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The beta sheet is a common motif of the regular protein secondary structure. Beta sheets consist of beta strands (β-strands) connected laterally by at least two or three backbone hydrogen bonds, forming a generally twisted, pleated sheet. A β-strand is a stretch of polypeptide chain typically 3 to 10 amino acids long with backbone in an extended conformation. The supramolecular association of β-sheets has been implicated in the formation of the fibrils and protein aggregates observed in amyloidosis, Alzheimer's disease and other proteinopathies.
In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences. Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix. Gaps are inserted between the residues so that identical or similar characters are aligned in successive columns. Sequence alignments are also used for non-biological sequences such as calculating the distance cost between strings in a natural language, or to display financial data.
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.
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.
Structural bioinformatics is the branch of bioinformatics that is related to the analysis and prediction of the three-dimensional structure of biological macromolecules such as proteins, RNA, and DNA. It deals with generalizations about macromolecular 3D structures such as comparisons of overall folds and local motifs, principles of molecular folding, evolution, binding interactions, and structure/function relationships, working both from experimentally solved structures and from computational models. The term structural has the same meaning as in structural biology, and structural bioinformatics can be seen as a part of computational structural biology. The main objective of structural bioinformatics is the creation of new methods of analysing and manipulating biological macromolecular data in order to solve problems in biology and generate new knowledge.
BioJava is an open-source software project dedicated to provide Java tools to process biological data. BioJava is a set of library functions written in the programming language Java for manipulating sequences, protein structures, file parsers, Common Object Request Broker Architecture (CORBA) interoperability, Distributed Annotation System (DAS), access to AceDB, dynamic programming, and simple statistical routines. BioJava supports a range of data, starting from DNA and protein sequences to the level of 3D protein structures. The BioJava libraries are useful for automating many daily and mundane bioinformatics tasks such as to parsing a Protein Data Bank (PDB) file, interacting with Jmol and many more. This application programming interface (API) provides various file parsers, data models and algorithms to facilitate working with the standard data formats and enables rapid application development and analysis.
In molecular biology, protein threading, also known as fold recognition, is a method of protein modeling which is used to model those proteins which have the same fold as proteins of known structures, but do not have homologous proteins with known structure. It differs from the homology modeling method of structure prediction as it is used for proteins which do not have their homologous protein structures deposited in the Protein Data Bank (PDB), whereas homology modeling is used for those proteins which do. Threading works by using statistical knowledge of the relationship between the structures deposited in the PDB and the sequence of the protein which one wishes to model.
Pfam is a database of protein families that includes their annotations and multiple sequence alignments generated using hidden Markov models. The latest version of Pfam, 37.0, was released in June 2024 and contains 21,979 families. It is currently provided through InterPro website.
Multiple sequence alignment (MSA) is the process or the result of sequence alignment of three or more biological sequences, generally protein, DNA, or RNA. These alignments are used to infer evolutionary relationships via phylogenetic analysis and can highlight homologous features between sequences. Alignments highlight mutation events such as point mutations, insertion mutations and deletion mutations, and alignments are used to assess sequence conservation and infer the presence and activity of protein domains, tertiary structures, secondary structures, and individual amino acids or nucleotides.
InterPro is a database of protein families, protein domains and functional sites in which identifiable features found in known proteins can be applied to new protein sequences in order to functionally characterise them.
T-Coffee is a multiple sequence alignment software using a progressive approach. It generates a library of pairwise alignments to guide the multiple sequence alignment. It can also combine multiple sequences alignments obtained previously and in the latest versions can use structural information from Protein Data Bank (PDB) files (3D-Coffee). It has advanced features to evaluate the quality of the alignments and some capacity for identifying occurrence of motifs (Mocca). It produces alignment in the aln format (Clustal) by default, but can also produce PIR, MSF, and FASTA format. The most common input formats are supported.
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.
In molecular biology, a protein domain is a region of a protein's polypeptide chain that is self-stabilizing and that folds independently from the rest. Each domain forms a compact folded three-dimensional structure. Many proteins consist of several domains, and a domain may appear in a variety of different proteins. Molecular evolution uses domains as building blocks and these may be recombined in different arrangements to create proteins with different functions. In general, domains vary in length from between about 50 amino acids up to 250 amino acids in length. The shortest domains, such as zinc fingers, are stabilized by metal ions or disulfide bridges. Domains often form functional units, such as the calcium-binding EF hand domain of calmodulin. Because they are independently stable, domains can be "swapped" by genetic engineering between one protein and another to make chimeric proteins.
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.
A protein superfamily is the largest grouping (clade) of proteins for which common ancestry can be inferred. Usually this common ancestry is inferred from structural alignment and mechanistic similarity, even if no sequence similarity is evident. Sequence homology can then be deduced even if not apparent. Superfamilies typically contain several protein families which show sequence similarity within each family. The term protein clan is commonly used for protease and glycosyl hydrolases superfamilies based on the MEROPS and CAZy classification systems.
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.
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.
An array of protein tandem repeats is defined as several adjacent copies having the same or similar sequence motifs. These periodic sequences are generated by internal duplications in both coding and non-coding genomic sequences. Repetitive units of protein tandem repeats are considerably diverse, ranging from the repetition of a single amino acid to domains of 100 or more residues.
References
- ↑ Bohdan D.R.; Bujnicki J.M.; Baulin E.F. (2024). "ARTEMIS: a method for topology-independent superposition of RNA 3D structures and structure-based sequence alignment". Nucleic Acids Research. doi: 10.1093/nar/gkae758 . PMC 11472068 .
