Proteins perform vital cell functions by interacting with other proteins to form protein-protein complexes, through non-covalent interactions. However, mutations in proteins can affect crucial interactions, cause changes in folding, stability and binding affinity, and may lead to diseases. In this work, we have developed PROXiMATE, a database with thermodynamic data for mutations such as binding free energy, changes in binding free energy as well as dissociation and association constants for heterodimeric and homodimeric protein-protein complexes. The database is available at https://urlprotection-tko.global.sonicwall.com/click?PV=1&MSGID=202007221115040125315&URLID=5&ESV=10.0.6.3447&IV=68985E7A11C8BC84B833652A347AC195&TT=1595416509002&ESN=NUyDD0YyMmfipwfIyEuZFfrd%2BrwB2914dG6gk%2FXAnOU%3D&KV=1536961729279&ENCODED_URL=http%3A%2F%2Fwww.iitm.ac.in%2Fbioinfo%2FPROXiMATE&HK=C3EDE9AD10F3EBBFB8C3CDD98284F18D728353C011D83B803A84C8AF6BD6CC67/. PROXiMATE was used to analyze the additivity of binding affinity of double mutants. While a majority of double mutations are additive, a significant proportion of double mutants are non-additive. Non-additive double mutants tend to be closer to each other and have more contacts. Further, we have developed ProAffiMuSeq, a sequence-based method to predict changes in binding free energy using functional class. The correlation between experimental and predicted changes in binding free energy is 0.73, with a MAE of 0.86 kcal/mol. The performance is comparable to structure-based methods in a blind dataset. Users can access the method at https://urlprotection-tko.global.sonicwall.com/click?PV=1&MSGID=202007221115040125315&URLID=3&ESV=10.0.6.3447&IV=86CF7A4BB4E20B865506B8D09021047C&TT=1595416509002&ESN=McUskcZHbR7Ds%2BCNM4YyRtlVZWOASTq755%2FXbBry9fY%3D&KV=1536961729279&ENCODED_URL=https%3A%2F%2Fweb.iitm.ac.in%2Fbioinfo2%2Fproaffimuseq&HK=E1ED8C1B91FFD829197116D07E1D4605ED64EB69F92A8E4BFCE514EF13B853E0/. We applied ProAffiMuSeq in a systematic analysis of the relationship between changes in binding affinity and disease-causing mutations. The majority of disease-causing mutations tend to decrease binding affinity. However, in certain cancers, a significant proportion may increase the binding affinity, and may have been selected to enhance cell survival and growth. Our research shows that incorporating the effects of mutations on binding affinity in protein-protein interaction network studies can be used in protein engineering, provide insights into disease mechanisms, and help to identify novel drug targets.

Publications:
1. Jemimah, S., K. Yugandhar, and M.M. Gromiha (2017). PROXiMATE: a database of mutant protein-protein complex thermodynamics and kinetics. Bioinformatics, 33(17), 27872788.
2. Gromiha M.M., K. Yugandhar and S. Jemimah (2017). Protein-protein interactions: scoring schemes and binding affinity. Curr Opin Struct Biol, 44:31-38.
3. Jemimah, S. and M.M. Gromiha (2018). Exploring additivity effects of double mutations on the binding affinity of protein-protein complexes. Proteins, 86(5), 536-547.
4. Jemimah, S., M. Sekijima, and M.M. Gromiha (2019). ProAffiMuSeq: sequence-based method to predict the binding free energy change of protein-protein complexes upon mutation using functional classification. Bioinformatics, 36(6), 1725-1730.
5. Jemimah, S. and M.M. Gromiha (2020). Insights into changes in binding affinity caused by disease mutations in protein-protein complexes. Comput Biol Med, In press. DOI: 10.1016/j.compbiomed.2020.103829
6. Jemimah, S., K. Yugandhar and M.M. Gromiha (2020). Binding affinity of protein-protein complexes: experimental techniques, databases and computational methods. In Gromiha MM (Ed.), Protein interactions: Computational methods, analysis and applications. pp. 87-108. Singapore: World Scientific (March 2020)