An optimized TOPS+ comparison method for enhanced TOPS models

This article has been made available through the Brunel Open Access Publishing Fund.Background Although methods based on highly abstract descriptions of protein structures, such as VAST and TOPS, can perform very fast protein structure comparison, the results can lack a high degree of biological significance. Previously we have discussed the basic mechanisms of our novel method for structure comparison based on our TOPS+ model (Topological descriptions of Protein Structures Enhanced with Ligand Information). In this paper we show how these results can be significantly improved using parameter optimization, and we call the resulting optimised TOPS+ method as advanced TOPS+ comparison method i.e. advTOPS+. Results We have developed a TOPS+ string model as an improvement to the TOPS [1-3] graph model by considering loops as secondary structure elements (SSEs) in addition to helices and strands, representing ligands as first class objects, and describing interactions between SSEs, and SSEs and ligands, by incoming and outgoing arcs, annotating SSEs with the interaction direction and type. Benchmarking results of an all-against-all pairwise comparison using a large dataset of 2,620 non-redundant structures from the PDB40 dataset [4] demonstrate the biological significance, in terms of SCOP classification at the superfamily level, of our TOPS+ comparison method. Conclusions Our advanced TOPS+ comparison shows better performance on the PDB40 dataset [4] compared to our basic TOPS+ method, giving 90 percent accuracy for SCOP alpha+beta; a 6 percent increase in accuracy compared to the TOPS and basic TOPS+ methods. It also outperforms the TOPS, basic TOPS+ and SSAP comparison methods on the Chew-Kedem dataset [5], achieving 98 percent accuracy. Software Availability: The TOPS+ comparison server is available at http://balabio.dcs.gla.ac.uk/mallika/WebTOPS/.This article is available through the Brunel Open Access Publishing Fun

Fr-TM-align: a new protein structural alignment method based on fragment alignments and the TM-score

©2008 Pandit and Skolnick; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This article is available from: http://www.biomedcentral.com/1471-2105/9/531doi:10.1186/1471-2105-9-531Background: Protein tertiary structure comparisons are employed in various fields of contemporary structural biology. Most structure comparison methods involve generation of an initial seed alignment, which is extended and/or refined to provide the best structural superposition between a pair of protein structures as assessed by a structure comparison metric. One such metric, the TM-score, was recently introduced to provide a combined structure quality measure of the coordinate root mean square deviation between a pair of structures and coverage. Using the TM-score, the TM-align structure alignment algorithm was developed that was often found to have better accuracy and coverage than the most commonly used structural alignment programs; however, there were a number of situations when this was not true. Results: To further improve structure alignment quality, the Fr-TM-align algorithm has been developed where aligned fragment pairs are used to generate the initial seed alignments that are then refined using dynamic programming to maximize the TM-score. For the assessment of the structural alignment quality from Fr-TM-align in comparison to other programs such as CE and TMalign, we examined various alignment quality assessment scores such as PSI and TM-score. The assessment showed that the structural alignment quality from Fr-TM-align is better in comparison to both CE and TM-align. On average, the structural alignments generated using Fr-TM-align have a higher TM-score (~9%) and coverage (~7%) in comparison to those generated by TM-align. Fr- TM-align uses an exhaustive procedure to generate initial seed alignments. Hence, the algorithm is computationally more expensive than TM-align. Conclusion: Fr-TM-align, a new algorithm that employs fragment alignment and assembly provides better structural alignments in comparison to TM-align. The source code and executables of Fr- TM-align are freely downloadable at: http://cssb.biology.gatech.edu/skolnick/files/FrTMalign/

Predicting residue contacts using pragmatic correlated mutations method: reducing the false positives

BACKGROUND: Predicting residues' contacts using primary amino acid sequence alone is an important task that can guide 3D structure modeling and can verify the quality of the predicted 3D structures. The correlated mutations (CM) method serves as the most promising approach and it has been used to predict amino acids pairs that are distant in the primary sequence but form contacts in the native 3D structure of homologous proteins. RESULTS: Here we report a new implementation of the CM method with an added set of selection rules (filters). The parameters of the algorithm were optimized against fifteen high resolution crystal structures with optimization criterion that maximized the confidentiality of the predictions. The optimization resulted in a true positive ratio (TPR) of 0.08 for the CM without filters and a TPR of 0.14 for the CM with filters. The protocol was further benchmarked against 65 high resolution structures that were not included in the optimization test. The benchmarking resulted in a TPR of 0.07 for the CM without filters and to a TPR of 0.09 for the CM with filters. CONCLUSION: Thus, the inclusion of selection rules resulted to an overall improvement of 30%. In addition, the pair-wise comparison of TPR for each protein without and with filters resulted in an average improvement of 1.7. The methodology was implemented into a web server that is freely available to the public. The purpose of this implementation is to provide the 3D structure predictors with a tool that can help with ranking alternative models by satisfying the largest number of predicted contacts, as well as it can provide a confidence score for contacts in cases where structure is known

