Exploiting high-energy hydration sites for the discovery of potent peptide aldehyde inhibitors of the SARS-CoV-2 main protease with cellular antiviral activity.

Small-molecule antivirals that prevent the replication of the SARS-CoV-2 virus by blocking the enzymatic activity of its main protease (Mpro) are and will be a tenet of pandemic preparedness. However, the peptidic nature of such compounds often precludes the design of compounds within favorable physical property ranges, limiting cellular activity. Here we describe the discovery of peptide aldehyde Mpro inhibitors with potent enzymatic and cellular antiviral activity. This structure-activity relationship (SAR) exploration was guided by the use of calculated hydration site thermodynamic maps (WaterMap) to drive potency via displacement of waters from high-energy sites. Thousands of diverse compounds were designed to target these high-energy hydration sites and then prioritized for synthesis by physics- and structure-based Free-Energy Perturbation (FEP+) simulations, which accurately predicted biochemical potencies. This approach ultimately led to the rapid discovery of lead compounds with unique SAR that exhibited potent enzymatic and cellular activity with excellent pan-coronavirus coverage.

Discovery of a Novel Class of d-Amino Acid Oxidase Inhibitors Using the Schrödinger Computational Platform.

d-Serine is a coagonist of the N-methyl d-aspartate (NMDA) receptor, a key excitatory neurotransmitter receptor. In the brain, d-serine is synthesized from its l-isomer by serine racemase and is metabolized by the D-amino acid oxidase (DAO, DAAO). Many studies have linked decreased d-serine concentration and/or increased DAO expression and enzyme activity to NMDA dysfunction and schizophrenia. Thus, it is feasible to employ DAO inhibitors for the treatment of schizophrenia and other indications. Powered by the Schrödinger computational modeling platform, we initiated a research program to identify novel DAO inhibitors with the best-in-class properties. The program execution leveraged an hDAO FEP+ model to prospectively predict compound potency. A new class of DAO inhibitors with desirable properties has been discovered from this endeavor. Our modeling technology on this program has not only enhanced the efficiency of structure-activity relationship development but also helped to identify a previously unexplored subpocket for further optimization.

From computer-aided drug discovery to computer-driven drug discovery.

Computational chemistry and structure-based design have traditionally been viewed as a subset of tools that could aid acceleration of the drug discovery process, but were not commonly regarded as a driving force in small molecule drug discovery. In the last decade however, there have been dramatic advances in the field, including (1) development of physics-based computational approaches to accurately predict a broad variety of endpoints from potency to solubility, (2) improvements in artificial intelligence and deep learning methods and (3) dramatic increases in computational power with the advent of GPUs and cloud computing, resulting in the ability to explore and accurately profile vast amounts of drug-like chemical space in silico. There have also been simultaneous advancements in structural biology such as cryogenic electron microscopy (cryo-EM) and computational protein-structure prediction, allowing for access to many more high-resolution 3D structures of novel drug-receptor complexes. The convergence of these breakthroughs has positioned structurally-enabled computational methods to be a driving force behind the discovery of novel small molecule therapeutics. This review will give a broad overview of the synergies in recent advances in the fields of computational chemistry, machine learning and structural biology, in particular in the areas of hit identification, hit-to-lead, and lead optimization.

Facilitators and Barriers Surrounding the Role of Administration in Employee Job Satisfaction in Long-Term Care Facilities: A Systematic Review.

Previous literature has shown how associate engagement has positively impacted on productivity, job satisfaction, safety, retention, consumer sentiment, and financial performance in hospitals and healthcare systems. However, a lack of research showing the relationship between associate engagement and job satisfaction within the long-term care environment has existed. Our objective was to investigate characteristics within the long-term care environment that promote and detract from associate job satisfaction and extrapolate the best practices in maintaining job satisfaction and engagement. This systematic review queried CINAHL, PubMed (MEDLINE), and Academic Search Ultimate databases for peer-reviewed publications for facilitators and barriers commensurate with employee job satisfaction in long-term care facilities using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) and the Kruse Protocols. The authors identified 11 facilitators for job satisfaction and 18 barriers to job satisfaction in the 60 selected articles. The top four facilitators were Supportive Leadership, Capable and Motivated Employees, Positive Organizational Values, and Social Support Mechanisms. The top four barriers were condescending management style, high job demands, lack of self-care, and lack of training with medically complex patients. The systematic review revealed the importance of maintaining satisfied employees in the long-term care workplace through am emphasis leadership and on the facilitators identified to best serve their associates and improve care for residents.

