Getting Insights into Structural and Energetic Properties of Reciprocal Peptide-Protein Interactions.

Peptide-protein interactions play a key role for many cellular and metabolic processes involved in the onset of largely spread diseases such as cancer and neurodegenerative pathologies. Despite the progress in the structural characterization of peptide-protein interfaces, the in-depth knowledge of the molecular details behind their interactions is still a daunting task. Here, we present the first comprehensive in silico morphological and energetic study of peptide binding sites by focusing on both peptide and protein standpoints. Starting from the PixelDB database, a nonredundant benchmark collection of high-quality 3D crystallographic structures of peptide-protein complexes, a classification analysis of the most representative categories based on the nature of each cocrystallized peptide has been carried out. Several interpretable geometrical and energetic descriptors have been computed both from peptide and target protein sides in the attempt to unveil physicochemical and structural causative correlations. Finally, we investigated the most frequent peptide-protein residue pairs at the binding interface and made extensive energetic analyses, based on GRID MIFs, with the aim to study the peptide affinity-enhancing interactions to be further exploited in rational drug design strategies.

An Integrated Machine Learning Model To Spot Peptide Binding Pockets in 3D Protein Screening.

The prediction of peptide-protein binding sites is of utmost importance to tackle the onset of severe neurodegenerative diseases and cancer. In this work, we detail a novel machine learning model based on Linear Discriminant Analysis (LDA) demonstrating to be highly predictive in detecting the putative protein binding regions of small peptides. Starting from 439 high-quality pockets derived from peptide-protein crystallographic complexes, three sets of well-established peptide-binding regions were first selected through a Partitioning Around Medoids (PAM) clustering algorithm based on morphological and energetic 3D GRID-MIF molecular descriptors. Next, the best combination between all the putative interacting peptide pockets and related GRID-MIF scores was automatically explored by using the LDA-based protocol implemented in BioGPS. This approach proved successful to recognize the actual interacting peptide regions (that is, AUC = 0.86 and partial ROC enrichment at 5% of 0.48) from all the other pockets of the protein. Validated on two external collections sets, including 445 and 347 crystallographic peptide-protein complexes, our LDA-based model could be effective to further run peptide-protein virtual screening campaigns.

CROMATIC: Cross-Relationship Map of Cavities from Coronaviruses.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of COVID-19 disease, has rapidly imposed an urgent need to identify effective drug candidates. In this context, the high resolution and non-redundant beta-Coronavirus protein cavities database is pivotal to help virtual screening protocols. Furthermore, the cross-relationship among cavities can lead to highlighting multitarget therapy chances. Here, we first collect all protein cavities on SARS-CoV-2, SARS-CoV, and MERS-CoV X-ray structures, and then, we compute a similarity map by using molecular interaction fields (MIFs). All the results come together in CROMATIC (CROss-relationship MAp of CaviTIes from Coronaviruses). CROMATIC encloses both a comprehensive and a non-redundant version of the cavities collection and a similarity map revealing, on the one hand, cavities that are conserved among the three Coronaviruses and, on the other hand, unexpected similarities among cavities that can represent a key starting point for multitarget therapy strategies. Similarity analysis was also performed for the available structures of SARS-CoV-2 spike variants, linking sequence mutations to three-dimensional interaction alterations. The CROMATIC repository is freely available to the scientific community at https://github.com/moldiscovery/sars-cromatic.

Exploring Ligand Binding Domain Dynamics in the NRs Superfamily.

Nuclear receptors (NRs) are transcription factors that play an important role in multiple diseases, such as cancer, inflammation, and metabolic disorders. They share a common structural organization composed of five domains, of which the ligand-binding domain (LBD) can adopt different conformations in response to substrate, agonist, and antagonist binding, leading to distinct transcription effects. A key feature of NRs is, indeed, their intrinsic dynamics that make them a challenging target in drug discovery. This work aims to provide a meaningful investigation of NR structural variability to outline a dynamic profile for each of them. To do that, we propose a methodology based on the computation and comparison of protein cavities among the crystallographic structures of NR LBDs. First, pockets were detected with the FLAPsite algorithm and then an "all against all" approach was applied by comparing each pair of pockets within the same sub-family on the basis of their similarity score. The analysis concerned all the detectable cavities in NRs, with particular attention paid to the active site pockets. This approach can guide the investigation of NR intrinsic dynamics, the selection of reference structures to be used in drug design and the easy identification of alternative binding sites.

