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Letter↗
Biomedical subjects
Mark Borodovsky
Publications and source records attributed to Mark Borodovsky.
Protein secondary structure prediction for a single-sequence using hidden semi-Markov models.
BACKGROUND: The accuracy of protein secondary structure prediction has been improving steadily towards the 88% estimated theoretical limit. There are two types of prediction algorithms: Single-sequence prediction algorithms imply that information about other (homologous) proteins is not available, while algorithms of the second type imply that information about homologous proteins is available, and use it intensively. The single-sequence algorithms could make an important contribution to studies of proteins with no detected homologs, however the accuracy of protein secondary structure prediction from a single-sequence is not as high as when the additional evolutionary information is present. RESULTS: In this paper, we further refine and extend the hidden semi-Markov model (HSMM) initially considered in the BSPSS algorithm. We introduce an improved residue dependency model by considering the patterns of statistically significant amino acid correlation at structural segment borders. We also derive models that specialize on different sections of the dependency structure and incorporate them into HSMM. In addition, we implement an iterative training method to refine estimates of HSMM parameters. The three-state-per-residue accuracy and other accuracy measures of the new method, IPSSP, are shown to be comparable or better than ones for BSPSS as well as for PSIPRED, tested under the single-sequence condition. CONCLUSIONS: We have shown that new dependency models and training methods bring further improvements to single-sequence protein secondary structure prediction. The results are obtained under cross-validation conditions using a dataset with no pair of sequences having significant sequence similarity. As new sequences are added to the database it is possible to augment the dependency structure and obtain even higher accuracy. Current and future advances should contribute to the improvement of function prediction for orphan proteins inscrutable to current similarity search methods.
Algorithms↗
Statistical significance in biological sequence analysis.
One of the major goals of computational sequence analysis is to find sequence similarities, which could serve as evidence of structural and functional conservation, as well as of evolutionary relations among the sequences. Since the degree of similarity is usually assessed by the sequence alignment score, it is necessary to know if a score is high enough to indicate a biologically interesting alignment. A powerful approach to defining score cutoffs is based on the evaluation of the statistical significance of alignments. The statistical significance of an alignment score is frequently assessed by its P-value, which is the probability that this score or a higher one can occur simply by chance, given the probabilistic models for the sequences. In this review we discuss the general role of P-value estimation in sequence analysis, and give a description of theoretical methods and computational approaches to the estimation of statistical signifiance for important classes of sequence analysis problems. In particular, we concentrate on the P-value estimation techniques for single sequence studies (both score-based and score-free), global and local pairwise sequence alignments, multiple alignments, sequence-to-profile alignments and alignments built with hidden Markov models. We anticipate that the review will be useful both to researchers professionally working in bioinformatics as well as to biomedical scientists interested in using contemporary methods of DNA and protein sequence analysis.
Computational Biology↗
Gene identification in novel eukaryotic genomes by self-training algorithm.
Finding new protein-coding genes is one of the most important goals of eukaryotic genome sequencing projects. However, genomic organization of novel eukaryotic genomes is diverse and ab initio gene finding tools tuned up for previously studied species are rarely suitable for efficacious gene hunting in DNA sequences of a new genome. Gene identification methods based on cDNA and expressed sequence tag (EST) mapping to genomic DNA or those using alignments to closely related genomes rely either on existence of abundant cDNA and EST data and/or availability on reference genomes. Conventional statistical ab initio methods require large training sets of validated genes for estimating gene model parameters. In practice, neither one of these types of data may be available in sufficient amount until rather late stages of the novel genome sequencing. Nevertheless, we have shown that gene finding in eukaryotic genomes could be carried out in parallel with statistical models estimation directly from yet anonymous genomic DNA. The suggested method of parallelization of gene prediction with the model parameters estimation follows the path of the iterative Viterbi training. Rounds of genomic sequence labeling into coding and non-coding regions are followed by the rounds of model parameters estimation. Several dynamically changing restrictions on the possible range of model parameters are added to filter out fluctuations in the initial steps of the algorithm that could redirect the iteration process away from the biologically relevant point in parameter space. Tests on well-studied eukaryotic genomes have shown that the new method performs comparably or better than conventional methods where the supervised model training precedes the gene prediction step. Several novel genomes have been analyzed and biologically interesting findings are discussed. Thus, a self-training algorithm that had been assumed feasible only for prokaryotic genomes has now been developed for ab initio eukaryotic gene identification.
