DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases

Quantum mechanics implementation in drug-design workflows: does it really help?

The pharmaceutical industry is progressively operating in an era where development costs are constantly under pressure, higher percentages of drugs are demanded, and the drug-discovery process is a trial-and-error run. The profit that flows in with the discovery of new drugs has always been the motivation for the industry to keep up the pace and keep abreast with the endless demand for medicines. The process of finding a molecule that binds to the target protein using in silico tools has made computational chemistry a valuable tool in drug discovery in both academic research and pharmaceutical industry. However, the complexity of many protein-ligand interactions challenges the accuracy and efficiency of the commonly used empirical methods. The usefulness of quantum mechanics (QM) in drug-protein interaction cannot be overemphasized; however, this approach has little significance in some empirical methods. In this review, we discuss recent developments in, and application of, QM to medically relevant biomolecules. We critically discuss the different types of QM-based methods and their proposed application to incorporating them into drug-design and -discovery workflows while trying to answer a critical question: are QM-based methods of real help in drug-design and -discovery research and industry?

Design, synthesis and α-amylase inhibitory activity of novel chromone derivatives

Quercetin is one of the naturally occurring polyphenol flavonoid predominantly known for antidiabetic activity. In the present study, by considering the structural requirements, twenty two novel chromone derivatives (5-26) as α-amylase inhibitor were designed and subsequently in silico evaluated for drug likeness behavior. Designed compounds were synthesized, characterized by spectral analysis and finally evaluated for the inhibition of α-amylase activity by in vitro assay. Tested compounds exhibited significant to weak activity with IC₅₀ range of 12-125µM. Among the tested compounds, analogues 5, 8, 12, 13, 15, 17 and 22 exhibited significant human α-amylase inhibitory activity with IC₅₀ values <25µM, which can be further explored as anti-hyperglycemic agents. Putative binding mode of the significant and least active α-amylase inhibitors with the target enzyme was also explored by the docking studies.

Superior Performance of the SQM/COSMO Scoring Functions in Native Pose Recognition of Diverse Protein-Ligand Complexes in Cognate Docking

General and reliable description of structures and energetics in protein-ligand (PL) binding using the docking/scoring methodology has until now been elusive. We address this urgent deficiency of scoring functions (SFs) by the systematic development of corrected semiempirical quantum mechanical (SQM) methods, which correctly describe all types of noncovalent interactions and are fast enough to treat systems of thousands of atoms. Two most accurate SQM methods, PM6-D3H4X and SCC-DFTB3-D3H4X, are coupled with the conductor-like screening model (COSMO) implicit solvation model in so-called "SQM/COSMO" SFs and have shown unique recognition of native ligand poses in cognate docking in four challenging PL systems, including metalloprotein. Here, we apply the two SQM/COSMO SFs to 17 diverse PL complexes and compare their performance with four widely used classical SFs (Glide XP, AutoDock4, AutoDock Vina, and UCSF Dock). We observe superior performance of the SQM/COSMO SFs and identify challenging systems. This method, due to its generality, comparability across the chemical space, and lack of need for any system-specific parameters, gives promise of becoming, after comprehensive large-scale testing in the near future, a useful computational tool in structure-based drug design and serving as a reference method for the development of other SFs.

Reversible manipulation of the G-quadruplex structures and enzymatic reactions through supramolecular host-guest interactions

Supramolecular chemistry addresses intermolecular forces and consequently promises great flexibility and precision. Biological systems are often the inspirations for supramolecular research. The G-quadruplex (G4) belongs to one of the most important secondary structures in nucleic acids. Until recently, the supramolecular manipulation of the G4 has not been reported. The present study is the first to disclose a supramolecular switch for the reversible control of human telomere G4s. Moreover, this supramolecular switch has been successfully used to manipulate an enzymatic reaction. Using various methods, we show that cucurbit[7]uril preferably locks and encapsulates the positively charged piperidines of Razo through supramolecular interactions. They can switch the conformations of the DNA inhibitor between a flexible state and the rigid G4 and are therefore responsible for the reversible control of the thrombin activity. Thus, our findings open a promising route and exhibit potential applications in future studies of chemical biology.

