In Silico Exploration of the Trypanothione Reductase (TryR) of L. mexicana.

Human leishmaniasis is a neglected tropical disease which affects nearly 1.5 million people every year, with Mexico being an important endemic region. One of the major defense mechanisms of these parasites is based in the polyamine metabolic pathway, as it provides the necessary compounds for its survival. Among the enzymes in this route, trypanothione reductase (TryR), an oxidoreductase enzyme, is crucial for the Leishmania genus' survival against oxidative stress. Thus, it poses as an attractive drug target, yet due to the size and features of its catalytic pocket, modeling techniques such as molecular docking focusing on that region is not convenient. Herein, we present a computational study using several structure-based approaches to assess the druggability of TryR from L. mexicana, the predominant Leishmania species in Mexico, beyond its catalytic site. Using this consensus methodology, three relevant pockets were found, of which the one we call σ-site promises to be the most favorable one. These findings may help the design of new drugs of trypanothione-related diseases.

Epilogue to the Gerald Maggiora Festschrift: a tribute to an exemplary mentor, colleague, collaborator, and innovator.

In May 2022, JCAMD published a Special Issue in honor of Gerald (Gerry) Maggiora, whose scientific leadership over many decades advanced the fields of computational chemistry and chemoinformatics for drug discovery. Along the way, he has impacted many researchers in both academia and the pharmaceutical industry. In this Epilogue, we explain the origins of the Festschrift and present a series of first-hand vignettes, in approximate chronological sequence, that together paint a picture of this remarkable man. Whether they highlight Gerry's endless curiosity about molecular life sciences or his willingness to challenge conventional wisdom or his generous support of junior colleagues and peers, these colleagues and collaborators are united in their appreciation of his positive influence. These tributes also reflect key trends and themes during the evolution of modern drug discovery, seen through the lens of people who worked with a visionary leader. Junior scientists will find an inspiring roadmap for creative collegiality and collaboration.

Membrane Curvature Revisited-the Archetype of Rhodopsin Studied by Time-Resolved Electronic Spectroscopy.

G-protein-coupled receptors (GPCRs) comprise the largest and most pharmacologically targeted membrane protein family. Here, we used the visual receptor rhodopsin as an archetype for understanding membrane lipid influences on conformational changes involved in GPCR activation. Visual rhodopsin was recombined with lipids varying in their degree of acyl chain unsaturation and polar headgroup size using 1-palmitoyl-2-oleoyl-sn-glycero- and 1,2-dioleoyl-sn-glycerophospholipids with phosphocholine (PC) or phosphoethanolamine (PE) substituents. The receptor activation profile after light excitation was measured using time-resolved ultraviolet-visible spectroscopy. We discovered that more saturated POPC lipids back shifted the equilibrium to the inactive state, whereas the small-headgroup, highly unsaturated DOPE lipids favored the active state. Increasing unsaturation and decreasing headgroup size have similar effects that combine to yield control of rhodopsin activation, and necessitate factors beyond proteolipid solvation energy and bilayer surface electrostatics. Hence, we consider a balance of curvature free energy with hydrophobic matching and demonstrate how our data support a flexible surface model (FSM) for the coupling between proteins and lipids. The FSM is based on the Helfrich formulation of membrane bending energy as we previously first applied to lipid-protein interactions. Membrane elasticity and curvature strain are induced by lateral pressure imbalances between the constituent lipids and drive key physiological processes at the membrane level. Spontaneous negative monolayer curvature toward water is mediated by unsaturated, small-headgroup lipids and couples directly to GPCR activation upon light absorption by rhodopsin. For the first time to our knowledge, we demonstrate this modulation in both the equilibrium and pre-equilibrium evolving states using a time-resolved approach.

Encoding mu-opioid receptor biased agonism with interaction fingerprints.

Opioids are potent painkillers, however, their therapeutic use requires close medical monitoring to diminish the risk of severe adverse effects. The G-protein biased agonists of the µ-opioid receptor (MOR) have shown safer therapeutic profiles than non-biased ligands. In this work, we performed extensive all-atom molecular dynamics simulations of two markedly biased ligands and a balanced reference molecule. From those simulations, we identified a protein-ligand interaction fingerprint that characterizes biased ligands. Then, we built and virtually screened a database containing 68,740 ligands with proven or potential GPCR agonistic activity. Exemplary molecules that fulfill the interacting pattern for biased agonism are showcased, illustrating the usefulness of this work for the search of biased MOR ligands and how this contributes to the understanding of MOR biased signaling.