Algorithms↗
GeneMark: web software for gene finding in prokaryotes, eukaryotes and viruses.
The task of gene identification frequently confronting researchers working with both novel and well studied genomes can be conveniently and reliably solved with the help of the GeneMark web software (http://opal.biology.gatech.edu/GeneMark/). The website provides interfaces to the GeneMark family of programs designed and tuned for gene prediction in prokaryotic, eukaryotic and viral genomic sequences. Currently, the server allows the analysis of nearly 200 prokaryotic and >10 eukaryotic genomes using species-specific versions of the software and pre-computed gene models. In addition, genes in prokaryotic sequences from novel genomes can be identified using models derived on the spot upon sequence submission, either by a relatively simple heuristic approach or by the full-fledged self-training program GeneMarkS. A database of reannotations of >1000 viral genomes by the GeneMarkS program is also available from the web site. The GeneMark website is frequently updated to provide the latest versions of the software and gene models.
Algorithms↗
Three new isoforms of Caenorhabditis elegans UNC-89 containing MLCK-like protein kinase domains.
In Caenorhabditis elegans, the gene unc-89 is required for A-band organization of striated muscle. In mammals, a likely homolog of UNC-89, called obscurin, has been described and found to be localized at both the M-lines and Z-discs of striated muscle. Here, we show that the coding sequence for unc-89 is larger than originally thought, and that the gene encodes at least four major isoforms: UNC-89-A (original isoform, 732 kDa), UNC-89-B (potentially 900 kDa), and UNC-89-C and UNC-89-D (each 156 kDa). UNC-89-C and -D, except for unique N-terminal tails of eight and 11 residues, respectively, are co-linear with the C terminus of UNC-89-B. The unc-89 complex transcription unit contains at least three promoters: one directing UNC-89-A and -B primarily in body-wall and pharyngeal muscle, one internal promoter directing expression of UNC-89-C primarily in body-wall muscle, and one internal promoter directing expression of UNC-89-D primarily in a few muscle cells of the tail. Isoform-specific RNA interference resulted in a muscle structural phenotype similar to a typical unc-89 mutant, but with varying degrees of severity. Antibodies generated to the interkinase region shared by the UNC-89-B, -C and -D isoforms localize to the middle of A-bands, like previously-described UNC-89 antibodies, and detect proteins on immunoblots consistent with the proposed gene organization and additional isoforms. The three new UNC-89 isoforms contain two protein kinase domains, of the myosin light chain kinase (MLCK) family. UNC-89-B contains two complete protein kinase domains, designated PK1 and PK2. UNC-89-C and -D begin with partial kinase domains, PK1-C and PK1-D. Homology modeling suggests that PK2 is catalytically active, PK1 is inactive, and that PK1-C and PK1-D have similar structures at their N termini that may create sites for interaction with other proteins.
Amino Acid Sequence↗
Identification of new human cadherin genes using a combination of protein motif search and gene finding methods.
We have combined protein motif search and gene finding methods to identify genes encoding proteins containing specific domains. Particularly, we have focused on finding new human genes of the cadherin superfamily proteins, which represent a major group of cell-cell adhesion receptors contributing to embryonic neuronal morphogenesis. Models for three cadherin protein motifs were generated from over 100 already annotated cadherin domains and used to search the complete translated human genome. The genomic sequence regions containing motif "hits" were analyzed by eukaryotic GeneMark.hmm to identify the exon-intron structure of new genes. Three new genes CDH-J, PCDH-J and FAT-J were found. The predicted proteins PCDH-J and FAT-J were classified into protocadherin and FAT-like subfamilies, respectively, based on the number and organization of cadherin domains and presence of subfamily-specific conserved amino acid residues. Expression of FAT-J was shown in almost all tested tissues. The exon-intron organization of CDH-J was experimentally verified by PCR with specifically designed primers and its tissue-specific expression was demonstrated. The described methodology can be applied to discover new genes encoding proteins from families with well-characterized structural and functional domains.
Amino Acid Motifs↗
Effects of choice of DNA sequence model structure on gene identification accuracy.
