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 Potensi Sinamaldehid sebagai Anti Hiperpigmentasi secara In Silico

2021 ◽  
Vol 7 (2) ◽  
pp. 95-101
Author(s):  
Ni Made Gani Pratiwi ◽  
Ni Made Atika Saraswati ◽  
Ni Made Irma Febby Prasasti Dewi ◽  
Luh Pande Putu Tirta
Keyword(s):  
Root Mean Square ◽  
In Silico ◽  
Mean Square ◽  
Inhibitor Protein ◽  
Protein Target ◽  
Root Mean Square Distance ◽  
Mean Square Distance

Permasalahan kulit yang sering ditemui yaitu hiperpigmentasi yang terjadi akibat adanya sintesis melanin berlebihan yang menyebabkan penggelapan warna kulit. Hiperpigmentasi dapat diatasi dengan agen anti hiperpigmentasi yang beraktivitas dalam menghambat proses sintesis melanin. Sintesis melanin dapat dihambat dengan berbagai cara salah satunya dengan menghambat aktivitas tyrosinase. Tyrosinase merupakan enzim yang berperan dalam mengkatalisis proses biosintesis melanin. Sinamaldehid merupakan senyawa bahan alam banyak ditemukan pada tanaman Cinnamomum burmanni mempunyai aktivitas sebagai antioksidan. Penelitian ini bertujuan untuk mengetahui potensi sinamaldehid dalam menghambat tyrosinase yang akan dibandingkan dengan native liganya secara in silico. Uji in silico dilakukan secara docking molecular dengan tahapan yaitu preparasi dan optimasi sinamaldehid, preparasi tyrosinase serta validasi dan docking. Metode docking molecular telah dinyatakan valid karena RMSD (root mean square distance) yang diperoleh tidak lebih dari 3 Å. Analisis data dilakukan dengan melihat energi ikatan yang dihasilkan dan ikatan yang terbentuk antara senyawa dengan residu asam amino pada protein. Nilai energi ikatan yang diperoleh antara ikatan sinamaldehid dengan tyrosinase adalah-6,21 kkal/mol. Sedangkan energi ikatan antara tyrosinase dengan native ligandnya -4,79 kkal/mol. Hal tersebut menunjukkan afinitas dari sinamaldehid pada protein tyrosinase lebih besar dibandingkan native ligandnya, sehingga sinamaldehid dikatakan memiliki potensi sebagai anti hiperpigmentasi dengan mekanisme molecular berupa inhibitor protein target tyrosinase sehingga dapat menghambat aktivitas enzim tyrosinase.

Percentile-based spread: a more accurate way to compare crystallographic models

2010 ◽  
Vol 66 (9) ◽  
pp. 970-978 ◽  
Author(s):  
Edwin Pozharski
Keyword(s):  
Crystal Structures ◽  
Root Mean Square ◽  
Conformational Changes ◽  
Alternative Measure ◽  
Mean Square ◽  
Root Mean Square Distance ◽  
Average Variation ◽  
Model Ensembles ◽  
Mean Square Distance

The comparison of biomacromolecular crystal structures is traditionally based on the root-mean-square distance between corresponding atoms. This measure is sensitive to the presence of outliers, which inflate it disproportionately to their fraction. An alternative measure, the percentile-based spread (p.b.s.), is proposed and is shown to represent the average variation in atomic positions more adequately. It is discussed in the context of isomorphous crystal structures, conformational changes and model ensembles generated by repetitive automated rebuilding.

scholarly journals MPDB: a unified multi-domain protein structure database integrating structural analogue detection

2021 ◽  
Author(s):  
Chunxiang Peng ◽  
Xiaogen Zhou ◽  
Yuhao Xia ◽  
Yang Zhang ◽  
Guijun Zhang
Keyword(s):  
Protein Structure ◽  
Structure Prediction ◽  
Protein Structures ◽  
Structural Similarity ◽  
Structure Database ◽  
Eukaryotic Proteins ◽  
Domain Models ◽  
Input Domain ◽  
Domain Protein ◽  
Multiple Domains

