Enhancing torsional sampling using fully adaptive simulated tempering.

Enhanced sampling algorithms are indispensable when working with highly disconnected multimodal distributions. An important application of these is the conformational exploration of particular internal degrees of freedom of molecular systems. However, despite the existence of many commonly used enhanced sampling algorithms to explore these internal motions, they often rely on system-dependent parameters, which negatively impact efficiency and reproducibility.

Targeting Cytotoxic Agents through EGFR-Mediated Covalent Binding and Release.

A major drawback of cytotoxic chemotherapy is the lack of selectivity toward noncancerous cells. The targeted delivery of cytotoxic drugs to tumor cells is a longstanding goal in cancer research. We proposed that covalent inhibitors could be adapted to deliver cytotoxic agents, conjugated to the β-position of the Michael acceptor, via an addition-elimination mechanism promoted by covalent binding.

Discovery of AZD4747, a Potent and Selective Inhibitor of Mutant GTPase KRAS with Demonstrable CNS Penetration.

The glycine to cysteine mutation at codon 12 of Kirsten rat sarcoma (KRAS) represents an Achilles heel that has now rendered this important GTPase druggable. Herein, we report our structure-based drug design approach that led to the identification of , AZD4747, a clinical development candidate for the treatment of KRAS-positive tumors, including the treatment of central nervous system (CNS) metastases. Building on our earlier discovery of C5-tethered quinazoline AZD4625, excision of a usually critical pyrimidine ring yielded a weak but brain-penetrant start point which was optimized for potency and DMPK.

Design of rigid protein-protein interaction inhibitors enables targeting of undruggable Mcl-1.

The structure-based design of small-molecule inhibitors targeting protein-protein interactions (PPIs) remains a huge challenge as the drug must bind typically wide and shallow protein sites. A PPI target of high interest for hematological cancer therapy is myeloid cell leukemia 1 (Mcl-1), a prosurvival guardian protein from the Bcl-2 family. Despite being previously considered undruggable, seven small-molecule Mcl-1 inhibitors have recently entered clinical trials.

Virtual Screening in the Cloud Identifies Potent and Selective ROS1 Kinase Inhibitors.

ROS1 rearrangements account for 1-2% of non-small cell lung cancer patients, yet there are no specifically designed, selective ROS1 therapies in the clinic. Previous knowledge of potent ROS1 inhibitors with selectivity over TrkA, a selected antitarget, enabled virtual screening as a hit finding approach in this project. The ligand-based virtual screening was focused on identifying molecules with a similar 3D shape and pharmacophore to the known actives.

Enhancing Ligand and Protein Sampling Using Sequential Monte Carlo.

The sampling problem is one of the most widely studied topics in computational chemistry. While various methods exist for sampling along a set of reaction coordinates, many require system-dependent hyperparameters to achieve maximum efficiency. In this work, we present an alchemical variation of adaptive sequential Monte Carlo (SMC), an irreversible importance resampling method that is part of a well-studied class of methods that have been used in various applications but have been underexplored in computational biophysics.

Discovery of AZD4625, a Covalent Allosteric Inhibitor of the Mutant GTPase KRAS.

KRAS is an archetypal high-value intractable oncology drug target. The glycine to cysteine mutation at codon 12 represents an Achilles heel that has now rendered this important GTPase druggable. Herein, we report our structure-based drug design approach that led to the identification of , AZD4625, a clinical development candidate for the treatment of KRAS positive tumors.

Drugging the undruggable: a computational chemist's view of KRAS.

In recent years, the emergence of targeted covalent inhibitors which bind to the G12C mutant of KRAS have offered a solution to this previously intractable target. Inhibitors of KRAS tend to be structurally complex, displaying features such as atropisomerism, chiral centres and a reactive covalent warhead. Such molecules result in lengthy and challenging syntheses, and as a consequence critical decisions need to be made at the design level to maximise the chances of success.

Virtual Screening Approach to Identifying a Novel and Tractable Series of Elastase Inhibitors.

Novel therapies are required to treat chronic bacterial infections in cystic fibrosis (CF) sufferers. The most common pathogen responsible for these infections is , which persists within the lungs of CF sufferers despite intensive antibiotic treatment. elastase (also known as LasB or pseudolysin) is a key virulence determinant that contributes to the pathogenesis and persistence of infections in CF patients.

Sensitivity of Binding Free Energy Calculations to Initial Protein Crystal Structure.

Binding free energy calculations using alchemical free energy (AFE) methods are widely considered to be the most rigorous tool in the computational drug discovery arsenal. Despite this, the calculations suffer from accuracy, precision, and reproducibility issues. In this publication, we perform a high-throughput study of more than a thousand AFE calculations, utilizing over 220 μs of total sampling time, on three different protein systems to investigate the impact of the initial crystal structure on the resulting binding free energy values.

Alkynyl Benzoxazines and Dihydroquinazolines as Cysteine Targeting Covalent Warheads and Their Application in Identification of Selective Irreversible Kinase Inhibitors.

With a resurgence in interest in covalent drugs, there is a need to identify new moieties capable of cysteine bond formation that are differentiated from commonly employed systems such as acrylamide. Herein, we report on the discovery of new alkynyl benzoxazine and dihydroquinazoline moieties capable of covalent reaction with cysteine. Their utility as alternative electrophilic warheads for chemical biological probes and drug molecules is demonstrated through site-selective protein modification and incorporation into kinase drug scaffolds.

ProtoCaller: Robust Automation of Binding Free Energy Calculations.

