Improving protein alignment algorithms using amino-acid hydrophobicities - Applications of TMATCH, A new algorithm
Cavanaugh, D. P.; Chittur, K.
Show abstract
MotivationSequence database search and matching algorithms are an important tool when trying to understand the structure (and so the function) of proteins. Proteins with similar structure and function often have very similar primary structure. There are however many cases where proteins with similar structure have very different primary structures. Substitution matrices (PAM, BLOSUM, Gonnett) can be used to identify proteins of similar structure, but they fail when the sequence similarity falls below about 25%. ResultsWe have described a new algorithm for examining the the primary structure of proteins against a database of known proteins with a new hydrophobicity index. In this paper, we examine the ability of TMATCH to identify proteins of similar structure using sequence matching with the hydrophobicity index. We compare results from TMATCH with those obtained using FASTA and PSI-BLAST. We show that by using similarity patterns spread across the entire length of two proteins we get a more robust indicator of remote relatedness than relying upon high similarity scoring pair regions. AvailabilityThe program TMATCH is available on request Contactchitturk@uah.edu
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Comparative in silico analysis of ftsZ gene from different bacteria reveals the preference for core set of codons in coding sequence structuring and secondary structural elements determination 95%
- SARS-CoV-2 protein structure and sequence mutations: evolutionary analysis and effects on virus variants SARS-CoV-2 protein structure and sequence mutations: 95%
- GenomeBits insight into omicron and delta variants of coronavirus pathogen 94%
Similar papers in this journal
- An Issue of Concern: Unique Truncated ORF8 Protein Variants of SARS-CoV-2 95%
- Identification of novel mutations in RNA-dependent RNA polymerases of SARS-CoV-2 and their implications on its protein structure 93%
- Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network 92%
Similar papers in this journal
Similar papers in this journal
- SubFeat: Feature Subspacing Ensemble Classifier for Function Prediction of DNA, RNA and Protein Sequences 94%
- Computational study and design of effective siRNAs to silence structural proteins associated genes of Indian SARS-CoV-2 strains 91%
- A Composite Ranking of Risk Factors for COVID-19 Time-To-Event Data from a Turkish Cohort 91%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.