MOTIVATION: Markov chain models of DNA sequences have frequently been used in gene finding algorithms. Performance of the algorithm critically depends on the model structure and parameters. Still, the issue of choosing the model structure has not been studied with sufficient attention. RESULTS: We have assessed performance of several types of Markov chain models, both fixed order (FO) models and models with interpolation, within the framework of the GeneMark algorithm. The performance was measured in two ways: (i) the accuracy of detection of protein-coding potential in artificial DNA sequences and (ii) the accuracy of identifying genes in real prokaryotic genomes. We observed that the models built by deleted interpolation (DI) slightly outperformed other models in detecting protein-coding potential in artificial DNA sequences with GC content in the medium range and also in detecting genes in real genomes with medium GC content. For artificial and real genomic DNA with high or low GC content, we observed that the models built by DI were in some cases slightly outperformed by FO models.
Algorithms↗
Probabilistic methods of identifying genes in prokaryotic genomes: connections to the HMM theory.
In this paper, we review developments in probabilistic methods of gene recognition in prokaryotic genomes with the emphasis on connections to the general theory of hidden Markov models (HMM). We show that the Bayesian method implemented in GeneMark, a frequently used gene-finding tool, can be augmented and reintroduced as a rigorous forward-backward (FB) algorithm for local posterior decoding described in the HMM theory. Another earlier developed method, prokaryotic GeneMark.hmm, uses a modification of the Viterbi algorithm for HMM with duration to identify the most likely global path through hidden functional states given the DNA sequence. GeneMark and GeneMark.hmm programs are worth using in concert for analysing prokaryotic DNA sequences that arguably do not follow any exact mathematical model. The new extension of GeneMark using the FB algorithm was implemented in the software program GeneMark.fba. Given the DNA sequence, this program determines an a posteriori probability for each nucleotide to belong to coding or non-coding region. Also, for any open reading frame (ORF), it assigns a score defined as a probabilistic measure of all paths through hidden states that traverse the ORF as a coding region. The prediction accuracy of GeneMark.fba determined in our tests was compared favourably to the accuracy of the initial (standard) GeneMark program. Comparison to the prokaryotic GeneMark.hmm has also demonstrated a certain, yet species-specific, degree of improvement in raw gene detection, ie detection of correct reading frame (and stop codon). The accuracy of exact gene prediction, which is concerned about precise prediction of gene start (which in a prokaryotic genome unambiguously defines the reading frame and stop codon, thus, the whole protein product), still remains more accurate in GeneMarkS, which uses more elaborate HMM to specifically address this task.
Algorithms↗
Identification of proteins associated with murine cytomegalovirus virions.
Proteins associated with the murine cytomegalovirus (MCMV) viral particle were identified by a combined approach of proteomic and genomic methods. Purified MCMV virions were dissociated by complete denaturation and subjected to either separation by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and in-gel digestion or treated directly by in-solution tryptic digestion. Peptides were separated by nanoflow liquid chromatography and analyzed by tandem mass spectrometry (LC-MS/MS). The MS/MS spectra obtained were searched against a database of MCMV open reading frames (ORFs) predicted to be protein coding by an MCMV-specific version of the gene prediction algorithm GeneMarkS. We identified 38 proteins from the capsid, tegument, glycoprotein, replication, and immunomodulatory protein families, as well as 20 genes of unknown function. Observed irregularities in coding potential suggested possible sequence errors in the 3'-proximal ends of m20 and M31. These errors were experimentally confirmed by sequencing analysis. The MS data further indicated the presence of peptides derived from the unannotated ORFs ORF(c225441-226898) (m166.5) and ORF(105932-106072). Immunoblot experiments confirmed expression of m166.5 during viral infection.
Amino Acid Sequence↗
Improving gene annotation of complete viral genomes.
