Literature DB >> 34475217

Energy penalties enhance flexible receptor docking in a model cavity.

Anna S Kamenik1,2, Isha Singh2, Parnian Lak2, Trent E Balius3, Klaus R Liedl4, Brian K Shoichet3.   

Abstract

Protein flexibility remains a major challenge in library docking because of difficulties in sampling conformational ensembles with accurate probabilities. Here, we use the model cavity site of T4 lysozyme L99A to test flexible receptor docking with energy penalties from molecular dynamics (MD) simulations. Crystallography with larger and smaller ligands indicates that this cavity can adopt three major conformations: open, intermediate, and closed. Since smaller ligands typically bind better to the cavity site, we anticipate an energy penalty for the cavity opening. To estimate its magnitude, we calculate conformational preferences from MD simulations. We find that including a penalty term is essential for retrospective ligand enrichment; otherwise, high-energy states dominate the docking. We then prospectively docked a library of over 900,000 compounds for new molecules binding to each conformational state. Absent a penalty term, the open conformation dominated the docking results; inclusion of this term led to a balanced sampling of ligands against each state. High ranked molecules were experimentally tested by Tm upshift and X-ray crystallography. From 33 selected molecules, we identified 18 ligands and determined 13 crystal structures. Most interesting were those bound to the open cavity, where the buried site opens to bulk solvent. Here, highly unusual ligands for this cavity had been predicted, including large ligands with polar tails; these were confirmed both by binding and by crystallography. In docking, incorporating protein flexibility with thermodynamic weightings may thus access new ligand chemotypes. The MD approach to accessing and, crucially, weighting such alternative states may find general applicability.

Entities:  

Keywords:  docking; flexible receptor; model cavity; molecular dynamics

Mesh:

Substances:

Year:  2021        PMID: 34475217      PMCID: PMC8433570          DOI: 10.1073/pnas.2106195118

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  76 in total

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3.  PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models.

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Journal:  J Chem Theory Comput       Date:  2015-10-14       Impact factor: 6.006

4.  Structural Characterization of Biomolecules through Atomistic Simulations Guided by DEER Measurements.

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Journal:  Structure       Date:  2018-12-06       Impact factor: 5.006

Review 5.  Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell.

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6.  Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo.

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Journal:  J Phys Chem B       Date:  2018-03-12       Impact factor: 2.991

7.  Modelling proteins' hidden conformations to predict antibiotic resistance.

Authors:  Kathryn M Hart; Chris M W Ho; Supratik Dutta; Michael L Gross; Gregory R Bowman
Journal:  Nat Commun       Date:  2016-10-06       Impact factor: 14.919

8.  Routine Access to Millisecond Time Scale Events with Accelerated Molecular Dynamics.

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Journal:  J Chem Theory Comput       Date:  2012-07-27       Impact factor: 6.006

9.  An improved relaxed complex scheme for receptor flexibility in computer-aided drug design.

Authors:  Rommie E Amaro; Riccardo Baron; J Andrew McCammon
Journal:  J Comput Aided Mol Des       Date:  2008-01-15       Impact factor: 3.686

10.  Sodium-induced population shift drives activation of thrombin.

Authors:  Ursula Kahler; Anna S Kamenik; Johannes Kraml; Klaus R Liedl
Journal:  Sci Rep       Date:  2020-01-23       Impact factor: 4.996

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  3 in total

Review 1.  Is There a Need for a More Precise Description of Biomolecule Interactions to Understand Cell Function?

Authors:  Pierre Bongrand
Journal:  Curr Issues Mol Biol       Date:  2022-01-21       Impact factor: 2.976

2.  AtomNet PoseRanker: Enriching Ligand Pose Quality for Dynamic Proteins in Virtual High-Throughput Screens.

Authors:  Kate A Stafford; Brandon M Anderson; Jon Sorenson; Henry van den Bedem
Journal:  J Chem Inf Model       Date:  2022-03-02       Impact factor: 4.956

3.  Modeling receptor flexibility in the structure-based design of KRASG12C inhibitors.

Authors:  Kai Zhu; Cui Li; Kingsley Y Wu; Christopher Mohr; Xun Li; Brian Lanman
Journal:  J Comput Aided Mol Des       Date:  2022-08-05       Impact factor: 4.179

  3 in total

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