Comparison of automated docking programs as virtual screening tools

Elucidating binding mechanisms of naringenin by alpha-chymotrypsin: Insights into non-binding interactions and complex formation

As an inevitable parameter in the description of enzyme properties, the investigation of enzyme-ligand interactions has attracted a lot of attention. Alpha-Chymotrypsin (α-Chy) is essential for protein digestion and plays an important role in human health. Naringenin (NAG) as a potent antioxidant has recently been applied in the pharmaceutical industry. Using multispectral methods and computational simulation techniques, the binding strength of NAG to α-Chy was investigated in this research. UV-vis and fluorescence quenching data showed significant spectral changes upon binding of NAG to α-Chy. As demonstrated by fluorescence techniques, NAG could employ a static quenching process to decrease the intrinsic fluorescence of α-Chy. Both circular dichroism (CD) and FTIR spectroscopic analyses revealed that binding of NAG to α-Chy caused more flexible conformation. The slight increases in RMSD (0.06 nm) were observed for the NAG-(α-Chy) compound was supported by the results of thermal stability data. Docking computation confirmed that hydrogen and Van der Waals interactions are the important forces, which is in exact agreement with thermodynamics studies. Kinetic analysis of the enzyme showed an increase in activity, which was consistent, with the MD simulation results. The findings from the in-silico studies were in complete agreement with the experimental results.

Mechanism of baixiangdan capsules on anti-neuroinflammation: combining dry and wet experiments

Neuroinflammation plays an important role in the pathogenesis of neurological disorders, and despite intensive research, treatment of neuroinflammation remains limited. BaiXiangDan capsule (BXD) is widely used in clinical practice. However, systematic studies on the direct role and mechanisms of BXD in neuroinflammation are still lacking. We systematically evaluated the potential pharmacological mechanisms of BXD on neuroinflammation using network pharmacological analysis combined with experimental validation. Multiple databases are used to mine potential targets for bioactive ingredients, drug targets and neuroinflammation. GO and KEGG pathway analysis was also performed. Interactions between active ingredients and pivotal targets were confirmed by molecular docking. An experimental model of neuroinflammation was used to evaluate possible therapeutic mechanisms for BXD. Network pharmacological analysis revealed that Chrysoeriol, Kaempferol and Luteolin in BXD exerted their anti-neuroinflammatory effects mainly by acting on targets such as NCOA2, PIK3CA and PTGS2. Molecular docking results showed that their average affinity was less than -5 kcal/mol, with an average affinity of -8.286 kcal/mol. Pathways in cancer was found to be a potentially important pathway, with involvement of PI3K/AKT signaling pathways. In addition, in vivo experiments showed that BXD treatment ameliorated neural damage and reduced neuronal cell death. Western blotting, RT-qPCR and ELISA analysis showed that BXD inhibited not only the expression of IL-1β, TNF-α and NO, but also NF-κB, MMP9 and PI3K/AKT signaling pathways. This study applied network pharmacology and in vivo experiments to explore the possible mechanisms of BXD against neuroinflammation, providing insight into the treatment of neuroinflammation.

In silico study of natural antioxidants

Antioxidants are the body's defense system against the damage of reactive oxygen species, which are usually produced in the body through various physiological processes. There are various sources of these antioxidants such as endogenous antioxidants in the body and exogenous food sources. This chapter provides important information on methods used to investigate antioxidant activity and sources of plant antioxidants. Over the past two decades, numerous studies have demonstrated the importance of in silico research in the development of novel natural and synthesized antioxidants. In silico methods such as quantitative structure-activity relationships (QSAR), pharmacophore, docking, and virtual screenings are play critical roles in designing effective antioxidants that may be synthesized and tested later. This chapter introduces the available in silico approaches for different classes of antioxidants. Many successful applications of in silico methods in the development and design of novel antioxidants are thoroughly discussed. The QSAR, pharmacophore, molecular docking techniques, and virtual screenings process summarized here would help readers to find out the proper mechanism for the interaction between the free radicals and antioxidant compounds. Furthermore, this chapter focuses on introducing new QSAR models in combination with other in silico methods to predict antioxidants activity and design more active antioxidants. In silico studies are essential to explore largely unknown plant tissue, food sources for antioxidant synthesis, as well as saving time and money in such studies.

Consensus scoring evaluated using the GPCR-Bench dataset: Reconsidering the role of MM/GBSA

The recent availability of large numbers of GPCR crystal structures has provided an unprecedented opportunity to evaluate their performance in virtual screening protocols using established benchmarking datasets. In this study, we evaluated the ability of MM/GBSA in consensus scoring-based virtual screening enrichment together with nine classical scoring functions, using the GPCR-Bench dataset consisting of 24 GPCR crystal structures and 254,646 actives and decoys. While the performance of consensus scoring was modest overall, combinations which included MM/GBSA performed relatively well compared to combinations of classical scoring functions. Combinations of MM/GBSA and good-performing scoring functions provided the highest proportion of improvements, with improvements observed in 32% and 19% of all combinations across all targets at the EF_1% and EF_5% levels respectively. Combinations of MM/GBSA and poor-performing scoring functions still outperformed classical scoring functions, with improvements observed in 26% and 17% of all combinations at the EF_1% and EF_5% levels. In comparison, only 14-22% and 6-11% of combinations of classical scoring functions produced improvements at EF_1% and EF_5% respectively. Efforts to improve performance by increasing the number of scoring functions in consensus scoring to three were mostly ineffective. We also observed that consensus scoring performed better for individual scoring functions possessing initially low enrichment factors, potentially implying their benefits are more relevant in such scenarios. Overall, this study demonstrated the first implementation of MM/GBSA in consensus scoring using the GPCR-Bench dataset and could provide a valuable benchmark of the performance of MM/GBSA in comparison to classical scoring functions in consensus scoring for GPCRs.

