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Lake trout (Salvelinus namaycush) are a top-predator species in the Laurentian Great Lakes that are often used as bioindicators of chemical stressors in the ecosystem. Although many studies are done using these fish to determine concentrations of stressors like legacy persistent, bioaccumulative and toxic chemicals, there are currently no proteomic studies on the biological effects these stressors have on the ecosystem. This lack of proteomic studies on Great Lakes lake trout is because there is currently no complete, comprehensive protein database for this species. Here, we employed proteomics approaches to develop a lake trout protein database that could aid in future research on this fish, in particular exposomics and adductomics. The current study utilized heart tissue and blood from two lake trout. Our previous work using lake trout liver revealed 4194 potential protein hits in the NCBI databases and 3811 potential protein hits in the UniProtKB databases. In the current study, using the NCBI databases we identified 838 proteins for the heart and 580 proteins for the blood tissues in the biological replicate 1 (BR1) and 1180 potential protein hits for the heart and 561 potential protein hits for the blood in BR2. Similar results were obtained using the UniProtKB databases. This study builds on our previous work by continuing to build the first comprehensive lake trout protein database and provides insight into protein homology through evolutionary relationships. This data is available via the PRIDE partner repository with the dataset identifier PXD023970.

Thermostability conferred by ensembles of several weak-interactions results in compactness-rigidity in proteins that impairs their flexibility/function. Understanding of protein's structural modification under stress is important.In this study, randomly selected 54 nonhomologous and variedly thermostable (0-20 °C/21-40 °C/41-60 °C/61-80 °C/81-100 °C) proteins were investigated to elucidate their thermostability utilizing multiple bioinformatics-tools. Important physico-chemical factors/peptide-nonplanarity(ω) and Ramachandran-plot were analyzed from the proteins' PDB-structure by SPSS-programme.Our ANOVA results suggest that temperature exerted significant and periodic influences on ω (F = 5.81/p = 0.0001) and ψ (F = 6.52/p = 0.0001) dihedral-angles in proteins where peptide nonplanarity obviously favored thermostability. The Pearson-correlation and further goodness of-fit model from ordinal-regression analysis suggest that nonplanarity is increasingly abundant in protein carboxy-terminal (χ2 = 37.9/p = 0.0001) at higher temperatures. Moreover, the ω is found to be highly correlated with ψ (r = 0.289/p = 0.0001) but not ɸ (r = -0.071/p = 0.365) which is supported by the regression-analysis (R2 = 0.085/F = 7.623/p 0.001). Consistent and paradoxical decrease in the protein-size is linked to protein-thermostability. Hydrophobicity/hydrophilicity/protein phosphorylation and iso-electric point/charge were found to be grossly increasing with the protein-thermostability. However, outlier/disallowed residue-number (mean ± SD) was lower in moderate- and hyper-thermostable proteins, indicating temperature as a better purifying naturalselection-pressure to generate consistent allelic-fitness.In the current study, irrespective to the protein homology, nonplanarity has been demonstrated as a significant determinant of protein-thermostability.

Background BRAKER is a suite of automatic pipelines, BRAKER1 and BRAKER2, for the accurate annotation of protein-coding genes in eukaryotic genomes. Each pipeline trains statistical models of protein-coding genes based on provided evidence and, then predicts protein-coding genes in genomic sequences using both the extrinsic evidence and statistical models. For training and prediction, BRAKER1 and BRAKER2 incorporate complementary extrinsic evidence: BRAKER1 uses only RNA-seq data while BRAKER2 uses only a database of cross-species proteins. The BRAKER suite has so far not been able to reliably exceed the accuracy of BRAKER1 and BRAKER2 when incorporating both types of evidence simultaneously. Currently, for a novel genome project where both RNA-seq and protein data are available, the best option is to run both pipelines independently, and to pick one, likely better output. Therefore, one or another type of the extrinsic evidence would remain unexploited. Results We present TSEBRA, a software that selects gene predictions (transcripts) from the sets generated by BRAKER1 and BRAKER2. TSEBRA uses a set of rules to compare scores of overlapping transcripts based on their support by RNA-seq and homologous protein evidence. We show in computational experiments on genomes of 11 species that TSEBRA achieves higher accuracy than either BRAKER1 or BRAKER2 running alone and that TSEBRA compares favorably with the combiner tool EVidenceModeler. Conclusion TSEBRA is an easy-to-use and fast software tool. It can be used in concert with the BRAKER pipeline to generate a gene prediction set supported by both RNA-seq and homologous protein evidence.

