SUMMARY: Intra- and intermolecular contact surfaces are routinely calculated for a large array of applications in bioinformatics but are typically approximated from differential solvent accessible surface area calculations and not calculated directly. These approximations do not properly take the effects of neighboring atoms into account and tend to deviate considerably from the true contact surface. We implemented an extension of the original Shrake-Rupley algorithm to accurately estimate interatomic contact surface areas of molecular structures and complexes. Our extended algorithm is able to calculate the contact area of an atom to all nearby atoms by directly calculating overlapping surface patches, taking into account the possible shielding effects of neighboring atoms. Here, we present a versatile software tool and web server for the calculation of contact surface areas, as well as buried surface areas and solvent accessible surface areas (SASA) for different types of biomolecules, such as proteins, nucleic acids and small organic molecules. Detailed results are provided in tab-separated values format for analysis and Protein Databank files for visualization. Direct contact surface area calculation resulted in improved accuracy in a benchmark with a non-redundant set of 245 protein-DNA complexes. SASA-based approximations underestimated protein-DNA contact surfaces on average by 40%. This software tool may be useful for surface-based intra- and intermolecular interaction analyses and scoring function development. AVAILABILITY AND IMPLEMENTATION: A web server, stand-alone binaries for Linux, MacOS and Windows and C++ source code are freely available from http://schuellerlab.org/dr_sasa/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The total number of known three-dimensional protein structures is rapidly increasing. Consequently, the need for fast structural search against complete databases without a significant loss of accuracy is increasingly demanding. Recently, TopSearch, an ultra-fast method for finding rigid structural relationships between a query structure and the complete Protein Data Bank (PDB), at the multi-chain level, has been released. However, comparable accurate flexible structural aligners to perform efficient whole database searches of multi-domain proteins are not yet available. The availability of such a tool is critical for a sustainable boosting of biological discovery. RESULTS: Here we report on the development of a new method for the fast and flexible comparison of protein structure chains. The method relies on the calculation of 2D matrices containing a description of the three-dimensional arrangement of secondary structure elements (angles and distances). The comparison involves the matching of an ensemble of substructures through a nested-two-steps dynamic programming algorithm. The unique features of this new approach are the integration and trade-off balancing of the following: 1) speed, 2) accuracy and 3) global and semiglobal flexible structure alignment by integration of local substructure matching. The comparison, and matching with competitive accuracy, of one medium sized (250-aa) query structure against the complete PDB database (216,322 protein chains) takes about 8 min using an average desktop computer. The method is at least 2-3 orders of magnitude faster than other tested tools with similar accuracy. We validate the performance of the method for fold and superfamily assignment in a large benchmark set of protein structures. We finally provide a series of examples to illustrate the usefulness of this method and its application in biological discovery. CONCLUSIONS: The method is able to detect partial structure matching, rigid body shifts, conformational changes and tolerates substantial structural variation arising from insertions, deletions and sequence divergence, as well as structural convergence of unrelated proteins.

UNLABELLED: Specific recognition of DNA by proteins is a crucial step of many biological processes. PDIviz is a plugin for the PyMOL molecular visualization system that analyzes protein-DNA binding interfaces by comparing the solvent accessible surface area of the complex against the free protein and free DNA. The plugin provides three distinct three-dimensional visualization modes to highlight interactions with DNA bases and backbone, major and minor groove, and with atoms of different pharmacophoric type (hydrogen bond donors/acceptors, hydrophobic and thymine methyl). Each mode comes in three styles to focus the visual analysis on the protein or DNA side of the interface, or on the nucleotide sequence. PDIviz allows for the generation of publication quality images, all calculated data can be written to disk, and a command line interface is provided for automating tasks. The plugin may be helpful for the detailed identification of regions involved in DNA base and shape readout, and can be particularly useful in rapidly pinpointing the overall mode of interaction. AVAILABILITY AND IMPLEMENTATION: Freely available at http://melolab.org/pdiviz/ as a PyMOL plugin. Tested with incentive, educational, and open source versions of PyMOL on Windows, Mac and Linux systems. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: The understanding of the biological role of RNA molecules has changed. Although it is widely accepted that RNAs play important regulatory roles without necessarily coding for proteins, the functions of many of these non-coding RNAs are unknown. Thus, determining or modeling the 3D structure of RNA molecules as well as assessing their accuracy and stability has become of great importance for characterizing their functional activity. Here, we introduce a new web application, WebRASP, that uses knowledge-based potentials for scoring RNA structures based on distance-dependent pairwise atomic interactions. This web server allows the users to upload a structure in PDB format, select several options to visualize the structure and calculate the energy profile. The server contains online help, tutorials and links to other related resources. We believe this server will be a useful tool for predicting and assessing the quality of RNA 3D structures. AVAILABILITY AND IMPLEMENTATION: The web server is available at http://melolab.org/webrasp. It has been tested on the most popular web browsers and requires Java plugin for Jmol visualization.

