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PubMed2002

Functional organization of the yeast proteome by systematic analysis of protein complexes.

Most cellular processes are carried out by multiprotein complexes. The identification and analysis of their components provides insight into how the ensemble of expressed proteins (proteome) is organized into functional units. We used tandem-affinity purification (TAP) and mass spectrometry in a large-scale approach to characterize multiprotein complexes in Saccharomyces cerevisiae. We processed 1,739 genes, including 1,143 human orthologues of relevance to human biology, and purified 589 protein assemblies. Bioinformatic analysis of these assemblies defined 232 distinct multiprotein complexes and proposed new cellular roles for 344 proteins, including 231 proteins with no previous functional annotation. Comparison of yeast and human complexes showed that conservation across species extends from single proteins to their molecular environment. Our analysis provides an outline of the eukaryotic proteome as a network of protein complexes at a level of organization beyond binary interactions. This higher-order map contains fundamental biological information and offers the context for a more reasoned and informed approach to drug discovery.

Cells, Cultured↗
PubMed2004

Quantifying structure-function uncertainty: a graph theoretical exploration into the origins and limitations of protein annotation.

Since the advent of investigations into structural genomics, research has focused on correctly identifying domain boundaries, as well as domain similarities and differences in the context of their evolutionary relationships. As the science of structural genomics ramps up adding more and more information into the databanks, questions about the accuracy and completeness of our classification and annotation systems appear on the forefront of this research. A central question of paramount importance is how structural similarity relates to functional similarity. Here, we begin to rigorously and quantitatively answer these questions by first exploring the consensus between the most common protein domain structure annotation databases CATH, SCOP and FSSP. Each of these databases explores the evolutionary relationships between protein domains using a combination of automatic and manual, structural and functional, continuous and discrete similarity measures. In order to examine the issue of consensus thoroughly, we build a generalized graph out of each of these databases and hierarchically cluster these graphs at interval thresholds. We then employ a distance measure to find regions of greatest overlap. Using this procedure we were able not only to enumerate the level of consensus between the different annotation systems, but also to define the graph-theoretical origins behind the annotation schema of class, family and superfamily by observing that the same thresholds that define the best consensus regions between FSSP, SCOP and CATH correspond to distinct, non-random phase-transitions in the structure comparison graph itself. To investigate the correspondence in divergence between structure and function further, we introduce a measure of functional entropy that calculates divergence in function space. First, we use this measure to calculate the general correlation between structural homology and functional proximity. We extend this analysis further by quantitatively calculating the average amount of functional information gained from our understanding of structural distance and the corollary inherent uncertainty that represents the theoretical limit of our ability to infer function from structural similarity. Finally we show how our measure of functional "entropy" translates into a more intuitive concept of functional annotation into similarity EC classes.

Biochemical Phenomena↗
PubMed2004

A protein interaction network links GIT1, an enhancer of huntingtin aggregation, to Huntington's disease.

Analysis of protein-protein interactions (PPIs) is a valuable approach for characterizing proteins of unknown function. Here, we have developed a strategy combining library and matrix yeast two-hybrid screens to generate a highly connected PPI network for Huntington's disease (HD). The network contains 186 PPIs among 35 bait and 51 prey proteins. It revealed 165 new potential interactions, 32 of which were confirmed by independent binding experiments. The network also permitted the functional annotation of 16 uncharacterized proteins and facilitated the discovery of GIT1, a G protein-coupled receptor kinase-interacting protein, which enhances huntingtin aggregation by recruitment of the protein into membranous vesicles. Coimmunoprecipitations and immunofluorescence studies revealed that GIT1 and huntingtin associate in mammalian cells under physiological conditions. Moreover, GIT1 localizes to neuronal inclusions, and is selectively cleaved in HD brains, indicating that its distribution and function is altered during disease pathogenesis.

Adaptor Proteins, Signal Transducing↗
PubMed2005

NMR solution structure of Thermotoga maritima protein TM1509 reveals a Zn-metalloprotease-like tertiary structure.