In-silico identification of lysine residue for α-Amylase immobilization on dialdehyde cellulose

Enzymes are the precious gift of nature to humans. The wise utilization of enzymes may reduce energy needs of humans and the Immobilization technique can help a lot in this regard. This aspect overcomes limitations of the enzymes, therefore providing an opportunity to explore enzymatic chemistry further. In the present context, it is quite cumbersome & costly to identify the amino acid of enzymes involved in the covalent mode of Immobilization. In the present study, molecular modeling techniques were used to do this difficult task. The present work used molecular modeling methods to extract information about the immobilization of α-Amylase (E.C.3.2.1.1) on Dialdehyde Cellulose. The Lysine residue is the most probable residue to interact with Dialdehyde Cellulose. In the present work, a total of 23 lysine residues were used to study covalent binding behavior with α-Amylase. It was found that if Lys142 is involved in binding with Dialdehyde Cellulose then binding affinity (-6.1 & -5.9 kcal mol-1), as well as the involvement of amino acids of both free α-Amylase and Lys142 immobilized α-Amylase with the starch substrate, were found to be similar. The technique reported here is used for the identification of amino acid residue for the Immobilization of enzymes.

Machine learning and graph neural network for finding potential drugs related to multiple myeloma

An innovative voting mechanism for virtual drug screening.

Computer-aided design of 1,4-naphthoquinone-based inhibitors targeting cruzain and rhodesain cysteine proteases

Chagas disease and Human African Trypanosomiasis (HAT) are caused by Trypanosoma cruzi and T. brucei parasites, respectively. Cruzain (CRZ) and Rhodesain (RhD) are cysteine proteases that share 70% of identity and play vital functions in these parasites. These macromolecules represent promising targets for designing new inhibitors. In this context, 26 CRZ and 5 RhD 3D-structures were evaluated by molecular redocking to identify the most accurate one to be utilized as a target. Posteriorly, a virtual screening of a library containing 120 small natural and nature-based compounds was performed on both of them. In total, 14 naphthoquinone-based analogs were identified, synthesized, and biologically evaluated. In total, five compounds were active against RhD, being three of them also active on CRZ. A derivative of 1,4-naphthoquinonepyridin-2-ylsulfonamide was found to be the most active molecule, exhibiting IC₅₀ values of 6.3 and 1.8 µM for CRZ and RhD, respectively. Dynamic simulations at 100 ns demonstrated good stability and do not alter the targets' structures. MM-PBSA calculations revealed that it presents a higher affinity for RhD (-25.3 Kcal mol^-1) than CRZ, in which van der Waals interactions were more relevant. A mechanistic hypothesis (via C3-Michael-addition reaction) involving a covalent mode of inhibition for this compound towards RhD was investigated by covalent molecular docking and DFT B3LYP/6-31 + G* calculations, exhibiting a low activation energy (ΔG^‡) and providing a stable product (ΔG), with values of 7.78 and - 39.72 Kcal mol^-1, respectively; similar to data found in the literature. Nevertheless, a reversibility assay by dilution revealed that JN-11 is a time-dependent and reversible inhibitor. Finally, this study applies modern computer-aided techniques to identify promising inhibitors from a well-known chemical class of natural products. Then, this work could inspire other future studies in the field, being useful for designing potent naphthoquinones as RhD inhibitors.

Hybrid In Silico and TR-FRET-Guided Discovery of Novel BCL-2 Inhibitors

B-Cell lymphoma 2 (BCL-2) regulates cell death in humans. In this study, combined multiscale in silico approaches and in vitro studies were employed. A small-molecule library that includes more than 210 000 compounds was used. The predicted therapeutic activity value (TAV) of the compounds in this library was computed with the binary cancer quantitative structure-activity relationships (QSAR) model. The molecules with a high calculated TAV were used in 26 individual toxicity QSAR models. As a result of this screening protocol, 288 nontoxic molecules with high predicted TAV were identified. These selected hits were then screened against the BCL-2 target protein using hybrid docking and molecular dynamics (MD) simulations. The interaction energies of identified compounds were compared with two known BCL-2 inhibitors. Then, the short MD simulations were carried out by initiating the best docking poses of 288 molecules. Average MM/GBSA energies were computed, and long MD simulations were employed to selected hits. The same calculations were also applied for two known BCL-2 inhibitors. Moreover, a five-site (AHRRR) structure-based pharmacophore model was constructed, and this model was used in the screening of the same database. On the basis of hybrid data-driven ligand identification study, final hits were selected and used in in vitro studies. Based on results of the time-resolved fluorescence resonance energy transfer (TR-FRET) analysis, further filtration was carried out for the U87-MG cell line tests. MTT cell proliferation assay analysis results showed that selected three potent compounds were significantly effective on glioma cells.

Baseline Comparisons of Complementary Sampling Methods for Assembly Driven by Short-Ranged Pair Potentials toward Fast and Flexible Hybridization

This work measures baseline sampling characteristics that highlight fundamental differences between sampling methods for assembly driven by short-ranged pair potentials. Such granular comparison is essential for fast, flexible, and accurate hybridization of complementary methods. Besides sampling speed, efficiency, and accuracy of uniform grid coverage, other sampling characteristics measured are (i) accuracy of covering narrow low energy regions that have low effective dimension (ii) ability to localize sampling to specific basins, and (iii) flexibility in sampling distributions. As a proof of concept, we compare a recently developed geometric methodology EASAL (Efficient Atlasing and Search of Assembly Landscapes) and the traditional Monte Carlo (MC) method for sampling the energy landscape of two assembling trans-membrane helices, driven by short-range pair potentials. By measuring the above-mentioned sampling characteristics, we demonstrate that EASAL provides localized and accurate coverage of crucial regions of the energy landscape of low effective dimension, under flexible sampling distributions, with much fewer samples and computational resources than MC sampling. EASAL's empirically validated theoretical guarantees permit credible extrapolation of these measurements and comparisons to arbitrary number and size of assembling units. Promising avenues for hybridizing the complementary advantages of the two methods are discussed.

