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. We report here a comparison between three such methodologies, Just Add Water Molecules (JAWS), Grand Canonical Monte Carlo (GCMC), and double-decoupling, in the hope of understanding the advantages and limitations of each method when applied to enclosed binding sites. As a result, we have adapted the JAWS scoring procedure, allowing the binding free energies of strongly bound water molecules to be calculated to a high degree of accuracy, requiring significantly less computational effort than more rigorous approaches. The combination of JAWS and GCMC offers a route to a rapid scheme capable of both locating and scoring water molecules for rational drug design

Computer aided drug design in the development of proteolysis targeting chimeras

Proteolysis targeting chimeras represent a class of drug molecules with a number of attractive properties, most notably a potential to work for targets that, so far, have been in-accessible for conventional small molecule inhibitors. Due to their different mechanism of action, and physico-chemical properties, many of the methods that have been designed and applied for computer aided design of traditional small molecule drugs are not applicable for proteolysis targeting chimeras. Here we review recent developments in this field focusing on three aspects: de-novo linker-design, estimation of absorption for beyond-rule-of-5 compounds, and the generation and ranking of ternary complex structures. In spite of this field still being young, we find that a good number of models and algorithms are available, with the potential to assist the design of such compounds in-silico, and accelerate applied pharmaceutical research

Bayesian optimization for ternary complex prediction (BOTCP)

Proximity-inducing compounds (PICs) are an emergent drug technology through which a protein of interest (POI), often a drug target, is brought into the vicinity of a second protein which modifies the POI’s function, abundance or localisation, giving rise to a therapeutic effect. One of the best-known examples for such compounds are heterobifunctional molecules known as proteolysis targeting chimeras (PROTACs). PROTACs reduce the abundance of the target protein by establishing proximity to an E3 ligase which labels the protein for degradation via the ubiquitin-proteasomal pathway. Design of PROTACs in silico requires the computational prediction of the ternary complex consisting of POI, PROTAC molecule, and the E3 ligase.We present a novel machine learning-based method for predicting PROTAC-mediated ternary complex structures using Bayesian optimization. We show how a fitness score combining an estimation of protein-protein interactions with PROTAC conformation energy calculations enables the sample-efficient exploration of candidate structures. Furthermore, our method presents two novel scores for filtering and reranking which take PROTAC stability (Autodock-Vina based PROTAC stability score) and protein interaction restraints (the TCP-AIR score) into account. We evaluate our method using DockQ scores on a number of available ternary complex structures (including previously unevaluated cases) and demonstrate that even with a clustering that requires members to have a high similarity, i.e., with smaller clusters, we can assign high ranks to those clusters that contain poses close to the experimentally determined native structure of the ternary complexes. We also demonstrate the resultant improved yield of near-native poses33 The near-native pose is defined as a pose that has DockQ score ≥0.23. in these clusters

Structure and mechanism of human UDP-xylose synthase: evidence for a promoting role of sugar ring distortion in a three-step catalytic conversion of UDP-glucuronic acid.

UDP-xylose synthase (UXS) catalyzes decarboxylation of UDP-D-glucuronic acid to UDP-xylose. In mammals, UDP-xylose serves to initiate glycosaminoglycan synthesis on the protein core of extracellular matrix proteoglycans. Lack of UXS activity leads to a defective extracellular matrix, resulting in strong interference with cell signaling pathways. We present comprehensive structural and mechanistic characterization of the human form of UXS. The 1.26-Å crystal structure of the enzyme bound with NAD(+) and UDP reveals a homodimeric short-chain dehydrogenase/reductase (SDR), belonging to the NDP-sugar epimerases/dehydratases subclass. We show that enzymatic reaction proceeds in three chemical steps via UDP-4-keto-D-glucuronic acid and UDP-4-keto-pentose intermediates. Molecular dynamics simulations reveal that the D-glucuronyl ring accommodated by UXS features a marked (4)C(1) chair to B(O,3) boat distortion that facilitates catalysis in two different ways. It promotes oxidation at C(4) (step 1) by aligning the enzymatic base Tyr(147) with the reactive substrate hydroxyl and it brings the carboxylate group at C(5) into an almost fully axial position, ideal for decarboxylation of UDP-4-keto-D-glucuronic acid in the second chemical step. The protonated side chain of Tyr(147) stabilizes the enolate of decarboxylated C(4) keto species ((2)H(1) half-chair) that is then protonated from the Si face at C(5), involving water coordinated by Glu(120). Arg(277), which is positioned by a salt-link interaction with Glu(120), closes up the catalytic site and prevents release of the UDP-4-keto-pentose and NADH intermediates. Hydrogenation of the C(4) keto group by NADH, assisted by Tyr(147) as catalytic proton donor, yields UDP-xylose adopting the relaxed (4)C(1) chair conformation (step 3)

Liquid ordering at the KDP {100}-solution interface

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Sensitivity of morphology prediction to the force field: paracetamol as an example

The growth morphology of paracetamol is known to show a strong supersaturation dependence. Most morphology prediction methods, like the attachment energy method, cannot include this dependence in their prediction. Monte Carlo simulations are able to use the supersaturation as an input parameter and can also include the growth mechanism. This makes the Monte Carlo technique a powerful tool to study the growth of organic crystals. Some studies in the literature show that the attachment energy method is only weakly influenced by the force field used to calculate the attachment energies. The present paper presents the sensitivity of the Monte Carlo simulation results to the force field and charge set using paracetamol as a case study. The force field and atomic point charges are found to influence the results to a large extent. This is due to subtle differences in step energies that determine the growth rates of the crystal faces