Sequence-based protein homology detection has emerged as one of the most sensitive and accurate approaches to protein structure prediction. Despite the success, homology detection remains very challenging for weakly homologous proteins with divergent evolutionary profile. Very recently, deep neural network architectures have shown promising progress in mining the coevolutionary signal encoded in multiple sequence alignments, leading to reasonably accurate estimation of inter-residue interaction maps, which serve as a rich source of additional information for improved homology detection. Here, we summarize the latest developments in protein homology detection driven by inter-residue interaction map threading. We highlight the emerging trends in distant-homology protein threading through the alignment of predicted interaction maps at various granularities ranging from binary contact maps to finer-grained distance and orientation maps as well as their combination. We also discuss some of the current limitations and possible future avenues to further enhance the sensitivity of protein homology detection.

As an important task in protein structure and function studies, protein fold recognition has attracted more and more attention. The existing computational predictors in this field treat this task as a multi-classification problem, ignoring the relationship among proteins in the dataset. However, previous studies showed that their relationship is critical for protein homology analysis. In this study, the protein fold recognition is treated as an information retrieval task. The Learning to Rank model (LTR) was employed to retrieve the query protein against the template proteins to find the template proteins in the same fold with the query protein in a supervised manner. The triadic closure principle (TCP) was performed on the ranking list generated by the LTR to improve its accuracy by considering the relationship among the query protein and the template proteins in the ranking list. Finally, a predictor called Fold-LTR-TCP was proposed. The rigorous test on the LE benchmark dataset showed that the Fold-LTR-TCP predictor achieved an accuracy of 73.2%, outperforming all the other competing methods.

The most frequently used approach for protein structure prediction is currently homology modeling. The 3D model building phase of this methodology is critical for obtaining an accurate and biologically useful prediction. The most widely employed tool to perform this task is MODELLER. This program implements the “modeling by satisfaction of spatial restraints” strategy and its core algorithm has not been altered significantly since the early 1990s. In this work, we have explored the idea of modifying MODELLER with two effective, yet computationally light strategies to improve its 3D modeling performance. Firstly, we have investigated how the level of accuracy in the estimation of structural variability between a target protein and its templates in the form of σ values profoundly influences 3D modeling. We show that the σ values produced by MODELLER are on average weakly correlated to the true level of structural divergence between target-template pairs and that increasing this correlation greatly improves the program’s predictions, especially in multiple-template modeling. Secondly, we have inquired into how the incorporation of statistical potential terms (such as the DOPE potential) in the MODELLER’s objective function impacts positively 3D modeling quality by providing a small but consistent improvement in metrics such as GDT-HA and lDDT and a large increase in stereochemical quality. Python modules to harness this second strategy are freely available at https://github.com/pymodproject/altmod. In summary, we show that there is a large room for improving MODELLER in terms of 3D modeling quality and we propose strategies that could be pursued in order to further increase its performance., Author summary Proteins are fundamental biological molecules that carry out countless activities in living beings. Since the function of proteins is dictated by their three-dimensional atomic structures, acquiring structural details of proteins provides deep insights into their function. Currently, the most frequently used computational approach for protein structure prediction is template-based modeling. In this approach, a target protein is modeled using the experimentally-derived structural information of a template protein assumed to have a similar structure to the target. MODELLER is the most frequently used program for template-based 3D model building. Despite its success, its predictions are not always accurate enough to be useful in Biomedical Research. Here, we show that it is possible to greatly increase the performance of MODELLER by modifying two aspects of its algorithm. First, we demonstrate that providing the program with accurate estimations of local target-template structural divergence greatly increases the quality of its predictions. Additionally, we show that modifying MODELLER’s scoring function with statistical potential energetic terms also helps to improve modeling quality. This work will be useful in future research, since it reports practical strategies to improve the performance of this core tool in Structural Bioinformatics.

Motivation Protein fold recognition has attracted increasing attention because it is critical for studies of the 3D structures of proteins and drug design. Researchers have been extensively studying this important task, and several features with high discriminative power have been proposed. However, the development of methods that efficiently combine these features to improve the predictive performance remains a challenging problem. Results In this study, we proposed two algorithms: MV-fold and MT-fold. MV-fold is a new computational predictor based on the multi-view learning model for fold recognition. Different features of proteins were treated as different views of proteins, including the evolutionary information, secondary structure information and physicochemical properties. These different views constituted the latent space. The ε-dragging technique was employed to enlarge the margins between different protein folds, improving the predictive performance of MV-fold. Then, MV-fold was combined with two template-based methods: HHblits and HMMER. The ensemble method is called MT-fold incorporating the advantages of both discriminative methods and template-based methods. Experimental results on five widely used benchmark datasets (DD, RDD, EDD, TG and LE) showed that the proposed methods outperformed some state-of-the-art methods in this field, indicating that MV-fold and MT-fold are useful computational tools for protein fold recognition and protein homology detection and would be efficient tools for protein sequence analysis. Finally, we constructed an update and rigorous benchmark dataset based on SCOPe (version 2.07) to fairly evaluate the performance of the proposed method, and our method achieved stable performance on this new dataset. This new benchmark dataset will become a widely used benchmark dataset to fairly evaluate the performance of different methods for fold recognition. Supplementary information Supplementary data are available at Bioinformatics online.

