Comparative performance of several flexible docking programs and scoring functions: enrichment studies for a diverse set of pharmaceutically relevant targets

Docking Score ML: Target-Specific Machine Learning Models Improving Docking-Based Virtual Screening in 155 Targets

In drug discovery, molecular docking methods face challenges in accurately predicting energy. Scoring functions used in molecular docking often fail to simulate complex protein-ligand interactions fully and accurately leading to biases and inaccuracies in virtual screening and target predictions. We introduce the "Docking Score ML", developed from an analysis of over 200,000 docked complexes from 155 known targets for cancer treatments. The scoring functions used are founded on bioactivity data sourced from ChEMBL and have been fine-tuned using both supervised machine learning and deep learning techniques. We validated our approach extensively using multiple data sets such as validation of selectivity mechanism, the DUDE, DUD-AD, and LIT-PCBA data sets, and performed a multitarget analysis on drugs like sunitinib. To enhance prediction accuracy, feature fusion techniques were explored. By merging the capabilities of the Graph Convolutional Network (GCN) with multiple docking functions, our results indicated a clear superiority of our methodologies over conventional approaches. These advantages demonstrate that Docking Score ML is an efficient and accurate tool for virtual screening and reverse docking.

Pharmacophore Model-Based Virtual Screening Workflow for Discovery of Inhibitors Targeting Plasmodium falciparum Hsp90

Plasmodium falciparum causes the most lethal and widespread form of malaria. Eradication of malaria remains a priority due to the increasing number of cases of drug resistance. The heat shock protein 90 of P. falciparum (PfHsp90) is a validated drug target essential for parasite survival. Most PfHsp90 inhibitors bind at the ATP binding pocket found in its N-terminal domain, abolishing the chaperone's activities, which leads to parasite death. The challenge is that the NTD of PfHsp90 is highly conserved, and its disruption requires selective inhibitors that can act without causing off-target human Hsp90 activities. We endeavored to discover selective inhibitors of PfHsp90 using pharmacophore modeling, virtual screening protocols, induced fit docking (IFD), and cell-based and biochemical assays. The pharmacophore model (DHHRR), composed of one hydrogen bond donor, two hydrophobic groups, and two aromatic rings, was used to mine commercial databases for initial hits, which were rescored to 20 potential hits using IFD. Eight of these compounds displayed moderate to high activity toward P. falciparum NF54 (i.e., IC50s ranging from 6.0 to 0.14 μM) and averaged >10 in terms of selectivity indices toward CHO and HepG2 cells. Additionally, four compounds inhibited PfHsp90 with greater selectivity than a known inhibitor, harmine, and bound to PfHsp90 with weak to moderate affinity. Our findings support the use of a pharmacophore model to discover diverse chemical scaffolds such as FM2, FM6, F10, and F11 exhibiting anti-Plasmodium activities and serving as valuable new PfHsp90 inhibitors. Optimization of these hits may enable their development into potent leads for future antimalarial drugs.

In-silico studies of 2-aminothiazole derivatives as anticancer agents by QSAR, molecular docking, MD simulation and MM-GBSA approaches

Targeting Hec1/Nek2 is considered as crucial target for cancer treatment due to its significant role in cell proliferation. In pursuit of this, a series of twenty-five 2-aminothiazoles derivatives, along with their Hec1/Nek2 inhibitory activities were subjected to QSAR studies utilizing QSARINS software. The significant three descriptor QSAR model was generated, showing noteworthy statistical parameters: a correlation coefficient of cross validation leave one out (Q2LOO) = 0.7965, coefficient of determination (R2) = 0.8436, (R2ext) = 0.6308, cross validation leave many out (Q2LMO) = 0.7656, Concordance Correlation Coefficient (CCCCV = 0.8875), CCCtr = 0.9151, and CCCext = 0.0.7241. The descriptors integral to generated QSAR model include Moreau-Broto autocorrelation, which represents the spatial autocorrelation of a property along the molecular graph's topological structure (ATSC1i), Moran autocorrelation at lag 8, which is weighted by charges (MATS8c) and RPSA representing the total molecular surface area. It was noted that these descriptors significantly influence Hec1/Nek2 inhibitory activity of 2-aminothiazoles derivatives. New lead molecules were designed and predicted for their Hec1/Nek2 inhibitory activity based on the developed three descriptor model. Further, the ADMET and Molecular docking studies were carried out for these designed molecules. The three molecules were selected based on their docking score and further subjected for MD simulation studies. Post-MD MM-GBSA analysis were also performed to predicted the free binding energies of molecules. The study helped us to understand the key interactions between 2-aminothiazoles derivatives and Hec1/Nek2 protein that may be necessary to develop new lead molecules against cancer.Communicated by Ramaswamy H. Sarma.

Computational Methods in Cooperation with Experimental Approaches to Design Protein Tyrosine Phosphatase 1B Inhibitors in Type 2 Diabetes Drug Design: A Review of the Achievements of This Century

Protein tyrosine phosphatase 1B (PTP1B) dephosphorylates phosphotyrosine residues and is an important regulator of several signaling pathways, such as insulin, leptin, and the ErbB signaling network, among others. Therefore, this enzyme is considered an attractive target to design new drugs against type 2 diabetes, obesity, and cancer. To date, a wide variety of PTP1B inhibitors that have been developed by experimental and computational approaches. In this review, we summarize the achievements with respect to PTP1B inhibitors discovered by applying computer-assisted drug design methodologies (virtual screening, molecular docking, pharmacophore modeling, and quantitative structure–activity relationships (QSAR)) as the principal strategy, in cooperation with experimental approaches, covering articles published from the beginning of the century until the time this review was submitted, with a focus on studies conducted with the aim of discovering new drugs against type 2 diabetes. This review encourages the use of computational techniques and includes helpful information that increases the knowledge generated to date about PTP1B inhibition, with a positive impact on the route toward obtaining a new drug against type 2 diabetes with PTP1B as a molecular target.

