GRID-Based Three-Dimensional Pharmacophores I: FLAPpharm, a Novel Approach for Pharmacophore Elucidation

Computational Methods for the Discovery and Optimization of TAAR1 and TAAR5 Ligands

G-protein-coupled receptors (GPCRs) represent a family of druggable targets when treating several diseases and continue to be a leading part of the drug discovery process. Trace amine-associated receptors (TAARs) are GPCRs involved in many physiological functions with TAAR1 having important roles within the central nervous system (CNS). By using homology modeling methods, the responsiveness of TAAR1 to endogenous and synthetic ligands has been explored. In addition, the discovery of different chemo-types as selective murine and/or human TAAR1 ligands has helped in the understanding of the species-specificity preferences. The availability of TAAR1-ligand complexes sheds light on how different ligands bind TAAR1. TAAR5 is considered an olfactory receptor but has specific involvement in some brain functions. In this case, the drug discovery effort has been limited. Here, we review the successful computational efforts developed in the search for novel TAAR1 and TAAR5 ligands. A specific focus on applying structure-based and/or ligand-based methods has been done. We also give a perspective of the experimental data available to guide the future drug design of new ligands, probing species-specificity preferences towards more selective ligands. Hints for applying repositioning approaches are also discussed.

Synthesis, biological evaluation and metadynamics simulations of novel N-methyl β-sheet breaker peptides as inhibitors of Alzheimer's β-amyloid fibrillogenesis

Several scientific evidences report that a central role in the pathogenesis of Alzheimer's disease is played by the deposition of insoluble aggregates of β-amyloid proteins in the brain. Because Aβ is self-assembling, one possible design strategy is to inhibit the aggregation of Aβ peptides using short peptide fragments homologous to the full-length wild-type Aβ protein. In the past years, several studies have reported on the synthesis of some short synthetic peptides called β-sheet breaker peptides (BSBPs). Herein, we present the synthesis of novel (cell-permeable) N-methyl BSBPs, designed based on literature information on the structural key features of BSBPs. Three-dimensional GRID-based pharmacophore peptide screening combined with PT-WTE metadynamics was performed to support the results of the design and microwave-assisted synthesis of peptides 2 and 3 prepared and analyzed for their fibrillogenesis inhibition activity and cytotoxicity. An HR-MS-based cell metabolomic approach highlighted their cell permeability properties.

PharmaCore: The Automatic Generation of 3D Structure-Based Pharmacophore Models from Protein/Ligand Complexes

In this work, we present PharmaCore: a new, completely automatic workflow aimed at generating three-dimensional (3D) structure-based pharmacophore models toward any target of interest. The proposed approach relies on using cocrystallized ligands to create the input files for generating the pharmacophore hypotheses, integrating not only the three-dimensional structural information on the ligand but also data concerning the binding mode of these molecules put in the protein cavity. We developed a Python library that, starting from the specific UniProt ID of the protein under investigation as the only element that requires user intervention, subsequently collects and aligns the corresponding structures bearing a known ligand in a fully automated fashion, bringing them all into the same coordinate system. The protocol includes a final phase in which the aligned small molecules are used to produce the pharmacophore hypotheses directly onto the protein structure using a specific software, e.g., Phase (Schrödinger LLC). To validate the entire procedure and highlight the possible applications in the field of drug discovery and repositioning, we first generated pharmacophores for soluble epoxide hydrolase (sEH) and compared with already-published ones. Then, we reproduced the binding profile of a reported selective binder of ATAD2 bromodomain (AM879), testing it against a panel of 1741 pharmacophores related to 16 epigenetic proteins and automatically generated with PharmaCore, finally disclosing putative unprecedented off-targets. The computational predictions were successfully validated with AlphaScreen assays, highlighting the applicability of the proposed workflow in drug discovery and repositioning. Finally, the process was also validated on tankyrase 2 and SARS-CoV-2 MPro, confirming the robustness of PharmaCore.

Discovery of a Novel Chemo-Type for TAAR1 Agonism via Molecular Modeling

The search for novel effective TAAR1 ligands continues to draw great attention due to the wide range of pharmacological applications related to TAAR1 targeting. Herein, molecular docking studies of known TAAR1 ligands, characterized by an oxazoline core, have been performed in order to identify novel promising chemo-types for the discovery of more active TAAR1 agonists. In particular, the oxazoline-based compound S18616 has been taken as a reference compound for the computational study, leading to the development of quite flat and conformationally locked ligands. The choice of a "Y-shape" conformation was suggested for the design of TAAR1 ligands, interacting with the protein cavity delimited by ASP103 and aromatic residues such as PHE186, PHE195, PHE268, and PHE267. The obtained results allowed us to preliminary in silico screen an in-house series of pyrimidinone-benzimidazoles (1a-10a) as a novel scaffold to target TAAR1. Combined ligand-based (LBCM) and structure based (SBCM) computational methods suggested the biological evaluation of compounds 1a-10a, leading to the identification of derivatives 1a-3a (hTAAR1 EC50 = 526.3-657.4 nM) as promising novel TAAR1 agonists.

A further pocket or conformational plasticity by mapping COX-1 catalytic site through modified-mofezolac structure-inhibitory activity relationships and their antiplatelet behavior

