Identifying coding exons by similarity search: alu-derived and other potentially misleading protein sequences.

The search for significant local similarities with known protein sequences is a powerful method for interpreting anonymous cDNA sequences or locating coding exons within genomic DNA sequences at a stage where the average contig size is still very small. The BLASTx program, implemented on the National Center for Biotechnology Information server, allows a sensitive search of all putative translations of a nucleotide query sequence against all known proteins in a matter of seconds. From an analysis of the current databases, I report a set of protein sequences exhibiting high local similarity to Alu repeat or vector sequences. These entries can lead to misleading interpretations of similarity searches. During the course of this study, the protease of a human spumaretrovirus was found to have integrated the 3' end half of the U2 snRNA.     

SW#db: GPU-Accelerated Exact Sequence Similarity Database Search

In recent years we have witnessed a growth in sequencing yield, the number of samples sequenced, and as a result–the growth of publicly maintained sequence databases. The increase of data present all around has put high requirements on protein similarity search algorithms with two ever-opposite goals: how to keep the running times acceptable while maintaining a high-enough level of sensitivity. The most time consuming step of similarity search are the local alignments between query and database sequences. This step is usually performed using exact local alignment algorithms such as Smith-Waterman. Due to its quadratic time complexity, alignments of a query to the whole database are usually too slow. Therefore, the majority of the protein similarity search methods prior to doing the exact local alignment apply heuristics to reduce the number of possible candidate sequences in the database. However, there is still a need for the alignment of a query sequence to a reduced database. In this paper we present the SW#db tool and a library for fast exact similarity search. Although its running times, as a standalone tool, are comparable to the running times of BLAST, it is primarily intended to be used for exact local alignment phase in which the database of sequences has already been reduced. It uses both GPU and CPU parallelization and was 4–5 times faster than SSEARCH, 6–25 times faster than CUDASW++ and more than 20 times faster than SSW at the time of writing, using multiple queries on Swiss-prot and Uniref90 databases     

Use of a structural alphabet to find compatible folds for amino acid sequences

The structural annotation of proteins with no detectable homologs of known 3D structure identified using sequence‐search methods is a major challenge today. We propose an original method that computes the conditional probabilities for the amino‐acid sequence of a protein to fit to known protein 3D structures using a structural alphabet, known as “Protein Blocks” (PBs). PBs constitute a library of 16 local structural prototypes that approximate every part of protein backbone structures. It is used to encode 3D protein structures into 1D PB sequences and to capture sequence to structure relationships. Our method relies on amino acid occurrence matrices, one for each PB, to score global and local threading of query amino acid sequences to protein folds encoded into PB sequences. It does not use any information from residue contacts or sequence‐search methods or explicit incorporation of hydrophobic effect. The performance of the method was assessed with independent test datasets derived from SCOP 1.75A. With a Z‐score cutoff that achieved 95% specificity (i.e., less than 5% false positives), global and local threading showed sensitivity of 64.1% and 34.2%, respectively. We further tested its performance on 57 difficult CASP10 targets that had no known homologs in PDB: 38 compatible templates were identified by our approach and 66% of these hits yielded correctly predicted structures. This method scales‐up well and offers promising perspectives for structural annotations at genomic level. It has been implemented in the form of a web‐server that is freely available at http://www.bo‐protscience.fr/forsa .     

Identification of Glyoxalase I Sequences in Brassica oleracea and Sporobolus stapfianus: Evidence for Gene Duplication Events

Glyoxalase I (GlxI) is the first of two enzymes involved in the cellular detoxification of methylglyoxal. A recent search of the National Center for Biotechnology Information (NCBI) databases with the protein sequence of Salmonella typhimurium GlxI identified two new hypothetical proteins with unassigned function. These two sequences, from Brassica oleracea and Sporobolus stapfianus, have significant sequence similarity to known GlxI sequences, suggesting that these two open reading frames encode for GlxI in these plants. Interestingly, analysis of these two new sequences indicates that they code for a protein composed of two fused monomers, a situation previously found solely in the yeast GlxI enzymes. Received: 10 May 1997 / Accepted: 15 October 1997     