Benchmarking ensemble docking methods in D3R Grand Challenge 4

The discovery of new drugs is a time consuming and expensive process. Methods such as virtual screening, which can filter out ineffective compounds from drug libraries prior to expensive experimental study, have become popular research topics. As the computational drug discovery community has grown, in order to benchmark the various advances in methodology, organizations such as the Drug Design Data Resource have begun hosting blinded grand challenges seeking to identify the best methods for ligand pose-prediction, ligand affinity ranking, and free energy calculations. Such open challenges offer a unique opportunity for researchers to partner with junior students (e.g., high school and undergraduate) to validate basic yet fundamental hypotheses considered to be uninteresting to domain experts. Here, we, a group of high school-aged students and their mentors, present the results of our participation in Grand Challenge 4 where we predicted ligand affinity rankings for the Cathepsin S protease, an important protein target for autoimmune diseases. To investigate the effect of incorporating receptor dynamics on ligand affinity rankings, we employed the Relaxed Complex Scheme, a molecular docking method paired with molecular dynamics-generated receptor conformations. We found that Cathepsin S is a difficult target for molecular docking and we explore some advanced methods such as distance-restrained docking to try to improve the correlation with experiments. This project has exemplified the capabilities of high school students when supported with a rigorous curriculum, and demonstrates the value of community-driven competitions for beginners in computational drug discovery.

Discovery of Novel Acetylcholinesterase Inhibitors by Virtual Screening, In Vitro Screening, and Molecular Dynamics Simulations

Alzheimer's disease is the most common neurodegenerative disease and currently poses a significant socioeconomic problem. This study describes the uses of computer-aided drug discovery techniques to identify novel inhibitors of acetylcholinesterase, a target for Alzheimer's disease. High-throughput virtual screening was employed to predict potential inhibitors of acetylcholinesterase. Validation of enrichment was performed with the DUD-E data set, showing that an ensemble of binding pocket conformations is critical when a diverse set of ligands are being screened. A total of 720 compounds were submitted for in vitro screening, which led to 25 hits being identified with IC₅₀ values of less than 50 μM. The majority of these hits belonged to two scaffolds: 1-ethyl-3-methoxy-3-methylpyrrolidine and 1H-pyrrolo[3,2-c]pyridin-6-amine both of which are noted to be promising compounds for further optimization. As various possible binding poses were suggested from molecular docking, molecular dynamics simulations were employed to validate the poses. In the case of the most active compounds identified, a critical, stable water bridge formed deep within the binding pocket was identified potentially explaining in part the lack of activity for subsets of compounds that are not able to form this water bridge.

Bio-physical and Computational Studies on Serum Albumin / Target Protein Binding of a Potential Anti-Cancer Agent

The successful evolution of an effective drug depends on its pharmacokinetics, efficiency and safety and these in turn depend on the drug-target/drug-carrier protein binding. This work, deals with the interaction of a pyridine derivative, 2-hydroxy-5-(4-methoxyphenyl)-6-phenylpyridine 3-carbonitrile (HDN) with serum albumins at physiological conditions utilizing the steady state and time-resolved fluorescence techniques by probing the emission behavior of Trp in BSA and HSA. In-silico studies revealed a combined static and dynamic quenching mechanism for the interactions. The binding studies suggests a spontaneous binding between HDN and the albumins with a moderate binding affinity (Kb ∼ 10-5 M-1) with a single class of binding site. The FRET mediated emission from HDN indicates preferential binding of HDN in subdomain IIA of the albumins with Trp residue in close proximity. Circular dichroism results indicate HDN induced conformational changes for BSA and HSA, but the α-helical secondary structure was well preserved even up to a concentration of 10 µM HDN. Moderate binding affinity of HDN with BSA and HSA and the unaltered secondary structure of proteins on binding propose the potential application of HDN as an efficient drug. The application of docking method on the affinity of HDN towards the proposed target/receptor is discussed.

Superfast Synthesis of Stabilized Silver Nanoparticles Using Aqueous Allium sativum (Garlic) Extract and Isoniazid Hydrazide Conjugates: Molecular Docking and In-Vitro Characterizations

Green synthesis of silver nanoparticles (AgNPs) was synthesized from fresh garlic extract coupled with isoniazid hydrazide (INH), a commonly used antibiotic to treat tuberculosis. A molecular docking study conducted with the selected compounds compared with anthranilate phosphoribosyltransferase (trpD) from Mycobacterium tuberculosis. The aqueous extract of garlic was prepared and mixed with silver nitrate (AgNO3) solution for the superfast synthesis of stable AgNPs. INH was then conjugated with AgNPs at different ratios (v/v) to obtain stable INH-AgNPs conjugates (AgNCs). The resulting AgNCs characterized by FTIR spectra revealed the ultrafast formation of AgNPs (<5 s) and perfectly conjugated with INH. The shifting of λmax to longer wavelength, as found from UV spectral analysis, confirmed the formation of AgNCs, among which ideal formulations (F7, F10, and F13) have been pre-selected. The zeta particle size (PS) and the zeta potential (ZP) of AgNPs were found to be 145.3 ± 2.1 nm and −33.1 mV, respectively. These data were significantly different compared to that of AgNCs (160 ± 2.7 nm and −14.4 mV for F7; 208.9 ± 2.9 nm and −19.8 mV for F10; and 281.3 ± 3.6 nm and −19.5 mV for F13), most probably due to INH conjugation. The results of XRD, SEM and EDX confirmed the formation of AgNCs. From UV spectral analysis, EE of INH as 51.6 ± 5.21, 53.6 ± 6.88, and 70.01 ± 7.11 %, for F7, F10, and F13, respectively. The stability of the three formulations was confirmed in various physiological conditions. Drug was released in a sustainable fashion. Besides, from the preferred 23 compounds, five compounds namely Sativoside R2, Degalactotigonin, Proto-desgalactotigonin, Eruboside B and Sativoside R1 showed a better docking score than trpD, and therefore may help in promoting anti-tubercular activity.