MOTIVATION: Pairwise alignment of protein structures is a fundamental task in structural bioinformatics. There are numerous computer programs in the public domain that produce alignments for a given pair of protein structures, but the results obtained by the various programs generally differ substantially. Hence, in the application of such programs the question arises which of the alignment programs are the most trustworthy in the sense of overall performance, and which programs provide the best result for a given pair of proteins. The major problem in comparing, evaluating and judging alignment results is that there is no clear notion of the optimality of an alignment. As a consequence, the numeric criteria and scores reported by the individual structure alignment programs are largely incomparable. RESULTS: Here we report on the development and application of a new approach for the evaluation of structure alignment results. The method uses the translation vector and rotation matrix to generate the superposition of two structures but discards the alignment reported by the individual programs. The optimal alignment is then generated in standardized form based on a suitably implemented dynamic programming algorithm where the length of the alignment is the single most informative parameter. We demonstrate that some of the most popular programs in protein structure research differ considerably in their overall performance. In particular, each of the programs investigated here produced in at least in one case the best and the worst alignment compared with all others. Hence, at the current state of development of structure comparison techniques, it is advisable to use several programs in parallel and to choose the optimal alignment in the way reported here. AVAILABILITY AND IMPLEMENTATION: The computer software that implement the method described here is freely available at http://melolab.org/stovca.

MOTIVATION: Over the recent years, the vision that RNA simply serves as information transfer molecule has dramatically changed. The study of the sequence/structure/function relationships in RNA is becoming more important. As a direct consequence, the total number of experimentally solved RNA structures has dramatically increased and new computer tools for predicting RNA structure from sequence are rapidly emerging. Therefore, new and accurate methods for assessing the accuracy of RNA structure models are clearly needed. RESULTS: Here, we introduce an all-atom knowledge-based potential for the assessment of RNA three-dimensional (3D) structures. We have benchmarked our new potential, called Ribonucleic Acids Statistical Potential (RASP), with two different decoy datasets composed of near-native RNA structures. In one of the benchmark sets, RASP was able to rank the closest model to the X-ray structure as the best and within the top 10 models for ∼93 and ∼95% of decoys, respectively. The average correlation coefficient between model accuracy, calculated as the root mean square deviation and global distance test-total score (GDT-TS) measures of C3' atoms, and the RASP score was 0.85 and 0.89, respectively. Based on a recently released benchmark dataset that contains hundreds of 3D models for 32 RNA motifs with non-canonical base pairs, RASP scoring function compared favorably to ROSETTA FARFAR force field in the selection of accurate models. Finally, using the self-splicing group I intron and the stem-loop IIIc from hepatitis C virus internal ribosome entry site as test cases, we show that RASP is able to discriminate between known structure-destabilizing mutations and compensatory mutations. AVAILABILITY: RASP can be readily applied to assess all-atom or coarse-grained RNA structures and thus should be of interest to both developers and end-users of RNA structure prediction methods. The computer software and knowledge-based potentials are freely available at http://melolab.org/supmat.html. CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Dynamic programming (DP) is a general optimization strategy that is successfully used across various disciplines of science. In bioinformatics, it is widely applied in calculating the optimal alignment between pairs of protein or DNA sequences. These alignments form the basis of new, verifiable biological hypothesis. Despite its importance, there are no interactive tools available for training and education on understanding the DP algorithm. Here, we introduce an interactive computer application with a graphical interface, for the purpose of educating students about DP. The program displays the DP scoring matrix and the resulting optimal alignment(s), while allowing the user to modify key parameters such as the values in the similarity matrix, the sequence alignment algorithm version and the gap opening/extension penalties. We hope that this software will be useful to teachers and students of bioinformatics courses, as well as researchers who implement the DP algorithm for diverse applications. AVAILABILITY AND IMPLEMENTATION: The software is freely available at: http:/melolab.org/sat. The software is written in the Java computer language, thus it runs on all major platforms and operating systems including Windows, Mac OS X and LINUX. CONTACT: All inquiries or comments about this software should be directed to Francisco Melo at [email protected].

The Protein-DNA Interface database (PDIdb) is a repository containing relevant structural information of Protein-DNA complexes solved by X-ray crystallography and available at the Protein Data Bank. The database includes a simple functional classification of the protein-DNA complexes that consists of three hierarchical levels: Class, Type and Subtype. This classification has been defined and manually curated by humans based on the information gathered from several sources that include PDB, PubMed, CATH, SCOP and COPS. The current version of the database contains only structures with resolution of 2.5 A or higher, accounting for a total of 922 entries. The major aim of this database is to contribute to the understanding of the main rules that underlie the molecular recognition process between DNA and proteins. To this end, the database is focused on each specific atomic interface rather than on the separated binding partners. Therefore, each entry in this database consists of a single and independent protein-DNA interface.We hope that PDIdb will be useful to many researchers working in fields such as the prediction of transcription factor binding sites in DNA, the study of specificity determinants that mediate enzyme recognition events, engineering and design of new DNA binding proteins with distinct binding specificity and affinity, among others. Finally, due to its friendly and easy-to-use web interface, we hope that PDIdb will also serve educational and teaching purposes.

