scholarly journals Molecular dynamics simulations for genetic interpretation in protein coding regions: where we are, where to go and when

2019 ◽  
Author(s):  
Juan J Galano-Frutos ◽  
Helena García-Cebollada ◽  
Javier Sancho
Keyword(s):  
Molecular Dynamics ◽  
Amino Acid ◽  
Large Scale ◽  
Binary Classification ◽  
Chemical Properties ◽  
Md Simulations ◽  
Single Amino Acid ◽  
Medical Decision ◽  
Evolutionary Information ◽  
Protein Variants

Abstract The increasing ease with which massive genetic information can be obtained from patients or healthy individuals has stimulated the development of interpretive bioinformatics tools as aids in clinical practice. Most such tools analyze evolutionary information and simple physical–chemical properties to predict whether replacement of one amino acid residue with another will be tolerated or cause disease. Those approaches achieve up to 80–85% accuracy as binary classifiers (neutral/pathogenic). As such accuracy is insufficient for medical decision to be based on, and it does not appear to be increasing, more precise methods, such as full-atom molecular dynamics (MD) simulations in explicit solvent, are also discussed. Then, to describe the goal of interpreting human genetic variations at large scale through MD simulations, we restrictively refer to all possible protein variants carrying single-amino-acid substitutions arising from single-nucleotide variations as the human variome. We calculate its size and develop a simple model that allows calculating the simulation time needed to have a 0.99 probability of observing unfolding events of any unstable variant. The knowledge of that time enables performing a binary classification of the variants (stable-potentially neutral/unstable-pathogenic). Our model indicates that the human variome cannot be simulated with present computing capabilities. However, if they continue to increase as per Moore’s law, it could be simulated (at 65°C) spending only 3 years in the task if we started in 2031. The simulation of individual protein variomes is achievable in short times starting at present. International coordination seems appropriate to embark upon massive MD simulations of protein variants.

Molecular Dynamics Simulation of a Pullout Test on a Carbon Nanotube in a Polymer Matrix

2014 ◽  
Vol 1700 ◽  
pp. 61-66
Author(s):  
Guttormur Arnar Ingvason ◽  
Virginie Rollin
Keyword(s):  
Molecular Dynamics ◽  
Polymer Matrix ◽  
Large Scale ◽  
Md Simulations ◽  
Dynamics Simulation ◽  
Atomistic Simulations ◽  
Polymer Molecules ◽  
Molecular Potential ◽  
Pull Out ◽  
Walled Carbon Nanotubes

ABSTRACTAdding single walled carbon nanotubes (SWCNT) to a polymer matrix can improve the delamination properties of the composite. Due to the complexity of polymer molecules and the curing process, few 3-D Molecular Dynamics (MD) simulations of a polymer-SWCNT composite have been run. Our model runs on the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), with a COMPASS (Condensed phase Optimized Molecular Potential for Atomistic Simulations Studies) potential. This potential includes non-bonded interactions, as well as bonds, angles and dihedrals to create a MD model for a SWCNT and EPON 862/DETDA (Diethyltoluenediamine) polymer matrix. Two simulations were performed in order to test the implementation of the COMPASS parameters. The first one was a tensile test on a SWCNT, leading to a Young’s modulus of 1.4 TPa at 300K. The second one was a pull-out test of a SWCNT from an originally uncured EPON 862/DETDA matrix.

scholarly journals Antimicrobial action of the cationic peptide, chrysophsin-3: a coarse-grained molecular dynamics study

Soft Matter ◽  
2018 ◽  
Vol 14 (15) ◽  
pp. 2796-2807 ◽  
Author(s):  
Andrea Catte ◽  
Mark R. Wilson ◽  
Martin Walker ◽  
Vasily S. Oganesyan
Keyword(s):  
Molecular Dynamics ◽  
Large Scale ◽  
Md Simulations ◽  
Antimicrobial Action ◽  
Coarse Grained ◽  
Cationic Peptide ◽  
Coarse Grained Molecular Dynamics

Antimicrobial action of a cationic peptide is modelled by large scale MD simulations.

scholarly journals Hydrogenation Dynamics of Biphenylene Carbon (Graphenylene) Membranes