In Silico Strategies in Tuberculosis Drug Discovery

Tuberculosis (TB) remains a serious threat to global public health, responsible for an estimated 1.5 million mortalities in 2018. While there are available therapeutics for this infection, slow-acting drugs, poor patient compliance, drug toxicity, and drug resistance require the discovery of novel TB drugs. Discovering new and more potent antibiotics that target novel TB protein targets is an attractive strategy towards controlling the global TB epidemic. In silico strategies can be applied at multiple stages of the drug discovery paradigm to expedite the identification of novel anti-TB therapeutics. In this paper, we discuss the current TB treatment, emergence of drug resistance, and the effective application of computational tools to the different stages of TB drug discovery when combined with traditional biochemical methods. We will also highlight the strengths and points of improvement in in silico TB drug discovery research, as well as possible future perspectives in this field.

Fast Screening of Inhibitor Binding/Unbinding Using Novel Software Tool CaverDock

Protein tunnels and channels are attractive targets for drug design. Drug molecules that block the access of substrates or release of products can be efficient modulators of biological activity. Here, we demonstrate the applicability of a newly developed software tool CaverDock for screening databases of drugs against pharmacologically relevant targets. First, we evaluated the effect of rigid and flexible side chains on sets of substrates and inhibitors of seven different proteins. In order to assess the accuracy of our software, we compared the results obtained from CaverDock calculation with experimental data previously collected with heat shock protein 90α. Finally, we tested the virtual screening capabilities of CaverDock with a set of oncological and anti-inflammatory FDA-approved drugs with two molecular targets—cytochrome P450 17A1 and leukotriene A4 hydrolase/aminopeptidase. Calculation of rigid trajectories using four processors took on average 53 min per molecule with 90% successfully calculated cases. The screening identified functional tunnels based on the profile of potential energies of binding and unbinding trajectories. We concluded that CaverDock is a sufficiently fast, robust, and accurate tool for screening binding/unbinding processes of pharmacologically important targets with buried functional sites. The standalone version of CaverDock is available freely at https://loschmidt.chemi.muni.cz/caverdock/ and the web version at https://loschmidt.chemi.muni.cz/caverweb/.

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.

In Silico Peptide Ligation: Iterative Residue Docking and Linking as a New Approach to Predict Protein-Peptide Interactions

Peptide⁻protein interactions are corner-stones of living functions involved in essential mechanisms, such as cell signaling. Given the difficulty of obtaining direct experimental structural biology data, prediction of those interactions is of crucial interest for the rational development of new drugs, notably to fight diseases, such as cancer or Alzheimer's disease. Because of the high flexibility of natural unconstrained linear peptides, prediction of their binding mode in a protein cavity remains challenging. Several theoretical approaches have been developed in the last decade to address this issue. Nevertheless, improvements are needed, such as the conformation prediction of peptide side-chains, which are dependent on peptide length and flexibility. Here, we present a novel in silico method, Iterative Residue Docking and Linking (IRDL), to efficiently predict peptide⁻protein interactions. In order to reduce the conformational space, this innovative method splits peptides into several short segments. Then, it uses the performance of intramolecular covalent docking to rebuild, sequentially, the complete peptide in the active site of its protein target. Once the peptide is constructed, a rescoring step is applied in order to correctly rank all IRDL solutions. Applied on a set of 11 crystallized peptide⁻protein complexes, the IRDL method shows promising results, since it is able to retrieve experimental binding conformations with a Root Mean Square Deviation (RMSD) below 2 Å in the top five ranked solutions. For some complexes, IRDL method outperforms two other docking protocols evaluated in this study. Hence, IRDL is a new tool that could be used in drug design projects to predict peptide⁻protein interactions.

In silico identification of inhibitors targeting N-Terminal domain of human Replication Protein A

Replication Protein A (RPA) mediates DNA Damage Response (DDR) pathways through protein-protein interactions (PPIs). Targeting the PPIs formed between RPA and other DNA Damage Response (DDR) mediators has become an intriguing area of research for cancer drug discovery. A number of studies applied different methods ranging from high throughput screening approaches to fragment-based drug design tools to discover RPA inhibitors. Although these methods are robust, virtual screening approaches may be allocated as an alternative to such experimental methods, especially for screening of large libraries. Here we report the comprehensive screening of the large database, ZINC15 composed of ∼750 M compounds and the comparison of the identified ligands with the previously known inhibitors by means of binding affinity and drug-likeness. Initially, a ligand library sharing similarity with a promising inhibitor of the N-terminal domain of the RPA70 subunit (RPA70N) was generated by screening of the ZINC15 library. 46,999 ligands were collected and screened by LeDock which produced a satisfactory correlation with the experimental values (R² = 0.77). 10 of the top-scoring ligands in LeDock were directly progressed to molecular dynamics (MD) simulations, while 10 additional ligands were also selected based on their LeDock scores and the presence of a functional group that could interact with the key amino acids in the RPA70N cleft. MD simulations were used to predict the binding free energy of the ligands by the MM-PBSA method which produced a high level of agreement with the experiments (R² = 0.85). Binding free energy predictions pointed out 2 ligands with higher binding affinity than any of the reference inhibitors. Particularly the ligand ZINC000753854163 exhibited superior drug-likeness features than any of the known inhibitors. Overall, this study reports ZINC000753854163 as a possible inhibitor of RPA70N, reflecting its possible use in RPA70N targeted cancer therapy.

