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Background: Rational design of synthetic phage-displayed libraries requires the identification of the most appropriate positions for randomization using defined amino acid sets to recapitulate the natural occurrence. The present study uses position-specific scoring matrixes (PSSMs) for identifying and randomizing Camelidae nanobody (VHH) CDR3. The functionality of a synthetic VHH repertoire designed by this method was tested for discovering new VHH binders to recombinant coagulation factor VII (rfVII)., Methods: Based on PSSM analysis, the CDR3 of cAbBCII10 VHH framework was identified, and a set of amino acids for the substitution of each PSSM-CDR3 position was defined. Using the Rosetta design SwiftLib tool, the final repertoire was back-translated to a degenerate nucleotide sequence. A synthetic phage-displayed library was constructed based on this repertoire and screened for anti-rfVII binders., Results: A synthetic phage-displayed VHH library with 1 × 10 8 variants was constructed. Three VHH binders to rfVII were isolated from this library with estimated dissociation constants (K D ) of 1 × 10 -8  M, 5.8 × 10 -8  M and 2.6 × 10 - 7  M., Conclusion: PSSM analysis is a simple and efficient way to design synthetic phage-displayed libraries., Competing Interests: Declaration of Competing interest The authors declare no conflict of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Abstract ᅟ We report a protein sequence analysis of the cell cycle regulatory protease, separase. The sequence and structural conservation of the C-terminal protease domain has long been recognized, whereas the N-terminal regulatory domain of separase was reported to lack detectable sequence similarity. Here we reveal significant sequence conservation of the separase regulatory domain and report a discovery of a cysteine motif (CxCxxC) conserved in major lineages of Metazoa including nematodes and vertebrates. This motif is found in a solvent exposed linker region connecting two TPR-like helical motifs. Mutation of this motif in Caenorhabditis elegans separase leads to a temperature sensitive hypomorphic protein. Conservation of this motif in organisms ranging from C. elegans to humans suggests its functional importance. Reviewers This article was reviewed by Lakshminarayan Iyer and Michael Galperin.

Abstract Background PSI-BLAST, an extremely popular tool for sequence similarity search, features the utilization of Position-Specific Scoring Matrix (PSSM) constructed from a multiple sequence alignment (MSA). PSSM allows the detection of more distant homologs than a general amino acid substitution matrix does. An accurate estimation of the weights for sequences in an MSA is crucially important for PSSM construction. PSI-BLAST divides a given MSA into multiple blocks, for which sequence weights are calculated. When the block width becomes very narrow, the sequence weight calculation can be odd. Results We demonstrate that PSI-BLAST indeed generates a significant fraction of blocks having width less than 5, thereby degrading the PSI-BLAST performance. We revised the code of PSI-BLAST to prevent the blocks from being narrower than a given minimum block width (MBW). We designate the modified application of PSI-BLAST as PSI-BLASTexB. When MBW is 25, PSI-BLASTexB notably outperforms PSI-BLAST consistently for three independent benchmark sets. The performance boost is even more drastic when an MSA, instead of a sequence, is used as a query. Conclusions Our results demonstrate that the generation of narrow-width blocks during the sequence weight calculation is a critically important factor that restricts the PSI-BLAST search performance. By preventing narrow blocks, PSI-BLASTexB upgrades the PSI-BLAST performance remarkably. Binaries and source codes of PSI-BLASTexB (MBW = 25) are available at https://github.com/kyungtaekLIM/PSI-BLASTexB .

The protein secondary structures are important information for studying the evolution, structure and function of proteins. Recently, deep learning methods have been actively applied to predict the secondary structure of proteins using only protein sequence information. In these methods, widely used input features are protein profiles transformed from protein sequences. In this paper, to obtain an effective protein profiles, protein profiles were constructed using protein sequence search methods such as PSI-BLAST and HHblits. We adjust the similarity threshold for determining the homologous protein sequence used in constructing the protein profile and the number of iterations of the profile construction using the homologous sequence information. We used the protein profiles as inputs to convolutional neural networks and recurrent neural networks to predict the secondary structures. The protein profile that was created by adding evolutionary information only once was effective. [ABSTRACT FROM AUTHOR]

Protein secondary structure is important for the study of protein evolution, structure and function of proteins which play crucial roles in most of biological processes. This paper try to effectively extract protein secondary structure information from the large protein structure database in order to predict the protein secondary structure of a query protein sequence. To find more remote homologous sequences of a query sequence in the protein database, we used PSI-BLAST which can perform gapped iterative searches and use profiles consisting of homologous protein sequences of a query protein. The secondary structures of the homologous sequences are weighed combined to the secondary structure prediction according to their relative degree of similarity to the query sequence. When homologous sequences with a neural network predictor were used, the accuracies were higher than those of current state-of-art techniques, achieving a Q3 accuracy of 92.28% and a Q8 accuracy of 88.79%. [ABSTRACT FROM AUTHOR]

