Protein Structure Idealization: How accurately is it possible to model protein structures with dihedral angles?

Protein structure prediction and evaluation is one of the major fields of computational biology. Estimation of dihedral angle can provide information about the acceptability of both theoretically predicted and experimentally determined structures. Here we report on the sequence specific dihedral angle distribution of high resolution protein structures available in PDB and have developed Sasichandran, a tool for sequence specific dihedral angle prediction and structure evaluation. This tool will allow evaluation of a protein structure in pdb format from the sequence specific distribution of Ramachandran angles. Additionally, it will allow retrieval of the most probable Ramachandran angles for a given sequence along with the sequence specific data. Key words: Torsion angle, φ-ψ distribution, sequence specific ramachandran plot, Ramasekharan, protein structure appraisal D.O.I. 10.3329/ptcb.v19i2.5439 Plant Tissue Cult. & Biotech. 19(2): 217-226, 2009 (December)

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Analysis of Oncogene Protein Structure Using Small World Network Concept

Current Bioinformatics ◽

10.2174/1574893614666191113143840 ◽

2020 ◽

Vol 15 (7) ◽

pp. 732-740

Author(s):

Neetu Kumari ◽

Anshul Verma

Keyword(s):

Amino Acids ◽

Protein Structure ◽

Degree Distribution ◽

Protein Structures ◽

Small World ◽

Extreme Condition ◽

Centrality Measures ◽

Small World Network ◽

Network Concept ◽

Oncogene Protein

Background: The basic building block of a body is protein which is a complex system whose structure plays a key role in activation, catalysis, messaging and disease states. Therefore, careful investigation of protein structure is necessary for the diagnosis of diseases and for the drug designing. Protein structures are described at their different levels of complexity: primary (chain), secondary (helical), tertiary (3D), and quaternary structure. Analyzing complex 3D structure of protein is a difficult task but it can be analyzed as a network of interconnection between its component, where amino acids are considered as nodes and interconnection between them are edges. Objective: Many literature works have proven that the small world network concept provides many new opportunities to investigate network of biological systems. The objective of this paper is analyzing the protein structure using small world concept. Methods: Protein is analyzed using small world network concept, specifically where extreme condition is having a degree distribution which follows power law. For the correct verification of the proposed approach, dataset of the Oncogene protein structure is analyzed using Python programming. Results: Protein structure is plotted as network of amino acids (Residue Interaction Graph (RIG)) using distance matrix of nodes with given threshold, then various centrality measures (i.e., degree distribution, Degree-Betweenness correlation, and Betweenness-Closeness correlation) are calculated for 1323 nodes and graphs are plotted. Conclusion: Ultimately, it is concluded that there exist hubs with higher centrality degree but less in number, and they are expected to be robust toward harmful effects of mutations with new functions.

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PROTEIN STRUCTURE ALIGNMENT AND FAST SIMILARITY SEARCH USING LOCAL SHAPE SIGNATURES

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720004000533 ◽

2004 ◽

Vol 02 (01) ◽

pp. 215-239 ◽

Cited By ~ 4

Author(s):

TOLGA CAN ◽

YUAN-FANG WANG

Keyword(s):

Protein Structure ◽

Protein Structures ◽

Structure Alignment ◽

Protein Structure Alignment ◽

Specific Information ◽

Alignment Algorithm ◽

Screening Process ◽

Domain Specific ◽

Local Sequence ◽

Shape Signatures

We present a new method for conducting protein structure similarity searches, which improves on the efficiency of some existing techniques. Our method is grounded in the theory of differential geometry on 3D space curve matching. We generate shape signatures for proteins that are invariant, localized, robust, compact, and biologically meaningful. The invariancy of the shape signatures allows us to improve similarity searching efficiency by adopting a hierarchical coarse-to-fine strategy. We index the shape signatures using an efficient hashing-based technique. With the help of this technique we screen out unlikely candidates and perform detailed pairwise alignments only for a small number of candidates that survive the screening process. Contrary to other hashing based techniques, our technique employs domain specific information (not just geometric information) in constructing the hash key, and hence, is more tuned to the domain of biology. Furthermore, the invariancy, localization, and compactness of the shape signatures allow us to utilize a well-known local sequence alignment algorithm for aligning two protein structures. One measure of the efficacy of the proposed technique is that we were able to perform structure alignment queries 36 times faster (on the average) than a well-known method while keeping the quality of the query results at an approximately similar level.

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Implementation of a Parallel Protein Structure Alignment Service on Cloud

International Journal of Genomics ◽

10.1155/2013/439681 ◽

2013 ◽

Vol 2013 ◽

pp. 1-8 ◽

Cited By ~ 17

Author(s):

Che-Lun Hung ◽

Yaw-Ling Lin

Keyword(s):

Protein Structure ◽

Programming Model ◽

Protein Structures ◽

Structure Alignment ◽

Evolutionary Relationships ◽

Protein Structure Alignment ◽

Alignment Algorithm ◽

Cloud Platform ◽

Computational Performance ◽

Refinement Algorithm

Protein structure alignment has become an important strategy by which to identify evolutionary relationships between protein sequences. Several alignment tools are currently available for online comparison of protein structures. In this paper, we propose a parallel protein structure alignment service based on the Hadoop distribution framework. This service includes a protein structure alignment algorithm, a refinement algorithm, and a MapReduce programming model. The refinement algorithm refines the result of alignment. To process vast numbers of protein structures in parallel, the alignment and refinement algorithms are implemented using MapReduce. We analyzed and compared the structure alignments produced by different methods using a dataset randomly selected from the PDB database. The experimental results verify that the proposed algorithm refines the resulting alignments more accurately than existing algorithms. Meanwhile, the computational performance of the proposed service is proportional to the number of processors used in our cloud platform.