First virtual screening and experimental validation of inhibitors targeting GES-5 carbapenemase.

The worldwide spread of beta-lactamases with hydrolytic activity extended to last resort carbapenems is aggravating the antibiotic resistance problem and endangers the successful antimicrobial treatment of clinically relevant pathogens. As recently highlighted by the World Health Organization, new strategies to contain antimicrobial resistance are urgently needed. Class A carbapenemases include members of the KPC, GES and SFC families. These enzymes have the ability to hydrolyse penicillins, cephalosporins and carbapenems, while also being less susceptible to available beta-lactam inhibitors, such as clavulanic acid. The KPC family is the most prevalent. It is mostly found on plasmids in Klebsiella pneumoniae, meaning that great amounts of attention, in terms of inhibitor design and structural biology, have been dedicated to it, whereas no efforts have yet been dedicated to GES-type enzymes, despite their ability to rapidly and horizontally disseminate. We herein report the first in silico screening against GES-5, which is the most dangerous GES-type beta-lactamase, using a library of 800K commercially available candidates that all share drug-like properties, such as their MW, logP, rotatable bonds and HBA/HBD atoms. The best screening results were filtered to enrich the number of different chemotypes, and then submitted to molecular docking. The 34 most promising candidates were selected for in vitro validation in biochemical assays against recombinant GES-5. Six hits acted as inhibitors, in the high micromolar range, towards GES-5 and led to the identification of the first, novel chemotypes with inhibitory activity against this clinically relevant carbapenemase.

BioGPS: navigating biological space to predict polypharmacology, off-targeting, and selectivity.

The structural comparison of protein binding sites is increasingly important in drug design; identifying structurally similar sites can be useful for techniques such as drug repurposing, and also in a polypharmacological approach to deliberately affect multiple targets in a disease pathway, or to explain unwanted off-target effects. Once similar sites are identified, identifying local differences can aid in the design of selectivity. Such an approach moves away from the classical "one target one drug" approach and toward a wider systems biology paradigm. Here, we report a semiautomated approach, called BioGPS, that is based on the software FLAP which combines GRID Molecular Interactions Fields (MIFs) and pharmacophoric fingerprints. BioGPS comprises the automatic preparation of protein structure data, identification of binding sites, and subsequent comparison by aligning the sites and directly comparing the MIFs. Chemometric approaches are included to reduce the complexity of the resulting data on large datasets, enabling focus on the most relevant information. Individual site similarities can be analyzed in terms of their Pharmacophoric Interaction Field (PIF) similarity, and importantly the differences in their PIFs can be extracted. Here we describe the BioGPS approach, and demonstrate its applicability to rationalize off-target effects (ERα and SERCA), to classify protein families and explain polypharmacology (ABL1 kinase and NQO2), and to rationalize selectivity between subfamilies (MAP kinases p38α/ERK2 and PPARδ/PPARγ). The examples shown demonstrate a significant validation of the method and illustrate the effectiveness of the approach.

A Pipeline To Enhance Ligand Virtual Screening: Integrating Molecular Dynamics and Fingerprints for Ligand and Proteins.

The importance of taking into account protein flexibility in drug design and virtual ligand screening (VS) has been widely debated in the literature, and molecular dynamics (MD) has been recognized as one of the most powerful tools for investigating intrinsic protein dynamics. Nevertheless, deciphering the amount of information hidden in MD simulations and recognizing a significant minimal set of states to be used in virtual screening experiments can be quite complicated. Here we present an integrated MD-FLAP (molecular dynamics-fingerprints for ligand and proteins) approach, comprising a pipeline of molecular dynamics, clustering and linear discriminant analysis, for enhancing accuracy and efficacy in VS campaigns. We first extracted a limited number of representative structures from tens of nanoseconds of MD trajectories by means of the k-medoids clustering algorithm as implemented in the BiKi Life Science Suite ( http://www.bikitech.com [accessed July 21, 2015]). Then, instead of applying arbitrary selection criteria, that is, RMSD, pharmacophore properties, or enrichment performances, we allowed the linear discriminant analysis algorithm implemented in FLAP ( http://www.moldiscovery.com [accessed July 21, 2015]) to automatically choose the best performing conformational states among medoids and X-ray structures. Retrospective virtual screenings confirmed that ensemble receptor protocols outperform single rigid receptor approaches, proved that computationally generated conformations comprise the same quantity/quality of information included in X-ray structures, and pointed to the MD-FLAP approach as a valuable tool for improving VS performances.

