On the construction of model Hamiltonians for adiabatic quantum computation and its application to finding low energy conformations of lattice protein models

In this report, we explore the use of a quantum optimization algorithm for obtaining low energy conformations of protein models. We discuss mappings between protein models and optimization variables, which are in turn mapped to a system of coupled quantum bits. General strategies are given for constructing Hamiltonians to be used to solve optimization problems of physical/chemical/biological interest via quantum computation by adiabatic evolution. As an example, we implement the Hamiltonian corresponding to the Hydrophobic-Polar (HP) model for protein folding. Furthermore, we present an approach to reduce the resulting Hamiltonian to two-body terms gearing towards an experimental realization.Comment: 35 pages, 8 figure

Improved Docking of Polypeptides with Glide

Predicting the binding mode of flexible polypeptides to proteins is an important task that falls outside the domain of applicability of most small molecule and protein–protein docking tools. Here, we test the small molecule flexible ligand docking program Glide on a set of 19 non-α-helical peptides and systematically improve pose prediction accuracy by enhancing Glide sampling for flexible polypeptides. In addition, scoring of the poses was improved by post-processing with physics-based implicit solvent MM-GBSA calculations. Using the best RMSD among the top 10 scoring poses as a metric, the success rate (RMSD ≤ 2.0 Å for the interface backbone atoms) increased from 21% with default Glide SP settings to 58% with the enhanced peptide sampling and scoring protocol in the case of redocking to the native protein structure. This approaches the accuracy of the recently developed Rosetta FlexPepDock method (63% success for these 19 peptides) while being over 100 times faster. Cross-docking was performed for a subset of cases where an unbound receptor structure was available, and in that case, 40% of peptides were docked successfully. We analyze the results and find that the optimized polypeptide protocol is most accurate for extended peptides of limited size and number of formal charges, defining a domain of applicability for this approach

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC50 50 < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns

WScore: A Flexible and Accurate Treatment of Explicit Water Molecules in Ligand–Receptor Docking

We have developed a new methodology for protein–ligand docking and scoring, WScore, incorporating a flexible description of explicit water molecules. The locations and thermodynamics of the waters are derived from a WaterMap molecular dynamics simulation. The water structure is employed to provide an atomic level description of ligand and protein desolvation. WScore also contains a detailed model for localized ligand and protein strain energy and integrates an MM-GBSA scoring component with these terms to assess delocalized strain of the complex. Ensemble docking is used to take into account induced fit effects on the receptor conformation, and protein reorganization free energies are assigned via fitting to experimental data. The performance of the method is evaluated for pose prediction, rank ordering of self-docked complexes, and enrichment in virtual screening, using a large data set of PDB complexes and compared with the Glide SP and Glide XP models; significant improvements are obtained

Water-assisted Proton Transfer in Ferredoxin I*

The role of water molecules in assisting proton transfer (PT) is investigated for the proton-pumping protein ferredoxin I (FdI) from Azotobacter vinelandii. It was shown previously that individual water molecules can stabilize between Asp15 and the buried [3Fe-4S]0 cluster and thus can potentially act as a proton relay in transferring H+ from the protein to the μ2 sulfur atom. Here, we generalize molecular mechanics with proton transfer to studying proton transfer reactions in the condensed phase. Both umbrella sampling simulations and electronic structure calculations suggest that the PT Asp15-COOH + H2O + [3Fe-4S]0 → Asp15-COO− + H2O + [3Fe-4S]0 H+ is concerted, and no stable intermediate hydronium ion (H3O+) is expected. The free energy difference of 11.7 kcal/mol for the forward reaction is in good agreement with the experimental value (13.3 kcal/mol). For the reverse reaction (Asp15-COO− + H2O + [3Fe-4S]0H+ → Asp15-COOH + H2O + [3Fe-4S]0), a larger barrier than for the forward reaction is correctly predicted, but it is quantitatively overestimated (23.1 kcal/mol from simulations versus 14.1 from experiment). Possible reasons for this discrepancy are discussed. Compared with the water-assisted process (ΔE ≈ 10 kcal/mol), water-unassisted proton transfer yields a considerably higher barrier of ΔE ≈ 35 kcal/mol