Cyclooxygenase enzymes have distinct roles in cardiovascular, neurological, and neurodegenerative disease. They are differently expressed in different type of cancers. Specific and selective COXs inhibitors are needed to be used alone or in combo-therapies. Fully understand the differences at the catalytic site of the two cyclooxygenase (COX) isoforms is still opened to investigation. Thus, two series of novel compounds were designed and synthesized in fair to good yields using the highly selective COX-1 inhibitor mofezolac as the lead compound to explore a COX-1 zone formed by the polar residues Q192, S353, H90 and Y355, as well as hydrophobic amino acids I523, F518 and L352. According to the structure of the COX-1:mofezolac complex, hydrophobic amino acids appear to have free volume eventually accessible to the more sterically hindering groups than the methoxy linked to the phenyl groups of mofezolac, in particular the methoxyphenyl at C4-mofezolac isoxazole. Mofezolac bears two methoxyphenyl groups linked to C3 and C4 of the isoxazole core ring. Thus, in the novel compounds, one or both methoxy groups were replaced by the higher homologous ethoxy, normal and isopropyl, normal and tertiary butyl, and phenyl and benzyl. Furthermore, a major difference between the two sets of compounds is the presence of either a methyl or acetic moiety at the C5 of the isoxazole. Among the C5-methyl series, 12 (direct precursor of mofezolac) (COX-1 IC50 = 0.076 μM and COX-2 IC50 = 0.35 μM) and 15a (ethoxy replacing the two methoxy groups in 12; COX-1 IC50 = 0.23 μM and COX-2 IC50 > 50 μM) were still active and with a Selectivity Index (SI = COX-2 IC50/COX-1 IC50) = 5 and 217, respectively. The other symmetrically substituted alkoxyphenyl moietis were inactive at 50 μM final concentration. Among the asymmetrically substituted, only the 16a (methoxyphenyl on C3-isoxazole and ethoxyphenyl on C4-isoxazole) and 16b (methoxyphenyl on C3-isoxazole and n-propoxyphenyl on C4-isoxazole) were active with SI = 1087 and 38, respectively. Among the set of compounds with the acetic moiety, structurally more similar to mofezolac (SI = 6329), SI ranged between 1.4 and 943. It is noteworthy that 17b (n-propoxyphenyl on both C3- and C4-isoxazole) were found to be a COX-2 slightly selective inhibitor with SI = 0.072 (COX-1 IC50 > 50 μM and COX-2 IC50 = 3.6 μM). Platelet aggregation induced by arachidonic acid (AA) can be in vitro suppressed by the synthesized compounds, without affecting of the secondary hemostasia, confirming the biological effect provided by the selective inhibition of COX-1. A positive profile of hemocompatibility in relation to erythrocyte and platelet toxicity was observed. Additionally, these compounds exhibited a positive profile of hemocompatibility and reduced cytotoxicity. Quantitative structure activity relationship (QSAR) models and molecular modelling (Ligand and Structure based virtual screening procedures) provide key information on the physicochemical and pharmacokinetic properties of the COX-1 inhibitors as well as new insights into the mechanisms of inhibition that will be used to guide the development of more effective and selective compounds. X-ray analysis was used to confirm the chemical structure of 14 (MSA17).

DeepGRID: Deep Learning Using GRID Descriptors for BBB Prediction

Deep Learning approaches are able to automatically extract relevant features from the input data and capture nonlinear relationships between the input and output. In this work, we present the GRID-derived AI (GrAId) descriptors, a simple modification to GRID MIFs that facilitate their use in combination with Convolutional Neural Networks (CNNs) to build Deep Learning models in a rotationally, conformationally, and alignment-independent approach we are calling DeepGRID. To our knowledge, this is the first time that GRID MIFs have been combined with CNNs in a Deep Learning approach. We applied the approach to build regression and classification models for blood-brain barrier permeation, an important factor when designing CNS drugs and conversely when designing to avoid off-target effects for CNS-inactive drugs. The VolSurf approach was one of the first to successfully model this property from three-dimensional structures, using descriptors derived from their GRID Molecular Interaction Fields (MIFs) in combination with PLS. We compared the DeepGRID models with others built using the hand-crafted VolSurf descriptors in combination with both PLS and Random Forest (RF). Both the DeepGRID and RF regression models performed best according to the % of compounds with a Geometric Mean Fold Error (GMFE) within 2-fold of the experimental data. Applying these regression models as classifiers, for the smaller 332 and 416 compound data sets all models performed well with ROC AUC values of ∼0.9 on the external test set. For the larger 2105 compound data set, the DeepGRID classifier performed the best with an AUC of 0.87 on the external test set with the RF model having an AUC of 0.84 and the original VolSurf lgBB model having an AUC of 0.83.

Inhibitors of ABCG2-mediated multidrug resistance: Lead generation through computer-aided drug design

Human breast cancer resistance protein (BCRP), known also as ABCG2, plays a major role in multiple drug resistance (MDR) in tumor cells. Through this ABC transporter, cancer cells acquire the ability of resistance to structurally and functionally unrelated anticancer drugs. Nowadays, the design of ABCG2 inhibitors as potential agents to enhance the chemotherapy efficacy is an interesting strategy. In this context, we have used computer-aided drug design (CADD) based on available data of a large series of potent inhibitors from our groups as an approach in guiding the design of effective ABCG2 inhibitors. We report therein the results on the use of the FLAPpharm method to elucidate the pharmacophoric features of one of the ABCG2 binding sites involved in the regulation of the basal ATPase activity of the transporter. The predictivity of the model was evaluated by testing three predicted compounds which were found to induce high inhibitory activity of BCRP, in the nanomolar range for the best of them.

Targeting and ultrabroad insight into molecular basis of Resistance-nodulation-cell division efflux pumps

Resistance-nodulation-cell devision (RND) efflux pump variants have attracted a great deal of attention for efflux of many antibiotic classes, which leads to multidrug-resistant bacteria. The present study aimed to discover the interaction between the RND efflux pumps and antibiotics, find the conserved and hot spot residues, and use this information to target the most frequent RND efflux pumps. Protein sequence and 3D conformational alignments, pharmacophore modeling, molecular docking, and molecular dynamics simulation were used in the first level for discovering the function of the residues in interaction with antibiotics. In the second level, pharmacophore-based screening, structural-based screening, multistep docking, GRID MIF, pharmacokinetic modeling, fragment molecular orbital, and MD simulation were utilized alongside the former level information to find the most proper inhibitors. Five conserved residues, containing Ala209, Tyr404, Leu415, Asp416, and Ala417, as well as their counterparts in other OMPs were evaluated as the crucial conserved residues. MD simulation confirmed that a number of these residues had a key role in the performance of the efflux antibiotics; therefore, some of them were hot spot residues. Fourteen ligands were selected, four of which interacted with all the crucial conserved residues. NPC100251 was the fittest OMP inhibitor after pharmacokinetic computations. The second-level MD simulation and FMO supported the efficacy of the NPC100251. It was exhibited that perhaps OMPs worked as the intelligent and programable protein. NPC100251 was the strongest OMPs inhibitor, and may be a potential therapeutic candidate for MDR infections.

Small Molecules as Toll-like Receptor 4 Modulators Drug and In-House Computational Repurposing

The innate immunity toll-like receptor 4 (TLR4) system is a receptor of paramount importance as a therapeutic target. Virtual screening following a “computer-aided drug repurposing” approach was applied to the discovery of novel TLR4 modulators with a non-lipopolysaccharide-like structure. We screened almost 29,000 approved drugs and drug-like molecules from commercial, public, and in-house academia chemical libraries and, after biological assays, identified several compounds with TLR4 antagonist activity. Our computational protocol showed to be a robust approach for the identification of hits with drug-like scaffolds as possible inhibitors of the TLR4 innate immune pathways. Our collaborative work broadens the chemical diversity for inspiration of new classes of TLR4 modulators.

