Transplant-insert-constrain-relax-assemble (TICRA): protein-ligand complex structure modeling and application to kinases.

We introduce TICRA (transplant-insert-constrain-relax-assemble), a method for modeling the structure of unknown protein-ligand complexes using the X-ray crystal structures of homologous proteins and ligands with known activity. We present results from modeling the structures of protein kinase-inhibitor complexes using p38 and Lck as examples. These examples show that the TICRA method may be used prospectively to create and refine models for protein kinase-inhibitor complexes with an overall backbone rmsd of less than 0.75 Å for the kinase domain, when compared to published X-ray crystal structures. Further refinement of the models of the kinase domains of p38 and Lck in complex with their cognate ligands from the published crystal structures was able to improve the rmsd's of the model complexes to below 0.5 Å. Our results show that TICRA is a useful approach to the problem of structure-based drug design in cases where little structural information is available for the target proteins and the binding mode of active compounds is unknown.

Computational approach to de novo discovery of fragment binding for novel protein states.

In silico fragment-based drug discovery has become an integral component of the new fragment-based approach that has evolved over the past decade. Protein structure of high quality is essential in carrying out computational designs, and protein flexibility has been shown to impact prospective designs or docking experiments. Here we introduce methodology to calculate protein normal modes and protein molecular dynamics in torsion space which enable the development of multiple protein states to address the natural flexibility of proteins. We also present two fragment-based sampling methods, grand canonical Monte Carlo and systematic sampling, which are used to study protein-fragment interactions by generating fragment ensembles and we discuss the process by which these ensembles are linked to design ligands.

Fragment-based computation of binding free energies by systematic sampling.

A fragment-based method for computing protein-ligand binding free energies by systematic sampling has been developed. Systematic sampling of fragment-protein interactions in translational and rotational space is followed by de novo assembly of fragments into molecules and computation of binding free energies for the molecules with statistical mechanics. The rigorous sampling provides independence from the choice of initial binding pose and assembling fragments enables evaluation of binding of a large number of molecule poses with relatively little computation. The method allows a full sampling of possible conformations and avoids the "conformational focusing" problem associated with free energy methods that sample only limited conformational and orientation changes from a starting pose. The direct computation of the entropy loss upon assembling fragments into molecules is an innovation for fragment-based methods. The computed binding free energies are compared to calorimetric data for a series of ligands for the T4 lysozyme L99A mutant and binding constants for a series of p38 MAP kinase ligands. In both cases, the standard error of prediction is close to 1 kcal/mol.

Fast protein structure prediction using Monte Carlo simulations with modal moves.

Using normal modes to generate torsion space moves in Monte Carlo simulations of peptides and proteins is not a new idea; nevertheless, despite its power it has not received widespread application. We show that such a "Modal Monte Carlo" approach is an efficient tool for ab initio predictions of small-protein structures. We apply this method to the Trp cage, a 20-residue polypeptide designed to fold rapidly into a structure that includes tertiary contacts, despite its short length. We achieve a high-quality ab initio structure prediction in about 2 orders of magnitude less computation time than state of the art molecular dynamics techniques.