ProSMoS server: a pattern-based search using interaction matrix representation of protein structures
TL;DR: The approach to protein structure pattern search (ProSMoS) is implemented as a web-server that converts 3D structure into an interaction matrix representation including the SSE types, handednesses of connections between SSEs, coordinates of SSE starts and ends, types of interactions between S SEs and β-sheet definitions.
Abstract: Assessing structural similarity and defining common regions through comparison of protein spatial structures is an important task in functional and evolutionary studies of proteins. There are many servers that compare structures and define sub-structures in common between proteins through superposition and closeness of either coordinates or contacts. However, a natural way to analyze a structure for experts working on structure classification is to look for specific three-dimensional (3D) motifs and patterns instead of finding common features in two proteins. Such motifs can be described by the architecture and topology of major secondary structural elements (SSEs) without consideration of subtle differences in 3D coordinates. Despite the importance of motif-based structure searches, currently there is a shortage of servers to perform this task. Widely known TOPS does not fully address this problem, as it finds only topological match but does not take into account other important spatial properties, such as interactions and chirality. Here, we implemented our approach to protein structure pattern search (ProSMoS) as a web-server. ProSMoS converts 3D structure into an interaction matrix representation including the SSE types, handednesses of connections between SSEs, coordinates of SSE starts and ends, types of interactions between SSEs and β-sheet definitions. For a user-defined structure pattern, ProSMoS lists all structures from a database that contain this pattern. ProSMoS server will be of interest to structural biologists who would like to analyze very general and distant structural similarities. The ProSMoS web server is available at: http://prodata.swmed.edu/ProSMoS/.
Citations
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TL;DR: The iPBA web server presents an improved alignment approach using (i) specialized PB Substitution Matrices (SM) and (ii) anchor-based alignment methodology, and the quality of ∼88% of alignments was improved and iPBA alignments were also better than DALI, MUSTANG and GANGSTA+ in >80% of the cases.
Abstract: With the immense growth in the number of available protein structures, fast and accurate structure comparison has been essential. We propose an efficient method for structure comparison, based on a structural alphabet. Protein Blocks (PBs) is a widely used structural alphabet with 16 pentapeptide conformations that can fairly approximate a complete protein chain. Thus a 3D structure can be translated into a 1D sequence of PBs. With a simple Needleman-Wunsch approach and a raw PB substitution matrix, PB-based structural alignments were better than many popular methods. iPBA web server presents an improved alignment approach using (i) specialized PB Substitution Matrices (SM) and (ii) anchor-based alignment methodology. With these developments, the quality of ∼88% of alignments was improved. iPBA alignments were also better than DALI, MUSTANG and GANGSTA(+) in >80% of the cases. The webserver is designed to for both pairwise comparisons and database searches. Outputs are given as sequence alignment and superposed 3D structures displayed using PyMol and Jmol. A local alignment option for detecting subs-structural similarity is also embedded. As a fast and efficient 'sequence-based' structure comparison tool, we believe that it will be quite useful to the scientific community. iPBA can be accessed at http://www.dsimb.inserm.fr/dsimb_tools/ipba/.
97 citations
TL;DR: The Critical assessment of protein structure prediction round 9 (CASP9) aimed to evaluate predictions for 129 experimentally determined protein structures by dividing them into domain‐based evaluation units that were then classified into two assessment categories: template based modeling (TBM) and template free modeling (FM).
Abstract: The Critical assessment of protein structure prediction round 9 (CASP9) aimed to evaluate predictions for 129 experimentally determined protein structures. To assess tertiary structure predictions, these target structures were divided into domain-based evaluation units that were then classified into two assessment categories: template based modeling (TBM) and template free modeling (FM). CASP9 targets were split into domains of structurally compact evolutionary modules. For the targets with more than one defined domain, the decision to split structures into domains for evaluation was based on server performance. Target domains were categorized based on their evolutionary relatedness to existing templates as well as their difficulty levels indicated by server performance. Those target domains with sequence-related templates and high server prediction performance were classified as TMB, whereas those targets without identifiable templates and low server performance were classified as FM. However, using these generalizations for classification resulted in a blurred boundary between CASP9 assessment categories. Thus, the FM category included those domains without sequence detectable templates (25 target domains) as well as some domains with difficult to detect templates whose predictions were as poor as those without templates (five target domains). Several interesting examples are discussed, including targets with sequence related templates that exhibit unusual structural differences, targets with homologous or analogous structure templates that are not detectable by sequence, and targets with new folds. Proteins 2011; © 2011 Wiley-Liss, Inc.