Synthesis and Activity of 2-Acyl-cyclohexane-1,3-dione Congeners Derived from Peperomia Natural Products against the Plant p-Hydroxyphenylpyruvate Dioxygenase Herbicidal Molecular Target Site

Plastoquinone is a key electron carrier in photosynthesis and an essential cofactor for the biosynthesis of carotenoids. p-Hydroxyphenylpyruvate dioxygenase (HPPD) is a vital enzymatic step in plastoquinone biosynthesis that is the target of triketone herbicides, such as those derived from the pharmacophore backbone of the natural product leptospermone. In this work, the inhibitory activity of a series of 2-acyl-cyclohexane-1,3-diones congeners derived from Peperomia natural products was tested on plant HPPD. The most active compound was a 2-acyl-cyclohexane-1,3-dione with a C₁₁ alkyl side chain (5d; I₅₀app: 0.18 ± 0.02 μM) that was slightly more potent than the commercial triketone herbicide sulcotrione (I₅₀app: 0.25 ± 0.02 μM). QSAR analysis and docking studies were performed to further characterize the key structural features imparting activity. A 1,3-dione feature was required for inhibition of HPPD. Molecules with a side chain of 11 carbons were found to be optimal for inhibition, while the presence of a double bond, hydroxy, or methyl beyond the required structural features on the cyclohexane ring generally decreased HPPD inhibiting activity.

Discovery of new chemotypes of dual 5-HT2A/D2 receptor antagonists with a strategy of drug design methodologies

Aim: Through the application of structure- and ligand-based methods, the authors aimed to create an integrative approach to developing a computational protocol for the rational drug design of potent dual 5-HT_2A/D₂ receptor antagonists without off-target activities on H₁ receptors. Materials & methods: Molecular dynamics and virtual docking methods were used to identify key interactions of the structurally diverse antagonists in the binding sites of the studied targets, and to generate their bioactive conformations for further 3D-quantitative structure-activity relationship modeling. Results & conclusion: Toward the goal of finding multi-potent drugs with a more effective and safer profile, the obtained results led to the design of a new set of dual antagonists and opened a new perspective on the therapy for complex brain diseases.

Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process

The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.

Expanding the Accessible Chemical Space of SIRT2 Inhibitors through Exploration of Binding Pocket Dynamics

Considerations of binding pocket dynamics are one of the crucial aspects of the rational design of binders. Identification of alternative conformational states or cryptic subpockets could lead to the discovery of completely novel groups of the ligands. However, experimental characterization of pocket dynamics, besides being expensive, may not be able to elucidate all of the conformational states relevant for drug discovery projects. In this study, we propose the protocol for computational simulations of sirtuin 2 (SIRT2) binding pocket dynamics and its integration into the structure-based virtual screening (SBVS) pipeline. Initially, unbiased molecular dynamics simulations of SIRT2:inhibitor complexes were performed using optimized force field parameters of SIRT2 inhibitors. Time-lagged independent component analysis (tICA) was used to design pocket-related collective variables (CVs) for enhanced sampling of SIRT2 pocket dynamics. Metadynamics simulations in the tICA eigenvector space revealed alternative conformational states of the SIRT2 binding pocket and the existence of a cryptic subpocket. Newly identified SIRT2 conformational states outperformed experimentally resolved states in retrospective SBVS validation. After performing prospective SBVS, compounds from the under-represented portions of the SIRT2 inhibitor chemical space were selected for in vitro evaluation. Two compounds, NDJ18 and NDJ85, were identified as potent and selective SIRT2 inhibitors, which validated the in silico protocol and opened up the possibility for generalization and broadening of its application. The anticancer effects of the most potent compound NDJ18 were examined on the triple-negative breast cancer cell line. Results indicated that NDJ18 represents a promising structure suitable for further evaluation.

FragExplorer: GRID-Based Fragment Growing and Replacement

Understanding which chemical modifications can be made to known ligands is a key aspect of structure-based drug design and one that was pioneered by the software GRID. We developed FragExplorer with the explicit aim of showing GRID users which fragments would best match the GRID molecular interaction fields in a protein binding site, given a bound ligand as a starting point. Users can grow ligands or replace existing moieties; the R-Group Exploration mode identifies all potential R-Groups and searches for replacements automatically; the Scaffold Exploration mode does the same for all potential scaffolds. For a ligand with three points of variation, R-Group Exploration will typically explore a chemical space of 10¹⁶ potential molecules; including Scaffold Exploration increases this to 10²². FragExplorer was designed to be integrated within an interactive 3D Editor/Designer; therefore, the speed of computation was an important consideration; a typical fragment search takes 20 seconds. In a fragment reprediction test, FragExplorer demonstrates an overall fragment retrieval rate of 55%, increasing to 69% for smaller fragments. At a 90% substructural match, the retrieval rate increases to ∼80%. We also show how the approach could have been used to hop from olmesartan to azilsartan or to optimize a p38 MAP kinase lead to a compound that bears similarity to a known nanomolar inhibitor.

Pharmacophore-Based Discovery of Substrates of a Novel Drug/Proton-Antiporter in the Human Brain Endothelial hCMEC/D3 Cell Line

A drug/proton-antiporter, whose the molecular structure is still unknown, was previously evidenced at the blood-brain barrier (BBB) by functional experiments. The computational method could help in the identification of substrates of this solute carrier (SLC) transporter. Two pharmacophore models for substrates of this transporter using the FLAPpharm approach were developed. The trans-stimulation potency of 40 selected compounds for already known specific substrates ([3H]-clonidine) were determined and compared in the human brain endothelial cell line hCMEC/D3. Results. The two pharmacophore models obtained were used as templates to screen xenobiotic and endogenous compounds from four databases (e.g., Specs), and 45 hypothetical new candidates were tested to determine their substrate capacity. Psychoactive drugs such as antidepressants (e.g., imipramine, desipramine), antipsychotics/neuroleptics such as phenothiazine derivatives (chlorpromazine), sedatives anti-histamine-H1 drugs (promazine, promethazine, triprolidine, pheniramine), opiates/opioids (e.g., hydrocodone), trihexyphenidyl and sibutramine were correctly predicted as proton-antiporter substrates. The best performing pharmacophore model for the proton-antiporter substrates appeared as a good predictor of known substrates and allowed the identification of new substrate compounds. This model marks a new step in the characterization of this drug/proton-antiporter and will be of great use in uncovering its substrates and designing chemical entities with an improved influx capability to cross the BBB.