Property-Unmatched Decoys in Docking Benchmarks

Enrichment of ligands versus property-matched decoys is widely used to test and optimize docking library screens. However, the unconstrained optimization of enrichment alone can mislead, leading to false confidence in prospective performance. This can arise by over-optimizing for enrichment against property-matched decoys, without considering the full spectrum of molecules to be found in a true large library screen. Adding decoys representing charge extrema helps mitigate over-optimizing for electrostatic interactions. Adding decoys that represent the overall characteristics of the library to be docked allows one to sample molecules not represented by ligands and property-matched decoys but that one will encounter in a prospective screen. An optimized version of the DUD-E set (DUDE-Z), as well as Extrema and sets representing broad features of the library (Goldilocks), is developed here. We also explore the variability that one can encounter in enrichment calculations and how that can temper one's confidence in small enrichment differences. The new tools and new decoy sets are freely available at http://tldr.docking.org and http://dudez.docking.org.

Fragment Binding to Kinase Hinge: If Charge Distribution and Local pKa Shifts Mislead Popular Bioisosterism Concepts

Medicinal-chemistry optimization follows strategies replacing functional groups and attaching larger substituents at a promising lead scaffold. Well-established bioisosterism rules are considered, however, it is difficult to estimate whether the introduced modifications really match the required properties at a binding site. The electron density distribution and pKa values are modulated influencing protonation states and bioavailability. Considering the adjacent H-bond donor/acceptor pattern of the hinge binding motif in a kinase, we studied by crystallography a set of fragments to map the required interaction pattern. Unexpectedly, benzoic acid and benzamidine, decorated with the correct substituents, are totally bioisosteric just as carboxamide and phenolic OH. A mono-dentate pyridine nitrogen out-performs bi-dentate functionalities. The importance of correctly designing pKa values of attached functional groups by additional substituents at the parent scaffold is rendered prominent.

Isolation and identification of quorum sensing antagonist from Cinnamomum verum leaves against Pseudomonas aeruginosa

PURPOSE

The study aimed at isolating and identifying potential anti-quorum sensing (QS) compounds from Cinnamomum verum leaves against Pseudomonas aeruginosa.

METHODOLOGY

Isolation of anti-QS compounds from C. verum leaf ethanol extract was carried out by column chromatography. The bioactive fraction was analysed by UV, IR, and GCMS spectroscopy. Various virulence assays were performed to assess the QS quenching ability of the purified compounds. In vivo toxicity of the purified compounds was examined in zebrafish model. The expression of the virulence genes was evaluated by qPCR analysis and in silico assessment was accomplished to check the binding ability of the compounds with the autoinducer molecule.

KEY FINDINGS

The QS inhibitors isolated and identified showed a remarkable ability in reducing the production of elastase, pyocyanin, swarming motility and biofilm formation in P. aeruginosa. In the presence of the characterized compounds, the expression of virulence genes of P. aeruginosa was significantly reduced. Toxicity studies in zebrafish model indicated no effects on development and organogenesis at a concentration below 100 mg/l. Further, in silico analysis demonstrated the binding efficiency of the anti-QS compounds to AHL molecules, thus proving the QS quenching ability of the isolated compounds.

SIGNIFICANCE

To the best of our knowledge this is the first report of isolation of anti-QS compounds from C. verum leaves against P. aeruginosa. The identified compounds qualify as potential QS antagonists. Further studies on these compounds can pave way for an effective and attractive anti-pathogenic therapy, to overcome the emergence of antibiotic resistance in bacteria.

Biological Evaluation and Molecular Docking Studies of Benzalkonium Ibuprofenate

The third-generation ionic liquids (ILs), which are being used to produce double active pharmaceutical ingredients (d-APIs) with tunable biological activity along with novel performance, enhancement, and delivery options, have been revolutionizing the area of drug discovery since the past few decades. Herein we report the in vitro antibacterial and anti-inflammatory activity of benzalkonium ibuprofenate (BaIb) that are being used as in-house d-API, with a particular focus on its interaction with respective protein target through molecular docking study. The evaluation of the biological activity of BaIb with the antibacterial and anti-inflammatory target at the molecular level revealed that the synthesized BaIb could be designed as a potential double active drug since it retains the antibacterial and anti-inflammatory activity of its parent drugs, benzalkonium chloride (BaCl) and sodium ibuprofenate (NaIb), respectively.

In-silico strategies for identification of potent inhibitor for MMP-1 to prevent metastasis of breast cancer

Matrix Metalloproteinase-1 (MMP-1) has been often upregulated in advanced breast cancers, known to participate in ECM degradation, migration, invasion, thus leading to metastasis. Due to these effects, the condition is often reported to inversely correlate with survival in advanced breast cancers. In the present study, in-silico method was adopted based on selective non zinc binding inhibitors of MMP-1. ADME properties were predicted for PASS filtered compounds and docking calculations were performed using Glide XP and IFD protocols of Schrodinger program. We identified six ligands as potent inhibitors and validated by observing structures and the interactions of MMP-1. The identified hits were validated using molecular dynamics simulation studies. Electronic structure analysis was performed for two top hit compounds myricetin and quercetin using density function theory (DFT) at B3LYP/6-31**G level to understand their molecular reactivity. Finally, one compound myricetin has emerged as the structurally stable compound with -7.801 kcal/mol and reasonable pose inside the binding site. Molecular dynamics results indicated that myricetin forms a stable interaction with the key amino acid residues such as Glu209, Glu219, Tyr240 and Pro238. In addition, it did not form any binding with the catalytic zinc at its active site. The interaction pattern of myricetin at its substrate binding site exhibited to be potent MMP-1 inhibitor. DFT study also showed that it has more potent inhibitory effect and solubility. These factors altogether show that myricetin could be considered as the best among the compounds evaluated in inhibiting MMP-1 thereby preventing metastasis of breast cancer. Communicated by Ramaswamy H. Sarma.