Background: Sequence matching is extremely important for applications throughout biology, particularly for discovering information such as functional and evolutionary relationships, and also for discriminating between unimportant and disease mutants. At present the functions of a large fraction of genes are unknown; improvements in sequence matching will improve gene annotations. Universal amino acid substitution matrices such as Blosum62 are used to measure sequence similarities and to identify distant homologues, regardless of the structure class. However, such single matrices do not take into account important structural information evident within the different topologies of proteins and treats substitutions within all protein folds identically. Others have suggested that the use of structural information can lead to significant improvements in sequence matching but this has not yet been very effective. Here we develop novel substitution matrices that include not only general sequence information but also have a topology specific component that is unique for each CATH topology. This novel feature of using a combination of sequence and structure information for each protein topology significantly improves the sequence matching scores for the sequence pairs tested. We have used a novel multi-structure alignment method for each homology level of CATH in order to extract topological information. Results: We obtain statistically significant improved sequence matching scores for 73 % of the alpha helical test cases. On average, 61 % of the test cases showed improvements in homology detection when structure information was incorporated into the substitution matrices. On average z-scores for homology detection are improved by more than 54 % for all cases, and some individual cases have z-scores more than twice those obtained using generic matrices. Our topology specific similarity matrices also outperform other traditional similarity matrices and single matrix based structure methods. When default amino acid substitution matrix in the Psi-blast algorithm is replaced by our structure-based matrices, the structure matching is significantly improved over conventional Psi-blast. It also outperforms results obtained for the corresponding HMM profiles generated for each topology. Conclusions: We show that by incorporating topology-specific structure information in addition to sequence information into specific amino acid substitution matrices, the sequence matching scores and homology detection are significantly improved. Our topology specific similarity matrices outperform other traditional similarity matrices, single matrix based structure methods, also show improvement over conventional Psi-blast and HMM profile based methods in sequence matching. The results support the discriminatory ability of the new amino acid similarity matrices to distinguish between distant homologs and structurally dissimilar pairs. [ABSTRACT FROM AUTHOR]

Evolutionary connections were analyzed for endo-β-xylanases, which possess the GH10 family catalytic domains. A homology search yielded thrice as many proteins as are available from the Carbohydrate-Active Enzymes (CAZy) database. Lateral gene transfer was shown to play an important role in evolution of bacterial proteins of the family, especially in the phyla Acidobacteria, Cyanobacteria, Planctomycetes, Spirochaetes, and Verrucomicrobia. In the case of Verrucomicrobia, 23 lateral transfers from organisms of other phyla were detected. Evolutionary relationships were observed between the GH10 family domains and domains with the TIM-barrel tertiary structure from several other glycosidase families. The GH39 family of glycoside hydrolases showed the closest relationship. Unclassified homologs were grouped into 12 novel families of putative glycoside hydrolases (GHL51-GHL62). [ABSTRACT FROM AUTHOR]

ABSTRACT The prediction of protein domain region is an advantageous process on the study of protein structure and function. In this study, we proposed a new method, which is composed of fuzzy mean operator and region division, to predict the particular positions of domains in a target protein based on its sequence. The whole sequence is aligned and scored by using fuzzy mean operator, and the final determination of domain region position is realized by region division. A published benchmark is used for the comparison with previous researches. In addition, we generate two extra datasets to examine the stability of this method. Finally, the prediction accuracy of independent test dataset achieved by our method was up to 84.13%. We wish that this method could be useful for related researches. Proteins 2015; 83:1462-1469. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

