Developing a move-set for protein model refinement.

MOTIVATION: A wide variety of methods for the construction of an atomic model for a given amino acid sequence are known, the more accurate being those that use experimentally determined structures as templates. However, far fewer methods are aimed at refining these models. The approach presented here carefully blends models created by several different means, in an attempt to combine the good quality regions from each into a final, more refined, model. RESULTS: We describe here a number of refinement operators (collectively, 'move-set') that enable a relatively large region of conformational space to be searched. This is used within a genetic algorithm that reshuffles and repacks structural components. The utility of the move-set is demonstrated by introducing a cost function, containing both physical and other components guiding the input structures towards the target structure. We show that our move-set has the potential to improve the conformation of models and that this improvement can be beyond even the best template for some comparative modelling targets. AVAILABILITY: The populus software package and the source code are available at http://bmm.cancerresearchuk.org/~offman01/populus.html.

Incorporation of flexibility into rigid-body docking: applications in rounds 3-5 of CAPRI.

We have submitted models for all 9 targets in Rounds 3-5 of CAPRI and have predicted at least 30% of the correct contacts for 4 of the targets and at least 10% of the correct contacts for another 4 targets. We have employed a variety of techniques but have had the greatest success by combining established rigid-body docking with a variety of initial conformations generated by molecular dynamics.

In silico protein recombination: enhancing template and sequence alignment selection for comparative protein modelling.

Comparative modelling of proteins is a predictive technique to build an atomic model for a given amino acid sequence, on the basis of the structures of other proteins (templates) that have been determined experimentally. Critical problems arise in this procedure: selecting the correct templates, aligning the query sequence with them and building the non-conserved surface loops. In this work, we apply a genetic algorithm, with crossover and mutation, as a new tool to overcome the first two. In silico protein recombination proves to be an effective way to exploit the variability of templates and sequence alignments to produce populations of optimized models by artificial selection. Despite some limitations, the procedure is shown to be robust to alignment errors, while simplifying the task of selecting templates, making it a good candidate for automatic building of reliable protein models.

Guided docking: first step to locate potential binding sites.

The long-range electrostatic forces of the targets in round 2 of the Critical Assessment of PRediction of Interactions (CAPRI) experiment were examined and a simple guided docking method, based on these forces, was applied. The method described consists of calculating an initial rigid body trajectory and an optional final, fully flexible refinement stage. Although only limited success was found in predicting the final complexes, some interesting information was discovered. In particular, the long-range forces seem to give some insight into the unusual binding mode of target 4 while raising some questions about target 7, which warrant further investigation.

Novel use of a genetic algorithm for protein structure prediction: searching template and sequence alignment space.

A novel genetic algorithm was applied to all CASP5 targets. The algorithm simultaneously searches template and alignment space. Results show that the current implementation of the method is perhaps most useful in recognizing and refining remote homology targets. This new method is briefly described and results are analyzed. Strengths and weaknesses of the current implementation of the algorithm are discussed.

Comparative modelling: an essential methodology for protein structure prediction in the post-genomic era.

The gap between the number of protein sequences and protein structures is increasing rapidly, exacerbated by the completion of numerous genome projects now flooding into public databases. To fill this gap, comparative protein modelling is widely considered the most accurate technique for predicting the three-dimensional shape of proteins. High-throughput, automatic protein modelling should considerably increase our access to protein structures other than those determined by experimental techniques such as X-ray crystallography and NMR (nuclear magnetic resonance) spectroscopy. The uses for these complete three-dimensional models are growing rapidly, ranging from guiding site-directed mutagenesis experiments to protein-protein interaction predictions. In recognition of this, a number of very useful comparative modelling servers have begun to emerge on the Web. Molecular biologists now have a powerful web-based toolkit to construct models, assess their accuracy, and use them to explain and predict experiments. There is, however, still much to do by those engaged in algorithmic development if comparative modelling is to compete on an equal footing with experimental protein structure determination techniques.

Probability-based model of protein-protein interactions on biological timescales.

BACKGROUND: Simulation methods can assist in describing and understanding complex networks of interacting proteins, providing fresh insights into the function and regulation of biological systems. Recent studies have investigated such processes by explicitly modelling the diffusion and interactions of individual molecules. In these approaches, two entities are considered to have interacted if they come within a set cutoff distance of each other. RESULTS: In this study, a new model of bimolecular interactions is presented that uses a simple, probability-based description of the reaction process. This description is well-suited to simulations on timescales relevant to biological systems (from seconds to hours), and provides an alternative to the previous description given by Smoluchowski. In the present approach (TFB) the diffusion process is explicitly taken into account in generating the probability that two freely diffusing chemical entities will interact within a given time interval. It is compared to the Smoluchowski method, as modified by Andrews and Bray (AB). CONCLUSION: When implemented, the AB & TFB methods give equivalent results in a variety of situations relevant to biology. Overall, the Smoluchowski method as modified by Andrews and Bray emerges as the most simple, robust and efficient method for simulating biological diffusion-reaction processes currently available.