Dorsal midbrain syndrome associated with persistent neck extension: Clinical and diagnostic imaging findings in 2 dogs.

Two young dogs were evaluated for an acute onset of abnormal head posture and eye movement. Neurological examination was characterized mostly by permanent neck extension, abnormalities of pupils, and eye movement. A mesencephalic mass lesion was detected on magnetic resonance imaging in both cases. Neurophysiological pathways likely responsible for this peculiar clinical presentation are discussed.

Syndrome du mésencéphale dorsal associé à une extension du cou persistante : résultats de l'évaluation clinique et de l'imagerie diagnostique chez 2 chiens. Deux jeunes chiens ont été évalués suite à l'apparition soudaine d'une posture de tête et d'un mouvement des yeux anormaux. Un examen neurologique a été caractérisé surtout par une extension permanente du cou, des anomalies des pupilles et un mouvement des yeux. Une masse mésencéphalique a été détectée à l'imagerie par résonance magnétique dans les deux cas. Les voies neurophysiologiques, qui étaient probablement responsables de cette présentation clinique, sont discutées.(Traduit par Isabelle Vallières).

Playing with opening and closing of heterocycles: using the cusmano-ruccia reaction to develop a novel class of oxadiazolothiazinones, active as calcium channel modulators and P-glycoprotein inhibitors.

As a result of the ring-into-ring conversion of nitrosoimidazole derivatives, we obtained a molecular scaffold that, when properly decorated, is able to decrease inotropy by blocking L-type calcium channels. Previously, we used this scaffold to develop a quantitative structure-activity relationship (QSAR) model, and we used the most potent oxadiazolothiazinone as a template for ligand-based virtual screening. Here, we enlarge the diversity of chemical decorations, present the synthesis and in vitro data for 11 new derivatives, and develop a new 3D-QSAR model with recent in silico techniques. We observed a key role played by the oxadiazolone moiety: given the presence of positively charged calcium ions in the transmembrane channel protein, we hypothesize the formation of a ternary complex between the oxadiazolothiazinone, the Ca2+ ion and the protein. We have supported this hypothesis by means of pharmacophore generation and through the docking of the pharmacophore into a homology model of the protein. We also studied with docking experiments the interaction with a homology model of P-glycoprotein, which is inhibited by this series of molecules, and provided further evidence toward the relevance of this scaffold in biological interactions.

Cytotoxicity on low-grade canine meningioma with the use of somatostatin analog (octreotide): An in vitro study.

Background: Meningioma is the most common tumor of the central nervous system of dogs. For this tumor, surgery remains the treatment of choice, either alone or in combination with radiotherapy. Unfortunately, chemotherapeutic strategies are practically absent in dogs and palliative therapies are the only option to surgery. Somatostatin receptor subtype 2 (SSTR2) is expressed in canine meningioma. Since the potent cell-proliferation inhibiting effect of somatostatin (SST), the aim of this study was to investigate in vitro the effects of octreotide, as SST analog, in the viability of canine meningioma. Methods: Four surgical canine meningiomas were used in this study to establish cell cultures. Expression of SSTR2 was verified with immunolabelling in FFPE samples and cell cultures. The effects of octreotide on cell viability were assessed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT). After 24 hours they were exposed to different concentrations of octreotide (0.1 nM, 1 nM, 10 nM, 100 nM) for 24 and 48 hours. Results: All meningiomas consisted of grade I tumors. The cultured neoplastic cells expressed SSTR2 from 80% to 100%. Octreotide significantly increased cell death after 48 hours of continuous exposure, with 10 and 100 nM octreotide doses. The percentage of cell viability was 80.92 ±â€…4.9 and 80.49 ±â€…3.61, compared to the control, respectively, consistent with decreased cell viability of about 20% for both doses. Conclusions: Octreotide reduced the alive neoplastic cultured cells of low-grade canine meningioma in a dose-dependent pattern with continuous exposition for 48 hours. These results support an alternative systemic treatment of meningioma with octreotide in the dog.

