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Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The subsequent process of analyzing and interpreting data is often referred to as computational biology, though the distinction between the two terms is often disputed.
The proteome is the entire set of proteins that is, or can be, expressed by a genome, cell, tissue, or organism at a certain time. It is the set of expressed proteins in a given type of cell or organism, at a given time, under defined conditions. Proteomics is the study of the proteome.
Proteomics is the large-scale study of proteins. Proteins are vital macromolecules of all living organisms, with many functions such as the formation of structural fibers of muscle tissue, enzymatic digestion of food, or synthesis and replication of DNA. In addition, other kinds of proteins include antibodies that protect an organism from infection, and hormones that send important signals throughout the body.
In bioinformatics, sequence analysis is the process of subjecting a DNA, RNA or peptide sequence to any of a wide range of analytical methods to understand its features, function, structure, or evolution. It can be performed on the entire genome, transcriptome or proteome of an organism, and can also involve only selected segments or regions, like tandem repeats and transposable elements. Methodologies used include sequence alignment, searches against biological databases, and others.
Structural genomics seeks to describe the 3-dimensional structure of every protein encoded by a given genome. This genome-based approach allows for a high-throughput method of structure determination by a combination of experimental and modeling approaches. The principal difference between structural genomics and traditional structural prediction is that structural genomics attempts to determine the structure of every protein encoded by the genome, rather than focusing on one particular protein. With full-genome sequences available, structure prediction can be done more quickly through a combination of experimental and modeling approaches, especially because the availability of large number of sequenced genomes and previously solved protein structures allows scientists to model protein structure on the structures of previously solved homologs.
Protein structure prediction is the inference of the three-dimensional structure of a protein from its amino acid sequence—that is, the prediction of its secondary and tertiary structure from primary structure. Structure prediction is different from the inverse problem of protein design. Protein structure prediction is one of the most important goals pursued by computational biology; it is important in medicine and biotechnology.
In computational biology, gene prediction or gene finding refers to the process of identifying the regions of genomic DNA that encode genes. This includes protein-coding genes as well as RNA genes, but may also include prediction of other functional elements such as regulatory regions. Gene finding is one of the first and most important steps in understanding the genome of a species once it has been sequenced.
Functional genomics is a field of molecular biology that attempts to describe gene functions and interactions. Functional genomics make use of the vast data generated by genomic and transcriptomic projects. Functional genomics focuses on the dynamic aspects such as gene transcription, translation, regulation of gene expression and protein–protein interactions, as opposed to the static aspects of the genomic information such as DNA sequence or structures. A key characteristic of functional genomics studies is their genome-wide approach to these questions, generally involving high-throughput methods rather than a more traditional "candidate-gene" approach.
In molecular biology, an interactome is the whole set of molecular interactions in a particular cell. The term specifically refers to physical interactions among molecules but can also describe sets of indirect interactions among genes.
World Community Grid (WCG) is an effort to create the world's largest volunteer computing platform to tackle scientific research that benefits humanity. Launched on November 16, 2004, with proprietary Grid MP client from United Devices and adding support for Berkeley Open Infrastructure for Network Computing (BOINC) in 2005, World Community Grid eventually discontinued the Grid MP client and consolidated on the BOINC platform in 2008. In September 2021, it was announced that IBM transferred ownership to the Krembil Research Institute of University Health Network in Toronto, Ontario.
Modelling biological systems is a significant task of systems biology and mathematical biology. Computational systems biology aims to develop and use efficient algorithms, data structures, visualization and communication tools with the goal of computer modelling of biological systems. It involves the use of computer simulations of biological systems, including cellular subsystems, to both analyze and visualize the complex connections of these cellular processes.
The Human Proteome Folding Project (HPF) is a collaborative effort between New York University, the Institute for Systems Biology (ISB) and the University of Washington, using the Rosetta software developed by the Rosetta Commons. The project is managed by the Bonneau lab.
Protein subcellular localization prediction involves the prediction of where a protein resides in a cell, its subcellular localization.
Halo bacterium is a genus in the family Halobacteriaceae.
Rosetta@home is a volunteer computing project researching protein structure prediction on the Berkeley Open Infrastructure for Network Computing (BOINC) platform, run by the Baker lab. Rosetta@home aims to predict protein–protein docking and design new proteins with the help of about fifty-five thousand active volunteered computers processing at over 487,946 GigaFLOPS on average as of September 19, 2020. Foldit, a Rosetta@home videogame, aims to reach these goals with a crowdsourcing approach. Though much of the project is oriented toward basic research to improve the accuracy and robustness of proteomics methods, Rosetta@home also does applied research on malaria, Alzheimer's disease, and other pathologies.
In computational biology, de novo protein structure prediction refers to an algorithmic process by which protein tertiary structure is predicted from its amino acid primary sequence. The problem itself has occupied leading scientists for decades while still remaining unsolved. According to Science, the problem remains one of the top 125 outstanding issues in modern science. At present, some of the most successful methods have a reasonable probability of predicting the folds of small, single-domain proteins within 1.5 angstroms over the entire structure.
Ram Samudrala is a professor of computational biology and bioinformatics at the University at Buffalo, United States. He researches protein folding, structure, function, interaction, design, and evolution.
