Feature functional theory–binding predictor (FFT–BP) for the blind prediction of binding free energies

Bao Wang, Zhixiong Zhao, Duc D. Nguyen, Guo Wei Wei

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

We present a feature functional theory–binding predictor (FFT–BP) for the protein–ligand binding affinity prediction. The underpinning assumptions of FFT–BP are as follows: (1) representability: There exists a microscopic feature vector that can uniquely characterize and distinguish one protein–ligand complex from another; (2) feature–function relationship: the macroscopic features, including binding free energy, of a complex is a functional of microscopic feature vectors; and (3) similarity: molecules with similar microscopic features have similar macroscopic features, such as binding affinity. Physical models, such as implicit solvent models and quantum theory, are utilized to extract microscopic features, while machine learning algorithms are employed to rank the similarity among protein–ligand complexes. A large variety of numerical validations and tests confirms the accuracy and robustness of the proposed FFT–BP model. The root-mean-square errors of FFT–BP blind predictions of a benchmark set of 100 complexes, the PDBBind v2007 core set of 195 complexes and the PDBBind v2015 core set of 195 complexes are 1.99, 2.02 and 1.92 kcal/mol, respectively. Their corresponding Pearson correlation coefficients are 0.75, 0.80, and 0.78, respectively.

Original languageEnglish
Article number55
JournalTheoretical Chemistry Accounts
Volume136
Issue number4
DOIs
StatePublished - Apr 1 2017

Bibliographical note

Publisher Copyright:
© 2017, Springer-Verlag Berlin Heidelberg.

Keywords

  • Implicit solvent model
  • Microscopic feature
  • Protein–ligand binding
  • Scoring function

ASJC Scopus subject areas

  • Physical and Theoretical Chemistry

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