REFERENCES
1. Cukuroglu E, Engin HB, Gursoy A, Keskin O. Hot spots in protein-protein interfaces: towards drug discovery. Progress in Biophysics and Molecular Biology . 2014; 116(2-3): 165-173. doi:10.1016/j.pbiomolbio.2014.06.003
2. Jubb H, Blundell TL, Ascher DB. Flexibility and small pockets at protein-protein interfaces: New insights into druggability.Progress in Biophysics and Molecular Biology . 2015; 119(1): 2-9. doi:10.1016/j.pbiomolbio.2015.01.009
3. Junaid M, Li CD, Shah M, Khan A, Guo H, Wei DQ. Extraction of molecular features for the drug discovery targeting protein-protein interaction of Helicobacter pylori CagA and tumor suppressor protein ASSP2. Proteins: Structure, Function and Bioinformatics . 2019; 87(10): 837-849. doi:10.1002/prot.25748
4. Lanzarotti E, Defelipe LA, Marti MA, Turjanski AG. Aromatic clusters in protein-protein and protein-drug complexes. Journal of Cheminformatics . 2020; 12(1): 1-9. doi:10.1186/s13321-020-00437-4
5. Rosell M, Fernández-Recio J. Hot-spot analysis for drug discovery targeting protein-protein interactions. Expert Opinion on Drug Discovery . 2018; 13(4): 327-338. doi:10.1080/17460441.2018.1430763
6. Sarvagalla S, Cheung CHA, Tsai JY, Hsieh HP, Coumar MS. Disruption of protein-protein interactions: Hot spot detection, structure-based virtual screening and: In vitro testing for the anti-cancer drug target-survivin. RSC Advances . 2016; 6(38): 31947-31959. doi:10.1039/c5ra22927h
7. Srivastava M, Suri C, Singh M, Mathur R, Asthana S. Molecular dynamics simulation reveals the possible druggable hotspots of USP7.Oncotarget . 2018; 9(76): 34289-34305. doi:10.18632/oncotarget.26136
8. Wang, L., Wang, N., Zhang, W., Cheng X, Yan Z, Shao G, Wang X, Wang R, Fu C. Therapeutic peptides: current applications and future directions. Sig Transduct Target Ther. 2022. 7, 48. https://doi.org/10.1038/s41392-022-00904-4.
9. Clackson T, Wells JA. A hot spot of binding energy in a hormone-receptor interface. Science . 1995; 267(5196): 383-386. doi:10.1126/science.7529940
10. Bogan AA, Thorn KS. Anatomy of hot spots in protein interfaces.J Mol Biol . 1998; 280(1): 1-9. doi:10.1006/jmbi.1998.1843
11. Csizmok V, Follis AV, Kriwacki RW, Forman-Kay JD. Dynamic protein interaction networks and new structural paradigms in signaling.Chem Rev . 2016; 116(11): 6424-6462. doi:10.1021/acs.chemrev.5b00548
12. Rajamani D, Thiel S, Vajda S, Camacho CJ. Anchor residues in protein–protein interactions. Proc Natl Acad Sci USA . 2004; 101(31): 11287. doi:10.1073/pnas.0401942101
13. Meireles LMC, Dömling AS, Camacho CJ. ANCHOR: a web server and database for analysis of protein-protein interaction binding pockets for drug discovery. Nucleic Acids Res . 2010; 38(Web Server issue): W407-411. doi:10.1093/nar/gkq502
14. Sirin S, Apgar JR, Bennett EM, Keating AE. AB-Bind: Antibody binding mutational database for computational affinity predictions.Protein Science . 2016; 25(2): 393-409. doi:10.1002/pro.2829