Detecting remote evolutionary relationships among proteins by large-scale semantic embedding

Virtually every molecular biologist has searched a protein or DNA sequence database to find sequences that are evolutionarily related to a given query. Pairwise sequence comparison methods--i.e., measures of similarity between query and target sequences--provide the engine for sequence database search and have been the subject of 30 years of computational research. For the difficult problem of detecting remote evolutionary relationships between protein sequences, the most successful pairwise comparison methods involve building local models (e.g., profile hidden Markov models) of protein sequences. However, recent work in massive data domains like web search and natural language processing demonstrate the advantage of exploiting the global structure of the data space. Motivated by this work, we present a large-scale algorithm called ProtEmbed, which learns an embedding of protein sequences into a low-dimensional "semantic space." Evolutionarily related proteins are embedded in close proximity, and additional pieces of evidence, such as 3D structural similarity or class labels, can be incorporated into the learning process. We find that ProtEmbed achieves superior accuracy to widely used pairwise sequence methods like PSI-BLAST and HHSearch for remote homology detection; it also outperforms our previous RankProp algorithm, which incorporates global structure in the form of a protein similarity network. Finally, the ProtEmbed embedding space can be visualized, both at the global level and local to a given query, yielding intuition about the structure of protein sequence space.     

A comparison of position-specific score matrices based on sequence and structure alignments

Sequence comparison methods based on position-specific score matrices (PSSMs) have proven a useful tool for recognition of the divergent members of a protein family and for annotation of functional sites. Here we investigate one of the factors that affects overall performance of PSSMs in a PSI-BLAST search, the algorithm used to construct the seed alignment upon which the PSSM is based. We compare PSSMs based on alignments constructed by global sequence similarity (ClustalW and ClustalW-pairwise), local sequence similarity (BLAST), and local structure similarity (VAST). To assess performance with respect to identification of conserved functional or structural sites, we examine the accuracy of the three-dimensional molecular models predicted by PSSM-sequence alignments. Using the known structures of those sequences as the standard of truth, we find that model accuracy varies with the algorithm used for seed alignment construction in the pattern local-structure (VAST) > local-sequence (BLAST) > global-sequence (ClustalW). Using structural similarity of query and database proteins as the standard of truth, we find that PSSM recognition sensitivity depends primarily on the diversity of the sequences included in the alignment, with an optimum around 30-50% average pairwise identity. We discuss these observations, and suggest a strategy for constructing seed alignments that optimize PSSM-sequence alignment accuracy and recognition sensitivity.     

Proteome-wide functional classification and identification of prokaryotic transmembrane proteins by transmembrane topology similarity comparison

We propose a new method for classifying and identifying transmembrane (TM) protein functions in proteome-scale by applying a single-linkage clustering method based on TM topology similarity, which is calculated simply from comparing the lengths of loop regions. In this study, we focused on 87 prokaryotic TM proteomes consisting of 31 proteobacteria, 22 gram-positive bacteria, 19 other bacteria, and 15 archaea. Prior to performing the clustering, we first categorized individual TM protein sequences as "known," "putative" (similar to "known" sequences), or "unknown" by using the homology search and the sequence similarity comparison against SWISS-PROT to assess the current status of the functional annotation of the TM proteomes based on sequence similarity only. More than three-quarters, that is, 75.7% of the TM protein sequences are functionally "unknown," with only 3.8% and 20.5% of them being classified as "known" and "putative," respectively. Using our clustering approach based on TM topology similarity, we succeeded in increasing the rate of TM protein sequences functionally classified and identified from 24.3% to 60.9%. Obtained clusters correspond well to functional superfamilies or families, and the functional classification and identification are successfully achieved by this approach. For example, in an obtained cluster of TM proteins with six TM segments, 109 sequences out of 119 sequences annotated as "ATP-binding cassette transporter" are properly included and 122 "unknown" sequences are also contained.     

