Evaluation of docking performance in a blinded virtual screening of fragment-like trypsin inhibitors

International audienceIn this study, we have "blindly" assessed the ability of several combinations of docking software and scoring functions to predict the binding of a fragment-like library of bovine trypsine inhibitors. The most suitable protocols (involving Gold software and GoldScore scoring function, with or without rescoring) were selected for this purpose using a training set of compounds with known biological activities. The selected virtual screening protocols provided good results with the SAMPL3-VS dataset, showing enrichment factors of about 10 for Top 20 compounds. This methodology should be useful in difficult cases of docking, with a special emphasis on the fragment-based virtual screening campaigns

Integrative approaches in fragment-based drug discovery

This thesis combines experimental and computational methods to investigate aspects of fragment identification and elaboration in fragment-based ligand design, a promising approach for identifying small molecule drugs, to target the pharmacologically relevant bromodomain PHIP(2). The research covers various aspects of the process, from initial crystallographic fragment screening to validation of follow-up compounds. Chapters 1 and 2 provide an overview of relevant perspectives and methodologies in fragment-based drug discovery. Chapter 3 reports a crystallographic fragment screening against PHIP(2), resolving 47 fragments at the acetylated-lysine binding site, and evaluates the abilities of crowdsourced computational methods to replicate fragment binding and crystallographic poses. This chapter highlights the challenges associated with using computational methods for reproducing crystallographic fragment screening results with submissions performing relatively weakly. Chapter 4 demonstrates the advantages of X-ray crystallographic screening of crude reaction mixtures generated robotically, showcasing reduced time, solvent, and hardware requirements. Soaking crude reaction mixtures maintains crystal integrity which led to the identification of 22 binders, 3 with an alternate pose caused by a single methyl addition to the core fragment and 1 hit in assays. It demonstrates how affordable methods can generate large amounts of crystallographic data of fragment elaborations. Chapter 5 develops an algorithmic approach to extract features associated with crystallographic binding, deriving simple binding scores using data from Chapter 4. The method identifies 26 false negatives with binding scores enriching binders over non-binders. Employing these scores prospectively in a virtual screening demonstrated how binding features can be exploited to select further follow-up compounds leading to low micromolar potencies. Chapter 6 attempts to integrate more computationally intensive methods to identify fragment follow-up compounds with increased potency through virtual screening enhanced with free energy calculations. Only two out of six synthesised follow-up compounds showed weak binding in assays, and none were resolved in crystal structures. This thesis tackles critical challenges in follow-up design, synthesis, and dataset analysis, underlining the limitations of existing methods in advancing fragment-based drug discovery. It emphasises the necessity of integrative approaches for an optimised “design, make, test” cycle in fragment-based drug discovery

Virtual fragment screening on GPCRs: A case study on dopamine D3 and histamine H4 receptors

Prospective structure based virtual fragment screening methodologies on two GPCR targets namely the dopamine D3 and the histamine H4 receptors with a library of 12,905 fragments were evaluated. Fragments were docked to the X-ray structure and the homology model of the D3 and H4 receptors, respectively. Representative receptor conformations for ensemble docking were obtained from molecular dynamics trajectories. In vitro confirmed hit rates ranged from 16% to 32%. Hits had high ligand efficiency (LE) values in the range of 0.31-0.74 and also acceptable lipophilic efficiency. The X-ray structure, the homology model and structural ensembles were all found suitable for docking based virtual screening of fragments against these GPCRs. However, there was little overlap among different hit sets and methodologies were thus complementary to each other. (C) 2014 Elsevier Masson SAS. All rights reserved

SAMPL6: calculation of macroscopic pKa values from ab initio quantum mechanical free energies

International audienceMacroscopic pKa values were calculated for all compounds in the SAMPL6 blind prediction challenge, based on quantum chemical calculations with a continuum solvation model and a linear correction derived from a small training set. Microscopic pKa values were derived from the gas-phase free energy difference between protonated and deprotonated forms together with the Conductor-like Polarizable Continuum Solvation Model and the experimental solvation free energy of the proton. pH-dependent microstate free energies were obtained from the microscopic pKas with a maximum likelihood estimator and appropriately summed to yield macroscopic pKa values or microstate populations as function of pH. We assessed the accuracy of three approaches to calculate the microscopic pKas: direct use of the quantum mechanical free energy differences and correction of the direct values for short-comings in the QM solvation model with two different linear models that we independently derived from a small training set of 38 compounds with known pKa. The predictions that were corrected with the linear models had much better accuracy [root-mean-square error (RMSE) 2.04 and 1.95 pKa units] than the direct calculation (RMSE 3.74). Statistical measures indicate that some systematic errors remain, likely due to differences in the SAMPL6 data set and the small training set with respect to their interactions with water. Overall, the current approach provides a viable physics-based route to estimate macroscopic pKa values for novel compounds with reasonable accuracy

Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge

The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pKa for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pKa challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pKa challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pKa values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pKa prediction methods

How Water's Properties Are Encoded in Its Molecular Structure and Energies.