CB1 receptor binding sites for NAM and PAM: A first approach for studying, new n‑butyl‑diphenylcarboxamides as allosteric modulators

The development of cannabinoid receptor type-1 (CB1R) modulators has been implicated in multiple pathophysiological events ranging from memory deficits to neurodegenerative disorders among others, even if their central psychiatric side effects such as depression, anxiety, and suicidal tendencies, have limited their clinical use. Thus, the identification of ligands which selectively act on peripheral CB1Rs, is becoming more interesting. A recent study reported a class of peripheral CB1R selective antagonists, characterized by a 5-aryl substituted nicotinamide core. These derivatives have structural similarities with the biphenyl compounds, endowed with CB2R antagonist activity, previously synthesized by our research group. In this work we combined the pharmacophoric portion of both classes, in order to obtain novel CBR antagonists. Among the synthesized compounds rather unexpectedly two compounds of this series, C7 and C10, did not show the radioligand ([³H]CP55940) displacement on CB1R but increased binding (∼ 150%), suggesting a possible allosteric behavior. Computational studies were performed to investigate the role of these compounds in CB1R modulation. The analysis of their binding poses in two different binding cavities of the CB1R surface, revealed a preferred interaction with the experimental binding site for negative allosteric modulators.

Combining machine learning and quantum mechanics yields more chemically aware molecular descriptors for medicinal chemistry applications

Molecular interaction fields (MIFs), describing molecules in terms of their ability to interact with any chemical entity, are one of the most established and versatile concepts in drug discovery. Improvement of this molecular description is highly desirable for in silico drug discovery and medicinal chemistry applications. In this work, we revised a well‐established molecular mechanics' force field and applied a hybrid quantum mechanics and machine learning approach to parametrize the hydrogen‐bonding (HB) potentials of small molecules, improving this aspect of the molecular description. Approximately 66,000 molecules were chosen from available drug databases and subjected to density functional theory calculations (DFT). For each atom, the molecular electrostatic potential (EP) was extracted and used to derive new HB energy contributions; this was subsequently combined with a fingerprint‐based description of the structural environment via partial least squares modeling, enabling the new potentials to be used for molecules outside of the training set. We demonstrate that parameter prediction for molecules outside of the training set correlates with their DFT‐derived EP, and that there is correlation of the new potentials with hydrogen‐bond acidity and basicity scales. We show the newly derived MIFs vary in strength for various ring substitution in accordance with chemical intuition. Finally, we report that this derived parameter, when extended to non‐HB atoms, can also be used to estimate sites of reaction.

From Anti-infective Agents to Cancer Therapy: a Drug Repositioning Study Revealed a New Use for Nitrofuran Derivatives

BACKGROUND

The progression of ovarian cancer seems to be related to HDAC1, HDAC3 and HDAC6 activity. A possible strategy for improving therapies for treating ovarian carcinoma, minimizing the preclinical screenings, is the repurposing of already approved pharmaceutical products as inhibitors of these enzymes.

OBJECTIVE

This work was aimed to implement a computational strategy for identifying new HDAC inhibitors for ovarian carcinoma treatment among approved drugs.

METHOD

The CHEMBL database was used to construct training, test and decoys sets for performing and validating HDAC1, HDAC3 and HDAC6 3D-QSAR models obtained by using FLAP program. Docking and MD simulations were used in combination with the generated models to identify novel potential HDAC inhibitors. Cell viability assays and Western blot analyses were performed on normal and cancer cells for a direct evaluation of the anti-proliferative activity and an in vitro estimation of HDAC inhibition of the compounds selected through in silico screening.

RESULT

The best quantitative prediction was obtained for the HDAC6 3D-QSAR model. The screening of approved drugs highlighted a new potential use as HDAC inhibitors for some compounds, in particular nitrofuran derivatives, usually known for their antibacterial activity, and frequently used as antimicrobial adjuvant therapy in cancer treatment. Experimental evaluation of these derivatives highlighted a significant antiproliferative activity against cancer cell lines overexpressing HDAC6, and an increase in acetylated alpha-tubulin levels.

CONCLUSION

Experimental results support the hypothesis of a potential direct interaction of nitrofuran derivatives with HDACs. In addition to the possible repurposing of already approved drugs, this work suggests the nitro group as a new zinc binding group, able to interact with the catalytic zinc ion of HDACs.

An Integrative in silico Drug Repurposing Approach for Identification of Potential Inhibitors of SARS-CoV-2 Main Protease

Considering the urgent need for novel therapeutics in ongoing COVID-19 pandemic, drug repurposing approach might offer rapid solutions comparing to de novo drug design. In this study, we designed an integrative in silico drug repurposing approach for rapid selection of potential candidates against SARS-CoV-2 Main Protease (Mpro ). To screen FDA-approved drugs, we implemented structure-based molecular modelling techniques, physiologically-based pharmacokinetic (PBPK) modelling of drugs disposition and data mining analysis of drug-gene-COVID-19 association. Through presented approach, we selected the most promising FDA approved drugs for further COVID-19 drug development campaigns and analysed them in context of available experimental data. To the best of our knowledge, this is unique in silico study which integrates structure-based molecular modeling of Mpro inhibitors with predictions of their tissue disposition, drug-gene-COVID-19 associations and prediction of pleiotropic effects of selected candidates.

Imidazothiazole-based potent inhibitors of V600E-B-RAF kinase with promising anti-melanoma activity: biological and computational studies

A series of imidazothiazole derivatives possessing potential activity against melanoma cells were investigated for molecular mechanism of action. The target compounds were tested against V600E-B-RAF and RAF1 kinases. Compound 1zb is the most potent against both kinases with IC₅₀ values 0.978 and 8.2 nM, respectively. It showed relative selectivity against V600E mutant B-RAF kinase. Compound 1zb was also tested against four melanoma cell lines and exerted superior potency (IC₅₀ 0.18-0.59 µM) compared to the reference standard drug, sorafenib (IC₅₀ 1.95-5.45 µM). Compound 1zb demonstrated also prominent selectivity towards melanoma cells than normal skin cells. It was further tested in whole-cell kinase assay and showed in-cell V600E-B-RAF kinase inhibition with IC₅₀ of 0.19 µM. Compound 1zb induces apoptosis not necrosis in the most sensitive melanoma cell line, UACC-62. Furthermore, molecular dynamic and 3D-QSAR studies were done to investigate the binding mode and understand the pharmacophoric features of this series of compounds.

