scholarly journals iPBAvizu: a PyMOL plugin for an efficient 3D protein structure superimposition approach

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Guilhem Faure ◽  
Agnel Praveen Joseph ◽  
Pierrick Craveur ◽  
Tarun J. Narwani ◽  
Narayanaswamy Srinivasan ◽  
...  
Keyword(s):  
Protein Structure ◽  
Sequence Alignment ◽  
Protein Structures ◽  
3D Structure ◽  
Structural Features ◽  
Substitution Matrix ◽  
Structural Alphabet ◽  
3D Protein Structure ◽  
Alignment Procedure ◽  
Protein Alignments

Abstract Background Protein 3D structure is the support of its function. Comparison of 3D protein structures provides insight on their evolution and their functional specificities and can be done efficiently via protein structure superimposition analysis. Multiple approaches have been developed to perform such task and are often based on structural superimposition deduced from sequence alignment, which does not take into account structural features. Our methodology is based on the use of a Structural Alphabet (SA), i.e. a library of 3D local protein prototypes able to approximate protein backbone. The interest of a SA is to translate into 1D sequences into the 3D structures. Results We used Protein blocks (PB), a widely used SA consisting of 16 prototypes, each representing a conformation of the pentapeptide skeleton defined in terms of dihedral angles. Proteins are described using PB from which we have previously developed a sequence alignment procedure based on dynamic programming with a dedicated PB Substitution Matrix. We improved the procedure with a specific two-step search: (i) very similar regions are selected using very high weights and aligned, and (ii) the alignment is completed (if possible) with less stringent parameters. Our approach, iPBA, has shown to perform better than other available tools in benchmark tests. To facilitate the usage of iPBA, we designed and implemented iPBAvizu, a plugin for PyMOL that allows users to run iPBA in an easy way and analyse protein superimpositions. Conclusions iPBAvizu is an implementation of iPBA within the well-known and widely used PyMOL software. iPBAvizu enables to generate iPBA alignments, create and interactively explore structural superimposition, and assess the quality of the protein alignments.

3D protein structure from genetic epistasis experiments

2018 ◽  
Author(s):  
Nathan J. Rollins ◽  
Kelly P. Brock ◽  
Frank J. Poelwijk ◽  
Michael A. Stiffler ◽  
Nicholas P. Gauthier ◽  
...  
Keyword(s):  
Protein Structure ◽  
3D Structure ◽  
Helical Conformation ◽  
Systematic Sampling ◽  
Sufficient Information ◽  
Single Mutation ◽  
Phenotypic Effect ◽  
3D Protein Structure ◽  
Small Proteins ◽  
The Individual

SummaryHigh-throughput experimental techniques have made possible the systematic sampling of the single mutation landscape for many proteins, defined as the change in protein fitness as the result of point mutation sequence changes. In a more limited number of cases, and for small proteins only, we also have nearly full coverage of all possible double mutants. By comparing the phenotypic effect of two simultaneous mutations with that of the individual amino acid changes, we can evaluate epistatic effects that reflect non-additive cooperative processes. The observation that epistatic residue pairs often are in contact in the 3D structure led to the hypothesis that a systematic epistatic screen contains sufficient information to identify the 3D fold of a protein. To test this hypothesis, we examined experimental double mutants for evidence of epistasis and identified residue contacts at 86% accuracy, including secondary structure elements and evidence for an alternative all-α-helical conformation. Positively epistatic contacts – corresponding to compensatory mutations, restoring fitness – were the most informative. Folded models generated from top-ranked epistatic pairs, when compared with the known structure, were accurate within 2.4 Å over 53 residues, indicating the possibility that 3D protein folds can be determined experimentally with good accuracy from functional assays of mutant libraries, at least for small proteins. These results suggest a new experimental approach for determining protein structure.

scholarly journals ANTIMICROBIAL POTENTIALS OF SOIL FUNGI METABOLITES, MOLECULAR DYNAMICS AND THEIR 3D PROTEIN STRUCTURAL PREDICTION USING BIOINFORMATICS TOOL

2020 ◽  
pp. 20-26
Author(s):  
MAJOLAGBE O. N. ◽  
AINA D. A. ◽  
OMOMOWO I. O. ◽  
THOMAS A.
Keyword(s):  
Escherichia Coli ◽  
Staphylococcus Aureus ◽  
Protein Structure ◽  
Secondary Metabolites ◽  
Soil Fungi ◽  
3D Structure ◽  
Antimicrobial Activities ◽  
Biological Properties ◽  
3D Protein Structure ◽  
Klebsiella Spp