With the development of protein structure prediction methods and biological experimental determination techniques, the structure of single-domain proteins can be relatively easier to be modeled or experimentally solved. However, more than 80% of eukaryotic proteins and 67% of prokaryotic proteins contain multiple domains. Constructing a unified multi-domain protein structure database will promote the research of multi-domain proteins, especially in the modeling of multi-domain protein structures. In this work, we develop a unified multi-domain protein structure database (MPDB). Based on MPDB, we also develop a server with two functional modules: (1) the culling module, which filters the whole MPDB according to input criteria; (2) the detection module, which identifies structural analogues of the full-chain according to the structural similarity between input domain models and the protein in MPDB. The module can discover the potential analogue structures, which will contribute to high-quality multi-domain protein structure modeling.

FAST SIMILARITY SEARCH FOR PROTEIN 3D STRUCTURES USING TOPOLOGICAL PATTERN MATCHING BASED ON SPATIAL RELATIONS

2005 ◽  
Vol 15 (04) ◽  
pp. 287-296 ◽  
Author(s):  
SUNG-HEE PARK ◽  
KEUN HO RYU ◽  
DAVID GILBERT
Keyword(s):  
Protein Structure ◽  
Secondary Structure ◽  
Similarity Search ◽  
Spatial Databases ◽  
Topological String ◽  
Protein Structures ◽  
Structural Similarity ◽  
Spatial Relations ◽  
3D Structures ◽  
Comparison Results

Similarity search for protein 3D structures become complex and computationally expensive due to the fact that the size of protein structure databases continues to grow tremendously. Recently, fast structural similarity search systems have been required to put them into practical use in protein structure classification whilst existing comparison systems do not provide comparison results on time. Our approach uses multi-step processing that composes of a preprocessing step to represent geometry of protein structures with spatial objects, a filter step to generate a small candidate set using approximate topological string matching, and a refinement step to compute a structural alignment. This paper describes the preprocessing and filtering for fast similarity search using the discovery of topological patterns of secondary structure elements based on spatial relations. Our system is fully implemented by using Oracle 8i spatial. We have previously shown1 that our approach has the advantage of speed of performance compared with other approach such as DALI. This work shows that the discovery of topological relations of secondary structure elements in protein structures by using spatial relations of spatial databases is practical for fast structural similarity search for proteins.

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 Molecular Dynamics Study of Insulin Mutants

2021 ◽  
Keyword(s):  
Molecular Dynamics ◽  
Protein Structure ◽  
Secondary Structure ◽  
Root Mean Square ◽  
Human Insulin ◽  
Dynamics Simulation ◽  
Solvent Accessible Surface Area ◽  
Mean Square ◽  
Wild Type ◽  
Secondary Structure Content

Human insulin, a small protein hormone consisting of A-chain (21 residues) and B-chain (30 residues) linked by three disulfide bonds, is crucial for controlling the hyperglycemia in type I diabetes. In the present work molecular dynamics simulation (MD) with human insulin and its mutants was used to assess the influence of 10 point mutations (HisA8, ValA10, AspB10, GlnB17, AlaB17, GlnB18, AspB25, ThrB26, GluB27, AspB28), 6 double mutations (GluA13+GluB10, SerA13+GluB27, GluB1+GluB27, SerB2+AspB10, AspB9+GluB27, GluB16+GluB27) and one triple mutation (GluA15+AspA18+AspB3) in the protein sequence on the structure and dynamics of human insulin. A series of thermal unfolding MD simulations with wild type (WT) human insulin and its mutants was performed at 400 K with GROMACS software (version 5.1) using the CHARMM36m force field. The MD results have been analyzed in terms of the parameters characterizing both the global and local protein structure, such as the backbone root mean-square deviation, gyration radius, solvent accessible surface area, the root mean-square fluctuations and the secondary structure content. The MD simulation data showed that depending on time evolution of integral characteristics, the examined mutants can be tentatively divided into three groups: 1) the mutants HisA8, ValA10, AlaB17, AspB25, ThrB26, GluB27, GluA13+GluB10, GluB1+GluB27 and GluB16+GluB27, which exert stabilizing effect on the protein structure in comparison with wild type insulin; 2) the mutants GlnB17, AspB10, SerB2+AspB10 and GluA15+AspA18+AspB3 that did not significantly affect the dynamical properties of human insulin with a minimal stabilizing impact; 3) the mutants AspB28, AspB9+GluB27 and SerA13+GluB27, GlnB18, destabilizing the protein structure. Analysis of the secondary structure content provided evidence for the influence of AspB28, AspB9+GluB27 and SerA13+GluB27, GlnB18 on the insulin unfolding. Our MD results indicate that the replacement of superficial nonpolar residues in the insulin structure by hydrophilic ones gives rise to the increase in protein stability in comparison with the wild type protein.

