Fully automated molecular mechanics based induced fit protein-ligand docking method.

We describe a method for docking a ligand into a protein receptor while allowing flexibility of the protein binding site. The method employs a multistep procedure that begins with the generation of protein and ligand conformations. An initial placement of the ligand is then performed by computing binding site hotspots. This initial placement is followed by a protein side-chain refinement stage that models protein flexibility. The final step of the process is an energy minimization of the ligand pose in the presence of the rigid receptor. Thus the algorithm models flexibility of the protein at two stages, before and after ligand placement. We validated this method by performing docking and cross docking studies of eight protein systems for which crystal structures were available for at least two bound ligands. The resulting rmsd values of the 21 docked protein-ligand complexes showed values of 2 A or less for all but one of the systems examined. The method has two critical benefits for high throughput virtual screening studies. First, no user intervention is required in the docking once the initial binding site selection has been made in the protein. Second, the initial protein conformation generation needs to be performed only once for a given binding region. Also, the method may be customized in various ways depending on the particular scenario in which dockings are being performed. Each of the individual steps of the method is fully independent making it straightforward to explore different variants of the high level workflow to further improve accuracy and performance.

LOOPER: a molecular mechanics-based algorithm for protein loop prediction.

We describe a new ab initio method and corresponding program, LOOPER, for the prediction of protein loop conformations. The method is based on a multi-step algorithm (developed as a set of CHARMm scripts) and uses standard CHARMm force field parameters for energy minimization and scoring. One of the main obstacles to ab initio computational loop modeling is the exponential growth of the backbone conformational states with the number of residues in the loop fragment. In contrast to many ab initio algorithms that use Monte-Carlo schemes or exhaustive sampling, LOOPER adopts a systematic search strategy with minimal sampling of the backbone torsion angles. During the initial conformational sampling, two representative states are sampled for each alanine-like residue based on pairs of initial varphi and psi dihedral angles, except glycine, which is sampled by four representative conformations. The initial (varphi, psi) values are determined from the analysis of a novel iso-energy contour map which is proposed as an alternative structure validation method to the widely used Ramachandra plot. The efficient sampling strategy is combined with energy minimization at each step. The initial energy minimization and scoring of the loop include the interactions of the protein core with loop backbone atoms only. Construction and optimization of the side-chain conformations is followed by a final ranking stage based on the CHARMm energy with a generalized Born solvation term as a scoring function. The systematic and efficient sampling strategy in LOOPER consistently finds near native loop conformations in our validation study. At the same time, the computational overhead of our method is significantly lower than many alternative approaches that use exhaustive search strategies.

The dominant role of side-chain backbone interactions in structural realization of amino acid code. ChiRotor: a side-chain prediction algorithm based on side-chain backbone interactions.

The basic differences between the 20 natural amino acid residues are due to differences in their side-chain structures. This characteristic design of protein building blocks implies that side-chain-side-chain interactions play an important, even dominant role in 3D-structural realization of amino acid codes. Here we present the results of a comparative analysis of the contributions of side-chain-side-chain (s-s) and side-chain-backbone (s-b) interactions to the stabilization of folded protein structures within the framework of the CHARMm molecular data model. Contrary to intuition, our results suggest that side-chain-backbone interactions play the major role in side-chain packing, in stabilizing the folded structures, and in differentiating the folded structures from the unfolded or misfolded structures, while the interactions between side chains have a secondary effect. An additional analysis of electrostatic energies suggests that combinatorial dominance of the interactions between opposite charges makes the electrostatic interactions act as an unspecific folding force that stabilizes not only native structure, but also compact random conformations. This observation is in agreement with experimental findings that, in the denatured state, the charge-charge interactions stabilize more compact conformations. Taking advantage of the dominant role of side-chain-backbone interactions in side-chain packing to reduce the combinatorial problem, we developed a new algorithm, ChiRotor, for rapid prediction of side-chain conformations. We present the results of a validation study of the method based on a set of high resolution X-ray structures.

A hidden Markov model with molecular mechanics energy-scoring function for transmembrane helix prediction.

A range of methods has been developed to predict transmembrane helices and their topologies. Although most of these algorithms give good predictions, no single method consistently outperforms the others. However, combining different algorithms is one approach that can potentially improve the accuracy of the prediction. We developed a new method that initially uses a hidden Markov model to predict alternative models for membrane spanning helices in proteins. The algorithm subsequently identifies the best among models by ranking them using a novel scoring function based on the folding energy of transmembrane helical fragments. This folding of helical fragments and the incorporation into membrane is modeled using CHARMm, extended with the Generalized Born surface area solvent model (GBSA/IM) with implicit membrane. The combined method reported here, TMHGB significantly increases the accuracy of the original hidden Markov model-based algorithm.

Assessment of putative protein targets derived from the SARS genome.

The ability to rapidly and reliably develop hypotheses on the function of newly discovered protein sequences requires systematic and comprehensive analysis. Such an analysis, embodied within the DS GeneAtlas pipeline, has been used to critically evaluate the severe acute respiratory syndrome (SARS) genome with the goal of identifying new potential targets for viral therapeutic intervention. This paper discusses several new functional hypotheses on the roles played by the constituent gene products of SARS, and will serve as an example of how such assignments can be developed or extended on other systems of interest.