62 citations
TL;DR: An improved heuristic for tableau-based protein structure and substructure searching using simulated annealing is developed, that is as fast or faster and comparable in accuracy, with some widely used existing methods.
Abstract: Searching a database of protein structures for matches to a query structure, or occurrences of a structural motif, is an important task in structural biology and bioinformatics. While there are many existing methods for structural similarity searching, faster and more accurate approaches are still required, and few current methods are capable of substructure (motif) searching. We developed an improved heuristic for tableau-based protein structure and substructure searching using simulated annealing, that is as fast or faster and comparable in accuracy, with some widely used existing methods. Furthermore, we created a parallel implementation on a modern graphics processing unit (GPU). The GPU implementation achieves up to 34 times speedup over the CPU implementation of tableau-based structure search with simulated annealing, making it one of the fastest available methods. To the best of our knowledge, this is the first application of a GPU to the protein structural search problem.
50 citations
TL;DR: This survey is focused on systematically presenting the available Web-based tools that aid in repositioning drugs, and aims to build user-friendly interfaces to extend the user-base beyond computational scientists, to include life scientists who may have deeper chemical and biological insights.
Abstract: Drug repurposing (a.k.a. drug repositioning) is the search for new indications or molecular targets distinct from a drug's putative activity, pharmacological effect or binding specificities. With the ever-increasing rates of termination of drugs in clinical trials, drug repositioning has risen as one of the effective solutions against the risk of drug failures. Repositioning finds a way to reverse the grim but real trend that Eroom's law portends for the pharmaceutical and biotech industry, and drug discovery in general. Further, the advent of high-throughput technologies to explore biological systems has enabled the generation of zeta bytes of data and a massive collection of databases that store them. Computational analytics and mining are frequently used as effective tools to explore this byzantine series of biological and biomedical data. However, advanced computational tools are often difficult to understand or use, thereby limiting their accessibility to scientists without a strong computational background. Hence it is of great importance to build user-friendly interfaces to extend the user-base beyond computational scientists, to include life scientists who may have deeper chemical and biological insights. This survey is focused on systematically presenting the available Web-based tools that aid in repositioning drugs.
45 citations
TL;DR: A model to approximate the compact parallel and antiparallel arrangement of α-helices and β-strands, enumerated all possible topologies formed by up to five secondary structural elements (SSEs), searched for their occurrence in spatial structures of proteins, and documented their frequencies of occurrence in the PDB.
19 citations
References
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TL;DR: This database provides a detailed and comprehensive description of the structural and evolutionary relationships of the proteins of known structure and provides for each entry links to co-ordinates, images of the structure, interactive viewers, sequence data and literature references.
6,603 citations
"ProSMoS server: a pattern-based sea..." refers background in this paper
...instead of finding common features in two proteins (7,8)....
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TL;DR: The present paper describes the SSM algorithm of protein structure comparison in three dimensions, which includes an original procedure of matching graphs built on the protein's secondary-structure elements, followed by an iterative three-dimensional alignment of protein backbone Calpha atoms.
Abstract: The present paper describes the SSM algorithm of protein structure comparison in three dimensions, which includes an original procedure of matching graphs built on the protein's secondary-structure elements, followed by an iterative three-dimensional alignment of protein backbone C_\alpha atoms. The SSM results are compared with those obtained from other protein comparison servers, and the advantages and disadvantages of different scores that are used for structure recognition are discussed. A new score, balancing the r.m.s.d. and alignment length N_{\rm align}, is proposed. It is found that different servers agree reasonably well on the new score, while showing considerable differences in r.m.s.d. and N_{\rm align}.
3,658 citations
"ProSMoS server: a pattern-based sea..." refers methods in this paper
...We compared the performance of ProSMoS, TOPS and SSM previously (9), and the results indicate that our method finds more matches than other two programs....
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...The only other similar web servers are SSM (14) and TableauSearch (15)....