Decoys Selection in Benchmarking Datasets: Overview and Perspectives

Virtual Screening (VS) is designed to prospectively help identifying potential hits, i.e., compounds capable of interacting with a given target and potentially modulate its activity, out of large compound collections. Among the variety of methodologies, it is crucial to select the protocol that is the most adapted to the query/target system under study and that yields the most reliable output. To this aim, the performance of VS methods is commonly evaluated and compared by computing their ability to retrieve active compounds in benchmarking datasets. The benchmarking datasets contain a subset of known active compounds together with a subset of decoys, i.e., assumed non-active molecules. The composition of both the active and the decoy compounds subsets is critical to limit the biases in the evaluation of the VS methods. In this review, we focus on the selection of decoy compounds that has considerably changed over the years, from randomly selected compounds to highly customized or experimentally validated negative compounds. We first outline the evolution of decoys selection in benchmarking databases as well as current benchmarking databases that tend to minimize the introduction of biases, and secondly, we propose recommendations for the selection and the design of benchmarking datasets.

Identification of chlamydial T3SS inhibitors through virtual screening against T3SS ATPase

Chlamydia trachomatis is a widespread sexually transmitted pathogen that resides within a special vacuole inside host cells. Although acute infection can be treated with antibiotics, chlamydia can enter persistent state, leading to chronic infection that is difficult to cure. Thus, novel anti-chlamydial compounds active against persistent chlamydia are required. Chlamydiae rely upon type III secretion system (T3SS) to inject effector proteins into host cell cytoplasm, and T3SS inhibitors are viewed as promising compounds for treatment of chlamydial infections. C. trachomatis ATPase SctN is an important T3SS component and has not been targeted before. We thus used virtual screening against homology modeled SctN structure to search for SctN inhibitors. Selected compounds were tested for their ability to inhibit chlamydial survival and development within eukaryotic cells, and for the ability to suppress normal T3SS functioning. We identified two compounds that were able to block normal protein translocation through T3SS and inhibit chlamydial survival within eukaryotic cells in 50-100 μm concentrations. These two novel T3SS inhibitors also possessed relatively low toxicity toward eukaryotic cells. A small series of derivatives was further synthesized for the most active of two inhibitors to probe SAR properties.

Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening

In structure-based virtual screening, compound ranking through a consensus of scores from a variety of docking programs or scoring functions, rather than ranking by scores from a single program, provides better predictive performance and reduces target performance variability. Here we compare traditional consensus scoring methods with a novel, unsupervised gradient boosting approach. We also observed increased score variation among active ligands and developed a statistical mixture model consensus score based on combining score means and variances. To evaluate performance, we used the common performance metrics ROCAUC and EF1 on 21 benchmark targets from DUD-E. Traditional consensus methods, such as taking the mean of quantile normalized docking scores, outperformed individual docking methods and are more robust to target variation. The mixture model and gradient boosting provided further improvements over the traditional consensus methods. These methods are readily applicable to new targets in academic research and overcome the potentially poor performance of using a single docking method on a new target.

Efficient conformational sampling and weak scoring in docking programs? Strategy of the wisdom of crowds

BACKGROUND

In drug design, an efficient structure-based optimization of a ligand needs the precise knowledge of the protein-ligand interactions. In the absence of experimental information, docking programs are necessary for ligand positioning, and the choice of a reliable program is essential for the success of such an optimization. The performances of four popular docking programs, Gold, Glide, Surflex and FlexX, were investigated using 100 crystal structures of complexes taken from the Directory of Useful Decoys-Enhanced database.

RESULTS

The ligand conformational sampling was rather efficient, with a correct pose found for a maximum of 84 complexes, obtained by Surflex. However, the ranking of the correct poses was not as efficient, with a maximum of 68 top-rank or 75 top-4 rank correct poses given by Glidescore. No relationship was found between either the sampling or the scoring performance of the four programs and the properties of either the targets or the small molecules, except for the number of ligand rotatable bonds. As well, no exploitable relationship was found between each program performance in docking and in virtual screening; a wrong top-rank pose may obtain a good score that allows it to be ranked among the most active compounds and vice versa. Also, to improve the results of docking, the strengths of the programs were combined either by using a rescoring procedure or the United Subset Consensus (USC). Oddly, positioning with Surflex and rescoring with Glidescore did not improve the results. However, USC based on docking allowed us to obtain a correct pose in the top-4 rank for 87 complexes. Finally, nine complexes were scrutinized, because a correct pose was found by at least one program but poorly ranked by all four programs. Contrarily to what was expected, except for one case, this was not due to weaknesses of the scoring functions.

CONCLUSIONS

We conclude that the scoring functions should be improved to detect the correct poses, but sometimes their failure may be due to other varied considerations. To increase the chances of success, we recommend to use several programs and combine their results. Graphical abstract Summary of the results obtained by semi-rigid docking of crystallographic ligands. The docking was done on 100 protein-ligand X-ray structures, taken from the DUD-E database, and using four programs, Glide, Gold, Surflex and FlexX. Based on the docking results, we applied our United Subset Consensus method (USC), for which only the top4-rank poses are relevant. The number of complexes for which the best pose is correct, is represented by the gray boxes, the blue and red boxes correspond to the number of complexes with a correct pose ranked as the top 1 or within the top 4. A pose is considered correct when its root-mean-square deviation from the crystal structure is less than 2 Å.

