Hidden Markov models for detecting remote protein homologies

By Kevin J. Karplus, Christian Barrett, Richard Hughey. 1998

Subfields: Probabilistic & Bayesian Methods, Computational Biology & Bioinformatics

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Citations in the corpus

Toward optimal fragment generations for ab initio protein structure assembly (2012, background): “The average error between the exact distance and the distance in the distance profile that has the highest probability is 0.83 A˚, while the error of distance prediction randomly chosen from [4 to 8 A˚] is 1.24 A˚.”
Hidden Markov model speed heuristic and iterative HMM search procedure (2010, methodology): “This subdatabase is then used in the iterative searches due to the speed issues inherent in using profile-HMM scoring algorithms on large databases [4,5].”
MRFalign: Protein Homology Detection through Alignment of Markov Random Fields (2014, background): “Sequence-based protein alignment and homology detection has been extensively studied and widely applied to many biological problems such as homology modeling [1-4], phylogeny inference [5-7] and protein function prediction [8-10].”
Learning generative models for protein fold families (2011, background): “Equation (3) describes the probability of a sequence x for a specific model M.”
Accelerated Profile HMM Searches (2011, methodology): “…have low relative entropy per position (about 0.6 bits/position) because HMMER3 model parameterization uses a technique called entropy-weighting [29,37], an ad hoc method to re-weight the effective number of observed sequences relative to the prior (pseudocounts) to achieve a desired relative…”
MMseqs2: sensitive protein sequence searching for the analysis of massive data sets (2017, methodology): “resentation of low complexity and repeat regions, we added reversed UniProt sequences [16].”
HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment (2011, methodology): “SAM2K[2] and HMMER3[3] represent the evolving query MSAs by proﬁle HMMs for better sensitivity.”
Template‐based modeling and free modeling by I‐TASSER in CASP7 (2007, background): “The force field consists of a variety of knowledge-based energy terms describing SC pair-wise interactions and short-range C a correlations,20,32 propensity to the consensus secondary structures predictions from PSIPRED27 and SAM,26 residue-based solvent accessibility by neural network training,21,3”
Identification of correct regions in protein models using structural, alignment, and consensus information (2006, methodology): “2000), Sam-T99 (Karplus et al. 1998), mGenTHREADER (Jones 1999a), INBGU, FUGUE (Shi et al.”
Improving protein structure prediction using multiple sequence-based contact predictions. (2011, background): “…Wu and Zhang, 2007), i.e., collecting contacts from solved homologous proteins whose structures can be found in the Protein Data Bank (PDB) by sequence similarity search (Altschul et al., 1997) or threading algorithms (Bowie et al., 1991; Karplus et al., 1998; Soding, 2005; Wu and Zhang, 2008b).”
A Probabilistic Model of Local Sequence Alignment That Simplifies Statistical Significance Estimation (2008, methodology): “We have confirmed previous observations that even an ad hoc method to reduce the relative entropy per position (‘‘entropy-weighting’’; [43]) greatly improves search sensitivity in HMMER [27], although, puzzlingly, the same effect was not seen by PSI-BLAST’s authors [48].”
Review: protein secondary structure prediction continues to rise. (2001, methodology): “However, the breakthrough for large-scale routine searches was achieved with the development of PSI-BLAST (10) and hidden Markov models ( 12 , 26).”
Homology Modeling a Fast Tool for Drug Discovery: Current Perspectives (2012, methodology): “A new HMM, SAM-T98 ID known for finding remote homologs of protein sequences.”