PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-13 (13)
 

Clipboard (0)
None

Select a Filter Below

Journals
Year of Publication
Document Types
1.  An Introduction to Sequence Similarity (“Homology”) Searching 
Sequence similarity searching, typically with BLAST (units 3.3, 3.4), is the most widely used, and most reliable, strategy for characterizing newly determined sequences. Sequence similarity searches can identify ”homologous” proteins or genes by detecting excess similarity – statistically significant similarity that reflects common ancestry. This unit provides an overview of the inference of homology from significant similarity, and introduces other units in this chapter that provide more details on effective strategies for identifying homologs.
doi:10.1002/0471250953.bi0301s42
PMCID: PMC3820096  PMID: 23749753
sequence similarity; homology; orthlogy; paralogy; sequence alignment; multiple alignment; sequence evolution; Bioinformatics; Bioinformatics Fundamentals; Finding Similarities and Inferring Homologies
2.  The Catalytic Site Atlas 2.0: cataloging catalytic sites and residues identified in enzymes 
Nucleic Acids Research  2013;42(D1):D485-D489.
Understanding which are the catalytic residues in an enzyme and what function they perform is crucial to many biology studies, particularly those leading to new therapeutics and enzyme design. The original version of the Catalytic Site Atlas (CSA) (http://www.ebi.ac.uk/thornton-srv/databases/CSA) published in 2004, which catalogs the residues involved in enzyme catalysis in experimentally determined protein structures, had only 177 curated entries and employed a simplistic approach to expanding these annotations to homologous enzyme structures. Here we present a new version of the CSA (CSA 2.0), which greatly expands the number of both curated (968) and automatically annotated catalytic sites in enzyme structures, utilizing a new method for annotation transfer. The curated entries are used, along with the variation in residue type from the sequence comparison, to generate 3D templates of the catalytic sites, which in turn can be used to find catalytic sites in new structures. To ease the transfer of CSA annotations to other resources a new ontology has been developed: the Enzyme Mechanism Ontology, which has permitted the transfer of annotations to Mechanism, Annotation and Classification in Enzymes (MACiE) and UniProt Knowledge Base (UniProtKB) resources. The CSA database schema has been re-designed and both the CSA data and search capabilities are presented in a new modern web interface.
doi:10.1093/nar/gkt1243
PMCID: PMC3964973  PMID: 24319146
3.  Adjusting scoring matrices to correct overextended alignments 
Bioinformatics  2013;29(23):3007-3013.
Motivation: Sequence similarity searches performed with BLAST, SSEARCH and FASTA achieve high sensitivity by using scoring matrices (e.g. BLOSUM62) that target low identity (<33%) alignments. Although such scoring matrices can effectively identify distant homologs, they can also produce local alignments that extend beyond the homologous regions.
Results: We measured local alignment start/stop boundary accuracy using a set of queries where the correct alignment boundaries were known, and found that 7% of BLASTP and 8% of SSEARCH alignment boundaries were overextended. Overextended alignments include non-homologous sequences; they occur most frequently between sequences that are more closely related (>33% identity). Adjusting the scoring matrix to reflect the identity of the homologous sequence can correct higher identity overextended alignment boundaries. In addition, the scoring matrix that produced a correct alignment could be reliably predicted based on the sequence identity seen in the original BLOSUM62 alignment. Realigning with the predicted scoring matrix corrected 37% of all overextended alignments, resulting in more correct alignments than using BLOSUM62 alone.
Availability: RefProtDom2 (RPD2) sequences and the FASTA software are available from http://faculty.virginia.edu/wrpearson/fasta.
Contact: wrp@virginia.edu
doi:10.1093/bioinformatics/btt517
PMCID: PMC3834790  PMID: 23995390
4.  The limits of protein sequence comparison? 
Modern sequence alignment algorithms are used routinely to identify homologous proteins, proteins that share a common ancestor. Homologous proteins always share similar structures and often have similar functions. Over the past 20 years, sequence comparison has become both more sensitive, largely because of profile-based methods, and more reliable, because of more accurate statistical estimates. As sequence and structure databases become larger, and comparison methods become more powerful, reliable statistical estimates will become even more important for distinguishing similarities that are due to homology from those that are due to analogy (convergence). The newest sequence alignment methods are more sensitive than older methods, but more accurate statistical estimates are needed for their full power to be realized.
doi:10.1016/j.sbi.2005.05.005
PMCID: PMC2845305  PMID: 15919194
5.  PSI-Search: iterative HOE-reduced profile SSEARCH searching 
Bioinformatics  2012;28(12):1650-1651.
