Mapping Ligand-Shape Space for Protein-Ligand Systems: Distinguishing Key-in-Lock and Hand-in-Glove Proteins.

Many of the recently developed methods to study the shape of molecules permit one conformation of one molecule to be compared to another conformation of the same or a different molecule: a relative shape. Other methods provide an absolute description of the shape of a conformation that does not rely on comparisons or overlays. Any absolute description of shape can be used to generate a self-organizing map (shape map) that places all molecular shapes relative to one another; in the studies reported here, the shape fingerprint and ultrafast shape recognition methods are employed to create such maps. In the shape maps, molecules that are near one another have similar shapes, and the maps for the 102 targets in the DUD-E set have been generated. By examining the distribution of actives in comparison with their physical-property-matched decoys, we show that the proteins of key-in-lock type (relatively rigid receptor and ligand) can be distinguished from those that are more of a hand-in-glove type (more flexible receptor and ligand). These are linked to known differences in protein flexibility and binding-site size.

Ligand binding: evaluating the contribution of the water molecules network using the Fragment Molecular Orbital method.

Water molecules play a crucial role in protein-ligand binding, and many tools exist that aim to predict the position and relative energies of these important, but challenging participants of biomolecular recognition. The available tools are, in general, capable of predicting the location of water molecules. However, predicting the effects of their displacement is still very challenging. In this work, a linear-scaling quantum mechanics-based approach was used to assess water network energetics and the changes in network stability upon ligand structural modifications. This approach offers a valuable way to improve understanding of SAR data and help guide compound design.

Clinical and veterinary trypanocidal benzoxaboroles target CPSF3.

African trypanosomes cause lethal and neglected tropical diseases, known as sleeping sickness in humans and nagana in animals. Current therapies are limited, but fortunately, promising therapies are in advanced clinical and veterinary development, including acoziborole (AN5568 or SCYX-7158) and AN11736, respectively. These benzoxaboroles will likely be key to the World Health Organization's target of disease control by 2030. Their mode of action was previously unknown. We have developed a high-coverage overexpression library and use it here to explore drug mode of action in Trypanosoma brucei Initially, an inhibitor with a known target was used to select for drug resistance and to test massive parallel library screening and genome-wide mapping; this effectively identified the known target and validated the approach. Subsequently, the overexpression screening approach was used to identify the target of the benzoxaboroles, Cleavage and Polyadenylation Specificity Factor 3 (CPSF3, Tb927.4.1340). We validated the CPSF3 endonuclease as the target, using independent overexpression strains. Knockdown provided genetic validation of CPSF3 as essential, and GFP tagging confirmed the expected nuclear localization. Molecular docking and CRISPR-Cas9-based editing demonstrated how acoziborole can specifically block the active site and mRNA processing by parasite, but not host CPSF3. Thus, our findings provide both genetic and chemical validation for CPSF3 as an important drug target in trypanosomes and reveal inhibition of mRNA maturation as the mode of action of the trypanocidal benzoxaboroles. Understanding the mechanism of action of benzoxaborole-based therapies can assist development of improved therapies, as well as the prediction and monitoring of resistance, if or when it arises.

Turbocharging Matched Molecular Pair Analysis: Optimizing the Identification and Analysis of Pairs.

We have applied the two most commonly used methods for automatic matched pair identification, obtained the optimum settings, and discovered that the two methods are synergistic. A turbocharging approach to matched pair analysis is advocated in which a first round (a conservative categorical approach that uses an analogy with coin flips, heads corresponding to an increase in a measured property, tails to a decrease, and a biased coin to a structural change that reliably causes a change in that property) provides the settings for a second round (which uses the magnitude of the change in properties). Increased chemical specificity allows reliable knowledge to be extracted from smaller sets of pairs, and an assay-specific upper limit can be placed on the number of pairs required before adequate sampling of variability has been achieved.

Cryptosporidium lysyl-tRNA synthetase inhibitors define the interplay between solubility and permeability required to achieve efficacy.

Cryptosporidiosis is a diarrheal disease caused by infection with Cryptosporidium spp. parasites and is a leading cause of death in malnourished children worldwide. The only approved treatment, nitazoxanide, has limited efficacy in this at-risk patient population. Additional safe therapeutics are urgently required to tackle this unmet medical need. However, the development of anti-cryptosporidial drugs is hindered by a lack of understanding of the optimal compound properties required to treat this gastrointestinal infection. To address this knowledge gap, a diverse set of potent lysyl-tRNA synthetase inhibitors was profiled to identify optimal physicochemical and pharmacokinetic properties required for efficacy in a chronic mouse model of infection. The results from this comprehensive study illustrated the importance of balancing solubility and permeability to achieve efficacy in vivo. Our results establish in vitro criteria for solubility and permeability that are predictive of compound efficacy in vivo to guide the optimization of anti-cryptosporidial drugs. Two compounds from chemically distinct series (DDD489 and DDD508) were identified as demonstrating superior efficacy and prioritized for further evaluation. Both compounds achieved marked parasite reduction in immunocompromised mouse models and a disease-relevant calf model of infection. On the basis of these promising data, these compounds have been selected for progression to preclinical safety studies, expanding the portfolio of potential treatments for this neglected infectious disease.