Background In bioinformatics community, many tasks associate with matching a set of protein query sequences in large sequence datasets. To conduct multiple queries in the database, a common used method is to run BLAST on each original querey or on the concatenated queries. It is inefficient since it doesn’t exploit the common subsequences shared by queries. Results We propose a compression and cluster based BLASTP (C2-BLASTP) algorithm to further exploit the joint information among the query sequences and the database. Firstly, the queries and database are compressed in turn by procedures of redundancy analysis, redundancy removal and distinction record. Secondly, the database is clustered according to Hamming distance among the subsequences. To improve the sensitivity and selectivity of sequence alignments, ten groups of reduced amino acid alphabets are used. Following this, the hits finding operator is implemented on the clustered database. Furthermore, an execution database is constructed based on the found potential hits, with the objective of mitigating the effect of increasing scale of the sequence database. Finally, the homology search is performed in the execution database. Experiments on NCBI NR database demonstrate the effectiveness of the proposed C2-BLASTP for batch searching of homology in sequence database. The results are evaluated in terms of homology accuracy, search speed and memory usage. Conclusions It can be seen that the C2-BLASTP achieves competitive results as compared with some state-of-the-art methods.

Motivation: The functional impact of small molecules is increasingly being assessed in different eukaryotic species through large-scale phenotypic screening initiatives. Identifying the targets of these molecules is crucial to mechanistically understand their function and uncover new therapeutically relevant modes of action. However, despite extensive work carried out in model organisms and human, it is still unclear to what extent one can use information obtained in one species to make predictions in other species. Results: Here, for the first time, we explore and validate at a large scale the use of protein homology relationships to predict the targets of small molecules across different species. Our results show that exploiting target homology can significantly improve the predictions, especially for molecules experimentally tested in other species. Interestingly, when considering separately orthology and paralogy relationships, we observe that mapping small molecule interactions among orthologs improves prediction accuracy, while including paralogs does not improve and even sometimes worsens the prediction accuracy. Overall, our results provide a novel approach to integrate chemical screening results across multiple species and highlight the promises and remaining challenges of using protein homology for small molecule target identification. Availability and implementation: Homology-based predictions can be tested on our website http://www.swisstargetprediction.ch. Contact: david.gfeller@unil.ch or vincent.zoete@isb-sib.ch. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Homology models of proteins are of great interest for planning and analysing biological experiments when no experimental three-dimensional structures are available. Building homology models requires specialized programs and up-to-date sequence and structural databases. Integrating all required tools, programs and databases into a single web-based workspace facilitates access to homology modelling from a computer with web connection without the need of downloading and installing large program packages and databases. Results: SWISS-MODEL workspace is a web-based integrated service dedicated to protein structure homology modelling. It assists and guides the user in building protein homology models at different levels of complexity. A personal working environment is provided for each user where several modelling projects can be carried out in parallel. Protein sequence and structure databases necessary for modelling are accessible from the workspace and are updated in regular intervals. Tools for template selection, model building and structure quality evaluation can be invoked from within the workspace. Workflow and usage of the workspace are illustrated by modelling human Cyclin A1 and human Transmembrane Protease 3. Availability: The SWISS-MODEL workspace can be accessed freely at Contact: Torsten.Schwede@unibas.ch Supplementary information: Supplementary data are available at Bioinformatics online.