Summary: Iterative similarity searches with PSI-BLAST position-specific score matrices (PSSMs) find many more homologs than single searches, but PSSMs can be contaminated when homologous alignments are extended into unrelated protein domains—homologous over-extension (HOE). PSI-Search combines an optimal Smith–Waterman local alignment sequence search, using SSEARCH, with the PSI-BLAST profile construction strategy. An optional sequence boundary-masking procedure, which prevents alignments from being extended after they are initially included, can reduce HOE errors in the PSSM profile. Preventing HOE improves selectivity for both PSI-BLAST and PSI-Search, but PSI-Search has ~4-fold better selectivity than PSI-BLAST and similar sensitivity at 50% and 60% family coverage. PSI-Search is also produces 2- for 4-fold fewer false-positives than JackHMMER, but is ~5% less sensitive.
Availability and implementation: PSI-Search is available from the authors as a standalone implementation written in Perl for Linux-compatible platforms. It is also available through a web interface (www.ebi.ac.uk/Tools/sss/psisearch) and SOAP and REST Web Services (www.ebi.ac.uk/Tools/webservices).
Contact: pearson@virginia.edu; rodrigo.lopez@ebi.ac.uk
doi:10.1093/bioinformatics/bts240
PMCID: PMC3371869  PMID: 22539666
6.  MACiE: exploring the diversity of biochemical reactions 
Nucleic Acids Research  2011;40(D1):D783-D789.
MACiE (which stands for Mechanism, Annotation and Classification in Enzymes) is a database of enzyme reaction mechanisms, and can be accessed from http://www.ebi.ac.uk/thornton-srv/databases/MACiE/. This article presents the release of Version 3 of MACiE, which not only extends the dataset to 335 entries, covering 182 of the EC sub-subclasses with a crystal structure available (∼90%), but also incorporates greater chemical and structural detail. This version of MACiE represents a shift in emphasis for new entries, from non-homologous representatives covering EC reaction space to enzymes with mechanisms of interest to our users and collaborators with a view to exploring the chemical diversity of life. We present new tools for exploring the data in MACiE and comparing entries as well as new analyses of the data and new searches, many of which can now be accessed via dedicated Perl scripts.
doi:10.1093/nar/gkr799
PMCID: PMC3244993  PMID: 22058127
7.  Accelerated Profile HMM Searches 
PLoS Computational Biology  2011;7(10):e1002195.
Profile hidden Markov models (profile HMMs) and probabilistic inference methods have made important contributions to the theory of sequence database homology search. However, practical use of profile HMM methods has been hindered by the computational expense of existing software implementations. Here I describe an acceleration heuristic for profile HMMs, the “multiple segment Viterbi” (MSV) algorithm. The MSV algorithm computes an optimal sum of multiple ungapped local alignment segments using a striped vector-parallel approach previously described for fast Smith/Waterman alignment. MSV scores follow the same statistical distribution as gapped optimal local alignment scores, allowing rapid evaluation of significance of an MSV score and thus facilitating its use as a heuristic filter. I also describe a 20-fold acceleration of the standard profile HMM Forward/Backward algorithms using a method I call “sparse rescaling”. These methods are assembled in a pipeline in which high-scoring MSV hits are passed on for reanalysis with the full HMM Forward/Backward algorithm. This accelerated pipeline is implemented in the freely available HMMER3 software package. Performance benchmarks show that the use of the heuristic MSV filter sacrifices negligible sensitivity compared to unaccelerated profile HMM searches. HMMER3 is substantially more sensitive and 100- to 1000-fold faster than HMMER2. HMMER3 is now about as fast as BLAST for protein searches.
Author Summary
Searching sequence databases is one of the most important applications in computational molecular biology. The main workhorse in the field is the BLAST suite of programs. Since the introduction of BLAST in the 1990's, important theoretical advances in homology search methodology have been made using probabilistic inference methods and hidden Markov models (HMMs). However, previous software implementations of these newer probabilistic methods were slower than BLAST by about 100-fold. This hindered their utility, because computation speed is so critical with the rapidly increasing size of modern sequence databases. Here I describe the acceleration methods I implemented in a new, freely available profile HMM software package, HMMER3. HMMER3 makes profile HMM searches about as fast as BLAST, while retaining the power of using probabilistic inference technology.
doi:10.1371/journal.pcbi.1002195
PMCID: PMC3197634  PMID: 22039361
8.  Globally, unrelated protein sequences appear random 
Bioinformatics  2009;26(3):310-318.
