Binding site characteristics in structure-based virtual screening: evaluation of current docking tools

Design and synthesis of photoaffinity-based probes for labeling β-glucuronidase

β-Glucuronidase (GUSB) plays an important role in human physiological and pathological activities. The activity level of GUSB is closely related to human health and diseases. It is imperative to detect the activity of GUSB for related disease diagnosis and treatment. However, exactly evaluating the activity of GUSB in complicated biological system remains a challenge. In this study, we developed photoaffinity-based probes (AfBPs) equipped with photosensitive benzophenone group for labeling active GUSB. Through molecule docking, we predicted the binding model of the AfBPs and GUSB, and the obtained results suggested thermodynamically favorable binding. The AfBPs indicated high efficiency and showed dose-/time-dependent labeling of Escherichia coli (E. coli) GUSB. The application of AfBPs toward GUSB provides a powerful tool to study the activity of target enzymes and contributes to huge potential of enzyme inhibitor discovery and biomedical diagnostics.

Prioritizing Small Sets of Molecules for Synthesis through in-silico Tools: A Comparison of Common Ranking Methods

Prioritizing molecules for synthesis is a key role of computational methods within medicinal chemistry. Multiple tools exist for ranking molecules, from the cheap and popular molecular docking methods to more computationally expensive molecular-dynamics (MD)-based methods. It is often questioned whether the accuracy of the more rigorous methods justifies the higher computational cost and associated calculation time. Here, we compared the performance on ranking the binding of small molecules for seven scoring functions from five docking programs, one end-point method (MM/GBSA), and two MD-based free energy methods (PMX, FEP+). We investigated 16 pharmaceutically relevant targets with a total of 423 known binders. The performance of docking methods for ligand ranking was strongly system dependent. We observed that MD-based methods predominantly outperformed docking algorithms and MM/GBSA calculations. Based on our results, we recommend the application of MD-based free energy methods for prioritization of molecules for synthesis in lead optimization, whenever feasible.

Computational Approaches to Designing Antiviral Drugs against COVID-19: A Comprehensive Review

The global impact of the COVID-19 pandemic caused by SARS-CoV-2 necessitates innovative strategies for the rapid development of effective treatments. Computational methodologies, such as molecular modelling, molecular dynamics simulations, and artificial intelligence, have emerged as indispensable tools in the drug discovery process. This review aimed to provide a comprehensive overview of these computational approaches and their application in the design of antiviral agents for COVID-19. Starting with an examination of ligand-based and structure-based drug discovery, the review has delved into the intricate ways through which molecular modelling can accelerate the identification of potential therapies. Additionally, the investigation extends to phytochemicals sourced from nature, which have shown promise as potential antiviral agents. Noteworthy compounds, including gallic acid, naringin, hesperidin, Tinospora cordifolia, curcumin, nimbin, azadironic acid, nimbionone, nimbionol, and nimocinol, have exhibited high affinity for COVID-19 Mpro and favourable binding energy profiles compared to current drugs. Although these compounds hold potential, their further validation through in vitro and in vivo experimentation is imperative. Throughout this exploration, the review has emphasized the pivotal role of computational biologists, bioinformaticians, and biotechnologists in driving rapid advancements in clinical research and therapeutic development. By combining state-of-the-art computational techniques with insights from structural and molecular biology, the search for potent antiviral agents has been accelerated. The collaboration between these disciplines holds immense promise in addressing the transmissibility and virulence of SARS-CoV-2.