Discovery of potential inhibitors against New Delhi metallo-β-lactamase-1 from natural compounds: in silico-based methods

New Delhi metallo-β-lactamase variants and different types of metallo-β-lactamases have attracted enormous consideration for hydrolyzing almost all β-lactam antibiotics, which leads to multi drug resistance bacteria. Metallo-β-lactamases genes have disseminated in hospitals and all parts of the world and became a public health concern. There is no inhibitor for New Delhi metallo-β-lactamase-1 and other metallo-β-lactamases classes, so metallo-β-lactamases inhibitor drugs became an urgent need. In this study, multi-steps virtual screening was done over the NPASS database with 35,032 natural compounds. At first Captopril was extracted from 4EXS PDB code and use as a template for the first structural screening and 500 compounds obtained as hit compounds by molecular docking. Then the best ligand, i.e. NPC120633 was used as templet and 800 similar compounds were obtained. As a final point, ten compounds i.e. NPC171932, NPC100251, NPC18185, NPC98583, NPC112380, NPC471403, NPC471404, NPC472454, NPC473010 and NPC300657 had proper docking scores, and a 50 ns molecular dynamics simulation was performed for calculation binding free energy of each compound with New Delhi metallo-β-lactamase. Protein sequence alignment, 3D conformational alignment, pharmacophore modeling on all New Delhi metallo-β-lactamase variants and all types of metallo-β-lactamases were done. Quantum chemical perspective based on the fragment molecular orbital (FMO) method was performed to discover conserved and crucial residues in the catalytic activity of metallo-β-lactamases. These residues had similar 3D coordinates of spatial location in the 3D conformational alignment. So it is posibble that all types of metallo-β-lactamases can inhibit by these ten compounds. Therefore, these compounds were proper to mostly inhibit all metallo-β-lactamases in experimental studies.

Instantaneous generation of protein hydration properties from static structures

Complex molecular simulation methods are typically required to calculate the thermodynamic properties of biochemical systems. One example thereof is the thermodynamic profiling of (de)solvation of proteins, which is an essential driving force for protein-ligand and protein-protein binding. The thermodynamic state of water molecules depends on its enthalpic and entropic components; the latter is governed by dynamic properties of the molecule. Here, we developed, to the best of our knowledge, two novel machine learning methods based on deep neural networks that are able to generate the converged thermodynamic state of dynamic water molecules in the heterogeneous protein environment based solely on the information of the static protein structure. The applicability of our machine learning methods to predict the hydration information is demonstrated in two different studies, the qualitative analysis and quantitative prediction of structure-activity relationships, and the prediction of protein-ligand binding modes.

Focus on Human Monoamine Transporter Selectivity. New Human DAT and NET Models, Experimental Validation, and SERT Affinity Exploration

The most commonly used antidepressant drugs are the serotonin transporter inhibitors. Their effects depend strongly on the selectivity for a single monoamine transporter compared to other amine transporters or receptors, and the selectivity is roughly influenced by the spatial protein structure. Here, we provide a computational study on three human monoamine transporters, i.e., DAT, NET, and SERT. Starting from the construction of hDAT and hNET models, whose three-dimensional structure is unknown, and the prediction of the binding pose for 19 known inhibitors, 3D-QSAR models of three human transporters were built. The training set variability, which was high in structure and activity profile, was validated using a set of in-house compounds. Results concern more than one aspect. First of all, hDAT and hNET three-dimensional structures were built, validated, and compared to the hSERT one; second, the computational study highlighted the differences in binding site arrangement statistically correlated to inhibitor selectivity; third, the profiling of new inhibitors pointed out a conservation of the inhibitory activity trend between rabbit and human SERT with a difference of about 1 order of magnitude; fourth, binding and functional studies confirmed 4-(benzyloxy)-4-phenylpiperidine 20a–d and 21a–d as potent SERT inhibitors. In particular, one of the compounds (compound 20b) revealed a higher affinity for SERT than paroxetine in human platelets.

Bcr-Abl Allosteric Inhibitors: Where We Are and Where We Are Going to

The fusion oncoprotein Bcr-Abl is an aberrant tyrosine kinase responsible for chronic myeloid leukemia and acute lymphoblastic leukemia. The auto-inhibition regulatory module observed in the progenitor kinase c-Abl is lost in the aberrant Bcr-Abl, because of the lack of the N-myristoylated cap able to bind the myristoyl binding pocket also conserved in the Bcr-Abl kinase domain. A way to overcome the occurrence of resistance phenomena frequently observed for Bcr-Abl orthosteric drugs is the rational design of allosteric ligands approaching the so-called myristoyl binding pocket. The discovery of these allosteric inhibitors although very difficult and extremely challenging, represents a valuable option to minimize drug resistance, mostly due to the occurrence of mutations more frequently affecting orthosteric pockets, and to enhance target selectivity with lower off-target effects. In this perspective, we will elucidate at a molecular level the structural bases behind the Bcr-Abl allosteric control and will show how artificial intelligence can be effective to drive the automated de novo design towards off-patent regions of the chemical space.

Synthetic Peptide Libraries: From Random Mixtures to In Vivo Testing

Combinatorially generated molecular repertoires have been largely used to identify novel bioactive compounds. Ever more sophisticated technological solutions have been proposed to simplify and speed up such process, expanding the chemical diversity space and increasing the prospect to select new molecular entities with specific and potent activities against targets of therapeutic relevance. In this context, random mixtures of oligomeric peptides were originally used and since 25 years they represent a continuous source of bioactive molecules with potencies ranging from the sub-nM to microM concentration. Synthetic peptide libraries are still employed as starting "synthetic broths" of structurally and chemically diversified molecular fragments from which lead compounds can be extracted and further modified. Thousands of studies have been reported describing the application of combinatorial mixtures of synthetic peptides with different complexity and engrafted on diverse structural scaffolds for the identification of new compounds which have been further developed and also tested in in vivo models of relevant diseases. We briefly review some of the most used methodologies for library preparation and screening and the most recent case studies appeared in the literature where compounds have reached at least in vivo testing in animal or similar models. Recent technological advancements in biotechnology, engineering and computer science have suggested new options to facilitate the discovery of new bioactive peptides. In this instance, we anticipate here a new approach for the design of simple but focused tripeptide libraries against druggable cavities of therapeutic targets and its complementation with existing approaches.