Software for molecular docking: a review

Molecular docking methodology explores the behavior of small molecules in the binding site of a target protein. As more protein structures are determined experimentally using X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy, molecular docking is increasingly used as a tool in drug discovery. Docking against homology-modeled targets also becomes possible for proteins whose structures are not known. With the docking strategies, the druggability of the compounds and their specificity against a particular target can be calculated for further lead optimization processes. Molecular docking programs perform a search algorithm in which the conformation of the ligand is evaluated recursively until the convergence to the minimum energy is reached. Finally, an affinity scoring function, ΔG [U total in kcal/mol], is employed to rank the candidate poses as the sum of the electrostatic and van der Waals energies. The driving forces for these specific interactions in biological systems aim toward complementarities between the shape and electrostatics of the binding site surfaces and the ligand or substrate.

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.

Computational drug design strategies applied to the modelling of human immunodeficiency virus-1 reverse transcriptase inhibitors

Reverse transcriptase (RT) is a multifunctional enzyme in the human immunodeficiency virus (HIV)-1 life cycle and represents a primary target for drug discovery efforts against HIV-1 infection. Two classes of RT inhibitors, the nucleoside RT inhibitors (NRTIs) and the nonnucleoside transcriptase inhibitors are prominently used in the highly active antiretroviral therapy in combination with other anti-HIV drugs. However, the rapid emergence of drug-resistant viral strains has limited the successful rate of the anti-HIV agents. Computational methods are a significant part of the drug design process and indispensable to study drug resistance. In this review, recent advances in computer-aided drug design for the rational design of new compounds against HIV-1 RT using methods such as molecular docking, molecular dynamics, free energy calculations, quantitative structure-activity relationships, pharmacophore modelling and absorption, distribution, metabolism, excretion and toxicity prediction are discussed. Successful applications of these methodologies are also highlighted.

Computational evidence to inhibition of human acetyl cholinesterase by withanolide a for Alzheimer treatment

Alzheimer's disease (AD), a neurodegenerative disorder, is the most common cause of dementia. So far only five drugs have been approved by US FDA that temporarily slow worsening of symptoms for about six to twelve months. The limited number of therapeutic options for AD drives the exploration of new drugs. Enhancement of the central cholinergic function by the inhibition of acetylcholinesterase is a prominent clinically effective approach for the treatment of AD. Recently withanolide A, a secondary metabolite from the ayurvedic plant Withania somnifera has shown substantial neuro-protective ability. The present study is an attempt to elucidate the cholinesterase inhibition potential of withanolide A along with the associated binding mechanism. Our docking simulation results predict high binding affinity of the ligand to the receptor. Further, long de novo simulations for 10 ns suggest that ligand interaction with the residues Thr78, Trp81, Ser120 and His442 of human acetylcholinesterase, all of which fall under one or other of the active sites/subsites, could be critical for its inhibitory activity. The study provides evidence for consideration of withanolide A as a valuable small ligand molecule in treatment and prevention of AD associated pathology. The present information could be of high value for computational screening of AD drugs with low toxicity to normal cells. Accurate knowledge of the 3D structure of human acetylcholinesterase would further enhance the potential of such analysis in understanding the molecular interaction basis between ligand and receptor.

vSDC: a method to improve early recognition in virtual screening when limited experimental resources are available

Background

In drug design, one may be confronted to the problem of finding hits for targets for which no small inhibiting molecules are known and only low-throughput experiments are available (like ITC or NMR studies), two common difficulties encountered in a typical academic setting. Using a virtual screening strategy like docking can alleviate some of the problems and save a considerable amount of time by selecting only top-ranking molecules, but only if the method is very efficient, i.e. when a good proportion of actives are found in the 1–10 % best ranked molecules.

Results

The use of several programs (in our study, Gold, Surflex, FlexX and Glide were considered) shows a divergence of the results, which presents a difficulty in guiding the experiments. To overcome this divergence and increase the yield of the virtual screening, we created the standard deviation consensus (SDC) and variable SDC (vSDC) methods, consisting of the intersection of molecule sets from several virtual screening programs, based on the standard deviations of their ranking distributions.

Conclusions

SDC allowed us to find hits for two new protein targets by testing only 9 and 11 small molecules from a chemical library of circa 15,000 compounds. Furthermore, vSDC, when applied to the 102 proteins of the DUD-E benchmarking database, succeeded in finding more hits than any of the four isolated programs for 13–60 % of the targets. In addition, when only 10 molecules of each of the 102 chemical libraries were considered, vSDC performed better in the number of hits found, with an improvement of 6–24 % over the 10 best-ranked molecules given by the individual docking programs.Graphical abstract

Quantum Mechanics/Molecular Mechanics Modeling of Enzymatic Processes: Caveats and Breakthroughs

Nature has developed large groups of enzymatic catalysts with the aim to transfer substrates into useful products, which enables biosystems to perform all their natural functions. As such, all biochemical processes in our body (we drink, we eat, we breath, we sleep, etc.) are governed by enzymes. One of the problems associated with research on biocatalysts is that they react so fast that details of their reaction mechanisms cannot be obtained with experimental work. In recent years, major advances in computational hardware and software have been made and now large (bio)chemical systems can be studied using accurate computational techniques. One such technique is the quantum mechanics/molecular mechanics (QM/MM) technique, which has gained major momentum in recent years. Unfortunately, it is not a black-box method that is easily applied, but requires careful set-up procedures. In this work we give an overview on the technical difficulties and caveats of QM/MM and discuss work-protocols developed in our groups for running successful QM/MM calculations.

