2 research outputs found
An Estimate of the Numbers and Density of Low-Energy Structures (or Decoys) in the Conformational Landscape of Proteins
The conformational energy landscape of a protein, as calculated by known potential energy functions, has several minima, and one of these corresponds to its native structure. It is however difficult to comprehensively estimate the actual numbers of low energy structures (or decoys), the relationships between them, and how the numbers scale with the size of the protein.We have developed an algorithm to rapidly and efficiently identify the low energy conformers of oligo peptides by using mutually orthogonal Latin squares to sample the potential energy hyper surface. Using this algorithm, and the ECEPP/3 potential function, we have made an exhaustive enumeration of the low-energy structures of peptides of different lengths, and have extrapolated these results to larger polypeptides.We show that the number of native-like structures for a polypeptide is, in general, an exponential function of its sequence length. The density of these structures in conformational space remains more or less constant and all the increase appears to come from an expansion in the volume of the space. These results are consistent with earlier reports that were based on other models and techniques
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