Enhancing Acute Oral Toxicity Predictions by using Consensus Modeling and Algebraic Form-Based 0D-to-2D Molecular Encodes.

Quantitative structure-activity relationships (QSAR) are introduced to predict acute oral toxicity (AOT), by using the QuBiLS-MAS (acronym for quadratic, bilinear and N-Linear maps based on graph-theoretic electronic-density matrices and atomic weightings) framework for the molecular encoding. Three training sets were employed to build the models: EPA training set (5931 compounds), EPA-full training set (7413 compounds), and Zhu training set (10 152 compounds). Additionally, the EPA test set (1482 compounds) was used for the validation of the QSAR models built on the EPA training set, while the ProTox (425 compounds) and T3DB (284 compounds) external sets were employed for the assessment of all the models. The k-nearest neighbor, multilayer perceptron, random forest, and support vector machine procedures were employed to build several base (individual) models. The base models with REPA-training ≥ 0.75 ( R = correlation coefficient) and MAEEPA-training ≤ 0.5 (MAE = mean absolute error) were retained to build consensus models. As a result, two consensus models based on the minimum operator and denoted as M19 and M22, as well as a consensus model based on the weighted average operator and denoted as M24, were selected as the best ones for each training set considered. According to the applicability domain (AD) analysis performed, model M19 (built on the EPA training set) has MAEtest-AD = 0.4044, MAEProTox-AD = 0.4067 and MAET3DB-AD = 0.2586 on the EPA test set, ProTox external set, and T3DB external set, respectively; whereas model M22 (built on the EPA-full set) and model M24 (built on the Zhu set) present MAEProTox-AD = 0.3992 and MAET3DB-AD = 0.2286, and MAEProTox-AD = 0.3773 and MAET3DB-AD = 0.2471 on the two external sets accounted for, respectively. These outcomes were compared and statistically validated with respect to 14 QSAR methods (e.g., admetSAR, ProTox-II) from the literature. As a result, model M22 presents the best overall performance. In addition, a retrospective study on 261 withdrawn drugs due to their toxic/side effects was performed, to assess the usefulness of prospectively using the QSAR models proposed in the labeling of chemicals. A comparison with regard to the methods from the literature was also made. As a result, model M22 has the best ability of labeling a compound as toxic according to the globally harmonized system of classification and labeling of chemicals. Therefore, it can be concluded that the models proposed, especially model M22, constitute prominent tools for studying AOT, at providing the best results among all the methods examined. A freely available software was also developed to be used in virtual screening tasks ( http://tomocomd.com/apps/ptoxra ).

Targeting cancer-specific glycans by cyclic peptide lectinomimics.

The transformation from normal to malignant phenotype in human cancers is associated with aberrant cell-surface glycosylation. Thus, targeting glycosylation changes in cancer is likely to provide not only better insight into the roles of carbohydrates in biological systems, but also facilitate the development of new molecular probes for bioanalytical and biomedical applications. In the reported study, we have synthesized lectinomimics based on odorranalectin 1; the smallest lectin-like cyclic peptide isolated from the frog Odorrana grahami skin, and assessed the ability of these peptides to bind specific carbohydrates on molecular and cellular levels. In addition, we have shown that the disulfide bond found in 1 can be replaced with a lactam bridge. However, the orientation of the lactam bridge, peptides 2 and 3, influenced cyclic peptide's conformation and thus these peptides' ability to bind carbohydrates. Naturally occurring 1 and its analog 3 that adopt similar conformation in water bind preferentially L-fucose, and to a lesser degree D-galactose and N-acetyl-D-galactosamine, typically found within the mucin O-glycan core structures. In cell-based assays, peptides 1 and 3 showed a similar binding profile to Aleuria aurantia lectin and these two peptides inhibited the migration of metastatic breast cancer cell lines in a Transwell assay. Altogether, the reported data demonstrate the feasibility of designing lectinomimics based on cyclic peptides.

Retinal ligand mobility explains internal hydration and reconciles active rhodopsin structures.

