scholarly journals Modeling the Mechanism of CLN025 Beta-Hairpin Formation

2017 ◽  
Author(s):  
Keri A. McKiernan ◽  
Brooke E. Husic ◽  
Vijay S. Pande
Keyword(s):  
Force Fields ◽  
Test Case ◽  
Conformational Ensemble ◽  
Folding Process ◽  
Hydrophobic Collapse ◽  
Rate Determining Step ◽  
Beta Hairpins ◽  
Beta Hairpin ◽  
State Models ◽  
Protein Force Fields

Beta-hairpins are a substructure found in proteins that can lend insight into more complex systems. Furthermore, the folding of beta-hairpins is a valuable test case for benchmarking experimental and theoretical methods. Here, we simulate the folding of CLN025, a miniprotein with a beta-hairpin structure, at its experimental melting temperature using a range of state-of-the-art protein force fields. We construct Markov state models in order to examine the thermodynamics, kinetics, mechanism, and rate-determining step of folding. Mechanistically, we find the folding process is rate-limited by the formation of the turn region hydrogen bonds, which occurs following the downhill hydrophobic collapse of the extended denatured protein. These results are presented in the context of established and contradictory theories of the beta-hairpin folding process. Furthermore, our analysis suggests that the AMBER-FB15 force field, at this temperature, best describes the characteristics of the full experimental CLN025 conformational ensemble, while the AMBER ff99SB-ILDN and CHARMM22* force fields display a tendency to overstabilize the native state.

scholarly journals Comparison Between Empirical Protein Force Fields for the Simulation of the Adsorption Behavior of Structured LK Peptides on Functionalized Surfaces

2012 ◽  
Vol 7 (1) ◽  
pp. 24 ◽  
Author(s):  
Galen Collier ◽  
Nadeem A. Vellore ◽  
Jeremy A. Yancey ◽  
Steven J. Stuart ◽  
Robert A. Latour
Keyword(s):  
Force Fields ◽  
Adsorption Behavior ◽  
Functionalized Surfaces ◽  
Protein Force Fields

scholarly journals "Dividing and Conquering" and "Caching" in Molecular Modeling

2021 ◽  
Author(s):  
Xiaoyong Cao ◽  
Pu Tian
Keyword(s):  
Molecular Modeling ◽  
Degrees Of Freedom ◽  
Materials Science ◽  
Force Fields ◽  
Coarse Graining ◽  
Divide And Conquer ◽  
Collective Variables ◽  
Molecular Systems ◽  
State Models ◽  
Molecular Science

Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, Most of important methodological advancements in more than half century of molecule modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science based on force fields parameterization by coarse graining, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes "dividing and conquering" and/or "caching" in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but no transferability is available. Deep learning has been utilized to realize more efficient and accurate ways of "dividing and conquering" and "caching" along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science and a third class of algorithm that facilitates molecular modeling through partially transferable in resolution "caching" of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for "dividing and conquering" and "caching" in complex molecular systems.

Thermodynamics of Helix formation in small peptides of varying lengthin vacuo, implicit solvent and explicit solvent: Comparison between AMBER force fields

2019 ◽  
Vol 18 (03) ◽  
pp. 1950015
Author(s):  
Zhaoxi Sun ◽  
Xiaohui Wang
Keyword(s):  
Amino Acids ◽  
Free Energy ◽  
Hydrogen Bonds ◽  
Force Fields ◽  
Implicit Solvent ◽  
Conformational Ensemble ◽  
Helix Formation ◽  
Solvent Models ◽  
Explicit Solvent ◽  
Amber Force Fields