Significance of Ligand-Anchored Polymers for Drug Targeting in the Treatment of Colonic Disorders

Treatment of a variety of bowel diseases like Crohn's disease, ulcerative colitis, colonic cancers, colonic pathologies, and systemic delivery of drugs at the target sites can be done with the help of targeted drug delivery technique. Conventional colon specific drug delivery systems lack specificity and release significant amount of drug prior reaching the target site. Hence, efficient drug delivery system that ensures effective release of the drug at the colon is still a sought after research arena. Ligand anchored therapy is a strong and effective approach to execute drug delivery in selective target cells, for both, diagnostic, as well as therapeutic reasons. Compared to the regular drugs, such ligand anchored therapy provides added benefit of minimum toxicity and few side effects. Discovery of overexpressed receptors on diseased cells, as compared to healthy cells led to the emergence of active drug targeting. Further, drug resistance constitutes one of the major reasons of the failure of chemotherapy and presents a major obstacle for the effective treatment. The reason behind drug resistance is exposure of pathological cells/pathogens to sub-therapeutic levels of drugs due lack of specificity of therapeutics. Active targeting, specifically taken up by the target cells, can warrant exposure of pathological cells/pathogens to high drug load at the target and sparing non-target cells hence minimal damage to normal cells and least chance of drug resistance. Many ligands like antibodies, aptamers, peptides, folate, and transferrin have been discovered in the past few years. The design of nanocarriers can be incorporated with many different functions which enables functions like imaging and triggered intracellular drug release. The present review article focuses on advances in ligand anchored therapy and its significance on the progress of targeted nanocarriers. It will also establish novel concepts like multi-targeting and multi-functional nanocarriers for the treatment of colonic disorders.

Exploring structural requirements of isoform selective histone deacetylase inhibitors: a comparative in silico study

Histone deacetylases (HDACs) are a widely popular class of epigenetic regulators, second only in importance to DNA methyltransferases. They are responsible for deacetylating the lysine residues of a wide range of proteins, both nuclear and cytoplasmic. Therefore, deregulated HDAC activity is implicated in disruption of important biological functions leading to cancerous, neuropathological, infectious and inflammatory diseased states. The current therapeutic strategies aimed at combating HDAC related pathologies consist of pan HDAC inhibitors that target multiple HDAC isoforms. Many side-effects of such therapeutics have been reported due to off-target effects. Hence, efforts need to be focused towards developing therapeutics targeting single isoforms. This work aims at recognizing structural features, both of receptors and inhibitors, that would help achieve selective inhibition of HDAC isoforms. Protein alignment studies have been carried out to define the receptor structure differences that can be exploited for this purpose. Binding modes of highly isoform selective inhibitors have been established through molecular docking studies to characterize the receptor-ligand interactions responsible for selective inhibition. This information is represented with the help of pharmacophore models.

Prospects for Discovering the Secondary Metabolites of Cordyceps Sensu Lato by the Integrated Strategy

BACKGROUND

Some species of Cordyceps sensu lato are famous Chinese herbs with significant biological activities, often used as edible food and traditional medicine in China. Cordyceps represents the largest entomopathogenic group of fungi, including 40 genera and 1339 species in three families and incertae sedis of Hypocreales.

OBJECTIVE

Most of the Cordyceps-derivatives have been approved clinically for the treatment of various diseases such as diabetes, cancers, inflammation, cardiovascular, renal and neurological disorders and are used worldwide as supplements and herbal drugs, but there is still need for highly efficient Cordyceps-derived drugs for fatal diseases with approval of the U.S. Food and Drug Administration.

METHODS

Computer-aided drug design concepts could improve the discovery of putative Cordyceps- derived medicine within less time and low budget. The integration of computer-aided drug design methods with experimental validation has contributed to the successful discovery of novel drugs.

RESULTS

This review focused on modern taxonomy, active metabolites, and modern drug design techniques that could accelerate conventional drug design and discovery of Cordyceps s. l. Successful application of computer-aided drug design methods in Cordyceps research has been discussed.

CONCLUSION

It has been concluded that computer-aided drug design techniques could influence the multiple target-focused drug design, because each metabolite of Cordyceps has shown significant activities for the various diseases with very few or no side effects.

Comparative Assessment of Seven Docking Programs on a Nonredundant Metalloprotein Subset of the PDBbind Refined

Extensive usage of molecular docking for computer-aided drug discovery resulted in development of numerous programs with versatile scoring and posing algorithms. Selection of the docking program among these vast number of options is central to the outcome of drug discovery. To this end, comparative assessment studies of docking offer valuable insights into the selection of the optimal tool. Despite the availability of various docking assessment studies, the performance difference of docking programs has not been well addressed on metalloproteins which comprise a substantial portion of the human proteome and have been increasingly targeted for treatment of a wide variety of diseases. This study reports comparative assessment of seven docking programs on a diverse metalloprotein set which was compiled for this study. The refined set of the PDBbind (2017) was screened to gather 710 complexes with metal ion(s) closely located to the ligands (<4 Å). The redundancy was eliminated by clustering and overall 213 complexes were compiled as the nonredundant metalloprotein subset of the PDBbind refined. The scoring, ranking, and posing powers of seven noncommercial docking programs, namely, AutoDock4, AutoDock4_Zn, AutoDock Vina, Quick Vina 2, LeDock, PLANTS, and UCSF DOCK6, were comprehensively evaluated on this nonredundant set. Results indicated that PLANTS (80%) followed by LeDock (77%), QVina (76%), and Vina (73%) had the most accurate posing algorithms while AutoDock4 (48%) and DOCK6 (56%) were the least successful in posing. Contrary to their moderate-to-high level of posing success, none of the programs was successful in scoring or ranking of the binding affinities (r² ≈ 0). Screening power was further evaluated by using active-decoy ligand sets for a large compilation of metalloprotein targets. PLANTS stood out among other programs to be able to enrich the active ligand for every target, underscoring its robustness for screening of metalloprotein inhibitors. This study provides useful information for drug discovery studies targeting metalloproteins.

Repurposing Drugs Based on Evolutionary Relationships Between Targets of Approved Drugs and Proteins of Interest

Drug repurposing has garnered much interest as an effective method for drug development among biopharmaceutical companies. The availability of information on complete sequences of genomes and their associated biological data, genotype-phenotype-disease relationships, and properties of small molecules offers opportunities to explore the repurpose-able potential of existing pharmacopoeia. This method gains further importance, especially, in the context of development of drugs against infectious diseases, some of which pose serious complications due to emergence of drug-resistant pathogens. In this article, we describe computational means to achieve potential repurpose-able drug candidates that may be used against infectious diseases by exploring evolutionary relationships between established targets of FDA-approved drugs and proteins of pathogen of interest.

