Monte Carlo on the manifold and MD refinement for binding pose prediction of protein-ligand complexes: 2017 D3R Grand Challenge

MELD in Action: Harnessing Data to Accelerate Molecular Dynamics

We review MELD, an accelerator of Molecular Dynamics simulations of biomolecules. MELD (Modeling Employing Limited Data) integrates molecular dynamics (MD) with a variety of types of structural information through Bayesian inference, generating ensembles of protein and DNA structures having proper Boltzmann populations. MELD minimizes the computational sampling of irrelevant regions of phase space by applying energetic penalties to areas that conflict with the available data. MELD is effective in refining protein structures using NMR or cryo-EM data or predicting protein-ligand binding poses. As a plugin for OpenMM, MELD is interoperable with other enhanced sampling methods, offering a versatile tool for structural determination in computational chemistry and biophysics.

Molecular mechanism of interactions between ACAD9 and binding partners in mitochondrial respiratory complex I assembly

The dual function protein ACAD9 catalyzes α,β-dehydrogenation of fatty acyl-CoA thioesters in fatty acid β-oxidation and is an essential chaperone for mitochondrial respiratory complex I (CI) assembly. ACAD9, ECSIT, and NDUFAF1 interact to form the core mitochondrial CI assembly complex. Current studies examine the molecular mechanism of ACAD9/ECSIT/NDUFAF1interactions. ACAD9 binds to the carboxy-terminal half and NDUFAF1 to the amino-terminal half of ECSIT. Binary complexes are unstable and aggregate easily, while the ACAD9/ECSIT/NDUFAF1 ternary complex is soluble and highly stable. Molecular modeling and small-angle X-ray scattering studies identified intra-complex interaction sites and binding sites for other assembly factors. Binding of ECSIT at the ETF binding site in the amino-terminal domain of ACAD9 is consistent with observed loss of FAD and enzymatic activity and demonstrates that the two functions of ACAD9 are mutually exclusive. Mapping of 42 known pathogenic mutations onto the homology-modeled ACAD9 structure provides structural insights into pathomechanisms of CI deficiency.

Rigorous Free Energy Simulations in Virtual Screening

Virtual high throughput screening (vHTS) in drug discovery is a powerful approach to identify hits: when applied successfully, it can be much faster and cheaper than experimental high-throughput screening approaches. However, mainstream vHTS tools have significant limitations: ligand-based methods depend on knowledge of existing chemical matter, while structure-based tools such as docking involve significant approximations that limit their accuracy. Recent advances in scientific methods coupled with dramatic speedups in computational processing with GPUs make this an opportune time to consider the role of more rigorous methods that could improve the predictive power of vHTS workflows. In this Perspective, we assert that alchemical binding free energy methods using all-atom molecular dynamics simulations have matured to the point where they can be applied in virtual screening campaigns as a final scoring stage to prioritize the top molecules for experimental testing. Specifically, we propose that alchemical absolute binding free energy (ABFE) calculations offer the most direct and computationally efficient approach within a rigorous statistical thermodynamic framework for computing binding energies of diverse molecules, as is required for virtual screening. ABFE calculations are particularly attractive for drug discovery at this point in time, where the confluence of large-scale genomics data and insights from chemical biology have unveiled a large number of promising disease targets for which no small molecule binders are known, precluding ligand-based approaches, and where traditional docking approaches have foundered to find progressible chemical matter.

D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies

The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.

Sampling and refinement protocols for template-based macrocycle docking: 2018 D3R Grand Challenge 4

We describe a new template-based method for docking flexible ligands such as macrocycles to proteins. It combines Monte-Carlo energy minimization on the manifold, a fast manifold search method, with BRIKARD for complex flexible ligand searching, and with the MELD accelerator of Replica-Exchange Molecular Dynamics simulations for atomistic degrees of freedom. Here we test the method in the Drug Design Data Resource blind Grand Challenge competition. This method was among the best performers in the competition, giving sub-angstrom prediction quality for the majority of the targets.

Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4

The D3R Grand Challenge 4 provided a brilliant opportunity to test macrocyclic docking protocols on a diverse high-quality experimental data. We participated in both pose and affinity prediction exercises. Overall, we aimed to use an automated structure-based docking pipeline built around a set of tools developed in our team. This exercise again demonstrated a crucial importance of the correct local ligand geometry for the overall success of docking. Starting from the second part of the pose prediction stage, we developed a stable pipeline for sampling macrocycle conformers. This resulted in the subangstrom average precision of our pose predictions. In the affinity prediction exercise we obtained average results. However, we could improve these when using docking poses submitted by the best predictors. Our docking tools including the Convex-PL scoring function are available at https://team.inria.fr/nano-d/software/.

