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Structural alignment attempts to establish homology between two or more polymer structures based on their shape and three-dimensional conformation. This process is usually applied to protein tertiary structures but can also be used for large RNA molecules. In contrast to simple structural superposition, where at least some equivalent residues of the two structures are known, structural alignment requires no a priori knowledge of equivalent positions. Structural alignment is a valuable tool for the comparison of proteins with low sequence similarity, where evolutionary relationships between proteins cannot be easily detected by standard sequence alignment techniques. Structural alignment can therefore be used to imply evolutionary relationships between proteins that share very little common sequence. However, caution should be used in using the results as evidence for shared evolutionary ancestry because of the possible confounding effects of convergent evolution by which multiple unrelated amino acid sequences converge on a common tertiary structure.
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.
A pseudoknot is a nucleic acid secondary structure containing at least two stem-loop structures in which half of one stem is intercalated between the two halves of another stem. The pseudoknot was first recognized in the turnip yellow mosaic virus in 1982. Pseudoknots fold into knot-shaped three-dimensional conformations but are not true topological knots. These structures are categorized as cross (X) topology within the circuit topology framework, which, in contrast to knot theory, is a contact-based approach.
Nucleoproteins are proteins conjugated with nucleic acids. Typical nucleoproteins include ribosomes, nucleosomes and viral nucleocapsid proteins.
Nucleic acid structure prediction is a computational method to determine secondary and tertiary nucleic acid structure from its sequence. Secondary structure can be predicted from one or several nucleic acid sequences. Tertiary structure can be predicted from the sequence, or by comparative modeling.
Nucleic acid design is the process of generating a set of nucleic acid base sequences that will associate into a desired conformation. Nucleic acid design is central to the fields of DNA nanotechnology and DNA computing. It is necessary because there are many possible sequences of nucleic acid strands that will fold into a given secondary structure, but many of these sequences will have undesired additional interactions which must be avoided. In addition, there are many tertiary structure considerations which affect the choice of a secondary structure for a given design.
This is a list of notable computer programs that are used for nucleic acids simulations.
Nucleic acid secondary structure is the basepairing interactions within a single nucleic acid polymer or between two polymers. It can be represented as a list of bases which are paired in a nucleic acid molecule. The secondary structures of biological DNAs and RNAs tend to be different: biological DNA mostly exists as fully base paired double helices, while biological RNA is single stranded and often forms complex and intricate base-pairing interactions due to its increased ability to form hydrogen bonds stemming from the extra hydroxyl group in the ribose sugar.
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.
αr7 is a family of bacterial small non-coding RNAs with representatives in a broad group of Alphaproteobacterial species from the order Hyphomicrobiales. The first member of this family was found in a Sinorhizobium meliloti 1021 locus located in the chromosome (C). Further homology and structure conservation analysis identified full-length homologs in several nitrogen-fixing symbiotic rhizobia, in the plant pathogens belonging to Agrobacterium species as well as in a broad spectrum of Brucella species. αr7 RNA species are 134-159 nucleotides (nt) long and share a well defined common secondary structure. αr7 transcripts can be catalogued as trans-acting sRNAs expressed from well-defined promoter regions of independent transcription units within intergenic regions (IGRs) of the Alphaproteobacterial genomes.
αr45 is a family of bacterial small non-coding RNAs with representatives in a broad group of α-proteobacteria from the order Hyphomicrobiales. The first member of this family (Smr45C) was found in a Sinorhizobium meliloti 1021 locus located in the chromosome (C). Further homology and structure conservation analysis identified homologs in several nitrogen-fixing symbiotic rhizobia, in the plant pathogens belonging to Agrobacterium species as well as in a broad spectrum of Brucella species, in Bartonella species, in several members of the Xanthobactereacea family, and in some representatives of the Beijerinckiaceae family. αr45C RNA species are 147-153 nt long and share a well defined common secondary structure. All of the αr45 transcripts can be catalogued as trans-acting sRNAs expressed from well-defined promoter regions of independent transcription units within intergenic regions (IGRs) of the α-proteobacterial genomes.
I-TASSER is a bioinformatics method for predicting three-dimensional structure model of protein molecules from amino acid sequences. It detects structure templates from the Protein Data Bank by a technique called fold recognition. The full-length structure models are constructed by reassembling structural fragments from threading templates using replica exchange Monte Carlo simulations. I-TASSER is one of the most successful protein structure prediction methods in the community-wide CASP experiments.
A neutral network is a set of genes all related by point mutations that have equivalent function or fitness. Each node represents a gene sequence and each line represents the mutation connecting two sequences. Neutral networks can be thought of as high, flat plateaus in a fitness landscape. During neutral evolution, genes can randomly move through neutral networks and traverse regions of sequence space which may have consequences for robustness and evolvability.