Using the multi-objective optimization replica exchange Monte Carlo enhanced sampling method for protein-small molecule docking

Background

In this study, we extended the replica exchange Monte Carlo (REMC) sampling method to protein–small molecule docking conformational prediction using RosettaLigand. In contrast to the traditional Monte Carlo (MC) and REMC sampling methods, these methods use multi-objective optimization Pareto front information to facilitate the selection of replicas for exchange.

Results

The Pareto front information generated to select lower energy conformations as representative conformation structure replicas can facilitate the convergence of the available conformational space, including available near-native structures. Furthermore, our approach directly provides min-min scenario Pareto optimal solutions, as well as a hybrid of the min-min and max-min scenario Pareto optimal solutions with lower energy conformations for use as structure templates in the REMC sampling method. These methods were validated based on a thorough analysis of a benchmark data set containing 16 benchmark test cases. An in-depth comparison between MC, REMC, multi-objective optimization-REMC (MO-REMC), and hybrid MO-REMC (HMO-REMC) sampling methods was performed to illustrate the differences between the four conformational search strategies.

Conclusions

Our findings demonstrate that the MO-REMC and HMO-REMC conformational sampling methods are powerful approaches for obtaining protein–small molecule docking conformational predictions based on the binding energy of complexes in RosettaLigand.

An Anti-Cancer Drug Candidate OSI-027 and its Analog as Inhibitors of mTOR: Computational Insights Into the Inhibitory Mechanisms

The mammalian target of rapamycin (mTOR) is a serine-threonine kinase, which regulates cellular metabolism and growth, and is a validated therapeutic target in various cancers. Recently, OSI-027, a selective ATP competitive inhibitor of mTOR, has been developed. The OSI-027 is an orally bioavailable compound whose anti-cancer activities were observed in various cancer cell lines and tumor xenograft models. The current study is the first attempt to explore the binding mode and the molecular-interactions of OSI-027 with mTOR using molecular docking and (un)binding simulation approaches. The study identified various interacting residues and their extent of involvement in binding was emphasized using different methods. The (un)binding simulation analyses provided snapshots of various phases in OSI-027 binding and identified residues important for binding but away from the catalytic site. Further, to explore a better binder for mTOR among OSI-027 analogs, the virtual screening led to propose an OSI-027 analog with CID: 73294902 as a better inhibitor than the OSI-027 and the native ligand PI-103. The binding mode of the proposed compound is compared with those of OSI-027 and other native inhibitors. The comparison of (un)binding simulation phases of proposed compound with that of OSI-027 revealed that both, bound to the same catalytic site, follow different (un)binding path. Thus, the current study presents computational insights into the OSI-027 mediated inhibition of mTOR kinase and proposed an OSI-027 analog as better mTOR inhibitor, and thus, a good drug for further research in experimental laboratories. J. Cell. Biochem. 118: 4558-4567, 2017. © 2017 Wiley Periodicals, Inc.

Searching for bioactive conformations of drug-like ligands with current force fields: how good are we?

Drug-like ligands obtained from protein-ligand complexes deposited in the Protein Databank were subjected to conformational searching using various force fields and solvation settings. For each ligand, the resulting conformer pool was examined for the presence of the bioactive (crystal pose-like) conformation. Similarity of conformers toward the crystal-pose was quantified as the best achievable root mean squared deviation (RMSD, heavy atoms only). Analyzing the conformer pools generated by various force fields revealed only small differences in the likelihood of finding a crystal pose-like conformation. However, employing different solvents in the conformational search was found to be very important for achieving RMSDs below 1.0 Å. The best statistical values of likelihood were observed with a recently released force field covering a large portion of dihedral angles occurring in drug-like compounds in combination with the water as solvent. In order to enable computational chemists and modelers to efficiently use available software tools, we have additionally performed several focused analyses on ligands, grouped according to descriptors most relevant for the rational drug design.

