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A Consistent Scheme for Gradient-Based Optimization of Protein-Ligand Poses.

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We developed a novel scoring and optimization scheme for protein-ligand docking. This method improves pose prediction accuracy and numerical stability, crucial for drug discovery research.

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Bioinformatics

Background:

  • Protein-ligand pose scoring and optimization are critical for molecular docking.
  • Existing scoring functions often lack continuous differentiability and are not optimized for numerical stability.
  • Analysis of numerical optimization behavior of scoring functions is rarely performed.

Purpose of the Study:

  • To present a consistent scheme for protein-ligand pose scoring and gradient-based pose optimization.
  • To introduce a novel scoring function (JAMDA) and an optimized BFGS algorithm (LSL-BFGS).
  • To evaluate the performance and numerical properties of the proposed scheme.

Main Methods:

  • Developed a novel variant of the BFGS algorithm with step-length control: LSL-BFGS (limited step length BFGS).
  • Created the empirical JAMDA scoring function, designed for pose prediction and numerical optimizability.
  • Validated the JAMDA scoring function using the CASF-2016 benchmark for docking power.

Main Results:

  • The JAMDA scoring function achieved high pose prediction performance, ranking top poses within 2 Å RMSD in approximately 89% of cases.
  • The combination of JAMDA scoring with LSL-BFGS demonstrated superior optimization locality compared to the classical BFGS algorithm.
  • The LSL-BFGS algorithm maintained a low number of scoring function evaluations, similar to the classical BFGS.

Conclusions:

  • The presented JAMDA scoring and LSL-BFGS optimization scheme offers a consistent and effective approach for protein-ligand pose prediction.
  • The novel scheme enhances optimization locality and maintains high prediction accuracy, outperforming traditional methods.
  • The JAMDA scoring and optimization tools are available for non-commercial and academic use, facilitating further research in drug discovery.