Selecting the Right Protein‐Scoring Matrix

Wheeler, David

doi:10.1002/0471250953.bi0305s00

Cited by 15 publications

(11 citation statements)

References 9 publications

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“…Therefore, at superfamily level, SCOP provides an excellent benchmark for testing how algorithms perform in cases, in which some related proteins have very low sequence similarity. Distance measure between two sequences is computed by the N-W alignment algorithm and PAM50 [10] mutation probability matrix. The distances is calculated between every pair of protein sequences in the dataset and stored in a square matrix of N×N, where N is the number proteins in a dataset to be clustered.…”

Section: Resultsmentioning

confidence: 99%

A graph-based clustering method applied to protein sequences

Mishra¹,

Pandey²

2011

Bioinformation

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The number of amino acid sequences is increasing very rapidly in the protein databases like Swiss-Prot, Uniprot, PIR and others, but the structure of only some amino acid sequences are found in the Protein Data Bank. Thus, an important problem in genomics is automatically clustering homologous protein sequences when only sequence information is available. Here, we use graph theoretic techniques for clustering amino acid sequences. A similarity graph is defined and clusters in that graph correspond to connected subgraphs. Cluster analysis seeks grouping of amino acid sequences into subsets based on distance or similarity score between pairs of sequences. Our goal is to find disjoint subsets, called clusters, such that two criteria are satisfied: homogeneity: sequences in the same cluster are highly similar to each other; and separation: sequences in different clusters have low similarity to each other. We tested our method on several subsets of SCOP (Structural Classification of proteins) database, a gold standard for protein structure classification. The results show that for a given set of proteins the number of clusters we obtained is close to the superfamilies in that set; there are fewer singeltons; and the method correctly groups most remote homologs.

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Section: Resultsmentioning

confidence: 99%

A graph-based clustering method applied to protein sequences

Mishra¹,

Pandey²

2011

Bioinformation

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“…being n ξ the minimal length of both subsequences under consideration, and D D D(ξ ξ ξ(l), ν ν ν(l)) the value of the scoring matrix for the respective l-th elements of ξ ξ ξ and ν ν ν. As scoring matrix D D D, the Point Accepted Mutation (PAM250) is used for the pairwise local alignment, as recommended in (Wheeler, 2002).…”

Section: Dissimilarity Space Representationmentioning

confidence: 99%

Predicting Molecular Functions in Plants using Wavelet-based Motifs

Arango-Argoty

Giraldo-Forero

Jaramillo-Garzón

et al. 2013

Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms

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Predicting molecular functions of proteins is a fundamental challenge in bioinformatics. Commonly used algorithms are based on sequence alignments and fail when the training sequences have low percentages of identity with query proteins, as it is the case for non-model organisms such as land plants. On the other hand, machine learning-based algorithms offer a good alternative for prediction, but most of them ignore that molecular functions are conditioned by functional domains instead of global features of the whole sequence. This work presents a novel application of the Wavelet Transform in order to detect discriminant sub-sequences (motifs) and use them as input for a pattern recognition classifier. The results show that the continuous wavelet transform is a suitable tool for the identification and characterization of motifs. Also, the proposed classification methodology shows good prediction capabilities for datasets with low percentage of identity among sequences, outperforming BLAST2GO on about 11, 5% and PEPSTATS-SVM on 16, 4%. Plus, it offers major interpretability of the obtained results.

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“…The main ones are the so-called PAM and BLOSUM (Wheeler, 2003). The most widely used PAM matrix is PAM 250.…”

Section: Scoring Matrices (I) Pam Vs Blosummentioning

confidence: 99%

“…Thus, the PAM250 matrix is derived by multiplying the PAM1 matrix against itself 250 times. Biologically, the PAM250 matrix means there have been 2.5 amino acid replacements at each site (Wheeler, 2003). In the derivation of PAM matrices, sequences that were represented many times were not excluded from the calculation.…”

Section: Scoring Matrices (I) Pam Vs Blosummentioning

confidence: 99%