Conformational Ensembles by NMR and MD Simulations in Model Heptapeptides with Select Tri-Peptide Motifs

Both nuclear magnetic resonance (NMR) and molecular dynamics (MD) simulations are routinely used in understanding the conformational space sampled by peptides in the solution state. To investigate the role of single-residue change in the ensemble of conformations sampled by a set of heptapeptides, AEVXEVG with X = L, F, A, or G, comprehensive NMR, and MD simulations were performed. The rationale for selecting the particular model peptides is based on the high variability in the occurrence of tri-peptide E*L between the transmembrane β-barrel (TMB) than in globular proteins. The ensemble of conformations sampled by E*L was compared between the three sets of ensembles derived from NMR spectroscopy, MD simulations with explicit solvent, and the random coil conformations. In addition to the estimation of global determinants such as the radius of gyration of a large sample of structures, the ensembles were analyzed using principal component analysis (PCA). In general, the results suggest that the -EVL- peptide indeed adopts a conformational preference that is distinctly different not only from a random distribution but also from other peptides studied here. The relatively straightforward approach presented herein could help understand the conformational preferences of small peptides in the solution state.

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Effects of Force Fields on the Conformational and Dynamic Properties of Amyloid β(1-40) Dimer Explored by Replica Exchange Molecular Dynamics Simulations

10.1101/210286 ◽

2017 ◽

Author(s):

Charles R. Watts ◽

Andrew Gregory ◽

Cole Frisbie ◽

Sándor Lovas

Keyword(s):

Molecular Dynamics ◽

Force Field ◽

Force Fields ◽

Dynamic Properties ◽

Amyloid Β ◽

Md Simulations ◽

Conformational Space ◽

Random Coil ◽

Replica Exchange ◽

Β Sheet

AbstractAlzheimer’s disease is histologically marked by fibrils of Amyloid beta (Aβ) peptide within the extracellular matrix. Fibrils themselves are benign compared to the cytotoxicity of the oligomers and pre-fibrillary aggregates. The conformational space and structural ensembles of Aβ peptides and their oligomers in solution are inherently disordered and proven to be challenging to study. Optimum force field selection for molecular dynamics (MD) simulations and the biophysical relevance of results are still unknown. We compared the conformational space of the Aβ(1–40) dimers by 300 ns replica exchange MD simulations at physiological temperature (310 K) using: the AMBER-ff99sb-ILDN, AMBER-ff99sb*-ILDN, AMBER-ff99sb-NMR, and CHARMM22* force fields. Statistical comparisons of simulation results to experimental data and previously published simulations utilizing the CHARMM22* and CHARMM36 force fields were performed. All force fields yield sampled ensembles of conformations with collision cross sectional areas for the dimer that are statistically significantly larger than experimental results. All force fields, with the exception of AMBER-ff99sb-ILDN (8.8±6.4%) and CHARMM36 (2.7±4.2%), tend to overestimate the α-helical content compared to experimental CD (5.3±5.2%). Using the AMBER-ff99sb-NMR force field resulted in the greatest degree of variance (41.3±12.9%). Except for the AMBER-ff99sb-NMR force field, the others tended to under estimate the expected amount of β-sheet and over estimate the amount of turn/bend/random coil conformations. All force fields, with the exception AMBER-ff99sb-NMR, reproduce a theoretically expected β-sheet-turn-β-sheet conformational motif, however, only the CHARMM22* and CHARMM36 force fields yield results compatible with collapse of the central and C-terminal hydrophobic cores from residues 17-21 and 30-36. Although analyses of essential subspace sampling showed only minor variations between force fields, secondary structures of lowest energy conformers are different.

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Effect of Ionic Strength on the Aggregation Propensity of Aβ1-42 Peptide: An In-silico Study

Current Chemical Biology ◽

10.2174/2212796814999200818103157 ◽

2020 ◽

Vol 14 (3) ◽

pp. 216-226

Author(s):

Priyanka Borah ◽

Venkata S.K. Mattaparthi

Keyword(s):