Background BLOSUM matrices belong to the most commonly used substitution matrix series for protein homology search and sequence alignments since their publication in 1992. In 2008, Styczynski et al. discovered miscalculations in the clustering step of the matrix computation. Still, the RBLOSUM64 matrix based on the corrected BLOSUM code was reported to perform worse at a statistically significant level than the BLOSUM62. Here, we present a further correction of the (R)BLOSUM code and provide a thorough performance analysis of BLOSUM-, RBLOSUM- and the newly derived CorBLOSUM-type matrices. Thereby, we assess homology search performance of these matrix-types derived from three different BLOCKS databases on all versions of the ASTRAL20, ASTRAL40 and ASTRAL70 subsets resulting in 51 different benchmarks in total. Our analysis is focused on two of the most popular BLOSUM matrices — BLOSUM50 and BLOSUM62. Results Our study shows that fixing small errors in the BLOSUM code results in substantially different substitution matrices with a beneficial influence on homology search performance when compared to the original matrices. The CorBLOSUM matrices introduced here performed at least as good as their BLOSUM counterparts in ∼75 % of all test cases. On up-to-date ASTRAL databases BLOSUM matrices were even outperformed by CorBLOSUM matrices in more than 86 % of the times. In contrast to the study by Styczynski et al., the tested RBLOSUM matrices also outperformed the corresponding BLOSUM matrices in most of the cases. Comparing the CorBLOSUM with the RBLOSUM matrices revealed no general performance advantages for either on older ASTRAL releases. On up-to-date ASTRAL databases however CorBLOSUM matrices performed better than their RBLOSUM counterparts in ∼74 % of the test cases. Conclusions Our results imply that CorBLOSUM type matrices outperform the BLOSUM matrices on a statistically significant level in most of the cases, especially on up-to-date databases such as ASTRAL ≥2.01. Additionally, CorBLOSUM matrices are closer to those originally intended by Henikoff and Henikoff on a conceptual level. Hence, we encourage the usage of CorBLOSUM over (R)BLOSUM matrices for the task of homology search. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1060-3) contains supplementary material, which is available to authorized users.

Candidatus Liberibacter asiaticus (Ca. L. asiaticus) is a Gram-negative bacterium and the pathogen of Citrus Greening disease (Huanglongbing, HLB). As a parasitic bacterium, Ca. L. asiaticus harbors ABC transporters that play important roles in exchanging chemical compounds between Ca. L. asiaticus and its host. Here, we analyzed all the ABC transporter-related proteins in Ca. L. asiaticus. We identified 14 ABC transporter systems and predicted their structures and substrate specificities. In-depth sequence and structure analysis including multiple sequence alignment, phylogenetic tree reconstruction, and structure comparison further support their function predictions. Our study shows that this bacterium could use these ABC transporters to import metabolites (amino acids and phosphates) and enzyme cofactors (choline, thiamine, iron, manganese, and zinc), resist to organic solvent, heavy metal, and lipid-like drugs, maintain the composition of the outer membrane (OM), and secrete virulence factors. Although the features of most ABC systems could be deduced from the abundant experimental data on their orthologs, we reported several novel observations within ABC system proteins. Moreover, we identified seven nontransport ABC systems that are likely involved in virulence gene expression regulation, transposon excision regulation, and DNA repair. Our analysis reveals several candidates for further studies to understand and control the disease, including the type I virulence factor secretion system and its substrate that are likely related to Ca. L. asiaticus pathogenicity and the ABC transporter systems responsible for bacterial OM biosynthesis that are good drug targets.

Angiosperm stigma supports compatible pollen germination and tube growth, resulting in fertilization and seed production. Stigmas are mainly divided into two types, dry and wet, according to the absence or presence of exudates on their surfaces. Here, we used 2DE and MS to identify proteins specifically and preferentially expressed in the stigmas of maize (Zea Mays, dry stigma) and tobacco (Nicotiana tabacum, wet stigma), as well as proteins rinsed from the surface of the tobacco stigma. We found that the specifically and preferentially expressed proteins in maize and tobacco stigmas share similar distributions in functional categories. However, these proteins showed important difference between dry and wet stigmas in a few aspects, such as protein homology in "signal transduction" and "lipid metabolism," relative expression levels of proteins containing signal peptides and proteins in "defense and stress response." These different features might be related to the specific structures and functions of dry and wet stigmas. The possible roles of some stigma-expressed proteins were discussed. Our results provide important information on functions of proteins in dry and wet stigmas and reveal aspects of conservation and divergence between dry and wet stigmas at the proteomic level.

