Protein Structure Refinement Guided by Atomic Packing Frustration Analysis

1AbstractRecent advances in machine learning, bioinformatics and the understanding of the folding problem have enabled efficient predictions of protein structures with moderate accuracy, even for targets when there is little information from templates. All-atom molecular dynamics simulations provide a route to refine such predicted structures, but unguided atomistic simulations, even when lengthy in time, often fail to eliminate incorrect structural features that would allow the structure to become more energetically favorable owing to the necessity of making large scale motions and overcoming energy barriers for side chain repacking. In this study, we show that localizing packing frustration at atomic resolution by examining the statistics of the energetic changes that occur when the local environment of a site is changed allows one to identify the most likely locations of incorrect contacts. The global statistics of atomic resolution frustration in structures that have been predicted using various algorithms provide strong indicators of structural quality when tested over a database of 20 targets from previous CASP experiments. Residues that are more correctly located turn out to be more minimally frustrated than more poorly positioned sites. These observations provide a diagnosis of both global and local quality of predicted structures, and thus can be used as guidance in all-atom refinement simulations of the 20 targets. Refinement simulations guided by atomic packing frustration turn out to be quite efficient and significantly improve the quality of the structures.

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Protein phasing at non-atomic resolution by combining Patterson andVLDtechniques

Acta Crystallographica Section D Biological Crystallography ◽

10.1107/s139900471401013x ◽

2014 ◽

Vol 70 (7) ◽

pp. 1994-2006 ◽

Cited By ~ 8

Author(s):

Rocco Caliandro ◽

Benedetta Carrozzini ◽

Giovanni Luca Cascarano ◽

Giuliana Comunale ◽

Carmelo Giacovazzo ◽

...

Keyword(s):

Model Building ◽

Protein Structures ◽

Experimental Information ◽

Atomic Resolution ◽

Data Sets ◽

Density Maps ◽

Combined Use ◽

Final Electron ◽

Automatic Model Building

Phasing proteins at non-atomic resolution is still a challenge for anyab initiomethod. A variety of algorithms [Patterson deconvolution, superposition techniques, a cross-correlation function (Cmap), theVLD(vive la difference) approach, the FF function, a nonlinear iterative peak-clipping algorithm (SNIP) for defining the background of a map and thefree lunchextrapolation method] have been combined to overcome the lack of experimental information at non-atomic resolution. The method has been applied to a large number of protein diffraction data sets with resolutions varying from atomic to 2.1 Å, with the condition that S or heavier atoms are present in the protein structure. The applications include the use ofARP/wARPto check the quality of the final electron-density maps in an objective way. The results show that resolution is still the maximum obstacle to protein phasing, but also suggest that the solution of protein structures at 2.1 Å resolution is a feasible, even if still an exceptional, task for the combined set of algorithms implemented in the phasing program. The approach described here is more efficient than the previously described procedures:e.g.the combined use of the algorithms mentioned above is frequently able to provide phases of sufficiently high quality to allow automatic model building. The method is implemented in the current version ofSIR2014.

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Multi-Scale Structural Analysis of Proteins by Deep Semantic Segmentation

10.1101/474627 ◽

2018 ◽

Author(s):

Raphael R. Eguchi ◽

Po-Ssu Huang

Keyword(s):

Image Classification ◽

Protein Design ◽

Large Scale ◽

De Novo ◽

Protein Structures ◽

Semantic Segmentation ◽

Amino Acid Sequences ◽

Structural Quality ◽

Small Subset ◽

Structural Prediction

AbstractRecent advancements in computational methods have facilitated large-scale sampling of protein structures, leading to breakthroughs in protein structural prediction and enabling de novo protein design. Establishing methods to identify candidate structures that can lead to native folds or designable structures remains a challenge, since few existing metrics capture high-level structural features such as architectures, folds, and conformity to conserved structural motifs. Convolutional Neural Networks (CNNs) have been successfully used in semantic segmentation — a subfield of image classification in which a class label is predicted for every pixel. Here, we apply semantic segmentation to protein structures as a novel strategy for fold identification and structural quality assessment. We represent protein structures as 2D α-carbon distance matrices (“contact maps”), and train a CNN that assigns each residue in a multi-domain protein to one of 38 architecture classes designated by the CATH database. Our model performs exceptionally well, achieving a per-residue accuracy of 90.8% on the test set (95.0% average accuracy over all classes; 87.8% average within-structure accuracy). The unique aspect of our classifier is that it encodes sequence agnostic residue environments from the PDB and can assess structural quality as quantitative probabilities. We demonstrate that individual class probabilities can be used as a metric that indicates the degree to which a randomly generated structure assumes a specific fold, as well as a metric that highlights non-conformative regions of a protein belonging to a known class. These capabilities yield a powerful tool for guiding structural sampling for both structural prediction and design.SignificanceRecent computational advances have allowed researchers to predict the structure of many proteins from their amino acid sequences, as well as designing new sequences that fold into predefined structures. However, these tasks are often challenging because they require selection of a small subset of promising structural models from a large pool of stochastically generated ones. Here, we describe a novel approach to protein model selection that uses 2D image classification techniques to evaluate 3D protein models. Our method can be used to select structures based on the fold that they adopt, and can also be used to identify regions of low structural quality. These capabilities yield a powerful tool for both protein design and structure prediction.