Identification of small molecules affecting the interaction between human hemoglobin and Staphylococcus aureus IsdB hemophore.

Human hemoglobin (Hb) is the preferred iron source of Staphylococcus aureus. This pathogenic bacterium exploits a sophisticated protein machinery called Iron-regulated surface determinant (Isd) system to bind Hb, extract and internalize heme, and finally degrade it to complete iron acquisition. IsdB, the surface exposed Hb receptor, is a proven virulence factor of S. aureus and the inhibition of its interaction with Hb can be pursued as a strategy to develop new classes of antimicrobials. To identify small molecules able to disrupt IsdB:Hb protein-protein interactions (PPIs), we carried out a structure-based virtual screening campaign and developed an ad hoc immunoassay to screen the retrieved set of commercially available compounds. Saturation-transfer difference (STD) NMR was applied to verify specific interactions of a sub-set of molecules, chosen based on their efficacy in reducing the amount of Hb bound to IsdB. Among molecules for which direct binding was verified, the best hit was submitted to ITC analysis to measure the binding affinity to Hb, which was found to be in the low micromolar range. The results demonstrate the viability of the proposed in silico/in vitro experimental pipeline to discover and test IsdB:Hb PPI inhibitors. The identified lead compound will be the starting point for future SAR and molecule optimization campaigns.

Exposition and reactivity optimization to predict sites of metabolism in chemicals.

Chemical modifications of drugs induced by phase I biotransformations significantly affect their pharmacokinetic properties. Because the metabolites produced can themselves have a pharmacological effect and an intrinsic toxicity, medicinal chemists need to accurately predict the sites of metabolism (SoM) of drugs as early as possible. However, site of metabolism prediction is rarely accompanied by a prediction of the relative abundance of the various metabolites. Such a prediction would be a great help in the study of drug­ drug interactions and in the process of reducing the toxicity of potential drug candidates. The aim of this paper is to present recent developments in the prediction of xenobiotic metabolism and to use concrete examples to explain the computational mechanism employed.

ELIOT: A platform to navigate the E3 pocketome and aid the design of new PROTACs.

Proteolysis-targeting chimeras (PROTACs) are novel therapeutics for the treatment of human disease. They exploit the enormous potential of the E3 ligases, a class of proteins that mark a target protein for degradation via the ubiquitin-proteasome system. Despite the existence of several E3 ligase-related databases, the choice of the functioning ligase is limited to only 1.6% of those available, probably due to the fragmentary understanding of their structures and their known ligands; in fact, none of the existing databases report detailed studies covering their 3D structure or their pockets. Here, we report ELIOT (E3 LIgase pocketOme navigaTor), an accurate and complete platform containing the E3 ligase pocketome to enable navigation and selection of new E3 ligases and new ligands for the design of new PROTACs. All E3 ligase pockets were characterized with innovative 3D descriptors including their PROTAC-ability score, and similarity analyses between E3 pockets are presented. Tissue specificity and their degree of involvement in patients with specific cancer types are also annotated for each E3 ligase, enabling appropriate selection for the design of a PROTAC with improved specificity. All data are available at https://eliot.moldiscovery.com.

Targeting protein-protein interactions with low molecular weight and short peptide modulators: insights on disease pathways and starting points for drug discovery.

INTRODUCTION: Protein-protein interactions (PPIs) have been often considered undruggable targets although they are attractive for the discovery of new therapeutics. The spread of artificial intelligence and machine learning complemented with experimental methods is likely to change the perspectives of protein-protein modulator research. Noteworthy, some novel low molecular weight (LMW) and short peptide modulators of PPIs are already in clinical trials for the treatment of relevant diseases. AREAS COVERED: This review focuses on the main molecular properties of protein-protein interfaces and on key concepts pertaining to the modulation of PPIs. The authors survey recently reported state-of-the-art methods dealing with the rational design of PPI modulators and highlight the role of several computer-based approaches. EXPERT OPINION: Interfering specifically with large protein interfaces is still an open challenge. The initial concerns about the unfavorable physicochemical properties of many of these modulators are nowadays less acute with several molecules lying beyond the rule of 5, orally available and successful in clinical trials. As the cost of biologics interfering with PPIs is very high, it would seem reasonable to put more effort, both in academia and the private sectors, on actively developing novel low molecular weight compounds and short peptides to perform this task.