SUMMARY Functional annotation of newly sequenced genomes is one of the major challenges in modern biology. With modern sequencing technologies, the protein sequence universe is rapidly expanding. Newly sequenced bacterial genomes alone contain over 7.5 million proteins. The rate of data generation has far surpassed that of protein annotation. The volume of protein data makes manual curation infeasible, whereas a high compute cost limits the utility of existing automated approaches. In this work, we present an improved and optmized automated workflow to enable large-scale protein annotation. The workflow uses high performance computing architectures and a low complexity classification algorithm to assign proteins into existing clusters of orthologous groups of proteins. On the basis of the Position-Specific Iterative Basic Local Alignment Search Tool the algorithm ensures at least 80% specificity and sensitivity of the resulting classifications. The workflow utilizes highly scalable parallel applications for classification and sequence alignment. Using Extreme Science and Engineering Discovery Environment supercomputers, the workflow processed 1,200,000 newly sequenced bacterial proteins. With the rapid expansion of the protein sequence universe, the proposed workflow will enable scientists to annotate big genome data. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Protein eight-state secondary structure prediction is challenging, but is necessary to determine protein structure and function. Here, we report the development of a novel approach, SPSSM8, to predict eight-state secondary structures of proteins accurately from sequences based on the structural position-specific scoring matrix (SPSSM). The SPSSM has been successfully utilized to predict three-state secondary structures. Now we employ an eight-state SPSSM as a feature that is obtained from sequence structure alignment against a large database of 9 million sequences with putative structural information. The SPSSM8 uses a low sequence identity dataset (9062 entries) as a training set and conditional random field for the classification algorithm. The SPSSM8 achieved an average eight-state secondary structure accuracy (Q8) of 71.7% (Q3, 81.6%) for an independent testing set (463 entries), which had an improved accuracy of 10.1% and 4.6% compared with SSPro8 and CNF, respectively, and significantly improved the accuracy of eight-state secondary structure prediction. For CASP 9 dataset (92 entries) the SPSSM8 achieved a Q8 accuracy of 80.1% (Q3, 83.0%). The SPSSM8 was confirmed as an outstanding predictor for eight-state secondary structures of proteins. SPSSM8 is freely available at http://cal.tongji.edu.cn/SPSSM8. [Copyright &y& Elsevier]

Genome scale metabolic model provides an overview of an organism’s metabolic capability. These genome-specific metabolic reconstructions are based on identification of gene to protein to reaction (GPR) associations and, in turn, on homology with annotated genes from other organisms. Cyanobacteria are photosynthetic prokaryotes which have diverged appreciably from their nonphotosynthetic counterparts. They also show significant evolutionary divergence from plants, which are well studied for their photosynthetic apparatus. We argue that context-specific sequence and domain similarity can add to the repertoire of the GPR associations and significantly expand our view of the metabolic capability of cyanobacteria. We took an approach that combines the results of context-specific sequence-to-sequence similarity search with those of sequence-to-profile searches. We employ PSI-BLAST for the former, and CDD, Pfam, and COG for the latter. An optimization algorithm was devised to arrive at a weighting scheme to combine the different evidences with KEGG-annotated GPRs as training data. We present the algorithm in the form of software “Systematic, Homology-based Automated Re-annotation for Prokaryotes (SHARP).” We predicted 3,781 new GPR associations for the 10 prokaryotes considered of which eight are cyanobacteria species. These new GPR associations fall in several metabolic pathways and were used to annotate 7,718 gaps in the metabolic network. These new annotations led to discovery of several pathways that may be active and thereby providing new directions for metabolic engineering of these species for production of useful products. Metabolic model developed on such a reconstructed network is likely to give better phenotypic predictions. [ABSTRACT FROM AUTHOR]

The furanosidase superfamily contains the GH32, GH43, GH62, GH68, GH117, DUF377 (GH130), and DUF1861 families of glycoside hydrolases and their homologues. Catalytic domains of these families have five-bladed β-propeller tertiary structure. Iterative screening of the protein database supports of their relationship as well as evolutionary connections with domains from GH33 and GH93 families of glycoside hydrolases. The latter two have the structure of the six-bladed β-propeller. Among detected homologues we found 441 unclassified proteins. These proteins are combined into 39 groups based on homology: FURAN1-FURAN39. FURAN8 and FURAN36 can be considered as separate subfamilies within the GH43 and GH32 families of glycoside hydrolases, respectively. The remaining 37 groups are new families of hypothetical glycoside hydrolases. [ABSTRACT FROM AUTHOR]

Endo-α-1,4-polygalactosaminidase is a rare enzyme. Its catalytic domain belongs to the GH114 family of glycoside hydrolases. It is shown by phylogenetic analysis that the evolution of the corresponding genes involved duplications, elimination, and horizontal transfer. The domain and secondary structures of endo-α-1,4-polygalactosaminidases are discussed. A hypothesis is put forward as to the structure of the active center of the enzyme. Iterative screening of a protein database reveals evolutionary relationships of the GH114 family with the GH13, GH18, GH20, GH27, GH29, GH31, GH35, GH36, and GH66 families of glycoside hydrolases and with the COG1306, COG1649, COG2342, GHL3, and GHL4 families of proteins with unknown enzymatic functions. Unclassified homologs are grouped into 13 new families of hypothetical glycoside hydrolases: GHL5-GHL15, GH36J, and GH36K. [ABSTRACT FROM AUTHOR]