scholarly journals A novel hyperthermophilic methylglyoxal synthase: molecular dynamic analysis on the regional fluctuations

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gyo-Yeon Seo ◽  
Hoe-Suk Lee ◽  
Hyeonsoo Kim ◽  
Sukhyeong Cho ◽  
Jeong-Geol Na ◽  
...  
Keyword(s):  
Protein Structure ◽  
Hydrogen Bonds ◽  
Root Mean Square ◽  
Residual Activity ◽  
Dynamics Simulation ◽  
Mean Square ◽  
Salt Bridges ◽  
Optimum Activity ◽  
Regional Fluctuations ◽  
Methylglyoxal Synthase

AbstractTwo putative methylglyoxal synthases, which catalyze the conversion of dihydroxyacetone phosphate to methylglyoxal, from Oceanithermus profundus DSM 14,977 and Clostridium difficile 630 have been characterized for activity and thermal stability. The enzyme from O. profundus was found to be hyperthermophilic, with the optimum activity at 80 °C and the residual activity up to 59% after incubation of 15 min at 95 °C, whereas the enzyme from C. difficile was mesophilic with the optimum activity at 40 °C and the residual activity less than 50% after the incubation at 55 °C or higher temperatures for 15 min. The structural analysis of the enzymes with molecular dynamics simulation indicated that the hyperthermophilic methylglyoxal synthase has a rigid protein structure with a lower overall root-mean-square-deviation value compared with the mesophilic or thermophilic counterparts. In addition, the simulation results identified distinct regions with high fluctuations throughout those of the mesophilic or thermophilic counterparts via root-mean-square-fluctuation analysis. Specific molecular interactions focusing on the hydrogen bonds and salt bridges in the distinct regions were analyzed in terms of interatomic distances and positions of the individual residues with respect to the secondary structures of the enzyme. Key interactions including specific salt bridges and hydrogen bonds between a rigid beta-sheet core and surrounding alpha helices were found to contribute to the stabilisation of the hyperthermophilic enzyme by reducing the regional fluctuations in the protein structure. The structural information and analysis approach in this study can be further exploited for the engineering and industrial application of the enzyme.

scholarly journals PUResNet: prediction of protein-ligand binding sites using deep residual neural network

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Jeevan Kandel ◽  
Hilal Tayara ◽  
Kil To Chong
Keyword(s):  
Protein Structure ◽  
Ligand Binding ◽  
Binding Sites ◽  
Protein Structures ◽  
Structural Similarity ◽  
Vital Role ◽  
Biological Functions ◽  
True Nature ◽  
Ligand Binding Sites ◽  
Deep Learning Model

Abstract Background Predicting protein-ligand binding sites is a fundamental step in understanding the functional characteristics of proteins, which plays a vital role in elucidating different biological functions and is a crucial step in drug discovery. A protein exhibits its true nature after binding to its interacting molecule known as a ligand that binds only in the favorable binding site of the protein structure. Different computational methods exploiting the features of proteins have been developed to identify the binding sites in the protein structure, but none seems to provide promising results, and therefore, further investigation is required. Results In this study, we present a deep learning model PUResNet and a novel data cleaning process based on structural similarity for predicting protein-ligand binding sites. From the whole scPDB (an annotated database of druggable binding sites extracted from the Protein DataBank) database, 5020 protein structures were selected to address this problem, which were used to train PUResNet. With this, we achieved better and justifiable performance than the existing methods while evaluating two independent sets using distance, volume and proportion metrics.

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