Retrospective group fusion similarity search based on eROCE evaluation metric

New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods

To facilitate the identification of novel MAO-B inhibitors, we elaborated a consolidated computational approach, including a pharmacophoric atom-based 3D quantitative structure-activity relationship (QSAR) model, activity cliffs, fingerprint, and molecular docking analysis on a dataset of 126 molecules. An AAHR.2 hypothesis with two hydrogen bond acceptors (A), one hydrophobic (H), and one aromatic ring (R) supplied a statistically significant 3D QSAR model reflected by the parameters: R² = 0.900 (training set); Q² = 0.774 and Pearson's R = 0.884 (test set), stability s = 0.736. Hydrophobic and electron-withdrawing fields portrayed the relationships between structural characteristics and inhibitory activity. The quinolin-2-one scaffold has a key role in selectivity towards MAO-B with an AUC of 0.962, as retrieved by ECFP4 analysis. Two activity cliffs showing meaningful potency variation in the MAO-B chemical space were observed. The docking study revealed interactions with crucial residues TYR:435, TYR:326, CYS:172, and GLN:206 responsible for MAO-B activity. Molecular docking is in consensus with and complementary to pharmacophoric 3D QSAR, ECFP4, and MM-GBSA analysis. The computational scenario provided here will assist chemists in quickly designing and predicting new potent and selective candidates as MAO-B inhibitors for MAO-B-driven diseases. This approach can also be used to identify MAO-B inhibitors from other libraries or screen top molecules for other targets involved in suitable diseases.

Extended many-item similarity indices for sets of nucleotide and protein sequences

Quantification of similarities between protein sequences or DNA/RNA strands is a (sub-)task that is ubiquitously present in bioinformatics workflows, and is usually accomplished by pairwise comparisons of sequences, utilizing simple (e.g. percent identity) or more intricate concepts (e.g. substitution scoring matrices). Complex tasks (such as clustering) rely on a large number of pairwise comparisons under the hood, instead of a direct quantification of set similarities. Based on our recently introduced framework that enables multiple comparisons of binary molecular fingerprints (i.e., direct calculation of the similarity of fingerprint sets), here we introduce novel symmetric similarity indices for analogous calculations on sets of character sequences with more than two (t) possible items (e.g. DNA/RNA sequences with t = 4, or protein sequences with t = 20). The features of these new indices are studied in detail with analysis of variance (ANOVA), and demonstrated with three case studies of protein/DNA sequences with varying degrees of similarity (or evolutionary proximity). The Python code for the extended many-item similarity indices is publicly available at: https://github.com/ramirandaq/tn_Comparisons.

Virtual screening and drug repurposing experiments to identify potential novel selective MAO-B inhibitors for Parkinson's disease treatment

The main study's purpose is to detect novel natural products (NPs) that are potentially selective MAO-B inhibitors and, additionally, to computationally reposition the marketed drugs with a new therapeutic role for Parkinson's disease. To reach the goals, 3D similarity search, docking, ADMETox, and drug repurposing approaches were employed. Thus, an unbiased benchmarking dataset was built including selective and nonselective inhibitors for MAO-B compliant with both ligand- and structure-based virtual screening approaches. A retrospective and prospective mining scenario was applied to SPECS NP and DrugBank databases to detect novel scaffolds with potential benefits for Parkinson's disease patients. Out of the three best selected natural products, cardamomin showed excellently predicted drug-like properties, superior pharmacological profile, and specific interactions with MAO-B active site, indicating a potential selectivity over MAO-B. Two marketed drugs, fenamisal and monobenzone, were proposed as promising candidates repurposed for Parkinson's disease. The application of shape, physicochemical, and electrostatic similarity searches protocol emerged as a plausible solution to explore MAO-B inhibitors selectivity. This protocol might serve as a rewarding tool in early drug discovery and can be extended to other protein targets.

Comprehensive investigation of selectivity landscape of glycogen synthase kinase-3 inhibitors

Interaction signatures of drug candidates are characteristic to off-target (neutral) and antitarget (negative) effects, inferring reduced efficiency, side-effects and high attrition rate. Today's retroactive scaled-down virtual screening (VS) experiments relying on benchmarking datasets are extensively involved to assess ligand enrichment in the real-world problem. In recent years, unbiased benchmarking sets turned into a tremendous need to assist virtual screening methodologies for emerging drug targets. To date, the benchmarking datasets are quite limited, whereas glycogen synthase kinase-3 (GSK-3) is not included into directories of benchmarking datasets such as DUD-e, MUV, etc. Herein we introduced our in-house algorithm to build an unbiased benchmarking dataset, including highly selective, moderately selective and nonselective inhibitors for a significant therapeutic target - GSK-3, suitable for both ligand-based and structure-based VS approaches. These datasets are unbiased in terms of physico-chemical properties and topological descriptors, as resulted from mean(ROC-AUC) leave-one-out cross-validation (LOO CV). and additional 2 D similarity search. Moreover, we investigated the gradual selectivity dataset by application of multiple 2 D similarity coefficients and distances, 3 D similarity and docking. Besides the resulted links between the enrichment of selective GSK-3 inhibitors and their chemical structures, a database of compounds and their 3 D similarity signatures including cut-off thresholds for enhanced selectivity was generated. 2 D similarity space analysis revealed that selectivity problem cannot be evaluated appropriately with 2 D similarity searching alone. The current analysis provided useful, comprehensive insights, which may facilitate the knowledge-based identification of novel selective GSK-3 inhibitors.Communicated by Ramaswamy H. Sarma.