Thyroid Hormone Receptor β (THRB) defects, typically transmitted as autosomal dominant traits, cause Resistance to Thyroid Hormone (RTH). We analyzed the THRB gene in thirteen South American patients with clinical evidence RTH from eleven unrelated families. Sequence analysis revealed seven novel missense mutations. Four novel mutations were identified in exon 9. The first, a c.991A>G transition which originates a substitution of asparagine by aspartic acid (p.N331D). The second nucleotide alteration consists of a guanine to cytosine transversion at position 1003 (c.1003G>C) and results in substitution of the alanine at codon 335 by proline (p.A335P). The third mutation, a c.1022T>C transition produces a change of leucine by proline (p.L341P). The fourth mutation detected in exon 9 was a c.1036C>T transition which replaces the leucine at codon 346 by phenylalanine (p.L346F). The sequencing of the exon 10 detected three novel missense mutations. The first, a c.1293A>G transition changing isoleucine 431 for methionine (p.I431M). The second, the cytosine at position 1339 was replaced by adenine (c.1339C>A) resulting in the replacement of proline by threonine (p.P447T). The third mutation detected in exon 10 was a c.1358C>T transition resulting in the substitution of proline at codon 453 by leucine (p.P453L). Finally, sequencing analysis of the THRB gene revealed three substitutions previously described (p.A268G, p.P453T and p.F459C). The p.P453T was found in two patients. In conclusion, we report thirteen patients with RTH caused by heterozygous mutations of the THRB gene. Seven of the identified mutations correspond to novel substitutions. Fil: Rivolta, Carina Marcela. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Genética y Biología Molecular; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Salamanca; España Fil: Olcese, María Cecilia. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Genética y Biología Molecular; Argentina Fil: Belforte, Fiorella Sabrina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Genética y Biología Molecular; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Chiesa, Ana Elena. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones Endocrinológicas "Dr. César Bergada". Gobierno de la Ciudad de Buenos Aires. Centro de Investigaciones Endocrinológicas "Dr. César Bergada". Fundación de Endocrinología Infantil. Centro de Investigaciones Endocrinológicas "Dr. César Bergada"; Argentina Fil: Gruñeiro Papendieck, Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones Endocrinológicas "Dr. César Bergada". Gobierno de la Ciudad de Buenos Aires. Centro de Investigaciones Endocrinológicas "Dr. César Bergada". Fundación de Endocrinología Infantil. Centro de Investigaciones Endocrinológicas "Dr. César Bergada"; Argentina Fil: Iorcansky, Sonia. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; Argentina Fil: Herzovich, Viviana. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; Argentina Fil: Cassorla, Fernando. Universidad de Santiago de Chile. Hospital Clinico San Borja Arriaran. Instituto de Investigaciones Materno Infantil; Chile Fil: Gauna, Alicia Teresa. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos "Ramos Mejía"; Argentina Fil: Gonzalez Sarmiento, Rogelio. Universidad de Salamanca; España Fil: Targovnik, Hector Manuel. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Genética y Biología Molecular; Argentina. Universidad de Salamanca; España. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina

Ebolavirus and Marburgvirus (belonging to the Filoviridae family) emerged four decades ago and cause epidemics of haemorrhagic fever with high case-fatality rates. The genome of filoviruses encodes seven proteins. No significant homology is observed between filovirus proteins and any known macromolecule. Moreover, Marburgvirus and Ebolavirus show significant differences in protein homology. The natural maintenance cycle of filoviruses is unknown, the natural reservoir, the mode of transmission, the epidemic disease generation, and temporal dynamics are unclear. Lastly, Ebolavirus and Marburgvirus are considered as potential biological weapons. Vaccine appears the unique therapeutic frontier. Here, molecular and clinical aspects of filoviral haemorrhagic fevers are summarized. (c) 2007 Published by Elsevier Ltd.

In this paper, we improve the homology search performance by the combination of the predicted protein secondary structures and protein sequences. Previous research suggested that the straightforward combination of predicted secondary structures did not improve the homology search performance, mostly because of the errors in the structure prediction. We solved this problem by taking into account the confidence scores output by the prediction programs.

Motivation: Remote homology detection between protein sequences is a central problem in computational biology. Discriminative methods involving support vector machines (SVMs) are currently the most effective methods for the problem of superfamily recognition in the Structural Classification Of Proteins (SCOP) database. The performance of SVMs depends critically on the kernel function used to quantify the similarity between sequences. Results: We propose new kernels for strings adapted to biological sequences, which we call local alignment kernels. These kernels measure the similarity between two sequences by summing up scores obtained from local alignments with gaps of the sequences. When tested in combination with SVM on their ability to recognize SCOP superfamilies on a benchmark dataset, the new kernels outperform state-of-the-art methods for remote homology detection. Availability: Software and data available upon request.

High-throughput proteomics technologies, especially the yeast twohybrid system, produce large volumes of protein– protein interaction data organized in networks. The complete sequencing of many genomes raises questions about the extent to which such networks can be transferred between organisms. We attempted to answer this question using the experimentally derived Helicobacter pylori interaction map and the recently described interacting domain profile pair (IDPP) method to predict a virtual map for Escherichia coli. The extensive literature concerning E. coli was used to assess all predicted interactions and to validate the IDPP method, which clusters protein domains by sequence and connectivity similarities. The IDPP method has a much better heuristic value than methods solely based on protein homology. The IDPP method was further applied to Campylobacter jejuni to generate a virtual interaction map. An in-depth comparison of the chemotaxis pathways predicted in E. coli and C. jejuni led to the proposition of new functional assignments. Finally, the prediction of protein –protein interaction maps across organisms enabled us to validate some of the interactions on the original experimental map. q 2002 Elsevier Science Ltd. All rights reserved

Finding related conformations in the Protein Data Bank is essential in many areas of bioscience. To assist this task, we designed a dihedral angle database for searching protein segment homologs. The search engine relies on encoding of the protein coordinates into text characters representing amino acid sequence, φ and ψ dihedral angles. The search engine is advantageous owing to its high speed and interactive nature and is expected to assist scientists in discovering conformation homologs and evolutionary kinship. The search engine is fast, with query times lasting a few seconds, and freely available at http://tarshish.md.biu.ac.il/∼samsona Contact: avraham.samson@biu.ac.il Supplementary information: Supplementary data are available at Bioinformatics online.