Correlation of cathepsin S with coronary stenosis degree, carotid thickness, blood pressure, glucose and lipid metabolism and vascular endothelial function in atherosclerosis

Correlation of cathepsin S with coronary stenosis degree, carotid thickness, blood pressure, glucose and lipid metabolism, and vascular endothelial function in patients with atherosclerosis was investigated. Data from 120 patients with increased cathepsin S levels (increased group) and 120 subjects with normal cathepsin S levels (normal group) were retrospectively analyzed. The serum cathepsin S level and Gensini score were compared between the healthy subjects and patients with coronary atherosclerotic heart disease, and the correlation between serum cathepsin S level and Gensini score was analyzed. The carotid thickness, mean arterial pressure, and indexes related to glucose and lipid metabolism, as well as vascular endothelial function were compared between the two groups. The correlation of the serum cathepsin S level with carotid intima-media thickness (IMT), mean arterial pressure, fasting blood glucose, total cholesterol (TC) and nitric oxide (NO) was also investigated. Patients with multi-vessel coronary artery disease (CAD) had higher serum cathepsin S level than those with double-vessel and single-vessel disease, and higher level than that of healthy subjects. The Gensini score of patients with multi-vessel CAD was higher than that of patients with double-vessel and single-vessel disease, as well as that of healthy subjects. The serum cathepsin S level was positively correlated with the Gensini score. Patients with increased cathepsin S level had greater IMT, higher mean arterial pressure, fasting blood glucose, fasting insulin (FINS), triglyceride (TG), TC, and endothelin-1 (ET-1), however, lower NO level than those of healthy subjects. The serum cathepsin S level was positively correlated with IMT, mean arterial pressure, fasting blood glucose, and TC, however, it was negatively correlated with the NO level. In conclusion, as the serum cathepsin S level is elevated, the coronary stenosis is aggravated, the carotid thickness and blood pressure are increased, and the glucose and lipid metabolism, as well as vascular endothelial function are significantly abnormal.

Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S

Cathepsin S (CatS), a member of cysteine cathepsin proteases, has been well studied due to its significant role in many pathological processes, including arthritis, cancer and cardiovascular diseases. CatS inhibitors have been included in D3R-GC3 for both docking pose prediction and affinity ranking, and in D3R-GC4 for binding affinity ranking. The difficulties posed by CatS inhibitors in D3R mainly come from three aspects: large size, high flexibility and similar chemical structures. We have participated in GC4; our best submitted model, which employs a similarity-based alignment docking and Vina scoring protocol, yielded Kendall's τ of 0.23 for 459 binders in GC4. In our further explorations with machine learning, by curating a CatS specific training set, adopting a similarity-based constrained docking method as well as an arm-based fragmentation strategy which can describe large inhibitors in a locality-sensitive fashion, our best structure-based ranking protocol can achieve Kendall's τ of 0.52 for all binders in GC4. In this exploration process, we have demonstrated the importance of training data, docking approaches and fragmentation strategies in inhibitor-ranking protocol development with machine learning.

Template-Based Method for Conformation Generation and Scoring for Congeneric Series of Ligands

Physics-based prediction of protein-ligand binding affinities for a congeneric series of ligands in lead optimization requires their geometries as a first step. In this paper, we report a method that uses the 3D conformation of a lead compound in complex with a protein as a template to generate conformations of a series of related analog compounds. The method uses the Maximal Common Substructure (MCS) computed between lead and analog ligands to assign coordinates for the atoms shared between the ligands. For the differing atoms, a conformation generation procedure is implemented that results in a diversity of conformations. The generated conformations are sorted using a score based on the Molecular Mechanics and Generalized Born with Solvent Accessible Surface Area contribution (MM-GBSA) method. The accuracy of the generated conformations is tested retrospectively using a cross-validation approach applied to four data sets obtained from the Drug Design Data Resource (D3R) by measuring the RMSD of the top scored conformation with respect to the crystallographic pose. The scoring ability of the method is independently assessed using data for the same protein targets to test the rank ordering ability and separating active and inactive ligands. We tested the effect of protein flexibility during structural optimization and scoring approaches with and without strain energies. Retrospective validation on data sets comprising 4 targets shows that the method outperforms random selection for all targets and outperforms a molecular weight-based null model in 3 out of 4 targets in separating active and inactive compounds. Therefore, the presented method is expected to be of utility in lead optimization for rapidly screening analog ligands and generating initial conformations for use in more detailed physics-based binding affinity prediction methods.