BACKGROUND: As in many different areas of science and technology, most important problems in bioinformatics rely on the proper development and assessment of binary classifiers. A generalized assessment of the performance of binary classifiers is typically carried out through the analysis of their receiver operating characteristic (ROC) curves. The area under the ROC curve (AUC) constitutes a popular indicator of the performance of a binary classifier. However, the assessment of the statistical significance of the difference between any two classifiers based on this measure is not a straightforward task, since not many freely available tools exist. Most existing software is either not free, difficult to use or not easy to automate when a comparative assessment of the performance of many binary classifiers is intended. This constitutes the typical scenario for the optimization of parameters when developing new classifiers and also for their performance validation through the comparison to previous art. RESULTS: In this work we describe and release new software to assess the statistical significance of the observed difference between the AUCs of any two classifiers for a common task estimated from paired data or unpaired balanced data. The software is able to perform a pairwise comparison of many classifiers in a single run, without requiring any expert or advanced knowledge to use it. The software relies on a non-parametric test for the difference of the AUCs that accounts for the correlation of the ROC curves. The results are displayed graphically and can be easily customized by the user. A human-readable report is generated and the complete data resulting from the analysis are also available for download, which can be used for further analysis with other software. The software is released as a web server that can be used in any client platform and also as a standalone application for the Linux operating system. CONCLUSION: A new software for the statistical comparison of ROC curves is released here as a web server and also as standalone software for the LINUX operating system.

BACKGROUND: In this study, we present a robust and reliable computational method for tag-to-gene assignment in serial analysis of gene expression (SAGE). The method relies on current genome information and annotation, incorporation of several new features, and key improvements over alternative methods, all of which are important to determine gene expression levels more accurately. The method provides a complete annotation of potential virtual SAGE tags within a genome, along with an estimation of their confidence for experimental observation that ranks tags that present multiple matches in the genome. RESULTS: We applied this method to the Saccharomyces cerevisiae genome, producing the most thorough and accurate annotation of potential virtual SAGE tags that is available today for this organism. The usefulness of this method is exemplified by the significant reduction of ambiguous cases in existing experimental SAGE data. In addition, we report new insights from the analysis of existing SAGE data. First, we found that experimental SAGE tags mapping onto introns, intron-exon boundaries, and non-coding RNA elements are observed in all available SAGE data. Second, a significant fraction of experimental SAGE tags was found to map onto genomic regions currently annotated as intergenic. Third, a significant number of existing experimental SAGE tags for yeast has been derived from truncated cDNAs, which are synthesized through oligo-d(T) priming to internal poly-(A) regions during reverse transcription. CONCLUSION: We conclude that an accurate and unambiguous tag mapping process is essential to increase the quality and the amount of information that can be extracted from SAGE experiments. This is supported by the results obtained here and also by the large impact that the erroneous interpretation of these data could have on downstream applications.

Abstract Motivation: The overall performance of several molecular biology techniques involving DNA/DNA hybridization depends on the accurate prediction of the experimental value of a critical parameter: the melting temperature Tm. Till date, many computer software programs based on different methods and/or parameterizations are available for the theoretical estimation of the experimental Tm value of any given short oligonucleotide sequence. However, in most cases, large and significant differences in the estimations of Tm were obtained while using different methods. Thus, it is difficult to decide which Tm value is the accurate one. In addition, it seems that most people who use these methods are unaware about the limitations, which are well described in the literature but not stated properly or restricted the inputs of most of the web servers and standalone software programs that implement them. Results: A quantitative comparison on the similarities and differences among some of the published DNA/DNA Tm calculation methods is reported. The comparison was carried out for a large set of short oligonucleotide sequences ranging from 16 to 30 nt long, which span the whole range of CG-content. The results showed that significant differences were observed in all the methods, which in some cases depend on the oligonucleotide length and CG-content in a non-trivial manner. Based on these results, the regions of consensus and disagreement for the methods in the oligonucleotide feature space were reported. Owing to the lack of sufficient experimental data, a fair and complete assessment of accuracy for the different methods is not yet possible. Inspite of this limitation, a consensus Tm with minimal error probability was calculated by averaging the values obtained from two or more methods that exhibit similar behavior to each particular combination of oligonucleotide length and CG-content class. Using a total of 348 DNA sequences in the size range between 16mer and 30mer, for which the experimental Tm data are available, we demonstrated that the consensus Tm is a robust and accurate measure. It is expected that the results of this work would be constituted as a useful set of guidelines to be followed for the successful experimental implementation of various molecular biology techniques, such as quantitative PCR, multiplex PCR and the design of optimal DNA microarrays. Availability: A binary software distribution to calculate the consensus Tm described in this work for thousands of oligonucleotides simultaneously for the LINUX operating system is freely available upon request to the authors or from our website http://protein.bio.puc.cl/melting-temperatures.html Contact: [email protected] Supplementary information: The large set of oligonucleotides, the detailed results of the comparative and accuracy benchmarks, and hundreds of comparative graphs generated during this work are available at our website http://protein.bio.puc.cl/melting-temperatures.html