A Novel Scoring Based Distributed Protein Docking Application to Improve Enrichment

Molecular docking is a computational technique which predicts the binding energy and the preferred binding mode of a ligand to a protein target. Virtual screening is a tool which uses docking to investigate large chemical libraries to identify ligands that bind favorably to a protein target. We have developed a novel scoring based distributed protein docking application to improve enrichment in virtual screening. The application addresses the issue of time and cost of screening in contrast to conventional systematic parallel virtual screening methods in two ways. Firstly, it automates the process of creating and launching multiple independent dockings on a high performance computing cluster. Secondly, it uses a Nȧi̇ve Bayes scoring function to calculate binding energy of un-docked ligands to identify and preferentially dock (Autodock predicted) better binders. The application was tested on four proteins using a library of 10,573 ligands. In all the experiments, (i). 200 of the 1,000 best binders are identified after docking only ~14 percent of the chemical library, (ii). 9 or 10 best-binders are identified after docking only ~19 percent of the chemical library, and (iii). no significant enrichment is observed after docking ~70 percent of the chemical library. The results show significant increase in enrichment of potential drug leads in early rounds of virtual screening.

In-silico Studies Show Potent Inhibition of HIV-1 Reverse Transcriptase Activity by a Herbal Drug

Acquired immunodeficiency syndrome (AIDS) is a life threatening disease of the human immune system caused by human immunodeficiency virus (HIV). Effective inhibition of reverse transcriptase activity is a prominent, clinically viable approach for the treatment of AIDS. Few non-nucleoside reverse transcriptase inhibitors (NNRTIs) have been approved by the United States Food and Drug Administration (US FDA) as drugs for AIDS. In order to enhance therapeutic options against AIDS we examined novel herbal compounds of 4-thiazolidinone and its derivatives that are known to have remarkable antiviral potency. Our molecular docking and simulation experiments have identified one such herbal molecule known as (5E)-3-(2-aminoethyl)-5-benzylidene-1, 3-thiazolidine-2,4-dione that may bind HIV-1RT with high affinity to cause noncompetitive inhibition. Results are also compared with other US FDA approved drugs. Long de novo simulations and docking study suggest that the ligand (5E)-3-(2-aminoethyl)-5-benzylidene-1, 3-thiazolidine-2,4-dione (CID: 1656714) has strong binding interactions with Asp113, Asp110, Asp185 and Asp186 amino acids, all of which belong to one or the other catalytic pockets of HIV-1RT. It is expected that these interactions could be critical in the inhibitory activity of the HIV-1RT. Therefore, this study provides an evidence for consideration of (5E)-3-(2-aminoethyl)-5-benzylidene-1, 3-thiazolidine-2,4-dione as a valuable natural molecule in the treatment and prevention of HIV-associated disorders.

Analysis of Enoyl-Acyl Carrier Protein Reductase Structure and Interactions Yields an Efficient Virtual Screening Approach and Suggests a Potential Allosteric Site

Enoyl-acyl carrier protein reductases have an important role in fatty acid biosynthesis and are considered essential for bacterial and protozoal survival. Here, we perform a computational assessment of enoyl-acyl carrier protein reductase structures, providing insights for inhibitor design that we incorporate into a virtual screening approach. Firstly, we analyse 80 crystal structures of 16 different enoyl-acyl carrier protein reductases for their active site characteristics and druggability, finding these sites contain a readily druggable pocket, of varying size and shape. Interestingly, a high affinity, potentially allosteric site was identified for pfFabl. Analysis of the ligand-protein interactions of four enoyl-acyl carrier protein reductases from different micro-organisms (InhA, pfFabl, saFabl and ecFabl), involving 59 available crystal structures, found three commonly shared interactions; constraining these interactions in docking improved enrichment of enoyl-acyl carrier protein reductase virtual screens, by up to 60% in the top 3% of the ranked library. This docking protocol also improved pose prediction, decreasing the root-mean-square deviation to crystallographic pose by up to 75% on average. The binding site analysis and knowledge-based docking protocol presented here can potentially assist in the structure-based design of new enoyl-acyl carrier protein reductase inhibitors.

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.

Validation of a homology model of Mycobacterium tuberculosis DXS: rationalization of observed activities of thiamine derivatives as potent inhibitors of two orthologues of DXS

We present the a homology model ofM. tuberculosisDXS that we validated by identifying thiamine and thiamine diphosphate analogues as potent inhibitors of DXS.

Monte Carlo loop refinement and virtual screening of the thyroid-stimulating hormone receptor transmembrane domain

Metropolis Monte Carlo (MMC) loop refinement has been performed on the three extracellular loops (ECLs) of rhodopsin and opsin-based homology models of the thyroid-stimulating hormone receptor transmembrane domain, a class A type G protein-coupled receptor. The Monte Carlo sampling technique, employing torsion angles of amino acid side chains and local moves for the six consecutive backbone torsion angles, has previously reproduced the conformation of several loops with known crystal structures with accuracy consistently less than 2 Å. A grid-based potential map, which includes van der Waals, electrostatics, hydrophobic as well as hydrogen-bond potentials for bulk protein environment and the solvation effect, has been used to significantly reduce the computational cost of energy evaluation. A modified sigmoidal distance-dependent dielectric function has been implemented in conjunction with the desolvation and hydrogen-bonding terms. A long high-temperature simulation with 2 kcal/mol repulsion potential resulted in extensive sampling of the conformational space. The slow annealing leading to the low-energy structures predicted secondary structure by the MMC technique. Molecular docking with the reported agonist reproduced the binding site within 1.5 Å. Virtual screening performed on the three lowest structures showed that the ligand-binding mode in the inter-helical region is dependent on the ECL conformations.