Partial Least Squares Discriminant Analysis and 3D Similarity Perspective Applied to Analyze Comprehensively the Selectivity of Glycogen Synthase Kinase 3 Inhibitors

The current work was conducted to investigate the effectiveness of two conceptually distinct in silico ligand-based tools: Partial Least Squares Discriminant Analysis (PLS-DA) and 3D similarity, including shape, physico-chemical and electrostatics to classify target-specific pharmacophores with enrichment power for selective GSK-3 inhibitors against the phylogenetically related CDK-2, CDK-4, CDK-5 and PKC. All virtual screens were performed on four data sets of targets matched pairwise, including selective and nonselective inhibitors for GSK-3. The classification method PLS-DA results revealed that all obtained models are statistically robust according to the cross-validation and response permutation tests. Regarding selective GSK-3 inhibitors differentiation in terms of selectivity (Se), specificity (Sp), and accuracy (ACC), the PLS-DA models for CDK-4/GSK-3, and PKC/GSK-3 datasets are highly efficient discriminative. 3D similarity searches for CDK-4/GSK-3, PKC/GSK-3, and CDK-2/GSK-3 datasets using the most selective reference molecules lead to highest enrichments of selective GSK-3 inhibitors. EON yields excellent early and overall enrichments for ET_ST and ET_combo for most selective query for CDK-4/GSK-3. CDK-5/GSK-3 dataset didn't show consistent statistically significant enrichments for 3D similarity virtual screening. The current methodology is reliable and could be used as a powerful tool for the detection of potentially selective molecules targeting GSK-3.

COX Inhibition Profile and Molecular Docking Studies of Some 2-(Trimethoxyphenyl)-Thiazoles

Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used therapeutic agents that exhibit frequent and sometimes severe adverse effects, including gastrointestinal ulcerations and cardiovascular disorders. In an effort to obtain safer NSAIDs, we assessed the direct cyclooxygenase (COX) inhibition activity and we investigated the potential COX binding mode of some previously reported 2-(trimethoxyphenyl)-thiazoles. The in vitro COX inhibition assays were performed against ovine COX-1 and human recombinant COX-2. Molecular docking studies were performed to explain the possible interactions between the inhibitors and both COX isoforms binding pockets. Four of the tested compounds proved to be good inhibitors of both COX isoforms, but only compound A3 showed a good COX-2 selectivity index, similar to meloxicam. The plausible binding mode of compound A3 revealed hydrogen bond interactions with binding site key residues including Arg120, Tyr355, Ser530, Met522 and Trp387, whereas hydrophobic contacts were detected with Leu352, Val349, Leu359, Phe518, Gly526, and Ala527. Computationally predicted pharmacokinetic profile revealed A3 as lead candidate. The present data prove that the investigated compounds inhibit COX and thus confirm the previously reported in vivo anti-inflammatory screening results suggesting that A3 is a suitable candidate for further development as a NSAID.

Pharmacophore-based screening and drug repurposing exemplified on glycogen synthase kinase-3 inhibitors

The current study was conducted to elaborate a novel pharmacophore model to accurately map selective glycogen synthase kinase-3 (GSK-3) inhibitors, and perform virtual screening and drug repurposing. Pharmacophore modeling was developed using PHASE on a data set of 203 maleimides. Two benchmarking validation data sets with focus on selectivity were assembled using ChEMBL and PubChem GSK-3 confirmatory assays. A drug repurposing experiment linking pharmacophore matching with drug information originating from multiple data sources was performed. A five-point pharmacophore model was built consisting of a hydrogen bond acceptor (A), hydrogen bond donor (D), hydrophobic (H), and two rings (RR). An atom-based 3D quantitative structure-activity relationship (QSAR) model showed good correlative and satisfactory predictive abilities (training set [Formula: see text]; test set: [Formula: see text]; whole data set: stability [Formula: see text]). Virtual screening experiments revealed that selective GSK-3 inhibitors are ranked preferentially by Hypo-1, but fail to retrieve nonselective compounds. The pharmacophore and 3D QSAR models can provide assistance to design novel, potential GSK-3 inhibitors with high potency and selectivity pattern, with potential application for the treatment of GSK-3-driven diseases. A class of purine nucleoside antileukemic drugs was identified as potential inhibitor of GSK-3, suggesting the reassessment of the target range of these drugs.

ColBioS-FlavRC: a collection of bioselective flavonoids and related compounds filtered from high-throughput screening outcomes

Flavonoids, the vastest class of natural polyphenols, are extensively investigated for their multiple benefits on human health. Due to their physicochemical or biological properties, many representatives are considered to exhibit low selectivity among various protein targets or to plague high-throughput screening (HTS) outcomes. The aim of this study is to highlight reliable, bioselective compounds sharing flavonoidic scaffolds in HTS experiments. A filtering scheme was applied to remove undesired flavonoids (and related compounds) from confirmatory PubChem bioassays. A number of 433 compounds addressing various protein targets form the core of the collection of bioselective flavonoids and related compounds (ColBioS-FlavRC). With an additional set of 2908 inactive related compounds, ColBioS-FlavRC offers the grounds for method optimization and validation. We exemplified the use of ColBioS-FlavRC by pharmacophore modeling, subsequently (externally) validated for virtual screening purposes. The early enrichment capabilities of the pharmacophore hypotheses were measured by means of the median exponential retriever operating curve enrichment (MeROCE), a suited metric in comparative evaluations of virtual screening methods. ColBioS-FlavRC is available in the Supporting Information and is freely accessible for further studies.