Structure-Based Optimization of 1,2,4-Triazole-3-Thione Derivatives: Improving Inhibition of NDM-/VIM-Type Metallo-ß-Lactamases and Synergistic Activity on Resistant Bacteria.

The worldwide emergence and dissemination of Gram-negative bacteria expressing metallo-ß-lactamases (MBLs) menace the efficacy of all ß-lactam antibiotics, including carbapenems, a last-line treatment usually restricted to severe pneumonia and urinary tract infections. Nonetheless, no MBL inhibitor is yet available in therapy. We previously identified a series of 1,2,4-triazole-3-thione derivatives acting as micromolar inhibitors of MBLs in vitro, but devoid of synergistic activity in microbiological assays. Here, via a multidisciplinary approach, including molecular modelling, synthesis, enzymology, microbiology, and X-ray crystallography, we optimized this series of compounds and identified low micromolar inhibitors active against clinically relevant MBLs (NDM-1- and VIM-type). The best inhibitors increased, to a certain extent, the susceptibility of NDM-1- and VIM-4-producing clinical isolates to meropenem. X-ray structures of three selected inhibitors in complex with NDM-1 elucidated molecular recognition at the base of potency improvement, confirmed in silico predicted orientation, and will guide further development steps.

GRID-based three-dimensional pharmacophores I: FLAPpharm, a novel approach for pharmacophore elucidation.

Pharmacophore elucidation approaches are routinely used in drug discovery, primarily with the aim of determining the three-dimensional arrangement of common features shared by ligands interacting at the site of interest; these features can then be used to investigate the structure-activity relationship between the ligands and also to screen for other molecules possessing the relevant features. Here we present a novel approach based on GRID molecular interaction fields and the derivative method FLAP that has been previously described, which provides a common reference framework to compare both small molecule ligands and macromolecular protein targets. Unlike classical pharmacophore elucidation approaches that extract simplistic molecular features, determine those which are common across the data set, and use these features to align the structures, FLAPpharm first aligns the structures and subsequently extracts the common interacting features in terms of their molecular interaction fields, pseudofields, and atomic points, representing the common pharmacophore as a more comprehensive pharmacophoric pseudomolecule. The approach is applied to a number of data sets to investigate performance in terms of reproducing the X-ray crystallography-based alignment, in terms of its discriminatory ability when applied to virtual screening and also to illustrate its ability to explain alternative binding modes. In part two of this publication, a comprehensive benchmark data set for pharmacophore elucidation is presented and the performance of FLAPpharm discussed.

GRID-based three-dimensional pharmacophores II: PharmBench, a benchmark data set for evaluating pharmacophore elucidation methods.

To date, published pharmacophore elucidation approaches typically use a handful of data sets for validation: here, we have assembled a data set for 81 targets, containing 960 ligands aligned using their cocrystallized protein targets, to provide the experimental "gold standard". The two-dimensional structures are also assembled to remove conformational bias; an ideal method would be able to take these structures as input, find the common features, and reproduce the bioactive conformations and their alignments to correspond with the X-ray-determined gold standard alignments. Here we present this data set and describe three objective measures to evaluate performance: the ability to identify the bioactive conformation, the ability to identify and correctly align this conformation for 50% of the molecules in each data set, and the pharmacophoric field similarity. We have applied this validation methodology to our pharmacophore elucidation method FLAPpharm, that is published in the first paper of this series and discuss the limitations of the data set and objective success criteria. Starting from two-dimensional structures and producing unbiased models, FLAPpharm was able to identify the bioactive conformations for 67% of the ligands and also to produce successful models according to the second metric for 67% of the Pharmbench data sets. Inspection of the unsuccessful models highlighted the limitation of this root mean square (rms)-derived metric, since many were found to be pharmacophorically reasonable, increasing the overall success rate to 83%. The PharmBench data set is available at http://www.moldiscovery.com/PharmBench , along with a web service to enable users to score model alignments coming from external methods in the same way that we have presented here and, therefore, establishes a pharmacophore elucidation benchmark data set available to be used by the community.