Discovery of potential inhibitors for stat3: ligand based 3D pharmacophore, virtual screening, molecular docking, dynamic studies and in vitro evaluation

A large analysis of the signal transducer and activator of transcription (STAT3) in cancer is currently being carried out. It regulates gene expression, which is required for normal cellular functions such as differentiation, cell growth, proliferation, survival, maturation, and immunity. A ligand-based pharmacophore model was created using 3 D QSAR pharmacophore generation methodology in Discovery studio 4.1 clients to imagine structurally diverse novel chemical entities as STAT3 inhibitors with improved efficacy. Chemical properties of 48 different derivatives were included in the training package. Hypo1 was chosen as the query model for screening 1,45,000 drug-like molecules from the SPECS database, with these molecules subjected to the Lipinski rule of 5, Verber's rule, and SMART filtration. After filtration, the molecule was examined further using molecular docking analysis on the active site of STAT3. The binding interaction(s) and pharmacophore mapping were used to select the 19 possible inhibitory molecules. These 19 hits were then tested for toxicity using the TOPKAT software. In MD simulations and MM-PBSA calculations, the tested compound specs 28 provided the best results, suggesting that this ligand has the ability to inhibit more effectively. Based in-silico finding 19 compounds are subjected to in vitro anticancer activity against MDA-MB-231 and MCF-7 cell lines. Based on results compounds specs 11 and specs 13 shows significant activity compared to other compounds and these compounds were subjected to apoptosis assay. The tested compounds induced morphologic changes were dose and time dependent by which all the tested compound exhibits stronger anti-tumor effects.Communicated by Ramaswamy H. Sarma.

Application of Computational Biology and Artificial Intelligence in Drug Design

Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.

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.

Data Mining Meets Machine Learning: A Novel ANN-based Multi-Body Interaction Docking Scoring Function (MBI-Score) based on Utilizing Frequent Geometric and Chemical Patterns of Interfacial Atoms in Native Protein-Ligand Complexes

Accurate prediction of binding poses is crucial to structure-based drug design. We employ two powerful artificial intelligence (AI) approaches, data-mining and machine-learning, to design artificial neural network (ANN) based pose-scoring function. It is a simple machine-learning-based statistical function that employs frequent geometric and chemical patterns of interacting atoms at protein-ligand interfaces. The patterns are derived by mining interfaces of "native" protein-ligand complexes. Each interface is represented by a graph where nodes are atoms and edges connect protein-ligand interfacial atoms located within certain cutoff distance of each other. Applying frequent subgraph mining to these interfaces provides "native" frequent patterns of interacting atoms. Subsequently, given a pose for a protein-ligand complex of interest, the pose-scoring function (the information-processing unit or neuron) calculates the degree of matching between the interaction patterns present at the pose's interface and the native frequent patterns. The pose-scoring function takes into account the frequency of occurrence of the matching native patterns, the size of the match, and the degree of geometrical similarity between pose-specific and matching native frequent patterns. This novel "multi-body interaction" pose-scoring function (MBI-Score) was validated using two databases, PDBbind and Astex-85, and it outperformed seven commonly used commercial scoring functions. MBI-Score is available at www.khashanlab.org/mbi-score.

Potential activity of Linezolid against SARS-CoV-2 using electronic and molecular docking study

The crescent evolution of a global pandemic COVID-19 and its respiratory syndrome (SARS-Cov-2) has been a constant concern (Ghosh 2021; Khan et al. 2021; Alazmi and Motwalli 2020; Vargas et al. 2020). The absence of a proven and effective medication has compelled all the scientific community to search for a new drug. The use of known drugs is a faster way to develop new therapies. Molecular docking is a powerful tool (Gao et al. J Mol Model 10: 44-54, 2004; Singh et al. J Mol Model 18: 39-51, 2012; Schulz-Gasch and Stahl J Mol Model 9:47-57, 2003) to study the interaction of potential drugs with SARS-CoV-2, Alsalme et al. (2020) and Sanders et al. (2020) spike protein as a consequence the main goal of this article is to present the result of the study of an interaction between (R and S)-Linezolid with receptor-binding domain (RBD) of SARS-Cov-2 spike protein complexed with human Angiostensin-converting enzyme 2 (ACE2) (6vW1 - from PDB). The Linezolid enantiomers were optimized at B3LYP/6-311++G(2d,p) level of theory. Molecular docking of the system (S)-Linezolid⋯RBD⋯ACE2 and (R)-Linezolid⋯RBD⋯ACE2 was performed, the analysis was made using LigPlot+ and NCIplot software packages, to understand the intermolecular interactions. The UV-Vis and ECD of the complexes - (R and S)-Linezolid⋯RBD⋯ACE2 were performed in two layers with DFT/6-311++G(3df,2p) and DFT/6-31G(d), respectively. The results showed that only the (S)-Linezolid had a stable interaction with - 8.05 kcal.mol- 1, whereas all the R-enantiomeric configurations had positive values of binding energy. The (S)-Linezolid had the same interactions as in the (S)-Linezolid ⋯ Haluarcula morismortui Ribosomal system, where it is well-known the fact that the latter has biological activity. A specific interaction on the fluorine ring justified an attenuation on the ECD signal, in comparison to isolated species. Therefore, some biological activity of (S)-Linezolid with SARS-CoV-2 RBD was expected, indicated by the modification of its ECD signal and justified by a similar interaction in the S-Linezolid⋯Haluarcula marismortui Ribosomal system.

