Validation of DBFOLD: An efficient algorithm for computing folding pathways of complex proteins.
Journal
PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922
Informations de publication
Date de publication:
11 2020
11 2020
Historique:
received:
26
08
2020
accepted:
17
10
2020
revised:
30
11
2020
pubmed:
17
11
2020
medline:
29
1
2021
entrez:
16
11
2020
Statut:
epublish
Résumé
Atomistic simulations can provide valuable, experimentally-verifiable insights into protein folding mechanisms, but existing ab initio simulation methods are restricted to only the smallest proteins due to severe computational speed limits. The folding of larger proteins has been studied using native-centric potential functions, but such models omit the potentially crucial role of non-native interactions. Here, we present an algorithm, entitled DBFOLD, which can predict folding pathways for a wide range of proteins while accounting for the effects of non-native contacts. In addition, DBFOLD can predict the relative rates of different transitions within a protein's folding pathway. To accomplish this, rather than directly simulating folding, our method combines equilibrium Monte-Carlo simulations, which deploy enhanced sampling, with unfolding simulations at high temperatures. We show that under certain conditions, trajectories from these two types of simulations can be jointly analyzed to compute unknown folding rates from detailed balance. This requires inferring free energies from the equilibrium simulations, and extrapolating transition rates from the unfolding simulations to lower, physiologically-reasonable temperatures at which the native state is marginally stable. As a proof of principle, we show that our method can accurately predict folding pathways and Monte-Carlo rates for the well-characterized Streptococcal protein G. We then show that our method significantly reduces the amount of computation time required to compute the folding pathways of large, misfolding-prone proteins that lie beyond the reach of existing direct simulation. Our algorithm, which is available online, can generate detailed atomistic models of protein folding mechanisms while shedding light on the role of non-native intermediates which may crucially affect organismal fitness and are frequently implicated in disease.
Identifiants
pubmed: 33196646
doi: 10.1371/journal.pcbi.1008323
pii: PCOMPBIOL-D-20-01549
pmc: PMC7704049
doi:
Substances chimiques
Bacterial Proteins
0
IgG Fc-binding protein, Streptococcus
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1008323Subventions
Organisme : NIGMS NIH HHS
ID : R01 GM068670
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM124044
Pays : United States
Organisme : NIGMS NIH HHS
ID : F32 GM116231
Pays : United States
Organisme : NIGMS NIH HHS
ID : T32 GM008313
Pays : United States
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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