Hidden Markov models for detecting remote protein homologies.

Karplus, Kevin; Barrett, Christian; Hughey, Richard

doi:10.1093/bioinformatics/14.10.846

Cited by 1,011 publications

(685 citation statements)

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“…7 The generative models were trained from an existing library of SAM-T99 HMMs. The SAM-T99 algorithm, described more fully in Karplus et al, 9 builds an HMM for a SCOP domain sequence by searching the nonredundant protein database Swissprot for a set of potential homologs of the sequence and then iteratively selecting positive training sequences from among these potential homologs and refining a model. The resulting model is stored as an alignment of the domain sequence and final set of homologs.…”

Section: Results and Discussion Setup Of Competing Methodsmentioning

confidence: 99%

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Remote homolog detection using local sequence–structure correlations

et al. 2004

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Remote homology detection refers to the detection of structural homology in proteins when there is little or no sequence similarity. In this article, we present a remote homolog detection method called SVM-HMMSTR that overcomes the reliance on detectable sequence similarity by transforming the sequences into strings of hidden Markov states that represent local folding motif patterns. These state strings are transformed into fixeddimension feature vectors for input to a support vector machine. Two sets of features are defined: an order-independent feature set that captures the amino acid and local structure composition; and an order-dependent feature set that captures the sequential ordering of the local structures. Tests using the Structural Classification of Proteins (SCOP) 1.53 data set show that the SVM-HMMSTR gives a significant improvement over several current methods. Proteins 2004;57:518 -530.

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Section: Results and Discussion Setup Of Competing Methodsmentioning

confidence: 99%

“…In order to identify remote homologs, methods such as profiles for protein families, 5 hidden Markov models (HMMs), 6,7 and iterative methods such as PSI-BLAST 8 and SAM 9 have been introduced. The basic idea behind these methods is to generate a representative model for each protein family.…”

Section: Introductionmentioning

confidence: 99%

Remote homolog detection using local sequence–structure correlations

et al. 2004

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“…The last stage is consisted of generative classification algorithm and discriminative classification algorithm. pHMMs software that are HMMER [38]and SAM [39] are used as the generative classsifier while SVMs software that are SVM-Struct [40] and SVM-Fold [41] are used as discriminative classification algorithm. Even though a similar approach has been conducted by Bernardes et al [42] in their work by comparing the performance of various multiple alignment software, yet they only use pHMMs for classification.…”

Section: Framework For Finding the Optimal Mesh Algorithmmentioning

confidence: 99%

“…Then, the multiple alignments are converted to .cn3 files using fa2cd (ftp://ftp.ncbi.nih.gov/pub/REFINER/). The multiple alignments have to be converted in such a way because the refinement algorithm only understand inputs in the form of CD format, that is the same format used by CDD [39] database. One or more iterations of refinement which contains a stage of 'block shifting' followed by a 'block editing' stage are performed in the algorithm.…”

Section: Refinement Algorithmmentioning

confidence: 99%

An Optimal Mesh Algorithm for Remote Protein Homology Detection

Abdullah

Othman

Kasim

et al. 2011

Communications in Computer and Information Science

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“…We therefore used these genes as seed sequences in more exhaustive searches. Initially, using the SAM-T99 search method described in www.cse.ucse.edu/research/compbio web page that uses a hidden Markov model [20], we found no homologies to T4denV in six different The score represents the number of false positives expected by chance. In this search the resulting matches are due to the presence of common helicase motifs and do not necessarily indicate that the genes identified have functions corresponding to the XP genes.…”

Section: Do We Know All Possible Strategies For Ner In All Organisms?mentioning

confidence: 99%

Nucleotide excision repair “a legacy of creativity”

Cleaver

Karplus

Kashani‐Sabet

et al. 2001

Mutation Research/DNA Repair

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The first half of the 20th century has seen an enormous growth in our knowledge of DNA repair, in no small part due to the work of Dirk Bootsma, Philip Hanawalt and Bryn Bridges; those honored by this issue. For the new millennium, we have asked three general questions: (A) Do we know all possible strategies of nucleotide excision repair (NER) in all organisms? (B) How is NER integrated and regulated in cells and tissues? (C) Does DNA replication represent a new frontier in the roles of DNA repair? We make some suggestions for the kinds of answers the next generation may provide. The kingdom of archea represents an untapped field for investigation of DNA repair in organisms with extreme lifestyles. NER appears to involve a similar strategy to the other kingdoms of prokaryotes and eukaryotes, but subtle differences suggest that individual components of the system may differ. NER appears to be regulated by several major factors, especially p53 and Rb which interact with transcription coupled repair and global genomic repair, respectively. Examples can be found of major regulatory changes in repair in testicular tissue and melanoma cells. Our understanding of replication of damaged DNA has undergone a revolution in recent years, with the discovery of multiple low-fidelity DNA polymerases that facilitate replicative bypass. A secondary mechanism of replication in the absence of NER or of one or more of these polymerases involves sister chromatid exchange and recombination (hMre11/hRad50/Nbs1). The relative importance of bypass and recombination is determined by the action of p53. We hypothesise that these polymerases may be involved in resolution of complex DNA structures during completion of replication and sister chromatid resolution. With these fascinating problems to investigate, the field of DNA repair will surely not disappoint the next generation.

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Hidden Markov models for detecting remote protein homologies.

Abstract: karplus@cse.ucsc.edu; http://www.cse.ucsc.edu/karplus

Cited by 1,011 publications

References 0 publications

Remote homolog detection using local sequence–structure correlations

Remote homolog detection using local sequence–structure correlations

An Optimal Mesh Algorithm for Remote Protein Homology Detection

Nucleotide excision repair “a legacy of creativity”

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