CytoASP: a Cytoscape app for qualitative consistency reasoning, prediction and repair in biological networks

Background: Qualitative reasoning frameworks, such as the Sign Consistency Model (SCM), enable modelling regulatory networks to check whether observed behaviour can be explained or if unobserved behaviour can be predicted. The BioASP software collection offers ideal tools for such analyses. Additionally, the Cytoscape platform can offer extensive functionality and visualisation capabilities. However, specialist programming knowledge is required to use BioASP and no methods exist to integrate both of these software platforms effectively. Results: We report the implementation of CytoASP, an app that allows the use of BioASP for influence graph consistency checking, prediction and repair operations through Cytoscape. While offering inherent benefits over traditional approaches using BioASP, it provides additional advantages such as customised visualisation of predictions and repairs, as well as the ability to analyse multiple networks in parallel, exploiting multi-core architecture. We demonstrate its usage in a case study of a yeast genetic network, and highlight its capabilities in reasoning over regulatory networks. Conclusion: We have presented a user-friendly Cytoscape app for the analysis of regulatory networks using BioASP. It allows easy integration of qualitative modelling, combining the functionality of BioASP with the visualisation and processing capability in Cytoscape, and thereby greatly simplifying qualitative network modelling, promoting its use in relevant projects

Current approaches to flexible loop modeling

International audienceLoops are key components of protein structures, involved in many biological functions. Due to their conformational variability, the structural investigation of loops is a difficult topic, requiring a combination of experimental and computational methods. This paper provides a brief overview of current computational approaches to flexible loop modeling, and presents the main ingredients of the most standard protocols. Despite great progress in recent years, accurately modeling the conformational variability of long flexible loops remains a challenging problem. Future advances in this field will likely come from a tight coupling of experimental and computational techniques, which would enable a better understanding of the relationships between loop sequence, structural flexibility, and functional roles. In fine, accurate loop modeling will open the road to loop design problems of interest for applications in biomedicine and biotechnology

A Reinforcement-Learning-Based Approach to Enhance Exhaustive Protein Loop Sampling

International audienceMotivation: Loop portions in proteins are involved in many molecular interaction processes. They often exhibit a high degree of flexibility, which can be essential for their function. However, molecular modeling approaches usually represent loops using a single conformation. Although this conformation may correspond to a (meta-)stable state, it does not always provide a realistic representation. Results: In this paper, we propose a method to exhaustively sample the conformational space of protein loops. It exploits structural information encoded in a large library of three-residue fragments, and enforces loop-closure using a closed-form inverse kinematics solver. A novel reinforcement-learning-based approach is applied to accelerate sampling while preserving diversity. The performance of our method is showcased on benchmark datasets involving 9-, 12-and 15-residue loops. In addition, more detailed results presented for streptavidin illustrate the ability of the method to exhaustively sample the conformational space of loops presenting several meta-stable conformations. Availability: We are developing a software package called MoMA (for Molecular Motion Algorithms), which includes modeling tools and algorithms to sample conformations and transition paths of biomolecules, including the application described in this work. The binaries can be provided upon request and a web application will also be implemented in the short future

Protein loops with multiple meta-stable conformations: a challenge for sampling and scoring methods

International audienceFlexible regions in proteins, such as loops, cannot be represented by a single conformation. Instead, conformational ensembles are needed to provide a more global picture. In this context, identifying statistically meaningful conforma-tions within an ensemble generated by loop sampling techniques remains an open problem. The difficulty is primarily related to the lack of structural data about these flexible regions. With the majority of structural data coming from X-ray crystallography and ignoring plasticity, the conception and evaluation of loop scoring methods is challenging. In this work, we compare the performance of various scoring methods on a set of 8 protein loops that are known to be flexible. The ability of each method to identify and select all of the known conformations is assessed, and the underlying energy landscapes are produced and projected to visualize the qualitative differences obtained when using the methods. Statistical potentials are found to provide considerable reliability despite their being designed to tradeoff accurac

MoMA-LoopSampler: A web server to exhaustively sample protein loop conformations

International audienceMoMA-LoopSampler is a sampling method that globally explores the conformational space of flexible protein loops. It combines a large structural library of three-residue fragments and a novel reinforcement-learning-based approach to accelerate the sampling process while maintaining diversity. The method generates a set of statistically-likely loop states satisfying geometric constraints, and its ability to sample experimentally observed conformations has been demonstrated. This paper presents a web user interface to MoMA-LoopSampler through the illustration of a typical use-case.Availability: MoMA-LoopSampler is freely available at: https://moma.laas.fr/applications/LoopSampler/ We recommend users to create an account, but anonymous access is possible. In most cases, jobs are completed within a few minutes. The waiting time may increase depending on the server load, but it very rarely exceeds an hour. For users requiring more intensive use, binaries can be provided upon request. Supplementary information Supplementary data are available at Bioinformatics online

Predicting secondary structure propensities in IDPs using simple statistics from three-residue fragments

International audienceIntrinsically Disordered Proteins (IDPs) play key functional roles facilitated by their inherent plasticity. In most of the cases, IDPs recognize their partners through partially-structured elements inserted in fully-disordered chains. The identification and characterization of these elements is fundamental to understand the functional mechanisms of IDPs. Although several computational methods have been developed to identify order within disordered chains, most of the current secondary structure predictors are focused on globular proteins and are not necessarily appropriate for IDPs. Here, we present a comprehen-sible method, called Local Structural Propensity Predictor (LS2P), to predict secondary structure elements from IDP sequences. LS2P performs statistical analyses from a database of three-residue fragments extracted from coil regions of high-resolution protein structures. In addition to identifying scarcely populated helical and extended regions, the method pinpoints short stretches triggering β-turn formation or promoting α-helices. The simplicity of the method enables a direct connection between experimental observations and structural features encoded in IDP sequences