Protein function prediction methods are techniques that bioinformatics researchers use to assign biological or biochemical roles to proteins. These proteins are usually ones that are poorly studied or predicted based on genomic sequence data. These predictions are often driven by data-intensive computational procedures. Information may come from nucleic acid sequence homology, gene expression profiles, protein domain structures, text mining of publications, phylogenetic profiles, phenotypic profiles, and protein-protein interaction. Protein function is a broad term: the roles of proteins range from catalysis of biochemical reactions to transport to signal transduction, and a single protein may play a role in multiple processes or cellular pathways.
Secretomics is a type of proteomics which involves the analysis of the secretome—all the secreted proteins of a cell, tissue or organism. Secreted proteins are involved in a variety of physiological processes, including cell signaling and matrix remodeling, but are also integral to invasion and metastasis of malignant cells. Secretomics has thus been especially important in the discovery of biomarkers for cancer and understanding molecular basis of pathogenesis. The analysis of the insoluble fraction of the secretome has been termed matrisomics.
AlphaFold is an artificial intelligence (AI) program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure. The program is designed as a deep learning system.
References
- ↑ Renfrew PD, Campbell G, Strauss CEM, Bonneau R (2011) The 2010 Rosetta Developers Meeting: Macromolecular Prediction and Design Meets Reproducible Publishing. PLoS ONE 6(8): e22431
- ↑ Renfrew PD, Choi EJ, Bonneau R, Kuhlman B (2012) Incorporation of Noncanonical AminoAcids into Rosetta and Use in Computational Protein-Peptide Interface Design.PLoS ONE 7(3): e32637.
- ↑ Drew K, Winters P, Butterfoss GL, Berstis V, Uplinger K, Armstrong J, Riffle M, Schweighofer E, Bovermann B, Goodlett DR, Davis TN, Shasha D, Malmström L, Bonneau R., Genome Res. 2011 Nov; 21(11):1981-94.
- ↑ The proteome folding project: Proteome-scale prediction of structure and function (2011) Kevin Drew, Patrick Winters, GlennL. Butterfoss, Viktors Berstis, Keith Uplinger, Jonathan Armstrong, MichaelRiffle, Erik Schweighofer, Bill Bovermann, David R. Goodlett, Trisha N. Davis, Dennis Shasha, Lars Malmstrom, and Richard Bonneau. Genome Research, August 8, 2011)
- ↑ Bonneau, R, Facciotti, MT, Reiss, DJ, Madar A, Baliga, NS, et al. A predictive model for transcriptional control of physiology in a free living cell. (2007) Cell. Dec 131:1354-1365
- ↑ Maria Ciofani, Aviv Madar, Carolina Galan, MacLean Sellars, Kieran Mace, Florencia Pauli, Ashish Agarwal, Wendy Huang, Christopher N. Parkurst, Michael Muratet, Kim M. Newberry, Sarah Meadows, Alex Greenfield, Yi Yang, Preti Jain, Francis K. Kirigin, Carmen Birchmeier, Erwin F. Wagner, Kenneth M. Murphy, Richard M. Myers, Richard Bonneau, Dan R. Littman. Cell, October 12, 2012 (Vol. 151, Issue 2, pp. 289-303)
Structure prediction
- Bonneau, R & Baker, D. (2001). Ab Initio Protein Structure Prediction: Progress and Prospects. Annu. Rev. Biophys. Biomol. Struct. 30, 173-89.
- Bonneau, R., Dylan Chivian, Charlie EM Strauss, Carol Rohl, David Baker. (2002) De Novo Prediction of Three Dimensional Structures for Major Protein Families. JMB, 322(1):65-78.
- Bonneau R, Baliga NS, Deutsch EW, Shannon P, Hood L. (2004) Comprehensive de novo structure prediction in a systems-biology context for the archaea Halobacterium sp. NRC-1. Genome Biology. 5(8):R52-68
- Mike Boxem, Zoltan Maliga, Niels J. Klitgord, Na Li, Irma Lemmens, Miyeko Mana, Lorenzo De Lichtervelde, Joram Mul, Diederik van de Peut, Maxime Devos, Nicolas Simonis, Anne-Lore Schlaitz, Murat Cokol, Muhammed A. Yildirim, Tong Hao, Changyu Fan, Chenwei Lin, Mike Tipsword, Kevin Drew, Matilde Galli, Kahn Rhrissorrakrai, David Drech-sel, David E. Hill, Richard Bonneau, Kristin C. Gunsalus, Frederick P. Roth, Fabio Piano, Jan Tavernier, Sander van den Heuvel, Anthony A. Hyman, Marc Vidal. A Protein Domain-Based Interactome Network for C. elegans Early Embryogenesis. (2008) Cell, 134(3) pp. 534 – 545.
- Andersen-Nissen E, Smith KD, Bonneau R, Strong RK, Aderem A. A conserved surface on Toll-like receptor 5 recognizes bacterial flagellin. (2007) J Exp Med. Feb 19;204(2):393-403.
Genomics and systems biology
- Bonneau, Richard. Learning biological networks: from modules to dynamics. Nature Chemical Biology 4, 658 - 664 (2008)
- Bonneau R, Reiss DJ, Shannon P, Hood L, Baliga NS, Thorsson V (2006) The Inferelator: a procedure for learning parsimonious regulatory networks from systems-biology data-sets de novo. Genome Biol. 7(5):R36.
- David J Reiss, Nitin S Baliga, Bonneau R. (2006) Integrated biclustering of heterogeneous genome-wide datasets. BMC Bioinformatics. 7(1):280.
