A high level interface to SCOP and ASTRAL implemented in Python

BACKGROUND: Benchmarking algorithms in structural bioinformatics often involves the construction of datasets of proteins with given sequence and structural properties. The SCOP database is a manually curated structural classification which groups together proteins on the basis of structural similarity. The ASTRAL compendium provides non redundant subsets of SCOP domains on the basis of sequence similarity such that no two domains in a given subset share more than a defined degree of sequence similarity. Taken together these two resources provide a 'ground truth' for assessing structural bioinformatics algorithms. We present a small and easy to use API written in python to enable construction of datasets from these resources. RESULTS: We have designed a set of python modules to provide an abstraction of the SCOP and ASTRAL databases. The modules are designed to work as part of the Biopython distribution. Python users can now manipulate and use the SCOP hierarchy from within python programs, and use ASTRAL to return sequences of domains in SCOP, as well as clustered representations of SCOP from ASTRAL. CONCLUSION: The modules make the analysis and generation of datasets for use in structural genomics easier and more principled

Tableau-based protein substructure search using quadratic programming

<p>Abstract</p> <p>Background</p> <p>Searching for proteins that contain similar substructures is an important task in structural biology. The exact solution of most formulations of this problem, including a recently published method based on tableaux, is too slow for practical use in scanning a large database.</p> <p>Results</p> <p>We developed an improved method for detecting substructural similarities in proteins using tableaux. Tableaux are compared efficiently by solving the quadratic program (QP) corresponding to the quadratic integer program (QIP) formulation of the extraction of maximally-similar tableaux. We compare the accuracy of the method in classifying protein folds with some existing techniques.</p> <p>Conclusion</p> <p>We find that including constraints based on the separation of secondary structure elements increases the accuracy of protein structure search using maximally-similar subtableau extraction, to a level where it has comparable or superior accuracy to existing techniques. We demonstrate that our implementation is able to search a structural database in a matter of hours on a standard PC.</p

Fast and accurate protein substructure searching with simulated annealing and GPUs

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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).</p> <p>Conclusions</p> <p>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.</p

Network modularity and local environment similarity as descriptors of protein structure

As the number of solved protein structures increases, the opportunities for meta-analysis of this dataset increase too. Here we explore two approaches for analysing protein structure, both starting from the three-dimensional co-ordinates of each atom within the structure, which are then abstracted into a more useful form. The first method transforms the protein into a network in which its amino acids are the nodes, and where the edges are generated using a simple proximity test. By applying the Infomap community detection algorithm, we can fragment the protein into highly intra-connected subregions - these subregions are compact and globular, and can be compared with known structural and functional subunits of the protein (also known as domains). By performing this fragmentation process systematically across a large set of proteins, and checking for structurally conserved fragments, we can search for novel candidate domains. This method for automatically decomposing a protein into compact substructures may also be useful in coarse-graining molecular dynamics, analysing the protein’s topology, in de novo protein design, or in fitting electron density maps derived from single particle electron microscopy. The second method calculates a descriptor for each atom of the protein based on its local environment, known as a Smooth Overlap of Atomic Positions (SOAP) descriptor. Using these descriptors we can perform overall comparisons of the subregions identified above. In addition, by comparing the descriptors of a set of proteins known to share common structural or functional features (such as binding of a particular ligand), we can automatically identify the most highly conserved atoms of the set. These atoms may line ligand binding pockets or correspond to allosteric sites, which could inform drug design