We present here a computational method based on the analysis of amino acid composition for performing comparisons between proteins. This user-friendly and reliable test is aimed at rapidly identifying, from data-base subsets, sequences--if necessary, partial sequences--which share similar amino acid compositions to the input composition (deduced from experimental results). Apparent molecular weight (as determined by SDS-PAGE) and artefactual modifications due to the experimental determination of the amino acid composition are taken into account to perform the comparison. This program thus constitutes a useful tool in searching for the probable identification of either non-sequenced proteins or peptides from hydrolysed proteins.

Motivation: Detecting homologous proteins is one of the fundamental problems in computational biology. Many tools to solve this problem have been developed, but development of a simple, effective and generally applicable method is still desirable. Results: We propose a simple but effective information retrieval approach, named SIMPRO, to identify homology relationship between proteins. The key idea of our approach is that by accumulating and comparing indirect signals from conventional homology search methods, the search sensitivity can be increased. We tested the idea on the problem of detecting homology relationship between Pfam families, as well as detecting structural homologs based on SCOP, and found that our method achieved significant improvement. Our results indicate that simple manipulation of conventional homology search outputs by SIMPRO algorithm can remarkably improve homology search accuracy. Contact: kds@kaist.ac.kr

Summary: We present a large-scale implementation of the Rankprop protein homology ranking algorithm in the form of an openly accessible web server. We use the NRDB40 PSI-BLAST all-versus-all protein similarity network of 1.1 million proteins to construct the graph for the Rankprop algorithm, whereas previously, results were only reported for a database of 108 000 proteins. We also describe two algorithmic improvements to the original algorithm, including propagation from multiple homologs of the query and better normalization of ranking scores, that lead to higher accuracy and to scores with a probabilistic interpretation. Availability: The Rankprop web server and source code are available at http://rankprop.gs.washington.edu Contact: iain@nec-labs.com; noble@gs.washington.edu

The comparative analysis of primary and secondary structures, and hydropathy plots of hepatitis B virus (HBV) and hepatitis delta virus (HDV) proteins was carried out. Two short regions belonging to the HBV terminal protein were shown to be homologous to two regions; one encoded by HDV ORF5, and the other encoded by small ORF of the HDV antigenomic RNA strand. We propose a new protein containing both these regions may be synthesized in HDV infected cells. Stiking structural homology between the terminal protein of HBV and this predicted protein called HDAg' of HDV may indicate a possible functional similarity.'We hypothesize the HDAg' may interact with and inhibit the polymerase activity of HBV.

Summary: PERMOL is a new restraint-based program for homology modeling of proteins. Restraints are generated from the information contained in structures of homologous template proteins. Employing the restraints generated by PERMOL, three-dimensional structures are obtained using MD programs such as DYANA or CNS. In contrast to other programs PERMOL is mainly based on the use of dihedral angle information which is optimally suited to preserve the local secondary structure. The global arrangement of these elements is then facilitated by a small number of distance restraints. Using PERMOL homology, models of high quality are obtained. A key advantage of the proposed method is its flexibility, which allows the inclusion of data from other sources, such as experimental restraints and the use of modern molecular dynamics programs to calculate structures. Availability: The software and a detailed manual are available free of charge (http://www.biologie.uni-regensburg.de/Biophysik/Kalbitzer/permol/permol.html) Contact: hans-robert.kalbitzer@biologie.uni-regensburg.de

Vinculin is thought to be an important cytoskeletal protein in the linkage between actin cytoskeleton and integrin transmembrane receptors. We identified Vinculin (Vinc) gene in the X chromosome of D. melanogaster. Drosophila vinculin is highly homologous in its N- and C-terminal domains both to mammalian and nematode vinculins, and contains internal repeats and proline-rich region typical for vinculins. The X chromosome rearrangement In(1LR)pn2a was found to disrupt Vinc so that the coding sequence is interrupted by the (AAGAG)n satellite DNA. Northern analysis revealed that the Vinc transcript is completely absent in the In(1LR)pn2a homozygous flies. Surprisingly, these Vinc flies are viable and fertile. This finding highlights plasticity and adaptive capacity of cellular cytoskeletal and anchorage system.