Objective: To determine the antimicrobial potentials of secondary metabolite of soil fungi and predict their 3D structure and molecular identity. Methods: Pure soil fungi were isolated from soil samples and cultured under submerged fermentation (Smf) for their metabolites using Potato Dextrose Agar and Broth. The secondary metabolites of the isolated fungi were obtained intracellularly after 21 d of incubation in a rotary shaker incubator. The antimicrobial potentials of the metabolites were investigated against four (4) clinical isolates, namely: Staphylococcus aureus, Klebsiella spp, Candida albicans and Escherichia coli. These soil fungi were further characterized to the molecular level and their evolutionary relationships established using bioinformatics tools. Protein structure of each of the fungi isolates was predicted using PHYRE-2. Results: Out of all the soil fungi isolated, the metabolite of Aspergillus aculeatus showed the highest antimicrobial activities against Staphylococcus aureus (23.00±2.34 mm), Escherichia coli (9.00±1.44 mm) and Klebsiella spp (24.00±3.45 mm). The 3D protein structure predicted showed that each of the organisms consists of different amino-acid compositions such as: serine, tyrosine, proline, arginine, glycine, phenylalanine leucine with other notable biological properties. Conclusion: The work revealed that secondary metabolites of the isolated fungi carry an important role in combating infectious agents thereby, providing roadmaps for the biosynthesis of many synthetic and semi-synthetic drugs and bio-products which are environmentally friendly.

scholarly journals PREMONITION - Preprocessing motifs in protein structures for search acceleration

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 217 ◽  
Author(s):  
Sandeep Chakraborty ◽  
Basuthkar J. Rao ◽  
Bjarni Asgeirsson ◽  
Ravindra Venkatramani ◽  
Abhaya M. Dandekar
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Active Site ◽  
Active Sites ◽  
Protein Structures ◽  
3D Structure ◽  
Search Space ◽  
Computational Method ◽  
Computational Time ◽  
Active Site Residues

The remarkable diversity in biological systems is rooted in the ability of the twenty naturally occurring amino acids to perform multifarious catalytic functions by creating unique structural scaffolds known as the active site. Finding such structrual motifs within the protein structure is a key aspect of many computational methods. The algorithm for obtaining combinations of motifs of a certain length, although polynomial in complexity, runs in non-trivial computer time. Also, the search space expands considerably if stereochemically equivalent residues are allowed to replace an amino acid in the motif. In the present work, we propose a method to precompile all possible motifs comprising of a set (n=4 in this case) of predefined amino acid residues from a protein structure that occur within a specified distance (R) of each other (PREMONITION). PREMONITION rolls a sphere of radius R along the protein fold centered at the C atom of each residue, and all possible motifs are extracted within this sphere. The number of residues that can occur within a sphere centered around a residue is bounded by physical constraints, thus setting an upper limit on the processing times. After such a pre-compilation step, the computational time required for querying a protein structure with multiple motifs is considerably reduced. Previously, we had proposed a computational method to estimate the promiscuity of proteins with known active site residues and 3D structure using a database of known active sites in proteins (CSA) by querying each protein with the active site motif of every other residue. The runtimes for such a comparison is reduced from days to hours using the PREMONITION methodology.

scholarly journals Protein structure from the essential amino acids to the 3D structure

2019 ◽  
Vol 9 (1) ◽  
Keyword(s):  
Amino Acids ◽  
Protein Structure ◽  
Significant Contribution ◽  
Polypeptide Chain ◽  
Protein Structures ◽  
3D Structure ◽  
Building Blocks ◽  
Essential Amino Acids ◽  
Different Types ◽  
Quaternary Structures

Protein structure is a hot topic, not only to the specialist, but with others like the physicists. So this review is targeting those who are not biologists and have to deal with the protein in their research. In this review we travel with the protein structures from the amino acids and its classifications, and how the polypeptide chain is formed from these building blocks up to the final 3D structure. We introduced the secondary structure species like helices with its different types and how it is formed; also the beta sheet formation and types are explained briefly. Finally the tertiary and quaternary structures are presented. The approaches of molecular modeling as well as other important computational methods present significant contribution to studying proteins.

Inferring Functional Annotation for Human Genes from Gene-PDB Structure Mapping

2012 ◽  
Vol 195-196 ◽  
pp. 391-396
Author(s):  
Xi Chen ◽  
Hao Jiang ◽  
Wai Ki Ching ◽  
Li Min Li
Keyword(s):  
Protein Structure ◽  
Structure Data ◽  
Gene Function ◽  
Function Analysis ◽  
Protein Structures ◽  
3D Structure ◽  
Structure Mapping ◽  
Gene Similarity ◽  
Gene Function Analysis ◽  
Protein Structure Data