15. Thorn KS, Bogan AA. ASEdb: a database of alanine mutations and their effects on the free energy of binding in protein interactions.Bioinformatics . 2001; 17(3): 284-285. doi:10.1093/bioinformatics/17.3.284
16. Borrman T, Cimons J, Cosiano M, et al. ATLAS: A database linking binding affinities with structures for wild-type and mutant TCR-pMHC complexes. Proteins . 2017; 85(5): 908-916. doi:10.1002/prot.25260
17. Fischer TB, Arunachalam KV, Bailey D, et al. Binding interface database (BID): a compilation of amino acid hot spots in protein interfaces. Bioinformatics . 2003; 19(11): 1453–1454. https://doi.org/10.1093/bioinformatics/btg163
18. Geng C, Vangone A, Bonvin AMJJ. Exploring the interplay between experimental methods and the performance of predictors of binding affinity change upon mutations in protein complexes. Protein Eng Des Sel . 2016; 29(8): 291-299. doi:10.1093/protein/gzw020
19. Liu L, Xiong Y, Gao H, Wei D-Q, Mitchell JC, Zhu X. dbAMEPNI: a database of alanine mutagenic effects for protein–nucleic acid interactions. Database (Oxford) . 2018; 2018. doi:10.1093/database/bay034
20. Liu Q, Chen P, Wang B, Zhang J, Li J. dbMPIKT: a database of kinetic and thermodynamic mutant protein interactions. BMC Bioinformatics . 2018; 19(1): 455. doi:10.1186/s12859-018-2493-7
21. Jemimah S, Yugandhar K, Michael Gromiha M. PROXiMATE: a database of mutant protein-protein complex thermodynamics and kinetics.Bioinformatics . 2017; 33(17): 2787-2788. doi:10.1093/bioinformatics/btx312
22. Jankauskaite J, Jiménez-García B, Dapkunas J, Fernández-Recio J, Moal IH. SKEMPI 2.0: an updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation.Bioinformatics . 2019; 35(3): 462-469. doi:10.1093/bioinformatics/bty635
23. Guerois R, Nielsen JE, Serrano L. Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J Mol Biol . 2002; 320(2): 369-387. doi:10.1016/S0022-2836(02)00442-4
24. Schymkowitz J, Borg J, Stricher F, Nys R, Rousseau F, Serrano L. The FoldX web server: an online force field. Nucleic Acids Research . 2005; 33(2): W382–W388. https://doi.org/10.1093/nar/gki387
25. Kortemme T, Baker D. A simple physical model for binding energy hot spots in protein–protein complexes. Proc Natl Acad Sci USA . 2002; 99(22): 14116-14121. doi:10.1073/pnas.202485799
26. Kortemme T, Kim DE, Baker D. Computational alanine scanning of protein-protein interfaces. Sci STKE . 2004; 2004(219): pl2. doi:10.1126/stke.2192004pl2
27. Barlow KA, Ó Conchúir S, Thompson S, Suresh P, Lucas JE, Heinonen M, Kortemme T. Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein–Protein Binding Affinity upon Mutation. J. Phys. Chem. B. 2018; 122, 5389–5299. 10.1021/acs.jpcb.7b11367.
28. Dehouck Y.; Kwasigroch J. M.; Rooman M.; Gilis D. BeAtMuSiC: prediction of changes in protein–protein binding affinity on mutations.Nucleic Acids Res . 2013; 41, W333–W339. 10.1093/nar/gkt450.
29. Pires D. E. V.; Ascher D. B.; Blundell T. L. mCSM: predicting the effects of mutations in proteins using graph-based signatures.Bioinformatics 2014; 30, 335–342. 10.1093/bioinformatics/btt691.
