An RNA folding method capable of identifying pseudoknots and base triples.

In this meeting report we give an overview of the talks and presentations from the Fourth International Society for Computational Biology (ISCB) Student Council Symposium held as part of the annual Intelligent Systems for Molecular Biology (ISMB) conference in Toronto, Canada. Furthermore, we detail the role of the Student Council (SC) as an international student body in organizing this symposium series in the context of large, international conferences.

This meeting report gives an overview of the keynote lectures and a selection of the student presentations at the 5th International Society for Computational Biology Student Council Symposium at the 17th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) and the 8th European Conference on Computational Biology (ECCB). The symposium was held in Stockholm, Sweden from June 27 to July 2, 2009. We also report on other Student Council events that were organized at ISMB/ECCB 2009.

The predictive limits of the amino acid composition for the secondary structural content (percentage of residues in the secondary structural states helix, sheet, and coil) in proteins are assessed quantitatively. For the first time, techniques for prediction of secondary structural content are presented which rely on the amino acid composition as the only information on the query protein. In our first method, the amino acid composition of an unknown protein is represented by the best (in a least square sense) linear combination of the characteristic amino acid compositions of the three secondary structural types computed from a learning set of tertiary structures. The second technique is a generalization of the first one and takes into account also possible compositional couplings between any two sorts of amino acids. Its mathematical formulation results in an eigenvalue/eigenvector problem of the second moment matrix describing the amino acid compositional fluctuations of secondary structural types in various proteins of a learning set. Possible correlations of the principal directions of the eigenspaces with physical properties of the amino acids were also checked. For example, the first two eigenvectors of the helical eigenspace correlate with the size and hydrophobicity of the residue types respectively. As learning and test sets of tertiary structures, we utilized representative, automatically generated subsets of Protein Data Bank (PDB) consisting of non-homologous protein structures at the resolution thresholds < or = 1.8A, < or = 2.0A, < or = 2.5A, and < or = 3.0 A. We show that the consideration of compositional couplings improves prediction accuracy, albeit not dramatically. Whereas in the self-consistency test (learning with the protein to be predicted), a clear decrease of prediction accuracy with worsening resolution is observed, the jackknife test (leave the predicted protein out) yielded best results for the largest dataset (< or = 3.0A, almost no difference to the self-consistency test!), i.e., only this set, with more than 400 proteins, is sufficient for stable computation of the parameters in the prediction function of the second method. The average absolute error in predicting the fraction of helix, sheet, and coil from amino acid composition of the query protein are 13.7, 12.6, and 11.4%, respectively with r.m.s. deviations in the range of 8.6 divided by 11.8% for the 3.0 A dataset in a jackknife test. The absolute precision of the average absolute errors is in the range of 1 divided by 3% as measured for other representative subsets of the PDB. Secondary structural content prediction methods found in the literature have been clustered in accordance with their prediction accuracies. To our surprise, much more complex secondary structure prediction methods utilized for the same purpose of secondary structural content prediction achieve prediction accuracies very similar to those of the present analytic techniques, implying that all the information beyond the amino acid composition is, in fact, mainly utilized for positioning the secondary structural state in the sequence but not for determination of the overall number of residues in a secondary structural type. This result implies that higher prediction accuracies cannot be achieved relying solely on the amino acid composition of an unknown query protein as prediction input. Our prediction program SSCP has been made available as a World Wide Web and E-mail service.

Most of the protein structure prediction methods use a multi-step process, which often includes secondary structure prediction, contact prediction, fragment generation, clustering, etc. For many years, secondary structure prediction has been the workhorse for numerous methods aimed at predicting protein structure and function. This paper presents a new mixed integer linear optimization (MILP)-based consensus method: a Consensus scheme based On a mixed integer liNear optimization method for seCOndary stRucture preDiction (CONCORD). Based on seven secondary structure prediction methods, SSpro, DSC, PROF, PROFphd, PSIPRED, Predator and GorIV, the MILP-based consensus method combines the strengths of different methods, maximizes the number of correctly predicted amino acids and achieves a better prediction accuracy. The method is shown to perform well compared with the seven individual methods when tested on the PDBselect25 training protein set using sixfold cross validation. It also performs well compared with another set of 10 online secondary structure prediction servers (including several recent ones) when tested on the CASP9 targets ( http://predictioncenter.org/casp9/ ). The average Q3 prediction accuracy is 83.04 per cent for the sixfold cross validation of the PDBselect25 set and 82.3 per cent for the CASP9 targets. We have developed a MILP-based consensus method for protein secondary structure prediction. A web server, CONCORD, is available to the scientific community at http://helios.princeton.edu/CONCORD .