MRS Advances ◽  
2017 ◽  
Vol 2 (29) ◽  
pp. 1571-1576
Author(s):  
Vinicius Splugues ◽  
Pedro Alves da Silva Autreto ◽  
Douglas S. Galvao
Keyword(s):  
Molecular Dynamics ◽  
Large Scale ◽  
Materials Science ◽  
Md Simulations ◽  
Structural Transformations ◽  
Extensive Study ◽  
Massively Parallel ◽  
Porous Membranes ◽  
Two Dimensional ◽  
Reactive Force

ABSTRACTThe advent of graphene created a revolution in materials science. Because of this there is a renewed interest in other carbon-based structures. Graphene is the ultimate (just one atom thick) membrane. It has been proposed that graphene can work as impermeable membrane to standard gases, such argon and helium. Graphene-like porous membranes, but presenting larger porosity and potential selectivity would have many technological applications. Biphenylene carbon (BPC), sometimes called graphenylene, is one of these structures. BPC is a porous two-dimensional (planar) allotrope carbon, with its pores resembling typical sieve cavities and/or some kind of zeolites. In this work, we have investigated the hydrogenation dynamics of BPC membranes under different conditions (hydrogenation plasma density, temperature, etc.). We have carried out an extensive study through fully atomistic molecular dynamics (MD) simulations using the reactive force field ReaxFF, as implemented in the well-known Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code. Our results show that the BPC hydrogenation processes exhibit very complex patterns and the formation of correlated domains (hydrogenated islands) observed in the case of graphene hydrogenation was also observed here. MD results also show that under hydrogenation BPC structure undergoes a change in its topology, the pores undergoing structural transformations and extensive hydrogenation can produce significant structural damages, with the formation of large defective areas and large structural holes, leading to structural collapse.

scholarly journals Plant expression of NifD protein variants resistant to mitochondrial degradation

2020 ◽  
Vol 117 (37) ◽  
pp. 23165-23173 ◽  
Author(s):  
Robert S. Allen ◽  
Christina M. Gregg ◽  
Shoko Okada ◽  
Amratha Menon ◽  
Dawar Hussain ◽  
...  
Keyword(s):  
Amino Acid ◽  
Protein Design ◽  
Nitrogenase Activity ◽  
Plant Mitochondria ◽  
Single Amino Acid ◽  
Synthetic Proteins ◽  
Amino Acid Variant ◽  
Absolute Requirement ◽  
Oxygen Sensitive ◽  
Protein Variants

To engineer Mo-dependent nitrogenase function in plants, expression of the structural proteins NifD and NifK will be an absolute requirement. Although mitochondria have been established as a suitable eukaryotic environment for biosynthesis of oxygen-sensitive enzymes such as NifH, expression of NifD in this organelle has proven difficult due to cryptic NifD degradation. Here, we describe a solution to this problem. Using molecular and proteomic methods, we found NifD degradation to be a consequence of mitochondrial endoprotease activity at a specific motif within NifD. Focusing on this functionally sensitive region, we designed NifD variants comprising between one and three amino acid substitutions and distinguished several that were resistant to degradation when expressed in both plant and yeast mitochondria. Nitrogenase activity assays of these resistant variants in Escherichia coli identified a subset that retained function, including a single amino acid variant (Y100Q). We found that other naturally occurring NifD proteins containing alternate amino acids at the Y100 position were also less susceptible to degradation. The Y100Q variant also enabled expression of a NifD(Y100Q)–linker–NifK translational polyprotein in plant mitochondria, confirmed by identification of the polyprotein in the soluble fraction of plant extracts. The NifD(Y100Q)–linker–NifK retained function in bacterial nitrogenase assays, demonstrating that this polyprotein permits expression of NifD and NifK in a defined stoichiometry supportive of activity. Our results exemplify how protein design can overcome impediments encountered when expressing synthetic proteins in novel environments. Specifically, these findings outline our progress toward the assembly of the catalytic unit of nitrogenase within mitochondria.

scholarly journals Indentation-induced plastic behaviour of nanotwinned Cu/high entropy alloy FeCoCrNi nanolaminate: an atomic simulation

