Shuangye Yin

MedusaScore: An accurate force field-based scoring function for virtual drug screening

Virtual screening is becoming an important tool for drug discovery. However, the application of virtual screening has been limited by the lack of accurate scoring functions. Here, we present a novel scoring function, MedusaScore, for evaluating protein-ligand binding. MedusaScore is based on models of physical interactions that include van der Waals, solvation, and hydrogen bonding energies. To ensure the best transferability of the scoring function, we do not use any protein-ligand experimental data for parameter training. We then test the MedusaScore for docking decoy recognition and binding affinity prediction and find superior performance compared to other widely used scoring functions. Statistical analysis indicates that one source of inaccuracy of MedusaScore may arise from the unaccounted entropic loss upon ligand binding, which suggests avenues of approach for further MedusaScore improvement.

Fast screening of protein surfaces using geometric invariant fingerprints

We develop a rapid and efficient method for the comparison of protein local surface similarities using geometric invariants (fingerprints). By combining fast fingerprint comparison with explicit alignment, we successfully screen the entire Protein Data Bank for proteins that possess local surface similarities. Our method is independent of sequence and fold similarities, and has potential application to protein structure annotation and protein-protein interface design.

Discrete Molecular Dynamics Distinguishes Nativelike Binding Poses from Decoys in Difficult Targets

Virtual screening is one of the major tools used in computer-aided drug discovery. In structure-based virtual screening, the scoring function is critical to identifying the correct docking pose and accurately predicting the binding affinities of compounds. However, the performance of existing scoring functions has been shown to be uneven for different targets, and some important drug targets have proven especially challenging. In these targets, scoring functions cannot accurately identify the native or near-native binding pose of the ligand from among decoy poses, which affects both the accuracy of the binding affinity prediction and the ability of virtual screening to identify true binders in chemical libraries. Here, we present an approach to discriminating native poses from decoys in difficult targets for which several scoring functions failed to correctly identify the native pose. Our approach employs Discrete Molecular Dynamics simulations to incorporate protein-ligand dynamics and the entropic effects of binding. We analyze a collection of poses generated by docking and find that the residence time of the ligand in the native and nativelike binding poses is distinctly longer than that in decoy poses. This finding suggests that molecular simulations offer a unique approach to distinguishing the native (or nativelike) binding pose from decoy poses that cannot be distinguished using scoring functions that evaluate static structures. The success of our method emphasizes the importance of protein-ligand dynamics in the accurate determination of the binding pose, an aspect that is not addressed in typical docking and scoring protocols.

Combined application of cheminformatics- and physical force field-based scoring functions improves binding affinity prediction for CSAR data sets

The curated CSAR-NRC benchmark sets provide valuable opportunity for testing or comparing the performance of both existing and novel scoring functions. We apply two different scoring functions, both independently and in combination, to predict the binding affinity of ligands in the CSAR-NRC data sets. One reported here for the first time employs multiple chemical-geometrical descriptors of the protein-ligand interface to develop Quantitative Structure Binding Affinity Relationships (QSBAR) models. These models are then used to predict binding affinity of ligands in the external data set. Second is a physical force field-based scoring function, MedusaScore. We show that both individual scoring functions achieve statistically significant prediction accuracies with the squared correlation coefficient (R(2)) between the actual and predicted binding affinity of 0.44/0.53 (Set1/Set2) with QSBAR models and 0.34/0.47 (Set1/Set2) with MedusaScore. Importantly, we find that the combination of QSBAR models and MedusaScore into consensus scoring function affords higher prediction accuracy than any of the contributing methods achieving R(2) values of 0.45/0.58 (Set1/Set2). Furthermore, we identify several chemical features and noncovalent interactions that may be responsible for the inaccurate prediction of binding affinity for several ligands by the scoring functions employed in this study.

Structural Basis for μ-Opioid Receptor Binding and Activation

Opioids that stimulate the μ-opioid receptor (MOR1) are the most frequently prescribed and effective analgesics. Here we present a structural model of MOR1. Molecular dynamics simulations show a ligand-dependent increase in the conformational flexibility of the third intracellular loop that couples with the G protein complex. These simulations likewise identified residues that form frequent contacts with ligands. We validated the binding residues using site-directed mutagenesis coupled with radioligand binding and functional assays. The model was used to blindly screen a library of ∼1.2 million compounds. From the 34 compounds predicted to be strong binders, the top three candidates were examined using biochemical assays. One compound showed high efficacy and potency. Post hoc testing revealed this compound to be nalmefene, a potent clinically used antagonist, thus further validating the model. In summary, the MOR1 model provides a tool for elucidating the structural mechanism of ligand-initiated cell signaling and for screening novel analgesics.

Fingerprint-based structure retrieval using electron density.

We present a computational approach that can quickly search a large protein structural database to identify structures that fit a given electron density, such as determined by cryo‐electron microscopy. We use geometric invariants (fingerprints) constructed using 3D Zernike moments to describe the electron density, and reduce the problem of fitting of the structure to the electron density to simple fingerprint comparison. Using this approach, we are able to screen the entire Protein Data Bank and identify structures that fit two experimental electron densities determined by cryo‐electron microscopy. Proteins 2011.

Computational evaluation of protein stability change upon mutations

When designing a mutagenesis experiment, it is often crucial to estimate the stability change of proteins induced by mutations (Delta DG). Despite the recent advances in computational methods, it is still challenging to estimate D DG quickly and accurately. We recently developed the Eris protocols for in silico evaluation of the Delta DG. Starting from the tertiary structure of the wide-type protein, the Eris protocols can model the structure of the mutant protein and estimate Delta DG using the structure models. The Eris protocols not only efficiently optimize the side chains conformations, taking advantage of a fast rotamer-based searching algorithm, but also allow protein backbone flexibility during the modeling. As a result, the Eris protocols effectively resolve steric clashes induced by certain mutations and have more accurate Delta DG predictions than a fixed-backbone approach. We discuss the general aspects of computational Delta DG estimations and discuss in detail the principles and methodologies of the Eris protocols.