Rhodopsin, the mammalian dim-light receptor, is one of the best-characterized G-protein-coupled receptors, a pharmaceutically important class of membrane proteins that has garnered a great deal of attention because of the recent availability of structural information. Yet the mechanism of rhodopsin activation is not fully understood. Here, we use microsecond-scale all-atom molecular dynamics simulations, validated by solid-state (2)H nuclear magnetic resonance spectroscopy, to understand the transition between the dark and metarhodopsin I (Meta I) states. Our analysis of these simulations reveals striking differences in ligand flexibility between the two states. Retinal is much more dynamic in Meta I, adopting an elongated conformation similar to that seen in the recent activelike crystal structures. Surprisingly, this elongation corresponds to both a dramatic influx of bulk water into the hydrophobic core of the protein and a concerted transition in the highly conserved Trp265(6.48) residue. In addition, enhanced ligand flexibility upon light activation provides an explanation for the different retinal orientations observed in X-ray crystal structures of active rhodopsin.

Synthesis, anti-inflammatory activity and modeling studies of cycloartane-type terpenes derivatives isolated from Parthenium argentatum.

The 12-O-tetradecanoylphorbol-13-acetate (TPA)-induced edema model in mice determined the anti-inflammatory activities in vivo of argentatins A, B and D, the main cycloartenol-type triterpenes present in Parthenium argentatum. Our results showed that argentatin B (ED50=1.5×10(-4)mmol/ear) and argentatin A (ED50=2.8×10(-4)mmol/ear) were more potent anti-inflammatory agents than indomethacin (ED50=4.5×10(-4)mmol/ear), the reference drug. Based on these findings, we decided to evaluate 13 derivatives of argentatins A and B. All the derivatives showed anti-inflammatory activity in the TPA-induced edema model in mice. The most active compound was 25-nor-cycloart-3, 16-dione-17-en-24-oic acid, obtained from argentatin A (ED50=1.4×10(-4)mmol/ear). Argentatin B was assayed as inhibitor of COX-2 activity one of the key enzymes involved in the TPA assay. The results showed that argentatin B at 15µM doses inhibited 77% COX-2 activity. Docking studies suggest that argentatin B interacts with Arg 120, a key residue for COX-2 activity.

The pursuit of accurate predictive models of the bioactivity of small molecules.

Property prediction is a key interest in chemistry. For several decades there has been a continued and incremental development of mathematical models to predict properties. As more data is generated and accumulated, there seems to be more areas of opportunity to develop models with increased accuracy. The same is true if one considers the large developments in machine and deep learning models. However, along with the same areas of opportunity and development, issues and challenges remain and, with more data, new challenges emerge such as the quality and quantity and reliability of the data, and model reproducibility. Herein, we discuss the status of the accuracy of predictive models and present the authors' perspective of the direction of the field, emphasizing on good practices. We focus on predictive models of bioactive properties of small molecules relevant for drug discovery, agrochemical, food chemistry, natural product research, and related fields.

The role of imidazole in peptide cyclization by transesterification: parallels to the catalytic triads of serine proteases.

The improved bioavailability, stability and selectivity of cyclic peptides over their linear counterparts make them attractive structures in the design and discovery of novel therapeutics. In our previous work, we developed an imidazole-promoted preparation of cyclic depsipeptides in which we observed that increasing the concentration of imidazole resulted in the concomitant increase in the yield of cyclic product and reduction in dimerization, but also resulted in the generation of an acyl-substituted side product. In this work, we used transition state analysis to explore the mechanism of the imidazole-catalyzed esterification of one such peptide, Ac-SAFYG-SCH2φ, and determined the acyl substitution product to be an intermediate in a competing reaction pathway involving acyl substitution of the thioester by imidazole. Our findings indicate that imidazole plays an essential role in this side-chain to C-terminal coupling, and by extension, in transesterifications in general, through a concerted mechanism wherein imidazole deprotonates the nucleophile as the nucleophile attacks the carbonyl. The system under study is identical to the histidine-serine portion of the catalytic triads in serine proteases and it is likely that these enzymes employ the same concerted mechanism in the first step of peptide cleavage. Additionally, relatively high concentrations of imidazole must be used to effectively catalyze reactions in aprotic solvents since the overall reaction involves imidazole acting both as an acid and as a base, existing in solution as an equilibrium distribution between the neutral form and its conjugate acid.