{{cite journal}}: CS1 maint: multiple names: authors list (link) - ↑ Bohdan D.R.; Voronina V.V.; Bujnicki J.M.; Baulin E.F. (2023). "A comprehensive survey of long-range tertiary interactions and motifs in non-coding RNA structures". Nucleic Acids Research. 51 (16): 8367–8382. doi: 10.1093/nar/gkad605 . PMC 10484739 . PMID 37471030.
{{cite journal}}: CS1 maint: multiple names: authors list (link) - ↑ Baulin E.F.; Bohdan D.R.; Kowalski D.; Serwatka M.; Świerczyńska J.; Żyra Z.; Bujnicki J.M. (2024). "ARTEM: a method for RNA tertiary motif identification with backbone permutations, and its example application to kink-turn-like motifs". bioRxiv. doi: 10.1101/2024.05.31.596898 .
{{cite journal}}: CS1 maint: multiple names: authors list (link) - ↑ van Kempen M.; Kim S.; Tumescheit C.; Mirdita M.; Lee J.; Gilchrist C.; Söding J.; Steinegger M. (2023). "Fast and accurate protein structure search with Foldseek" (PDF). Nature Biotechnology. 42 (2): 243–246. doi:10.1038/s41587-023-01773-0.
{{cite journal}}: CS1 maint: multiple names: authors list (link) - ↑ Zemla A (2003). "LGA: A method for finding 3D similarities in protein structures". Nucleic Acids Research. 31 (13): 3370–3374. doi:10.1093/nar/gkg571. PMC 168977 . PMID 12824330.
- ↑ Aung, Zeyar; Kian-Lee Tan (Dec 2006). "MatAlign: Precise protein structure comparison by matrix alignment". Journal of Bioinformatics and Computational Biology. 4 (6): 1197–216. doi:10.1142/s0219720006002417. PMID 17245810.
- ↑ Sippl, M.; Wiederstein, M. (2012). "Detection of spatial correlations in protein structures and molecular complexes". Structure. 20 (4): 718–728. doi:10.1016/j.str.2012.01.024. PMC 3320710 . PMID 22483118.
- ↑ Janez Konc; Dušanka Janežič (2010). "ProBiS algorithm for detection of structurally similar protein binding sites by local structural alignment". Bioinformatics. 26 (9): 1160–1168. doi:10.1093/bioinformatics/btq100. PMC 2859123 . PMID 20305268.
- ↑ Ritchie, David W. (September 2016). "Calculating and scoring high quality multiple flexible protein structure alignments". Bioinformatics. 32 (17): 2650–2658. doi: 10.1093/bioinformatics/btw300 . PMID 27187202.
- ↑ Wang, Sheng; Jianzhu Ma; Jian Peng; Jinbo Xu (March 2013). "Protein structure alignment beyond spatial proximity". Scientific Reports. 3: 1448. Bibcode:2013NatSR...3E1448W. doi:10.1038/srep01448. PMC 3596798 . PMID 23486213.
- ↑ Wang, Sheng; Jian Peng; Jinbo Xu (Sep 2011). "Alignment of distantly related protein structures: algorithm, bound and implications to homology modeling". Bioinformatics. 27 (18): 2537–45. doi:10.1093/bioinformatics/btr432. PMC 3167051 . PMID 21791532.
- ↑ Razmara, Jafar; Safaai Deris; Sepideh Parvizpour (Feb 2012). "TS-AMIR: a topology string alignment method for intensive rapid protein structure comparison". Algorithms for Molecular Biology. 7 (4): 4. doi: 10.1186/1748-7188-7-4 . PMC 3298807 . PMID 22336468.
- ↑ Minami, S.; Sawada K.; Chikenji G. (Jan 2013). "MICAN : a protein structure alignment algorithm that can handle Multiple-chains, Inverse alignments, C α only models, Alternative alignments, and Non-sequential alignments". BMC Bioinformatics. 14 (24): 24. doi: 10.1186/1471-2105-14-24 . PMC 3637537 . PMID 23331634.
- ↑ Brown, P.; Pullan W.; Yang Y.; Zhou Y. (Oct 2015). "Fast and accurate non-sequential protein structure alignment using a new asymmetric linear sum assignment heuristic". Bioinformatics. 32 (3): 370–7. doi: 10.1093/bioinformatics/btv580 . hdl: 10072/101971 . PMID 26454279.
- ↑ Kaiser, F.; Eisold A.; Bittrich S.; Labudde D. (Oct 2015). "Fit3D: a web application for highly accurate screening of spatial resiudue patterns in protein structure data". Bioinformatics. 32 (5): 792–4. doi: 10.1093/bioinformatics/btv637 . PMID 26519504.
- ↑ Collier, J.; Allison L.; Lesk A.; Stuckey P.; Garcia de la Banda M.; Konagurthu A. (Apr 2017). "Statistical inference of protein structural alignments using information and compression". Bioinformatics. 33 (7): 1005–13. doi: 10.1093/bioinformatics/btw757 . PMID 28065899.
- ↑ Bittrich S, Burley SK, Rose AS (2020). "Real-time structural motif searching in proteins using an inverted index strategy". PLOS Comput Biol. 16 (12): e1008502. Bibcode:2020PLSCB..16E8502B. doi: 10.1371/journal.pcbi.1008502 . PMC 7746303 . PMID 33284792.
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