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In situ structural analysis of SARS-CoV-2 spike reveals flexibility mediated by three hinges

10.1101/2020.06.26.173476 ◽

2020 ◽

Cited By ~ 12

Author(s):

Beata Turoňová ◽

Mateusz Sikora ◽

Christoph Schürmann ◽

Wim J. H. Hagen ◽

Sonja Welsch ◽

...

Keyword(s):

Large Scale ◽

Vaccine Development ◽

Electron Tomography ◽

Structural Features ◽

Globular Domain ◽

Data Set ◽

Host Cell Surface ◽

Subtomogram Averaging ◽

Dynamics Simulations

AbstractThe spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is required for cell entry and is the major focus for vaccine development. We combine cryo electron tomography, subtomogram averaging and molecular dynamics simulations to structurally analyze S in situ. Compared to recombinant S, the viral S is more heavily glycosylated and occurs predominantly in a closed pre-fusion conformation. We show that the stalk domain of S contains three hinges that give the globular domain unexpected orientational freedom. We propose that the hinges allow S to scan the host cell surface, shielded from antibodies by an extensive glycan coat. The structure of native S contributes to our understanding of SARS-CoV-2 infection and the development of safe vaccines. The large scale tomography data set of SARS-CoV-2 used for this study is therefore sufficient to resolve structural features to below 5 Ångstrom, and is publicly available at EMPIAR-10453.

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EQUILIBRIUM MOLECULAR DYNAMICS CALCULATIONS OF THERMAL CONDUCTIVITY: A “HOW-TO” FOR THE BEGINNERS

CNL Nuclear Review ◽

10.12943/cnr.2018.00009 ◽

2020 ◽

Vol 9 (1) ◽

pp. 11-25

Author(s):

Jude S. Alexander ◽

Christopher Maxwell ◽

Jeremy Pencer ◽

Mouna Saoudi

Keyword(s):

Thermal Conductivity ◽

Molecular Dynamics ◽

Large Scale ◽

Interatomic Potential ◽

Negative Impact ◽

Significant Risk ◽

Nonequilibrium Molecular Dynamics ◽

Atomistic Modelling ◽

Dynamics Simulations

The ready availability of codes such as LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator) for molecular dynamics simulations has opened up the realm of atomistic modelling to novice code users with an interest in computational materials modelling but who lack the appropriate theoretical or computational background. As such, there is significant risk of the “user effect” having a negative impact on the quality of results obtained using such codes. Here, we present a “how-to” procedure for equilibrium molecular dynamics-based nuclear fuel thermal conductivity calculations using the Green–Kubo method with an interatomic potential developed by Cooper et al. [ 1 ]. The various steps of the simulation are identified and explained, along with criteria to assess the quality of the intermediate and final results, discussion of some problems that can arise during a simulation, and some inherent limitations of the method. Calculated thermal conductivities for UO2 and ThO2 will be compared with the available experimental data and also with similar thermal conductivity calculations using nonequilibrium molecular dynamics, reported in the open literature.