Gene annotation in viruses often relies upon similarity search methods. These methods possess high specificity but some genes may be missed, either those unique to a particular genome or those highly divergent from known homologs. To identify potentially missing viral genes we have analyzed all complete viral genomes currently available in GenBank with a specialized and augmented version of the gene finding program GeneMarkS. In particular, by implementing genome-specific self-training protocols we have better adjusted the GeneMarkS statistical models to sequences of viral genomes. Hundreds of new genes were identified, some in well studied viral genomes. For example, a new gene predicted in the genome of the Epstein-Barr virus was shown to encode a protein similar to alpha-herpesvirus minor tegument protein UL14 with heat shock functions. Convincing evidence of this similarity was obtained after only 12 PSI-BLAST iterations. In another example, several iterations of PSI-BLAST were required to demonstrate that a gene predicted in the genome of Alcelaphine herpesvirus 1 encodes a BALF1-like protein which is thought to be involved in apoptosis regulation and, potentially, carcinogenesis. New predictions were used to refine annotations of viral genomes in the RefSeq collection curated by the National Center for Biotechnology Information. Importantly, even in those cases where no sequence similarities were detected, GeneMarkS significantly reduced the number of primary targets for experimental characterization by identifying the most probable candidate genes. The new genome annotations were stored in VIOLIN, an interactive database which provides access to similarity search tools for up-to-date analysis of predicted viral proteins.
Amino Acid Sequence↗
Complete sequence and comparative analysis of the genome of herpes B virus (Cercopithecine herpesvirus 1) from a rhesus monkey.
The complete DNA sequence of herpes B virus (Cercopithecine herpesvirus 1) strain E2490, isolated from a rhesus macaque, was determined. The total genome length is 156,789 bp, with 74.5% G+C composition and overall genome organization characteristic of alphaherpesviruses. The first and last residues of the genome were defined by sequencing the cloned genomic termini. There were six origins of DNA replication in the genome due to tandem duplication of both oriL and oriS regions. Seventy-four genes were identified, and sequence homology to proteins known in herpes simplex viruses (HSVs) was observed in all cases but one. The degree of amino acid identity between B virus and HSV proteins ranged from 26.6% (US5) to 87.7% (US15). Unexpectedly, B virus lacked a homolog of the HSV gamma(1)34.5 gene, which encodes a neurovirulence factor. Absence of this gene was verified in two low-passage clinical isolates derived from a rhesus macaque and a zoonotically infected human. This finding suggests that B virus most likely utilizes mechanisms distinct from those of HSV to sustain efficient replication in neuronal cells. Despite the considerable differences in G+C content of the macaque and B virus genes (51% and 74.2%, respectively), codons used by B virus are optimal for the tRNA population of macaque cells. Complete sequence of the B virus genome will certainly facilitate identification of the genetic basis and possible molecular mechanisms of enhanced B virus neurovirulence in humans, which results in an 80% mortality rate following zoonotic infection.
Animals↗
Titins in C.elegans with unusual features: coiled-coil domains, novel regulation of kinase activity and two new possible elastic regions.
We report that there are previously unrecognized proteins in Caenorhabditis elegans that are similar to the giant muscle proteins called titins, and these are encoded by a single approximately 90kb gene. The gene structure was predicted by GeneMark.hmm and then experimentally verified. The Ce titin gene encodes polypeptides of 2.2MDa, 1.2MDa and 301kDa. The 2.2MDa isoform resembles twitchin and UNC-89 in that it contains multiple Ig (56) and FnIII (11) domains, and a single protein kinase domain. In addition, however, the 2.2MDa isoform contains four classes of short, 14-51 residue, repeat motifs arranged mostly in many tandem copies. One of these tandem repeat regions is similar to the PEVK regions of vertebrate and fly titins. As the PEVK region is one of the main elastic elements of the titins and is also composed of short tandem repeats, this suggests that the repeat motifs in the Ce titins may have a similar elastic function. An interesting aspect of the two largest Ce titin isoforms, is that in contrast to other members of the twitchin/titin family, there are multiple regions which are likely to form coiled-coil structure. In transgenic animals, the first approximately 100 residues of the largest isoforms targets to dense bodies, the worm analogs of Z-discs. Anti-Ce titin antibodies show localization to muscle I-bands beginning at the L2-L3 larval stages and this pattern continues into adult muscle. Ce titins may not have a role in early myofibril assembly: (1) Ce titins are too short to span half a sarcomere, and the onset of their expression is well after the initial assembly of thick filaments. (2) Ce titins are not localized to I-bands in embryonic or L1 larval muscle. The Ce titin protein kinase domain is most similar to the kinase domains of the twitchins and projectin. The Ce titin kinase has protein kinase activity in vitro, and this activity is regulated by a novel mechanism.
Amino Acid Motifs↗