Structure-Based Discovery and Development of Highly Potent Dihydroorotate Dehydrogenase Inhibitors for Malaria Chemoprevention.

Malaria remains a serious global health challenge, yet treatment and control programs are threatened by drug resistance. Dihydroorotate dehydrogenase (DHODH) was clinically validated as a target for treatment and prevention of malaria through human studies with DSM265, but currently no drugs against this target are in clinical use. We used structure-based computational tools including free energy perturbation (FEP+) to discover highly ligand efficient, potent, and selective pyrazole-based DHODH inhibitors through a scaffold hop from a pyrrole-based series.

Enabled Discovery of KAI-11101, a Preclinical DLK Inhibitor for the Treatment of Neurodegenerative Disease and Neuronal Injury.

Dual leucine zipper kinase (DLK), expressed primarily in neuronal cells, is a regulator of neuronal degeneration in response to cellular stress from chronic disease or neuronal injury. This makes it an attractive target for the treatment of neurodegenerative diseases such as Alzheimer's, Parkinson's, and amyotrophic lateral sclerosis, and neuronal injury, such as chemotherapy-induced peripheral neuropathy. Here, we describe the discovery of a potent, selective, brain-penetrant DLK inhibitor, KAI-11101 ().

Elucidating Protein Structures in the Gas Phase: Traversing Configuration Space with Biasing Methods.

Achieving accurate characterization of protein structures in the gas phase continues to be a formidable challenge. To tackle this issue, the present study employs Molecular Dynamics (MD) simulations in tandem with enhanced sampling techniques (methods designed to efficiently explore protein conformations). The objective is to identify suitable structures of proteins by contrasting their calculated Collision Cross-Section (CCS) with those observed experimentally.

A Computational Physics-based Approach to Predict Unbound Brain-to-Plasma Partition Coefficient, K.

The blood-brain barrier (BBB) plays a critical role in preventing harmful endogenous and exogenous substances from penetrating the brain. Optimal brain penetration of small-molecule central nervous system (CNS) drugs is characterized by a high unbound brain/plasma ratio (K). While various medicinal chemistry strategies and models have been reported to improve BBB penetration, they have limited application in predicting K directly.

A network of molecular switches controls the activation of the two-component response regulator NtrC.

Recent successes in simulating protein structure and folding dynamics have demonstrated the power of molecular dynamics to predict the long timescale behaviour of proteins. Here, we extend and improve these methods to predict molecular switches that characterize conformational change pathways between the active and inactive state of nitrogen regulatory protein C (NtrC). By employing unbiased Markov state model-based molecular dynamics simulations, we construct a dynamic picture of the activation pathways of this key bacterial signalling protein that is consistent with experimental observations and predicts new mutants that could be used for validation of the mechanism.

Elucidating Ligand-Modulated Conformational Landscape of GPCRs Using Cloud-Computing Approaches.

G-protein-coupled receptors (GPCRs) are a versatile family of membrane-bound signaling proteins. Despite the recent successes in obtaining crystal structures of GPCRs, much needs to be learned about the conformational changes associated with their activation. Furthermore, the mechanism by which ligands modulate the activation of GPCRs has remained elusive.

Cloud computing approaches for prediction of ligand binding poses and pathways.

We describe an innovative protocol for ab initio prediction of ligand crystallographic binding poses and highly effective analysis of large datasets generated for protein-ligand dynamics. We include a procedure for setup and performance of distributed molecular dynamics simulations on cloud computing architectures, a model for efficient analysis of simulation data, and a metric for evaluation of model convergence. We give accurate binding pose predictions for five ligands ranging in affinity from 7 nM to > 200 μM for the immunophilin protein FKBP12, for expedited results in cases where experimental structures are difficult to produce.

Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways.

Simulations can provide tremendous insight into the atomistic details of biological mechanisms, but micro- to millisecond timescales are historically only accessible on dedicated supercomputers. We demonstrate that cloud computing is a viable alternative that brings long-timescale processes within reach of a broader community. We used Google's Exacycle cloud-computing platform to simulate two milliseconds of dynamics of a major drug target, the G-protein-coupled receptor β2AR.

Thermodynamic integration to predict host-guest binding affinities.

An alchemical free energy method with explicit solvent molecular dynamics simulations was applied as part of the blind prediction contest SAMPL3 to calculate binding free energies for seven guests to an acyclic cucurbit-[n]uril host. The predictions included determination of protonation states for both host and guests, docking pose generation, and binding free energy calculations using thermodynamic integration. We found a root mean square error (RMSE) of 3.

Independent-Trajectory Thermodynamic Integration: a practical guide to protein-drug binding free energy calculations using distributed computing.

The Independent-Trajectory Thermodynamic Integration (IT-TI) approach for free energy calculation with distributed computing is described. IT-TI utilizes diverse conformational sampling obtained from multiple, independent simulations to obtain more reliable free energy estimates compared to single TI predictions. The latter may significantly under- or over-estimate the binding free energy due to finite sampling.

Effects of Biomolecular Flexibility on Alchemical Calculations of Absolute Binding Free Energies.

The independent trajectory thermodynamic integration (IT-TI) approach (Lawrenz et. al J. Chem.

Porous protein frameworks with unsaturated metal centers in sterically encumbered coordination sites.

Described is an engineered metal-binding protein, MBPPhen2, which forms porous crystalline frameworks that feature coordinatively unsaturated Zn- and Ni-centers.

Impact of calcium on N1 influenza neuraminidase dynamics and binding free energy.

The highly pathogenic influenza strains H5N1 and H1N1 are currently treated with inhibitors of the viral surface protein neuraminidase (N1). Crystal structures of N1 indicate a conserved, high affinity calcium binding site located near the active site. The specific role of this calcium in the enzyme mechanism is unknown, though it has been shown to be important for enzymatic activity and thermostability.

Independent-Trajectories Thermodynamic-Integration Free-Energy Changes for Biomolecular Systems: Determinants of H5N1 Avian Influenza Virus Neuraminidase Inhibition by Peramivir.

Free-energy changes are essential physicochemical quantities for understanding most biochemical processes. Yet, the application of accurate thermodynamic-integration (TI) computation to biological and macromolecular systems is limited by finite-sampling artifacts. In this paper, we employ independent-trajectories thermodynamic-integration (IT-TI) computation to estimate improved free-energy changes and their uncertainties for (bio)molecular systems.