This review deals with structural and functional features of glycoside hydrolases, a widespread group of enzymes present in almost all living organisms. Their catalytic domains are grouped into 120 amino acid sequence-based families in the international classification of the carbohydrate-active enzymes (CAZy database). At a higher hierarchical level some of these families are combined in 14 clans. Enzymes of the same clan have common evolutionary origin of their genes and share the most important functional characteristics such as composition of the active center, anomeric configuration of cleaved glycosidic bonds, and molecular mechanism of the catalyzed reaction (either inverting, or retaining). There are now extensive data in the literature concerning the relationship between glycoside hydrolase families belonging to different clans and/or included in none of them, as well as information on phylogenetic protein relationship within particular families. Summarizing these data allows us to propose a multilevel hierarchical classification of glycoside hydrolases and their homologs. It is shown that almost the whole variety of the enzyme catalytic domains can be brought into six main folds, large groups of proteins having the same three-dimensional structure and the supposed common evolutionary origin. [ABSTRACT FROM AUTHOR]

Abstract: Claudins (Cldn) are essential membrane proteins of tight junctions (TJs), which form the paracellular permselective barrier. They are produced by a multi-gene family of 24 reported members in mouse and human. Based on a comprehensive search combined with phylogenetic analyses, we identified three novel claudins (claudin-25, -26, and -27). Quantitative RT-PCR revealed that the three novel claudins were expressed in a tissue- and/or developmental stage-dependent manner. Claudins-25 and -26, but not claudin-27, were immunofluorescently localized to TJs when exogenously expressed in cultured MDCK and Eph epithelial cell lines. These findings expand the claudin family to include at least 27 members. Structured summary: Claudin-25 and ZO-1 colocalize by fluorescence microscopy (View interaction) ZO-1 and Claudin-26 colocalize by fluorescence microscopy (View interaction) [Copyright &y& Elsevier]

The GH101 family is composed of endo-α-N-acetylgalactosaminidases and their homologues. Pairwise sequence comparison and phylogenetic analysis allowed us to distinguish five to six subfamilies in this family. Diverse domain structures were found among the family members. Usually they have five irreplaceable and some optional domains. Iterative screening of the protein database revealed an evolutionary relationship of the GH101 catalytic domain with glycoside hydrolase domains from GH13, GH31, and GH70 families. Among other homologous proteins we have found representatives of COG1649, as well as members of four new families of predicted glycoside hydrolases (GHL1-GHL4). [ABSTRACT FROM AUTHOR]

Abstract: Various hypotheses have been proposed on the evolutionary origin of eukaryotic nucleus. Because one of the major cargoes in the nucleocytoplasmic export in the eukaryotic cell is the ribosome, its stimulating proteins called Ribosome Export Factors (REFs) might have an evolutionary history of inscribing the origin of eukaryotic nucleus. With the aim of understanding the evolutionary origin of the nucleus, here we employed the yeast REFs and searched for their evolutionary origin in more than 500 genomes of archaea and eubacteria by the PSI-BLAST search. Our results showed that the non-membranous REFs (non-mREFs) originated exclusively from eubacterial proteins, whereas the membranous REFs (mREFs) are from both archaeal and eubacterial proteins. Since the non-mREFs just work inside the nucleus while the mREFs shuttle between the nucleus and the cytoplasm, these results suggest that the extant REFs working inside the nucleus have derived exclusively from eubacterial proteins, implying that the nucleus arose in a cell that contained chromosomes possessing a substantial fraction of eubacterial genes, in line with the predictions of several models entailing endosymbiosis at eukaryote origins. [Copyright &y& Elsevier]

Knowledge of structural classes is useful in understanding of folding patterns in proteins. Although existing structural class prediction methods applied virtually all state-of-the-art classifiers, many of them use a relatively simple protein sequence representation that often includes amino acid (AA) composition. To this end, we propose a novel sequence representation that incorporates evolutionary information encoded using PSI-BLAST profile-based collocation of AA pairs. We used six benchmark datasets and five representative classifiers to quantify and compare the quality of the structural class prediction with the proposed representation. The best, classifier support vector machine achieved 61–96% accuracy on the six datasets. These predictions were comprehensively compared with a wide range of recently proposed methods for prediction of structural classes. Our comprehensive comparison shows superiority of the proposed representation, which results in error rate reductions that range between 14% and 26% when compared with predictions of the best-performing, previously published classifiers on the considered datasets. The study also shows that, for the benchmark dataset that includes sequences characterized by low identity (i.e., 25%, 30%, and 40%), the prediction accuracies are 20–35% lower than for the other three datasets that include sequences with a higher degree of similarity. In conclusion, the proposed representation is shown to substantially improve the accuracy of the structural class prediction. A web server that implements the presented prediction method is freely available at http://biomine.ece.ualberta.ca/Structural_Class/SCEC.html. © 2008 Wiley Periodicals, Inc. J Comput Chem 2008 [ABSTRACT FROM AUTHOR]