Protein-Ligand Docking Methodologies and Its Application in Drug Discovery

Molecular docking is a key tool in structural biology and computer-assisted drug design. Molecular docking is a method which predicts the preferred orientation of a ligand when bound in an active site to form a stable complex. It is the most common method used as a structure-based drug design. Here, the authors intend to discuss the various types of docking methods and their development and applications in modern drug discovery. The important basic theories such as sampling algorithm and scoring functions have been discussed briefly. The performances of the different available docking software have also been discussed. This chapter also includes some application examples of docking studies in modern drug discovery such as targeted drug delivery using carbon nanotubes, docking of nucleic acids to find the binding modes and a comparative study between high-throughput screening and structure-based virtual screening.

Protein-Protein Interactions (PPIs) as an Alternative to Targeting the ATP Binding Site of Kinase

Most of the developed kinase inhibitor drugs are ATP competitive and suffer from drawbacks such as off-target kinase activity, development of resistance due to mutation in the ATP binding pocket and unfavorable intellectual property situations. Besides the ATP binding pocket, protein kinases have binding sites that are involved in Protein-Protein Interactions (PPIs); these PPIs directly or indirectly regulate the protein kinase activity. Of recent, small molecule inhibitors of PPIs are emerging as an alternative to ATP competitive agents. Rational design of inhibitors for kinase PPIs could be carried out using molecular modeling techniques. In silico tools available for the prediction of hot spot residues and cavities at the PPI sites and the means to utilize this information for the identification of inhibitors are discussed. Moreover, in silico studies to target the Aurora B-INCENP PPI sites are discussed in context. Overall, this chapter provides detailed in silico strategies that are available to the researchers for carrying out structure-based drug design of PPI inhibitors.

Computer-Aided Drug Design Methods

Computational approaches are useful tools to interpret and guide experiments to expedite the antibiotic drug design process. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are the two general types of computer-aided drug design (CADD) approaches in existence. SBDD methods analyze macromolecular target 3-dimensional structural information, typically of proteins or RNA, to identify key sites and interactions that are important for their respective biological functions. Such information can then be utilized to design antibiotic drugs that can compete with essential interactions involving the target and thus interrupt the biological pathways essential for survival of the microorganism(s). LBDD methods focus on known antibiotic ligands for a target to establish a relationship between their physiochemical properties and antibiotic activities, referred to as a structure-activity relationship (SAR), information that can be used for optimization of known drugs or guide the design of new drugs with improved activity. In this chapter, standard CADD protocols for both SBDD and LBDD will be presented with a special focus on methodologies and targets routinely studied in our laboratory for antibiotic drug discoveries.

Structure-Based Virtual Screening

Structure-based virtual screening (SBVS) is a computational approach used in the early-stage drug discovery campaign to search a chemical compound library for novel bioactive molecules against a certain drug target. It utilizes the three-dimensional (3D) structure of the biological target, obtained from X-ray, NMR, or computational modeling, to dock a collection of chemical compounds into the binding site and select a subset of these compounds based on the predicted binding scores for further biological evaluation. In the present work, we illustrate the basic process of conducting a SBVS with examples using freely accessible tools and resources.

The Comparison of Docking Search Algorithms and Scoring Functions

This chapter, composed of two parts, firstly provides molecular docking overview and secondly two molecular docking case studies are described. In overview, basic information about molecular docking are presented such as description of search algorithms and scoring functions applied in various docking programs. Brief description of methods utilized in some of the most popular docking programs also applied in our experimental work is provided. AutoDock, CDOCKER, GOLD, FlexX and FRED were used for docking studies of the DC-SIGN protein, while GOLD was further used for docking studies of cathepsin K protein. Methods and results of our studies with their contribution to science and medicine are presented. Content of the chapter is therefore appropriate for public of Medicinal and Organic Chemistry as an overview of docking studies, and also for Computational Chemists at the beginning of their work as the introduction to application of molecular docking programs.

