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PubMed2024

WilsonGenAI a deep learning approach to classify pathogenic variants in Wilson Disease.

BACKGROUND: Advances in Next Generation Sequencing have made rapid variant discovery and detection widely accessible. To facilitate a better understanding of the nature of these variants, American College of Medical Genetics and Genomics and the Association of Molecular Pathologists (ACMG-AMP) have issued a set of guidelines for variant classification. However, given the vast number of variants associated with any disorder, it is impossible to manually apply these guidelines to all known variants. Machine learning methodologies offer a rapid way to classify large numbers of variants, as well as variants of uncertain significance as either pathogenic or benign. Here we classify ATP7B genetic variants by employing ML and AI algorithms trained on our well-annotated WilsonGen dataset. METHODS: We have trained and validated two algorithms: TabNet and XGBoost on a high-confidence dataset of manually annotated, ACMG & AMP classified variants of the ATP7B gene associated with Wilson's Disease. RESULTS: Using an independent validation dataset of ACMG & AMP classified variants, as well as a patient set of functionally validated variants, we showed how both algorithms perform and can be used to classify large numbers of variants in clinical as well as research settings. CONCLUSION: We have created a ready to deploy tool, that can classify variants linked with Wilson's disease as pathogenic or benign, which can be utilized by both clinicians and researchers to better understand the disease through the nature of genetic variants associated with it.

Hepatolenticular Degeneration↗
PubMed1999

Comparative sequence analysis of the mouse and human Lgn1/SMA interval.

Human chromosome 5q11.2-q13.3 and its ortholog on mouse chromosome 13 contain candidate genes for an inherited human neurodegenerative disorder called spinal muscular atrophy (SMA) and for an inherited mouse susceptibility to infection with Legionella pneumophila (Lgn1). These homologous genomic regions also have unusual repetitive organizations that create practical difficulties in mapping and raise interesting issues about the evolutionary origin of the repeats. In an attempt to analyze this region in detail, and as a way to identify additional candidate genes for these diseases, we have determined the sequence of 179 kb of the mouse Lgn1/SMA interval. We have analyzed this sequence using BLAST searches and various exon prediction programs to identify potential genes. Since these methods can generate false-positive exon declarations, our alignments of the mouse sequence with available human orthologous sequence allowed us to discriminate rapidly among this collection of potential coding regions by indicating which regions were well conserved and were more likely to represent actual coding sequence. As a result of our analysis, we accurately mapped two additional genes in the SMA interval that can be tested for involvement in the pathogenesis of SMA. While no new Lgn1 candidates emerged, we have identified new genetic markers that exclude Smn as an Lgn1 candidate. In addition to providing important resources for studying SMA and Lgn1, our data provide further evidence of the value of sequencing the mouse genome as a means to help with the annotation of the human genomic sequence and vice versa.

Animals↗
PubMed2001

Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure.

Of the sequence comparison methods, profile-based methods perform with greater selectively than those that use pairwise comparisons. Of the profile methods, hidden Markov models (HMMs) are apparently the best. The first part of this paper describes calculations that (i) improve the performance of HMMs and (ii) determine a good procedure for creating HMMs for sequences of proteins of known structure. For a family of related proteins, more homologues are detected using multiple models built from diverse single seed sequences than from one model built from a good alignment of those sequences. A new procedure is described for detecting and correcting those errors that arise at the model-building stage of the procedure. These two improvements greatly increase selectivity and coverage. The second part of the paper describes the construction of a library of HMMs, called SUPERFAMILY, that represent essentially all proteins of known structure. The sequences of the domains in proteins of known structure, that have identities less than 95 %, are used as seeds to build the models. Using the current data, this gives a library with 4894 models. The third part of the paper describes the use of the SUPERFAMILY model library to annotate the sequences of over 50 genomes. The models match twice as many target sequences as are matched by pairwise sequence comparison methods. For each genome, close to half of the sequences are matched in all or in part and, overall, the matches cover 35 % of eukaryotic genomes and 45 % of bacterial genomes. On average roughly 15% of genome sequences are labelled as being hypothetical yet homologous to proteins of known structure. The annotations derived from these matches are available from a public web server at: http://stash.mrc-lmb.cam.ac.uk/SUPERFAMILY. This server also enables users to match their own sequences against the SUPERFAMILY model library.