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.

Molecular Docking Study of Active Diazenyl Scaffolds as Inhibitors of Essential Targets Towards Antimicrobial Drug Discovery

BACKGROUND

The diazenyl compounds (-N=N- linkage) have been reported to have antimicrobial activity. In modern drug discovery, the drug-receptor interactions are generally explored by the molecular docking studies.

MATERIALS AND METHODS

Three categories of diazenyl scaffolds were screened for the docking studies to explore the binding mechanism of interaction with various microbial targets. The diazenyl Schiff bases (SBN-20, SBN-21, SBN-25, SBN-33, SBN-39, SBN-40 and SBN-42), naphthol pharmacophore based diazenyl Schiff bases (NS-2, NS-8, NS-12, NS-15, NS-21, and NS-23), morpholine based diazenyl chalcones (MD-6, MD-9, MD-14, MD-16, MD-20, and MD-21) were docked against various bacterial and fungal proteins in comparison with different standard drugs. Further, the drug likeliness and ADME properties of these molecules were predicted by QikProp module of the Schrodinger software.

RESULTS

Most of the derivatives had shown less docking scores and binding energies towards bacterial proteins, such as dihydropteroate synthase (PDB:2VEG), glucosamine-6-phosphate synthase (PDB:2VF5), dihydrofolate reductase (PDB:3SRW) in comparison with the standard drugs. The naphthol based diazenyl Schiff bases NS-21 and NS-23 were predicted to act on the cytochrome P450 sterol 14-alpha-demethylase (CYP51) (PDB:5FSA) involved in sterol biosynthesis, an essential target for antifungal drugs. The derivative MD-6, NS-2, NS-21, and NS-23 had shown high docking scores against bacterial DNA topoisomerase (PDB:3TTZ) in comparison with the standard drug ciprofloxacin. Further, most of the synthesized derivatives had shown drug like characters.

CONCLUSION

Hence, these compounds can be developed as novel antibacterial agents as potent DNA topoisomerase inhibitors and antifungal agents as CYP51 inhibitors.

Rational drug repurposing for cancer by inclusion of the unbiased molecular dynamics simulation in the structure-based virtual screening approach: Challenges and breakthroughs

Managing cancer is now one of the biggest concerns of health organizations. Many strategies have been developed in drug discovery pipelines to help rectify this problem and two of the best ones are drug repurposing and computational methods. The combination of these approaches can have immense impact on the course of drug discovery. In silico drug repurposing can significantly reduce the time, the cost and the effort of drug development. Computational methods such as structure-based drug design (SBDD) and virtual screening can predict the potentials of small molecule binders, such as drugs, for having favorable effect on a particular molecular target. However, the demand for accuracy and efficiency of SBDD requires more sophisticated and complicated approaches such as unbiased molecular dynamics (UMD) simulation that has been recently introduced. As a complementary strategy, the knowledge acquired from UMD simulations can increase the chance of finding the right candidates and the pipeline of its administration is introduced and discussed in this review. An elaboration of this pipeline is also made by detailing an example, the binding and unbinding pathways of dasatinib-c-Src kinase complex, which shows that how influential this method can be in rational drug repurposing in cancer treatment.