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.

Correlation Between Molecular Modelling and Spectroscopic Techniques in Investigation With DNA Binding Interaction of Ruthenium(II) Complexes

The DNA binding studies of rutheniumu(II) polypyridyl complexes {[Ru(phen)₂Mipc]²⁺, [Ru(bpy)₂Mipc]²⁺, [Ru(dmb)₂Mipc]²⁺, [Ru(phen)₂BrIPC]²⁺, [Ru(bpy)₂BrIPC]²⁺, [Ru(dmb)₂BrIPC]²⁺, [Ru(phen)₂PIP-Cl]²⁺, [Ru(bpy)₂PIP-Cl]²⁺, [Ru(dmb)₂PIP-Cl]²⁺, [Ru(phen)₂IPPBA]²⁺, [Ru(bpy)₂IPPBA]²⁺, [Ru(dmb)₂IPPBA]²⁺} with DNA investigated by electronic absorption titration, emission and molecular modelling studies to identify the binding interactions. All these complexes are showing good binding constant values ~10⁴ to 10⁵. The intercalative ligands makes the binding of the ruthenium(II) complex with DNA as intercalation mode. The ancillary ligands 1,10-phenanthroline (phen), 4,4'-Dimethyl-2,2'-dipyridyl (dmb) and 2,2'-dipyridine (bpy) having been discovered found to be involved in bond formation with the phosphate backbone of nucleotide base pairs in ruthenium(II) complex-DNA docked complex. The molecular docking results are good agreement with experimental results. The molecular modelling technic should help to extend knowledge about the nature (or) mode of binding of these ruthenium(II) complexes with (calf thymus) CT-DNA.

The Effect of Conformational Flexibility on Binding Free Energy Estimation between Kinases and Their Inhibitors

Accurate prediction of binding affinities of drug candidates to their targets remains challenging because of protein flexibility in solution. Conformational flexibility of the ATP-binding site in the CDK2 and ERK2 kinases was identified using molecular dynamics simulations. The binding free energy (ΔG) of twenty-four ATP-competitive inhibitors toward these kinases was assessed using an alchemical free energy perturbation method, MP-CAFEE. However, large calculation errors of 2-3 kcal/mol were observed using this method, where the free energy simulation starts from a single equilibrated conformation. Here, we developed a new ΔG computation method, where the starting structure was set to multiconformations to cover flexibility. The calculation accuracy was successfully improved, especially for larger molecular size compounds, leading to reliable prediction of a broader range of drug candidates. The present study demonstrates that conformational flexibility of interactions between a compound and the glycine-rich loop in the kinases is a key factor in ΔG estimation.

Identification of Novel Inhibitors against Coactivator Associated Arginine Methyltransferase 1 Based on Virtual Screening and Biological Assays

Overexpression of coactivator associated arginine methyltransferase 1 (CARM1), a protein arginine N-methyltransferase (PRMT) family enzyme, is associated with various diseases including cancers. Consequently, the development of small-molecule inhibitors targeting PRMTs has significant value for both research and therapeutic purposes. In this study, together with structure-based virtual screening with biochemical assays, two compounds DC_C11 and DC_C66 were identified as novel inhibitors of CARM1. Cellular studies revealed that the two inhibitors are cell membrane permeable and effectively blocked proliferation of cancer cells including HELA, K562, and MCF7. We further predicted the binding mode of these inhibitors through molecular docking analysis, which indicated that the inhibitors competitively occupied the binding site of the substrate and destroyed the protein-protein interactions between CARM1 and its substrates. Overall, this study has shed light on the development of small-molecule CARM1 inhibitors with novel scaffolds.

Discovery of the molecular mechanisms of the novel chalcone-based Magnaporthe oryzae inhibitor C1 using transcriptomic profiling and co-expression network analysis

Background

In our previous studies, we discovered a series of chalcone-based phytopathogenic fungus inhibitors. However, knowledge of their effects, detailed targets and molecular mechanisms in Magnaporthe oryzae (M. oryzae) remained limited.

