Resources and Infrastructure for Structural Bioinformatics

2007 ◽  
pp. 207-227
Author(s):  
Dong Xu ◽  
Jie Liang ◽  
Ying Xu
Keyword(s):  
Structural Bioinformatics

scholarly journals BioCommons: a robust java library for RNA structural bioinformatics

2021 ◽  
Author(s):  
Tomasz Zok
Keyword(s):  
Source Code ◽  
Structural Bioinformatics ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Bioinformatic Tools ◽  
Data Formats ◽  
Central Repository ◽  
Diverse Data ◽  
2D And 3D ◽  
Java Library

Abstract Motivation Biomolecular structures come in multiple representations and diverse data formats. Their incompatibility with the requirements of data analysis programs significantly hinders the analytics and the creation of new structure-oriented bioinformatic tools. Therefore, the need for robust libraries of data processing functions is still growing. Results BioCommons is an open-source, Java library for structural bioinformatics. It contains many functions working with the 2D and 3D structures of biomolecules, with a particular emphasis on RNA. Availability and implementation The library is available in Maven Central Repository and its source code is hosted on GitHub: https://github.com/tzok/BioCommons Supplementary information Supplementary data are available at Bioinformatics online.

Structural bioinformatics: Deriving biological insights from protein structures

2010 ◽  
Vol 2 (4) ◽  
pp. 347-366 ◽  
Author(s):  
Nagasuma Chandra ◽  
Praveen Anand ◽  
Kalidas Yeturu
Keyword(s):  
Protein Structures ◽  
Structural Bioinformatics

scholarly journals Computational Prediction of Heteromeric Protein Complex Disassembly Order with Hybrid Monte Carlo/Molecular Dynamics Simulation

2022 ◽  
Author(s):  
Ikuo Kurisaki ◽  
Shigenori Tanaka
Keyword(s):  
Molecular Dynamics ◽  
Monte Carlo ◽  
Protein Complex ◽  
Protein Complexes ◽  
Structural Bioinformatics ◽  
Dynamics Simulation ◽  
Tryptophan Synthase ◽  
Selection Scheme ◽  
Hybrid Monte Carlo ◽  
Multimeric Protein

The physicochemical entity of biological phenomenon in the cell is a network of biochemical reactions and the activity of such a network is regulated by multimeric protein complexes. Mass spectroscopy (MS) experiments and multimeric protein docking simulations based on structural bioinformatics techniques have revealed the molecular-level stoichiometry and static configuration of subcomplexes in their bound forms, then revealing the subcomplex populations and formation orders. Meanwhile, these methodologies are not designed to straightforwardly examine temporal dynamics of multimeric protein assembly and disassembly, essential physicochemical properties to understand functional expression mechanisms of proteins in the biological environment. To address the problem, we had developed an atomistic simulation in the framework of the hybrid Monte Carlo/Molecular Dynamics (hMC/MD) method and succeeded in observing disassembly of homomeric pentamer of the serum amyloid P component protein in experimentally consistent order. In this study, we improved the hMC/MD method to examine disassembly processes of the tryptophan synthase tetramer, a paradigmatic heteromeric protein complex in MS studies. We employed the likelihood-based selection scheme to determine a dissociation-prone subunit pair at each hMC/MD simulation cycle and achieved highly reliable predictions of the disassembly orders with the success rate over 0.9 without a priori knowledge of the MS experiments and structural bioinformatics simulations. We similarly succeeded in reliable predictions for the other three tetrameric protein complexes. These achievements indicate the potential availability of our hMC/MD approach as the general purpose methodology to obtain microscopic and physicochemical insights into multimeric protein complex formation.

Protein Structural Bioinformatics: An Overview

2019 ◽  
pp. 445-459
Author(s):  
M. Michael Gromiha ◽  
Raju Nagarajan ◽  
Samuel Selvaraj
Keyword(s):  
Structural Bioinformatics

Graph Applications in Chemoinformatics and Structural Bioinformatics

2011 ◽  
pp. 386-420
Author(s):  
Eleanor Joyce Gardiner
Keyword(s):  
Graph Theory ◽  
Molecular Graph ◽  
Structural Bioinformatics ◽  
Three Dimensions ◽  
Chemical Graph ◽  
Labeled Graph ◽  
3D Space ◽  
Chemical Graph Theory ◽  
Computer Representation ◽  
History Of

The focus of this chapter will be the uses of graph theory in chemoinformatics and in structural bioinformatics. There is a long history of chemical graph theory dating back to the 1860’s and Kekule’s structural theory. It is natural to regard the atoms of a molecule as nodes and the bonds as edges (2D representations) of a labeled graph (a molecular graph). This chapter will concentrate on the algorithms developed to exploit the computer representation of such graphs and their extensions in both two and three dimensions (where an edge represents the distance in 3D space between a pair of atoms), together with the algorithms developed to exploit them. The algorithms will generally be summarized rather than detailed. The methods were later extended to larger macromolecules (such as proteins); these will be covered in less detail.

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