Modifications in the piperazine ring of nucleozin affect anti-influenza activity.

The infection caused by the influenza virus is a latent tret. The limited access to vaccines and approved drugs highlights the need for additional antiviral agents. Nucleozin and its analogs have gain attention for their promising anti-influenza activity. To contribute to the advancement of the discovery and design of nucleozin analogs, we analyzed piperazine-modified nucleozin analogs to increase conformational freedom. Also, we describe a new synthetic strategy to obtain nucleozin and its analogues, three molecules were synthesized and two of them were biologically evaluated in vitro. Although the analogues were less active than nucleozin, the loss of activity highlights the need for the piperazine ring to maintain the activity of nucleozin analogs. Interestingly, this result agrees with the prediction of anti-influenza activity made with a QSAR model presented in this work. The proposed model and the synthetic route will be useful for the further development of nucleozin analogs with antiviral activity.

School of cheminformatics in Latin America.

We report the major highlights of the School of Cheminformatics in Latin America, Mexico City, November 24-25, 2022. Six lectures, one workshop, and one roundtable with four editors were presented during an online public event with speakers from academia, big pharma, and public research institutions. One thousand one hundred eighty-one students and academics from seventy-nine countries registered for the meeting. As part of the meeting, advances in enumeration and visualization of chemical space, applications in natural product-based drug discovery, drug discovery for neglected diseases, toxicity prediction, and general guidelines for data analysis were discussed. Experts from ChEMBL presented a workshop on how to use the resources of this major compounds database used in cheminformatics. The school also included a round table with editors of cheminformatics journals. The full program of the meeting and the recordings of the sessions are publicly available at https://www.youtube.com/@SchoolChemInfLA/featured .

Molecular recognition of the Thomsen-Friedenreich antigen-threonine conjugate by adhesion/growth regulatory galectin-3: nuclear magnetic resonance studies and molecular dynamics simulations.

Nuclear magnetic resonance (NMR) spectroscopy and molecular modeling methods have been strategically combined to elucidate the molecular recognition features of the binding of threonine O-linked Thomsen-Friedenreich (TF) antigen to chimera-type avian galectin-3 (CG-3). Saturation transfer difference (STD) NMR experiments revealed the highest intensities for the H4 protons of both the ß-D-Galp and α-D-GalpNAc moieties, with 100 and 71% of relative STD, respectively. The methyl protons of the threonine residue exhibited a small STD effect, <15%, indicating that the interaction of the amino acid with the protein is rather transient. Two-dimensional transferred nuclear Overhauser effect spectroscopy NMR experiments and molecular modeling suggested some differences in conformer populations between the free and bound states. A dynamic binding mode for the TF antigen-CG-3 complex consisting of two poses has been deduced. In one pose, intermolecular interactions were formed between the terminal threonine residue and the receptor. In the second pose, intermolecular interactions involved the internal GalpNAc. The difference in the trend of some shifts in the heteronuclear single-quantum coherence titration spectra indicates some disparities in the binding interactions of CG-3 with lactose and TF antigen. The results obtained from this model of the avian orthologue of human galectin-3 will allow detailed interspecies comparison to give sequence deviations in phylogeny a structural and functional meaning. Moreover, the results indicate that the peptide scaffold presenting TF antigen could be relevant for binding and thus provides a possible route for the design of galectin-3 inhibitors with improved affinity and selectivity.

Inhibition of prohormone convertases PC1/3 and PC2 by 2,5-dideoxystreptamine derivatives.