The subcellular locations of proteins are important functional annotations. An effective and reliable subcellular localization method is necessary for proteomics research. This paper introduces a new method - PairProSVM - to automatically predict the subcellular locations of proteins. The profiles of all protein sequences in the training set are constructed by PSI-BLAST, and the pairwise profile alignment scores are used to form feature vectors for training a support vector machine (SVM) classifier. It was found that PairProSVM outperforms the methods that are based on sequence alignment and amino acid compositions even if most of the homologous sequences have been removed. PairProSVM was evaluated on Huang and Li's and Gardy et al.'s protein data sets. The overall accuracies on these data sets reach 75.3 percent and 91.9 percent, respectively, which are higher than or comparable to those obtained by sequence alignment and composition-based methods. [ABSTRACT FROM PUBLISHER]

Most of the prediction methods for secretory proteins require the presence of a correct N-terminal end of the pre-protein for correct classification. As large scale genome sequencing projects sometimes assign the 5'-end of genes incorrectly, many proteins are encoded without the correct N-terminus leading to incorrect prediction. In this study, a systematic attempt has been made to predict secretory proteins irrespective of presence or absence of N-terminal signal peptides (also known as classical and non-classical secreted proteins respectively), using machine-learning techniques; artificial neural network (ANN) and support vector machine (SVM). We trained and tested our methods on a dataset of 3321 secretory and 3654 non-secretory mammalian proteins using five-fold cross-validation technique. First, ANN-based modules have been developed for predicting secretory proteins using 33 physico-chemical properties, amino acid composition and dipeptide composition and achieved accuracies of 73.1%, 76.1% and 77.1%, respectively. Similarly, SVM-based modules using 33 physico-chemical properties, amino acid, and dipeptide composition have been able to achieve accuracies of 77.4%, 79.4% and 79.9%, respectively. In addition, BLAST and PSI-BLAST modules designed for predicting secretory proteins based on similarity search achieved 23.4% and 26.9% accuracy, respectively. Finally, we developed a hybrid-approach by integrating amino acid and dipeptide composition based SVM modules and PSI-BLAST module that increased the accuracy to 83.2%, which is significantly better than individual modules. We also achieved high sensitivity of 60.4% with low value of 5% false positive predictions using hybrid module. A web server SRTpred has been developed based on above study for predicting classical and non-classical secreted proteins from whole sequence of mammalian proteins, which is available from http://www.imtech.res.in/raghava/srtpred/. [ABSTRACT FROM AUTHOR]

Ubiquitin functions to regulate protein turnover in a cell by closely regulating the degradation of specific proteins. Such a regulatory role is very important, and thus I have analyzed the proteins that are ubiquitin-like, using an artificial neural network, support vector machines and a hidden Markov model (HMM). The methods were trained and tested on a set of 373 ubiquitin proteins and 373 non-ubiquitin proteins, obtained from Entrez protein database. The artificial neural network and support vector machine are trained and tested using both the physicochemical properties and PSSM matrices generated from PSI-BLAST, while in the HMM based method direct sequences are used for training-testing procedures. Further, the performance measures of the methods are calculated for test sequences, i.e. accuracy, specificity, sensitivity and Matthew's correlation coefficients of the methods are calculated. The highest accuracy of 90.2%, specificity of 87.04% and sensitivity of 94.08% was achieved using the support vector machine model with PSSM matrices. While accuracies of 86.82%, 83.37%, 80.18% and 72.11% were obtained for the support vector machine with physicochemical properties, neural network with PSSM matrices, neural networks with physicochemical properties, and hidden Markov model, respectively. As the accuracy for SVM model is better both using physicochemical properties and the PSSM matrices, it is concluded that kernel methods such as SVM outperforms neural networks and hidden Markov models. [ABSTRACT FROM AUTHOR]

Subcellular location is an important functional annotation of proteins. An automatic, reliable and efficient prediction system for protein subcellular localization is necessary for large-scale genome analysis. This paper describes a protein subcellular localization method which extracts features from protein profiles rather than from amino acid sequences. The protein profile represents a protein family, discards part of the sequence information that is not conserved throughout the family and therefore is more sensitive than the amino acid sequence. The amino acid compositions of whole profile and the N-terminus of the profile are extracted, respectively, to train and test the probabilistic neural network classifiers. On two benchmark datasets, the overall accuracies of the proposed method reach 89.1% and 68.9%, respectively. The prediction results show that the proposed method perform better than those methods based on amino acid sequences. The prediction results of the proposed method are also compared with Subloc on two redundance-reduced datasets. [ABSTRACT FROM AUTHOR]