The 150-residue protein TM1509 is encoded in gene YF09_THEMA of Thermotoga maritima. TM1509 has so far no functional annotation and belongs to protein family UPF0054 (PFAM accession number: PF02130) which contains at least 146 members. The NMR structure of TM1509 reveals an alpha+beta fold comprising a four stranded beta-sheet with topology A( upward arrow), B( upward arrow), D( upward arrow), C( downward arrow) as well as five alpha-helices I-V. The structures of most members of family PF02130 can be reliably constructed using the TM1509 NMR structure, demonstrating high leverage for exploration of fold space. A multiple sequence alignment of TM1509 with homologues of family UPF0054 shows that three polypeptide segments, as well as a putative zinc-binding consensus motif HGXLHLXGYDH located at the C-terminal end of alpha-helix IV, are highly conserved. The spatial arrangement of the three His residues of this UPF0054 consensus motif is similar to the arrangement found for the His residues in the HEXXHXXGXXH zinc-binding consensus motif of matrix metallo-proteases (MMPs). Moreover, the other conserved polypeptide segments form a large cavity which encloses the putative Zn-binding pocket and might confer specificity during catalysis. However, TM1509 and the other members of the UPF0054 family do not have the crucial Glu residue in position 2 of the MMP consensus motif. Intriguingly, the TM1509 structure indicates that the Asp in the UPF0054 consensus motif (Asp 111 in TM1509) may overtake the catalytic role of the Glu. This suggests that protein family UPF0054 might contain members of a hitherto uncharacterized class of metalloproteases.

Amino Acid Sequence↗
PubMed2025

Chromosome-level assembly and annotation of the yellow-shelled fish (Barbodes Wynaadensis).

Barbodes wynaadensis, a unique cyprinid species native to Yunnan Province in China, stands out as an allotetraploid (AABB) fish with a complex evolutionary history. Leveraging a multi-platform sequencing strategy combining MGI short-read, PacBio long-read, and Hi-C scaffolding technologies, we assembled the first chromosome-level genome for B. wynaadensis. The final assembled genome spans 1.76 Gb in length with a contig N50 of 33.53 Mb, demonstrating high assembly continuity. Hi-C scaffolding enabled the reconstruction of 50 pseudochromosomes, representing 99.94% of the total genome assembly. Genome annotation identified 46,121 protein-coding genes, with a functional annotation rate of 99.76%. Repetitive elements constituted 48.26% of the genomic sequences, including lineage-specific expansions of DNA transposons (29.26%) and LTRs (6.36%). This high-quality assembly resolves challenges in polyploid genome reconstruction and provides a critical resource for investigating Cyprinidae evolution, particularly subgenome divergence and adaptation. The dataset also enables practical applications, such as molecular marker development for population monitoring, supporting conservation efforts for this threatened endemic species amid habitat degradation in the Nujiang River basin.

Animals↗
PubMed2006

SNP Function Portal: a web database for exploring the function implication of SNP alleles.

MOTIVATION: Finding the potential functional significance of SNPs is a major bottleneck in understanding genome-wide SNP scanning results, as the related functional data are distributed across many different databases. The SNP Function Portal is designed to be a clearing house for all public domain SNP functional annotation data, as well as in-house functional annotations derived from different data sources. It currently contains SNP functional annotations in six major categories including genomic elements, transcription regulation, protein function, pathway, disease and population genetics. Besides extensive SNP functional annotations, the SNP Function Portal includes a powerful search engine that accepts different types of genetic markers as input and identifies all genetically related SNPs based on the HapMap Phase II data as well as the relationship of different markers to known genes. As a result, our system allows users to identify the potential biological impact of genetic markers and complex relationships among genetic markers and genes, and it greatly facilitates knowledge discovery in genome-wide SNP scanning experiments. AVAILABILITY: http://brainarray.mbni.med.umich.edu/Brainarray/Database/SearchSNP/snpfunc.aspx.

Alleles↗
PubMed2001

Automatic rule generation for protein annotation with the C4.5 data mining algorithm applied on SWISS-PROT.

