scholarly journals The IDP-Specific Force Field ff14IDPSFF Improves the Conformer Sampling of Intrinsically Disordered Proteins

2017 ◽  
Vol 57 (5) ◽  
pp. 1166-1178 ◽  
Author(s):  
Dong Song ◽  
Ray Luo ◽  
Hai-Feng Chen
Keyword(s):  
Force Field ◽  
Intrinsically Disordered Proteins ◽  
Disordered Proteins ◽  
Specific Force ◽  
Intrinsically Disordered

Extensive Evaluation of Environment-specific Force Field for Ordered and Disordered Proteins

2021 ◽  
Author(s):  
Xiaocheng Cui ◽  
Hao Liu ◽  
Ashfaq Ur Rehman ◽  
Haifeng Chen
Keyword(s):  
Force Field ◽  
Intrinsically Disordered Proteins ◽  
Tertiary Structure ◽  
Experimental Methods ◽  
Human Diseases ◽  
Disordered Proteins ◽  
Specific Force ◽  
Intrinsically Disordered ◽  
Extensive Evaluation

Intrinsically disordered proteins (IDPs) have not fixed tertiary structure under physiology condition and associate with many human diseases. Because IDPs have the characters of diverse conformation, current experimental methods can...

scholarly journals Rescuing the Over-Collapse of Intrinsically Disordered Proteins using a Force Field Derived by a New Paradigm

2016 ◽  
Vol 110 (3) ◽  
pp. 556a
Author(s):  
Davide Mercadante ◽  
Sigrid Milles ◽  
Gustavo Fuertes ◽  
Dmitri Svergun ◽  
Edward A. Lemke ◽  
...  
Keyword(s):  
Force Field ◽  
Intrinsically Disordered Proteins ◽  
Disordered Proteins ◽  
New Paradigm ◽  
Intrinsically Disordered

scholarly journals CHARMM36: An Improved Force Field for Folded and Intrinsically Disordered Proteins

2017 ◽  
Vol 112 (3) ◽  
pp. 175a-176a ◽  
Author(s):  
Jing Huang ◽  
Sarah Rauscher ◽  
Grzegorz Nawrocki ◽  
Ting Ran ◽  
Michael Feig ◽  
...  
Keyword(s):  
Force Field ◽  
Intrinsically Disordered Proteins ◽  
Disordered Proteins ◽  
Intrinsically Disordered

scholarly journals Structural Ensembles of Intrinsically Disordered Proteins Depend Strongly on Force Field

2014 ◽  
Vol 106 (2) ◽  
pp. 271a ◽  
Author(s):  
Sarah Rauscher ◽  
Vytautas Gapsys ◽  
Andreas Volkhardt ◽  
Christian Blau ◽  
Bert L. de Groot ◽  
...  
Keyword(s):  
Force Field ◽  
Intrinsically Disordered Proteins ◽  
Disordered Proteins ◽  
Intrinsically Disordered ◽  
Structural Ensembles

scholarly journals Bayesian inference of protein ensembles from SAXS data

2016 ◽  
Vol 18 (8) ◽  
pp. 5832-5838 ◽  
Author(s):  
L. D. Antonov ◽  
S. Olsson ◽  
W. Boomsma ◽  
T. Hamelryck
Keyword(s):  
Bayesian Inference ◽  
Force Field ◽  
Intrinsically Disordered Proteins ◽  
Probabilistic Method ◽  
Disordered Proteins ◽  
Saxs Data ◽  
Intrinsically Disordered ◽  
Protein Ensembles

A probabilistic method infers ensembles of intrinsically disordered proteins (IDPs) by combining SAXS data with a force field.

scholarly journals Modelling the structure and interactions of intrinsically disordered peptides with multiple-replica, metadynamics-based sampling methods and force-field combinations

2021 ◽  
Author(s):  
Lunna Li ◽  
Tommaso Casalini ◽  
Paolo Arosio ◽  
Matteo Salvalaglio
Keyword(s):  
Force Field ◽  
Sampling Method ◽  
Intrinsically Disordered Proteins ◽  
Thermodynamic Characteristics ◽  
Building Blocks ◽  
Disordered Proteins ◽  
Intrinsically Disordered ◽  
Self Association ◽  
Disordered Peptides

Intrinsically disordered proteins (IDPs) play a key role in many biological processes, including the formation of biomolecular condensates within cells. A detailed characterization of their configurational ensemble and structure-function paradigm is crucial for understanding their biological activity and for exploiting them as building blocks in material sciences. In this work, we incorporate bias-exchange metadynamics and parallel-tempering well-tempered metadynamics with CHARMM36m and CHARMM22* to explore the structural and thermodynamic characteristics of a short archetypal disordered sequence derived from a DEAD-box protein. The conformational landscapes emerging from our simulations are largely congruent across methods and forcefields. Nevertheless, differences in fine details emerge from varying forcefield/sampling method combinations. For this protein, our analysis identifies features that help to explain the low propensity of this sequence to undergo self-association in vitro, which can be common to all force-field/sampling method combinations. Overall, our work demonstrates the importance of using multiple force-field/enhanced sampling method combinations for accurate structural and thermodynamic information in the study of general disordered proteins.

scholarly journals Different force fields give rise to different amyloid aggregation pathways in molecular dynamics simulations

2020 ◽  
Author(s):  
Suman Samantray ◽  
Feng Yin ◽  
Batuhan Kav ◽  
Birgit Strodel
Keyword(s):  
Molecular Dynamics ◽  
Force Field ◽  
Intrinsically Disordered Proteins ◽  
Force Fields ◽  
Md Simulations ◽  
Peptide Sequence ◽  
Disordered Proteins ◽  
Test Case ◽  
Peptide Aggregation ◽  
Intrinsically Disordered

AbstractThe progress towards understanding the molecular basis of Alzheimers’s disease is strongly connected to elucidating the early aggregation events of the amyloid-β (Aβ) peptide. Molecular dynamics (MD) simulations provide a viable technique to study the aggregation of Aβ into oligomers with high spatial and temporal resolution. However, the results of an MD simulation can only be as good as the underlying force field. A recent study by our group showed that none of the force fields tested can distinguish between aggregation-prone and non-aggregating peptide sequences, producing the same and in most cases too fast aggregation kinetics for all peptides. Since then, new force fields specially designed for intrinsically disordered proteins such as Aβ were developed. Here, we assess the applicability of these new force fields to studying peptide aggregation using the Aβ16−22 peptide and mutations of it as test case. We investigate their performance in modeling the monomeric state, the aggregation into oligomers, and the stability of the aggregation end product, i.e., the fibrillar state. A main finding is that changing the force field has a stronger effect on the simulated aggregation pathway than changing the peptide sequence. Also the new force fields are not able to reproduce the experimental aggregation propensity order of the peptides. Dissecting the various energy contributions shows that AMBER99SB-disp overestimates the interactions between the peptides and water, thereby inhibiting peptide aggregation. More promising results are obtained with CHARMM36m and especially its version with increased protein–water interactions. It is thus recommended to use this force field for peptide aggregation simulations and base future reparameterizations on it.

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