Protein tyrosine phosphatases: Ligand interaction analysis and optimisation of virtual screening

Docking-based virtual screening is an established component of structure-based drug discovery. Nevertheless, scoring and ranking of computationally docked ligand libraries still suffer from many false positives. Identifying optimal docking parameters for a target protein prior to virtual screening can improve experimental hit rates. Here, we examine protocols for virtual screening against the important but challenging class of drug target, protein tyrosine phosphatases. In this study, common interaction features were identified from analysis of protein-ligand binding geometries of more than 50 complexed phosphatase crystal structures. It was found that two interactions were consistently formed across all phosphatase inhibitors: (1) a polar contact with the conserved arginine residue, and (2) at least one interaction with the P-loop backbone amide. In order to investigate the significance of these features on phosphatase-ligand binding, a series of seeded virtual screening experiments were conducted on three phosphatase enzymes, PTP1B, Cdc25b and IF2. It was observed that when the conserved arginine and P-loop amide interactions were used as pharmacophoric constraints during docking, enrichment of the virtual screen significantly increased in the three studied phosphatases, by up to a factor of two in some cases. Additionally, the use of such pharmacophoric constraints considerably improved the ability of docking to predict the inhibitor's bound pose, decreasing RMSD to the crystallographic geometry by 43% on average. Constrained docking improved enrichment of screens against both open and closed conformations of PTP1B. Incorporation of an ordered water molecule in PTP1B screening was also found to generally improve enrichment. The knowledge-based computational strategies explored here can potentially inform structure-based design of new phosphatase inhibitors using docking-based virtual screening.

Comparative assessment of scoring functions on an updated benchmark: 1. Compilation of the test set

Scoring functions are often applied in combination with molecular docking methods to predict ligand binding poses and ligand binding affinities or to identify active compounds through virtual screening. An objective benchmark for assessing the performance of current scoring functions is expected to provide practical guidance for the users to make smart choices among available methods. It can also elucidate the common weakness in current methods for future improvements. The primary goal of our comparative assessment of scoring functions (CASF) project is to provide a high-standard, publicly accessible benchmark of this type. Our latest study, i.e., CASF-2013, evaluated 20 popular scoring functions on an updated set of protein-ligand complexes. This data set was selected out of 8302 protein-ligand complexes recorded in the PDBbind database (version 2013) through a fairly complicated process. Sample selection was made by considering the quality of complex structures as well as binding data. Finally, qualified complexes were clustered by 90% similarity in protein sequences. Three representative complexes were chosen from each cluster to control sample redundancy. The final outcome, namely, the PDBbind core set (version 2013), consists of 195 protein-ligand complexes in 65 clusters with binding constants spanning nearly 10 orders of magnitude. In this data set, 82% of the ligand molecules are "druglike" and 78% of the protein molecules are validated or potential drug targets. Correlation between binding constants and several key properties of ligands are discussed. Methods and results of the scoring function evaluation will be described in a companion work in this issue (doi: 10.1021/ci500081m ).

Comparative assessment of scoring functions on an updated benchmark: 2. Evaluation methods and general results

Our comparative assessment of scoring functions (CASF) benchmark is created to provide an objective evaluation of current scoring functions. The key idea of CASF is to compare the general performance of scoring functions on a diverse set of protein-ligand complexes. In order to avoid testing scoring functions in the context of molecular docking, the scoring process is separated from the docking (or sampling) process by using ensembles of ligand binding poses that are generated in prior. Here, we describe the technical methods and evaluation results of the latest CASF-2013 study. The PDBbind core set (version 2013) was employed as the primary test set in this study, which consists of 195 protein-ligand complexes with high-quality three-dimensional structures and reliable binding constants. A panel of 20 scoring functions, most of which are implemented in main-stream commercial software, were evaluated in terms of "scoring power" (binding affinity prediction), "ranking power" (relative ranking prediction), "docking power" (binding pose prediction), and "screening power" (discrimination of true binders from random molecules). Our results reveal that the performance of these scoring functions is generally more promising in the docking/screening power tests than in the scoring/ranking power tests. Top-ranked scoring functions in the scoring power test, such as X-Score(HM), ChemScore@SYBYL, ChemPLP@GOLD, and PLP@DS, are also top-ranked in the ranking power test. Top-ranked scoring functions in the docking power test, such as ChemPLP@GOLD, Chemscore@GOLD, GlidScore-SP, LigScore@DS, and PLP@DS, are also top-ranked in the screening power test. Our results obtained on the entire test set and its subsets suggest that the real challenge in protein-ligand binding affinity prediction lies in polar interactions and associated desolvation effect. Nonadditive features observed among high-affinity protein-ligand complexes also need attention.

DiSCuS: an open platform for (not only) virtual screening results management

DiSCuS, a "Database System for Compound Selection", has been developed. The primary goal of DiSCuS is to aid researchers in the steps subsequent to generating high-throughput virtual screening (HTVS) results, such as selection of compounds for further study, purchase, or synthesis. To do so, DiSCuS provides (1) a storage facility for ligand-receptor complexes (generated with external programs), (2) a number of tools for validating these complexes, such as scoring functions, potential energy contributions, and med-chem features with ligand similarity estimates, and (3) powerful searching and filtering options with logical operators. DiSCuS supports multiple receptor targets for a single ligand, so it can be used either to evaluate different variants of an active site or for selectivity studies. DiSCuS documentation, installation instructions, and source code can be found at http://discus.ibb.waw.pl .