MOTIVATION: The gap between the amount of newly submitted protein data and reliable functional annotation in public databases is growing. Traditional manual annotation by literature curation and sequence analysis tools without the use of automated annotation systems is not able to keep up with the ever increasing quantity of data that is submitted. Automated supplements to manually curated databases such as TrEMBL or GenPept cover raw data but provide only limited annotation. To improve this situation automatic tools are needed that support manual annotation, automatically increase the amount of reliable information and help to detect inconsistencies in manually generated annotations. RESULTS: A standard data mining algorithm was successfully applied to gain knowledge about the Keyword annotation in SWISS-PROT. 11 306 rules were generated, which are provided in a database and can be applied to yet unannotated protein sequences and viewed using a web browser. They rely on the taxonomy of the organism, in which the protein was found and on signature matches of its sequence. The statistical evaluation of the generated rules by cross-validation suggests that by applying them on arbitrary proteins 33% of their keyword annotation can be generated with an error rate of 1.5%. The coverage rate of the keyword annotation can be increased to 60% by tolerating a higher error rate of 5%. AVAILABILITY: The results of the automatic data mining process can be browsed on http://golgi.ebi.ac.uk:8080/Spearmint/ Source code is available upon request. CONTACT: kretsch@ebi.ac.uk.

Algorithms↗
PubMed2005

Percolation of annotation errors through hierarchically structured protein sequence databases.

Databases of protein sequences have grown rapidly in recent years as a result of genome sequencing projects. Annotating protein sequences with descriptions of their biological function ideally requires careful experimentation, but this work lags far behind. Instead, biological function is often imputed by copying annotations from similar protein sequences. This gives rise to annotation errors, and more seriously, to chains of misannotation. [Percolation of annotation errors in a database of protein sequences (2002)] developed a probabilistic framework for exploring the consequences of this percolation of errors through protein databases, and applied their theory to a simple database model. Here we apply the theory to hierarchically structured protein sequence databases, and draw conclusions about database quality at different levels of the hierarchy.

Amino Acid Sequence↗
PubMed2005

QuasiMotiFinder: protein annotation by searching for evolutionarily conserved motif-like patterns.

Sequence signature databases such as PROSITE, which include amino acid segments that are indicative of a protein's function, are useful for protein annotation. Lamentably, the annotation is not always accurate. A signature may be falsely detected in a protein that does not carry out the associated function (false positive prediction, FP) or may be overlooked in a protein that does carry out the function (false negative prediction, FN). A new approach has emerged in which a signature is replaced with a sequence profile, calculated based on multiple sequence alignment (MSA) of homologous proteins that share the same function. This approach, which is superior to the simple pattern search, essentially searches with the sequence of the query protein against an MSA library. We suggest here an alternative approach, implemented in the QuasiMotiFinder web server (http://quasimotifinder.tau.ac.il/), which is based on a search with an MSA of homologous query proteins against the original PROSITE signatures. The explicit use of the average evolutionary conservation of the signature in the query proteins significantly reduces the rate of FP prediction compared with the simple pattern search. QuasiMotiFinder also has a reduced rate of FN prediction compared with simple pattern searches, since the traditional search for precise signatures has been replaced by a permissive search for signature-like patterns that are physicochemically similar to known signatures. Overall, QuasiMotiFinder and the profile search are comparable to each other in terms of performance. They are also complementary to each other in that signatures that are falsely detected in (or overlooked by) one may be correctly detected by the other.

Amino Acid Motifs↗
PubMed2026

GBSC: graph-based sequence clustering method for similar short tandem repeats in protein sequences.

MOTIVATION: Short tandem repeats (STRs) are abundant in protein sequences and play important role in determining their structures and functions. Strikingly, the unusual compositional characteristics of tandem repeats break classical sequence analysis tools. RESULTS: Here, we establish the first algorithm to effectively identify and cluster STRs: Graph-Based Sequence Clustering (GBSC) features linear time complexity, and clusters protein sequence fragments based on their STRs, while allowing for insertions and mutations and supporting the analysis of imperfect or cryptic repeats. Due to its computational efficacy, our algorithm can be used to systematically scan for patterns in large datasets. We compare our method both to state-of-the-art methods for identifying STRs in proteins and alternative clustering approaches. Unlike existing STR analysis methods, GBSC clusters repeat patterns rather than raw sequences, operating at the level of structural repeat identity, while tolerating biological variations and preventing erroneous merging of structurally and functionally distinct motifs. Whereas functional annotation is typically only available at the protein level, the functions of individual STRs and sequences of adjacent STRs remain largely unknown. On a challenging use case we here demonstrate and discuss how our method can be used to associate previously unannotated repetitive protein fragments with similar ones, allowing the transfer of annotation by similarity. For the first time, GBSC offers a tool that systematically extends this fundamental bioinformatics principle to low-complexity regions across large datasets. AVAILABILITY AND IMPLEMENTATION: GBSC is available at GitHub https://github.com/patryk-jarnot/GBSC and https://doi.org/10.5281/zenodo.18965247. The data and scripts to reproduce the analysis are available at https://doi.org/10.5281/zenodo.16906653.