AlphaSpace 2.0: Representing Concave Biomolecular Surfaces Using β-Clusters

Modern rational modulator design and structure-function characterization often concentrate on concave regions of biomolecular surfaces, ranging from well-defined small-molecule binding sites to large protein-protein interaction interfaces. Here, we introduce a β-cluster as a pseudomolecular representation of fragment-centric pockets detected by AlphaSpace [J. Chem. Inf. Model. 2015, 55, 1585], a recently developed computational analysis tool for topographical mapping of biomolecular concavities. By mimicking the shape as well as atomic details of potential molecular binders, this new β-cluster representation allows direct pocket-to-ligand shape comparison and can be used to guide ligand optimization. Furthermore, we defined the β-score, the optimal Vina score of the β-cluster, as an indicator of pocket ligandability and developed an ensemble β-cluster approach, which allows one-to-one pocket mapping and comparison among aligned protein structures. We demonstrated the utility of β-cluster representation by applying the approach to a wide variety of problems including binding site detection and comparison, characterization of protein-protein interactions, and fragment-based ligand optimization. These new β-cluster functionalities have been implemented in AlphaSpace 2.0, which is freely available on the web at http://www.nyu.edu/projects/yzhang/AlphaSpace2.

Bacillus subtilis Lipase A—Lipase or Esterase?

The question of how to distinguish between lipases and esterases is about as old as the definition of the subclassification is. Many different criteria have been proposed to this end, all indicative but not decisive. Here, the activity of lipases in dry organic solvents as a criterion is probed on a minimal α/β hydrolase fold enzyme, the Bacillus subtilis lipase A (BSLA), and compared to Candida antarctica lipase B (CALB), a proven lipase. Both hydrolases show activity in dry solvents and this proves BSLA to be a lipase. Overall, this demonstrates the value of this additional parameter to distinguish between lipases and esterases. Lipases tend to be active in dry organic solvents, while esterases are not active under these circumstances.

A novel small-molecule inhibitor of the human papillomavirus E6-p53 interaction that reactivates p53 function and blocks cancer cells growth

Despite prophylactic vaccination campaigns, human papillomavirus (HPV)-induced cancers still represent a major medical issue for global population, thus specific anti-HPV drugs are needed. Since the ability of HPV E6 oncoprotein to promote p53 degradation is linked to tumor progression, E6 has been proposed as an ideal target for cancer treatment. Using the crystal structure of the E6/E6AP/p53 complex, we performed an in silico screening of small-molecule libraries against a highly conserved alpha-helix in the N-terminal domain of E6 involved in the E6-p53 interaction. We discovered a compound able to inhibit the E6-mediated degradation of p53 through disruption of E6-p53 binding both in vitro and in cells. This compound could restore p53 intracellular levels and transcriptional activity, reduce the viability and proliferation of HPV-positive cancer cells, and block 3D cervospheres formation. Mechanistic studies revealed that the compound anti-tumor activity mainly relies on induction of cell cycle arrest and senescence. Our data demonstrate that the disruption of the direct E6-p53 interaction can be obtained with a small-molecule compound leading to specific antitumoral activity in HPV-positive cancer cells and thus represents a new approach for anti-HPV drug development.

Molecular modelling of epitopes recognized by neoplastic B lymphocytes in Chronic Lymphocytic Leukemia

Identification of epitopes recognized by tumour B cells could provide insights into the molecular mechanisms of B cell tumorigenesis through aberrant B cell receptor (BCR) signalling. Here, we analysed the structure of eleven peptides binders of BCRs expressed in Chronic Lymphocytic Leukemia (CLL) patients in order to identify the chemical features required for cross-reactive binding to different CLL clonotypes. Four cross-reactive (CR) and seven no-cross-reactive (NCR) peptides were analysed by means of GRID molecular interaction fields, ligand-based pharmacophore and 3D-QSAR approaches. Based on pharmacophore model, two peptides were generated by specific amino acids substitutions of the parental NCR peptides; these new peptides resumed the common chemical features of CR peptides and bound the CLL BCR clonotypes recognized by CR peptides and parental NCR peptides. Thus, our computational approach guided the pharmacophore modelling of CR peptides. In perspective, peptide binders of CLL BCR clonotypes could represent a powerful tool for computational modelling of epitopes recognized by tumour B cells clones.

Computer-based techniques for lead identification and optimization I: Basics

This chapter focuses on computational techniques for identifying and optimizing lead molecules, with a special emphasis on natural compounds. A number of case studies have been specifically discussed, such as the case of the naphthyridine scaffold, discovered through a structure-based virtual screening (SBVS) and proposed as the starting point for further lead optimization process, to enhance its telomeric RNA selectivity. Another example is the case of Liphagal, a tetracyclic meroterpenoid extracted fromAka coralliphaga, known as PI3Kα inhibitor, provide an evidence for the design of new active congeners against PI3Kα using molecular dynamics (MD) simulations. These are only two of the numerous examples of the computational techniques’ powerful in drug design and drug discovery fields. Finally, the design of drugs that can simultaneously interact with multiple targets as a promising approach for treating complicated diseases has been reported. An example of polypharmacological agents are the compounds extracted from mushrooms identified by means of molecular docking experiments. This chapter may be a useful manual of molecular modeling techniques used in the lead-optimization and lead identification processes.

Navigating within thiamine diphosphate-dependent decarboxylases: Sequences, structures, functional positions, and binding sites

Thiamine diphosphate-dependent decarboxylases catalyze both cleavage and formation of CC bonds in various reactions, which have been assigned to different homologous sequence families. This work compares 53 ThDP-dependent decarboxylases with known crystal structures. Both sequence and structural information were analyzed synergistically and data were analyzed for global and local properties by means of statistical approaches (principle component analysis and principal coordinate analysis) enabling complexity reduction. The different results obtained both locally and globally, that is, individual positions compared with the overall protein sequence or structure, revealed challenges in the assignment of separated homologous families. The methods applied herein support the comparison of enzyme families and the identification of functionally relevant positions. The findings for the family of ThDP-dependent decarboxylases underline that global sequence identity alone is not sufficient to distinguish enzyme function. Instead, local sequence similarity, defined by comparisons of structurally equivalent positions, allows for a better navigation within several groups of homologous enzymes. The differentiation between homologous sequences is further enhanced by taking structural information into account, such as BioGPS analysis of the active site properties or pairwise structural superimpositions. The methods applied herein are expected to be transferrable to other enzyme families, to facilitate family assignments for homologous protein sequences.