Diffusion Coefficient ◽

Ionic Strength ◽

Marked Effect ◽

Computational Study ◽

Conformational Dynamics ◽

Rapid Change ◽

Md Simulations ◽

Radius Of Gyration ◽

Aggregation Propensity ◽

In Silico Study

Background: Aggregation of misfolded proteins under stress conditions in the cell might lead to several neurodegenerative disorders. Amyloid-beta (Aβ1-42) peptide, the causative agent of Alzheimer’s disease, has the propensity to fold into β-sheets under stress, forming aggregated amyloid plaques. This is influenced by factors such as pH, temperature, metal ions, mutation of residues, and ionic strength of the solution. There are several studies that have highlighted the importance of ionic strength in affecting the folding and aggregation propensity of Aβ1-42 peptide. Objective: To understand the effect of ionic strength of the solution on the aggregation propensity of Aβ1-42 peptide, using computational approaches. Materials and Methods: In this study, Molecular Dynamics (MD) simulations were performed on Aβ1-42 peptide monomer placed in (i) 0 M, (ii) 0.15 M, and (iii) 0.30 M concentration of NaCl solution. To prepare the input files for the MD simulations, we have used the Amberff99SB force field. The conformational dynamics of Aβ1-42 peptide monomer in different ionic strengths of the solutions were illustrated from the analysis of the corresponding MD trajectory using the CPPtraj tool. Results: From the MD trajectory analysis, we observe that with an increase in the ionic strength of the solution, Aβ1-42 peptide monomer shows a lesser tendency to undergo aggregation. From RMSD and SASA analysis, we noticed that Aβ1-42 peptide monomer undergoes a rapid change in conformation with an increase in the ionic strength of the solution. In addition, from the radius of gyration (Rg) analysis, we observed Aβ1-42 peptide monomer to be more compact at moderate ionic strength of the solution. Aβ1-42 peptide was also found to hold its helical secondary structure at moderate and higher ionic strengths of the solution. The diffusion coefficient of Aβ1-42 peptide monomer was also found to vary with the ionic strength of the solution. We observed a relatively higher diffusion coefficient value for Aβ1-42 peptide at moderate ionic strength of the solution. Conclusion: Our findings from this computational study highlight the marked effect of ionic strength of the solution on the conformational dynamics and aggregation propensity of Aβ1-42 peptide monomer.

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On the Use of the Discrete Constant pH Molecular Dynamics to Describe the Conformational Space of Peptides

Polymers ◽

10.3390/polym13010099 ◽

2020 ◽

Vol 13 (1) ◽

pp. 99

Author(s):

Cristian Privat ◽

Sergio Madurga ◽

Francesc Mas ◽

Jaime Rubio-Martínez

Keyword(s):

Molecular Dynamics ◽

Amino Acids ◽

Md Simulations ◽

Point Of View ◽

Conformational Space ◽

Conformational Sampling ◽

Protonation State ◽

Constant Ph ◽

Biochemical Systems ◽

Constant Ph Molecular Dynamics

Solvent pH is an important property that defines the protonation state of the amino acids and, therefore, modulates the interactions and the conformational space of the biochemical systems. Generally, this thermodynamic variable is poorly considered in Molecular Dynamics (MD) simulations. Fortunately, this lack has been overcome by means of the Constant pH Molecular Dynamics (CPHMD) methods in the recent decades. Several studies have reported promising results from these approaches that include pH in simulations but focus on the prediction of the effective pKa of the amino acids. In this work, we want to shed some light on the CPHMD method and its implementation in the AMBER suitcase from a conformational point of view. To achieve this goal, we performed CPHMD and conventional MD (CMD) simulations of six protonatable amino acids in a blocked tripeptide structure to compare the conformational sampling and energy distributions of both methods. The results reveal strengths and weaknesses of the CPHMD method in the implementation of AMBER18 version. The change of the protonation state according to the chemical environment is presumably an improvement in the accuracy of the simulations. However, the simulations of the deprotonated forms are not consistent, which is related to an inaccurate assignment of the partial charges of the backbone atoms in the CPHMD residues. Therefore, we recommend the CPHMD methods of AMBER program but pointing out the need to compare structural properties with experimental data to bring reliability to the conformational sampling of the simulations.

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Evaluating the Effect of the Financial Status to the Mobility Customs

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10050328 ◽

2021 ◽

Vol 10 (5) ◽

pp. 328

Author(s):

Gergo Pintér ◽

Imre Felde

Keyword(s):

Socioeconomic Status ◽

Economic Status ◽

Housing Prices ◽

Principal Component ◽

Cellular Phone ◽

Radius Of Gyration ◽

Financial Status ◽

Property Price ◽

Statistical Relationships ◽

Commuting Patterns

In this article, we explore the relationship between cellular phone data and housing prices in Budapest, Hungary. We determine mobility indicators from one months of Call Detail Records (CDR) data, while the property price data are used to characterize the socioeconomic status at the Capital of Hungary. First, we validated the proposed methodology by comparing the Home and Work locations estimation and the commuting patterns derived from the cellular network dataset with reports of the national mini census. We investigated the statistical relationships between mobile phone indicators, such as Radius of Gyration, the distance between Home and Work locations or the Entropy of visited cells, and measures of economic status based on housing prices. Our findings show that the mobility correlates significantly with the socioeconomic status. We performed Principal Component Analysis (PCA) on combined vectors of mobility indicators in order to characterize the dependence of mobility habits on socioeconomic status. The results of the PCA investigation showed remarkable correlation of housing prices and mobility customs.

