scholarly journals A Fast 3 x N Matrix Multiply Routine for Calculation of Protein RMSD

2014 ◽  
Author(s):  
Imran S Haque ◽  
Kyle A Beauchamp ◽  
Vijay S Pande
Keyword(s):  
Root Mean Square ◽  
Linear Algebra ◽  
High Performance ◽  
Protein Structures ◽  
Matrix Product ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Large Numbers ◽  
Rapid Calculation ◽  
Atomic Coordinates

The bottleneck for the rapid calculation of the root-mean-square deviation in atomic coordinates (RMSD) between pairs of protein structures for large numbers of conformations is the evaluation of a (3xN) x (Nx3) matrix product over conformation pairs. Here we describe two matrix multiply routines specialized for the 3xN case that are able to significantly outperform (by up to 3X) off- the-shelf high-performance linear algebra libraries for this computation, reaching machine limits on performance. The routines are implemented in C and Python libraries, and are available at https://github.com/simtk/IRMSD.

scholarly journals Identification of Terpenoids From Abrus precatorius Against Parkinson’s Disease Proteins Using In Silico Approach

2021 ◽  
Vol 15 ◽  
pp. 117793222110507
Author(s):  
Damilola Alex Omoboyowa ◽  
Toheeb Adewale Balogun ◽  
Oluwaseun Motunrayo Omomule ◽  
Oluwatosin A Saibu
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
In Silico ◽  
High Performance ◽  
Md Simulation ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Abrus Precatorius

Parkinson’s disease (PD) is the second major neuro-degenrative disorder that causes morbidity and mortality among older populations. Terpenoids were reported as potential neuro-protective agents. Therefore, this study seeks to unlock the inhibitory potential of terpenoids from Abrus precatorius seeds against proteins involve in PD pathogenesis. In this study, in silico molecular docking of 5 terpenoids derived from high-performance liquid chromatography (HPLC) analysis of A. precatorius seeds against α-synuclein, catechol-o-methyltransferase, and monoamine oxidase B which are markers of PD was performed using Autodock vina. The absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) of the hits were done using Swiss ADME predictor and molecular dynamic (MD) simulation of the hit-protein complex was performed using Desmond Schrodinger software. Five out of 6 compounds satisfied the ADME/Tox parameters and showed varying degrees of binding affinities with selected proteins. Drimenin-α-synuclein complex showed the lowest binding energy of −9.1 kcal/mol followed by interaction with key amino acid residues necessary for α-synuclein inhibition. The selection of this complex was justified by its stability in MD simulation conducted for 10 ns and exhibited stable interaction in terms of root mean square deviation (RMSD) and root mean square deviation error fluctuation (RMSF) values.

scholarly journals Distance-based metrics for comparing conformational ensembles of intrinsically disordered proteins

2020 ◽  
Author(s):  
Tamas Lazar ◽  
Mainak Guharoy ◽  
Wim Vranken ◽  
Sarah Rauscher ◽  
Shoshana J. Wodak ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Intrinsically Disordered Proteins ◽  
Statistical Significance ◽  
Protein Structures ◽  
Disordered Proteins ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Conformational Ensembles ◽  
Intrinsically Disordered

AbstractIntrinsically disordered proteins (IDPs) are proteins whose native functional states represent ensembles of highly diverse conformations. Such ensembles are a challenge for quantitative structure comparisons as their conformational diversity precludes optimal superimposition of the atomic coordinates, necessary for deriving common similarity measures such as the root-mean-square deviation (RMSD) of these coordinates. Here we introduce superimposition-free metrics, which are based on computing matrices of Cα-Cα distance distributions within ensembles and comparing these matrices between ensembles. Differences between two matrices yield information on the similarity between specific regions of the polypeptide, whereas the global structural similarity is captured by the ens_dRMS, defined as the root-mean-square difference between the medians of the Cα-Cαdistance distributions of two ensembles. Together, our metrics enable rigorous investigations of structure-function relationships in conformational ensembles of IDPs derived using experimental restraints or by molecular simulations, and for proteins containing both structured and disordered regions.Statement of SignificanceImportant biological insight is obtained from comparing the high-resolution structures of proteins. Such comparisons commonly involve superimposing two protein structures and computing the residual root-mean-square deviation of the atomic positions. This approach cannot be applied to intrinsically disordered proteins (IDPs) because IDPs do not adopt well-defined 3D structures, rather, their native functional state is defined by ensembles of heterogeneous conformations that cannot be meaningfully superimposed. We report new measures that quantify the local and global similarity between different conformational ensembles by evaluating differences between the distributions of residue-residue distances and their statistical significance. Applying these measures to IDP ensembles and to a protein containing both structured and intrinsically disordered domains provides deeper insights into how structural features relate to function.

scholarly journals GNINA 1.0: molecular docking with deep learning

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Andrew T. McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

AbstractMolecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2Å root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under an open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

Fast collocation for Moho estimation from GOCE gravity data: the Iran case study

2020 ◽  
Vol 221 (1) ◽  
pp. 651-664
Author(s):  
H Heydarizadeh Shali ◽  
D Sampietro ◽  
A Safari ◽  
M Capponi ◽  
A Bahroudi
Keyword(s):  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Seismic Data ◽  
Active Faults ◽  
Earth’S Crust ◽  
Arabian Plate ◽  
Moho Depth ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Earth's Crust