SophosQM: Accurate Binding Affinity Prediction in Compound Optimization.

The optimization of compounds' binding affinity for a biological target is a crucial aspect of the drug development process. Being able to accurately predict binding energies in advance of synthesizing compounds would have a massive impact on the speed of the drug discovery process. The ideal binding affinity prediction method should combine accuracy, reliability, and speed. In this paper, we present SophosQM, a quantum mechanics (QM)-based approach, which can accurately predict the binding affinities of compounds to proteins. The binding affinity predictive models generated by SophosQM are based on the fragment molecular orbital (FMO) method to estimate the enthalpic component of the binding free energy, and a macroscopic descriptor, clog P, is used as an approximation of the entropic component. The affinity prediction is performed using multilinear regression, fitting the experimental values against the FMO-computed enthalpic term and clog P. The quality of the prediction can be assessed in terms of the correlation coefficient between experimental and predicted values. In this work, the method's reliability and accuracy are exemplified by applying SophosQM to 70 compounds binding to six different targets of pharmaceutical relevance. Overall, the results show a very satisfactory performance with a global correlation coefficient in the order of 0.9. Our predictions also show a satisfactory performance compared to data based on free energy perturbation. Finally, SophosQM can also be applied in high-throughput mode by using semiempirical QM methods to evaluate large portions of chemical space, while retaining a good level of accuracy, but decreasing the computing time to just a few seconds per compound.

Identification and Optimization of Novel Inhibitors of the Polyketide Synthase 13 Thioesterase Domain with Antitubercular Activity.

There is an urgent need for new tuberculosis (TB) treatments, with novel modes of action, to reduce the incidence/mortality of TB and to combat resistance to current treatments. Through both chemical and genetic methodologies, polyketide synthase 13 (Pks13) has been validated as essential for mycobacterial survival and as an attractive target for Mycobacterium tuberculosis growth inhibitors. A benzofuran series of inhibitors that targeted the Pks13 thioesterase domain, failed to progress to preclinical development due to concerns over cardiotoxicity. Herein, we report the identification of a novel oxadiazole series of Pks13 inhibitors, derived from a high-throughput screening hit and structure-guided optimization. This new series binds in the Pks13 thioesterase domain, with a distinct binding mode compared to the benzofuran series. Through iterative rounds of design, assisted by structural information, lead compounds were identified with improved antitubercular potencies (MIC < 1 µM) and in vitro ADMET profiles.

Scaffold-Hopping Strategy on a Series of Proteasome Inhibitors Led to a Preclinical Candidate for the Treatment of Visceral Leishmaniasis.

There is an urgent need for new treatments for visceral leishmaniasis (VL), a parasitic infection which impacts heavily large areas of East Africa, Asia, and South America. We previously reported on the discovery of GSK3494245/DDD01305143 (1) as a preclinical candidate for VL and, herein, we report on the medicinal chemistry program that led to its identification. A hit from a phenotypic screen was optimized to give a compound with in vivo efficacy, which was hampered by poor solubility and genotoxicity. The work on the original scaffold failed to lead to developable compounds, so an extensive scaffold-hopping exercise involving medicinal chemistry design, in silico profiling, and subsequent synthesis was utilized, leading to the preclinical candidate. The compound was shown to act via proteasome inhibition, and we report on the modeling of different scaffolds into a cryo-EM structure and the impact this has on our understanding of the series' structure-activity relationships.

DNDI-6148: A Novel Benzoxaborole Preclinical Candidate for the Treatment of Visceral Leishmaniasis.

Visceral leishmaniasis (VL) is a parasitic disease endemic across multiple regions of the world and is fatal if untreated. Current therapies are unsuitable, and there is an urgent need for safe, short-course, and low-cost oral treatments to combat this neglected disease. The benzoxaborole chemotype has previously delivered clinical candidates for the treatment of other parasitic diseases. Here, we describe the development and optimization of this series, leading to the identification of compounds with potent in vitro and in vivo antileishmanial activity. The lead compound (DNDI-6148) combines impressive in vivo efficacy (>98% reduction in parasite burden) with pharmaceutical properties suitable for onward development and an acceptable safety profile. Detailed mode of action studies confirm that DNDI-6148 acts principally through the inhibition of Leishmania cleavage and polyadenylation specificity factor (CPSF3) endonuclease. As a result of these studies and its promising profile, DNDI-6148 has been declared a preclinical candidate for the treatment of VL.

