期刊
JOURNAL OF PHYSICAL CHEMISTRY B
卷 122, 期 49, 页码 11115-11125出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpcb.8b05791
关键词
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资金
- Amazon AWS Machine Learning Research Award
- NIH [R01 GM44557]
- Rice University [C-0016]
The associative memory, water-mediated, structure and energy model (AWSEM) has been successfully used to study protein folding, binding, and aggregation problems. In this work, we introduce AWSEM-IDP, a new AWSEM branch for simulating intrinsically disordered proteins (IDPs), where the weights of the potentials determining secondary structure formation have been finely tuned, and a novel potential is introduced that helps to precisely control both the average extent of protein chain collapse and the chain's fluctuations in size. AWSEM-IDP can efficiently sample large conformational spaces, while retaining sufficient molecular accuracy to realistically model proteins. We applied this new model to two IDPs, demonstrating that AWSEM-IDP can reasonably well reproduce higher-resolution reference data, thus providing the foundation for a transferable IDP force field. Finally, we used thermodynamic perturbation theory to show that, in general, the conformational ensembles of IDPs are highly sensitive to fine-tuning of force field parameters.
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