Motivation: To test whether protein folding constraints and secondary structure sequence preferences significantly reduce the space of amino acid words in proteins, we compared the frequencies of four- and five-amino acid word clumps (independent words) in proteins to the frequencies predicted by four random sequence models.
Results: While the human proteome has many overrepresented word clumps, these words come from large protein families with biased compositions (e.g. Zn-fingers). In contrast, in a non-redundant sample of Pfam-AB, only 1% of four-amino acid word clumps (4.7% of 5mer words) are 2-fold overrepresented compared with our simplest random model [MC(0)], and 0.1% (4mers) to 0.5% (5mers) are 2-fold overrepresented compared with a window-shuffled random model. Using a false discovery rate q-value analysis, the number of exceptional four- or five-letter words in real proteins is similar to the number found when comparing words from one random model to another. Consensus overrepresented words are not enriched in conserved regions of proteins, but four-letter words are enriched 1.18- to 1.56-fold in α-helical secondary structures (but not β-strands). Five-residue consensus exceptional words are enriched for α-helix 1.43- to 1.61-fold. Protein word preferences in regular secondary structure do not appear to significantly restrict the use of sequence words in unrelated proteins, although the consensus exceptional words have a secondary structure bias for α-helix. Globally, words in protein sequences appear to be under very few constraints; for the most part, they appear to be random.
Contact: wrp@virginia.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btp660
PMCID: PMC2852211  PMID: 19948773
9.  RefProtDom: a protein database with improved domain boundaries and homology relationships 
Bioinformatics  2010;26(18):2361-2362.
Summary: RefProtDom provides a set of divergent query domains, originally selected from Pfam, and full-length proteins containing their homologous domains, with diverse architectures, for evaluating pair-wise and iterative sequence similarity searches. Pfam homology and domain boundary annotations in the target library were supplemented using local and semi-global searches, PSI-BLAST searches, and SCOP and CATH classifications.
Availability: RefProtDom is available from http://faculty.virginia.edu/wrpearson/fasta/PUBS/gonzalez09a
Contact: miledywgonzalez@gmail.com; pearson@virginia.edu
doi:10.1093/bioinformatics/btq426
PMCID: PMC2935417  PMID: 20693322
10.  Improving pairwise sequence alignment accuracy using near-optimal protein sequence alignments 
BMC Bioinformatics  2010;11:146.
Background
While the pairwise alignments produced by sequence similarity searches are a powerful tool for identifying homologous proteins - proteins that share a common ancestor and a similar structure; pairwise sequence alignments often fail to represent accurately the structural alignments inferred from three-dimensional coordinates. Since sequence alignment algorithms produce optimal alignments, the best structural alignments must reflect suboptimal sequence alignment scores. Thus, we have examined a range of suboptimal sequence alignments and a range of scoring parameters to understand better which sequence alignments are likely to be more structurally accurate.
Results
We compared near-optimal protein sequence alignments produced by the Zuker algorithm and a set of probabilistic alignments produced by the probA program with structural alignments produced by four different structure alignment algorithms. There is significant overlap between the solution spaces of structural alignments and both the near-optimal sequence alignments produced by commonly used scoring parameters for sequences that share significant sequence similarity (E-values < 10-5) and the ensemble of probA alignments. We constructed a logistic regression model incorporating three input variables derived from sets of near-optimal alignments: robustness, edge frequency, and maximum bits-per-position. A ROC analysis shows that this model more accurately classifies amino acid pairs (edges in the alignment path graph) according to the likelihood of appearance in structural alignments than the robustness score alone. We investigated various trimming protocols for removing incorrect edges from the optimal sequence alignment; the most effective protocol is to remove matches from the semi-global optimal alignment that are outside the boundaries of the local alignment, although trimming according to the model-generated probabilities achieves a similar level of improvement. The model can also be used to generate novel alignments by using the probabilities in lieu of a scoring matrix. These alignments are typically better than the optimal sequence alignment, and include novel correct structural edges. We find that the probA alignments sample a larger variety of alignments than the Zuker set, which more frequently results in alignments that are closer to the structural alignments, but that using the probA alignments as input to the regression model does not increase performance.