Amino Acid Sequence↗
PubMed2006

Decoding the fine-scale structure of a breast cancer genome and transcriptome.

A comprehensive understanding of cancer is predicated upon knowledge of the structure of malignant genomes underlying its many variant forms and the molecular mechanisms giving rise to them. It is well established that solid tumor genomes accumulate a large number of genome rearrangements during tumorigenesis. End Sequence Profiling (ESP) maps and clones genome breakpoints associated with all types of genome rearrangements elucidating the structural organization of tumor genomes. Here we extend the ESP methodology in several directions using the breast cancer cell line MCF-7. First, targeted ESP is applied to multiple amplified loci, revealing a complex process of rearrangement and co-amplification in these regions reminiscent of breakage/fusion/bridge cycles. Second, genome breakpoints identified by ESP are confirmed using a combination of DNA sequencing and PCR. Third, in vitro functional studies assign biological function to a rearranged tumor BAC clone, demonstrating that it encodes anti-apoptotic activity. Finally, ESP is extended to the transcriptome identifying four novel fusion transcripts and providing evidence that expression of fusion genes may be common in tumors. These results demonstrate the distinct advantages of ESP including: (1) the ability to detect all types of rearrangements and copy number changes; (2) straightforward integration of ESP data with the annotated genome sequence; (3) immortalization of the genome; (4) ability to generate tumor-specific reagents for in vitro and in vivo functional studies. Given these properties, ESP could play an important role in a tumor genome project.

Breast Neoplasms↗
PubMed2007

Statistical and conformational analysis of the electron density of protein side chains.

Protein side chains make most of the specific contacts between proteins and other molecules, and their conformational properties have been studied for many years. These properties have been analyzed primarily in the form of rotamer libraries, which cluster the observed conformations into groups and provide frequencies and average dihedral angles for these groups. In recent years, these libraries have improved with higher resolution structures and using various criteria such as high thermal factors to eliminate side chains that may be misplaced within the crystallographic model coordinates. Many of these side chains have highly non-rotameric dihedral angles. The origin of side chains with high B-factors and/or with non-rotameric dihedral angles is of interest in the determination of protein structures and in assessing the prediction of side chain conformations. In this paper, using a statistical analysis of the electron density of a large set of proteins, it is shown that: (1) most non-rotameric side chains have low electron density compared to rotameric side chains; (2) up to 15% of chi1 non-rotameric side chains in PDB models can clearly be fit to density at a single rotameric conformation and in some cases multiple rotameric conformations; (3) a further 47% of non-rotameric side chains have highly dispersed electron density, indicating potentially interconverting rotameric conformations; (4) the entropy of these side chains is close to that of side chains annotated as having more than one chi(1) rotamer in the crystallographic model; (5) many rotameric side chains with high entropy clearly show multiple conformations that are not annotated in the crystallographic model. These results indicate that modeling of side chains alternating between rotamers in the electron density is important and needs further improvement, both in structure determination and in structure prediction.

Algorithms↗
PubMed2001

Kiddo, a new transposable element family closely associated with rice genes.

The promoter region of the rice ubiquitin2 (rubq2) gene was found to be polymorphic between japonica (T309) and indica (IR24) lines as the result of a 270-bp deletion in T309. A TTATA footprint in the T309 rubq2 promoter suggested that an excision event had occurred, and inspection of the 270-bp region present in IR24 revealed that it had all the characteristics of a miniature inverted repeat transposable element (MITE). Database searches showed that this element is a member of a new MITE family, which we have named Kiddo. Thirty-five complete Kiddo sequences were identified in existing rice genomic sequence databases. They could be arranged into four groups, within-group sequence identity was over 90%, with 65-75% identity between groups. The high sequence similarity within a group indicates that some Kiddo members were recently mobile and may still be active. An additional 24 decayed Kiddo sequences were detected. Interestingly, approximately 80% of 18 Kiddo members from annotated accessions lie within 530 bp of a coding sequence. That approximately 40% of Kiddo members present in genic regions reside in introns suggests that Kiddo transposition entails the use of both DNA and RNA intermediates, and may provide some insight into the origins of individual groups. DNA blot analysis showed that Kiddo is a rice-specific element, although one sequence with limited (72%) similarity to Kiddo group A was detected as a wheat EST. Kiddo family members may represent new molecular and phylogenetic markers, as well as representing valuable materials for studying the molecular mechanisms of MITE transposition.