30. Witvliet DK, Strokach A, Giraldo-Forero AF, Teyra J, Colak R, Kim PM. ELASPIC web-server: proteome-wide structure-based prediction of mutation effects on protein stability and binding affinity.Bioinformatics . 2016; 32(10): 1589-1591. doi:10.1093/bioinformatics/btw031
31. Geng C, Vangone A, Folkers GE, Xue LC, Bonvin AMJJ. iSEE: Interface structure, evolution, and energy‐based machine learning predictor of binding affinity changes upon mutations. Proteins . 2019; 87(2): 110-119. doi:10.1002/prot.25630
32. Li M, Simonetti FL, Goncearenco A, Panchenko AR. MutaBind estimates and interprets the effects of sequence variants on protein–protein interactions. Nucleic Acids Res . 2016; 44(Web Server issue): W494-W501. doi:10.1093/nar/gkw374
33. Benedix A, Becker CM, de Groot BL, Caflisch A, Böckmann RA. Predicting free energy changes using structural ensembles. Nature Methods . 2009; 6(1): 3-4. doi:10.1038/nmeth0109-3
34. Brender JR, Zhang Y. Predicting the effect of mutations on protein-protein binding interactions through structure-based interface profiles. PLOS Computational Biology . 2015; 11(10): e1004494. doi:10.1371/journal.pcbi.1004494
35. Petukh M, Dai L, Alexov E. SAAMBE: Webserver to predict the charge of binding free energy caused by amino acids mutations. Int J Mol Sci . 2016; 17(4). doi:10.3390/ijms17040547
36. Rosell M, Fernández-Recio J. Hot-spot analysis for drug discovery targeting protein-protein interactions. Expert Opin Drug Discov . 2018;13(4):327-338. doi:10.1080/17460441.2018.1430763
37. Beard H, Cholleti A, Pearlman D, Sherman W, Loving KA. Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes. Plos One . 2013; 8(12): e82849. doi:10.1371/journal.pone.0082849
38. Schrödinger Release 2019–3, Maestro, Protein Preparation Wizard, Prime, MM-GBSA, Schrödinger, LLC, New York, NY, 2020.
39. Vreven T, Moal IH, Vangone A, et al. Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2. J Mol Biol . 2015;427(19):3031-3041. doi:10.1016/j.jmb.2015.07.016
40. Hauser AS, Windshügel B. LEADS-PEP: A Benchmark Data Set for Assessment of Peptide Docking Performance. J ChemInf Model . 2016;56(1):188-200. doi:10.1021/acs.jcim.5b00234
41 . Xiang Z, Honig B. Extending the accuracy limits of prediction for side-chain conformations. J Mol Biol . 2001;311(2):421-430. doi:10.1006/jmbi.2001.4865
42. Reddy MR, Reddy CR, Rathore RS, et al. Free energy calculations to estimate ligand-binding affinities in structure-based drug design.Curr Pharm Des . 2014; 20(20): 3323-3337. doi:10.2174/13816128113199990604
43. Genheden S, Ryde U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities .Expert Opin Drug Discov . 2015;10(5):449-461. doi:10.1517/17460441.2015.1032936
44.Sirin S, Pearlman DA, Sherman W. Physics-based enzyme design: predicting binding affinity and catalytic activity. Proteins . 2014;82(12):3397-3409. doi:10.1002/prot.24694
45. Negron C, Pearlman DA, Del Angel G. Predicting mutations deleterious to function in beta-lactamase TEM1 using MM-GBSA. PLoS ONE . 2019;14(3):e0214015. doi:10.1371/journal.pone.0214015
46. Mason PE, Neilson GW, Dempsey CE, Barnes AC, Cruickshank JM. The hydration structure of guanidinium and thiocyanate ions: implications for protein stability in aqueous solution. Proc Natl Acad Sci USA. 2003; 100(8): 4557-61. doi: 10.1073/pnas.0735920100.
47. Samanta U, Bahadur RP, Chakrabarti P. Quantifying the accessible surface area of protein residues in their local environment.Protein Eng. 2002; 15(8): 659-67. doi: 10.1093/protein/15.8.659.
48. Pace CN, Grimsley GR, Scholtz JM. Protein ionizable groups: pK values and their contribution to protein stability and solubility.J Biol Chem. 2009; 284(20): 13285-9. doi: 10.1074/jbc.R800080200.
49. Chakrabarti P, Bhattacharyya R. Geometry of nonbonded interactions involving planar groups in proteins. Prog Biophys Mol Biol. 2007; 95(1-3): 83-137. doi: 10.1016/j.pbiomolbio.2007.03.016.
50. Koide S, Sidhu SS. The importance of being tyrosine: lessons in molecular recognition from minimalist synthetic binding proteins. ACS Chem Biol. 2009; 4(5): 325-34. doi: 10.1021/cb800314v.
51. Keskin O, Ma B, Nussinov R. Hot regions in protein–protein interactions: the organization and contribution of structurally conserved hot spot residues. J Mol Biol. 2005; 345(5): 1281-94. doi: 10.1016/j.jmb.2004.10.077.