Methods

To explore the expression and function of differentially expressed genes in M. oryzae after treatment with compound C1, we analyzed the expression profile of mRNAs using a microarray analysis and GO, KEGG and WGCNA analysis, followed by qRT-PCR and Western blots to validate our findings.

Results

A total of 1013 up-regulated and 995 down-regulated mRNAs were differentially expressed after M. oryzae was treated with C1 compared to those of the control samples. Among these, cytochrome P450, glycylpeptide N-myristoyltransferase (NMT) and peroxisomal membrane protein 4 were identified as the most significant DEGs and were validated by experiments.

Conclusion

In conclusion, our study suggests that the combination of transcriptomic microarray, bioinformatics analysis and weighted gene co-expression networks can be used to predict potential therapeutic targets and to map the pathways regulated by small molecular natural product-like drugs.

Electronic supplementary material

The online version of this article (doi:10.1186/s40064-016-3385-9) contains supplementary material, which is available to authorized users.

Novel small molecule binders of human N-glycanase 1, a key player in the endoplasmic reticulum associated degradation pathway

Peptide:N-glycanase (NGLY1) is an enzyme responsible for cleaving oligosaccharide moieties from misfolded glycoproteins to enable their proper degradation. Deletion and truncation mutations in this gene are responsible for an inherited disorder of the endoplasmic reticulum-associated degradation pathway. However, the literature is unclear whether the disorder is a result of mutations leading to loss-of-function, loss of substrate specificity, loss of protein stability or a combination of these factors. In this communication, without burdening ourselves with the mechanistic underpinning of disease causation because of mutations on the NGLY1 protein, we demonstrate the successful application of virtual ligand screening (VLS) combined with experimental high-throughput validation to the discovery of novel small-molecules that show binding to the transglutaminase domain of NGLY1. Attempts at recombinant expression and purification of six different constructs led to successful expression of five, with three constructs purified to homogeneity. Most mutant variants failed to purify possibly because of misfolding and the resultant exposure of surface hydrophobicity that led to protein aggregation. For the purified constructs, our threading/structure-based VLS algorithm, FINDSITE(comb), was employed to predict ligands that may bind to the protein. Then, the predictions were assessed by high-throughput differential scanning fluorimetry. This led to the identification of nine different ligands that bind to the protein of interest and provide clues to the nature of pharmacophore that facilitates binding. This is the first study that has identified novel ligands that bind to the NGLY1 protein as a possible starting point in the discovery of ligands with potential therapeutic applications in the treatment of the disorder caused by NGLY1 mutants.

Virtual Screening Approaches towards the Discovery of Toll-Like Receptor Modulators

This review aims to summarize the latest efforts performed in the search for novel chemical entities such as Toll-like receptor (TLR) modulators by means of virtual screening techniques. This is an emergent research field with only very recent (and successful) contributions. Identification of drug-like molecules with potential therapeutic applications for the treatment of a variety of TLR-regulated diseases has attracted considerable interest due to the clinical potential. Additionally, the virtual screening databases and computational tools employed have been overviewed in a descriptive way, widening the scope for researchers interested in the field.

A D3R prospective evaluation of machine learning for protein-ligand scoring

We assess the performance of several machine learning-based scoring methods at protein-ligand pose prediction, virtual screening, and binding affinity prediction. The methods and the manner in which they were trained make them sufficiently diverse to evaluate the utility of various strategies for training set curation and binding pose generation, but they share a novel approach to classification in the context of protein-ligand scoring. Rather than explicitly using structural data such as affinity values or information extracted from crystal binding poses for training, we instead exploit the abundance of data available from high-throughput screening to approach the problem as one of discriminating binders from non-binders. We evaluate the performance of our various scoring methods in the 2015 D3R Grand Challenge and find that although the merits of some features of our approach remain inconclusive, our scoring methods performed comparably to a state-of-the-art scoring function that was fit to binding affinity data.

