How missense mutations in receptors tyrosine kinases impact constitutive activity and alternate drug sensitivity: insights from molecular dynamics simulations

The fundamental oncology-related research is required for a deeper understanding of the molecular mechanisms associated with the normal and/or abnormal protein functions, which are closely related with structure and dynamics of the macromolecules involved in these process. The most common origin of oncogenic events is related to missense mutations. Mutation-induced structural effects promoted by oncogenic mutations in receptor tyrosine kinases (RTKs), are not yet fully characterized. Computational biology completes and enriches experimental data, producing a novel vision of molecular mechanisms governing RTKs activity. In series of our papers, we studied the structural and dynamical features of native and mutated RTKs from III family (KIT and CSF-1R), yielding a detailed description of their mechanisms of activation, ligand-depend for the native proteins and constitutive for the distinct mutants. The mechanisms of RTKs activation are described in terms of allosteric regulation between coupled regulating fragments of the protein, juxta-membrane region (JMR) and activation (A-) loop. As some mutations promote resistance to the clinically-used drugs, we analyzed the affinity of imatinib to these therapeutic targets. The computationally-obtained (in silico) data were correlated with in vivo and in vitro observations, thus validating our numerically-based accounts. Going forward, clinical validation of cancer-related models and simulations are cornerstones key of translation of in silico data into biomedical research, at clinical and pharmacological levels

EROS-DOCK: Protein-Protein Docking Using Exhaustive Branch-and-Bound Rotational Search

International audienceMotivation: Protein-protein docking algorithms aim to predict the 3D structure of a binary complex using the structures of the individual proteins. This typically involves searching and scoring in a six-dimensional space. Many docking algorithms use FFT techniques to exhaustively cover the search space and to accelerate the scoring calculation. However, FFT docking results often depend on the initial protein orientations with respect to the Fourier sampling grid. Furthermore, Fourier-transforming a physics-base force field can involve a serious loss of precision.Results: Here, we present EROS-DOCK, an algorithm to rigidly dock two proteins using a series of exhaustive 3D rotational searches in which non-clashing orientations are scored using the ATTRACT coarse-grained force field model. The rotational space is represented as a quaternion "π-ball", which is systematically subdivided in a "branch-and-bound" manner, allowing efficient pruning of rotations that will give steric clashes. The algorithm was tested on 173 Docking Benchmark complexes, and results were compared with those of ATTRACT and ZDOCK. According to the CAPRI quality criteria, EROS-DOCK typcially gives more acceptable or medium quality solutions than ATTRACT and ZDOCK.Availability: The EROS-DOCK program is available for download at http://erosdock.loria.fr

EROS: A Protein Docking Algorithm Using a Quaternion pi- Ball Representation for Exhaustive and Accelerated Exploration of 3D Rotational Space

International audienceProteins are involved in many essential cellular processes of living organisms. Proteins form macro complexes joining themselves to other proteins to carry out these processes. Therefore, to know the 3D structures of such complexes is of biomedical interest. Proteinprotein docking algorithms aim to predict how two proteins interact with each other to form a 3D complex. Docking algorithms need to fulfill two main tasks: (1) sampling all the possible relative positions of the two proteins and (2) computing the interaction energy at each position to find the minimum energy (= the best solution). Obtaining the interaction energy is a computationally expensive task. We are developing a new algorithm based on the ATTRACT coarsegrained forcefield [1] and using a quaternion ball representation to accelerate the search of the 3D rotational space

EROS-DOCK for Pairwise and Multi-body Protein-Protein Docking

International audienceProtein-protein docking algorithms aim to predict the 3D structure of a complex using the structures of the individual proteins. For binary complexes, this involves searching and scoring in a six-dimensional space. Many docking algorithms use FFT techniques to exhaustively cover the search space and to accelerate the scoring calculation. However, the results often depend on the initial protein orientations with respect to the Fourier sampling grid. Furthermore, Fourier-transforming a physics-base force field can involve a serious loss of precision. Here, we present a novel docking algorithm called EROS-DOCK (Exhaustive Rotational Search based Docking) [1] to rigidly dock two proteins using a series of exhaustive 3D rotational searches in which non-clashing orientations are scored using ATTRACT coarse-grained force field (ff) [2]. Initial positions are defined by putting each attractive pair of surface pseudo-atoms at their optimal distance in the ff. Thus, EROS-DOCK retains the exhaustive nature of FFT-based search algorithms while using a sensitive physics-based scoring function. Rather than calculating an O(N M) interaction energy * explicitly at every grid point, we use a quaternion "π-ball" to represent the space of all possible 3D Euler angle rotations [3], and we recursively subdivide the π-ball in order to cover the rotational space systematically, from each initial position. An associated tree-like data structure allows rotations that give steric clashes to be pruned efficiently using a "branch-and-bound" technique. To our knowledge, this is the first time that such a branch-and-bound pruning technique has been applied to the rigid-body protein docking problem. The EROS-DOCK algorithm was tested on 173 target complexes from the Protein Docking Benchmark (v4) [4], and results were compared with those of ATTRACT and ZDOCK [5]. Overall, EROS-DOCK was able to find local minima that were not explored by the ATTRACT gradient-driven atom-based search. After refinement by a short coarse-grained minimization, the EROS-DOCK results were generally better than those of ATTRACT and ZDOCK, according to the standard CAPRI criteria. EROS-DOCK can use contact restraints as an additional pruning criteria. Our results show that using even just one residue-residue restraint in each interaction interface is sufficient to increase the number of cases with acceptable solutions within the top 10 from 51 to 121 out of 173 pairwise docking cases. We used EROS-DOCK with restraints to dock trimeric complexes by combinatorial assembly of pairwise solutions. We expected that all interfaces in a multi-body docking solution should be similar to at least one interface in each lists of pairwise docking solutions. Thus, we used a new fast technique to calculate the RMSD between pairs of transformation matrices [6], and an adaptation of the branch-and-bound rotational search algorithm to accelerate the search for low RMSD docking solutions. By test on a home-made benchmark of 11 three-body cases, 7 obtained at least one acceptable quality solution in the top 50 solutions