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Влияние морфологии буферного слоя AlN на структурное качество полуполярного слоя GaN, выращенного на подложке Si(001), по данным просвечивающей электронной микроскопии

Письма в журнал технической физики ◽

10.21883/pjtf.2022.05.52159.18932 ◽

2022 ◽

Vol 48 (5) ◽

pp. 51

Author(s):

Д.А. Кириленко ◽

А.В. Мясоедов ◽

А.Е. Калмыков ◽

Л.М. Сорокин

Keyword(s):

Gallium Nitride ◽

Buffer Layer ◽

Structural Features ◽

Nitride Layer ◽

Structural Quality ◽

Threading Dislocations ◽

Interface Morphology ◽

Transmission Electron ◽

Dislocations Density

Structural features of the interface between semipolar gallium nitride layer and buffer layer of aluminum nitride grown on a SiC/Si(001) template misoriented by an angle of 7° were studied by high-resolution transmission electron microscopy. The effect of interface morphology on the structural quality of the gallium nitride layer is revealed: faceted structure the surface of the buffer layer reduces the threading dislocations density.

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Multi-scale structural analysis of proteins by deep semantic segmentation

Bioinformatics ◽

10.1093/bioinformatics/btz650 ◽

2019 ◽

Vol 36 (6) ◽

pp. 1740-1749 ◽

Cited By ~ 2

Author(s):

Raphael R Eguchi ◽

Po-Ssu Huang

Keyword(s):

Protein Design ◽

Large Scale ◽

De Novo ◽

Protein Structures ◽

Semantic Segmentation ◽

Structural Features ◽

Supplementary Information ◽

Structural Prediction ◽

Structure Accuracy ◽

High Level

Abstract Motivation Recent advances in computational methods have facilitated large-scale sampling of protein structures, leading to breakthroughs in protein structural prediction and enabling de novo protein design. Establishing methods to identify candidate structures that can lead to native folds or designable structures remains a challenge, since few existing metrics capture high-level structural features such as architectures, folds and conformity to conserved structural motifs. Convolutional Neural Networks (CNNs) have been successfully used in semantic segmentation—a subfield of image classification in which a class label is predicted for every pixel. Here, we apply semantic segmentation to protein structures as a novel strategy for fold identification and structure quality assessment. Results We train a CNN that assigns each residue in a multi-domain protein to one of 38 architecture classes designated by the CATH database. Our model achieves a high per-residue accuracy of 90.8% on the test set (95.0% average per-class accuracy; 87.8% average per-structure accuracy). We demonstrate that individual class probabilities can be used as a metric that indicates the degree to which a randomly generated structure assumes a specific fold, as well as a metric that highlights non-conformative regions of a protein belonging to a known class. These capabilities yield a powerful tool for guiding structural sampling for both structural prediction and design. Availability and implementation The trained classifier network, parser network, and entropy calculation scripts are available for download at https://git.io/fp6bd, with detailed usage instructions provided at the download page. A step-by-step tutorial for setup is provided at https://goo.gl/e8GB2S. All Rosetta commands, RosettaRemodel blueprints, and predictions for all datasets used in the study are available in the Supplementary Information. Supplementary information Supplementary data are available at Bioinformatics online.

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Effect of molecular solvents of varying polarity on the self-assembly of 1-n-dodecyl-3-methylimidazolium octylsulfate ionic liquid

Journal of Theoretical and Computational Chemistry ◽

10.1142/s0219633618400047 ◽

2018 ◽

Vol 17 (03) ◽

pp. 1840004 ◽

Cited By ~ 3

Author(s):

Utkarsh Kapoor ◽

Jindal K. Shah

Keyword(s):

Ionic Liquid ◽

Self Assembly ◽

Large Scale ◽

Structural Features ◽

Nematic Order ◽

Structure Factors ◽

Dynamics Simulations ◽

Multiple Domains ◽

Molecular Solvents

Large-scale molecular dynamics simulations consisting of more than 88,000–106,000 atoms for approximately 250 ns (including equilibration and production) were conducted to assess the effect of polar, nonpolar and amphiphilic molecular solvents on the nanoscale structuring of 1-[Formula: see text]-dodecyl-3-methylimidazolium [C[Formula: see text]mim] octylsulfate [C8SO4] ionic liquid (IL). Water [H2O], [Formula: see text]-octane [C8H[Formula: see text]] and 1-octanol [C8H[Formula: see text]OH] are employed as examples of polar, nonpolar, and amphiphilic molecules, respectively. The results indicate that each of these molecular solvents modify the nanosegregation behavior of the ionic liquid in a unique way. Water induces a high order of structuring of the ionic liquid as indicated by extremely high nematic order parameter for the system. In addition, the morphology of the neat ionic liquid is transformed from layer-like to that of bilayer-like in which the polar and nonpolar domains alternate. The presence of water also causes the stretching of the nonpolar domain, thus, increasing its size. At the concentration examined in this work, [Formula: see text]-octane is found to be only partially miscible with the ionic liquid. The polar network is maintained; however, the continuous cationic nonpolar domain is split into multiple domains. [Formula: see text]-octane is accommodated in the ionic liquid nonpolar domain. Similarly, the amphiphilicity of 1-octanol leads to an increase in the number of cationic as well as anionic domains. The overall nonpolar domain length, however, remains nearly identical to that found for the pure ionic liquid. Additional characterization of structural features of the three systems is discussed in terms of one-dimensional number densities, nematic order parameters for the overall systems and their components and structure factors.