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A method for validating the accuracy of NMR protein structures

Nature Communications ◽

10.1038/s41467-020-20177-1 ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Nicholas J. Fowler ◽

Adnan Sljoka ◽

Mike P. Williamson

Keyword(s):

Secondary Structure ◽

Crystal Structures ◽

Protein Structures ◽

Random Coil ◽

Local Rigidity ◽

Correlation Score ◽

Nmr Structures ◽

Explicit Solvent ◽

Structure Ensemble ◽

Better Than

AbstractWe present a method that measures the accuracy of NMR protein structures. It compares random coil index [RCI] against local rigidity predicted by mathematical rigidity theory, calculated from NMR structures [FIRST], using a correlation score (which assesses secondary structure), and an RMSD score (which measures overall rigidity). We test its performance using: structures refined in explicit solvent, which are much better than unrefined structures; decoy structures generated for 89 NMR structures; and conventional predictors of accuracy such as number of restraints per residue, restraint violations, energy of structure, ensemble RMSD, Ramachandran distribution, and clashscore. Restraint violations and RMSD are poor measures of accuracy. Comparisons of NMR to crystal structures show that secondary structure is equally accurate, but crystal structures are typically too rigid in loops, whereas NMR structures are typically too floppy overall. We show that the method is a useful addition to existing measures of accuracy.

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High-resolution mining of the SARS-CoV-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling

Chemical Science ◽

10.1039/d1sc00145k ◽

2021 ◽

Author(s):

Théo Jaffrelot Inizan ◽

Frédéric Célerse ◽

Olivier Adjoua ◽

Dina El Ahdab ◽

Luc-Henri Jolly ◽

...

Keyword(s):

Molecular Dynamics ◽

High Resolution ◽

Adaptive Sampling ◽

Force Fields ◽

Sampling Strategy ◽

Md Simulations ◽

Conformational Space ◽

Many Body ◽

Polarizable Force Fields ◽

Main Protease

We provide an unsupervised adaptive sampling strategy capable of producing μs-timescale molecular dynamics (MD) simulations of large biosystems using many-body polarizable force fields (PFFs).

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Investigating the Disordered and Membrane-Active Peptide A-Cage-C Using Conformational Ensembles

Molecules ◽

10.3390/molecules26123607 ◽

2021 ◽

Vol 26 (12) ◽

pp. 3607

Author(s):

Olena Dobrovolska ◽

Øyvind Strømland ◽

Ørjan Sele Handegård ◽

Martin Jakubec ◽

Morten L. Govasli ◽

...

Keyword(s):

Conformational Changes ◽

Protein Misfolding ◽

Driving Forces ◽

Conformational Space ◽

Supported Bilayers ◽

Conformational Ensembles ◽

Protein Membrane ◽

Chimeric Polypeptide ◽

Protein Misfolding And Aggregation ◽

In Silico Analyses

The driving forces and conformational pathways leading to amphitropic protein-membrane binding and in some cases also to protein misfolding and aggregation is the subject of intensive research. In this study, a chimeric polypeptide, A-Cage-C, derived from α-Lactalbumin is investigated with the aim of elucidating conformational changes promoting interaction with bilayers. From previous studies, it is known that A-Cage-C causes membrane leakages associated with the sporadic formation of amorphous aggregates on solid-supported bilayers. Here we express and purify double-labelled A-Cage-C and prepare partially deuterated bicelles as a membrane mimicking system. We investigate A-Cage-C in the presence and absence of these bicelles at non-binding (pH 7.0) and binding (pH 4.5) conditions. Using in silico analyses, NMR, conformational clustering, and Molecular Dynamics, we provide tentative insights into the conformations of bound and unbound A-Cage-C. The conformation of each state is dynamic and samples a large amount of overlapping conformational space. We identify one of the clusters as likely representing the binding conformation and conclude tentatively that the unfolding around the central W23 segment and its reorientation may be necessary for full intercalation at binding conditions (pH 4.5). We also see evidence for an overall elongation of A-Cage-C in the presence of model bilayers.