Reactive pipelines for integrated structural bioinformatics resources

International audienceIntegration of structural bioinformatics resources is a tremendous challenge, for the following reasons:1. Structural bioinformatics is much more fragmented than sequence-based bioinformatics. Structure prediction tools use a plethora of formats to describe protein models: rotation matrices (for docking), normal mode amplitudes, and discretized backbone and sidechain rotamers (for folding). When converting to simple atomic coordinates, much information is lost. To achieve interoperability, it is not sufficient to have a semantic ontology for protein structure. Multiple syntactic ontologies (and their pairwise conversions) for different protein model formats are necessary.2. Structure prediction tools are typically full-stack protocols: sampling, scoring and refinement all occur within the same tool. Tools are optimized to return a handful of models, to be used directly by biologists. This makes tool integration extremely difficult. Instead, tools should be decomposed into their constituent stages. At each stage, large numbers (up to millions) of models should be kept, to be re-ranked or filtered by downstream tools (e. g. using additional experimental information). This allows labs to focus on single-purpose tools that work well, rather than building competing and incompatible protocol stacks.3. Structural biology databases are full of implicit dependencies, including time. For example, PDB codes are not stable URIs: PDB code 1XYZ may point to different coordinates over time, changing when the PDB entry gets updated. This means that computations are not reproducible from input parameters with PDB codes. To make computations reproducible, their inputs should be defined by the values of the input data, not their database URIs. These values should be stored as checksums, and the database should resolve any requested checksum to its value.At the RPBS, we are developing technologies to deal with the above problems:- Syntactic ontologies (using a superset of JSON schema) to describe the input and output data formats of structural biology tools.- Tracking the dependencies of a computation (including code dependencies) into a computation tree of checksums. This computation tree uniquely and deterministically defines the result of the computation.- Checksum servers listing URI resources that serve the value of the requested checksum.- A server to map the checksum of a computation result to its computation tree. As the inputs are often computation results themselves, this allows a computation to be tracked all the way down to the original experimental data.- Reactive pipelines to re-evaluate computation trees as they change. This allows the automatic re-computation of a structure prediction if any of its inputs change (e. g. because of new experimental data, or if the tool itself is improved)

EROS: A Protein Docking Algorithm Using a Quaternion Pi-Ball Representation for Exhaustive and Accelerated Exploration of 3D Rotational Space

International audienceProteins are involved in many essential cellular processes of living organisms. Proteins form macro complexes joining themselves to other proteins to carry out these processes. Therefore, to know the 3D structures of such complexes is of biomedical interest. Protein­protein docking algorithms aim to predict how two proteins interact with each other to form a 3D complex. Docking algorithms need to fulfill two main tasks: (1) sampling all the possible relative positions of the two proteins and (2) computing the interaction energy at each position to find the minimum energy (= the best solution). Obtaining the interaction energy is a computationally expensive task. We are developing a new algorithm based on the ATTRACT coarse­grained force­field [1] and using a quaternion Pi-­ball representation to accelerate the search of the 3D rotational space

Modeling large protein–glycosaminoglycan complexes using a fragment‐based approach

International audienceGlycosaminoglycans (GAGs), a major constituant of the extracellular matrix, participate in cell-signaling by binding specific proteins. Structural data on protein-GAG interactions is crucial to understand and modulate these signaling processes, with potential applications in regenerative medicine. However, experimental and theoretical approaches used to study GAG-protein systems are challenged by GAGs high flexibility limiting the conformational sampling above a certain size, and by the scarcity of GAG-specific computational tools. We present for the first-time an automated fragment-based method for docking GAGs on a protein binding site. In this approach, trimeric GAG fragments are flexibly docked to the protein, assembled based on their spacial overlap, and refined by molecular dynamics. The method appeared more successful than the classical full-ligand approach for most of 13 tested complexes with known structure. The approach is particularly promising for docking of long GAG chains, which represents a bottleneck for classical docking approaches applied to these systems