Abstract: BRCA1 is a large protein that exhibits a multiplicity of functions in its apparent role in DNA repair. Certain mutations of BRCA1 are known to have exceptionally high penetrance with respect to familial breast and ovarian cancers. The structures of the N-terminus and C-terminus of the protein have been determined. The C-terminus unit consists of two α-β-α domains designated BRCT. We predicated two homologous BRCT regions in the BRCA1 internal region, and subsequently produced and purified these protein domains. Both recombinant domains show significant self-association capabilities as well as a preferential tendency to interact with each other. These results suggest a possible regulatory mechanism for BRCA1 function. We have demonstrated p53-binding activity by an additional region, and confirmed previous results showing that two regions of BRCA1 protein bind p53 in vitro. Based on sequence analysis, we predict five p53-binding sites. Our comparison of binding by wild-type and mutant domains indicates the sequence specificity of BRCA1–p53 interaction. [Copyright &y& Elsevier]

The impetus for the recent development and emergence of automated function prediction methods is an exponentially growing flood of new experimental data, the interpretation of which is hindered by a shortage of reliable annotations for proteins that lack experimental characterization or significant homologs in current databases. Here we introduce PFP, an automated function prediction server that provides the most probable annotations for a query sequence in each of the three branches of the Gene Ontology: biological process, molecular function, and cellular component. Rather than utilizing precise pattern matching to identify functional motifs in the sequences and structures of these proteins, we designed PFP to increase the coverage of function annotation by lowering resolution of predictions when a detailed function is not predictable. To do this we extend a traditional PSI-BLAST search by extracting and scoring annotations (GO terms) individually, including annotations from distantly related sequences, and applying a novel data mining tool, the Function Association Matrix, to score strongly associated pairs of annotations. We show that PFP can correctly assign function using only weakly similar sequences with a significantly better accuracy and coverage than a standard PSI-BLAST search, improving it more than fivefold. The most descriptive annotations predicted by PFP (GO depth ≥8) can identify a significant subgraph in the GO with >60% accuracy and ∼100% coverage for our benchmark set. We also provide examples of the superb performance of PFP in an assessment of automated function prediction servers at the Automated Function Prediction Special Interest Group meeting at ISMB 2005 (AFP-SIG '05). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Protein existing as an ensemble of structures, called intrinsically disordered, has been shown to be responsible for a wide variety of biological functions and to be common in nature. Here we focus on improving sequence-based predictions of long (>30 amino acid residues) regions lacking specific 3-D structure by means of four new neural-network-based Predictors Of Natural Disordered Regions (PONDRs): VL3, VL3H, VL3P, and VL3E. PONDR VL3 used several features from a previously introduced PONDR VL2, but benefitted from optimized predictor models and a slightly larger (152 vs. 145) set of disordered proteins that were cleaned of mislabeling errors found in the smaller set. PONDR VL3H utilized homologues of the disordered proteins in the training stage, while PONDR VL3P used attributes derived from sequence profiles obtained by PSI-BLAST searches. The measure of accuracy was the average between accuracies on disordered and ordered protein regions. By this measure, the 30-fold cross-validation accuracies of VL3, VL3H, and VL3P were, respectively, 83.6 ± 1.4%, 85.3 ± 1.4%, and 85.2 ± 1.5%. By combining VL3H and VL3P, the resulting PONDR VL3E achieved an accuracy of 86.7 ± 1.4%. This is a significant improvement over our previous PONDRs VLXT (71.6 ± 1.3%) and VL2 (80.9 ± 1.4%). The new disorder predictors with the corresponding datasets are freely accessible through the web server at . [ABSTRACT FROM AUTHOR]