A machine learning-driven study indicates emodin improves cardiac hypertrophy by modulation of mitochondrial SIRT3 signaling

Cardiac hypertrophy (CH) is an enormous risk factor in the process of heart failure development, however, there is still lack of effective treatment for CH. Mitochondrial protection is an effective way against CH. Rheum palmatum L. (rhubarb) has been used to treat chronic heart diseases such as heart failure, especially to inhibit cardiac compensatory enlargement. The aim of this study was to explore the pharmacodynamic component of rhubarb and reveal its pharmacological effects and targets in the treatment of CH. Based on network pharmacology and machine learning approach, ingredients of rhubarb and targets for CH were extracted and surflex docking was conducted for obtaining the optimal ingredient-target combination(s) and emodin-SIRT3 was identified for further functional analysis. Transverse aortic constriction or isoproterenol induced CH mice and phenylephrine injured cardiomyocytes were used to verify the mitochondria protection effect and CH improvement of emodin in vivo and in vitro by modulation of mitochondrial SIRT3 signaling. The results showed that emodin could block agonist-induced and pressure overload-mediated CH. Emodin prevented mitochondrial dysfunction and its underlying mechanism was attributed to the activation of SIRT3, but the effect was not obvious with the presence of SIRT3 inhibitors (3-TYP)/SIRT3 siRNA. Furthermore, PGC-1ɑ was involved in the process of emodin regulating SIRT3 signaling pathway as an upstream target. Our findings clarified the main material basis and mechanism of rhubarb in the treatment of CH. Emodin, as the major ingredient of rhubarb, has therapeutic potential for CH through mitochondrial protection due to the modulation of SIRT3 signaling.

The fine art of preparing membrane transport proteins for biomolecular simulations: Concepts and practical considerations

Molecular dynamics (MD) simulations have developed into an invaluable tool in bimolecular research, due to the capability of the method in capturing molecular events and structural transitions that describe the function as well as the physiochemical properties of biomolecular systems. Due to the progressive development of more efficient algorithms, expansion of the available computational resources, as well as the emergence of more advanced methodologies, the scope of computational studies has increased vastly over time. We now have access to a multitude of online databases, software packages, larger molecular systems and novel ligands due to the phenomenon of emerging novel psychoactive substances (NPS). With so many advances in the field, it is understandable that novices will no doubt find it challenging setting up a protein-ligand system even before they run their first MD simulation. These initial steps, such as homology modelling, ligand docking, parameterization, protein preparation and membrane setup have become a fundamental part of the drug discovery pipeline, and many areas of biomolecular sciences benefit from the applications provided by these technologies. However, there still remains no standard on their usage. Therefore, our aim within this review is to provide a clear overview of a variety of concepts and methodologies to consider, providing a workflow for a case study of a membrane transport protein, the full-length human dopamine transporter (hDAT) in complex with different stimulants, where MD simulations have recently been applied successfully.

Comprehensive assessment of flexible-ligand docking algorithms: current effectiveness and challenges

Protein-ligand docking has been playing an important role in modern drug discovery. To model drug-target binding in real systems, a number of flexible-ligand docking algorithms with different sampling strategies and scoring methods have been subsequently developed over the past three decades, while rigid-ligand docking is still being used because of its compelling computational efficiency. Here, a comprehensive assessment has been conducted to investigate the effectiveness of flexible-ligand docking versus rigid-ligand docking for three representative docking algorithms (global optimization, incremental construction and multi-conformer docking) in virtual screening and pose prediction on the Directory of Useful Decoys. It was found that overall flexible-ligand docking did not achieve a statistically significant improvement in enrichments over rigid-ligand docking in virtual screening, but all docking programs significantly improved the success rates when considering ligand flexibility in pose prediction. The worse effectiveness of flexible-ligand docking in virtual screening than in pose prediction suggests that the challenges of current docking algorithms exist in ranking more than docking, although the use of flexible-ligand docking in virtual screening was justified by its better effectiveness for more flexible ligand in virtual screening. Challenges for scoring, including internal energy, charge polarization, entropy and flexibility, were investigated and discussed. An empirical way was also proposed to consider loss of ligand conformational entropy for virtual screening.