A structure filter for the Eukaryotic Linear Motif Resource

<p>Abstract</p> <p>Background</p> <p>Many proteins are highly modular, being assembled from globular domains and segments of natively disordered polypeptides. Linear motifs, short sequence modules functioning independently of protein tertiary structure, are most abundant in natively disordered polypeptides but are also found in accessible parts of globular domains, such as exposed loops. The prediction of novel occurrences of known linear motifs attempts the difficult task of distinguishing functional matches from stochastically occurring non-functional matches. Although functionality can only be confirmed experimentally, confidence in a putative motif is increased if a motif exhibits attributes associated with functional instances such as occurrence in the correct taxonomic range, cellular compartment, conservation in homologues and accessibility to interacting partners. Several tools now use these attributes to classify putative motifs based on confidence of functionality.</p> <p>Results</p> <p>Current methods assessing motif accessibility do not consider much of the information available, either predicting accessibility from primary sequence or regarding any motif occurring in a globular region as low confidence. We present a method considering accessibility and secondary structural context derived from experimentally solved protein structures to rectify this situation. Putatively functional motif occurrences are mapped onto a representative domain, given that a high quality reference SCOP domain structure is available for the protein itself or a close relative. Candidate motifs can then be scored for solvent-accessibility and secondary structure context. The scores are calibrated on a benchmark set of experimentally verified motif instances compared with a set of random matches. A combined score yields 3-fold enrichment for functional motifs assigned to high confidence classifications and 2.5-fold enrichment for random motifs assigned to low confidence classifications. The structure filter is implemented as a pipeline with both a graphical interface via the ELM resource <url>http://elm.eu.org/</url> and through a Web Service protocol.</p> <p>Conclusion</p> <p>New occurrences of known linear motifs require experimental validation as the bioinformatics tools currently have limited reliability. The ELM structure filter will aid users assessing candidate motifs presenting in globular structural regions. Most importantly, it will help users to decide whether to expend their valuable time and resources on experimental testing of interesting motif candidates.</p

canSAR: an integrated cancer public translational research and drug discovery resource

canSAR is a fully integrated cancer research and drug discovery resource developed to utilize the growing publicly available biological annotation, chemical screening, RNA interference screening, expression, amplification and 3D structural data. Scientists can, in a single place, rapidly identify biological annotation of a target, its structural characterization, expression levels and protein interaction data, as well as suitable cell lines for experiments, potential tool compounds and similarity to known drug targets. canSAR has, from the outset, been completely use-case driven which has dramatically influenced the design of the back-end and the functionality provided through the interfaces. The Web interface at http://cansar.icr.ac.uk provides flexible, multipoint entry into canSAR. This allows easy access to the multidisciplinary data within, including target and compound synopses, bioactivity views and expert tools for chemogenomic, expression and protein interaction network data

HMMerThread: Detecting Remote, Functional Conserved Domains in Entire Genomes by Combining Relaxed Sequence-Database Searches with Fold Recognition

Conserved domains in proteins are one of the major sources of functional information for experimental design and genome-level annotation. Though search tools for conserved domain databases such as Hidden Markov Models (HMMs) are sensitive in detecting conserved domains in proteins when they share sufficient sequence similarity, they tend to miss more divergent family members, as they lack a reliable statistical framework for the detection of low sequence similarity. We have developed a greatly improved HMMerThread algorithm that can detect remotely conserved domains in highly divergent sequences. HMMerThread combines relaxed conserved domain searches with fold recognition to eliminate false positive, sequence-based identifications. With an accuracy of 90%, our software is able to automatically predict highly divergent members of conserved domain families with an associated 3-dimensional structure. We give additional confidence to our predictions by validation across species. We have run HMMerThread searches on eight proteomes including human and present a rich resource of remotely conserved domains, which adds significantly to the functional annotation of entire proteomes. We find ∼4500 cross-species validated, remotely conserved domain predictions in the human proteome alone. As an example, we find a DNA-binding domain in the C-terminal part of the A-kinase anchor protein 10 (AKAP10), a PKA adaptor that has been implicated in cardiac arrhythmias and premature cardiac death, which upon stress likely translocates from mitochondria to the nucleus/nucleolus. Based on our prediction, we propose that with this HLH-domain, AKAP10 is involved in the transcriptional control of stress response. Further remotely conserved domains we discuss are examples from areas such as sporulation, chromosome segregation and signalling during immune response. The HMMerThread algorithm is able to automatically detect the presence of remotely conserved domains in proteins based on weak sequence similarity. Our predictions open up new avenues for biological and medical studies. Genome-wide HMMerThread domains are available at http://vm1-hmmerthread.age.mpg.de

Bioinformatic studies of small disulphide-rich proteins (SDPs)

Ph.DDOCTOR OF PHILOSOPH

DeepREx-WS: A web server for characterising protein–solvent interaction starting from sequence

