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The residence time of a ligand-protein complex is a crucial aspect in determining biological effect in vivo. Despite its importance, the prediction of ligand koff still remains challenging for modern computational chemistry. We have developed aMetaD, a fast and generally applicable computational protocol to predict ligand-protein unbinding events using a molecular dynamics (MD) method based on adiabatic-bias MD and metadynamics. This physics-based, fully flexible, and pose-dependent ligand scoring function evaluates the maximum energy (RTscore) required to move the ligand from the bound-state energy basin to the next. Unbinding trajectories are automatically analyzed and translated into atomic solvation factor (SF) values representing the water dynamics during the unbinding event. This novel computational protocol was initially tested on two M3 muscarinic receptor and two adenosine A2A receptor antagonists and then evaluated on a test set of 12 CRF1R ligands. The resulting RTscores were used successfully to classify ligands with different residence times. Additionally, the SF analysis was used to detect key differences in the degree of accessibility to water molecules during the predicted ligand unbinding events. The protocol provides actionable working hypotheses that are applicable in a drug discovery program for the rational optimization of ligand binding kinetics.

Morphing was initially developed as a cinematic effect, where one image is seamlessly transformed into another image. The technique was widely adopted by biologists to visualize the transition between protein conformational states, generating an interpolated pathway from an initial to a final protein structure. Geometric morphing seeks to create visually suggestive movies that illustrate structural changes between conformations but do not necessarily represent a biologically relevant pathway, while minimum energy path (MEP) interpolations aim at describing the true transition state between the crystal structure minima in the energy landscape.

G-protein-coupled receptors (GPCRs) are key signaling molecules and are intensely studied. Whereas GPCRs recognizing small-molecules have been successfully targeted for drug discovery, protein-recognizing GPCRs, such as the chemokine receptors, claim few drugs or even useful small molecule reagents. This reflects both the difficulties that attend protein-protein interface inhibitor discovery, and the lack of structures for these targets. Imminent structure determination of chemokine receptor CXCR4 motivated docking screens for new ligands against a homology model and subsequently the crystal structure. More than 3 million molecules were docked against the model and then against the crystal structure; 24 and 23 high-scoring compounds from the respective screens were tested experimentally. Docking against the model yielded only one antagonist, which resembled known ligands and lacked specificity, whereas the crystal structure docking yielded four that were dissimilar to previously known scaffolds and apparently specific. Intriguingly, several were potent and relatively small, with IC(50) values as low as 306 nM, ligand efficiencies as high as 0.36, and with efficacy in cellular chemotaxis. The potency and efficiency of these molecules has few precedents among protein-protein interface inhibitors, and supports structure-based efforts to discover leads for chemokine GPCRs.

We present what to our knowledge is a new method of optimized torsion-angle normal-mode analysis, in which the normal modes move along curved paths in Cartesian space. We show that optimized torsion-angle normal modes reproduce protein conformational changes more accurately than Cartesian normal modes. We also show that orthogonalizing the displacement vectors from torsion-angle normal-mode analysis and projecting them as straight lines in Cartesian space does not lead to better performance than Cartesian normal modes. Clearly, protein motion is more naturally described by curved paths in Cartesian space., (Copyright © 2011 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

Movement is crucial to the biological function of many proteins, yet crystallographic structures of proteins can give us only a static snapshot. The protein dynamics that are important to biological function often happen on a timescale that is unattainable through detailed simulation methods such as molecular dynamics as they often involve crossing high-energy barriers. To address this coarse-grained motion, several methods have been implemented as web servers in which a set of coordinates is usually linearly interpolated from an initial crystallographic structure to a final crystallographic structure. We present a new morphing method that does not extrapolate linearly and can therefore go around high-energy barriers and which can produce different trajectories between the same two starting points. In this work, we evaluate our method and other established coarse-grained methods according to an objective measure: how close a coarse-grained dynamics method comes to a crystallographically determined intermediate structure when calculating a trajectory between the initial and final crystal protein structure. We test this with a set of five proteins with at least three crystallographically determined on-pathway high-resolution intermediate structures from the Protein Data Bank. For simple hinging motions involving a small conformational change, segmentation of the protein into two rigid sections outperforms other more computationally involved methods. However, large-scale conformational change is best addressed using a nonlinear approach and we suggest that there is merit in further developing such methods.