FLORA: a novel method to predict protein function from structure in diverse superfamilies

Predicting protein function from structure remains an active area of interest, particularly for the structural genomics initiatives where a substantial number of structures are initially solved with little or no functional characterisation. Although global structure comparison methods can be used to transfer functional annotations, the relationship between fold and function is complex, particularly in functionally diverse superfamilies that have evolved through different secondary structure embellishments to a common structural core. The majority of prediction algorithms employ local templates built on known or predicted functional residues. Here, we present a novel method (FLORA) that automatically generates structural motifs associated with different functional sub-families (FSGs) within functionally diverse domain superfamilies. Templates are created purely on the basis of their specificity for a given FSG, and the method makes no prior prediction of functional sites, nor assumes specific physico-chemical properties of residues. FLORA is able to accurately discriminate between homologous domains with different functions and substantially outperforms (a 2–3 fold increase in coverage at low error rates) popular structure comparison methods and a leading function prediction method. We benchmark FLORA on a large data set of enzyme superfamilies from all three major protein classes (α, β, αβ) and demonstrate the functional relevance of the motifs it identifies. We also provide novel predictions of enzymatic activity for a large number of structures solved by the Protein Structure Initiative. Overall, we show that FLORA is able to effectively detect functionally similar protein domain structures by purely using patterns of structural conservation of all residues

SECRET domain of variola virus CrmB protein can be a member of poxviral type II chemokine-binding proteins family

<p>Abstract</p> <p>Background</p> <p>Variola virus (VARV) the causative agent of smallpox, eradicated in 1980, have wide spectrum of immunomodulatory proteins to evade host immunity. Recently additional biological activity was discovered for VARV CrmB protein, known to bind and inhibit tumour necrosis factor (TNF) through its N-terminal domain homologous to cellular TNF receptors. Besides binding TNF, this protein was also shown to bind with high affinity several chemokines which recruit B- and T-lymphocytes and dendritic cells to sites of viral entry and replication. Ability to bind chemokines was shown to be associated with unique C-terminal domain of CrmB protein. This domain named SECRET (Smallpox virus-Encoded Chemokine Receptor) is unrelated to the host proteins and lacks significant homology with other known viral chemokine-binding proteins or any other known protein.</p> <p>Findings</p> <p><it>De novo </it>modelling of VARV-CrmB SECRET domain spatial structure revealed its apparent structural homology with cowpox virus CC-chemokine binding protein (vCCI) and vaccinia virus A41 protein, despite low sequence identity between these three proteins. Potential ligand-binding surface of modelled VARV-CrmB SECRET domain was also predicted to bear prominent electronegative charge which is characteristic to known orthopoxviral chemokine-binding proteins.</p> <p>Conclusions</p> <p>Our results suggest that SECRET should be included into the family of poxviral type II chemokine-binding proteins and that it might have been evolved from the vCCI-like predecessor protein.</p

Building Science Gateways for Analysing Molecular Docking Results Using a Generic Framework and Methodology

Molecular docking and virtual screening experiments require large computational and data resources and high-level user interfaces in the form of science gateways. While science gateways supporting such experiments are relatively common, there is a clearly identified need to design and implement more complex environments for further analysis of docking results. This paper describes a generic framework and a related methodology that supports the efficient development of such environments. The framework is modular enabling the reuse of already existing components. The methodology, which proposes three techniques that the development team can use, is agile and encourages active participation of end-users. Based on the framework and methodology, two prototype implementations of science-gateway-based docking environments are presented and evaluated. The first system recommends a receptor-ligand pair for the next docking experiment, and the second filters docking results based on ligand properties

A framework for protein structure classification and identification of novel protein structures

BACKGROUND: Protein structure classification plays a central role in understanding the function of a protein molecule with respect to all known proteins in a structure database. With the rapid increase in the number of new protein structures, the need for automated and accurate methods for protein classification is increasingly important. RESULTS: In this paper we present a unified framework for protein structure classification and identification of novel protein structures. The framework consists of a set of components for comparing, classifying, and clustering protein structures. These components allow us to accurately classify proteins into known folds, to detect new protein folds, and to provide a way of clustering the new folds. In our evaluation with SCOP 1.69, our method correctly classifies 86.0%, 87.7%, and 90.5% of new domains at family, superfamily, and fold levels. Furthermore, for protein domains that belong to new domain families, our method is able to produce clusters that closely correspond to the new families in SCOP 1.69. As a result, our method can also be used to suggest new classification groups that contain novel folds. CONCLUSION: We have developed a method called proCC for automatically classifying and clustering domains. The method is effective in classifying new domains and suggesting new domain families, and it is also very efficient. A web site offering access to proCC is freely available a