RSC Advances ◽  
2020 ◽  
Vol 10 (16) ◽  
pp. 9187-9192 ◽  
Author(s):  
Hui Feng ◽  
Jingwen Tang ◽  
Haotian Chen ◽  
Yuanyuan Tian ◽  
Qihong Fang ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Large Scale ◽  
Md Simulations ◽  
High Entropy Alloy ◽  
High Entropy ◽  
Atomic Simulation ◽  
Layer Number ◽  
Plastic Behaviour

Using large-scale molecular dynamics (MD) simulations, the effects of interface and layer number in the nanoindentation response of experimentally observed nanotwinned Cu/high entropy alloy (HEA) FeCoCrNi nanolaminate are studied.

scholarly journals A Virtual Reality Ensemble Molecular Dynamics Workflow to Study Complex Conformational Changes in Proteins

2020 ◽  
Author(s):  
Jordi Juárez-Jiménez ◽  
Philip Tew ◽  
Michael o'connor ◽  
Salome Llabres ◽  
Rebecca Sage ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Virtual Reality ◽  
Conformational Changes ◽  
Large Scale ◽  
A Priori ◽  
Computational Cost ◽  
Md Simulations ◽  
Collective Variables ◽  
Sampling Protocols ◽  
State Models

<p>Molecular dynamics (MD) simulations are increasingly used to elucidate relationships between protein structure, dynamics and their biological function. Currently it is extremely challenging to perform MD simulations of large-scale structural rearrangements in proteins that occur on millisecond timescales or beyond, as this requires very significant computational resources, or the use of cumbersome ‘collective variable’ enhanced sampling protocols. Here we describe a framework that combines ensemble MD simulations and virtual-reality visualization (eMD-VR) to enable users to interactively generate realistic descriptions of large amplitude, millisecond timescale protein conformational changes in proteins. Detailed tests demonstrate that eMD-VR substantially decreases the computational cost of folding simulations of a WW domain, without the need to define collective variables <i>a priori</i>. We further show that eMD-VR generated pathways can be combined with Markov State Models to describe the thermodynamics and kinetics of large-scale loop motions in the enzyme cyclophilin A. Our results suggest eMD-VR is a powerful tool for exploring protein energy landscapes in bioengineering efforts. </p>

scholarly journals Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations

2019 ◽  
Author(s):  
Junghoon Chae ◽  
Debsindhu Bhowmik ◽  
Heng Ma ◽  
Arvind Ramanathan ◽  
Chad Steed
Keyword(s):  
Molecular Dynamics ◽  
Visual Analytics ◽  
Large Scale ◽  
Physical Phenomenon ◽  
Living Organism ◽  
Md Simulations ◽  
High Dimensional ◽  
Molecular Characteristics ◽  
Complex Simulation ◽  
Dynamics Simulations

AbstractMolecular Dynamics (MD) simulation have been emerging as an excellent candidate for understanding complex atomic and molecular scale mechanism of bio-molecules that control essential bio-physical phenomenon in a living organism. But this MD technique produces large-size and long-timescale data that are inherently high-dimensional and occupies many terabytes of data. Processing this immense amount of data in a meaningful way is becoming increasingly difficult. Therefore, specific dimensionality reduction algorithm using deep learning technique has been employed here to embed the high-dimensional data in a lower-dimension latent space that still preserves the inherent molecular characteristics i.e. retains biologically meaningful information. Subsequently, the results of the embedding models are visualized for model evaluation and analysis of the extracted underlying features. However, most of the existing visualizations for embeddings have limitations in evaluating the embedding models and understanding the complex simulation data. We propose an interactive visual analytics system for embeddings of MD simulations to not only evaluate and explain an embedding model but also analyze various characteristics of the simulations. Our system enables exploration and discovery of meaningful and semantic embedding results and supports the understanding and evaluation of results by the quantitatively described features of the MD simulations (even without specific labels).

scholarly journals Large-scale clinical interpretation of genetic variants using evolutionary data and deep learning

2020 ◽  
Author(s):  
Jonathan Frazer ◽  
Pascal Notin ◽  
Mafalda Dias ◽  
Aidan Gomez ◽  
Kelly Brock ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Large Scale ◽  
Protein Sequences ◽  
Evolutionary Model ◽  
Generative Models ◽  
Evolutionary Information ◽  
Disease Genes ◽  
Independent Evidence ◽  
Variants Of Unknown Significance ◽  
Protein Variants