Accelerating Protein Docking Calculations using the ATTRACT Coarse­-Grained Force Field and 3D Rotation Maps

International audienceProtein­protein docking algorithms aim to predict how two proteins interact with each other to form a complex. Docking algorithms need to fulfil two main tasks: (1) sampling the possible relative positions of the two proteins and (2) computing the interaction energy at each position. Although the docking problem has been studied for over 25 years, doing this accurately and thoroughly remains a computationally expensive task. We are therefore developing a new algorithm to face the protein docking problem in a novel and efficient way. We are interested in performing an exhaustive search of the rigid­body docking space, where the positioning of the two proteins is not driven by any prior knowledge (e.g. from homology), while still using an accurate force field interaction energy model. In order to reduce the O(N^2) cost of atomistic force­field models, we use a coarse­grained (CG) bead model taken from ATTRACT [1]. However, a naive energy calculation for every trial orientation would still cost O(N^2) energy evaluations in the number of beads. We are therefore developing a method to detect the locations of all possible clashes before performing any energy calculations, thus allowing us to avoid calculating energies for many millions of useless trial orientations. Based on a preliminary study of protein­protein interfaces in complexes from the Protein Docking Benchmark [2], we found that a large number of interfaces contain at least one pair of CG beads at almost their optimal distance. Therefore, our idea is to perform a series of restricted docking searches in which one surface bead from each docking partner is placed in contact at the coordinate origin. This leaves a 3D rotational search, in which ligand may rotate around a fixed receptor. Of course, a full docking search requires all possible pairs of surface beads to be placed together. However, within each rotational sub­problem, we can exploit the fact that rotations do not change any distances from the origin. Thus, we can pre­calculate a "3D rotational map" of all of the rotations that will cause the beads to clash. We can then restrict the remaining rotational search and energy calculation to a small region near the forbidden rotations in the clash map

Fragment­ based modeling of protein­-GAG complexes

International audienceGlycosaminoglycans (GAGs) are linear anionic periodic poly-saccharides that bind to their protein targets in the extracellular matrix, and so participate in many cell-signaling processes. They are very promising targets for the design of novel functional biomaterials with medical applications such as bone and skin regeneration [Scharnweber et al, JMSM 2015].Useful biological and therapeutical insights can be obtained from the structure of complexes between GAGs and their target proteins. However the experimental resolution as well as the modeling of their structure is highly challenging. The reasons are both GAG intrinsic properties, such as the high flexibility and conformational diversity of glycans, and the lack for computational tools particularly designed for protein-GAG systems. This currently limits the success of GAG docking to very short fragments [Samsonov and Pisabarro, Glycobiology 2016].We present here a new fragment-based method to dock GAGs on a coarsly known protein binding site. We combine flexible docking of trimeric GAG fragments with Autodock and combinatorial assembly of the compatible poses into GAGs chains, followed by fully flexible refinement. Tested on a benchmark of 13 complexes with various GAG types (heparin, chondroitin sulfate and hyaluronic acids), the method could model 5-mers to 7-mers with the accuracy of 5 Å RMSD to the experimental structure for all of them, and 3 Å RMSD for half of them. This is the first reported automatized fragment-based docking method to successfully dock such diverse GAGs. In principle, the independence of this approach on the ligand's length allows to dock very long GAG chains, which has been a bottleneck for previously applied docking approaches for these systems. The results of this work contribute to enrich the sparse pool of computational tools specifically developed for protein-GAG complexes

Using Restraints in EROS-Dock Improves Model Quality in Pairwise and Multicomponent Protein Docking

International audienceProtein docking algorithms aim to predict the 3D structure of a protein complex from the structures of its separated components. In the past, most docking algorithms focused on docking pairs of proteins to form dimeric complexes. However, attention is now turning towards the more difficult problem of using docking methods to predict the structures of multi-component complexes. In both cases, however, the constituent proteins often change conformation upon complex formation, and this can cause many algorithms to fail to detect near-native binding orientations due to the high number of atomic steric clashes in the list of candidate solutions. An increasingly popular way to retain more near-native orientations is to define one or more restraints that serve to modulate or override the effect of steric clashes. Here, we present an updated version of our “EROS-DOCK” docking algorithm which has been extended to dock arbitrary dimeric and trimeric complexes, and to allow the user to define residue-residue or atom-atom interaction restraints. Our results show that using even just one residue-residue restraint in each interaction interface is sufficient to increase the number of cases with acceptable solutions within the top 10 from 51 to 121 out of 173 pairwise docking cases, and to successfully dock 8 out of 11 trimeric complexes