Minimalist Protein Model As a Diagnostic Tool for Misfolding and Aggregation
Overview
Molecular Biology
Authors
Affiliations
We propose a realistic coarse-grained protein model and a technique to "anchor" the model to available experimental data. We apply this procedure to characterize the effect of multiple mutations on the folding mechanism of protein S6. We show that the mutation of a few "gatekeeper" residues triggers significant changes on the folding landscape of S6. These results suggest that gatekeeper residues control the flexibility of critical regions of S6, that in turn regulates the delicate balance between folding and aggregation. Although obtained with a minimalist protein model, these results are fully consistent with experimental evidence and offer a clue to understand the interplay between folding and aggregation in protein S6.
Markov state modeling reveals alternative unbinding pathways for peptide-MHC complexes.
Abella J, Antunes D, Jackson K, Lizee G, Clementi C, Kavraki L Proc Natl Acad Sci U S A. 2020; 117(48):30610-30618.
PMID: 33184174 PMC: 7720115. DOI: 10.1073/pnas.2007246117.
Machine Learning of Coarse-Grained Molecular Dynamics Force Fields.
Wang J, Olsson S, Wehmeyer C, Perez A, Charron N, De Fabritiis G ACS Cent Sci. 2019; 5(5):755-767.
PMID: 31139712 PMC: 6535777. DOI: 10.1021/acscentsci.8b00913.
A Discontinuous Potential Model for Protein-Protein Interactions.
Shao Q, Hall C Found Mol Model Simul (2015). 2017; 2016:1-20.
PMID: 28580454 PMC: 5453719. DOI: 10.1007/978-981-10-1128-3_1.
Adaptive local learning in sampling based motion planning for protein folding.
Ekenna C, Thomas S, Amato N BMC Syst Biol. 2016; 10 Suppl 2:49.
PMID: 27490494 PMC: 4977477. DOI: 10.1186/s12918-016-0297-9.
Multiscale approach to the determination of the photoactive yellow protein signaling state ensemble.
Rohrdanz M, Zheng W, Lambeth B, Vreede J, Clementi C PLoS Comput Biol. 2014; 10(10):e1003797.
PMID: 25356903 PMC: 4214557. DOI: 10.1371/journal.pcbi.1003797.