Microsatellite Repeats↗
PubMed2003

Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs.

MOTIVATION: The subcellular location of a protein is closely correlated to its function. Thus, computational prediction of subcellular locations from the amino acid sequence information would help annotation and functional prediction of protein coding genes in complete genomes. We have developed a method based on support vector machines (SVMs). RESULTS: We considered 12 subcellular locations in eukaryotic cells: chloroplast, cytoplasm, cytoskeleton, endoplasmic reticulum, extracellular medium, Golgi apparatus, lysosome, mitochondrion, nucleus, peroxisome, plasma membrane, and vacuole. We constructed a data set of proteins with known locations from the SWISS-PROT database. A set of SVMs was trained to predict the subcellular location of a given protein based on its amino acid, amino acid pair, and gapped amino acid pair compositions. The predictors based on these different compositions were then combined using a voting scheme. Results obtained through 5-fold cross-validation tests showed an improvement in prediction accuracy over the algorithm based on the amino acid composition only. This prediction method is available via the Internet.

Algorithms↗
PubMed2005

3did: interacting protein domains of known three-dimensional structure.

The database of 3D Interacting Domains (3did) is a collection of domain-domain interactions in proteins for which high-resolution three-dimensional structures are known. 3did exploits structural information to provide critical molecular details necessary for understanding how interactions occur. It also offers an overview of how similar in structure are interactions between different members of the same protein family. The database also contains Gene Ontology-based functional annotations and interactions between yeast proteins from large-scale interaction discovery studies. A web-based tool to query 3did is available at http://3did.embl.de.

Databases, Protein↗
PubMed2006

Understanding the cell in terms of structure and function: insights from structural genomics.

Structural genomics programs are only now moving into the large-scale production phase, yet have already produced around 2000 protein structures. Through a widespread if not exclusive emphasis on structural novelty, our knowledge of the protein fold universe is improving rapidly. With this information comes the challenge of structure-based function annotation for the many target proteins about which little or nothing is known. Recent years have therefore seen the emergence of impressively diverse bioinformatics approaches to predict the function of a protein structure. Attention is now turning to means of combining these predictions with information from various other sources.

Computational Biology↗
PubMed2026

On the state of protein function prediction: a report on the fourth CAFA challenge.

BACKGROUND: The Critical Assessment of Functional Annotation (CAFA) is a community effort held to understand the field of computational protein function prediction. Every three years, since 2010, the organizers initiate an experiment to collect function predictions on a large set of proteins and then evaluate the performance of predicting methods on a subset of proteins that have accumulated experimental annotations between the submission deadline and the evaluation time. CAFA provides an independent and rigorous assessment of the current state of the art, thus leveling the playing field, highlighting successes, revealing bottlenecks, and offering a forum for the exchange of ideas in protein science. Here, we report the results of the fourth CAFA experiment (CAFA4). RESULTS: CAFA4 featured the participation of 148 methods from 70 research groups on a total of 46,205 unique proteins over a 5-year annotation accumulation phase, the longest in any CAFA. In a comparison across CAFA2-CAFA4 methods, the prediction of Gene Ontology (GO) terms has clearly improved across all three GO aspects and traditional evaluation settings. While not achieving the first rank, several CAFA2 and CAFA3 methods featured in the top ten methods in many evaluations, suggesting that earlier methods still hold relevance. The performance is weaker in the newly introduced "partial knowledge" evaluation category (proteins with experimental annotations before submission deadline that gained additional annotations in the same GO aspect during the annotation accumulation phase), highlighting the need for a new class of methods. The rankings of the methods were stable over the years in traditional evaluation settings, but less so in the new partial knowledge evaluation. Overall, the field continues to progress with some influx of new participants. Sustained efforts will be necessary to substantially advance it.

Journal Article↗
PubMed2005

CYGD: the Comprehensive Yeast Genome Database.