Design, 3D QSAR modeling and docking of TGF-β type I inhibitors to target cancer

Transforming growth factor-β (TGF-β) family members plays a vital role in regulating hormonal function, bone formation, tissue remodeling, and erythropoiesis, cell growth and apoptosis. TGF-β super-family members mediate signal transduction via serine/threonine kinase receptors located on the cell membrane. Variation in expression of the TGF-β type I and II receptors in the cancer cells compromise its tumor suppressor activities which direct to oncogenic functions. The present study was aimed to screen the potent TGF-β type I inhibitors through atom based 3D-QSAR and pharmacophore modelling. For this purpose, we have chosen known TGF-β type I inhibitors from the binding database. The PHASE module of Schrodinger identified the best Pharmacophore model which includes three hydrogen bond acceptors (A), one hydrophobic region (H), and one ring (R) as the structural features. The top pharmacophore model AAAHR.27 was chosen with the R² value of 0.94 and validated externally using molecules of the test set. Moreover the AAAHR.27 model underwent virtual screening using the molecules from the NCI, ZINC and Maybridge database. The screened molecules were further filtered using molecular docking and ADME properties prediction. Additionally, the 7 lead molecules were compared with a newly identified compound "SB431542" (well known TGF-β type I receptor inhibitor) and Galunisertib, a small molecule inhibitor of TGF-β type I, under clinical development (phase II trials) using the docking score and other binding properties. Also a top scored screened molecule from our study has been compared and confirmed using molecular dynamic simulation studies. By this way, we have obtained 7 distinct drug-like TGF-β type I inhibitors which can be beneficial in suppressing cancers reported with up-regulation of TGF-β type I. This result highlights the guidelines for designing molecules with TGF-β Type I inhibitory properties.

Improving Prediction Accuracy of Binding Free Energies and Poses of HIV Integrase Complexes Using the Binding Energy Distribution Analysis Method with Flattening Potentials

To accelerate conformation sampling of slow dynamics from receptor or ligand, we introduced flattening potentials on selected bonded and nonbonded intramolecular interactions to the binding energy distribution analysis method (BEDAM) for calculating absolute binding free energies of protein-ligand complexes using an implicit solvent model and implemented flattening BEDAM using the asynchronous replica exchange (AsyncRE) framework for performing large scale replica exchange molecular dynamics (REMD) simulations. The advantage of using the flattening feature to reduce high energy barriers was exhibited first by the p-xylene-T4 lysozyme complex, where the intramolecular interactions of a protein side chain on the binding site were flattened to accelerate the conformational transition of the side chain from the trans to the gauche state when the p-xylene ligand is present in the binding site. Much more extensive flattening BEDAM simulations were performed for 53 experimental binders and 248 nonbinders of HIV-1 integrase which formed the SAMPL4 challenge, with the total simulation time of 24.3 μs. We demonstrated that the flattening BEDAM simulations not only substantially increase the number of true positives (and reduce false negatives) but also improve the prediction accuracy of binding poses of experimental binders. Furthermore, the values of area under the curve (AUC) of receiver operating characteristic (ROC) and the enrichment factors at 20% cutoff calculated from the flattening BEDAM simulations were improved significantly in comparison with that of simulations without flattening as we previously reported for the whole SAMPL4 database. Detailed analysis found that the improved ability to discriminate the binding free energies between the binders and nonbinders is due to the fact that the flattening simulations reduce the reorganization free energy penalties of binders and decrease the overlap of binding free energy distributions of binders relative to that of nonbinders. This happens because the conformational ensemble distributions for both the ligand and protein in solution match those at the fully coupled (complex) state more closely when the systems are more fully sampled after the flattening potentials are applied to the intermediate states.

A Component Formula of Chinese Medicine for Hypercholesterolemia Based on Virtual Screening and Biology Network

Hypercholesterolemia is a risk factor to atherosclerosis and coronary heart disease II. The abnormal rise of cholesterol in plasma is the main symptom. Cholesterol synthesis pathway is an important pathway of the origin of cholesterol, which is an essential pathway for the therapy of hypercholesterolemia. The 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMG-CoA reductase), squalene synthase (SQS), and sterol regulatory element binding protein-2 (SREBP-2) are closely connected with the synthesis of cholesterol. The inhibition of these targets can reduce the cholesterol in plasma. This study aimed to build a component formula including three Traditional Chinese Medicines (TCM) components with the inhibition activity of these targets by using virtual screening and biological network. Structure-based pharmacophore models of HMG-CoA reductase and SQS and ligand-based pharmacophore model of SREBP-2 were constructed to screen the Traditional Chinese Medicine Database (TCMD). Molecular docking was used for further screening of components of HMG-CoA reductase and SQS. Then, metabolic network was constructed to elucidate the comprehensive interaction of three targets for lipid metabolism. Finally, three potential active compounds were obtained, which are poncimarin, hexahydrocurcumin, and forsythoside C. The source plants of the compounds were also taken into account, which should have known action of lowering hyperlipidemia. The lipid-lowering effect of hexahydrocurcumin was verified by experiment in vitro. The components that originated from TCMs with lipid-lowering efficacy made up a formula with a synergistic effect through the computer aid drug design methods. The research provides a fast and efficient method to build TCM component formula and it may inspire the study of the explanation of TCM formula mechanism.

