschmidt, J., Trellet, M., Gümüş, Z. H., Costa, J., & Bonvin, A. M. J. J.
- Affiliations
- Utrecht University (NL)
- University of Lisbon (PT)
- Universidade do Porto (PT)
- Corresponding author
- Moreira, Irina S.
- Bonvin, Alexandre M. J. J.
- ORCID
- Venue
- Scientific Reports
- Published
- 2017-08-14
- Volume
- 7
- Issue
- 1
- Pages
- 8007-8007
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5556074
- OpenAlex
- W2742299251
- Cited by
- 98
- References
- 50
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 14 · 2023: 11 · 2022: 12 · 2021: 18 · 2020: 17 · 2019: 11 · 2018: 7
- FWCI
- 3.89
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Funders
- European Commission (659826)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (TOP-PUNT grant 718.015.001, 718.015.001, 675728)
- Programa Operacional Regional do Centro (CENTRO‐01‐0145‐FEDER‐000008:BrainHealth2020, POCI-01-0145-FEDER-007440, CENTRO-01-0145-FEDER-000008)
- Horizon 2020 Framework Programme (675858, 675728)
- Fundação para a Ciência e a Tecnologia (01-0145, COMPETE, CENTRO-01-0145-FEDER-000008), POCI-01-0145, PO-CI-01-0145-FEDER-007440, CENTRO-01-0145-FEDER, IF/00578/2014, H2020, POCI-01-0145-FEDER-, COMPETE_2020, POCI-01-, SFRH/BPD/97650/2013)
- European Regional Development Fund (COMPETE, project CENTRO-01-0145-FEDER-000008: BrainHealth 2020, H2020, POCI-01, POCI-01-0145-FEDER-007440, CENTRO-01-0145-FEDER-000008:BrainHealth2020, 01-0145, COMPETE2020, POCI-01-0145, CENTRO-01-0145-FEDER-000008, POCI-01-0145- FEDER)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- protein-protein interfaces, hot spots, interfacial residues, protein-protein complex, residue classification, ensemble learning, class imbalance, web server, sequence features
- MeSH terms
- Machine Learning, Animals, Binding Sites, Humans, Protein Binding, Software, Sequence Analysis, Protein, Protein Interaction Mapping
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