Protein–solvent interaction provides important features for protein surface engineering when the structure is absent or partially solved. Presently, we can integrate the notion of solvent exposed/buried residues with that of their flexibility and intrinsic disorder to highlight regions where mutations may increase or decrease protein stability in order to modify proteins for biotechnological reasons, while preserving their functional integrity. Here we describe a web server, which provides the unique possibility of integrating knowledge of solvent and non-solvent exposure with that of residue conservation, flexibility and disorder of a protein sequence, for a better understanding of which regions are relevant for protein integrity. The core of the webserver is DeepREx, a novel deep learning-based tool that classifies each residue in the sequence as buried or exposed. DeepREx is trained on a high-quality, non-redundant dataset derived from the Protein Data Bank comprising 2332 monomeric protein chains and benchmarked on a blind test set including 200 protein sequences unrelated with the training set. Results show that DeepREx performs at the state-of-the-art in the field. In turn, the Web Server, DeepREx-WS, supplements the predictions of DeepREx with features that allow a better characterisation of exposed and buried regions: i) residue conservation derived from multiple sequence alignment; ii) local sequence hydrophobicity; iii) residue flexibility computed with MEDUSA; iv) a predictor of secondary structure; v) the presence of disordered regions as derived from MobiDB-Lite3.0. The web server allows browsing, selecting and intersecting the different features. We demonstrate a possible application of the DeepREx-WS for assisting the identification of residues to be variated in protein surface engineering processes

Efficient algorithms and architectures for protein 3-D structure comparison

Η σύγκριση δομών πρωτεϊνών είναι ανεπτυγμένος τομέας της υπολογιστικής πρωτεϊνωμικής που χρησιμοποιείται ευρέως στη δομική βιολογία και την ανακάλυψη φαρμάκων. Οι αυξανόμενες υπολογιστικές απαιτήσεις του είναι αποτέλεσμα τριών παραγόντων: ταχεία επέκταση των βάσεων δεδομένων με νέες δομές πρωτεϊνών, υψηλή υπολογιστική πολυπλοκότητα των αλγορίθμων σύγκρισης δομών πρωτεϊνών κατά ζεύγη (PSC), και τάση χρήσης πολλαπλών μεθόδων σύγκρισης και συνδυασμού των αποτελεσμάτων τους (multi criteria protein structure comparison-MCPSC-), μιας και δεν υπάρχει PSC μέθοδος κοινά αποδεκτή ως η καλύτερη. Αναπτύξαμε πλαίσιο λογισμικού που εκμεταλλεύεται επεξεργαστές πολλών πυρήνων για την υλοποίηση παράλληλων στρατηγικών MCPSC με βάση τρεις δημοφιλείς PSC μεθόδους, τις TMalign, CE και USM. Συγκρίνουμε την απόδοση και αποδοτικότητα δύο παράλληλων υλοποιήσεων MCPSC στον πειραματικό επεξεργαστή δικτύου σε ψηφίδα (Network on Chip)  Intel Single-Chip Cloud Computer και τον δημοφιλή επεξεργαστή Intel Core i7. Επιπλέον, αναπτύξαμε εκτενές υπολογιστικό pipeline και υλοποίησή του με πρόγραμμα Python, που ονομάζεται pyMCPSC, που επιτρέπει στους χρήστες να εκτελούν MCPSC διεργασίες σε επεξεργαστές πολλαπλών πυρήνων. Το pyMCPSC, το οποίο συνδυάζει πέντε μεθόδους PSC και υποστηρίζει πέντε διαφορετικά σχήματα συναίνεσης MCPSC, υποστηρίζει τη συγκριτική ανάλυση μεγάλων συνόλων με δομές πρωτεϊνών και μπορεί να επεκταθεί ώστε να ενσωματώσει και νέες μεθόδους PSC στις βαθμολογίες συναίνεσης, καθώς αυτές καθίστανται διαθέσιμες.Protein Structure Comparison (PSC) is a well developed field of computational proteomics with active interest since it is widely used in structural biology and drug discovery. Fast increasing computational demand for all-to-all protein structures comparison is a result of mainly three factors: rapidly expanding structural proteomics databases, high computational complexity of pairwise PSC algorithms, and the trend towards using multiple criteria for comparison and combining their results (MCPSC). In this thesis we have developed a software framework that exploits many-core and multi-core CPUs to implement efficient parallel MCPSC schemes in modern processors based on three popular PSC methods, namely, TMalign, CE, and USM. We evaluate and compare the performance and efficiency of two parallel MCPSC implementations using Intel’s experimental many-core Single-Chip Cloud Computer (SCC) CPU as well as Intel’s Core i7 multi-core processor. Further, we have developed a dataset processing pipeline and implemented it in a Python utility, called pyMCPSC, allowing users to perform MCPSC efficiently on multi-core CPU. pyMCPSC, which combines five PSC methods and five different consensus scoring schemes, facilitates the analysis of similarities in protein domain datasets and can be easily extended to incorporate more PSC methods in the consensus scoring as they are becoming available