Protein homology modelling typically involves the prediction of side-chain conformations in the modelled protein while assuming a main-chain trace taken from a known tertiary structure of a protein with homologous sequence. It is generally believed that the need to examine all possible combinations of side-chain conformations poses the major obstacle to accurate homology modelling. Methods proposed heretofore use only discrete or limited searches of the side-chain torsion angle space to mitigate the combinatorial problem and also rely on simplified energy functions for calculational speed. The configurational constraints are typically based upon use of frequently observed torsion angles, fixed steps in torsion angles, or oligopeptide segments taken from tertiary structural databanks that are similar in sequence and conformation with the target structure. In the present work, a more fundamental approach is explored for several protein structures and it is demonstrated that the combinatorial barrier in side-chain placement hardly exists. Each side-group can be configured individually in the environment of only the backbone atoms using a systematic search procedure combined with extensive local energy minimization. Tests, using the main-chain or both the main-chain and remaining side-charm atoms to calculate low energy geometries for each residue, established the dominance of the main-chain contribution. The final structure is achieved by combining the individually placed side-chains followed by a full energy refinement of the structure. The prediction accuracy of the present homology modelling technique was assessed relative to other automated procedures and was found to yield improved predictions relative to the known side-chain conformations determined by X-ray crystallography.

Homeobox-containing sequences were isolated from a genomic library of zebrafish (Brachydanio rerio). A λ clone containing two homeobox cross-hybridizing regions was characterized. DNA sequencing of one of these regions (ZF-21) revealed that it contains a homeobox closely related to the Antennapedia class of Drosophila homeobox sequences. Moreover, the deduced amino acid sequence of the C-terminal end (81 residues including the homeobox) is identical to the corresponding part of the murine Hox-2.1 protein. Similar to Hox-2.1, a ZF-21 derived transcript of 2.3 kb is present in embryos at the somite forming stages.

The N-terminal amino acid sequences of two chloroform/methanol soluble globulins from barley and one form wheat are reported. They are homologous with N-terminal sequences previously reported for α-amylase and trypsin inhibitors from cereals and 2 S storage proteins from castor bean and rape. Three albumins were also purified from Aegilops squarrosa and Triticum monococcum. These had N-terminal amino acid sequences most closely related to the α-amylase and trypsin inhibitors. The relationships of this superfamily of seed proteins are discussed.

The sequences of three mitochondrial carriers involved in energy transfer, the ADP/ATP carrier, phosphate carrier and uncoupling carrier, are analyzed. Similarly to what has been previously reported for the ADP/ATP carrier and the uncoupling protein, now also the phosphate carrier is found to have a tripartite structure comprising three similar repeats of approx. 100 residues each. The three sequences show a fair overall homology with each other. More significant homologies are found by comparing the repeats within and between the carriers in a scheme where the sequences are spliced into repeats, which are arranged for maximum homology by allowing possible insertions or deletions. A striking conservation of critical residues, glycine, proline, of charged and of aromatic residues is found throughout all nine repeats. This is indicative of a similar structural principle in the repeats. Hydropathy profiles of the three proteins and a search for amphipathic α-spans reveal six membrane-spanning segments for each carrier, providing further support for the basic structural identity of the repeats. The proposed folding pattern of the carriers in the membrane is exemplified with the phosphate carrier. A possible tertiary arrangement of the repeats and the membranespanning helices is shown. The emergence of a mitochondrial carrier family by triplication and by divergent evolution from a common gene of about 100 residues is discussed.

Neural networks provide a basis for semiempirical studies of pattern matching between the primary and secondary structures of proteins. Networks of the perceptron class have been trained to classify the amino-acid residues into two categories for each of three types of secondary feature: α-helix or not, β-sheet or not, and random coil or not. The explicit prediction for the helices in rhodopsin is compared with both electron microscopy results and those of the Chou-Fasman method. A new measure of homology between proteins is provided by the network approach, which thereby leads to quantification of the differences between the primary structures of proteins.

The zebrafish genome was found to contain two sequences which cross-hybridize strongly with the engrailed gene of Drosophila. Several independent clones containing one of these cross-hybridizing sequences were isolated from a zebrafish genomic library. Characterization of this region (ZF-EN) by DNA sequencing showed that it shares about 70% sequence identity with the engrailed homeobox. More extensive homeobox homology (greater than 90%) was found relative to the murine En genes. The closest relationship exists between ZF-EN and En-2 where the C-terminal domains (104 amino acids) encoded by these genes are almost identical. We also observed that ZF-EN and En-2 are very similar with respect to their transcript sizes and temporal expression patterns.

