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Relevant bibliographies by topics / Substitution matrices

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Journal articles

Academic literature on the topic 'Substitution matrices'

Author: Grafiati

Published: 14 March 2023

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Journal articles on the topic "Substitution matrices"

1

Koshi, J. M., and R. A. Goldstein. "Context-dependent optimal substitution matrices." Protein Engineering Design and Selection 8, no. 7 (1995): 641–45. http://dx.doi.org/10.1093/protein/8.7.641.

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2

Hautus, M. L. J. "Substitution of matrices over rings." Linear Algebra and its Applications 226-228 (September 1995): 353–70. http://dx.doi.org/10.1016/0024-3795(95)00155-k.

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3

BARBÉ, ANDRÉ M. "FRACTALS BY NUMBERS." Fractals 03, no. 04 (1995): 651–61. http://dx.doi.org/10.1142/s0218348x95000588.

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We introduce an extension of an earlier defined simple, number-based matrix substitution system for obtaining fractal matrices, by considering cyclic substitutions. The elements of the resulting matrices are related to representations of their addresses in a mixed number base. The Hutchinson operator for the limit form of a geometrical representation of the fractal matrix is derived. It is shown that the class of fractal limit sets obtainable from cyclic substitutions does not extend the class obtainable from the simple substitutions.

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4

Barlowe, Scott, Heather B. Coan, and Robert T. Youker. "SubVis: an interactive R package for exploring the effects of multiple substitution matrices on pairwise sequence alignment." PeerJ 5 (June 27, 2017): e3492. http://dx.doi.org/10.7717/peerj.3492.

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Understanding how proteins mutate is critical to solving a host of biological problems. Mutations occur when an amino acid is substituted for another in a protein sequence. The set of likelihoods for amino acid substitutions is stored in a matrix and input to alignment algorithms. The quality of the resulting alignment is used to assess the similarity of two or more sequences and can vary according to assumptions modeled by the substitution matrix. Substitution strategies with minor parameter variations are often grouped together in families. For example, the BLOSUM and PAM matrix families are

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5

BALDI, PIERRE. "Substitution Matrices and Hidden Markov Models." Journal of Computational Biology 2, no. 3 (1995): 487–91. http://dx.doi.org/10.1089/cmb.1995.2.487.

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6

Dosztányi, Zs, and A. E. Torda. "SUBSTITUTION MATRICES BASED ON FORCE FIELDS." Biochemical Society Transactions 28, no. 5 (2000): A146. http://dx.doi.org/10.1042/bst028a146.

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7

G., Renganayaki, and Achuthsankar S. Nair. "Hubsm: A Novel Amino Acid Substitution Matrix for Comparing Hub Proteins." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (2017): 212. http://dx.doi.org/10.23956/ijarcsse.v7i8.53.

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Sequence alignment algorithms and database search methods use BLOSUM and PAM substitution matrices constructed from general proteins. These de facto matrices are not optimal to align sequences accurately, for the proteins with markedly different compositional bias in the amino acid. In this work, a new amino acid substitution matrix is calculated for the disorder and low complexity rich region of Hub proteins, based on residue characteristics. Insights into the amino acid background frequencies and the substitution scores obtained from the Hubsm unveils the residue substitution patterns which

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8

Aledo, Pablo, and Juan Carlos Aledo. "Proteome-Wide Structural Computations Provide Insights into Empirical Amino Acid Substitution Matrices." International Journal of Molecular Sciences 24, no. 1 (2023): 796. http://dx.doi.org/10.3390/ijms24010796.

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The relative contribution of mutation and selection to the amino acid substitution rates observed in empirical matrices is unclear. Herein, we present a neutral continuous fitness-stability model, inspired by the Arrhenius law (qij=aije−ΔΔGij). The model postulates that the rate of amino acid substitution (i→j) is determined by the product of a pre-exponential factor, which is influenced by the genetic code structure, and an exponential term reflecting the relative fitness of the amino acid substitutions. To assess the validity of our model, we computed changes in stability of 14,094 proteins,

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9

LIU, XIN, and WEI-MOU ZHENG. "AN AMINO ACID SUBSTITUTION MATRIX FOR PROTEIN CONFORMATION IDENTIFICATION." Journal of Bioinformatics and Computational Biology 04, no. 03 (2006): 769–82. http://dx.doi.org/10.1142/s0219720006002156.

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Amino acid substitution matrices play an essential role in protein sequence alignment, a fundamental task in bioinformatics. Most widely used matrices, such as PAM matrices derived from homologous sequences and BLOSUM matrices derived from aligned segments of PROSITE, did not integrate conformation information in their construction. There are a few structure-based matrices, which are derived from limited data of structure alignment. Using databases PDB_SELECT and DSSP, we create a database of sequence-conformation blocks which explicitly represent sequence-structure relationship. Members in a

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10

Vilim, R. B., R. M. Cunningham, B. Lu, P. Kheradpour, and F. J. Stevens. "Fold-specific substitution matrices for protein classification." Bioinformatics 20, no. 6 (2004): 847–53. http://dx.doi.org/10.1093/bioinformatics/btg492.

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