Helix formation is of great significance in protein folding. The helix-forming tendencies of amino acids are accumulated along the sequence to determine the helix-forming tendency of peptides. Computer simulation can be used to model this process in atomic details and give structural insights. In the current work, we employ equilibrate-state free energy simulation to systematically study the folding/unfolding thermodynamics of a series of mutated peptides. Two AMBER force fields including AMBER99SB and AMBER14SB are compared. The new 14SB force field uses refitted torsion parameters compared with 99SB and they share the same atomic charge scheme. We find that in vacuo the helix formation is mutation dependent, which reflects the different helix propensities of different amino acids. In general, there are helix formers, helix indifferent groups and helix breakers. The helical structure becomes more favored when the length of the sequence becomes longer, which arises from the formation of additional backbone hydrogen bonds in the lengthened sequence. Therefore, the helix indifferent groups and helix breakers will become helix formers in long sequences. Also, protonation-dependent helix formation is observed for ionizable groups. In 14SB, the helical structures are more stable than in 99SB and differences can be observed in their grouping schemes, especially in the helix indifferent group. In solvents, all mutations are helix indifferent due to protein–solvent interactions. The decrease in the number of backbone hydrogen bonds is the same with the increase in the number of protein–water hydrogen bonds. The 14SB in explicit solvent is able to capture the free energy minima in the helical state while 14SB in implicit solvent, 99SB in explicit solvent and 99SB in implicit solvent cannot. The helix propensities calculated under 14SB agree with the corresponding experimental values, while the 99SB results obviously deviate from the references. Hence, implicit solvent models are unable to correctly describe the thermodynamics even for the simple helix formation in isolated peptides. Well-developed force fields and explicit solvents are needed to correctly describe the protein dynamics. Aside from the free energy, differences in conformational ensemble under different force fields in different solvent models are observed. The numbers of hydrogen bonds formed under different force fields agree and they are mostly determined by the solvent model.

scholarly journals Evolution of All-Atom Protein Force Fields to Improve Local and Global Properties

2019 ◽  
Vol 10 (9) ◽  
pp. 2227-2234 ◽  
Author(s):  
Gül H. Zerze ◽  
Wenwei Zheng ◽  
Robert B. Best ◽  
Jeetain Mittal
Keyword(s):  
Force Fields ◽  
Global Properties ◽  
Protein Force Fields

Using path sampling to build better Markovian state models: Predicting the folding rate and mechanism of a tryptophan zipper beta hairpin

2004 ◽  
Vol 121 (1) ◽  
pp. 415 ◽  
Author(s):  
Nina Singhal ◽  
Christopher D. Snow ◽  
Vijay S. Pande
Keyword(s):  
Path Sampling ◽  
Folding Rate ◽  
Beta Hairpin ◽  
State Models ◽  
Tryptophan Zipper

scholarly journals Four classes of β-hairpins in proteins

1986 ◽  
Vol 240 (1) ◽  
pp. 289-292 ◽  
Author(s):  
E J Milner-White ◽  
R Poet
Keyword(s):  
Hydrogen Bonds ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Frequency Of Occurrence ◽  
Single Class ◽  
Three Dimensional Structure ◽  
Beta Hairpins ◽  
Beta Hairpin

We show that beta-hairpins can be divided into four classes, each with a number of members. Hairpins from a single class are readily interconverted by loss or gain of hydrogen bonds, but interconversion between classes requires complete unzipping and reformation of the entire beta-hairpin. Sibanda & Thornton [(1985) Nature (London) 316, 170-174] have classified beta-hairpins as either two-residue, three-residue, four-residue etc., loops. We point out that their nomenclature, by itself, gives rise to ambiguities, but that, if the class (one of the four mentioned above) is also specified, the description of beta-hairpins becomes straightforward. A range of proteins of known three-dimensional structure has been examined; it provides examples of hairpins of each of the four classes and give some indication of their frequency of occurrence. The distribution observed is substantially different from that described by Sibanda & Thornton [(1985) Nature (London) 316, 170-174].

scholarly journals Systematic Validation of Protein Force Fields against Experimental Data

PLoS ONE ◽  
2012 ◽  
Vol 7 (2) ◽  
pp. e32131 ◽  
Author(s):  
Kresten Lindorff-Larsen ◽  
Paul Maragakis ◽  
Stefano Piana ◽  
Michael P. Eastwood ◽  
Ron O. Dror ◽  
...  
Keyword(s):  
Experimental Data ◽  
Force Fields ◽  
Protein Force Fields
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