The four electron transfer energy metabolism systems, photosynthesis, aerobic respiration, denitrification, and sulfur respiration, are thought to be evolutionarily related because of the similarity of electron transfer patterns and the existence of some homologous proteins. How these systems have evolved is elusive. We therefore conducted a comprehensive homology search using PSI-BLAST, and phylogenetic analyses were conducted for the three homologous groups (groups 1-3) based on multiple alignments of domains defined in the Pfam database. There are five electron transfer types important for catalytic reaction in group 1, and many proteins bind molybdenum. Deletions of two domains led to loss of the function of binding molybdenum and ferredoxin, and these deletions seem to be critical for the electron transfer pattern changes in group 1. Two types of electron transfer were found in group 2, and all its member proteins bind siroheme and ferredoxin. Insertion of the pyridine nucleotide disulfide oxidoreductase domain seemed to be the critical point for the electron transfer pattern change in this group. The proteins belonging to group 3 are all flavin enzymes, and they bind flavin adenine dinucleotide (FAD) or flavin mononucleotide (FMN). Types of electron transfer in this group are divergent, but there are two common characteristics. NAD(P)H works as an electron donor or acceptor, and FAD or FMN transfers electrons from/to NAD(P)H. Electron transfer functions might be added to these common characteristics by the addition of functional domains through the evolution of group 3 proteins. Based on the phylogenetic analyses in this study and previous studies, we inferred the phylogeny of the energy metabolism systems as follows: photosynthesis (and possibly aerobic respiration) and the sulfur/nitrogen assimilation system first diverged, then the sulfur/nitrogen dissimilation system was produced from the latter system. [ABSTRACT FROM AUTHOR]

Sequence alignment profiles have been shown to be very powerful in creating accurate sequence alignments. Profiles are often used to search a sequence database with a local alignment algorithm. More accurate and longer alignments have been obtained with profile-to-profile comparison. There are several steps that must be performed in creating profile-profile alignments, and each involves choices in parameters and algorithms. These steps include (1) what sequences to include in a multiple alignment used to build each profile, (2) how to weight similar sequences in the multiple alignment and how to determine amino acid frequencies from the weighted alignment, (3) how to score a column from one profile aligned to a column of the other profile, (4) how to score gaps in the profile-profile alignment, and (5) how to include structural information. Large-scale benchmarks consisting of pairs of homologous proteins with structurally determined sequence alignments are necessary for evaluating the efficacy of each scoring scheme. With such a benchmark, we have investigated the properties of profile-profile alignments and found that (1) with optimized gap penalties, most column-column scoring functions behave similarly to one another in alignment accuracy; (2) some functions, however, have much higher search sensitivity and specificity; (3) position-specific weighting schemes in determining amino acid counts in columns of multiple sequence alignments are better than sequence-specific schemes; (4) removing positions in the profile with gaps in the query sequence results in better alignments; and (5) adding predicted and known secondary structure information improves alignments. [ABSTRACT FROM AUTHOR]

Enzyme function conservation has been used to derive the threshold of sequence identity necessary to transfer function from a protein of known function to an unknown protein. Using pairwise sequence comparison, several studies suggested that when the sequence identity is above 40%, enzyme function is well conserved. In contrast, Rost argued that because of database bias, the results from such simple pairwise comparisons might be misleading. Thus, by grouping enzyme sequences into families based on sequence similarity and selecting representative sequences for comparison, he showed that enzyme function starts to diverge quickly when the sequence identity is below 70%. Here, we employ a strategy similar to Rost''s to reduce the database bias; however, we classify enzyme families based not only on sequence similarity, but also on functional similarity, i.e. sequences in each family must have the same four digits or the same first three digits of the enzyme commission (EC) number. Furthermore, instead of selecting representative sequences for comparison, we calculate the function conservation of each enzyme family and then average the degree of enzyme function conservation across all enzyme families. Our analysis suggests that for functional transferability, 40% sequence identity can still be used as a confident threshold to transfer the first three digits of an EC number; however, to transfer all four digits of an EC number, above 60% sequence identity is needed to have at least 90% accuracy. Moreover, when PSI-BLAST is used, the magnitude of the E-value is found to be weakly correlated with the extent of enzyme function conservation in the third iteration of PSI-BLAST. As a result, functional annotation based on the E-values from PSI-BLAST should be used with caution. We also show that by employing an enzyme family-specific sequence identity threshold above which 100% functional conservation is required, functional inference of unknown sequences can be accurately accomplished. However, this comes at a cost: those true positive sequences below this threshold cannot be uniquely identified. [Copyright &y& Elsevier]

Phosphorylase kinase is a four-subunit enzyme involved in the regulation of glycogen breakdown. The traditional textbook view is that only the γ subunit has enzymatic activity, whereas the other three subunits have a regulatory role. Evidence from homology searches and sequence alignments, however, shows that the α- and β-subunits possess amino-terminal glucoamylase-like domains and suggests that they might possess a previously overlooked amylase activity. If true, this would have important implications for the understanding, diagnosis, and management of glycogen storage diseases. There is thus a clear need to test this hypothesis through enzymatic assays and structural studies. [ABSTRACT FROM AUTHOR]