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Coupled molecular dynamics mediate long- and short-range epistasis between mutations that affect stability and aggregation kinetics

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1810324115 ◽

2018 ◽

Vol 115 (47) ◽

pp. E11043-E11052 ◽

Cited By ~ 16

Author(s):

Haoran Yu ◽

Paul A. Dalby

Keyword(s):

Molecular Dynamics ◽

Protein Engineering ◽

Protein Stability ◽

Local Environment ◽

Structural Features ◽

Aggregation Kinetics ◽

Free Energy Barrier ◽

Epistatic Interactions ◽

Cross Correlation Analysis ◽

Dynamics Simulations

Multiple mutations are typically required to significantly improve protein stability or aggregation kinetics. However, when several substitutions are made in a single protein, the mutations can potentially interact in a nonadditive manner, resulting in epistatic effects, which can hamper protein-engineering strategies to improve thermostability or aggregation kinetics. Here, we have examined the role of protein dynamics in mediating epistasis between pairs of mutations. With Escherichia coli transketolase (TK) as a model, we explored the epistatic interactions between two single variants H192P and A282P, and also between the double-mutant H192P/A282P and two single variants, I365L or G506A. Epistasis was determined for several measures of protein stability, including the following: the free-energy barrier to kinetic inactivation, ∆∆G‡; thermal transition midpoint temperatures, Tm; and aggregation onset temperatures, Tagg. Nonadditive epistasis was observed between neighboring mutations as expected, but also for distant mutations located in the surface and core regions of different domains. Surprisingly, the epistatic behaviors for each measure of stability were often different for any given pairwise recombination, highlighting that kinetic and thermodynamic stabilities do not always depend on the same structural features. Molecular-dynamics simulations and a pairwise cross-correlation analysis revealed that mutations influence the dynamics of their local environment, but also in some cases the dynamics of regions distant in the structure. This effect was found to mediate epistatic interactions between distant mutations and could therefore be exploited in future protein-engineering strategies.

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Computational Modulation of the V3 Region of Glycoprotein gp125 of HIV-2

International Journal of Molecular Sciences ◽

10.3390/ijms22041948 ◽

2021 ◽

Vol 22 (4) ◽

pp. 1948

Author(s):

Patrícia A. Serra ◽

Nuno Taveira ◽

Rita C. Guedes

Keyword(s):

Viral Entry ◽

Network Formation ◽

Protein Structures ◽

3D Structure ◽

3D Models ◽

Structural Features ◽

Host Cells ◽

Computer Assisted ◽

Function Relationship ◽

Dynamics Simulations

HIV-2 infection is frequently neglected in HIV/AIDS campaigns. However, a special emphasis must be given to HIV-2 as an untreated infection that also leads to AIDS and death, and for which the efficacy of most available drugs is limited against HIV-2. HIV envelope glycoproteins mediate binding to the receptor CD4 and co-receptors at the surface of the target cell, enabling fusion with the cell membrane and viral entry. Here, we developed and optimized a computer-assisted drug design approach of an important HIV-2 glycoprotein that allows us to explore and gain further insights at the molecular level into protein structures and interactions crucial for the inhibition of HIV-2 cell entry. The 3D structure of a key HIV-2ROD gp125 region was generated by a homology modeling campaign. To disclose the importance of the main structural features and compare them with experimental results, 3D-models of six mutants were also generated. These mutations revealed the selective impact on the behavior of the protein. Furthermore, molecular dynamics simulations were performed to optimize the models, and the dynamic behavior was tackled to account for structure flexibility and interactions network formation. Structurally, the mutations studied lead to a loss of aromatic features, which is very important for the establishment of π-π interactions and could induce a structural preference by a specific coreceptor. These new insights into the structure-function relationship of HIV-2 gp125 V3 and surrounding regions will help in the design of better models and the design of new small molecules capable to inhibit the attachment and binding of HIV with host cells.

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