Arabidopsis Proteins↗
PubMed2004

A preliminary investigation of the Nostoc punctiforme proteome.

Nostoc punctiforme ATCC 29133 is a filamentous terrestrial cyanobacterium (prokaryote) that expresses several different phenotypes in response to environmental cues. When grown in nitrogen-deficient media the most abundant proteins in addition to phycobiliproteins were superoxide dismutase, ATP synthase, and peptidyl-prolyl cis-trans isomerases. A methylated peptide from an akinete marker protein was also identified, suggesting that methylation could potentially play a regulatory role through signaling. C-phycocyanin alpha-chain was methylated at the C-terminal end of the protein and tandem mass spectrometric data also identified peptides that were deamidated. Since a significant number of putative polyketide/non-ribosomal peptide synthase genes are present in the annotated genome, an analysis of a methanolic extract of whole cells was also performed, and a series of nostopeptolides were identified.

Amino Acid Sequence↗
PubMed2002

The cadherin superfamily database.

The cadherin superfamily is a large protein family with diverse structures and functions. Because of this diversity and the growing biological interest in cell adhesion and signaling processes, in which many members of the cadherin superfamily play a crucial role, it is becoming increasingly important to develop tools to manage, distribute and analyze sequences in this protein family. Current profile and motif databases classify protein sequences into a broad spectrum of protein superfamilies, however to provide a more specific functional annotation, the next step should include classification of subfamilies of these protein superfamilies. Here, we present a tool that classified greater than 90% of the proteins belonging to the cadherin superfamily found in the SWISS PROT database. Therefore, for most members of the cadherin superfamily, this tool can assist in adding more specific functional annotations than can be achieved with current profile and motif databases. Finally, the classification tool and the results of our analysis were integrated into a web-accessible database (http://calcium.uhnres. utoronto.ca/cadherin).

Amino Acid Motifs↗
PubMed2003

The apoptosis database.

The apoptosis database is a public resource for researchers and students interested in the molecular biology of apoptosis. The resource provides functional annotation, literature references, diagrams/images, and alternative nomenclatures on a set of proteins having 'apoptotic domains'. These are the distinctive domains that are often, if not exclusively, found in proteins involved in apoptosis. The initial choice of proteins to be included is defined by apoptosis experts and bioinformatics tools. Users can browse through the web accessible lists of domains, proteins containing these domains and their associated homologs. The database can also be searched by sequence homology using basic local alignment search tool, text word matches of the annotation, and identifiers for specific records. The resource is available at http://www.apoptosis-db.org and is updated on a regular basis.

Animals↗
PubMed2006

ECRbase: database of evolutionary conserved regions, promoters, and transcription factor binding sites in vertebrate genomes.

Evolutionary conservation of DNA sequences provides a tool for the identification of functional elements in genomes. We have created a database of evolutionary conserved regions (ECRs) in vertebrate genomes, entitled ECRbase, which is constructed from a collection of whole-genome alignments produced by the ECR Browser. ECRbase features a database of syntenic blocks that recapitulate the evolution of rearrangements in vertebrates and a comprehensive collection of promoters in all vertebrate genomes generated using multiple sources of gene annotation. The database also contains a collection of annotated transcription factor binding sites (TFBSs) in evolutionary conserved and promoter elements. ECRbase currently includes human, rhesus macaque, dog, opossum, rat, mouse, chicken, frog, zebrafish and fugu genomes. It is freely accessible at http://ecrbase.dcode.org.

Animals↗
PubMed2006

MetaCyc: a multiorganism database of metabolic pathways and enzymes.

MetaCyc is a database of metabolic pathways and enzymes located at http://MetaCyc.org/. Its goal is to serve as a metabolic encyclopedia, containing a collection of non-redundant pathways central to small molecule metabolism, which have been reported in the experimental literature. Most of the pathways in MetaCyc occur in microorganisms and plants, although animal pathways are also represented. MetaCyc contains metabolic pathways, enzymatic reactions, enzymes, chemical compounds, genes and review-level comments. Enzyme information includes substrate specificity, kinetic properties, activators, inhibitors, cofactor requirements and links to sequence and structure databases. Data are curated from the primary literature by curators with expertise in biochemistry and molecular biology. MetaCyc serves as a readily accessible comprehensive resource on microbial and plant pathways for genome analysis, basic research, education, metabolic engineering and systems biology. Querying, visualization and curation of the database is supported by SRI's Pathway Tools software. The PathoLogic component of Pathway Tools is used in conjunction with MetaCyc to predict the metabolic network of an organism from its annotated genome. SRI and the European Bioinformatics Institute employed this tool to create pathway/genome databases (PGDBs) for 165 organisms, available at the BioCyc.org website. These PGDBs also include predicted operons and pathway hole fillers.