Modeling ErbB2-p130Cas interaction to design new potential anticancer agents

The ErbB2 receptor tyrosine kinase is overexpressed in approximately 15–20% of breast tumors and associated with aggressive disease and poor clinical outcome. p130Cas represents a nodal scaffold protein regulating cell survival, migration and proliferation in normal and pathological contexts. p130Cas overexpression in ErbB2 human breast cancer correlates with poor prognosis and metastasis formation. Recent data indicate that p130Cas association to ErbB2 protects ErbB2 from degradation, thus enhancing tumorigenesis. Therefore, inhibiting p130Cas/ErbB2 interaction might represent a new therapeutic strategy to target breast cancer. Here we demonstrate by performing Molecular Modeling, Molecular Dynamics, dot blot, ELISA and fluorescence quenching experiments, that p130Cas binds directly to ErbB2. Then, by structure-based virtual screening, we identified two potential inhibitors of p130Cas/ErbB2 interaction. Their experimental validation was performed in vitro and in ErbB2-positive breast cancer cellular models. The results highlight that both compounds interfere with p130Cas/ErbB2 binding and significantly affect cell proliferation and sensitivity to Trastuzumab. Overall, this study identifies p130Cas/ErbB2 complex as a potential breast cancer target revealing new therapeutic perspectives for protein-protein interaction (PPI).

Receptor pharmacophore ensemble (REPHARMBLE): a probabilistic pharmacophore modeling approach using multiple protein-ligand complexes

Ensemble methods are gaining more importance in structure-based approaches as single protein-ligand complexes strongly influence the outcomes of virtual screening. Structure-based pharmacophore modeling based on a single protein-ligand complex with complex feature combinations is often limited to certain chemical classes. The REPHARMBLE (receptor pharmacophore ensemble) approach presented here examines the ability of an ensemble of selected protein-ligand complexes to populate pharmacophore space in the ligand binding site, rigorously assesses the importance of pharmacophore features using Poisson statistic and information theory-based entropy calculations, and generates pharmacophore models with high probabilities. In addition, an ensemble scoring function that combines all the resultant high-scoring pharmacophore models to score molecules is derived. The REPHARMBLE approach was evaluated on ten DUD-E benchmark datasets and afforded good screening performance, as measured by receiver operating characteristic, enrichment factor and Güner-Henry score. Although one of the high-scoring models achieved superior statistical results in each dataset, the ensemble scoring function balanced the shortcomings of each model and passed with close performance measures. This approach offers a reliable way of choosing the best-scoring features to build four-feature pharmacophore queries and customize a target-biased 'pharmacophore ensemble' scoring function for subsequent virtual screening.

Truly Target-Focused Pharmacophore Modeling: A Novel Tool for Mapping Intermolecular Surfaces

Pharmacophore models are an accurate and minimal tridimensional abstraction of intermolecular interactions between chemical structures, usually derived from a group of molecules or from a ligand-target complex. Only a limited amount of solutions exists to model comprehensive pharmacophores using the information of a particular target structure without knowledge of any binding ligand. In this work, an automated and customable tool for truly target-focused (T²F) pharmacophore modeling is introduced. Key molecular interaction fields of a macromolecular structure are calculated using the AutoGRID energy functions. The most relevant points are selected by a newly developed filtering cascade and clustered to pharmacophore features with a density-based algorithm. Using five different protein classes, the ability of this method to identify essential pharmacophore features was compared to structure-based pharmacophores derived from ligand-target interactions. This method represents an extremely valuable instrument for drug design in a situation of scarce ligand information available, but also in the case of underexplored therapeutic targets, as well as to investigate protein allosteric pockets and protein-protein interactions.

Identification of potential aryl hydrocarbon receptor ligands by virtual screening of industrial chemicals

We have developed a virtual screening procedure to identify potential ligands to the aryl hydrocarbon receptor (AhR) among a set of industrial chemicals. AhR is a key target for dioxin-like compounds, which is related to these compounds' potential to induce cancer and a wide range of endocrine and immune system-related effects. The virtual screening procedure included an initial filtration aiming at identifying chemicals with structural similarities to 66 known AhR binders, followed by 3 enrichment methods run in parallel. These include two ligand-based methods (structural fingerprints and nearest neighbor analysis) and one structure-based method using an AhR homology model. A set of 6445 commonly used industrial chemicals was processed, and each step identified unique potential ligands. Seven compounds were identified by all three enrichment methods, and these compounds included known activators and suppressors of AhR. Only approximately 0.7% (41 compounds) of the studied industrial compounds was identified as potential AhR ligands and among these, 28 compounds have to our knowledge not been tested for AhR-mediated effects or have been screened with low purity. We suggest assessment of AhR-related activities of these compounds and in particular 2-chlorotrityl chloride, 3-p-hydroxyanilino-carbazole, and 3-(2-chloro-4-nitrophenyl)-5-(1,1-dimethylethyl)-1,3,4-oxadiazol-2(3H)-one.

A Rational Approach for the Identification of Non-Hydroxamate HDAC6-Selective Inhibitors

The human histone deacetylase isoform 6 (HDAC6) has been demonstrated to play a major role in cell motility and aggresome formation, being interesting for the treatment of multiple tumour types and neurodegenerative conditions. Currently, most HDAC inhibitors in preclinical or clinical evaluations are non-selective inhibitors, characterised by a hydroxamate zinc-binding group (ZBG) showing off-target effects and mutagenicity. The identification of selective HDAC6 inhibitors with novel chemical properties has not been successful yet, also because of the absence of crystallographic information that makes the rational design of HDAC6 selective inhibitors difficult. Using HDAC inhibitory data retrieved from the ChEMBL database and ligand-based computational strategies, we identified 8 original new non-hydroxamate HDAC6 inhibitors from the SPECS database, with activity in the low μM range. The most potent and selective compound, bearing a hydrazide ZBG, was shown to increase tubulin acetylation in human cells. No effects on histone H4 acetylation were observed. To the best of our knowledge, this is the first report of an HDAC6 selective inhibitor bearing a hydrazide ZBG. Its capability to passively cross the blood-brain barrier (BBB), as observed through PAMPA assays, and its low cytotoxicity in vitro, suggested its potential for drug development.