The Comprehensive Yeast Genome Database (CYGD) compiles a comprehensive data resource for information on the cellular functions of the yeast Saccharomyces cerevisiae and related species, chosen as the best understood model organism for eukaryotes. The database serves as a common resource generated by a European consortium, going beyond the provision of sequence information and functional annotations on individual genes and proteins. In addition, it provides information on the physical and functional interactions among proteins as well as other genetic elements. These cellular networks include metabolic and regulatory pathways, signal transduction and transport processes as well as co-regulated gene clusters. As more yeast genomes are published, their annotation becomes greatly facilitated using S.cerevisiae as a reference. CYGD provides a way of exploring related genomes with the aid of the S.cerevisiae genome as a backbone and SIMAP, the Similarity Matrix of Proteins. The comprehensive resource is available under http://mips.gsf.de/genre/proj/yeast/.

Binding Sites↗
PubMed2005

Assigning new GO annotations to protein data bank sequences by combining structure and sequence homology.

Accompanying the discovery of an increasing number of proteins, there is the need to provide functional annotation that is both highly accurate and consistent. The Gene Ontology (GO) provides consistent annotation in a computer readable and usable form; hence, GO annotation (GOA) has been assigned to a large number of protein sequences based on direct experimental evidence and through inference determined by sequence homology. Here we show that this annotation can be extended and corrected for cases where protein structures are available. Specifically, using the Combinatorial Extension (CE) algorithm for structure comparison, we extend the protein annotation currently provided by GOA at the European Bioinformatics Institute (EBI) to further describe the contents of the Protein Data Bank (PDB). Specific cases of biologically interesting annotations derived by this method are given. Given that the relationship between sequence, structure, and function is complicated, we explore the impact of this relationship on assigning GOA. The effect of superfolds (folds with many functions) is considered and, by comparison to the Structural Classification of Proteins (SCOP), the individual effects of family, superfamily, and fold.

Algorithms↗
PubMed2006

The SWISS-MODEL Repository: new features and functionalities.

The SWISS-MODEL Repository is a database of annotated 3D protein structure models generated by the SWISS-MODEL homology-modelling pipeline. As of September 2005, the repository contained 675,000 models for 604,000 different protein sequences of the UniProt database. Regular updates ensure that the content of the repository reflects the current state of sequence and structure databases, integrating new or modified target sequences, and making use of new template structures. Each Repository entry consists of one or more 3D models accompanied by detailed information about the target protein and the model building process: functional annotation, a detailed template selection log, target-template alignment, summary of the model building and model quality assessment. The SWISS-MODEL Repository is freely accessible at http://swissmodel.expasy.org/repository/.

Computer Graphics↗
PubMed2006

Identification of similar regions of protein structures using integrated sequence and structure analysis tools.

BACKGROUND: Understanding protein function from its structure is a challenging problem. Sequence based approaches for finding homology have broad use for annotation of both structure and function. 3D structural information of protein domains and their interactions provide a complementary view to structure function relationships to sequence information. We have developed a web site http://www.sblest.org/ and an API of web services that enables users to submit protein structures and identify statistically significant neighbors and the underlying structural environments that make that match using a suite of sequence and structure analysis tools. To do this, we have integrated S-BLEST, PSI-BLAST and HMMer based superfamily predictions to give a unique integrated view to prediction of SCOP superfamilies, EC number, and GO term, as well as identification of the protein structural environments that are associated with that prediction. Additionally, we have extended UCSF Chimera and PyMOL to support our web services, so that users can characterize their own proteins of interest. RESULTS: Users are able to submit their own queries or use a structure already in the PDB. Currently the databases that a user can query include the popular structural datasets ASTRAL 40 v1.69, ASTRAL 95 v1.69, CLUSTER50, CLUSTER70 and CLUSTER90 and PDBSELECT25. The results can be downloaded directly from the site and include function prediction, analysis of the most conserved environments and automated annotation of query proteins. These results reflect both the hits found with PSI-BLAST, HMMer and with S-BLEST. We have evaluated how well annotation transfer can be performed on SCOP ID's, Gene Ontology (GO) ID's and EC Numbers. The method is very efficient and totally automated, generally taking around fifteen minutes for a 400 residue protein. CONCLUSION: With structural genomics initiatives determining structures with little, if any, functional characterization, development of protein structure and function analysis tools are a necessary endeavor. We have developed a useful application towards a solution to this problem using common structural and sequence based analysis tools. These approaches are able to find statistically significant environments in a database of protein structure, and the method is able to quantify how closely associated each environment is to a predicted functional annotation.

Computational Biology↗