Optimal strategies for virtual screening of induced-fit and flexible target in the 2015 D3R Grand Challenge

Induced fit or protein flexibility can make a given structure less useful for docking and/or scoring. The 2015 Drug Design Data Resource (D3R) Grand Challenge provided a unique opportunity to prospectively test optimal strategies for virtual screening in these type of targets: heat shock protein 90 (HSP90), a protein with multiple ligand-induced binding modes; and mitogen-activated protein kinase kinase kinase kinase 4 (MAP4K4), a kinase with a large flexible pocket. Using previously known co-crystal structures, we tested predictions from methods that keep the receptor structure fixed and used (a) multiple receptor/ligand co-crystals as binding templates for minimization or docking ("close"), (b) methods that align or dock to a single receptor ("cross"), and (c) a hybrid approach that chose from multiple bound ligands as initial templates for minimization to a single receptor ("min-cross"). Pose prediction using our "close" models resulted in average ligand RMSDs of 0.32 and 1.6 Å for HSP90 and MAP4K4, respectively, the most accurate models of the community-wide challenge. On the other hand, affinity ranking using our "cross" methods performed well overall despite the fact that a fixed receptor cannot model ligand-induced structural changes,. In addition, "close" methods that leverage the co-crystals of the different binding modes of HSP90 also predicted the best affinity ranking. Our studies suggest that analysis of changes on the receptor structure upon ligand binding can help select an optimal virtual screening strategy.

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.

Potent selective monoamine oxidase B inhibition by maackiain, a pterocarpan from the roots of Sophora flavescens

Monoamine oxidase (MAO) catalyzes the oxidation of monoamines and its two isoforms, MAO-A and MAO-B, break down neurotransmitter amines. Of the compounds isolated from the roots of Sophora flavescens, (-)-maackiain (4), a pterocarpan, was found to potently and selectively inhibit human MAO-B, with an IC50 of 0.68μM, and to have a selectivity index of 126.2 for MAO-B. As compared with other herbal natural products, the IC50 value of 4 for MAO-B is one of the lowest reported to date. Genistein (1) highly, effectively and non-selectively inhibited MAO-A and MAO-B with IC50 values of 3.9μM and 4.1μM, respectively. (-)-4-Hydroxy-3-methoxy-8,9-methylenedioxypterocarpan (2) effectively and non-selectively inhibited MAO-A and MAO-B with IC50 values of 20.3μM and 10.3μM, respectively. In addition, compound 4 reversibly and competitively inhibited MAO-B with a Ki value of 0.054μM. Molecular docking simulation revealed that the binding affinity of 4 for MAO-B (-26.6kcal/mol) was greater than its affinity for MAO-A (-8.3kcal/mol), which was in-line with our inhibitory activity findings. Furthermore, Cys172 of MAO-B was found to be a key residue for hydrogen bonding with compound 4. The findings of this study suggest compound 4 be viewed as a new potent, selective, and reversible MAO-B inhibitor, and that compounds 1 and 2 be considered useful lead compounds for the developments of nonselective and reversible MAO inhibitors for the treatment of disorders like Parkinson's disease, Alzheimer disease, and depression.

Driving Structure-Based Drug Discovery through Cosolvent Molecular Dynamics

Identifying binding hotspots on protein surfaces is of prime interest in structure-based drug discovery, either to assess the tractability of pursuing a protein target or to drive improved potency of lead compounds. Computational approaches to detect such regions have traditionally relied on energy minimization of probe molecules onto static protein conformations in the absence of the natural aqueous environment. Advances in high performance computing now allow us to assess hotspots using molecular dynamics (MD) simulations. MD simulations integrate protein flexibility and the complicated role of water, thereby providing a more realistic assessment of the complex kinetics and thermodynamics at play. In this review, we describe the evolution of various cosolvent-based MD techniques and highlight a myriad of potential applications for such technologies in computational drug development.

