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Predicting Reactive Cysteines with Implicit-Solvent-Based Continuous Constant pH Molecular Dynamics in Amber.

Robert C Harris1, Ruibin Liu2, Jana Shen1

  • 1Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, United States.

Journal of Chemical Theory and Computation
|April 25, 2020
PubMed
Summary
This summary is machine-generated.

Predicting cysteine protonation states is crucial for understanding biological functions and drug design. A new generalized Born constant pH molecular dynamics (GB-CpHMD) method accurately predicts cysteine pKa values, outperforming traditional methods.

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Area of Science:

  • Computational Chemistry
  • Biophysics
  • Pharmacology

Background:

  • Cysteines play vital roles in cellular redox functions and covalent drug design.
  • Accurate prediction of cysteine protonation states is essential but challenging.
  • Most computational studies inaccurately assume protonated cysteines.

Purpose of the Study:

  • To benchmark the performance of a generalized Born (GB) based continuous constant pH molecular dynamics (CpHMD) method for predicting cysteine pKa values and reactivities.
  • To evaluate the accuracy and reliability of GB-CpHMD for cysteine protonation state predictions in proteins.

Main Methods:

  • Implementation of a GB-based CpHMD method in Amber for protein pKa calculations.
  • Benchmarking using a dataset of 24 proteins with varying cysteine pKa shifts.
  • Performing 10 ns single-pH or 4 ns replica-exchange CpHMD titrations.

Main Results:

  • GB-CpHMD achieved root-mean-square errors of 1.2-1.3 and correlation coefficients of 0.8-0.9 with experimental data.
  • Single-pH titrations accurately predicted thiolates (86% accuracy, 100% precision) and reactive cysteines (81% accuracy, 90% precision) at physiological pH.
  • Performance significantly surpassed traditional structure-based methods, including a widely used empirical pKa tool (accuracy < 50%).

Conclusions:

  • GB-CpHMD provides accurate and reliable predictions of cysteine pKa values and protonation states.
  • The method is computationally efficient, executable on a desktop GPU, making it accessible for various applications.
  • This tool aids in understanding biological functions and advancing covalent drug design by accurately modeling cysteine reactivity.