SUMMARY The study of the discontinuity between crust and mantle beneath Iran is still an open issue in the geophysical community due to its various tectonic features created by the collision between the Iranian and Arabian Plate. For instance in regions such as Zagros, Alborz or Makran, despite the number of studies performed, both by exploiting gravity or seismic data, the depth of the Moho and also interior structure is still highly uncertain. This is due to the complexity of the crust and to the presence of large short wavelength signals in the Moho depth. GOCE observations are capable and useful products to describe the Earth’s crust structure either at the regional or global scale. Furthermore, it is plausible to retrieve important information regarding the structure of the Earth’s crust by combining the GOCE observations with seismic data and considering additional information. In the current study, we used as observation a grid of second radial derivative of the anomalous gravitational potential computed at an altitude of 221 km by means of the space-wise approach, to study the depth of the Moho. The observations have been reduced for the gravitational effects of topography, bathymetry and sediments. The residual gravity has been inverted accordingly to a simple two-layer model. In particular, this guarantees the uniqueness of the solution of the inverse problem which has been regularized by means of a collocation approach in the frequency domain. Although results of this study show a general good agreement with seismically derived depths with a root mean square deviation of 6 km, there are some discrepancies under the Alborz zone and also Oman sea with a root mean square deviation up 10 km for the former and an average difference of 3 km for the latter. Further comparisons with the natural feature of the study area, for instance, active faults, show that the resulting Moho features can be directly associated with geophysical and tectonic blocks.

How root-mean-square distance (r.m.s.d.) values depend on the resolution of protein structures that are compared

2003 ◽  
Vol 36 (1) ◽  
pp. 125-128 ◽  
Author(s):  
Oliviero Carugo
Keyword(s):  
Protein Structure ◽  
Root Mean Square ◽  
Protein Pair ◽  
Protein Structures ◽  
Structural Similarity ◽  
Protein Crystal ◽  
Mean Square ◽  
Root Mean Square Distance ◽  
Open Question ◽  
Mean Square Distance

The most popular estimator of structural similarity is the root-mean-square distance (r.m.s.d.) between equivalent atoms, computed after optimal superposition of the two structures that are compared. It is known that r.m.s.d. values do not depend only on conformational differences but also on other features, for example the dimensions of the structures that are compared. An open question is how they might depend on the accuracy of the experimentally determined protein structures. Given that the accuracy of the protein crystal structures is generally estimated through the crystallographic resolution, it is important to know the dependence of the r.m.s.d. on the crystallographic resolution of the two structures that are compared. 14458 protein structure pairs of identical sequence were compared and the resulting r.m.s.d. values were normalized to 100-residue length to avoid the bias introduced by the dependence of the r.m.s.d. values on the protein-pair dimensions. On average, smaller r.m.s.d. values are associated with protein structure pairs at better resolution and the r.m.s.d. values tend to increase if the two proteins that are compared have been refined at different resolutions. For crystallographic resolutions ranging between 1.6 and 2.9 Å, both relationships appear to be linear: r.m.s.d. = −0.73 + 0.48 resolution and delta_r.m.s.d. = 0.20 + 0.30 delta_resolution (`delta' indicating difference). Although the linearity of these relationships is not expected to hold outside the 1.6–2.9 Å resolution range, they are useful in making the r.m.s.d. values more reliable.

scholarly journals A Variable Data Fusion Approach for Electromechanical Impedance-Based Damage Detection

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4204
Author(s):  
Shishir Kumar Singh ◽  
Rohan Soman ◽  
Tomasz Wandowski ◽  
Pawel Malinowski
Keyword(s):  
Data Fusion ◽  
Damage Detection ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Health Monitoring ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Damage Scenarios ◽  
Electromechanical Impedance ◽  
Fusion Approach

There is continuing research in the area of structural health monitoring (SHM) as it may allow a reduction in maintenance costs as well as lifetime extension. The search for a low-cost health monitoring system that is able to detect small levels of damage is still on-going. The present study is one more step in this direction. This paper describes a data fusion technique by combining the information for robust damage detection using the electromechanical impedance (EMI) method. The EMI method is commonly used for damage detection due to its sensitivity to low levels of damage. In this paper, the information of resistance (R) and conductance (G) is studied in a selected frequency band and a novel data fusion approach is proposed. A novel fused parameter (F) is developed by combining the information from G and R. The difference in the new metric under different damage conditions is then quantified using established indices such as the root mean square deviation (RMSD) index, mean absolute percentage deviation (MAPD), and root mean square deviation using k-th state as the reference (RMSDk). The paper presents an application of the new metric for detection of damage in three structures, namely, a thin aluminum (Al) plate with increasing damage severity (simulated with a drilled hole of increasing size), a glass fiber reinforced polymer (GFRP) composite beam with increasing delamination and another GFRP plate with impact-induced damage scenarios. Based on the experimental results, it is apparent that the variable F increases the robustness of the damage detection as compared to the quantities R and G.

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