SAXS-restrained ensemble simulations of intrinsically disordered proteins with commitment to the principle of maximum entropy.

SAXS-restrained ensemble simulations of intrinsically disordered proteins with commitment to the principle of maximum entropy.

Hermann, Markus R;Hub, Jochen S;
journal of chemical theory and computation 2019
287
hermann2019saxsrestrainedjournal

Abstract

Intrinsically disordered proteins (IDPs) play key roles in biology and disease, rationalizing the wide interest in deriving accurate solution ensembles of IDPs. Molecular dynamics (MD) simulations of IDPs often suffer from force field inaccuracies, suggesting that simulations must be complemented by experimental data to obtain physically correct ensembles. We present a method for integrating small-angle X-ray scattering (SAXS) data on-the-fly into MD simulations of disordered systems, with the aim to bias the conformational sampling towards agreement with ensemble-averaged SAXS data. By coupling a set of parallel replicas to the data and following the principle of maximum entropy, this method applies only a minimal bias. Using the RS peptide as a test case, we analyse the influence of (i) the number of parallel replicas, (ii) the force constant for the SAXS-derived biasing energy, and (iii) of the force field. The refined ensembles are cross-validated against experimental J couplings and further compared in terms of C distance maps and secondary structure content. Remarkably, we find that the applied force field only has a small influence on the SAXS-refined ensemble, suggesting that incorporating SAXS data into MD simulations may greatly reduce the force field bias.

Citation

ID: 13057
Ref Key: hermann2019saxsrestrainedjournal
Use this key to autocite in SciMatic or Thesis Manager

References

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
13057
Unique Identifier:
10.1021/acs.jctc.9b00338
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet

Saymatik Web3.0 Wallet