Animals↗
PubMed2006

Domain deletions and substitutions in the modular protein evolution.

The main mechanisms shaping the modular evolution of proteins are gene duplication, fusion and fission, recombination and loss of fragments. While a large body of research has focused on duplications and fusions, we concentrated, in this study, on how domains are lost. We investigated motif databases and introduced a measure of protein similarity that is based on domain arrangements. Proteins are represented as strings of domains and comparison was based on the classic dynamic alignment scheme. We found that domain losses and duplications were more frequent at the ends of proteins. We showed that losses can be explained by the introduction of start and stop codons which render the terminal domains nonfunctional, such that further shortening, until the whole domain is lost, is not evolutionarily selected against. We demonstrated that domains which also occur as single-domain proteins are less likely to be lost at the N terminus and in the middle, than at the C terminus. We conclude that fission/fusion events with single-domain proteins occur mostly at the C terminus. We found that domain substitutions are rare, in particular in the middle of proteins. We also showed that many cases of substitutions or losses result from erroneous annotations, but we were also able to find courses of evolutionary events where domains vanish over time. This is explained by a case study on the bacterial formate dehydrogenases.

Amino Acid Motifs↗
PubMed2006

The transcriptome of the sea urchin embryo.

The sea urchin Strongylocentrotus purpuratus is a model organism for study of the genomic control circuitry underlying embryonic development. We examined the complete repertoire of genes expressed in the S. purpuratus embryo, up to late gastrula stage, by means of high-resolution custom tiling arrays covering the whole genome. We detected complete spliced structures even for genes known to be expressed at low levels in only a few cells. At least 11,000 to 12,000 genes are used in embryogenesis. These include most of the genes encoding transcription factors and signaling proteins, as well as some classes of general cytoskeletal and metabolic proteins, but only a minor fraction of genes encoding immune functions and sensory receptors. Thousands of small asymmetric transcripts of unknown function were also detected in intergenic regions throughout the genome. The tiling array data were used to correct and authenticate several thousand gene models during the genome annotation process.

Animals↗
PubMed2005

A comprehensive update of the sequence and structure classification of kinases.

BACKGROUND: A comprehensive update of the classification of all available kinases was carried out. This survey presents a complete global picture of this large functional class of proteins and confirms the soundness of our initial kinase classification scheme. RESULTS: The new survey found the total number of kinase sequences in the protein database has increased more than three-fold (from 17,310 to 59,402), and the number of determined kinase structures increased two-fold (from 359 to 702) in the past three years. However, the framework of the original two-tier classification scheme (in families and fold groups) remains sufficient to describe all available kinases. Overall, the kinase sequences were classified into 25 families of homologous proteins, wherein 22 families (approximately 98.8% of all sequences) for which three-dimensional structures are known fall into 10 fold groups. These fold groups not only include some of the most widely spread proteins folds, such as the Rossmann-like fold, ferredoxin-like fold, TIM-barrel fold, and antiparallel beta-barrel fold, but also all major classes (all alpha, all beta, alpha+beta, alpha/beta) of protein structures. Fold predictions are made for remaining kinase families without a close homolog with solved structure. We also highlight two novel kinase structural folds, riboflavin kinase and dihydroxyacetone kinase, which have recently been characterized. Two protein families previously annotated as kinases are removed from the classification based on new experimental data. CONCLUSION: Structural annotations of all kinase families are now revealed, including fold descriptions for all globular kinases, making this the first large functional class of proteins with a comprehensive structural annotation. Potential uses for this classification include deduction of protein function, structural fold, or enzymatic mechanism of poorly studied or newly discovered kinases based on proteins in the same family.

Algorithms↗
PubMed2006

Automatic discovery of cross-family sequence features associated with protein function.