Accelerating drug discovery through tight integration of expert molecular design and predictive scoring.

Modeling protein-ligand interactions has been a central goal of computational chemistry for many years. We here review recent progress toward this goal, and highlight the role free energy calculation methods and computational solvent analysis techniques are now having in drug discovery. We further describe recent use of these methodologies to advance two separate drug discovery programs targeting acetyl-CoA carboxylase and tyrosine kinase 2. These examples suggest that tight integration of sophisticated chemistry teams with state-of-the-art computational methods can dramatically improve the efficiency of small molecule drug discovery.

Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field.

Designing tight-binding ligands is a primary objective of small-molecule drug discovery. Over the past few decades, free-energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (∼5× in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.

Locating a plausible binding site for an open-channel blocker, GlyH-101, in the pore of the cystic fibrosis transmembrane conductance regulator.

High-throughput screening has led to the identification of small-molecule blockers of the cystic fibrosis transmembrane conductance regulator (CFTR) chloride channel, but the structural basis of blocker binding remains to be defined. We developed molecular models of the CFTR channel on the basis of homology to the bacterial transporter Sav1866, which could permit blocker binding to be analyzed in silico. The models accurately predicted the existence of a narrow region in the pore that is a likely candidate for the binding site of an open-channel pore blocker such as N-(2-naphthalenyl)-[(3,5-dibromo-2,4-dihydroxyphenyl)methylene]glycine hydrazide (GlyH-101), which is thought to act by entering the channel from the extracellular side. As a more-stringent test of predictions of the CFTR pore model, we applied induced-fit, virtual, ligand-docking techniques to identify potential binding sites for GlyH-101 within the CFTR pore. The highest-scoring docked position was near two pore-lining residues, Phe337 and Thr338, and the rates of reactions of anionic, thiol-directed reagents with cysteines substituted at these positions were slowed in the presence of the blocker, consistent with the predicted repulsive effect of the net negative charge on GlyH-101. When a bulky phenylalanine that forms part of the predicted binding pocket (Phe342) was replaced with alanine, the apparent affinity of the blocker was increased ∼200-fold. A molecular mechanics-generalized Born/surface area analysis of GlyH-101 binding predicted that substitution of Phe342 with alanine would substantially increase blocker affinity, primarily because of decreased intramolecular strain within the blocker-protein complex. This study suggests that GlyH-101 blocks the CFTR channel by binding within the pore bottleneck.

Structure-based identification and neutralization mechanism of tyrosine sulfate mimetics that inhibit HIV-1 entry.

Tyrosine sulfate-mediated interactions play an important role in HIV-1 entry. After engaging the CD4 receptor at the cell surface, the HIV-1 gp120 glycoprotein binds to the CCR5 co-receptor via an interaction that requires two tyrosine sulfates, at positions 10 and 14 in the CCR5-N terminus. Building on previous structure determinations of this interaction, here we report the targeting of these tyrosine sulfate binding sites for drug design through in silico screening of small molecule libraries, identification of lead compounds, and characterization of biological activity. A class of tyrosine sulfate-mimicking small molecules containing a "phenyl sulfonate-linker-aromatic" motif was identified that specifically inhibited binding of gp120 to the CCR5-N terminus as well as to sulfated antibodies that recognize the co-receptor binding region on gp120. The most potent of these compounds bound gp120 with low micromolar affinity and its CD4-induced conformation with K(D)'s as tight as ∼50 nM. Neutralization experiments suggested the targeted site to be conformationally inaccessible prior to CD4 engagement. Primary HIV-1 isolates were weakly neutralized, preincubation with soluble CD4 enhanced neutralization, and engineered isolates with increased dependence on the N terminus of CCR5 or with reduced conformational barriers were neutralized with IC(50) values as low as ∼1 µM. These results reveal the potential of targeting the tyrosine sulfate interactions of HIV-1 and provide insight into how mechanistic barriers, evolved by HIV-1 to evade antibody recognition, also restrict small-molecule-mediated neutralization.