Polymerase Acidic Protein-Basic Protein 1 (PA-PB1) Protein-Protein Interaction as a Target for Next-Generation Anti-influenza Therapeutics

The limited therapeutic options against the influenza virus (flu) and increasing challenges in drug resistance make the search for next-generation agents imperative. In this context, heterotrimeric viral PA/PB1/PB2 RNA-dependent RNA polymerase is an attractive target for a challenging but strategic protein-protein interaction (PPI) inhibition approach. Since 2012, the inhibition of the polymerase PA-PB1 subunit interface has become an active field of research following the publication of PA-PB1 crystal structures. In this Perspective, we briefly discuss the validity of flu polymerase as a drug target and its inhibition through a PPI inhibition strategy, including a comprehensive analysis of available PA-PB1 structures. An overview of all of the reported PA-PB1 complex formation inhibitors is provided, and approaches used for identification of the inhibitors, the hit-to-lead studies, and the emerged structure-activity relationship are described. In addition to highlighting the strengths and weaknesses of all of the PA-PB1 heterodimerization inhibitors, we analyze their hypothesized binding modes and alignment with a pharmacophore model that we have developed.

Comparing pharmacophore models derived from crystal structures and from molecular dynamics simulations

ABSTRACT

Pharmacophore modeling is a widely used technique in computer-aided drug discovery. Structure-based pharmacophore models of a ligand in complex with a protein have proven to be useful for supporting in silico hit discovery, hit to lead expansion, and lead optimization. As a structure-based approach it depends on the correct interpretation of ligand-protein interactions. There are legitimate concerns about the fidelity of the bound ligand and about non-physiological contacts with parts of the crystal and the solvent effects that influence the protein structure. A possible way to refine the structure of a protein-ligand system is to use the final structure of a given MD simulation. In this study we compare pharmacophore models built using the initial protein-ligand structure obtained from the protein data bank (PDB) with pharmacophore models built with the final structure of a molecular dynamics simulation. We show that the pharmacophore models differ in feature number and feature type and that the pharmacophore models built from the last structure of a MD simulation shows in some cases better ability to distinguish between active and decoy ligand structures.

GRAPHICAL ABSTRACT

Pharmacophore-based discovery of inhibitors of a novel drug/proton antiporter in human brain endothelial hCMEC/D3 cell line

BACKGROUND AND PURPOSE

An influx drug/proton antiporter of unknown structure has been functionally demonstrated at the blood-brain barrier. This transporter, which handles some psychoactive drugs like diphenhydramine, clonidine, oxycodone, nicotine and cocaine, could represent a new pharmacological target in drug addiction therapy. However, at present there are no known drugs/inhibitors that effectively inhibit/modulate this transporter in vivo.

EXPERIMENTAL APPROACH

The FLAPpharm approach was used to establish a pharmacophore model for inhibitors of this transporter. The inhibitory potency of 44 selected compounds was determined against the specific substrate, [(3)H]-clonidine, in the human cerebral endothelial cell line hCMEC/D3 and ranked as good, medium, weak or non-inhibitor.

KEY RESULTS

The pharmacophore model obtained was used as a template to screen xenobiotic and endogenous compounds from databases [Specs, Recon2, Human Metabolome Database (HMDB), human intestinal transporter database], and hypothetical candidates were tested in vitro to determine their inhibitory capacity with [(3)H]-clonidine. According to the transporter database, 80% of the proton antiporter inhibitor candidates could inhibit P-glycoprotein/MDR1/ABCB1 and specificity is improved by reducing inhibitor size/shape and increasing water solubility. Virtual screening results using HMDB and Recon2 for endogenous compounds appropriately scored tryptamine as an inhibitor.

CONCLUSIONS AND IMPLICATIONS

The pharmacophore model for the proton-antiporter inhibitors was a good predictor of known inhibitors and allowed us to identify new good inhibitors. This model marks a new step towards the discovery of this drug/proton antiporter and will be of great use for the discovery and design of potent inhibitors that could potentially help to assess and validate its pharmacological role in drug addiction in vivo.

WONKA: objective novel complex analysis for ensembles of protein-ligand structures

WONKA is a tool for the systematic analysis of an ensemble of protein-ligand structures. It makes the identification of conserved and unusual features within such an ensemble straightforward. WONKA uses an intuitive workflow to process structural co-ordinates. Ligand and protein features are summarised and then presented within an interactive web application. WONKA's power in consolidating and summarising large amounts of data is described through the analysis of three bromodomain datasets. Furthermore, and in contrast to many current methods, WONKA relates analysis to individual ligands, from which we find unusual and erroneous binding modes. Finally the use of WONKA as an annotation tool to share observations about structures is demonstrated. WONKA is freely available to download and install locally or can be used online at http://wonka.sgc.ox.ac.uk.

Pharmacophore modeling using site-identification by ligand competitive saturation (SILCS) with multiple probe molecules

Receptor-based pharmacophore modeling is an efficient computer-aided drug design technique that uses the structure of the target protein to identify novel leads. However, most methods consider protein flexibility and desolvation effects in a very approximate way, which may limit their use in practice. The Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling protocol (SILCS-Pharm) was introduced recently to address these issues, as SILCS naturally takes both protein flexibility and desolvation effects into account by using full molecular dynamics simulations to determine 3D maps of the functional group-affinity patterns on a target receptor. In the present work, the SILCS-Pharm protocol is extended to use a wider range of probe molecules including benzene, propane, methanol, formamide, acetaldehyde, methylammonium, acetate and water. This approach removes the previous ambiguity brought by using water as both the hydrogen-bond donor and acceptor probe molecule. The new SILCS-Pharm protocol is shown to yield improved screening results, as compared to the previous approach based on three target proteins. Further validation of the new protocol using five additional protein targets showed improved screening compared to those using common docking methods, further indicating improvements brought by the explicit inclusion of additional feature types associated with the wider collection of probe molecules in the SILCS simulations. The advantage of using complementary features and volume constraints, based on exclusion maps of the protein defined from the SILCS simulations, is presented. In addition, reranking using SILCS-based ligand grid free energies is shown to enhance the diversity of identified ligands for the majority of targets. These results suggest that the SILCS-Pharm protocol will be of utility in rational drug design.