Inhibitory Reactivity of Capsaicin with α-Amylase and α-Glucosidase Related to Antidiabetes using Molecular Docking and Quantum Calculation Methods

This work aims to investigate the inhibitory activity of capsaicin, which is one of capsaicinoid compounds, on these enzymes using a molecular docking and quantum calculation. Acarbose, a commercial diabetes drug, was also investigated for comparison. The docking results revealed that acarbose yields better inhibition efficiency with binding free energy (ΔGbinding) of about -8.2 to -11.9 kcal/mol, and inhibition constant (Ki) of about 0.0002 to 0.4 µM, whereas capsaicin provided the ΔGbinding of -5.8 to -6.1 kcal/mol and Ki of 23.7 to 45.9 µM. The total binding energy (ΔEbinding) between each inhibitor and amino acids in active site of enzyme obtained from quantum calculation with MP2/6-31G(d,p) level is in agreement with the ΔGbinding, i.e. the ΔEbinding of acarbose was larger negative than that of capsaicin. The amino acids interacting with inhibitor as hydrogen bond mainly contribute to the total binding energy. Nevertheless, it could be concluded that capsaicinoids have high potential to be developed as an alternative drug for diabetes disease.

Homology Modeling and Protein Interaction Map of CHRNA7 Neurogenesis Protein

CHRNA7 is a neurodevelopmental protein involved in differentiation and neurogenesis, which is also named as nicotinic acetylcholine receptors, cholinergic receptor, nicotinic, alpha 7 (neuronal). The protein encoded by this gene forms a homo-oligomeric channel. It is a major component of brain nicotinic receptors displays that are blocked by and sensitive to alpha-bungarotoxin. Studies reports involvement of CHRNA7 protein in different neurological diseases. Non-availability of 3-dimensional (3D) structure leads the study toward structure 3D prediction along with its interaction analysis. The current paper is focused on the structure prediction through homology modeling of CHRNA7 along with binding site prediction using Schrödinger software suite. In continuation of the study, protein-protein interaction analysis is carried out by using string database. Tertiary structure along with binding sites was obtained, and visualized CHRAN7 protein have interaction with CHRNA protein family along with JAK2, AKT1, PICK1 protein that are involved in neurological disease. Structure formation analysis is an important aspect of proteomics studies. Hence, this predicted structure can be used for further advance studies and drug designing. Protein interaction analysis shows that CHRNA7 protein also interact with AKT1 protein which regulate neuronal differentiation and development, that signifies the role of CHRNA7 protein in neurological diseases.

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.

Benchmark of four popular virtual screening programs: construction of the active/decoy dataset remains a major determinant of measured performance

Background

In a structure-based virtual screening, the choice of the docking program is essential for the success of a hit identification. Benchmarks are meant to help in guiding this choice, especially when undertaken on a large variety of protein targets. Here, the performance of four popular virtual screening programs, Gold, Glide, Surflex and FlexX, is compared using the Directory of Useful Decoys-Enhanced database (DUD-E), which includes 102 targets with an average of 224 ligands per target and 50 decoys per ligand, generated to avoid biases in the benchmarking. Then, a relationship between these program performances and the properties of the targets or the small molecules was investigated.

Results

The comparison was based on two metrics, with three different parameters each. The BEDROC scores with α = 80.5, indicated that, on the overall database, Glide succeeded (score > 0.5) for 30 targets, Gold for 27, FlexX for 14 and Surflex for 11. The performance did not depend on the hydrophobicity nor the openness of the protein cavities, neither on the families to which the proteins belong. However, despite the care in the construction of the DUD-E database, the small differences that remain between the actives and the decoys likely explain the successes of Gold, Surflex and FlexX. Moreover, the similarity between the actives of a target and its crystal structure ligand seems to be at the basis of the good performance of Glide. When all targets with significant biases are removed from the benchmarking, a subset of 47 targets remains, for which Glide succeeded for only 5 targets, Gold for 4 and FlexX and Surflex for 2.