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...Although SSM is based on the secondary structure matching, it is a structure similarity search rather than a pattern search program....
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TL;DR: This note reports paradigm revisions that enable maintaining such a knowledge base up-to-date on a PC using the Dali server, a frugal solution to reduce the total computational cost by pruning search space using prior knowledge about the distribution of structures in fold space.
Abstract: The Red Queen said, ‘It takes all the running you can do, to keep in the same place.’ Lewis Carrol
Motivation: Newly solved protein structures are routinely scanned against structures already in the Protein Data Bank (PDB) using Internet servers. In favourable cases, comparing 3D structures may reveal biologically interesting similarities that are not detectable by comparing sequences. The number of known structures continues to grow exponentially. Sensitive—thorough but slow—search algorithms are challenged to deliver results in a reasonable time, as there are now more structures in the PDB than seconds in a day. The brute-force solution would be to distribute the individual comparisons on a massively parallel computer. A frugal solution, as implemented in the Dali server, is to reduce the total computational cost by pruning search space using prior knowledge about the distribution of structures in fold space. This note reports paradigm revisions that enable maintaining such a knowledge base up-to-date on a PC.
Availability: The Dali server for protein structure database searching at http://ekhidna.biocenter.helsinki.fi/dali_server is running DaliLite v.3. The software can be downloaded for academic use from http://ekhidna.biocenter.helsinki.fi/dali_lite/downloads/v3.
Contact: liisa.holm@helsinki.fi
1,013 citations
Journal Article•
TL;DR: The Structural Classification of Proteins (SCOP) database as discussed by the authors is a comprehensive ordering of all proteins of known structure, according to their evolutionary and structural relationships, including species, protein, family, superfamily, fold and class.
Abstract: The Structural Classification of Proteins (SCOP) database is a comprehensive ordering of all proteins of known structure, according to their evolutionary and structural relationships. The SCOP hierarchy comprises the following levels: Species, Protein, Family, Superfamily, Fold and Class. While keeping the original classification scheme intact, we have changed the production of SCOP in order to cope with a rapid growth of new structural data and to facilitate the discovery of new protein relationships. We describe ongoing developments and new features implemented in SCOP. A new update protocol supports batch classification of new protein structures by their detected relationships at Family and Superfamily levels in contrast to our previous sequential handling of new structural data by release date. We introduce pre-SCOP, a preview of the SCOP developmental version that enables earlier access to the information on new relationships. We also discuss the impact of worldwide Structural Genomics initiatives, which are producing new protein structures at an increasing rate, on the rates of discovery and growth of protein families and superfamilies. SCOP can be accessed at http://scop.mrc-lmb.cam.ac.uk/scop.
928 citations
TL;DR: Pre-SCOP, a preview of the SCOP developmental version that enables earlier access to the information on new relationships, is introduced and the impact of worldwide Structural Genomics initiatives, which are producing new protein structures at an increasing rate, on the rates of discovery and growth of protein families and superfamilies is discussed.
Abstract: The Structural Classification of Proteins (SCOP) database is a comprehensive ordering of all proteins of known structure, according to their evolutionary and structural relationships. The SCOP hierarchy comprises the following levels: Species, Protein, Family, Superfamily, Fold and Class. While keeping the original classification scheme intact, we have changed the production of SCOP in order to cope with a rapid growth of new structural data and to facilitate the discovery of new protein relationships. We describe ongoing developments and new features implemented in SCOP. A new update protocol supports batch classification of new protein structures by their detected relationships at Family and Superfamily levels in contrast to our previous sequential handling of new structural data by release date. We introduce pre-SCOP, a preview of the SCOP developmental version that enables earlier access to the information on new relationships. We also discuss the impact of worldwide Structural Genomics initiatives, which are producing new protein structures at an increasing rate, on the rates of discovery and growth of protein families and superfamilies. SCOP can be accessed at http:// scop.mrc-lmb.cam.ac.uk/scop.
880 citations
"ProSMoS server: a pattern-based sea..." refers background or methods in this paper
...base [PDB or SCOP (10)] and reports structures matching...
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...Here, we use the ferredoxin-like fold (10,19) to illustrate the output of the ProSMoS Server....
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