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Use of secondary structural information and Cα-Cα distance restraints to model protein structures with MODELLER

Journal of Biosciences ◽

10.1007/s12038-007-0093-1 ◽

2007 ◽

Vol 32 (S1) ◽

pp. 929-936 ◽

Cited By ~ 3

Author(s):

Boojala V. B. Reddy ◽

Yiannis N. Kaznessis

Keyword(s):

Structural Information ◽

Protein Structures ◽

Distance Restraints ◽

Model Protein

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Structural Modeling and Analysis of Pregnancy-Associated Glycoprotein-1 of Buffalo (Bubalus bubalis)

ISRN Molecular Biology ◽

10.5402/2012/481539 ◽

2012 ◽

Vol 2012 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Jerome Andonissamy ◽

S. K. Singh ◽

S. K. Agarwal

Keyword(s):

Protein Structure ◽

Active Sites ◽

Structural Model ◽

Protein Structures ◽

Structural Modeling ◽

Bubalus Bubalis ◽

High Energy ◽

Docking Studies ◽

Modeling And Analysis ◽

Structural Conformity

The present study was conducted to design and analyze the structural model of buffalo pregnancy-associated glycoprotein-1 (PAG-1) using bioinformatics. Structural modeling of the deduced buffalo PAG-1 protein was done using PHYRE, CONSURF servers and its structure was subsequently constructed using MODELLER 9.9 and PyMOL softwares Buffalo PAG-1 structural conformity was analyzed using PROSA, WHATIF, and 3D-PSSM servers. Designed buffalo PAG-1 protein structure on BLAST analysis retrieved protein structures belonging to aspartic proteinase family. Moreover in silico analysis revealed buffalo PAG-1 protein retained bilobed structure with pepstatin-binding clefts near the active sites by docking studies with pepstatin A using PatchDock server. Structural studies revealed that the amino and carboxy terminal containing aspartic residues are highly conserved and buried within the protein structure. Structural conformity studies showed that more than 90% of the residues lie inside favored and allowed regions. It was also deduced that buffalo PAG-1 possesses low and high energy zones with a very low threshold for proteolysis ascertaining the stableness of the buffalo PAG-1 protein structure. This study depicts the structural conformity and stability of buffalo PAG-1 protein.

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Prediction of Structural and Functional Aspects of Protein

Advances in Secure Computing, Internet Services, and Applications - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-4666-4940-8.ch016 ◽

2014 ◽

pp. 317-333

Author(s):

Arun G. Ingale

Keyword(s):

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Tertiary Structure ◽

Protein Structures ◽

Three Dimensional ◽

Dimensional Structure ◽

Sequence Information ◽

Predict Protein Structure ◽

Basic Ideas

To predict the structure of protein from a primary amino acid sequence is computationally difficult. An investigation of the methods and algorithms used to predict protein structure and a thorough knowledge of the function and structure of proteins are critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this chapter sheds light on the methods used for protein structure prediction. This chapter covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, it presents an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction, giving unique insight into the future applications of the modeled protein structures. In this chapter, current protein structure prediction methods are reviewed for a milieu on structure prediction, the prediction of structural fundamentals, tertiary structure prediction, and functional imminent. The basic ideas and advances of these directions are discussed in detail.

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3. Proteins

Biochemistry: A Very Short Introduction ◽

10.1093/actrade/9780198833871.003.0003 ◽

2021 ◽

pp. 34-51

Author(s):

Mark Lorch

Keyword(s):

Amino Acids ◽

Protein Folding ◽

Protein Structure ◽

Protein Structures ◽

Structure And Function ◽

Vast Array ◽

A Cell ◽

Cellular Machinery ◽

And Function ◽

The Relationship

This chapter examines proteins, the dominant proportion of cellular machinery, and the relationship between protein structure and function. The multitude of biological processes needed to keep cells functioning are managed in the organism or cell by a massive cohort of proteins, together known as the proteome. The twenty amino acids that make up the bulk of proteins produce the vast array of protein structures. However, amino acids alone do not provide quite enough chemical variety to complete all of the biochemical activity of a cell, so the chapter also explores post-translation modifications. It finishes by looking as some dynamic aspects of proteins, including enzyme kinetics and the protein folding problem.

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ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism

Nucleic Acids Research ◽

10.1093/nar/gkz390 ◽

2019 ◽

Vol 47 (W1) ◽

pp. W471-W476 ◽

Cited By ~ 5

Author(s):

Rasim Murat Aydınkal ◽

Onur Serçinoğlu ◽

Pemra Ozbek

Keyword(s):

Protein Structure ◽

Normal Mode Analysis ◽

Shortest Paths ◽

Protein Structures ◽

Interaction Strength ◽

Biological Information ◽

Coarse Grained ◽

Mode Analysis ◽

Residue Interaction ◽

Natural Variant

AbstractProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.

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