Computational approaches to predicting the impact of novel bases on RNA structure and stability

The use of computational modeling techniques to gain insight into nucleobase interactions has been a challenging endeavor to date. Accurate treatment requires the tackling of many challenges but also holds the promise of great rewards. The development of effective computational approaches to predict the binding affinities of nucleobases and analogues can, for example, streamline the process of developing novel nucleobase modifications, which should facilitate the development of new RNAi-based therapeutics. This brief review focuses on available computational approaches to predicting base pairing affinity in RNA-based contexts such as nucleobase-nucleobase interactions in duplexes and nucleobase-protein interactions. The challenges associated with such modeling along with potential future directions for the field are highlighted.

Synthesis and biological evaluation of direct thrombin inhibitors bearing 4-(piperidin-1-yl)pyridine at the P1 position with potent anticoagulant activity

The design and synthesis of a new class of nonpeptide direct thrombin inhibitors, built on the structure of 1-(pyridin-4-yl)piperidine-4-carboxamide, are described. Starting from a strongly basic 1-amidinopiperidine derivative (6) showing poor thrombin (fIIa) and factor Xa (fXa) inhibition activities, anti-fIIa activity and artificial membrane permeability were considerably improved by optimizing the basic P1 and the X-substituted phenyl P4 binding moieties. Structure-activity relationship studies, usefully complemented with molecular modeling results, led us to identify compound 13b, which showed excellent fIIa inhibition (Ki = 6 nM), weak anti-Xa activity (Ki = 5.64 μM), and remarkable selectivity over other serine proteases (e.g., trypsin). Compound 13b showed in vitro anticoagulant activity in the low micromolar range and significant membrane permeability. In mice (ex vivo), 13b demonstrated anticoagulant effects at 2 h after oral dosing (100 mg·kg(-1)), with a significant 43% prolongation of the activated partial thromboplastin time (aPTT), over controls (P < 0.05).

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.

Improving the scoring of protein-ligand binding affinity by including the effects of structural water and electronic polarization

Docking programs that use scoring functions to estimate binding affinities of small molecules to biological targets are widely applied in drug design and drug screening with partial success. But accurate and efficient scoring functions for protein-ligand binding affinity still present a grand challenge to computational chemists. In this study, the polarized protein-specific charge model (PPC) is incorporated into the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) method to rescore the binding poses of some protein-ligand complexes, for which docking programs, such as Autodock, could not predict their binding modes correctly. Different sampling techniques (single minimized conformation and multiple molecular dynamics (MD) snapshots) are used to test the performance of MM/PBSA combined with the PPC model. Our results show the availability and effectiveness of this approach in correctly ranking the binding poses. More importantly, the bridging water molecules are found to play an important role in correctly determining the protein-ligand binding modes. Explicitly including these bridging water molecules in MM/PBSA calculations improves the prediction accuracy significantly. Our study sheds light on the importance of both bridging water molecules and the electronic polarization in the development of more reliable scoring functions for predicting molecular docking and protein-ligand binding affinity.

Fragment-based drug discovery using a multidomain, parallel MD-MM/PBSA screening protocol

We have developed a rigorous computational screening protocol to identify novel fragment-like inhibitors of N(5)-CAIR mutase (PurE), a key enzyme involved in de novo purine synthesis that represents a novel target for the design of antibacterial agents. This computational screening protocol utilizes molecular docking, graphics processing unit (GPU)-accelerated molecular dynamics, and Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) free energy estimations to investigate the binding modes and energies of fragments in the active sites of PurE. PurE is a functional octamer comprised of identical subunits. The octameric structure, with its eight active sites, provided a distinct advantage in these studies because, for a given simulation length, we were able to place eight separate fragment compounds in the active sites to increase the throughput of the MM/PBSA analysis. To validate this protocol, we have screened an in-house fragment library consisting of 352 compounds. The theoretical results were then compared with the results of two experimental fragment screens, Nuclear Magnetic Resonance (NMR) and Surface Plasmon Resonance (SPR) binding analyses. In these validation studies, the protocol was able to effectively identify the competitive binders that had been independently identified by experimental testing, suggesting the potential utility of this method for the identification of novel fragments for future development as PurE inhibitors.

Design, synthesis, and biological evaluation of indenoisoquinoline rexinoids with chemopreventive potential

Nuclear receptors, such as the retinoid X receptor (RXR), are proteins that regulate a myriad of cellular processes. Molecules that function as RXR agonists are of special interest for the prevention and control of carcinogenesis. The majority of these ligands possess an acidic moiety that is believed to be key for RXR activation. This communication presents the design, synthesis, and biological evaluation of both acidic and nonacidic indenoisoquinolines as new RXR ligands. In addition, a comprehensive structure-activity relationship study is presented that identifies the important features of the indenoisoquinoline rexinoids. The ease of modification of the indenoisoquinoline core and the lack of the necessity of a carboxyl group for activity make them an attractive and unusual family of RXR agonists. This work establishes a structural foundation for the design of new and novel rexinoid cancer chemopreventive agents.

Target based virtual screening by docking into automatically generated GPCR models

Target based virtual screening (VS) combined with high-throughput measurements is an extremely useful tool to identify small molecule hits for proteins and in particular for G-protein coupled receptors (GPCRs). However, this is a quite difficult process for GPCRs due to the paucity of 3D structural information on these receptors. Therefore, the only possibility for target based VS is to build a structural model of the GPCR to be used for docking. However, GPCR model building is a very time consuming process, if the model should be able to explain all experimental findings and this investment is not always justified, if the model is only used for VS. Thus, a fully automated workflow is presented here, where a large number of GPCR models is built, and the best model is identified to be used for docking. The workflow leads to moderate enrichments with a very low effort. The inputs required are the sequence of the targeted GPCR, a reference ligand with experimental information and a database of small molecules to be used for docking. Manual intervention is recommended at various points, but it is strictly speaking not necessary.