Prevalence, clinical features, and outcome of intervertebral disc extrusion associated with extensive epidural hemorrhage in a population of French Bulldogs compared to Dachshunds.

Intervertebral disc extrusion associated with extensive epidural hemorrhage (DEEH) is a well-documented pathological condition in veterinary medicine. This retrospective study aimed to evaluate the prevalence and clinical features of DEEH in a population of French Bulldogs affected by intervertebral disc extrusion (n=75), compare the findings with those from a group of Dachshunds (n=98) and identify possible predictive factors of DEEH and outcomes in surgically treated patients. The study showed that the prevalence of DEEH observed in Dachshunds (11.2% [95% confidence interval [CI]: 5.7-19.2%]) was significantly lower than that observed in French Bulldogs (41.3% [95% CI: 30.1-53.3%]). The multiple logistic regression model highlighted that the patients presenting with an acute onset of clinical signs (>24 hr) (odds ratio [OR]: 13.08; 95% CI: 4.63-37.03, P=0.00), presence of clinical signs progression (OR: 5.04; P=0.01), and French Bulldogs (OR: 5.15; 95% CI: 1.71-15.54, P=0.00) were at increased risk of developing DEEH. Secondary analysis showed that patients with DEEH were at an increased risk of being non-ambulatory at discharge (OR: 3.43; P=0.017). Overall, the surgically treated patients had favorable outcomes.

Analytical and computational workflow for in-depth analysis of oxidized complex lipids in blood plasma.

Lipids are a structurally diverse class of biomolecules which can undergo a variety of chemical modifications. Among them, lipid (per)oxidation attracts most of the attention due to its significance in the regulation of inflammation, cell proliferation and death programs. Despite their apparent regulatory significance, the molecular repertoire of oxidized lipids remains largely elusive as accurate annotation of lipid modifications is complicated by their low abundance and often unknown, biological context-dependent structural diversity. Here, we provide a workflow based on the combination of bioinformatics and LC-MS/MS technologies to support identification and relative quantification of oxidized complex lipids in a modification type- and position-specific manner. The developed methodology is used to identify epilipidomics signatures of lean and obese individuals with and without type 2 diabetes. The characteristic signature of lipid modifications in lean individuals, dominated by the presence of modified octadecanoid acyl chains in phospho- and neutral lipids, is drastically shifted towards lipid peroxidation-driven accumulation of oxidized eicosanoids, suggesting significant alteration of endocrine signalling by oxidized lipids in metabolic disorders.

Incidence and type of brain herniation associated with intracranial meningioma in dogs and cats.

The incidence of brain herniation (BH) in association with intracranial meningioma (ICM) in dogs and cats is poorly described. The aim of this study was to evaluate the rate and type of brain herniations in client-owned dogs and cats with ICMs and to determine the meningioma volume (MV) relative to cranial cavity volume (CCV). A retrospective magnetic resonance imaging (MRI) analysis study of 24 cats and 45 dogs with ICMs was conducted to ascertain the presence and characteristics of BH. MV and CCV were measured and their ratio was calculated for each animal. Correlations of MV/CCV with independent variables were analyzed. BH was encountered in 24/24 cats (100%) and 30/45 dogs (66.7%) with ICMs. In cats, the most frequent presentation was foramenal herniation (FMH; 23/24, 95.8%), followed by caudotentorial (CTH; 21/24, 87.5%) and subfalcine (SH; 18/24, 75.0%) herniation. In dogs, the most frequent presentation was SH (28/45; 62.2%), followed by CTH (9/45; 20%) and FMH (2/45; 4.4%). Relative to dogs, cats with ICM had greater incidences of FMH (P<0.001) and CTH (P<0.001). Mean MV/CCV ratio was higher in cats (0.098) than in dogs (0.038; P<0.001). The most common clinical sign of ICM was altered behavior in cats (43%, P<0.01) and seizures in dogs (74.4%, P<0.001). In conclusion, cats were found to be more likely than dogs to present FMH and CTH, with a proportionally greater neoplasia volume.