Epik: a software program for pK( a ) prediction and protonation state generation for drug-like molecules.

Epik is a computer program for predicting pK(a) values for drug-like molecules. Epik can use this capability in combination with technology for tautomerization to adjust the protonation state of small drug-like molecules to automatically generate one or more of the most probable forms for use in further molecular modeling studies. Many medicinal chemicals can exchange protons with their environment, resulting in various ionization and tautomeric states, collectively known as protonation states. The protonation state of a drug can affect its solubility and membrane permeability. In modeling, the protonation state of a ligand will also affect which conformations are predicted for the molecule, as well as predictions for binding modes and ligand affinities based upon protein-ligand interactions. Despite the importance of the protonation state, many databases of candidate molecules used in drug development do not store reliable information on the most probable protonation states. Epik is sufficiently rapid and accurate to process large databases of drug-like molecules to provide this information. Several new technologies are employed. Extensions to the well-established Hammett and Taft approaches are used for pK(a) prediction, namely, mesomer standardization, charge cancellation, and charge spreading to make the predicted results reflect the nature of the molecule itself rather just for the particular Lewis structure used on input. In addition, a new iterative technology for generating, ranking and culling the generated protonation states is employed.

Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes.

A novel scoring function to estimate protein-ligand binding affinities has been developed and implemented as the Glide 4.0 XP scoring function and docking protocol. In addition to unique water desolvation energy terms, protein-ligand structural motifs leading to enhanced binding affinity are included: (1) hydrophobic enclosure where groups of lipophilic ligand atoms are enclosed on opposite faces by lipophilic protein atoms, (2) neutral-neutral single or correlated hydrogen bonds in a hydrophobically enclosed environment, and (3) five categories of charged-charged hydrogen bonds. The XP scoring function and docking protocol have been developed to reproduce experimental binding affinities for a set of 198 complexes (RMSDs of 2.26 and 1.73 kcal/mol over all and well-docked ligands, respectively) and to yield quality enrichments for a set of fifteen screens of pharmaceutical importance. Enrichment results demonstrate the importance of the novel XP molecular recognition and water scoring in separating active and inactive ligands and avoiding false positives.

Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening.

Glide's ability to identify active compounds in a database screen is characterized by applying Glide to a diverse set of nine protein receptors. In many cases, two, or even three, protein sites are employed to probe the sensitivity of the results to the site geometry. To make the database screens as realistic as possible, the screens use sets of "druglike" decoy ligands that have been selected to be representative of what we believe is likely to be found in the compound collection of a pharmaceutical or biotechnology company. Results are presented for releases 1.8, 2.0, and 2.5 of Glide. The comparisons show that average measures for both "early" and "global" enrichment for Glide 2.5 are 3 times higher than for Glide 1.8 and more than 2 times higher than for Glide 2.0 because of better results for the least well-handled screens. This improvement in enrichment stems largely from the better balance of the more widely parametrized GlideScore 2.5 function and the inclusion of terms that penalize ligand-protein interactions that violate established principles of physical chemistry, particularly as it concerns the exposure to solvent of charged protein and ligand groups. Comparisons to results for the thymidine kinase and estrogen receptors published by Rognan and co-workers (J. Med. Chem. 2000, 43, 4759-4767) show that Glide 2.5 performs better than GOLD 1.1, FlexX 1.8, or DOCK 4.01.

Design and synthesis of dipeptide nitriles as reversible and potent Cathepsin S inhibitors.

The specificity of the immune response relies on processing of foreign proteins and presentation of antigenic peptides at the cell surface. Inhibition of antigen presentation, and the subsequent activation of T-cells, should, in theory, modulate the immune response. The cysteine protease Cathepsin S performs a fundamental step in antigen presentation and therefore represents an attractive target for inhibition. Herein, we report a series of potent and reversible Cathepsin S inhibitors based on dipeptide nitriles. These inhibitors show nanomolar inhibition of the target enzyme as well as cellular potency in a human B cell line. The first X-ray crystal structure of a reversible inhibitor cocrystallized with Cathepsin S is also reported.