ProtoCaller is a Python library distributed through Anaconda which automates relative protein-ligand binding free energy calculations in GROMACS. It links a number of popular specialized tools used to perform protein setup and parametrization, such as PDB2PQR, Modeller, and AmberTools. ProtoCaller supports commonly used AMBER force fields with additional cofactor parameters, and AM1-BCC is used to derive ligand charges.

Structure-Based Design and Pharmacokinetic Optimization of Covalent Allosteric Inhibitors of the Mutant GTPase KRAS.

Attempts to directly drug the important oncogene KRAS have met with limited success despite numerous efforts across industry and academia. The KRAS mutant represents an "Achilles heel" and has recently yielded to covalent targeting with small molecules that bind the mutant cysteine and create an allosteric pocket on GDP-bound RAS, locking it in an inactive state. A weak inhibitor at this site was optimized through conformational locking of a piperazine-quinazoline motif and linker modification.

Utilizing Grand Canonical Monte Carlo Methods in Drug Discovery.

The concepts behind targeting waters for potency and selectivity gains have been well documented and explored, although maximizing such potential gains can prove to be challenging. This problem is exacerbated in cases where there are multiple interacting waters, wherein perturbation of one water can affect the free energy landscape of the remaining waters. Knowing the right modification is challenging, and computational approaches are ideally suited to help answer the key question of which substitution is best to try.

Heteroarylamide smoothened inhibitors: Discovery of N-[2,4-dimethyl-5-(1-methylimidazol-4-yl)phenyl]-4-(2-pyridylmethoxy)benzamide (AZD8542) and N-[5-(1H-imidazol-2-yl)-2,4-dimethyl-phenyl]-4-(2- pyridylmethoxy)benzamide (AZD7254).

Aberrant hedgehog (Hh) pathway signaling is implicated in multiple cancer types and targeting the Smoothened (SMO) receptor, a key protein of the Hh pathway, has proven effective in treating metastasized basal cell carcinoma. Our lead optimization effort focused on a series of heteroarylamides. We observed that a methyl substitution ortho to the heteroaryl groups on an aniline core significantly improved the potency of this series of compounds.

Lowering Lipophilicity by Adding Carbon: AzaSpiroHeptanes, a log Lowering Twist.

We have conducted an analysis of azaspiro[3.3]heptanes used as replacements for morpholines, piperidines, and piperazines in a medicinal chemistry context. In most cases, introducing a spirocyclic center lowered the measured log of the corresponding molecules by as much as -1.

DPP1 Inhibitors: Exploring the Role of Water in the S2 Pocket of DPP1 with Substituted Pyrrolidines.

A series of pyrrolidine amino nitrile DPP1 inhibitors have been developed and characterized. The S2 pocket structure-activity relationship for these compounds shows significant gains in potency for DPP1 from interacting further with target residues and a network of water molecules in the binding pocket. Herein we describe the X-ray crystal structures of several of these compounds alongside an analysis of factors influencing the inhibitory potency toward DPP1 of which stabilization of the water network, demonstrated using Grand Canonical Monte Carlo simulations and free energy calculations, is attributed as a main factor.

Lowering Lipophilicity by Adding Carbon: One-Carbon Bridges of Morpholines and Piperazines.

In this article, we report our investigation of a phenomenon by which bridging morpholines across the ring with one-carbon tethers leads to a counterintuitive reduction in lipophilicity. This effect was also found to occur in piperazines and piperidines and lowered the measured log D of the bridged molecules by as much as -0.8 relative to their unbridged counterparts.

Water, water, everywhere… It's time to stop and think.

Despite the numerous methods available for predicting the location and affinity of water molecules, there is still a degree of scepticism and reluctance towards using such information within a drug discovery program. Here, I review some of the most common and popular methods to assess whether these apparent concerns are justified. I suggest that the field is approaching maturity and that some methods are capable of giving quantitative predictions, which are confirmed experimentally.

Water Sites, Networks, And Free Energies with Grand Canonical Monte Carlo.

Water molecules play integral roles in the formation of many protein-ligand complexes, and recent computational efforts have been focused on predicting the thermodynamic properties of individual waters and how they may be exploited in rational drug design. However, when water molecules form highly coupled hydrogen-bonding networks, there is, as yet, no method that can rigorously calculate the free energy to bind the entire network or assess the degree of cooperativity between waters. In this work, we report theoretical and methodological developments to the grand canonical Monte Carlo simulation technique.

A molecular dynamics study of CaCO3 nanoparticles in a hydrophobic solvent with a stearate co-surfactant.

Stearates containing overbased detergent nanoparticles (NPs) are used as acid neutralising additives in automotive and marine engine oils. Molecular dynamics (MD) simulations of the self-assembly of calcium carbonate, calcium stearate as a co-surfactant and stabilising surfactants of such NPs in a model explicit molecular hydrophobic solvent have been carried out using a methodology described first by Bodnarchuk et al. [J.

Strategies to calculate water binding free energies in protein-ligand complexes.

Water molecules are commonplace in protein binding pockets, where they can typically form a complex between the protein and a ligand or become displaced upon ligand binding. As a result, it is often of great interest to establish both the binding free energy and location of such molecules. Several approaches to predicting the location and affinity of water molecules to proteins have been proposed and utilized in the literature, although it is often unclear which method should be used under what circumstances.

Water network perturbation in ligand binding: adenosine A(2A) antagonists as a case study.

Recent efforts in the computational evaluation of the thermodynamic properties of water molecules have resulted in the development of promising new in silico methods to evaluate the role of water in ligand binding. These methods include WaterMap, SZMAP, GRID/CRY probe, and Grand Canonical Monte Carlo simulations. They allow the prediction of the position and relative free energy of the water molecule in the protein active site and the analysis of the perturbation of an explicit water network (WNP) as a consequence of ligand binding.