Simple Model of Protein Energetics To Identify Ab Initio Folding Transitions from All-Atom MD Simulations of Proteins

被引:9
|
作者
Meli, Massimiliano [1 ]
Morra, Giulia [1 ,2 ]
Colombo, Giorgio [1 ,3 ]
机构
[1] SCITEC CNR, I-20131 Milan, Italy
[2] Weill Cornell Med, New York, NY 10065 USA
[3] Univ Pavia, Dept Chem, I-27100 Pavia, Italy
关键词
MARKOV STATE MODELS; MOLECULAR-DYNAMICS; STRUCTURAL STABILITY; EPITOPE DISCOVERY; TRP-CAGE; PATHWAYS; MODULATION; LANDSCAPE; INSIGHTS; VARIANTS;
D O I
10.1021/acs.jctc.0c00524
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
A fundamental requirement to predict the native conformation, address questions of sequence design and optimization, and gain insights into the folding mechanisms of proteins lies in the definition of an unbiased reaction coordinate that reports on the folding state without the need to compare it to reference values, which might be unavailable for new (designed) sequences. Here, we introduce such a reaction coordinate, which does not depend on previous structural knowledge of the native state but relies solely on the energy partition within the protein: the spectral gap of the pair nonbonded energy matrix (ENergy Gap, ENG). This quantity can be simply calculated along unbiased MD trajectories. We show that upon folding the gap increases significantly, while its fluctuations are reduced to a minimum. This is consistently observed for a diverse set of systems and trajectories. Our approach allows one to promptly identify residues that belong to the folding core as well as residues involved in non-native contacts that need to be disrupted to guide polypeptides to the folded state. The energy gap and fluctuations criteria are then used to develop an automatic detection system which allows us to extract and analyze folding transitions from a generic MD trajectory. We speculate that our method can be used to detect conformational ensembles in dynamic and intrinsically disordered proteins, revealing potential preorganization for binding.
引用
收藏
页码:5960 / 5971
页数:12
相关论文
共 50 条
  • [41] Membrane protein simulations with a united-atom lipid and all-atom protein model: lipid-protein interactions, side chain transfer free energies and model proteins
    Tieleman, D. Peter
    MacCallum, Justin L.
    Ash, Walter L.
    Kandt, Christian
    Xu, Zhitao
    Monticelli, Luca
    JOURNAL OF PHYSICS-CONDENSED MATTER, 2006, 18 (28) : S1221 - S1234
  • [43] All-Atom Direct Folding Simulation for Proteins Using the Accelerated Molecular Dynamics in Implicit Solvent Model
    Li Zong-Chao
    Duan Li-Li
    Feng Guo-Qiang
    Zhang Qing-Gang
    CHINESE PHYSICS LETTERS, 2015, 32 (11)
  • [44] All-Atom Direct Folding Simulation for Proteins Using the Accelerated Molecular Dynamics in Implicit Solvent Model
    李宗超
    段莉莉
    冯国强
    张庆刚
    Chinese Physics Letters, 2015, (11) : 173 - 176
  • [45] All-Atom Direct Folding Simulation for Proteins Using the Accelerated Molecular Dynamics in Implicit Solvent Model
    李宗超
    段莉莉
    冯国强
    张庆刚
    Chinese Physics Letters, 2015, 32 (11) : 173 - 176
  • [46] Folding Mechanism of Proteins Im7 and Im9: Insight from All-Atom Simulations in Implicit and Explicit Solvent
    Wang, F.
    Cazzolli, G.
    Wintrode, P.
    Faccioli, P.
    JOURNAL OF PHYSICAL CHEMISTRY B, 2016, 120 (35): : 9297 - 9307
  • [47] Coarse-grained protein-protein stiffnesses and dynamics from all-atom simulations
    Hicks, Stephen D.
    Henley, C. L.
    PHYSICAL REVIEW E, 2010, 81 (03):
  • [48] RNA folding pathways from all-atom simulations with a variationally improved history-dependent bias
    Lazzeri, Gianmarco
    Micheletti, Cristian
    Pasquali, Samuela
    Faccioli, Pietro
    BIOPHYSICAL JOURNAL, 2023, 122 (15) : 3089 - 3098
  • [49] All-atom folding simulations of the villin headpiece from stochastically selected coarse-grained structures
    De Mori, GMS
    Micheletti, C
    Colombo, G
    JOURNAL OF PHYSICAL CHEMISTRY B, 2004, 108 (33): : 12267 - 12270
  • [50] Protein Conformational Transitions from All-Atom Adaptively Biased Path Optimization (ABPO)
    Wu, Heng
    Post, Carol
    PROTEIN SCIENCE, 2018, 27 : 194 - 194