How are water's material properties encoded within the structure of the water molecule? This is pertinent to understanding Earth's living systems, its materials, its geochemistry and geophysics, and a broad spectrum of its industrial chemistry. Water has distinctive liquid and solid properties: It is highly cohesive. It has volumetric anomalies-water's solid (ice) floats on its liquid; pressure can melt the solid rather than freezing the liquid; heating can shrink the liquid. It has more solid phases than other materials. Its supercooled liquid has divergent thermodynamic response functions. Its glassy state is neither fragile nor strong. Its component ions-hydroxide and protons-diffuse much faster than other ions. Aqueous solvation of ions or oils entails large entropies and heat capacities. We review how these properties are encoded within water's molecular structure and energies, as understood from theories, simulations, and experiments. Like simpler liquids, water molecules are nearly spherical and interact with each other through van der Waals forces. Unlike simpler liquids, water's orientation-dependent hydrogen bonding leads to open tetrahedral cage-like structuring that contributes to its remarkable volumetric and thermal properties

Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks

Free energy calculations are rapidly becoming indispensable in structure-enabled drug discovery programs. As new methods, force fields, and implementations are developed, assessing their expected accuracy on real-world systems (benchmarking) becomes critical to provide users with an assessment of the accuracy expected when these methods are applied within their domain of applicability, and developers with a way to assess the expected impact of new methodologies. These assessments require construction of a benchmark - a set of well-prepared, high quality systems with corresponding experimental measurements designed to ensure the resulting calculations provide a realistic assessment of expected performance when these methods are deployed within their domains of applicability. To date, the community has not yet adopted a common standardized benchmark, and existing benchmark reports suffer from a myriad of issues, including poor data quality, limited statistical power, and statistically deficient analyses, all of which can conspire to produce benchmarks that are poorly predictive of real-world performance. Here, we address these issues by presenting guidelines for (1) curating experimental data to develop meaningful benchmark sets, (2) preparing benchmark inputs according to best practices to facilitate widespread adoption, and (3) analysis of the resulting predictions to enable statistically meaningful comparisons among methods and force fields

Evaluating parameterization protocols for hydration free energy calculations with the AMOEBA polarizable force field

Hydration free energy (HFE) calculations are often used to assess the performance of biomolecular force fields and the quality of assigned parameters. The AMOEBA polarizable force field moves beyond traditional pairwise additive models of electrostatics and may be expected to improve upon predictions of thermodynamic quantities such as HFEs over and above fixed point charge models. The recent SAMPL4 challenge evaluated the AMOEBA polarizable force field in this regard, but showed substantially worse results than those using the fixed point charge GAFF model. Starting with a set of automatically generated AMOEBA parameters for the SAMPL4 dataset, we evaluate the cumulative effects of a series of incremental improvements in parameterization protocol, including both solute and solvent model changes. Ultimately the optimized AMOEBA parameters give a set of results that are not statistically significantly different from those of GAFF in terms of signed and unsigned error metrics. This allows us to propose a number of guidelines for new molecule parameter derivation with AMOEBA, which we expect to have benefits for a range of biomolecular simulation applications such as protein ligand binding studie

Solvation thermodynamics of organic molecules by the molecular integral equation theory : approaching chemical accuracy

The integral equation theory (IET) of molecular liquids has been an active area of academic research in theoretical and computational physical chemistry for over 40 years because it provides a consistent theoretical framework to describe the structural and thermodynamic properties of liquid-phase solutions. The theory can describe pure and mixed solvent systems (including anisotropic and nonequilibrium systems) and has already been used for theoretical studies of a vast range of problems in chemical physics / physical chemistry, molecular biology, colloids, soft matter, and electrochemistry. A consider- able advantage of IET is that it can be used to study speci fi c solute − solvent interactions, unlike continuum solvent models, but yet it requires considerably less computational expense than explicit solvent simulations