Predicting protein-ligand binding affinity and correcting crystal structures with quantum mechanical calculations: lactate dehydrogenase A.

Accurately computing the geometry and energy of host-guest and protein-ligand interactions requires a physically accurate description of the forces in action. Quantum mechanics can provide this accuracy but the calculations can require a prohibitive quantity of computational resources. The size of the calculations can be reduced by including only the atoms of the receptor that are in close proximity to the ligand. We show that when combined with log P values for the ligand (which can be computed easily) this approach can significantly improve the agreement between computed and measured binding energies. When the approach is applied to lactate dehydrogenase A, it can make quantitative predictions about conformational, tautomeric and protonation state preferences as well as stereoselectivity and even identifies potential errors in structures deposited in the Protein Data Bank for this enzyme. By broadening the evidence base for these structures from only the diffraction data, more chemically realistic structures can be proposed.

Designing Hydroxamates and Reversed Hydroxamates to Inhibit Zinc-containing Proteases but not Cytochrome P450s: Insights from Quantum Mechanics and Protein-ligand Crystal Structures.

The Hydroxamate is a useful functional group that binds to metals in a range of enzymes, notably zinc in matrix metalloproteases and histone deacetylases. The group is also able to form interactions with iron leading to inhibition of the cytochromes P450, particularly the 3A4 isoform. We have studied the available crystal structures of zinc-containing proteins bound to hydroxamates and compared the observed geometries with those found by quantum mechanical calculations. This has revealed the likely binding mode preferences for neutral and anionic protonation states and highlighted the importance of electrostatic complementarity. Calculations were also performed for the interaction of the hydroxamate with iron in a heme environment, as found in the cytochromes P450. These reveal that the preferred binding mode of hydroxamates in this environment involves the s-trans conformation. These calculations provide design guidelines for those interested in designing inhibitors of metalloenzymes that do not block metabolism of other drugs. The ability to predict the geometries and energies of binding modes that cannot be studied experimentally is an advantage offered by this kind of study.

Resistant hypertension and chronotherapy.

Resistant hypertension is defined as blood pressure that remains above 140/90 mmHg in spite of the continuous use of three antihypertensive agents in optimal dose, including diuretic, and lifestyle changes. According to data from United States of America and Europe, the prevalence ranges from 10 up to 30% in patients with hypertension. Numerous biological and lifestyle factors can contribute to the development of resistant hypertension: medications, volume overload, obesity, diabetes mellitus, older age, renal parenchymal and renovascular disease, primary aldosteronism, obstructive sleep apnea, pheochormocytoma, Cushing's syndrome, thyroid diseases, aortic coarctation. For diagnosing patient's history is important, assessing compliance, regular blood pressure measurement, physical examination, biochemical evaluation and noninvasive imaging. The evaluation including 24h ambulatory monitoring of blood pressure (ABPM) in the identification of "non-dipper" hypertension. Non-dipper has particular importance and the prevalence of abnormally high sleep blood pressure is very often in chronic kidney patients. Therapeutic restoration of normal physiologic blood pressure reduction during night-time sleep (circadial variation) is the most significant independent predictor of decreased risk and the basis for the chronotherapy. The resistant hypertension treatment is achieved with nonpharmacological and pharmacological approach, treating secondary hypertension causes and invasive procedures.

Structural basis of protein oxidation resistance: a lysozyme study.

Accumulation of oxidative damage in proteins correlates with aging since it can cause irreversible and progressive degeneration of almost all cellular functions. Apparently, native protein structures have evolved intrinsic resistance to oxidation since perfectly folded proteins are, by large most robust. Here we explore the structural basis of protein resistance to radiation-induced oxidation using chicken egg white lysozyme in the native and misfolded form. We study the differential resistance to oxidative damage of six different parts of native and misfolded lysozyme by a targeted tandem/mass spectrometry approach of its tryptic fragments. The decay of the amount of each lysozyme fragment with increasing radiation dose is found to be a two steps process, characterized by a double exponential evolution of their amounts: the first one can be largely attributed to oxidation of specific amino acids, while the second one corresponds to further degradation of the protein. By correlating these results to the structural parameters computed from molecular dynamics (MD) simulations, we find the protein parts with increased root-mean-square deviation (RMSD) to be more susceptible to modifications. In addition, involvement of amino acid side-chains in hydrogen bonds has a protective effect against oxidation Increased exposure to solvent of individual amino acid side chains correlates with high susceptibility to oxidative and other modifications like side chain fragmentation. Generally, while none of the structural parameters alone can account for the fate of peptides during radiation, together they provide an insight into the relationship between protein structure and susceptibility to oxidation.