Fragment-Guided Discovery of Pyrazole Carboxylic Acid Inhibitors of the Kelch-like ECH-Associated Protein 1: Nuclear Factor Erythroid 2 Related Factor 2 (KEAP1:NRF2) Protein-Protein Interaction.

The NRF2-mediated cytoprotective response is central to cellular homoeostasis, and there is increasing interest in developing small-molecule activators of this pathway as therapeutics for diseases involving chronic oxidative stress. The protein KEAP1, which regulates NRF2, is a key point for pharmacological intervention, and we recently described the use of fragment-based drug discovery to develop a tool compound that directly disrupts the protein-protein interaction between NRF2 and KEAP1. We now present the identification of a second, chemically distinct series of KEAP1 inhibitors, which provided an alternative chemotype for lead optimization.

Generation of Quantum Configurational Ensembles Using Approximate Potentials.

Conformational analysis is of paramount importance in drug design: it is crucial to determine pharmacological properties, understand molecular recognition processes, and characterize the conformations of ligands when unbound. Molecular Mechanics (MM) simulation methods, such as Monte Carlo (MC) and molecular dynamics (MD), are usually employed to generate ensembles of structures due to their ability to extensively sample the conformational space of molecules. The accuracy of these MM-based schemes strongly depends on the functional form of the force field (FF) and its parametrization, components that often hinder their performance.

ParaMol: A Package for Automatic Parameterization of Molecular Mechanics Force Fields.

The ensemble of structures generated by molecular mechanics (MM) simulations is determined by the functional form of the force field employed and its parameterization. For a given functional form, the quality of the parameterization is crucial and will determine how accurately we can compute observable properties from simulations. While accurate force field parameterizations are available for biomolecules, such as proteins or DNA, the parameterization of new molecules, such as drug candidates, is particularly challenging as these may involve functional groups and interactions for which accurate parameters may not be available.

Practical High-Quality Electrostatic Potential Surfaces for Drug Discovery Using a Graph-Convolutional Deep Neural Network.

Inspecting protein and ligand electrostatic potential (ESP) surfaces in order to optimize electrostatic complementarity is a key activity in drug design. These ESP surfaces need to reflect the true electrostatic nature of the molecules, which typically means time-consuming high-level quantum mechanics (QM) calculations are required. For interactive design much faster alternative methods are required.

COSMIC-3D provides structural perspectives on cancer genetics for drug discovery.

COSMIC-3D is a comprehensive integration of cancer mutations with protein structure across the human genome and structural proteome, seeking to support the identification and characterisation of protein targets for novel drug design in precision oncology. As an interactive system to explore cancer mutations in three-dimensions, COSMIC-3D is designed to enable a greater understanding of the functional impact of mutations, generate new hypotheses on which mutations are cancer drivers, and provide new opportunities for addressing these mutations pharmaceutically. This combination of genetics, structural proteomics, and drug development, can be best described as “mutation-guided drug design”.

The Fragment Network: A Chemistry Recommendation Engine Built Using a Graph Database.

The hit validation stage of a fragment-based drug discovery campaign involves probing the SAR around one or more fragment hits. This often requires a search for similar compounds in a corporate collection or from commercial suppliers. The Fragment Network is a graph database that allows a user to efficiently search chemical space around a compound of interest.

Predicting "Hot" and "Warm" Spots for Fragment Binding.

Computational fragment mapping methods aim to predict hotspots on protein surfaces where small fragments will bind. Such methods are popular for druggability assessment as well as structure-based design. However, to date researchers developing or using such tools have had no clear way of assessing the performance of these methods.

Protein-Ligand Informatics Force Field (PLIff): Toward a Fully Knowledge Driven "Force Field" for Biomolecular Interactions.

The Protein Data Bank (PDB) contains a wealth of data on nonbonded biomolecular interactions. If this information could be distilled down to nonbonded interaction potentials, these would have some key advantages over standard force fields. However, there are some important outstanding issues to address in order to do this successfully.

Detection of secondary binding sites in proteins using fragment screening.

Proteins need to be tightly regulated as they control biological processes in most normal cellular functions. The precise mechanisms of regulation are rarely completely understood but can involve binding of endogenous ligands and/or partner proteins at specific locations on a protein that can modulate function. Often, these additional secondary binding sites appear separate to the primary binding site, which, for example for an enzyme, may bind a substrate.

Potential and limitations of ensemble docking.

A major problem in structure-based virtual screening applications is the appropriate selection of a single or even multiple protein structures to be used in the virtual screening process. A priori it is unknown which protein structure(s) will perform best in a virtual screening experiment. We investigated the performance of ensemble docking, as a function of ensemble size, for eight targets of pharmaceutical interest.

Experiences in fragment-based drug discovery.

Fragment-based drug discovery (FBDD) has become established in both industry and academia as an alternative approach to high-throughput screening for the generation of chemical leads for drug targets. In FBDD, specialised detection methods are used to identify small chemical compounds (fragments) that bind to the drug target, and structural biology is usually employed to establish their binding mode and to facilitate their optimisation. In this article, we present three recent and successful case histories in FBDD.

Docking performance of fragments and druglike compounds.

This paper addresses two questions of key interest to researchers working with protein-ligand docking methods: (i) Why is there such a large variation in docking performance between different test sets reported in the literature? (ii) Are fragments more difficult to dock than druglike compounds? To answer these, we construct a test set of in-house X-ray structures of protein-ligand complexes from drug discovery projects, half of which contain fragment ligands, the other half druglike ligands. We find that a key factor affecting docking performance is ligand efficiency (LE). High LE compounds are significantly easier to dock than low LE compounds, which we believe could explain the differences observed between test sets reported in the literature.