CMASA: an accurate algorithm for detecting local protein structural similarity and its application to enzyme catalytic site annotation

<p>Abstract</p> <p>Background</p> <p>The rapid development of structural genomics has resulted in many "unknown function" proteins being deposited in Protein Data Bank (PDB), thus, the functional prediction of these proteins has become a challenge for structural bioinformatics. Several sequence-based and structure-based methods have been developed to predict protein function, but these methods need to be improved further, such as, enhancing the accuracy, sensitivity, and the computational speed. Here, an accurate algorithm, the CMASA (Contact MAtrix based local Structural Alignment algorithm), has been developed to predict unknown functions of proteins based on the local protein structural similarity. This algorithm has been evaluated by building a test set including 164 enzyme families, and also been compared to other methods.</p> <p>Results</p> <p>The evaluation of CMASA shows that the CMASA is highly accurate (0.96), sensitive (0.86), and fast enough to be used in the large-scale functional annotation. Comparing to both sequence-based and global structure-based methods, not only the CMASA can find remote homologous proteins, but also can find the active site convergence. Comparing to other local structure comparison-based methods, the CMASA can obtain the better performance than both FFF (a method using geometry to predict protein function) and SPASM (a local structure alignment method); and the CMASA is more sensitive than PINTS and is more accurate than JESS (both are local structure alignment methods). The CMASA was applied to annotate the enzyme catalytic sites of the non-redundant PDB, and at least 166 putative catalytic sites have been suggested, these sites can not be observed by the Catalytic Site Atlas (CSA).</p> <p>Conclusions</p> <p>The CMASA is an accurate algorithm for detecting local protein structural similarity, and it holds several advantages in predicting enzyme active sites. The CMASA can be used in large-scale enzyme active site annotation. The CMASA can be available by the mail-based server (<url>http://159.226.149.45/other1/CMASA/CMASA.htm</url>).</p

Integrating sequence and structural biology with DAS.

BACKGROUND: The Distributed Annotation System (DAS) is a network protocol for exchanging biological data. It is frequently used to share annotations of genomes and protein sequence. RESULTS: Here we present several extensions to the current DAS 1.5 protocol. These provide new commands to share alignments, three dimensional molecular structure data, add the possibility for registration and discovery of DAS servers, and provide a convention how to provide different types of data plots. We present examples of web sites and applications that use the new extensions. We operate a public registry of DAS sources, which now includes entries for more than 250 distinct sources. CONCLUSION: Our DAS extensions are essential for the management of the growing number of services and exchange of diverse biological data sets. In addition the extensions allow new types of applications to be developed and scientific questions to be addressed. The registry of DAS sources is available at http://www.dasregistry.org.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

TOPS++FATCAT: Fast flexible structural alignment using constraints derived from TOPS+ Strings Model

<p>Abstract</p> <p>Background</p> <p>Protein structure analysis and comparison are major challenges in structural bioinformatics. Despite the existence of many tools and algorithms, very few of them have managed to capture the intuitive understanding of protein structures developed in structural biology, especially in the context of rapid database searches. Such intuitions could help speed up similarity searches and make it easier to understand the results of such analyses.</p> <p>Results</p> <p>We developed a TOPS++FATCAT algorithm that uses an intuitive description of the proteins' structures as captured in the popular TOPS diagrams to limit the search space of the aligned fragment pairs (AFPs) in the flexible alignment of protein structures performed by the FATCAT algorithm. The TOPS++FATCAT algorithm is faster than FATCAT by more than an order of magnitude with a minimal cost in classification and alignment accuracy. For beta-rich proteins its accuracy is better than FATCAT, because the TOPS+ strings models contains important information of the parallel and anti-parallel hydrogen-bond patterns between the beta-strand SSEs (Secondary Structural Elements). We show that the TOPS++FATCAT errors, rare as they are, can be clearly linked to oversimplifications of the TOPS diagrams and can be corrected by the development of more precise secondary structure element definitions.</p> <p>Software Availability</p> <p>The benchmark analysis results and the compressed archive of the TOPS++FATCAT program for Linux platform can be downloaded from the following web site: <url>http://fatcat.burnham.org/TOPS/</url></p> <p>Conclusion</p> <p>TOPS++FATCAT provides FATCAT accuracy and insights into protein structural changes at a speed comparable to sequence alignments, opening up a possibility of interactive protein structure similarity searches.</p