Fragment and torsion biasing algorithms for construction of small organic molecules in proteins using DOCK.

The computational construction of small organic molecules (de novo design), directly in a protein binding site, is an effective means for generating novel ligands tailored to fit the pocket environment. In this work, we present two new methods, which aim to improve de novo design outcomes using (1) biasing algorithms to prioritize selection and/or acceptance of fragments and torsions during growth, and (2) parallel-based clustering and pruning algorithms to remove duplicate molecules as candidate fragment are added. Large-scale testing encompassing thousands of simulations were employed to interrogate the methods in terms of multiple metrics which include numbers of duplicate molecules generated, pairwise-similarity, focused library reconstruction rates, fragment and torsion frequencies, fragment and torsion rank scores, interaction energy and drug-likeness scores, and 3D pose comparisons. The biasing algorithms, particularly those that include fragment and torsion components simultaneously, led to molecules that more closely mimicked the distributions of fragments and torsions found in drug-like libraries. The new parallel-based clustering and pruning algorithms, compared with the existing serial approach, also led to larger ensembles comprised of topologically unique molecules with much greater efficiency by removing redundant growth paths.

Rapid Rescoring and Refinement of Ligand-Receptor Complexes Using Replica Exchange Molecular Dynamics with a Monte Carlo Pose Reservoir.

Virtual screening (VS) involves generation of poses for a library of ligands and ranking using simplified energy functions and limited flexibility. Top-scored poses are used to rank and prioritize ligands. Here, we adapt the reservoir replica exchange molecular dynamics (res-REMD) method to rerank poses generated through VS. REMD simulations are carried out but with occasional Monte Carlo jumps to alternate VS-generated poses using a Metropolis criterion. The simulations converge within 10 ns for all systems, generating populations of alternate poses in the context of fully flexible ligand and protein side chains. The protocol is applied to four model protein-ligand complexes, where DOCK resulted in two successes and two scoring failures. In all four systems, the most populated cluster from the final ensemble exhibits high similarity to the crystallographic pose with ligand RMSD values under 2.0 Å. Both DOCK failures were rescued. For one DOCK success, the protocol identified the correct pose but also sampled an alternate pose at equal probability. Opportunities for future improvements and extensions are discussed.

Neutral ceramidase-active site inhibitor chemotypes and binding modes.

Ceramides impact a diverse array of biological functions and have been implicated in disease pathogenesis. The enzyme neutral ceramidase (nCDase) is a zinc-containing hydrolase and mediates the metabolism of ceramide to sphingosine (Sph), both in cells and in the intestinal lumen. nCDase inhibitors based on substrate mimetics, for example C6-urea ceramide, have limited potency, aqueous solubility, and micelle-free fraction. To identify non-ceramide mimetic nCDase inhibitors, hit compounds from an HTS campaign were evaluated in biochemical, cell based and in silico modeling approaches. A majority of small molecule nCDase inhibitors contained pharmacophores capable of zinc interaction but retained specificity for nCDase over zinc-containing acid and alkaline ceramidases, as well as matrix metalloprotease-3 and histone deacetylase-1. nCDase inhibitors were refined by SAR, were shown to be substrate competitive and were active in cellular assays. nCDase inhibitor compounds were modeled by in silico DOCK screening and by molecular simulation. Modeling data supports zinc interaction and a similar compound binding pose with ceramide. nCDase inhibitors were identified with notably improved activity and solubility in comparison with the reference lipid-mimetic C6-urea ceramide.

A molecular evolution algorithm for ligand design in DOCK.

As a complement to virtual screening, de novo design of small molecules is an alternative approach for identifying potential drug candidates. Here, we present a new 3D genetic algorithm to evolve molecules through breeding, mutation, fitness pressure, and selection. The method, termed DOCK_GA, builds upon and leverages powerful sampling, scoring, and searching routines previously implemented into DOCK6. Three primary experiments were used during development: Single-molecule evolution evaluated three selection methods (elitism, tournament, and roulette), in four clinically relevant systems, in terms of mutation type and crossover success, chemical properties, ensemble diversity, and fitness convergence, among others. Large scale benchmarking assessed performance across 651 different protein-ligand systems. Ensemble-based evolution demonstrated using multiple inhibitors simultaneously to seed growth in a SARS-CoV-2 target. Key takeaways include: (1) The algorithm is robust as demonstrated by the successful evolution of molecules across a large diverse dataset. (2) Users have flexibility with regards to parent input, selection method, fitness function, and molecular descriptors. (3) The program is straightforward to run and only requires a single executable and input file at run-time. (4) The elitism selection method yields more tightly clustered molecules in terms of 2D/3D similarity, with more favorable fitness, followed by tournament and roulette.