Assessment of a Single Decoupling Alchemical Approach for the Calculation of the Absolute Binding Free Energies of Protein-Peptide Complexes

The computational modeling of peptide inhibitors to target protein-protein binding interfaces is growing in interest as these are often too large, too shallow, and too feature-less for conventional small molecule compounds. Here, we present a rare successful application of an alchemical binding free energy method for the calculation of converged absolute binding free energies of a series of protein-peptide complexes. Specifically, we report the binding free energies of a series of cyclic peptides derived from the LEDGF/p75 protein to the integrase receptor of the HIV1 virus. The simulations recapitulate the effect of mutations relative to the wild-type binding motif of LEDGF/p75, providing structural, energetic and dynamical interpretations of the observed trends. The equilibration and convergence of the calculations are carefully analyzed. Convergence is aided by the adoption of a single-decoupling alchemical approach with implicit solvation, which circumvents the convergence difficulties of conventional double-decoupling protocols. We hereby present the single-decoupling methodology and critically evaluate its advantages and limitations. We also discuss some of the challenges and potential pitfalls of binding free energy calculations for complex molecular systems which have generally limited their applicability to the quantitative study of protein-peptide binding equilibria.

In Silico HTS and Structure Based Optimization of Indazole-Derived ULK1 Inhibitors

We present the outcome of an in silico high throughput screen (HTS) and optimization of a small molecule Unc-51-Like Kinase 1 (ULK1) inhibitor hit, SR-17398, with an indazole core. Docking studies guided design efforts that led to inhibitors with increased activity vs ULK1 (IC₅₀ < 50 nM). The most advanced molecules in this inhibitor series (3a and 3g) hold promise for further development into selective ULK1 molecular probes to interrogate the biology of ULK1 and to assess whether selectively targeting autophagy is an effective anticancer strategy.

Combinatorial In Silico Strategy towards Identifying Potential Hotspots during Inhibition of Structurally Identical HDAC1 and HDAC2 Enzymes for Effective Chemotherapy against Neurological Disorders

Histone deacetylases (HDACs) regulate epigenetic gene expression programs by modulating chromatin architecture and are required for neuronal development. Dysregulation of HDACs and aberrant chromatin acetylation homeostasis have been implicated in various diseases ranging from cancer to neurodegenerative disorders. Histone deacetylase inhibitors (HDACi), the small molecules interfering HDACs have shown enhanced acetylation of the genome and are gaining great attention as potent drugs for treating cancer and neurodegeneration. HDAC2 overexpression has implications in decreasing dendrite spine density, synaptic plasticity and in triggering neurodegenerative signaling. Pharmacological intervention against HDAC2 though promising also targets neuroprotective HDAC1 due to high sequence identity (94%) with former in catalytic domain, culminating in debilitating off-target effects and creating hindrance in the defined intervention. This emphasizes the need of designing HDAC2-selective inhibitors to overcome these vicious effects and for escalating the therapeutic efficacy. Here we report a top-down combinatorial in silico approach for identifying the structural variants that are substantial for interactions against HDAC1 and HDAC2 enzymes. We used extra-precision (XP)-molecular docking, Molecular Mechanics Generalized Born Surface Area (MMGBSA) for predicting affinity of inhibitors against the HDAC1 and HDAC2 enzymes. Importantly, we employed a novel in silico strategy of coupling the state-of-the-art molecular dynamics simulation (MDS) to energetically-optimized structure based pharmacophores (e-Pharmacophores) method via MDS trajectory clustering for hypothesizing the e-Pharmacophore models. Further, we performed e-Pharmacophores based virtual screening against phase database containing millions of compounds. We validated the data by performing the molecular docking and MM-GBSA studies for the selected hits among the retrieved ones. Our studies attributed inhibitor potency to the ability of forming multiple interactions and infirm potency to least interactions. Moreover, our studies delineated that a single HDAC inhibitor portrays differential features against HDAC1 and HDAC2 enzymes. The high affinity and selective HDAC2 inhibitors retrieved through e-Pharmacophores based virtual screening will play a critical role in ameliorating neurodegenerative signaling without hampering the neuroprotective isoform (HDAC1).

Best Practices of Computer-Aided Drug Discovery: Lessons Learned from the Development of a Preclinical Candidate for Prostate Cancer with a New Mechanism of Action

Small-molecule drug design is a complex and iterative decision-making process relying on pre-existing knowledge and driven by experimental data. Low-molecular-weight chemicals represent an attractive therapeutic option, as they are readily accessible to organic synthesis and can easily be characterized.1 Their potency as well as pharmacokinetic and pharmacodynamic properties can be systematically and rationally investigated and ultimately optimized via expert science behind medicinal chemistry and methods of computer-aided drug design (CADD). In recent years, significant advances in molecular modeling techniques have afforded a variety of tools to effectively identify potential binding pockets on prospective targets, to map key interactions between ligands and their binding sites, to construct and assess energetics of the resulting complexes, to predict ADMET properties of candidate compounds, and to systematically analyze experimental and computational data to derive meaningful structure-activity relationships leading to the creation of a drug candidate. This Perspective describes a real case of a drug discovery campaign accomplished in a relatively short time with limited resources. The study integrated an arsenal of available molecular modeling techniques with an array of experimental tools to successfully develop a novel class of potent and selective androgen receptor inhibitors with a novel mode of action. It resulted in the largest academic licensing deal in Canadian history, totaling $142M. This project exemplifies the importance of team science, an integrative approach to drug discovery, and the use of best practices in CADD. We posit that the lessons learned and best practices for executing an effective CADD project can be applied, with similar success, to many drug discovery projects in both academia and industry.

Computational design of ligand-binding membrane receptors with high selectivity

Accurate modeling and design of protein-ligand interactions have broad applications in cell, synthetic biology and drug discovery but remain challenging without experimental protein structures. Here we developed an integrated protein homology modeling-ligand docking-protein design approach that reconstructs protein-ligand binding sites from homolog protein structures in the presence of protein-bound ligand poses to capture conformational selection and induced fit modes of ligand binding. In structure modeling tests, we blindly predicted near-atomic accuracy ligand conformations bound to G protein-coupled receptors (GPCRs) that were rarely identified by traditional approaches. We also quantitatively predicted the binding selectivity of diverse ligands to structurally-uncharacterized GPCRs. We then applied the technique to design functional human dopamine receptors with novel ligand binding selectivity. Most blindly predicted ligand binding specificities closely agreed with experimental validations. Our method should prove useful in ligand discovery approaches and in reprogramming the ligand binding profile of membrane receptors that remain difficult to crystallize.

