SUMMARY: MoMA-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 AND IMPLEMENTATION: 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.

MOTIVATION: 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 AND IMPLEMENTATION: 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The study of the conformational energy landscape of a molecule is essential for the understanding of its physicochemical properties . This requires the exploration of a continuous, high-dimensional space to identify the most probable conformations and the transition paths between them. The problem is computationally difficult, in particular for highly-flexible biomolecules such as Intrinsically Disordered Proteins (IDPs). In recent years, a robotics-inspired algorithm called Transition-based Rapidly-exploring Random Tree (TRRT) has been proposed to solve this problem, and has been shown to provide good results with small and middle-sized biomolecules. Aiming to treat larger systems, we propose a hybrid strategy for the efficient parallelization of a multi-tree variant of TRRT, called Multi-TRRT, enabling an efficient execution in (possibly large) computer clusters. The parallel algorithm uses OpenMP multi-threading for computation inside each multi-core processor and MPI to perform the communication between processors. Results show a near-linear speedup for a wide range of cluster configurations. Although the paper mainly deals with the application of the proposed parallel algorithm to the investigation of biomolecules, the explanations concerning the methods are general, aiming to inspire future work on the parallelization of related algorithms.

BACKGROUND: Due to rapid sequencing of genomes, there are now millions of deposited protein sequences with no known function. Fast sequence-based comparisons allow detecting close homologs for a protein of interest to transfer functional information from the homologs to the given protein. Sequence-based comparison cannot detect remote homologs, in which evolution has adjusted the sequence while largely preserving structure. Structure-based comparisons can detect remote homologs but most methods for doing so are too expensive to apply at a large scale over structural databases of proteins. Recently, fragment-based structural representations have been proposed that allow fast detection of remote homologs with reasonable accuracy. These representations have also been used to obtain linearly-reducible maps of protein structure space. It has been shown, as additionally supported from analysis in this paper that such maps preserve functional co-localization of the protein structure space. METHODS: Inspired by a recent application of the Latent Dirichlet Allocation (LDA) model for conducting structural comparisons of proteins, we propose higher-order LDA-obtained topic-based representations of protein structures to provide an alternative route for remote homology detection and organization of the protein structure space in few dimensions. Various techniques based on natural language processing are proposed and employed to aid the analysis of topics in the protein structure domain. RESULTS: We show that a topic-based representation is just as effective as a fragment-based one at automated detection of remote homologs and organization of protein structure space. We conduct a detailed analysis of the information content in the topic-based representation, showing that topics have semantic meaning. The fragment-based and topic-based representations are also shown to allow prediction of superfamily membership. CONCLUSIONS: This work opens exciting venues in designing novel representations to extract information about protein structures, as well as organizing and mining protein structure space with mature text mining tools.

Adequate sampling of the conformational space is a central challenge in ab initio protein structure prediction. In the absence of a template structure, a conformational search procedure guided by an energy function explores the conformational space, gathering an ensemble of low-energy decoy conformations. If the sampling is inadequate, the native structure may be missed altogether. Even if reproduced, a subsequent stage that selects a subset of decoys for further structural detail and energetic refinement may discard near-native decoys if they are high energy or insufficiently represented in the ensemble. Sampling should produce a decoy ensemble that facilitates the subsequent selection of near-native decoys. In this paper, we investigate a robotics-inspired framework that allows directly measuring the role of energy in guiding sampling. Testing demonstrates that a soft energy bias steers sampling toward a diverse decoy ensemble less prone to exploiting energetic artifacts and thus more likely to facilitate retainment of near-native conformations by selection techniques. We employ two different energy functions, the associative memory Hamiltonian with water and Rosetta. Results show that enhanced sampling provides a rigorous testing of energy functions and exposes different deficiencies in them, thus promising to guide development of more accurate representations and energy functions.