PremPLI Method

PremPLI predicts the effects of single mutations on protein-ligand interactions by calculating the binding affinity changes quantitatively. The predictions are based on the 3D structure of the protein-ligand complex.

The PremPLI structure optimization protocol. The 3D structures of protein-ligand complexes were taken from the Protein Data Bank (PDB) (1), mutant structures were produced using the BuildModel module of FoldX (2). FoldX only optimizes the neighboring side chains around the mutation site when creating a mutant structure. Missing heavy side-chain and hydrogen atoms in protein were added via VMD program (3) using the topology parameters of the CHARMM36 force field (4). Hydrogen atoms of ligands were added via Chimera (5).

The PremPLI model uses random forest (RF) regression scoring function, training on experimental data of binding affinity changes (ΔΔG) for 796 mutations from 360 protein-ligand complexes.


The PremPLI scoring function is composed of 11 distinct features that contribute significantly to the quality of the model. The importance and the description of each feature are shown below:



1. Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N. and Bourne, P.E. (2000) The Protein Data Bank. Nucleic acids research, 28, 235-242.

2. Guerois, R., Nielsen, J.E. and Serrano, L. (2002) Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. Journal of molecular biology, 320, 369-387.

3. Humphrey, W., Dalke, A. and Schulten, K. (1996) VMD: visual molecular dynamics. Journal of molecular graphics, 14, 33-38, 27-38.

4. MacKerell, A.D., Bashford, D., Bellott, M., Dunbrack, R.L., Evanseck, J.D., Field, M.J., Fischer, S., Gao, J., Guo, H., Ha, S. et al. (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. The journal of physical chemistry, B, 102, 3586-3616.

5. Pettersen, E.F., Goddard, T.D., Huang, C.C., Couch, G.S., Greenblatt, D.M., Meng, E.C. and Ferrin, T.E. (2004) UCSF Chimera--a visualization system for exploratory research and analysis. Journal of computational chemistry, 25, 1605-1612.

6. Bhagwat, M. and Aravind, L. (2007) PSI-BLAST tutorial. Methods in molecular biology (Clifton, N.J.), 395, 177-186.

7. Choi, Y., Sims, G.E., Murphy, S., Miller, J.R. and Chan, A.P. (2012) Predicting the functional effect of amino acid substitutions and indels. PloS one, 7, e46688.

8. Jubb, H.C., Higueruelo, A.P., Ochoa-Montano, B., Pitt, W.R., Ascher, D.B. and Blundell, T.L. (2017) Arpeggio: A Web Server for Calculating and Visualising Interatomic Interactions in Protein Structures. Journal of molecular biology, 429, 365-371.

9. Cheng, T., Zhao, Y., Li, X., Lin, F., Xu, Y., Zhang, X., Li, Y., Wang, R. and Lai, L. (2007) Computation of Octanol−Water Partition Coefficients by Guiding an Additive Model with Knowledge. Journal of chemical information and modeling, 47, 2140-2148.

10. Petukh, M., Li, M. and Alexov, E. (2015) Predicting Binding Free Energy Change Caused by Point Mutations with Knowledge-Modified MM/PBSA Method. PLoS computational biology, 11, e1004276.

11. Hou, Q., Kwasigroch, J.M., Rooman, M. and Pucci, F. (2020) SOLart: a structure-based method to predict protein solubility and aggregation. Bioinformatics, 36, 1445-1452.

12. Azarya-Sprinzak, E., Naor, D., Wolfson, H.J. and Nussinov, R. (1997) Interchanges of spatially neighbouring residues in structurally conserved environments. Protein engineering, 10, 1109-1122.

13. Risler, J.L., Delorme, M.O., Delacroix, H. and Henaut, A. (1988) Amino acid substitutions in structurally related proteins. A pattern recognition approach. Determination of a new and efficient scoring matrix. Journal of molecular biology, 204, 1019-1029.


More details can be found in our paper.


School of Biology & Basic Medical Sciences, Soochow University
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