5 research outputs found
On the Characterization and Selection of Diverse Conformational Ensembles, with Applications to Flexible Docking
To address challenging flexible docking problems, a number of docking algorithms pre-generate large collections of candidate conformers. To remove the redundancy from such ensembles, a central problem in this context is to report a selection of conformers maximizing some geometric diversity criterion. We make three contributions to this problem. First, we resort to geometric optimization so as to report selections maximizing the molecular volume or molecular surface area (MSA) of the selection. Greedy strategies are developed, together with approximation bounds. Second, to assess the efficacy of our algorithms, we investigate two conformer ensembles corresponding to a flexible loop of four protein complexes. By focusing on the MSA of the selection, we show that our strategy matches the MSA of standard selection methods, but resorting to a number of conformers between one and two orders of magnitude smaller. This observation is qualitatively explained using the Betti numbers of the union of balls of the selection. Finally, we replace the conformer selection problem in the context of multiple-copy flexible docking. On the afore-mentioned systems, we show that using the loops selected by our strategy can improve the result of the docking process
Accounting for Large Amplitude Protein Deformation during in Silico Macromolecular Docking
Rapid progress of theoretical methods and computer calculation resources has turned in silico methods into a conceivable tool to predict the 3D structure of macromolecular assemblages, starting from the structure of their separate elements. Still, some classes of complexes represent a real challenge for macromolecular docking methods. In these complexes, protein parts like loops or domains undergo large amplitude deformations upon association, thus remodeling the surface accessible to the partner protein or DNA. We discuss the problems linked with managing such rearrangements in docking methods and we review strategies that are presently being explored, as well as their limitations and success
On the Characterization and Selection of Diverse Conformational Ensembles, with Applications to Flexible Docking
International audienceTo address challenging flexible docking problems, a number of docking algorithms pre-generate large collections of candidate conformers. To further remove the redundancy from such ensembles, a central question in this context is the following one: report a selection of conformers maximizing some geometric diversity criterion. In this context, we make three contributions. First, we tackle this problem resorting to geometric optimization so as to report selections maximizing the molecular volume or molecular surface area (MSA) of the selection. Greedy strategies are developed, together with approximation bounds. Second, to assess the efficacy of our algorithms, we investigate two conformer ensembles corresponding to a flexible loop of four protein complexes. By focusing on the MSA of the selection, we show that our strategy matches the MSA of standard selection methods, but resorting to a number of conformers between one and two orders of magnitude smaller. This observation is qualitatively explained using the Betti numbers of the union of balls of the selection. Finally, we replace the conformer selection problem in the context of multiple-copy flexible docking. On the systems above, we show that using the loops selected by our strategy can significantly improve the result of the docking process
Predicting and characterising protein-protein complexes
Macromolecular interactions play a key role in all life processes. The construction
and annotation of protein interaction networks is pivotal for the
understanding of these processes, and how their perturbation leads to disease.
However the extent of the human interactome and the limitations of
the experimental techniques which can be brought to bear upon it necessitate
theoretical approaches. Presented here are computational investigations
into the interactions between biological macromolecules, focusing on the
structural prediction of interactions, docking, and their kinetic and thermodynamic
characterisation via empirical functions. Firstly, the use of normal
modes in docking is investigated. Vibrational analysis of proteins are shown
to indicate the motions which proteins are intrinsically disposed to undertake,
and the use of this information to model flexible deformations upon
protein-protein binding is evaluated. Subsequently SwarmDock, a docking
algorithm which models flexibility as a linear combination of normal modes,
is presented and benchmarked on a wide variety of test cases. This algorithm
utilises state of the art energy functions and metaheuristics to navigate the
free energy landscape. Information derived from Langevin dynamics simulations
of encounter complex formation in the crowded cytosolic environment
can be incorporated into SwarmDock and enhances its performance.
Finally, a benchmark of binding free energies derived from the literature is
presented. For this benchmark, a large number of molecular descriptors are
derived. Machine learning methods are then applied to these in order to
derive empirical binding free energy, association rate and dissociation rate
functions which take account of the conformational changes which occur
upon complexation