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Dynamical Behavior and Conformational Selection Mechanism of the Intrinsically Disordered Sic1 Kinase-Inhibitor Domain

Life ◽

10.3390/life10070110 ◽

2020 ◽

Vol 10 (7) ◽

pp. 110 ◽

Cited By ~ 1

Author(s):

Davide Sala ◽

Ugo Cosentino ◽

Anna Ranaudo ◽

Claudio Greco ◽

Giorgio Moro

Keyword(s):

Kinase Inhibitor ◽

Dynamical Behavior ◽

Md Simulations ◽

Molecular Shape ◽

Conformational Space ◽

Coarse Grained ◽

Conformational Ensemble ◽

Selection Mechanism ◽

Conformational Selection ◽

Intrinsically Disordered

Intrinsically Disordered Peptides and Proteins (IDPs) in solution can span a broad range of conformations that often are hard to characterize by both experimental and computational methods. However, obtaining a significant representation of the conformational space is important to understand mechanisms underlying protein functions such as partner recognition. In this work, we investigated the behavior of the Sic1 Kinase-Inhibitor Domain (KID) in solution by Molecular Dynamics (MD) simulations. Our results point out that application of common descriptors of molecular shape such as Solvent Accessible Surface (SAS) area can lead to misleading outcomes. Instead, more appropriate molecular descriptors can be used to define 3D structures. In particular, we exploited Weighted Holistic Invariant Molecular (WHIM) descriptors to get a coarse-grained but accurate definition of the variegated Sic1 KID conformational ensemble. We found that Sic1 is able to form a variable amount of folded structures even in absence of partners. Among them, there were some conformations very close to the structure that Sic1 is supposed to assume in the binding with its physiological complexes. Therefore, our results support the hypothesis that this protein relies on the conformational selection mechanism to recognize the correct molecular partners.

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Encounter complexes and dimensionality reduction in protein–protein association

eLife ◽

10.7554/elife.01370 ◽

2014 ◽

Vol 3 ◽

Cited By ~ 37

Author(s):

Dima Kozakov ◽

Keyong Li ◽

David R Hall ◽

Dmitri Beglov ◽

Jiefu Zheng ◽

...

Keyword(s):

Energy Surface ◽

Energy Landscape ◽

Dna Recognition ◽

Principal Component ◽

Dimensional Subspace ◽

Relative Orientation ◽

Conformational Space ◽

Native Structure ◽

Protein Association ◽

Solid Bodies

An outstanding challenge has been to understand the mechanism whereby proteins associate. We report here the results of exhaustively sampling the conformational space in protein–protein association using a physics-based energy function. The agreement between experimental intermolecular paramagnetic relaxation enhancement (PRE) data and the PRE profiles calculated from the docked structures shows that the method captures both specific and non-specific encounter complexes. To explore the energy landscape in the vicinity of the native structure, the nonlinear manifold describing the relative orientation of two solid bodies is projected onto a Euclidean space in which the shape of low energy regions is studied by principal component analysis. Results show that the energy surface is canyon-like, with a smooth funnel within a two dimensional subspace capturing over 75% of the total motion. Thus, proteins tend to associate along preferred pathways, similar to sliding of a protein along DNA in the process of protein-DNA recognition.

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Making high-dimensional molecular distribution functions tractable through Belief Propagation on Factor Graphs

10.1101/2021.06.28.450193 ◽

2021 ◽

Author(s):

Zachary Smith ◽

Pratyush Tiwary

Keyword(s):

Belief Propagation ◽

Conformational Dynamics ◽

Joint Probability ◽

Md Simulations ◽

Distribution Functions ◽

Factor Graph ◽

High Dimensional ◽

Enhanced Sampling ◽

Small Peptides ◽

Approximate Probability

Molecular dynamics (MD) simulations provide a wealth of high-dimensional data at all-atom and femtosecond resolution but deciphering mechanistic information from this data is an ongoing challenge in physical chemistry and biophysics. Theoretically speaking, joint probabilities of the equilibrium distribution contain all thermodynamic information, but they prove increasingly difficult to compute and interpret as the dimensionality increases. Here, inspired by tools in probabilistic graphical modeling, we develop a factor graph trained through belief propagation that helps factorize the joint probability into an approximate tractable form that can be easily visualized and used. We validate the study through the analysis of the conformational dynamics of two small peptides with 5 and 9 residues. Our validations include testing the conditional dependency predictions through an intervention scheme inspired by Judea Pearl. Secondly we directly use the belief propagation based approximate probability distribution as a high-dimensional static bias for enhanced sampling, where we achieve spontaneous back-and-forth motion between metastable states that is up to 350 times faster than unbiased MD. We believe this work opens up useful ways to thinking about and dealing with high-dimensional molecular simulations.

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