A gene coding for an RPS2 protein is present in the mitochondrial genome of several cereals, but not in dicotyledons

A gene coding for a protein that shows homologies to prokaryotic ribosomal protein S2 is present in the mitochondrial (mt) genome of wheat (Triticum aestivum). The wheat gene is transcribed as a single mRNA which is edited by C-to-U conversions at seven positions, all resulting in alteration of the encoded amino acid. Homologous gene sequences are also present in the mt genomes of rice and maize, but we failed to identify the corresponding sequences in the mtDNA of all dicotyledonous species tested; in these species the mitochondrial RPS2 is probably encoded in the nucleus. The protein sequence deduced from the wheat rps2 gene sequence has a long C-terminal extension when compared to other prokaryotic RPS2 sequences. This extension presents no similarity with any known sequence and is not conserved in the maize or rice mitochondrial rps2 gene. Most probably, after translation, this peptide extension is processed by a specific peptidase to give rise to the mature wheat mitochondrial RPS2. Received: 20 November 1997 / Accepted: 29 January 1998     

SProt: sphere-based protein structure similarity algorithm

Background

Similarity search in protein databases is one of the most essential issues in computational proteomics. With the growing number of experimentally resolved protein structures, the focus shifted from sequences to structures. The area of structure similarity forms a big challenge since even no standard definition of optimal structure similarity exists in the field.

Results

We propose a protein structure similarity measure called SProt. SProt concentrates on high-quality modeling of local similarity in the process of feature extraction. SProt’s features are based on spherical spatial neighborhood of amino acids where similarity can be well-defined. On top of the partial local similarities, global measure assessing similarity to a pair of protein structures is built. Finally, indexing is applied making the search process by an order of magnitude faster.

Conclusions

The proposed method outperforms other methods in classification accuracy on SCOP superfamily and fold level, while it is at least comparable to the best existing solutions in terms of precision-recall or quality of alignment.

     

Functional similarities between the dictyostelium protein AprA and the human protein dipeptidyl‐peptidase IV

Autocrine proliferation repressor protein A (AprA) is a protein secreted by Dictyostelium discoideum cells. Although there is very little sequence similarity between AprA and any human protein, AprA has a predicted structural similarity to the human protein dipeptidyl peptidase IV (DPPIV). AprA is a chemorepellent for Dictyostelium cells, and DPPIV is a chemorepellent for neutrophils. This led us to investigate if AprA and DPPIV have additional functional similarities. We find that like AprA, DPPIV is a chemorepellent for, and inhibits the proliferation of, D. discoideum cells, and that AprA binds some DPPIV binding partners such as fibronectin. Conversely, rAprA has DPPIV‐like protease activity. These results indicate a functional similarity between two eukaryotic chemorepellent proteins with very little sequence similarity, and emphasize the usefulness of using a predicted protein structure to search a protein structure database, in addition to searching for proteins with similar sequences.     

Construction and profiling of a cDNA library from young fruit of satsuma mandarin

To profile gene expression in the early stage of fruit development from ‘Nichinan No. 1’ satsuma mandarin (Citrus unshiu Marc.), we isolated total mRNA at 30 d after flowering. A cDNA library was prepared from mature mRNAs and a total of 2350 cDNA clones were partially sequenced. In all, 1914 ESTs were acquired after the removal of the vector sequence and filtering over a minimum length of 150 nucleotides. A total of 763 unigenes, consisting of 138 contigs and 625 singletons, was identified after assembly of those ESTs. According to our homology search with BLASTX against the NCBI database, the deduced amino acid sequences of 253 unigenes were homologous to proteins with known function and 242 unigenes were significantly matched to proteins with putative or unknown functions. The remaining 268 showed no significant similarity to any protein sequences found in the public database with matches higher than an E value of 10^-5. The 253 unigenes matched to proteins with known function were then manually assigned to 10 cellular functional categories using a modified MIPS MATDB classification. The expression level of each gene was analyzed based on the redundancy of cDNA clones in each contig that comprised more than 10 ESTs. Here, the most abundant gene expressed in young fruits was for a chitinase precursor. A miraculin-like protein and a lectin-related protein precursor were also abundant.     