Birthdays, &lt;strong&gt; BOSC, Beatles and Bioinformatics with a Merseybeat &lt;/strong&gt; Conference season is upon us, and the &lt;em&gt; GigaScience &lt;/em&gt; team have just returned from a magical mystery tour to Liverpool. Regular readers will know &lt;em&gt; GigaScience &lt;/em&gt; launched at the ISMB (International Conference on Intelligent Systems for Molecular Biology) in 2012, and every year we attend and celebrate our birthday at the meeting.

BackgroundIn recent years protein structure prediction methods using local structure information have shown promising improvements. The quality of new fold predictions has risen significantly and in fold recognition incorporation of local structure predictions led to improvements in the accuracy of results.We developed a local structure prediction method to be integrated into either fold recognition or new fold prediction methods. For each local sequence window of a protein sequence the method predicts probability estimates for the sequence to attain particular local structures from a set of predefined local structure candidates.The first step is to define a set of local structure representatives based on clustering recurrent local structures. In the second step a discriminative model is trained to predict the local structure representative given local sequence information.ResultsThe step of clustering local structures yields an average RMSD quantization error of 1.19 Å for 27 structural representatives (for a fragment length of 7 residues). In the prediction step the area under the ROC curve for detection of the 27 classes ranges from 0.68 to 0.88.ConclusionThe described method yields probability estimates for local protein structure candidates, giving signals for all kinds of local structure. These local structure predictions can be incorporated either into fold recognition algorithms to improve alignment quality and the overall prediction accuracy or into new fold prediction methods.

The structure of RNA has been the subject of intense research over the last decades due to its importance for the correct functioning of RNA molecules in biological processes. Hence, a large number of models for RNA folding and corresponding algorithms for structure prediction have been developed. However, previous models often only consider base pairs, although every base is capable of up to three edge-to-edge interactions with other bases. Recently, Höner zu Siederdissen et al. presented an extended model of RNA secondary structure, including base triples together with a folding algorithm-the first thermodynamics-based algorithm that allows the prediction of secondary structures with base triples. In this article, we investigate the search space processed by this new algorithm, that is, the combinatorics of extended RNA secondary structures with base triples. We present generalized definitions for structural motifs like hairpins, stems, bulges, or interior loops occurring in structures with base triples. Furthermore, we prove precise asymptotic results for the number of different structures (size of search space) and expectations for various parameters associated with structural motifs (typical shape of folding). Our analysis shows that the asymptotic number of secondary structures of size n increases exponentially to [Formula: see text] compared to the classic model by Stein and Waterman for which [Formula: see text] structures exist. A comparison with the classic model reveals large deviations in the expected structural appearance, too. The inclusion of base triples constitutes a significant refinement of the combinatorial model of RNA secondary structure, which, by our findings, is quantitatively characterized. Our results are of special theoretical interest, because a closer look at the numbers involved suggests that extended RNA secondary structures constitute a new combinatorial class not bijective with any other combinatorial objects studied so far.

Discovering the intelligence in molecular biology

The prediction of RNA structure with pseudoknots is a nondeterministic polynomial-time hard (NP-hard) problem; according to minimum free energy models and computational methods, we investigate the RNA-pseudoknotted structure. Our paper presents an efficient algorithm for predicting RNA structure with pseudoknots, and the algorithm takes O([Formula: see text]) time and O([Formula: see text]) space, the experimental tests in Rfam10.1 and PseudoBase indicate that the algorithm is more effective and precise. The predicting accuracy, the time complexity and space complexity outperform existing algorithms, such as Maximum Weight Matching (MWM) algorithm, PKNOTS algorithm and Inner Limiting Layer (ILM) algorithm, and the algorithm can predict arbitrary pseudoknots. And there exists a [Formula: see text] ([Formula: see text]) polynomial time approximation scheme in searching maximum number of stackings, and we give the proof of the approximation scheme in RNA-pseudoknotted structure. We have improved several types of pseudoknots considered in RNA folding structure, and analyze their possible transitions between types of pseudoknots.

Carcinoembryonic antigen (CEA) is used as a biomarker for colorectal cancer. It is expressed during fetal development but in healthy adult cells the expression is low. Due to its size and the high degree of glycosylation, there are no structures available for mature CEA. By employing novel structure prediction methods, we aim to investigate CEA tertiary structure and interactions. Alphafold 3 server has increased the accuracy of structure predictions and allows for modelling of glycans in proteins and complexes. Models were created for a monomeric CEA, dimeric CEA and for CEA in complex with the antibody Tusamitamab. The structure of the monomeric glycosylated CEA exhibit two bends, one in the domain interface B1-A2 and one in the domain interface B2-A3. The dimer structure pairs in a parallel manner, with direct contacts in the N and the A2 domains of the two chains. The complex of CEA with Tusamitamab closely resembles the EM structure of the complex that was released after the training of Alphafold 3 was completed. Overall, the investigations give new angles to investigate for CEA. The predicted bend, primarily in the B2 and A3 domain interface, would allow for dimer formation of CEA from both the same cell as from adjacent cells and could help to explain the outstanding issue on how it can fulfil both tasks. The prediction of the antibody binding to CEA was accurate, the all-atom RMSD was 1.3Å. This is encouraging for other antibody - protein complexes predictions as the complex structure was not part of the training set for Alphafold 3.