Protein-ligand docking against non-native protein conformers.

In the validation of protein-ligand docking protocols, performance is mostly measured against native protein conformers, i.e. each ligand is docked into the protein conformation from the structure that contained that ligand.

Protein-Ligand Complexes: Computation of the Relative Free Energy of Different Scaffolds and Binding Modes.

A methodology for the calculation of the free energy difference between a pair of molecules of arbitrary topology is proposed. The protocol relies on a dual-topology paradigm, a softening of the intermolecular interactions, and a constraint that prevents the perturbed molecules from drifting away from each other at the end states. The equivalence and the performance of the methodology against a single-topology approach are demonstrated on a pair of harmonic oscillators, the calculation of the relative solvation free energy of ethane and methanol, and the relative binding free energy of two congeneric inhibitors of cyclooxygenase 2.

Identification of inhibitors of protein kinase B using fragment-based lead discovery.

Using fragment-based screening techniques, 5-methyl-4-phenyl-1H-pyrazole (IC50 80 microM) was identified as a novel, low molecular weight inhibitor of protein kinase B (PKB). Herein we describe the rapid elaboration of highly potent and ligand efficient analogues using a fragment growing approach. Iterative structure-based design was supported by protein-ligand structure determinations using a PKA-PKB "chimera" and a final protein-ligand structure of a lead compound in PKBbeta itself.

Diverse, high-quality test set for the validation of protein-ligand docking performance.

A procedure for analyzing and classifying publicly available crystal structures has been developed. It has been used to identify high-resolution protein-ligand complexes that can be assessed by reconstructing the electron density for the ligand using the deposited structure factors. The complexes have been clustered according to the protein sequences, and clusters have been discarded if they do not represent proteins thought to be of direct interest to the pharmaceutical or agrochemical industry.

A structural comparison of inhibitor binding to PKB, PKA and PKA-PKB chimera.

Although the crystal structure of the anti-cancer target protein kinase B (PKBbeta/Akt-2) has been useful in guiding inhibitor design, the closely related kinase PKA has generally been used as a structural mimic due to its facile crystallization with a range of ligands. The use of PKB-inhibitor crystallography would bring important benefits, including a more rigorous understanding of factors dictating PKA/PKB selectivity, and the opportunity to validate the utility of PKA-based surrogates. We present a "back-soaking" method for obtaining PKBbeta-ligand crystal structures, and provide a structural comparison of inhibitor binding to PKB, PKA, and PKA-PKB chimera.

Protein-ligand binding affinity predictions by implicit solvent simulations: a tool for lead optimization?

Continuum electrostatics is combined with rigorous free-energy calculations in an effort to deliver a reliable and efficient method for in silico lead optimization. The methodology is tested by calculation of the relative binding free energies of a set of inhibitors of neuraminidase, cyclooxygenase2, and cyclin-dependent kinase 2. The calculated free energies are compared to the results obtained with explicit solvent simulations and empirical scoring functions.

Automated protein-ligand crystallography for structure-based drug design.

An approach to automate protein-ligand crystallography is presented, with the aim of increasing the number of structures available to structure-based drug design. The methods we propose deal with the automatic interpretation of diffraction data for targets with known protein structures, and provide easy access to the results. Central to the system is a novel procedure that fully automates the placement of ligands into electron density maps.

Modeling water molecules in protein-ligand docking using GOLD.

We implemented a novel approach to score water mediation and displacement in the protein-ligand docking program GOLD. The method allows water molecules to switch on and off and to rotate around their three principal axes. A constant penalty, sigma(p), representing the loss of rigid-body entropy, is added for water molecules that are switched on, hence rewarding water displacement.

General and targeted statistical potentials for protein-ligand interactions.

We present a novel atom-atom potential derived from a database of protein-ligand complexes. First, we clarify the similarities and differences between two statistical potentials described in the literature, PMF and Drugscore. We highlight shortcomings caused by an important factor unaccounted for in their reference states, and describe a new potential, which we name the Astex Statistical Potential (ASP).

Prediction of binding modes for ligands in the cytochromes P450 and other heme-containing proteins.

The cytochromes P450 (P450s) are a family of heme-containing monooxygenase enzymes involved in a variety of functions, including the metabolism of endogenous and exogenous substances in the human body. During lead optimization, and in drug development, many potential drug candidates are rejected because of the affinity they display for drug-metabolising P450s. Recently, crystal structures of human enzymes involved in drug metabolism have been determined, significantly augmenting the prospect of using structure-based design to modulate the binding and metabolizing properties of compounds against P450 proteins.

Structure-guided fragment screening for lead discovery.

Fragment-based ligand screening can be a highly effective strategy for drug discovery. In general, fragment hits interact efficiently with the target, and although the potency of these small binders is often low, their optimization into potent leads is tractable. For a hit optimization phase to take full advantage of a good quality fragment binder, we believe it is essential to obtain reliable structural data for the hits.

Virtual screening using protein-ligand docking: avoiding artificial enrichment.

This study addresses a number of topical issues around the use of protein-ligand docking in virtual screening. We show that, for the validation of such methods, it is key to use focused libraries (containing compounds with one-dimensional properties, similar to the actives), rather than "random" or "drug-like" libraries to test the actives against. We also show that, to obtain good enrichments, the docking program needs to produce reliable binding modes.