Robert Dirks was an American chemist known for his theoretical and experimental work in DNA nanotechnology. Born in Thailand to a Thai Chinese mother and American father, he moved to Spokane, Washington at a young age. Dirks was the first graduate student in Niles Pierce's research group at the California Institute of Technology, where his dissertation work was on algorithms and computational tools to analyze nucleic acid thermodynamics and predict their structure. He also performed experimental work developing a biochemical chain reaction to self-assemble nucleic acid devices. Dirks later worked at D. E. Shaw Research on algorithms for protein folding that could be used to design new pharmaceuticals.
Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems biology, evolution, and text mining.
Non-coding RNAs have been discovered using both experimental and bioinformatic approaches. Bioinformatic approaches can be divided into three main categories. The first involves homology search, although these techniques are by definition unable to find new classes of ncRNAs. The second category includes algorithms designed to discover specific types of ncRNAs that have similar properties. Finally, some discovery methods are based on very general properties of RNA, and are thus able to discover entirely new kinds of ncRNAs.
DIMPL is a bioinformatic pipeline that enables the extraction and selection of bacterial GC-rich intergenic regions (IGRs) that are enriched for structured non-coding RNAs (ncRNAs). The method of enriching bacterial IGRs for ncRNA motif discovery was first reported for a study in "Genome-wide discovery of structured noncoding RNAs in bacteria".
Sfold is a software program developed to predict probable RNA secondary structures through structure ensemble sampling and centroid predictions with a focus on assessment of RNA target accessibility, for major applications to the rational design of siRNAs in the suppression of gene expressions, and to the identification of targets for regulatory RNAs particularly microRNAs.
References
- 1 2 Hofacker, I. L.; Fontana, W.; Stadler, P. F.; Bonhoeffer, L. S.; Tacker, M.; Schuster, P. (1 February 1994). "Fast folding and comparison of RNA secondary structures". Monatshefte für Chemie. 125 (2): 167–188. doi:10.1007/BF00818163. ISSN 0026-9247. S2CID 19344304.
- ↑ Vella, F. (1992). "Introduction to Protein Structure". Biochemical Education. 20 (2): 122. doi:10.1016/0307-4412(92)90132-6.
- ↑ Whisstock, James C.; Lesk, Arthur M. (1 August 2003). "Prediction of protein function from protein sequence and structure". Quarterly Reviews of Biophysics. 36 (3): 307–340. doi:10.1017/S0033583503003901. ISSN 1469-8994. PMID 15029827. S2CID 27123114.
- ↑ Lee, David; Redfern, Oliver; Orengo, Christine (2007). "Predicting protein function from sequence and structure". Nature Reviews Molecular Cell Biology. 8 (12): 995–1005. doi:10.1038/nrm2281. PMID 18037900. S2CID 14432468.
- ↑ Mathews, David H.; Disney, Matthew D.; Childs, Jessica L.; Schroeder, Susan J.; Zuker, Michael; Turner, Douglas H. (11 May 2004). "Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure". Proceedings of the National Academy of Sciences of the United States of America. 101 (19): 7287–7292. Bibcode:2004PNAS..101.7287M. doi: 10.1073/pnas.0401799101 . ISSN 0027-8424. PMC 409911 . PMID 15123812.
- ↑ Lorenz, Ronny; Bernhart, Stephan H; Siederdissen, Christian Höner zu; Tafer, Hakim; Flamm, Christoph; Stadler, Peter F; Hofacker, Ivo L (24 November 2011). "ViennaRNA Package 2.0". Algorithms for Molecular Biology. 6 (1): 26. doi: 10.1186/1748-7188-6-26 . PMC 3319429 . PMID 22115189.
- ↑ Gruber, Andreas R.; Lorenz, Ronny; Bernhart, Stephan H.; Neuböck, Richard; Hofacker, Ivo L. (1 July 2008). "The Vienna RNA Websuite". Nucleic Acids Research. 36 (suppl 2): W70–W74. doi:10.1093/nar/gkn188. ISSN 0305-1048. PMC 2447809 . PMID 18424795.
- ↑ Hofacker, Ivo L. (1 July 2003). "Vienna RNA secondary structure server". Nucleic Acids Research. 31 (13): 3429–3431. doi:10.1093/nar/gkg599. ISSN 0305-1048. PMC 169005 . PMID 12824340.
- ↑ "TBI - ViennaRNA Package 2". www.tbi.univie.ac.at. Retrieved 11 January 2016.