The prohormone convertases PC1/3 and PC2 are eukaryotic serine proteases involved in the proteolytic maturation of peptide hormone precursors and are implicated in a variety of pathological conditions, including obesity, diabetes, and neurodegenerative diseases. In this work, we screened 45 compounds obtained by derivatization of a 2,5-dideoxystreptamine scaffold with guanidinyl and aryl substitutions for convertase inhibition. We identified four promising PC1/3 competitive inhibitors and three PC2 inhibitors that exhibited various inhibition mechanisms (competitive, noncompetitive, and mixed), with sub- and low micromolar inhibitory potency against a fluorogenic substrate. Low micromolar concentrations of certain compounds blocked the processing of the physiological substrate proglucagon. The best PC2 inhibitor effectively inhibited glucagon synthesis, a known PC2-mediated process, in a pancreatic cell line; no cytotoxicity was observed. We also identified compounds that were able to stimulate both 87 kDa PC1/3 and PC2 activity, behavior related to the presence of aryl groups on the dideoxystreptamine scaffold. By contrast, inhibitory activity was associated with the presence of guanidinyl groups. Molecular modeling revealed interactions of the PC1/3 inhibitors with the active site that suggest structural modifications to further enhance potency. In support of kinetic data suggesting that PC2 inhibition probably occurs via an allosteric mechanism, we identified several possible allosteric binding sites using computational searches. It is noteworthy that one compound was found to both inhibit PC2 and stimulate PC1/3. Because glucagon acts in functional opposition to insulin in blood glucose homeostasis, blocking glucagon formation and enhancing proinsulin cleavage with a single compound could represent an attractive therapeutic approach in diabetes.

Recognizing pitfalls in virtual screening: a critical review.

The aim of virtual screening (VS) is to identify bioactive compounds through computational means, by employing knowledge about the protein target (structure-based VS) or known bioactive ligands (ligand-based VS). In VS, a large number of molecules are ranked according to their likelihood to be bioactive compounds, with the aim to enrich the top fraction of the resulting list (which can be tested in bioassays afterward). At its core, VS attempts to improve the odds of identifying bioactive molecules by maximizing the true positive rate, that is, by ranking the truly active molecules as high as possible (and, correspondingly, the truly inactive ones as low as possible). In choosing the right approach, the researcher is faced with many questions: where does the optimal balance between efficiency and accuracy lie when evaluating a particular algorithm; do some methods perform better than others and in what particular situations; and what do retrospective results tell us about the prospective utility of a particular method? Given the multitude of settings, parameters, and data sets the practitioner can choose from, there are many pitfalls that lurk along the way which might render VS less efficient or downright useless. This review attempts to catalogue published and unpublished problems, shortcomings, failures, and technical traps of VS methods with the aim to avoid pitfalls by making the user aware of them in the first place.

Furin inhibitors: importance of the positive formal charge and beyond.

Furin is the prototype member of the proprotein convertases superfamily. Proprotein convertases are associated with hormonal response, neural degeneration, viral and bacterial activation, and cancer. Several studies over the last decade have examined small molecules, natural products, peptides and peptide derivatives as furin inhibitors. Currently, subnanomolar inhibition of furin is possible. Herein, we report the analysis of 115 furin inhibitors reported in the literature. Analysis of the physicochemical properties of these compounds highlights the dependence of the inhibitory potency with the total formal charge and also shows how the most potent (peptide-based) furin inhibitors have physicochemical properties similar to drugs. In addition, we report docking studies of 26 furin inhibitors using Glide XP. Inspection of binding interactions shows that the two putative binding modes derived from our study are reasonable. Analysis of the binding modes and protein-ligand interaction fingerprints, used here as postdocking procedure, shows that electrostatic interactions predominate on S1, S2 and S4 subsites but are seldom in S3. Our models also show that the benzimidamide group, present in the most active inhibitors, can be accommodated in the S1 subsite. These results are valuable for the design of new furin inhibitors.

Identification of benzoylisoquinolines as potential anti-Chagas agents.

A set of three 3-benzoyl substituted isoquinolones was synthesized in good yields and assayed for in vitro trypanocidal activity against Trypanosoma cruzi, the protozoan parasite that causes Chagas' disease. Depending on the concentration evaluated, a greater or equivalent reduction in the number of bloodborne trypomastigotes compared to that observed with benznidazole, a drug currently used to attack the parasite, was observed for two of the samples. In order to assess the potential of the 3-benzoylisoquinolone nucleus as a possible scaffold in the design of novel anti-trypanosomal lead structures, a computational analysis was performed using structural and inhibition information from both functional and target assays archived in the online database, ChEMBL. Chemical space projection of the synthesized compounds along with 3067 structures with known activities against T. cruzi shows that the isoquinolones occupy a sparsely-populated region of chemical space, indicating their potential for development as a novel class of trypanocidals. In addition, 2D and 3D structural similarity analyses revealed micromolar and submicromolar inhibitors of T. cruzi in ChEMBL with high similarity to the synthesized structures.

Subtle structural differences of nucleotide analogs may impact SARS-CoV-2 RNA-dependent RNA polymerase and exoribonuclease activity.