BACKGROUND: Methods for predicting protein function directly from amino acid sequences are useful tools in the study of uncharacterized protein families and in comparative genomics. Until now, this problem has been approached using machine learning techniques that attempt to predict membership, or otherwise, to predefined functional categories or subcellular locations. A potential drawback of this approach is that the human-designated functional classes may not accurately reflect the underlying biology, and consequently important sequence-to-function relationships may be missed. RESULTS: We show that a self-supervised data mining approach is able to find relationships between sequence features and functional annotations. No preconceived ideas about functional categories are required, and the training data is simply a set of protein sequences and their UniProt/Swiss-Prot annotations. The main technical aspect of the approach is the co-evolution of amino acid-based regular expressions and keyword-based logical expressions with genetic programming. Our experiments on a strictly non-redundant set of eukaryotic proteins reveal that the strongest and most easily detected sequence-to-function relationships are concerned with targeting to various cellular compartments, which is an area already well studied both experimentally and computationally. Of more interest are a number of broad functional roles which can also be correlated with sequence features. These include inhibition, biosynthesis, transcription and defence against bacteria. Despite substantial overlaps between these functions and their corresponding cellular compartments, we find clear differences in the sequence motifs used to predict some of these functions. For example, the presence of polyglutamine repeats appears to be linked more strongly to the "transcription" function than to the general "nuclear" function/location. CONCLUSION: We have developed a novel and useful approach for knowledge discovery in annotated sequence data. The technique is able to identify functionally important sequence features and does not require expert knowledge. By viewing protein function from a sequence perspective, the approach is also suitable for discovering unexpected links between biological processes, such as the recently discovered role of ubiquitination in transcription.

Algorithms↗
PubMed2005

Effective function annotation through catalytic residue conservation.

Because of the extreme impact of genome sequencing projects, protein sequences without accompanying experimental data now dominate public databases. Homology searches, by providing an opportunity to transfer functional information between related proteins, have become the de facto way to address this. Although a single, well annotated, close relationship will often facilitate sufficient annotation, this situation is not always the case, particularly if mutations are present in important functional residues. When only distant relationships are available, the transfer of function information is more tenuous, and the likelihood of encountering several well annotated proteins with different functions is increased. The consequence for a researcher is a range of candidate functions with little way of knowing which, if any, are correct. Here, we address the problem directly by introducing a computational approach to accurately identify and segregate related proteins into those with a functional similarity and those where function differs. This approach should find a wide range of applications, including the interpretation of genomics/proteomics data and the prioritization of targets for high-throughput structure determination. The method is generic, but here we concentrate on enzymes and apply high-quality catalytic site data. In addition to providing a series of comprehensive benchmarks to show the overall performance of our approach, we illustrate its utility with specific examples that include the correct identification of haptoglobin as a nonenzymatic relative of trypsin, discrimination of acid-d-amino acid ligases from a much larger ligase pool, and the successful annotation of BioH, a structural genomics target.

Amino Acid Sequence↗
PubMed2004

CD-Search: protein domain annotations on the fly.

We describe the Conserved Domain Search service (CD-Search), a web-based tool for the detection of structural and functional domains in protein sequences. CD-Search uses BLAST(R) heuristics to provide a fast, interactive service, and searches a comprehensive collection of domain models. Search results are displayed as domain architecture cartoons and pairwise alignments between the query and domain-model consensus sequences. Search results may be visualized in further detail by embedding the query sequence into multiple alignment displays and by mapping onto three-dimensional molecular graphic displays of known structures within the domain family. CD-Search can be accessed at http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi.

Amino Acid Sequence↗
PubMed2002

Singular value decomposition analysis of protein sequence alignment score data.

One of the standard tools for the analysis of data arranged in matrix form is singular value decomposition (SVD). Few applications to genomic data have been reported to date mainly for the analysis of gene expression microarray data. We review SVD properties, examine mathematical terms and assumptions implicit in the SVD formalism, and show that SVD can be applied to the analysis of matrices representing pairwise alignment scores between large sets of protein sequences. In particular, we illustrate SVD capabilities for data dimension reduction and for clustering protein sequences. A comparison is performed between SVD-generated clusters of proteins and annotation reported in the SWISS-PROT Database for a set of protein sequences forming the calycin superfamily, entailing all entries corresponding to the lipocalin, cytosolic fatty acid-binding protein, and avidin-streptavidin Prosite patterns.

Amino Acid Sequence↗