Protein 3D structure is one of the main factors in reecting gene functions. The availability of protein structure data in Protein Data Bank (PDB) allows us to conduct gene function analysis based on protein structure data. However, the molecules in PDB, whose structures having been determined, are always not corresponding to a unique gene. That is to say, the mapping from a gene to the PDB is not one-to-one. This feature complicates the situation and increases the difculty of gene function analysis. In this paper, we attempt to tackle this problem and also study the problem of predicting gene function from protein structures based on the gene-PDB mapping. We rst obtain the gene-PDB mapping, which is used to represent a gene by the structure set of all its corresponding PDB molecules. We then dene a new gene-gene similarity measurement based on the structure similarity between PDB molecules, and we further show that this new measurement matches with the gene functional similarity. This means that the measurement we dened here can be used effectively for gene function prediction.

scholarly journals Pemodelan Protein dengan Homology Modeling menggunakan SWISS-MODEL

2020 ◽  
Vol 2 (2) ◽  
pp. 65-70
Author(s):  
Noer Komari ◽  
Samsul Hadi ◽  
Eko Suhartono
Keyword(s):  
Protein Structure ◽  
Homology Modeling ◽  
In Silico ◽  
3D Structure ◽  
Three Dimensional ◽  
Structure Model ◽  
3D Protein Structure ◽  
Laboratory Equipment ◽  
Model Visualization ◽  
Structure Of Proteins

The three-dimensional (3D) structure of proteins is necessary to understand the properties and functions of proteins. Determining protein structure by laboratory equipment is quite complicated and expensive. An alternative method to predict the 3D structure of proteins in the in silico method. One of the in silico methods is homology modeling. Homology modeling is done using the SWISS-MODEL server. Proteins that will be modeled in the 3D structure are proteins that do not yet have a structure in the RCSB PDB database. Protein sequences were obtained from the UniProt database with code A0A0B6VWS2. The results showed that there were two models selected, namely model-1 with the PDB code template 1q0e and model-2 with the PDB code template 3gtv. The results of sequence alignment and model visualization show that model-1 and model-2 are identical. The evaluation and assessment of model-1 on the Ramachandran Plot have a Favored area of ??97.36%, a MolProbity score of 0.79, and a QMEAN value is 1.13. Model-1 is a good 3D protein structure model.

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

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.

scholarly journals Prediction of HIV drug resistance based on the 3D protein structure: Proposal of molecular field mapping

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255693
Author(s):  
Ryosaku Ota ◽  
Kanako So ◽  
Masahiro Tsuda ◽  
Yuriko Higuchi ◽  
Fumiyoshi Yamashita
Keyword(s):  
Drug Resistance ◽  
Protein Structure ◽  
Prediction Models ◽  
3D Structure ◽  
Field Potential ◽  
Amino Acid Sequences ◽  
Molecular Field ◽  
3D Protein Structure ◽  
Hiv Drug Resistance ◽  
Field Mapping

A method for predicting HIV drug resistance by using genotypes would greatly assist in selecting appropriate combinations of antiviral drugs. Models reported previously have had two major problems: lack of information on the 3D protein structure and processing of incomplete sequencing data in the modeling procedure. We propose obtaining the 3D structural information of viral proteins by using homology modeling and molecular field mapping, instead of just their primary amino acid sequences. The molecular field potential parameters reflect the physicochemical characteristics associated with the 3D structure of the proteins. We also introduce the Bayesian conditional mutual information theory to estimate the probabilities of occurrence of all possible protein candidates from an incomplete sequencing sample. This approach allows for the effective use of uncertain information for the modeling process. We applied these data analysis techniques to the HIV-1 protease inhibitor dataset and developed drug resistance prediction models with reasonable performance.

scholarly journals DeepCDpred: Inter-residue Distance and Contact Prediction for Improved Prediction of Protein Structure

2018 ◽  
Author(s):  
Shuangxi Ji ◽  
Tŭgçe Oruç ◽  
Liam Mead ◽  
Muhammad Fayyaz Rehman ◽  
Christopher M Thomas ◽  
...  
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Assessment Tool ◽  
Protein Structures ◽  
3D Structure ◽  
Disease Diagnosis ◽  
Accurate Prediction ◽  
Prediction Methods ◽  
Model Assessment ◽  
Contact Prediction

AbstractRapid, accurate prediction of protein structure from amino acid sequence would accelerate fields as diverse as drug discovery, synthetic biology and disease diagnosis. Massively improved prediction of protein structures has been driven by improving the prediction of the amino acid residues that contact in their 3D structure. For an average globular protein, around 92% of all residue pairs are non-contacting, therefore accurate prediction of only a small percentage of inter-amino acid distances could increase the number of constraints to guide structure determination. We have trained deep neural networks to predict inter-residue contacts and distances. Distances are predicted with an accuracy better than most contact prediction techniques. Addition of distance constraints improved de novo structure predictions for test sets of 158 protein structures, as compared to using the best contact prediction methods alone. Importantly, usage of distance predictions allows the selection of better models from the structure pool without a need for an external model assessment tool. The results also indicate how the accuracy of distance prediction methods might be improved further.

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