The rapid spread and public health impact of the novel SARS-CoV-2 variants that cause COVID-19 continue to produce major global impacts and social distress. Several vaccines were developed in record time to prevent and limit the spread of the infection, thus playing a pivotal role in controlling the pandemic. Although the repurposing of available drugs attempts to provide therapies of immediate access against COVID-19, there is still a need for developing specific treatments for this disease. Remdesivir, molnupiravir and Paxlovid remain the only evidence-supported antiviral drugs to treat COVID-19 patients, and only in severe cases. To contribute on the search of potential Covid-19 therapeutic agents, we targeted the viral RNA-dependent RNA polymerase (RdRp) and the exoribonuclease (ExoN) following two strategies. First, we modeled and analyzed nucleoside analogs sofosbuvir, remdesivir, favipiravir, ribavirin, and molnupiravir at three key binding sites on the RdRp-ExoN complex. Second, we curated and virtually screened a database containing 517 nucleotide analogs in the same binding sites. Finally, we characterized key interactions and pharmacophoric features presumably involved in viral replication halting at multiple sites. Our results highlight structural modifications that might lead to more potent SARS-CoV-2 inhibitors against an expansive range of variants and provide a collection of nucleotide analogs useful for screening campaigns.

Consensus models of activity landscapes with multiple chemical, conformer, and property representations.

We report consensus Structure-Activity Similarity (SAS) maps that address the dependence of activity landscapes on molecular representation. As a case study, we characterized the activity landscape of 54 compounds with activities against human cathepsin B (hCatB), human cathepsin L (hCatL), and Trypanosoma brucei cathepsin B (TbCatB). Starting from an initial set of 28 descriptors we selected ten representations that capture different aspects of the chemical structures. These included four 2D (MACCS keys, GpiDAPH3, pairwise, and radial fingerprints) and six 3D (4p and piDAPH4 fingerprints with each including three conformers) representations. Multiple conformers are used for the first time in consensus activity landscape modeling. The results emphasize the feasibility of identifying consensus data points that are consistently formed in different reference spaces generated with several fingerprint models, including multiple 3D conformers. Consensus data points are not meant to eliminate data, disregarding, for example, "true" activity cliffs that are not identified by some molecular representations. Instead, consensus models are designed to prioritize the SAR analysis of activity cliffs and other consistent regions in the activity landscape that are captured by several molecular representations. Systematic description of the SARs of two targets give rise to the identification of pairs of compounds located in the same region of the activity landscape of hCatL and TbCatB suggesting similar mechanisms of action for the pairs involved. We also explored the relationship between property similarity and activity similarity and found that property similarities are suitable to characterize SARs. We also introduce the concept of structure-property-activity (SPA) similarity in SAR studies.

Multitarget structure-activity relationships characterized by activity-difference maps and consensus similarity measure.

Dual and triple activity-difference (DAD/TAD) maps are tools for the systematic characterization of structure-activity relationships (SAR) of compound data sets screened against two or three targets. DAD and TAD maps are two- and three- dimensional representations of the pairwise activity differences of compound data sets, respectively. Adding pairwise structural similarity information into these maps readily reveals activity cliff regions in the SAR for one, two, or three targets. In addition, pairs of compounds in the smooth regions of the SAR and scaffold hops are also easily identified in these maps. Herein, DAD and TAD maps are employed for the systematic characterization of the SAR of a benchmark set of 299 compounds screened against dopamine, norepinephrine, and serotonin transporters. To reduce the well-known dependence of the activity landscape on the structural representation, five selected 2D and 3D structure representations were used to characterize the SAR. Systematic analysis of the DAD and TAD maps reveals regions in the landscape with similar SAR for two or the three targets as well as regions with inverse SAR, i.e., changes in structure that increase activity for one target, but decrease activity for the other target. Focusing the analysis on pairs of compounds with high structure similarity revealed the presence of single-, dual-, and triple-target activity cliffs, i.e., small changes in structure with high changes in potency for one, two, or the three targets, respectively. Triple-target scaffold hops are also discussed. Activity cliffs and scaffold hops were also quantified and represented using two recently proposed approaches namely, mean Structure Activity Landscape Index (mean SALI) and Consensus Structure-Activity Similarity (SAS) maps.