Rational Drug Design Rational Drug Design

For the past three decades rationale drug design (RDD) has been developing as an innovative, rapid and successful way to discover new drug candidates. Many strategies have been followed and several targets with diverse structures and different biological roles have been investigated. Despite the variety of computational tools available, one can broadly divide them into two major classes that can be adopted either separately or in combination. The first class involves structure-based drug design, when the target's 3-dimensional structure is available or it can be computationally generated using homology modeling. On the other hand, when only a set of active molecules is available, and the structure of the target is unknown, ligand-based drug design tools are usually used. This review describes some recent advances in rational drug design, summarizes a number of their practical applications, and discusses both the advantages and shortcomings of the various techniques used.

Computational methods in drug discovery

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

Synthetic α-Hydroxytropolones as Inhibitors of HIV Reverse Transcriptase Ribonuclease H Activity

HIV Reverse Transcriptase-associated ribonuclease H activity is a promising enzymatic target for drug development that has not been successfully targeted in the clinic. While the α-hydroxytropolone-containing natural products β-thujaplicinol and manicol have emerged as some of the most potent leads described to date, structure-function studies have been limited to the natural products and semi-synthetic derivatives of manicol. Thus, a library of α-hydroxytropolones synthesized through a convenient oxidopyrylium cycloaddition/ring-opening sequence have been tested in in vitro and cell-based assays, and have been analyzed using computational support. These studies reveal new synthetic α-hydroxytropolones that, unlike the natural product leads they are derived from, demonstrate protective antiviral activity in cellular assays.

Recognizing drug targets using evolutionary information: implications for repurposing FDA-approved drugs against Mycobacterium tuberculosis H37Rv

Drug repurposing to explore target space has been gaining pace over the past decade with the upsurge in the use of systematic approaches for computational drug discovery. Such a cost and time-saving approach gains immense importance for pathogens of special interest, such as Mycobacterium tuberculosis H37Rv. We report a comprehensive approach to repurpose drugs, based on the exploration of evolutionary relationships inferred from the comparative sequence and structural analyses between targets of FDA-approved drugs and the proteins of M. tuberculosis. This approach has facilitated the identification of several polypharmacological drugs that could potentially target unexploited M. tuberculosis proteins. A total of 130 FDA-approved drugs, originally intended against other diseases, could be repurposed against 78 potential targets in M. tuberculosis. Additionally, we have also made an attempt to augment the chemical space by recognizing compounds structurally similar to FDA-approved drugs. For three of the attractive cases we have investigated the probable binding modes of the drugs in their corresponding M. tuberculosis targets by means of structural modelling. Such prospective targets and small molecules could be prioritized for experimental endeavours, and could significantly influence drug-discovery and drug-development programmes for tuberculosis.

Recent Advances in Computational Models for the Study of Protein-Peptide Interactions

We review computational models and software tools in current use for the study of protein-peptide interactions. Peptides and peptide derivatives are growing in interest as therapeutic agents to target protein-protein interactions. Protein-protein interactions are pervasive in biological systems and are responsible for the regulation of critical functions within the cell. Mutations or dysregulation of expression can alter the network of interactions among proteins and cause diseases such as cancer. Protein-protein binding interfaces, which are often large, shallow, and relatively feature-less, are difficult to target with small-molecule inhibitors. Peptide derivatives based on the binding motifs present in the target protein complex are increasingly drawing interest as superior alternatives to conventional small-molecule inhibitors. However, the design of peptide-based inhibitors also presents novel challenges. Peptides are more complex and more flexible than standard medicinal compounds. They also tend to form more extended and more complex interactions with their protein targets. Computational modeling is increasingly being employed to supplement synthetic and biochemical work to offer guidance and energetic and structural insights. In this review, we discuss recent in silico structure-based and physics-based approaches currently employed to model protein-peptide interactions with a few examples of their applications.

Non-covalent interactions involving halogenated derivatives of capecitabine and thymidylate synthase: a computational approach

Capecitabine, a fluoropyrimidine prodrug, has been a frequently chosen ligand for the last one and half decades to inhibit thymidylate synthase (TYMS) for treatment of colorectal cancer. TYMS is a key enzyme for de novo synthesis of deoxythymidine monophosphate and subsequent synthesis of DNA. Recent years have also seen the trait of modifying ligands using halogens and trifluoromethyl (–CF₃) group to ensure enhanced drug performance. In this study, in silico modification of capecitabine with Cl, Br, I atoms and –CF₃ group has been performed. Density functional theory has been employed to optimize the drug molecules and elucidate their thermodynamic and electrical properties such as Gibbs free energy, enthalpy, electronic energy, dipole moment and frontier orbital features (HOMO–LUMO gap, hardness and softness). Flexible and rigid molecular docking have been implemented between drugs and the receptor TYMS. Both inter- and intra-molecular non-covalent interactions involving the amino acid residues of TYMS and the drug molecules are explored in details. The drugs were superimposed on the resolved crystal structure (at 1.9 Å) of ZD1694/dUMP/TYMS system to shed light on similarity of the binding of capecitabine, and its modifiers, to that of ZD1694. Together, these results may provide more insights prior to synthesizing halogen-directed derivatives of capecitabine for anticancer treatment.

Identification of Glucose-Binding Pockets in Human Serum Albumin Using Support Vector Machine and Molecular Dynamics Simulations

Human Serum Albumin (HSA) has been suggested to be an alternate biomarker to the existing Hemoglobin-A1c (HbA1c) marker for glycemic monitoring. Development and usage of HSA as an alternate biomarker requires the identification of glycation sites, or equivalently, glucose-binding pockets. In this work, we combine molecular dynamics simulations of HSA and the state-of-art machine learning method Support Vector Machine (SVM) to predict glucose-binding pockets in HSA. SVM uses the three dimensional arrangement of atoms and their chemical properties to predict glucose-binding ability of a pocket. Feature selection reveals that the arrangement of atoms and their chemical properties within the first 4Å from the centroid of the pocket play an important role in the binding of glucose. With a 10-fold cross validation accuracy of 84 percent, our SVM model reveals seven new potential glucose-binding sites in HSA of which two are exposed only during the dynamics of HSA. The predictions are further corroborated using docking studies. These findings can complement studies directed towards the development of HSA as an alternate biomarker for glycemic monitoring.