Conclusion

The performance dramatic drop of all four programs when the biases are removed shows that we should beware of virtual screening benchmarks, because good performances may be due to wrong reasons. Therefore, benchmarking would hardly provide guidelines for virtual screening experiments, despite the tendency that is maintained, i.e., Glide and Gold display better performance than FlexX and Surflex. We recommend to always use several programs and combine their results. Graphical Abstract

FlexAID: Revisiting Docking on Non-Native-Complex Structures

Small-molecule protein docking is an essential tool in drug design and to understand molecular recognition. In the present work we introduce FlexAID, a small-molecule docking algorithm that accounts for target side-chain flexibility and utilizes a soft scoring function, i.e. one that is not highly dependent on specific geometric criteria, based on surface complementarity. The pairwise energy parameters were derived from a large dataset of true positive poses and negative decoys from the PDBbind database through an iterative process using Monte Carlo simulations. The prediction of binding poses is tested using the widely used Astex dataset as well as the HAP2 dataset, while performance in virtual screening is evaluated using a subset of the DUD dataset. We compare FlexAID to AutoDock Vina, FlexX, and rDock in an extensive number of scenarios to understand the strengths and limitations of the different programs as well as to reported results for Glide, GOLD, and DOCK6 where applicable. The most relevant among these scenarios is that of docking on flexible non-native-complex structures where as is the case in reality, the target conformation in the bound form is not known a priori. We demonstrate that FlexAID, unlike other programs, is robust against increasing structural variability. FlexAID obtains equivalent sampling success as GOLD and performs better than AutoDock Vina or FlexX in all scenarios against non-native-complex structures. FlexAID is better than rDock when there is at least one critical side-chain movement required upon ligand binding. In virtual screening, FlexAID results are lower on average than those of AutoDock Vina and rDock. The higher accuracy in flexible targets where critical movements are required, intuitive PyMOL-integrated graphical user interface and free source code as well as precompiled executables for Windows, Linux, and Mac OS make FlexAID a welcome addition to the arsenal of existing small-molecule protein docking methods.

Optimal ligand descriptor for pocket recognition based on the Beta-shape

Structure-based virtual screening is one of the most important and common computational methods for the identification of predicted hit at the beginning of drug discovery. Pocket recognition and definition is frequently a prerequisite of structure-based virtual screening, reducing the search space of the predicted protein-ligand complex. In this paper, we present an optimal ligand shape descriptor for a pocket recognition algorithm based on the beta-shape, which is a derivative structure of the Voronoi diagram of atoms. We investigate six candidates for a shape descriptor for a ligand using statistical analysis: the minimum enclosing sphere, three measures from the principal component analysis of atoms, the van der Waals volume, and the beta-shape volume. Among them, the van der Waals volume of a ligand is the optimal shape descriptor for pocket recognition and best tunes the pocket recognition algorithm based on the beta-shape for efficient virtual screening. The performance of the proposed algorithm is verified by a benchmark test.

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.

Mechanistic insights of SrtA–LPXTG blockers targeting the transpeptidase mechanism in Streptococcus mutans

The SrtA–LPXTG interaction plays a key role in transpeptidation reaction, cell wall and biofilm formations. This study explains the blocking of LEU interactions with SrtA will results as SrtA inhibitors through MD simulation and energy calculations methods.

Virtual screening: an in silico tool for interlacing the chemical universe with the proteome

In silico screening both in the forward (traditional virtual screening) and reverse sense (inverse virtual screening (IVS)) are helpful techniques for interlacing the chemical universe of small molecules with the proteome. The former, which is using a protein structure and a large chemical database, is well-known by the scientific community. We have chosen here to provide an overview on the latter, focusing on validation and target prioritization strategies. By comparing it to complementary or alternative wet-lab approaches, we put IVS in the broader context of chemical genomics, target discovery and drug design. By giving examples from the literature and an own example on how to validate the approach, we provide guidance on the issues related to IVS.

Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methods

The thyroid hormone receptor (THR) is an important member of the nuclear receptor family that can be activated by endocrine disrupting chemicals (EDC). Quantitative Structure-Activity Relationship (QSAR) models have been developed to facilitate the prioritization of THR-mediated EDC for the experimental validation. The largest database of binding affinities available at the time of the study for ligand binding domain (LBD) of THRβ was assembled to generate both continuous and classification QSAR models with an external accuracy of R(2)=0.55 and CCR=0.76, respectively. In addition, for the first time a QSAR model was developed to predict binding affinities of antagonists inhibiting the interaction of coactivators with the AF-2 domain of THRβ (R(2)=0.70). Furthermore, molecular docking studies were performed for a set of THRβ ligands (57 agonists and 15 antagonists of LBD, 210 antagonists of the AF-2 domain, supplemented by putative decoys/non-binders) using several THRβ structures retrieved from the Protein Data Bank. We found that two agonist-bound THRβ conformations could effectively discriminate their corresponding ligands from presumed non-binders. Moreover, one of the agonist conformations could discriminate agonists from antagonists. Finally, we have conducted virtual screening of a chemical library compiled by the EPA as part of the Tox21 program to identify potential THRβ-mediated EDCs using both QSAR models and docking. We concluded that the library is unlikely to have any EDC that would bind to the THRβ. Models developed in this study can be employed either to identify environmental chemicals interacting with the THR or, conversely, to eliminate the THR-mediated mechanism of action for chemicals of concern.

An exhaustive yet simple virtual screening campaign against Sortase A from multiple drug resistant Staphylococcus aureus

Methicillin resistant Staphylococcus aureus (MRSA) is one of the challenging bacterial pathogen due to its acquired resistance to the β lactam antibiotics. The Sortase A is an enzyme of Gram-positive bacteria including S. aureus to anchor surface proteins to the cell wall. Sortase A is well studied enzyme and considered as the drug target against MRSA. Sortase A plays active role in anchoring the virulence proteins on the cell wall of the Gram-positive bacteria. The inhibition of Sortase A activity results in the separation of S. aureus from the host cells and ultimately alleviation of the infection. Here, we adapted a structure-based virtual screening protocol which helped in identification of novel potential inhibitors of Sortase A. The protocol involved the docking of a chemical library of druglike compounds with the Sortase A binding site represented by multiple crystal structures. The compounds were ranked by multiple scoring functions and shortlisted for future experimental screening. The method resulted in shortlisting of three compounds as potential novel inhibitors of Sortase A out of a large chemical library. The high rankings of shortlisted compounds estimated by multiple scoring functions showed their binding potential with Sortase A. The results are proved to be a simple yet efficient choice of structure-based virtual screening. The identified compounds are druglike and show high rankings among all set protocols of the virtual screening. We hope that the study would eventually help to expedite the discovery of novel drug candidates against MRSA.

Comparing neural-network scoring functions and the state of the art: applications to common library screening

We compare established docking programs, AutoDock Vina and Schrödinger's Glide, to the recently published NNScore scoring functions. As expected, the best protocol to use in a virtual-screening project is highly dependent on the target receptor being studied. However, the mean screening performance obtained when candidate ligands are docked with Vina and rescored with NNScore 1.0 is not statistically different than the mean performance obtained when docking and scoring with Glide. We further demonstrate that the Vina and NNScore docking scores both correlate with chemical properties like small-molecule size and polarizability. Compensating for these potential biases leads to improvements in virtual screen performance. Composite NNScore-based scoring functions suited to a specific receptor further improve performance. We are hopeful that the current study will prove useful for those interested in computer-aided drug design.