AbstractQuantifying the pathogenicity of protein variants in human disease-related genes would have a profound impact on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences1–3. In principle, computational methods could support the large-scale interpretation of genetic variants. However, prior methods4–7 have relied on training machine learning models on available clinical labels. Since these labels are sparse, biased, and of variable quality, the resulting models have been considered insufficiently reliable8. By contrast, our approach leverages deep generative models to predict the clinical significance of protein variants without relying on labels. The natural distribution of protein sequences we observe across organisms is the result of billions of evolutionary experiments9,10. By modeling that distribution, we implicitly capture constraints on the protein sequences that maintain fitness. Our model EVE (Evolutionary model of Variant Effect) not only outperforms computational approaches that rely on labelled data, but also performs on par, if not better than, high-throughput assays which are increasingly used as strong evidence for variant classification11–23. After thorough validation on clinical labels, we predict the pathogenicity of 11 million variants across 1,081 disease genes, and assign high-confidence reclassification for 72k Variants of Unknown Significance8. Our work suggests that models of evolutionary information can provide a strong source of independent evidence for variant interpretation and that the approach will be widely useful in research and clinical settings.

scholarly journals Molecular Insights Into the Gating Kinetics of the Cardiac hERG Channel, Illuminated by Structure and Molecular Dynamics

2021 ◽  
Vol 12 ◽  
Author(s):  
Zheng Zequn ◽  
Lian Jiangfang
Keyword(s):  
Molecular Dynamics ◽  
Amino Acid ◽  
Molecular Dynamics Simulation ◽  
Dynamics Simulation ◽  
Herg Channel ◽  
Single Amino Acid ◽  
Delayed Rectifier ◽  
Amino Acid Residues ◽  
Structural Domains ◽  
Gating Kinetics

The rapidly activating delayed rectifier K+ current generated by the cardiac hERG potassium channel encoded by KCNH2 is the most important reserve current for cardiac repolarization. The unique inward rectification characteristics of the hERG channel depend on the gating regulation, which involves crucial structural domains and key single amino acid residues in the full-length hERG channel. Identifying critical molecules involved in the regulation of gating kinetics for the hERG channel requires high-resolution structures and molecular dynamics simulation models. Based on the latest progress in hERG structure and molecular dynamics simulation research, summarizing the molecules involved in the changes in the channel state helps to elucidate the unique gating characteristics of the channel and the reason for its high affinity to cardiotoxic drugs. In this review, we aim to summarize the significant advances in understanding the voltage gating regulation of the hERG channel based on its structure obtained from cryo-electron microscopy and computer simulations, which reveal the critical roles of several specific structural domains and amino acid residues.

scholarly journals Combining Virtual Reality Visualization with Ensemble Molecular Dynamics to Study Complex Protein Conformational Changes

2020 ◽  
Author(s):  
Jordi Juárez-Jiménez ◽  
Philip Tew ◽  
Michael o'connor ◽  
Salome Llabres ◽  
Rebecca Sage ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Virtual Reality ◽  
Conformational Changes ◽  
Large Scale ◽  
Computational Cost ◽  
Md Simulations ◽  
Collective Variables ◽  
Protein Conformational Changes ◽  
Sampling Protocols ◽  
State Models

<p>Molecular dynamics (MD) simulations are increasingly used to elucidate relationships between protein structure, dynamics and their biological function. Currently it is extremely challenging to perform MD simulations of large-scale structural rearrangements in proteins that occur on millisecond timescales or beyond, as this requires very significant computational resources, or the use of cumbersome ‘collective variable’ enhanced sampling protocols. Here we describe a framework that combines ensemble MD simulations and virtual-reality visualization (eMD-VR) to enable users to interactively generate realistic descriptions of large amplitude, millisecond timescale protein conformational changes in proteins. Detailed tests demonstrate that eMD-VR substantially decreases the computational cost of folding simulations of a WW domain, without the need to define collective variables <i>a priori</i>. We further show that eMD-VR generated pathways can be combined with Markov State Models to describe the thermodynamics and kinetics of large-scale loop motions in the enzyme cyclophilin A. Our results suggest eMD-VR is a powerful tool for exploring protein energy landscapes in bioengineering efforts. </p>

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