An artificial neural network model to predict structure-based protein-protein free energy of binding from Rosetta-calculated properties

被引:2
|
作者
Ferraz, Matheus V. F. [1 ,2 ,3 ]
Neto, Jose C. S. [4 ]
Lins, Roberto D. [1 ,2 ]
Teixeira, Erico S. [4 ]
机构
[1] Fiocruz MS, Oswaldo Cruz Fdn, Aggeu Magalhaes Inst, Dept Virol, Recife, PE, Brazil
[2] Univ Fed Pernambuco, UFPE, Dept Fundamental Chem, Recife, PE, Brazil
[3] HITS, Heidelberg Inst Theoret Studies, Heidelberg, Germany
[4] CESAR, Recife Ctr Adv Studies & Syst, Recife, PE, Brazil
关键词
DE-NOVO DESIGN; AFFINITY PREDICTION; CONTINUUM SOLVENT; STABILITY; MOLECULES; CLASSIFICATION; OPTIMIZATION; COMPUTATION; NANOBODIES; COMPLEXES;
D O I
10.1039/d2cp05644e
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
The prediction of the free energy (Delta G) of binding for protein-protein complexes is of general scientific interest as it has a variety of applications in the fields of molecular and chemical biology, materials science, and biotechnology. Despite its centrality in understanding protein association phenomena and protein engineering, the Delta G of binding is a daunting quantity to obtain theoretically. In this work, we devise a novel Artificial Neural Network (ANN) model to predict the Delta G of binding for a given three-dimensional structure of a protein-protein complex with Rosetta-calculated properties. Our model was tested using two data sets, and it presented a root-mean-square error ranging from 1.67 kcal mol(-1) to 2.45 kcal mol(-1), showing a better performance compared to the available state-of-the-art tools. Validation of the model for a variety of protein-protein complexes is showcased.
引用
收藏
页码:7257 / 7267
页数:11
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