Literature DB >> 24191692

Binding affinity prediction for protein-ligand complexes based on β contacts and B factor.

Qian Liu1, Chee Keong Kwoh, Jinyan Li.   

Abstract

Accurate determination of protein-ligand binding affinity is a fundamental problem in biochemistry useful for many applications including drug design and protein-ligand docking. A number of scoring functions have been proposed for the prediction of protein-ligand binding affinity. However, accurate prediction is still a challenging problem because poor performance is often seen in the evaluation under the leave-one-cluster-out cross-validation (LCOCV). We introduce a new scoring function named B2BScore to improve the prediction performance. B2BScore integrates two physicochemical properties for protein-ligand binding affinity prediction. One is the property of β contacts. A β contact between two atoms requires no other atoms to interrupt the atomic contact and assumes that the two atoms should have enough direct contact area. The other is the property of B factor to capture the atomic mobility in the dynamic protein-ligand binding process. Tested on the PDBBind2009 data set, B2BScore shows superior prediction performance to existing methods on independent test data as well as under the LCOCV evaluation framework. In particular, B2BScore achieves a significant LCOCV improvement across 26 protein clusters-a big increase of the averaged Pearson's correlation coefficients from 0.418 to 0.518 and a significant decrease of standard deviation of the coefficients from 0.352 to 0.196. We also identified several important and intuitive contact descriptors of protein-ligand binding through the random forest learning in B2BScore. Some of these descriptors are closely related to contacts between carbon atoms without covalent-bond oxygen/nitrogen, preferred contacts of metal ions, interfacial backbone atoms from proteins, or π rings. Some others are negative descriptors relating to those contacts with nitrogen atoms without covalent-bond hydrogens or nonpreferred contacts of metal ions. These descriptors can be directly used to guide protein-ligand docking.

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Year:  2013        PMID: 24191692     DOI: 10.1021/ci400450h

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  20 in total

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2.  Using diverse potentials and scoring functions for the development of improved machine-learned models for protein-ligand affinity and docking pose prediction.

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Review 4.  Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery.

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Journal:  Biomed Res Int       Date:  2019-11-11       Impact factor: 3.411

5.  Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction.

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7.  PLIP: fully automated protein-ligand interaction profiler.

Authors:  Sebastian Salentin; Sven Schreiber; V Joachim Haupt; Melissa F Adasme; Michael Schroeder
Journal:  Nucleic Acids Res       Date:  2015-04-14       Impact factor: 16.971

8.  A structural dissection of large protein-protein crystal packing contacts.

Authors:  Jiesi Luo; Zhongyu Liu; Yanzhi Guo; Menglong Li
Journal:  Sci Rep       Date:  2015-09-15       Impact factor: 4.379

9.  Integrating water exclusion theory into β contacts to predict binding free energy changes and binding hot spots.

Authors:  Qian Liu; Steven C H Hoi; Chee Keong Kwoh; Limsoon Wong; Jinyan Li
Journal:  BMC Bioinformatics       Date:  2014-02-26       Impact factor: 3.169

10.  Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study.

Authors:  Hongjian Li; Kwong-Sak Leung; Man-Hon Wong; Pedro J Ballester
Journal:  BMC Bioinformatics       Date:  2014-08-27       Impact factor: 3.169

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