Middle-way flexible docking

Pose prediction using mixed-resolution Monte Carlo in estrogen receptor α

Justin Spiriti, Sundar Raman Subramanian, Rohith Palli, Maria Wu, Daniel Zuckerman

Research output: Contribution to journalArticle

Abstract

There is a vast gulf between the two primary strategies for simulating protein-ligand interactions. Docking methods significantly limit or eliminate protein flexibility to gain great speed at the price of uncontrolled inaccuracy, whereas fully flexible atomistic molecular dynamics simulations are expensive and often suffer from limited sampling. We have developed a flexible docking approach geared especially for highly flexible or poorly resolved targets based on mixed-resolution Monte Carlo (MRMC), which is intended to offer a balance among speed, protein flexibility, and sampling power. The binding region of the protein is treated with a standard atomistic force field, while the remainder of the protein is modeled at the residue level with a Gō model that permits protein flexibility while saving computational cost. Implicit solvation is used. Here we assess three facets of the MRMC approach with implications for other docking studies: (i) the role of receptor flexibility in cross-docking pose prediction; (ii) the use of non-equilibrium candidate Monte Carlo (NCMC) and (iii) the use of pose-clustering in scoring. We examine 61 co-crystallized ligands of estrogen receptor α, an important cancer target known for its flexibility. We also compare the performance of the MRMC approach with Autodock smina. Adding protein flexibility, not surprisingly, leads to significantly lower total energies and stronger interactions between protein and ligand, but notably we document the important role of backbone flexibility in the improvement. The improved backbone flexibility also leads to improved performance relative to smina. Somewhat unexpectedly, our implementation of NCMC leads to only modestly improved sampling of ligand poses. Overall, the addition of protein flexibility improves the performance of docking, as measured by energy-ranked poses, but we do not find significant improvements based on cluster information or the use of NCMC. We discuss possible improvements for the model including alternative coarse-grained force fields, improvements to the treatment of solvation, and adding additional types of NCMC moves.

Original languageEnglish (US)
Article numbere0215694
JournalPloS one
Volume14
Issue number4
DOIs
StatePublished - Apr 1 2019

Fingerprint

Estrogen Receptors
prediction
Proteins
proteins
Ligands
Solvation
Sampling
estrogen receptors
Molecular Dynamics Simulation
molecular dynamics
energy
Cluster Analysis
sampling
Carrier Proteins
Molecular dynamics
Costs and Cost Analysis
receptors
neoplasms
ligands
Computer simulation

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

Middle-way flexible docking : Pose prediction using mixed-resolution Monte Carlo in estrogen receptor α. / Spiriti, Justin; Subramanian, Sundar Raman; Palli, Rohith; Wu, Maria; Zuckerman, Daniel.

In: PloS one, Vol. 14, No. 4, e0215694, 01.04.2019.

Research output: Contribution to journalArticle

Spiriti, Justin ; Subramanian, Sundar Raman ; Palli, Rohith ; Wu, Maria ; Zuckerman, Daniel. / Middle-way flexible docking : Pose prediction using mixed-resolution Monte Carlo in estrogen receptor α. In: PloS one. 2019 ; Vol. 14, No. 4.
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