Surface‐based protein binding pocket similarity

Protein similarity comparisons may be made on a local or global basis and may consider sequence information or differing levels of structural information. We present a local three‐dimensional method that compares protein binding site surfaces in full atomic detail. The approach is based on the morphological similarity method which has been widely applied for global comparison of small molecules. We apply the method to all‐by‐all comparisons two sets of human protein kinases, a very diverse set of ATP‐bound proteins from multiple species, and three heterogeneous benchmark protein binding site data sets. Cases of disagreement between sequence‐based similarity and binding site similarity yield informative examples. Where sequence similarity is very low, high pocket similarity can reliably identify important binding motifs. Where sequence similarity is very high, significant differences in pocket similarity are related to ligand binding specificity and similarity. Local protein binding pocket similarity provides qualitatively complementary information to other approaches, and it can yield quantitative information in support of functional annotation. Proteins 2011; © 2011 Wiley‐Liss, Inc.     

UniRef: comprehensive and non-redundant UniProt reference clusters

MOTIVATION: Redundant protein sequences in biological databases hinder sequence similarity searches and make interpretation of search results difficult. Clustering of protein sequence space based on sequence similarity helps organize all sequences into manageable datasets and reduces sampling bias and overrepresentation of sequences. RESULTS: The UniRef (UniProt Reference Clusters) provide clustered sets of sequences from the UniProt Knowledgebase (UniProtKB) and selected UniProt Archive records to obtain complete coverage of sequence space at several resolutions while hiding redundant sequences. Currently covering >4 million source sequences, the UniRef100 database combines identical sequences and subfragments from any source organism into a single UniRef entry. UniRef90 and UniRef50 are built by clustering UniRef100 sequences at the 90 or 50% sequence identity levels. UniRef100, UniRef90 and UniRef50 yield a database size reduction of approximately 10, 40 and 70%, respectively, from the source sequence set. The reduced redundancy increases the speed of similarity searches and improves detection of distant relationships. UniRef entries contain summary cluster and membership information, including the sequence of a representative protein, member count and common taxonomy of the cluster, the accession numbers of all the merged entries and links to rich functional annotation in UniProtKB to facilitate biological discovery. UniRef has already been applied to broad research areas ranging from genome annotation to proteomics data analysis. AVAILABILITY: UniRef is updated biweekly and is available for online search and retrieval at http://www.uniprot.org, as well as for download at ftp://ftp.uniprot.org/pub/databases/uniprot/uniref. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

IsoSVM – Distinguishing isoforms and paralogs on the protein level

Background  

Recent progress in cDNA and EST sequencing is yielding a deluge of sequence data. Like database search results and proteome databases, this data gives rise to inferred protein sequences without ready access to the underlying genomic data. Analysis of this information (e.g. for EST clustering or phylogenetic reconstruction from proteome data) is hampered because it is not known if two protein sequences are isoforms (splice variants) or not (i.e. paralogs/orthologs). However, even without knowing the intron/exon structure, visual analysis of the pattern of similarity across the alignment of the two protein sequences is usually helpful since paralogs and orthologs feature substitutions with respect to each other, as opposed to isoforms, which do not.     

Optimizing amino acid substitution matrices with a local alignment kernel

Background  

Detecting remote homologies by direct comparison of protein sequences remains a challenging task. We had previously developed a similarity score between sequences, called a local alignment kernel, that exhibits good performance for this task in combination with a support vector machine. The local alignment kernel depends on an amino acid substitution matrix. Since commonly used BLOSUM or PAM matrices for scoring amino acid matches have been optimized to be used in combination with the Smith-Waterman algorithm, the matrices optimal for the local alignment kernel can be different.     