The EMI domain, first named after its presence in proteins of the EMILIN family, was identified here in several metazoan proteins with various domain architectures, among which the mammalian NEU1/NG3 proteins and Caenorhabditis elegans CED-1, identified as a transmembrane receptor that mediates cell corpse engulfment. Functional data available for EMILIN proteins suggest that the EMI domain could be a protein–protein interaction module. Sequence profiles specific of the EMI family of domains led to identify the probable orthologs of the C. elegans CED-1 protein in mammals and insects, which were yet uncovered. [Copyright &y& Elsevier]

ADP-ribosyltransferases including toxins secreted by Vibrio cholera, Pseudomonas aerurginosa, and other pathogenic bacteria inactivate the function of human target proteins by attaching ADP-ribose onto a critical amino acid residue. Cross-species polymerase chain reaction (PCR) and database mining identified the orthologs of these ADP-ribosylating toxins in humans and the mouse. The human genome contains four functional toxin-related ADP-ribosyltransferase genes ( ARTs) and two related intron-containing pseudogenes; the mouse has six functional orthologs. The human and mouse ART genes map to chromosomal regions with conserved linkage synteny. The individual ART genes reveal highly restricted expression patterns, which are largely conserved in humans and the mouse. We confirmed the predicted extracellular location of the ART proteins by expressing recombinant ARTs in insect cells. Two human and four mouse ARTs contain the active site motif (R-S-EXE) typical of arginine-specific ADP-ribosyltransferases and exhibit the predicted enzyme activities. Two other human ARTs and their murine orthologues deviate in the active site motif and lack detectable enzyme activity. Conceivably, these ARTs may have acquired a new specificity or function. The position-sensitive iterative database search program PSI-BLAST connected the mammalian ARTs with most known bacterial ADP-ribosylating toxins. In contrast, no related open reading frames occur in the four completed genomes of lower eucaryotes (yeast, worm, fly, and mustard weed). Interestingly, these organisms also lack genes for ADP-ribosylhydrolases, the enzymes that reverse protein ADP-ribosylation. This suggests that the two enzyme families that catalyze reversible mono-ADP-ribosylation either were lost from the genomes of these nonchordata eucaryotes or were subject to horizontal gene transfer between kingdoms. [ABSTRACT FROM AUTHOR]

Fold recognition predicts protein three-dimensional structure by establishing relationships between a protein sequence and known protein structures. Most methods explicitly use information derived from the secondary and tertiary structure of the templates. Here we show that rigorous application of a sequence search method (PSI-BLAST) with no reference to secondary or tertiary structure information is able to perform as well as traditional fold recognition methods. Since the method, SENSER, does not require knowledge of the three-dimensional structure, it can be used to infer relationships that are not tractable by methods dependent on structural templates. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to profile-profile comparison. The method compares two input profiles (like those that are generated by PSI-BLAST) and assigns a similarity score to assess their statistical similarity. Our profile-profile comparison tool, which allows for gaps, can be used to detect weak similarities between protein families. It has also been optimized to produce alignments that are in very good agreement with structural alignments. Tests show that the profile-profile alignments are indeed highly correlated with similarities between secondary structure elements and tertiary structure. Exhaustive evaluations show that our method is significantly more sensitive in detecting distant homologies than the popular profile-based search programs PSI-BLAST and IMPALA. The relative improvement is the same order of magnitude as the improvement of PSI-BLAST relative to BLAST. Our new tool often detects similarities that fall within the twilight zone of sequence similarity. [Copyright &y& Elsevier]

Distant homologies between proteins are often discovered only after three-dimensional structures of both proteins are solved. The sequence divergence for such proteins can be so large that simple comparison of their sequences fails to identify any similarity. New generation of sensitive alignment tools use averaged sequences of entire homologous families (profiles) to detect such homologies. Several algorithms, including the newest generation of BLAST algorithms and BASIC, an algorithm used in our group to assign fold predictions for proteins from several genomes, are compared to each other on the large set of structurally similar proteins with little sequence similarity. Proteins in the benchmark are classified according to the level of their similarity, which allows us to demonstrate that most of the improvement of the new algorithms is achieved for proteins with strong functional similarities, with almost no progress in recognizing distant fold similarities.It is also shown that details of profile calculation strongly influence its sensitivity in recognizing distant homologies. The most important choice is how to include information from diverging members of the family, avoiding generating false predictions, while accounting for entire sequence divergence within a family. PSI-BLAST takes a conservative approach, deriving a profile from core members of the family, providing a solid improvement without almost any false predictions. BASIC strives for better sensitivity by increasing the weight of divergent family members and paying the price in lower reliability. A new FFAS algorithm introduced here uses a new procedure for profile generation that takes into account all the relations within the family and matches BASIC sensitivity with PSI-BLAST like reliability. [ABSTRACT FROM PUBLISHER]