This is the slide deck for the talk given by Bhavesh Patel at the Bioinformatics Open Source Conference (BOSC) 2024 conference, part of the International Conference on Intelligent Systems for Molecular Biology ( ISMB ) held in Montreal, Canada. Details about the conference are available at https://www.open-bio.org/events/bosc-2024. The talk instroduced Codefair, an open source and free GitHub app for making biomedical research software FAIR. Abstract: Abstract: We present Codefair, an innovative solution that helps researchers make their biomedical research software Findable, Accessible, Interoperable, and Reusable (FAIR), i.e. optimally reusable by humans and machines. The FAIR Biomedical Research Software (FAIR-BioRS) guidelines provide actionable instructions for making biomedical research software FAIR. While designed to be convenient to follow, we learned that their implementation can still be time consuming for researchers. To address this challenge, we are developing Codefair, a free and open source GitHub app that acts as a personal assistant for making research software FAIR. Researchers simply need to install Codefair from the GitHub marketplace and proceed with their software development as usual. By leveraging GitHub’s tools such as Probot, Codefair monitors activities on the software repository, communicates via GitHub issues, and submits pull requests to help researchers make their software FAIR. Currently, Codefair helps with including essential metadata elements such as license file, CITATION.cff metadata file, and codemeta.json metadata file. Additional features are being added to provide support for complying with best coding practices, archiving on Zenodo, registering on bio.tools, and much more to cover all the steps for making software FAIR. By alleviating their burden in the process, we believe Codefair will empower and encourage biomedical researchers into adopting FAIR and open practices for their research software. We present here our approach to developing Codefair, highlight the current and planned features, and explain how the community can benefit from and contribute to Codefair.

Terminal loops with a GNRA consensus sequence are a prominent feature of large self-assembling RNA molecules. In order to investigate tertiary interactions involving GNRA loops, we have devised an in vitro selection system derived from a group I ribozyme. Two selections, destined to isolate RNA sequences that would recognize two of the most widespread loops (GUGA and GAAA), yielded variants of previously identified receptors for those loops, and also some yet unrecognized, high-affinity binders with novel specificities towards members of the GNRA family. By taking advantage of available crystal structures, we have attempted to rationalize these results in terms of RNA-RNA contacts and to expose some of the structural principles that govern GNRA loop-mediated tertiary interactions; the role of loop nucleotide 2 in ensuring specific recognition by receptors is emphasized. More generally, comparison of the products of in vitro and natural selection is shown to provide insights into the mechanisms underlying the in vivo evolution of self-assembling RNA molecules.

Ribonucleic Acid (RNA) has important structural and functional roles in the cell and plays roles in many stages of protein synthesis. The structure of RNA largely determines its function. Current physical methods for structure determination are time-consuming and expensive, thus the methods for the computational prediction of structure are necessary. Various algorithms that have been used for RNA structure prediction based in minimum free energy include dynamic programming (DP) and meta heuristic algorithms. One of the most recent meta heuristic algorithms is Musician's behavior-inspired harmony search (HS) algorithm that has been successful in numerous complex optimization problems. This paper builds on the previous work of the harmony search algorithm (HSRNAFold) which was used to find the RNA secondary structure with minimum free energy. In this paper, the accuracy of prediction is compared to the dynamic programming technique RNAFold. The results show that HSRNAFold is able to predict more accurate structures than RNAFold for all test sequences.

This presentation was part of the ELIXIR Special Track at the joint 25th Annual International Conference on Intelligent Systems for Molecular Biology and the 16th European Conference for Computational Biology (ISMB/ECCB) on 25 July 2017. ELIXIR Track one showcased to delegates the activities and outputs of ELIXIR’s five Platforms (Tools, Data, Interoperability, Compute and Training). Through talks from ELIXIR partners and interactive discussions, delegates saw the services and resources made available via ELIXIR, and how they could engage in and contribute to their development. ELIXIR Track two followed a similar format and showcased the outputs of ELIXIR’s Use Cases (Marine Metagenomics, Plant Sciences, Rare Diseases and Human Data), the Capacity Building programme and Industry Engagement.

BOSC 2010 is currently in the planning stages. It will be held for 2 days in conjunction with the 18th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB 2010) in Boston, Massachusetts, USA. The dates of BOSC 2010 are July 9-10; the main ISMB Conference runs July 11-13, 2010.&amp;nbsp; The BOSC 2010 web site can be accessed here:&amp;nbsp; /wiki/BOSC_2010.