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Using the multi-objective optimization replica exchange Monte Carlo enhanced sampling method for protein-small

Hongrui Wang1, Hongwei Liu2, Leixin Cai2

  • 1School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, 215006, People's Republic of China. riihon@yeah.net.

BMC Bioinformatics
|July 12, 2017
PubMed
Summary
This summary is machine-generated.

We enhanced protein-small molecule docking using multi-objective optimization-replica exchange Monte Carlo (MO-REMC) and hybrid MO-REMC (HMO-REMC) methods. These approaches improve conformational sampling and prediction accuracy in RosettaLigand.

Keywords:
Complex structure predictionEnhanced sampling methodMonte CarloMulti-objective optimizationProtein–small molecule docking

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Protein-small molecule docking is crucial for drug discovery.
  • Traditional sampling methods like Monte Carlo (MC) and replica exchange Monte Carlo (REMC) have limitations in conformational prediction.
  • The study introduces multi-objective optimization (MO) to enhance REMC for docking.

Purpose of the Study:

  • To extend the REMC sampling method for protein-small molecule docking conformational prediction using RosettaLigand.
  • To leverage Pareto front information for improved replica selection and conformational space exploration.
  • To develop and validate novel MO-REMC and hybrid MO-REMC (HMO-REMC) sampling strategies.

Main Methods:

  • Utilized multi-objective optimization Pareto front information for replica exchange selection.
  • Implemented MO-REMC and HMO-REMC sampling methods within the RosettaLigand framework.
  • Validated the methods using a benchmark dataset of 16 test cases and compared them against MC and standard REMC.

Main Results:

  • Pareto front information facilitated convergence of conformational space, including near-native structures.
  • MO-REMC and HMO-REMC directly provided Pareto optimal solutions (min-min and hybrid min-min/max-min).
  • Comparative analysis demonstrated the distinct advantages of MO-REMC and HMO-REMC over traditional methods.

Conclusions:

  • MO-REMC and HMO-REMC are powerful methods for protein-small molecule docking conformational prediction.
  • These advanced sampling techniques enhance the accuracy of binding energy predictions in RosettaLigand.
  • The study provides improved computational tools for structure-based drug design.