Virtual medicinal chemistry: in silico pre-docking functional group transformation for discovery of novel inhibitors of botulinum toxin serotype A light chain

A novel method for applying high-throughput docking to challenging metalloenzyme targets is described. The method utilizes information-based virtual transformation of library carboxylates to hydroxamic acids prior to docking, followed by compound acquisition, one-pot (two steps) chemical synthesis and in vitro screening. In two experiments targeting the botulinum neurotoxin serotype A metalloprotease light chain, hit rates of 32% and 18% were observed.

FDA approved drugs complexed to their targets: evaluating pose prediction accuracy of docking protocols

Efficient drug discovery programs can be designed by utilizing existing pools of knowledge from the already approved drugs. This can be achieved in one way by repositioning of drugs approved for some indications to newer indications. Complex of drug to its target gives fundamental insight into molecular recognition and a clear understanding of putative binding site. Five popular docking protocols, Glide, Gold, FlexX, Cdocker and LigandFit have been evaluated on a dataset of 199 FDA approved drug-target complexes for their accuracy in predicting the experimental pose. Performance for all the protocols is assessed at default settings, with root mean square deviation (RMSD) between the experimental ligand pose and the docked pose of less than 2.0 Å as the success criteria in predicting the pose. Glide (38.7 %) is found to be the most accurate in top ranked pose and Cdocker (58.8 %) in top RMSD pose. Ligand flexibility is a major bottleneck in failure of docking protocols to correctly predict the pose. Resolution of the crystal structure shows an inverse relationship with the performance of docking protocol. All the protocols perform optimally when a balanced type of hydrophilic and hydrophobic interaction or dominant hydrophilic interaction exists. Overall in 16 different target classes, hydrophobic interactions dominate in the binding site and maximum success is achieved for all the docking protocols in nuclear hormone receptor class while performance for the rest of the classes varied based on individual protocol.

How well do the substrates KISS the enzyme? Molecular docking program selection for feruloyl esterases

Molecular docking is the most commonly used technique in the modern drug discovery process where computational approaches involving docking algorithms are used to dock small molecules into macromolecular target structures. Over the recent years several evaluation studies have been reported by independent scientists comparing the performance of the docking programs by using default ‘black box’ protocols supplied by the software companies. Such studies have to be considered carefully as the docking programs can be tweaked towards optimum performance by selecting the parameters suitable for the target of interest. In this study we address the problem of selecting an appropriate docking and scoring function combination (88 docking algorithm-scoring functions) for substrate specificity predictions for feruloyl esterases, an industrially relevant enzyme family. We also propose the ‘Key Interaction Score System’ (KISS), a more biochemically meaningful measure for evaluation of docking programs based on pose prediction accuracy.

Leveraging structural information for the discovery of new drugs: computational methods

Escalating problems with drug resistance continue to compromise the effectiveness of commercial antibiotics, necessitating the search for novel classes of antimicrobial agents. To circumvent problems with resistance, a multitarget single-pharmacophore approach has been employed to discover inhibitors that possess balanced activity against multiple target enzymes. In this chapter, we examine the application of computational techniques, in particular, structure-based drug design approaches, to design new dual-targeting antibacterial agents against bacterial topoisomerases.

Improving molecular docking through eHiTS' tunable scoring function

We present three complementary approaches for score-tuning that improve docking performance in pose prediction, virtual screening and binding affinity assessment. The methodology utilizes experimental data to customize the scoring function for the system of interest considering the specific docking scenario. The tuning approach, which has been implemented as an automated utility in eHiTS, is introduced as a solution to one of the conundrums of the molecular docking paradigm, namely, the lack of a universally well performing scoring function. The accuracy of scoring functions has been shown to be generally system-dependent, and particularly lacking for binding energy and bio-activity predictions. In the proposed approach, pose and energy predictions are enhanced by adjusting the relative weights of the eHiTS energy terms to improve score-RMSD or score-affinity correlations. In a virtual screening context ligand-based similarity is used to rescale the docking score such that better enrichment factors are achieved. We discuss the algorithmic details of the methods, and demonstrate the effects of score tuning on a variety of targets, including CDK2, BACE1 and neuraminidase, as well as on the popular benchmarks--the Directory of Useful Decoys and the PDBBind database.