The SBASE domain sequence library,release 10: domain architecture prediction

SBASE (http://www.icgeb.trieste.it/sbase) is an on-line collection of protein domain sequences and related computational tools designed to facilitate detection of domain homologies based on simple database search. The 10th 'jubilee release' of the SBASE library of protein domain sequences contains 1 052 904 protein sequence segments annotated by structure, function, ligand-binding or cellular topology, clustered into over 6000 domain groups. Domain identification and functional prediction are based on a comparison of BLAST search outputs with a knowledge base of biologically significant similarities extracted from known domain groups. The knowledge base is generated automatically for each domain group from the comparison of within-group ('self') and out-of-group ('non-self') similarities. This is a memory-based approach wherein group-specific similarity functions are automatically learned from the database.     

QAUST: Protein Function Prediction Using Structure Similarity,Protein Interaction,and Functional Motifs

The number of available protein sequences in public databases is increasing exponentially. However, a significant percentage of these sequences lack functional annotation, which is essential for the understanding of how biological systems operate. Here, we propose a novel method, Quantitative Annotation of Unknown STructure (QAUST), to infer protein functions, specifically Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. QAUST uses three sources of information: structure information encoded by global and local structure similarity search, biological network information inferred by protein–protein interaction data, and sequence information extracted from functionally discriminative sequence motifs. These three pieces of information are combined by consensus averaging to make the final prediction. Our approach has been tested on 500 protein targets from the Critical Assessment of Functional Annotation (CAFA) benchmark set. The results show that our method provides accurate functional annotation and outperforms other prediction methods based on sequence similarity search or threading. We further demonstrate that a previously unknown function of human tripartite motif-containing 22 (TRIM22) protein predicted by QAUST can be experimentally validated.     

ENVIRON: a software package to compare protein three-dimensional structures with homologous sequences using local structural motifs

This work presents a method to compare local clusters of interactingresidues as observed in a known three-dimensional protein structurewith corresponding clusters inferred from homologous proteinsequences, assuming conserved protein folding. For this purposethe local environment of a selected residue in a known proteinstructure is defined as the ensemble of amino acids in contactwith it in the folded state. Using a multiple sequence alignmentto identify corresponding residues in homologous proteins, adetailed comparison can be performed between the local environmentof a selected amino acid in the template protein structure andthe expected local environments at the sets of equivalent residues,derived from the aligned protein sequences. The comparison makesit possible to detect conserved local features such as hydrogenbonding or complementarity in residue substitution. A globalmeasure of environmental similarity is also defined, to searchfor conserved amino acid clusters subject to functional or structural constraints. The proposed approach is useful for investigatingprotein function as well as for site-directed mutagenesis experiments,where appropriate amino acid substitutions can be suggestedby observing naturally occurring protein variants.     

Efficient recognition of protein fold at low sequence identity by conservative application of Psi-BLAST: validation

A substantial fraction of protein sequences derived from genomic analyses is currently classified as representing 'hypothetical proteins of unknown function'. In part, this reflects the limitations of methods for comparison of sequences with very low identity. We evaluated the effectiveness of a Psi-BLAST search strategy to identify proteins of similar fold at low sequence identity. Psi-BLAST searches for structurally characterized low-sequence-identity matches were carried out on a set of over 300 proteins of known structure. Searches were conducted in NCBI's non-redundant database and were limited to three rounds. Some 614 potential homologs with 25% or lower sequence identity to 166 members of the search set were obtained. Disregarding the expect value, level of sequence identity and span of alignment, correspondence of fold between the target and potential homolog was found in more than 95% of the Psi-BLAST matches. Restrictions on expect value or span of alignment improved the false positive rate at the expense of eliminating many true homologs. Approximately three-quarters of the putative homologs obtained by three rounds of Psi-BLAST revealed no significant sequence similarity to the target protein upon direct sequence comparison by BLAST, and therefore could not be found by a conventional search. Although three rounds of Psi-BLAST identified many more homologs than a standard BLAST search, most homologs were undetected. It appears that more than 80% of all homologs to a target protein may be characterized by a lack of significant sequence similarity. We suggest that conservative use of Psi-BLAST has the potential to propose experimentally testable functions for the majority of proteins currently annotated as 'hypothetical proteins of unknown function'.