We have explored the structural features of cobalamin binding proteins by peptide mapping. The present report is a comparison of the radioiodinated tryptic peptides of intrinsic factor, transcobalamin and haptocorrin from man, hog and rabbit. The results show that the homology between analogous proteins from different species is close for intrinsic factor and transcobalamin and weaker for haptocorrin. The results also suggest the existence of one or more regions which, with minor changes, are conserved among all proteins investigated. This implies a common evolutionary origin for all the cobalamin binding proteins studied.

By means of comparative analysis of primary and secondary structures, and hydropathy plots of hepadnavirus P proteins new functional domains were revealed additionally to the polymerase domain which had been found earlier in these proteins. The C-terminal part of P proteins was revealed to be significantly similar to ribonuclease H of E. coli. The ribonuclease H functional domain is known to be an integral entity of retrovirus reverse transcriptase as a rule. Availability of this domain indicates once more the putative reverse transcriptase properties of the P products. The proteins of hepadnaviruses were compared to terminal proteins of picornaviruses, adenoviruses and bacteriophages. The data obtained suggested that a conservative N-terminal region of P proteins functions as protein primer for DNA synthesis in hepadnaviruses.

Beta-lactoglobulin has been found to be a member of a super family of proteins that bind specific ligands and which share common features in their amino acid sequences. Here we show that these features are grouped spatially on the surface of the proteins and suggests that they may be concerned with binding to cell-surface receptors.

Evidence is presented, based on sequence comparison according to Hydrophobic Cluster Analysis, of a structural and evolutionary relationship between the human alpha/beta-interferon receptor and the human and mouse gamma-interferon receptor. These results predict that the human alpha/beta-interferon receptor extracellular part is organised in two homologous subdomains connected by a proline linker. They also predict that both subdomains present some homologies to the external domain of mouse and human gamma interferon receptor.

Protein homology studies with catalase as a reference point were carried out with coagulase-positive staphylococci belonging to Staphylococcus aureus, S. intermedius and S. hyicus. Antisera against catalases of S. aureus ATCC 12600 and S. aureus ATCC12601 reacted very weakly employing double immunodiffusion and quantitative microcomplement-fixation assay with cell-free extracts or catalase enriched preparations of S. intermedius or S. hyicus. The differences between coagulase-positive staphylococci could be confirmed by using the antiserum against S. intermedius H 11 catalase. Within the strains of the species S. intermedius immunological distances ranging up to 25 indicate a heterogeneity which justify the separation of the biotypes E and F on a subspecies level. Coagulase-positive strains of S. hyicus revealed neither a close relationship to S. aureus nor to S. intermedius.

Protein S14 was isolated in pure form from Escherichia coli ribosomal 30 S subunits. Its complete amino acid sequence was determined by a combination of various approaches, such as enzymatic and chemical cleavage of the protein chain, isolation of the resulting peptides as well as manual and automatic sequence determination by the Edman degradation technique. The protein has an Mr of 11 191 and consists of 98 amino acid residues, 26 of which are basic and 9 acidic. One residue each of cysteine, histidine, tyrosine and tryptophan is present in the protein. The secondary structure of protein S14 as predicted according to 4 different programs shows a long α-helix in the N-terminal region and a short α-helix near the C-terminus of the protein chain. When the amino acid sequence of protein S14 was compared with that of all other E. coli ribosomal proteins with computer search programs, only relatively short homologous regions were found. A comparison between protein S14 of E. coli and the homologous protein from Bacillus stearothermophilus revealed ∼35% identity within the protein regions available for comparison.

This study describes the generation of a three-dimensional quantitative structure activity relationship (3D-QSAR) model for 29 structurally diverse, competitive CYP2C9 inhibitors defined experimentally from an initial data set of 73 compounds. In parallel, a homology model for CYP2C9 using the rabbit CYP2C5 coordinates was built. For molecules with a known interaction mode with CYP2C9, this homology model, in combination with the docking program GOLD, was used to select conformers to use in the 3D-QSAR analysis. The remaining molecules were docked, and the GRID interaction energies for all conformers proposed by GOLD were calculated. This was followed by a principal component analysis (PCA) of the GRID energies for all conformers of all compounds. Based on the similarity in the PCA plot to the inhibitors with a known interaction mode, the conformer to be used in the 3D-QSAR analysis was selected. The compounds were randomly divided into two groups, the training data set (n = 21) to build the model and the external validation set (n = 8). The PLS (partial least-squares) analysis of the interaction energies against the K(i) values generated a model with r(2) = 0.947 and a cross-validation of q(2) = 0.730. The model was able to predict the entire external data set within 0.5 log units of the experimental K(i) values. The amino acids in the active site showed complementary features to the grid interaction energies in the 3D-QSAR model and were also in agreement with mutagenesis studies.