Interaction model of steviol glycosides from Stevia rebaudiana (Bertoni) with sweet taste receptors: A computational approach

Docking studies were performed on natural sweeteners from Stevia rebaudiana by constructing homology models of T1R2 and T1R3 subunits of human sweet taste receptors. Ramachandran plot, PROCHECK results and ERRAT overall quality factor were used to validate the quality of models. Furthermore, docking results of steviol glycosides (SG's) were correlated significantly with data available in the literature which enabled to predict the exact sweetness rank order of SG's. The binding pattern indicated that Asn 44, Ans 52, Ala 345, Pro 343, Ile 352, Gly 346, Gly 47, Ala 354, Ser 336, Thr 326 and Ser 329 are the main interacting amino acid residues in case of T1R2 and Arg 56, Glu 105, Asp 215, Asp 216, Glu 148, Asp 258, Lys 255, Ser 104, Glu 217, Leu 51, Arg 52 for T1R3, respectively. Amino acids interact with SG's mainly by forming hydrogen bonds with the hydroxyl group of glucose moieties. Significant variation in docked poses of all the SG's were found. In this study, we have proposed the mechanism of the sweetness of the SG's in the form of multiple point stimulation model by considering the diverse binding patterns of various SG's, as well as their structural features. It will give further insight in understanding the differences in the quality of taste and will be used to improve the taste of SG's using semi-synthetic approaches.

BEDAM binding free energy predictions for the SAMPL4 octa-acid host challenge

The binding energy distribution analysis method (BEDAM) protocol has been employed as part of the SAMPL4 blind challenge to predict the binding free energies of a set of octa-acid host-guest complexes. The resulting predictions were consistently judged as some of the most accurate predictions in this category of the SAMPL4 challenge in terms of quantitative accuracy and statistical correlation relative to the experimental values, which were not known at the time the predictions were made. The work has been conducted as part of a hands-on graduate class laboratory session. Collectively the students, aided by automated setup and analysis tools, performed the bulk of the calculations and the numerical and structural analysis. The success of the experiment confirms the reliability of the BEDAM methodology and it shows that physics-based atomistic binding free energy estimation models, when properly streamlined and automated, can be successfully employed by non-specialists.

MoDock: A multi-objective strategy improves the accuracy for molecular docking

BACKGROUND

As a main method of structure-based virtual screening, molecular docking is the most widely used in practice. However, the non-ideal efficacy of scoring functions is thought as the biggest barrier which hinders the improvement of the molecular docking method.

RESULTS

A new multi-objective strategy for molecular docking, named as MoDock, is presented to further improve the docking accuracy with available scoring functions. Instead of simple combination of multiple objectives with fixed weight factors, an aggregate function is adopted to approximate the real solution of the original multi-objective and multi-constraint problem, which will simultaneously smooth the energy surface of the combined scoring functions. Then, method of centers and genetic algorithm are used to find the optimal solution. Tests of MoDock against the GOLD test data set reveal the multi-objective strategy improves the docking accuracy over the individual scoring functions. Meanwhile, a 70% ratio of the good docking solutions with the RMSD value below 1.0 Å outperforms other 6 commonly used docking programs, even with a flexible receptor docking program included.

CONCLUSIONS

The results show MoDock is an effective strategy to overcome the deviations brought by single scoring function, and improves the prediction power of molecular docking.

Comparing sixteen scoring functions for predicting biological activities of ligands for protein targets

Accurately predicting relative binding affinities and biological potencies for ligands that interact with proteins remains a significant challenge for computational chemists. Most evaluations of docking and scoring algorithms have focused on enhancing ligand affinity for a protein by optimizing docking poses and enrichment factors during virtual screening. However, there is still relatively limited information on the accuracy of commercially available docking and scoring software programs for correctly predicting binding affinities and biological activities of structurally related inhibitors of different enzyme classes. Presented here is a comparative evaluation of eight molecular docking programs (Autodock Vina, Fitted, FlexX, Fred, Glide, GOLD, LibDock, MolDock) using sixteen docking and scoring functions to predict the rank-order activity of different ligand series for six pharmacologically important protein and enzyme targets (Factor Xa, Cdk2 kinase, Aurora A kinase, COX-2, pla2g2a, β Estrogen receptor). Use of Fitted gave an excellent correlation (Pearson 0.86, Spearman 0.91) between predicted and experimental binding only for Cdk2 kinase inhibitors. FlexX and GOLDScore produced good correlations (Pearson>0.6) for hydrophilic targets such as Factor Xa, Cdk2 kinase and Aurora A kinase. By contrast, pla2g2a and COX-2 emerged as difficult targets for scoring functions to predict ligand activities. Although possessing a high hydrophobicity in its binding site, β Estrogen receptor produced reasonable correlations using LibDock (Pearson 0.75, Spearman 0.68). These findings can assist medicinal chemists to better match scoring functions with ligand-target systems for hit-to-lead optimization using computer-aided drug design approaches.

Benchmarking methods and data sets for ligand enrichment assessment in virtual screening

Retrospective small-scale virtual screening (VS) based on benchmarking data sets has been widely used to estimate ligand enrichments of VS approaches in the prospective (i.e. real-world) efforts. However, the intrinsic differences of benchmarking sets to the real screening chemical libraries can cause biased assessment. Herein, we summarize the history of benchmarking methods as well as data sets and highlight three main types of biases found in benchmarking sets, i.e. "analogue bias", "artificial enrichment" and "false negative". In addition, we introduce our recent algorithm to build maximum-unbiased benchmarking sets applicable to both ligand-based and structure-based VS approaches, and its implementations to three important human histone deacetylases (HDACs) isoforms, i.e. HDAC1, HDAC6 and HDAC8. The leave-one-out cross-validation (LOO CV) demonstrates that the benchmarking sets built by our algorithm are maximum-unbiased as measured by property matching, ROC curves and AUCs.