Conclusions
The pool of suboptimal pairwise protein sequence alignments substantially overlaps structure-based alignments for pairs with statistically significant similarity, and a regression model based on information contained in this alignment pool improves the accuracy of pairwise alignments with respect to structure-based alignments.
doi:10.1186/1471-2105-11-146
PMCID: PMC2850363  PMID: 20307279
11.  Visualization of near-optimal sequence alignments 
Bioinformatics (Oxford, England)  2004;20(6):953-958.
Motivation
Mathematically optimal alignments do not always properly align active site residues or well-recognized structural elements. Most near-optimal sequence alignment algorithms display alternative alignment paths, rather than the conventional residue-by-residue pairwise alignment. Typically, these methods do not provide mechanisms for finding effectively the most biologically meaningful alignment in the potentially large set of options.
Results
We have developed Web-based software that displays near optimal or alternative alignments of two protein or DNA sequences as a continuous moving picture. A WWW interface to a C++ program generates near optimal alignments, which are sent to a Java Applet, which displays them in a series of alignment frames. The Applet aligns residues so that consistently aligned regions remain at a fixed position on the display, while variable regions move. The display can be stopped to examine alignment details.
Availability
Available at http://fasta.bioch.virginia.edu/ noptalign. For source code contact the authors at wrp@virginia.edu
Contact
wrp@virginia.edu
doi:10.1093/bioinformatics/bth013
PMCID: PMC2836811  PMID: 14751975
12.  Homologous over-extension: a challenge for iterative similarity searches 
Nucleic Acids Research  2010;38(7):2177-2189.
We have characterized a novel type of PSI-BLAST error, homologous over-extension (HOE), using embedded PFAM domain queries on searches against a reference library containing Pfam-annotated UniProt sequences and random synthetic sequences. PSI-BLAST makes two types of errors: alignments to non-homologous regions and HOE alignments that begin in a homologous region, but extend beyond the homology into neighboring sequence regions. When the neighboring sequence region contains a non-homologous domain, PSI-BLAST can incorporate the unrelated sequence into its position specific scoring matrix, which then finds non-homologous proteins with significant expectation values. HOE accounts for the largest fraction of the initial false positive (FP) errors, and the largest fraction of FPs at iteration 5. In searches against complete protein sequences, 5–9% of alignments at iteration 5 are non-homologous. HOE frequently begins in a partial protein domain; when partial domains are removed from the library, HOE errors decrease from 16 to 3% of weighted coverage (hard queries; 35–5% for sampled queries) and no-error searches increase from 2 to 58% weighed coverage (hard; 16–78% sampled). When HOE is reduced by not extending previously found sequences, PSI-BLAST specificity improves 4–8-fold, with little loss in sensitivity.
doi:10.1093/nar/gkp1219
PMCID: PMC2853128  PMID: 20064877
13.  CRP: Cleavage of Radiolabeled Phosphoproteins 
Nucleic Acids Research  2003;31(13):3859-3861.
The CRP (Cleavage of Radiolabeled Phosphoproteins) program guides the design and interpretation of experiments to identify protein phosphorylation sites by Edman sequencing of unseparated peptides. Traditionally, phosphorylation sites are determined by cleaving the phosphoprotein and separating the peptides for Edman 32P-phosphate release sequencing. CRP analysis of a phosphoprotein's sequence accelerates this process by omitting the separation step: given a protein sequence of interest, the CRP program performs an in silico proteolytic cleavage of the sequence and reports the predicted Edman cycles in which radioactivity would be observed if a given serine, threonine or tyrosine were phosphorylated. Experimentally observed cycles containing 32P can be compared with CRP predictions to confirm candidate sites and/or explore the ability of additional cleavage experiments to resolve remaining ambiguities. To reduce ambiguity, the phosphorylated residue (P-Tyr, P-Ser or P-Thr) can be determined experimentally, and CRP will ignore sites with alternative residues. CRP also provides simple predictions of likely phosphorylation sites using known kinase recognition motifs. The CRP interface is available at http://fasta.bioch.virginia.edu/crp.
PMCID: PMC168920  PMID: 12824437

Results 1-13 (13)