Interaction of small molecules with human tyrosinase: A surface plasmon resonance and molecular docking study

Studies on tyrosinase have recently gained the attention of researchers due to the enzyme's biological functions and potential applications in food and cosmetic applications. In this present study, screening and kinetic evaluation of small molecules (>300Da) on human tyrosinase was carried out using surface plasmon resonance and molecular docking studies. Four molecules showed significant binding were further characterized. The binding constant K_D (M) values obtained for crocin, curcumin, tannic acid, pyrogallol and hydroquinone are 5.60×10^-5, 1.52×10^-3, 6.45×10^-5, 1.34×10^-5 and 2.433×10^-7M respectively. In silico docking studies using autodock indicated significant binding and revealed the binding pocket residues for all molecules. The study shows the Biacore/SPR sensor's ability for screening and detection of inhibitors for human tyrosinase. This study can be used to rapidly screen and optimize various lead compounds as binders/inhibitors/modulators of human tyrosinase enzyme activity.

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

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

"Rules of Engagement" of Protein-Glycoconjugate Interactions: A Molecular View Achievable by using NMR Spectroscopy and Molecular Modeling

Understanding the dynamics of protein-ligand interactions, which lie at the heart of host-pathogen recognition, represents a crucial step to clarify the molecular determinants implicated in binding events, as well as to optimize the design of new molecules with therapeutic aims. Over the last decade, advances in complementary biophysical and spectroscopic methods permitted us to deeply dissect the fine structural details of biologically relevant molecular recognition processes with high resolution. This Review focuses on the development and use of modern nuclear magnetic resonance (NMR) techniques to dissect binding events. These spectroscopic methods, complementing X-ray crystallography and molecular modeling methodologies, will be taken into account as indispensable tools to provide a complete picture of protein-glycoconjugate binding mechanisms related to biomedicine applications against infectious diseases.

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.

Discovering Severe Acute Respiratory Syndrome Coronavirus 3CL Protease Inhibitors: Virtual Screening, Surface Plasmon Resonance, and Fluorescence Resonance Energy Transfer Assays

An integrated system has been developed for discovering potent inhibitors of severe acute respiratory syndrome coronavirus 3C-like protease (SARS-CoV 3CLpro) by virtual screening correlating with surface plasmon resonance (SPR) and fluorescence resonance energy transfer (FRET) technologies-based assays. The authors screened 81,287 small molecular compounds against SPECS database by virtual screening; 256 compounds were subsequently selected for biological evaluation. Through SPR technology-based assay, 52 from these 256 compounds were discovered to show binding to SARS-CoV 3CLpro. The enzymatic inhibition activities of these 52 SARS-CoV 3CLpro binders were further applied to FRET-based assay, and IC50 values were determined. Based on this integrated assay platform, 8 new SARS-CoV 3CLpro inhibitors were discovered. The fact that the obtained IC50 values for the inhibitors are in good accordance with the discovered dissociation equilibrium constants (KDs) assayed by SPR implied the reliability of this platform. Our current work is hoped to supply a powerful approach in the discovery of potent SARS-CoV 3CLpro inhibitors, and the determined inhibitors could be used as possible lead compounds for further research.

Molecular modelling, synthesis and biological evaluation of peptide inhibitors as anti-angiogenic agent targeting neuropilin-1 for anticancer application

Vascular endothelial growth factor (VEGF) and its co-receptor neuropilin-1 (NRP-1) are important targets of many pro-angiogenic factors. In this study, nine peptides were synthesized and evaluated for their molecular interaction with NRP-1 and compared to our previous peptide ATWLPPR. Docking study showed that the investigated peptides shared the same binding region as shown by tuftsin known to bind selectively to NRP-1. Four pentapeptides (DKPPR, DKPRR, TKPPR and TKPRR) and a hexapeptide CDKPRR demonstrated good inhibitory activity against NRP-1. In contrast, peptides having arginine residue at sites other than the C-terminus exhibited low activity towards NRP-1 and this is confirmed by their inability to displace the VEGF₁₆₅ binding to NRP-1. Docking study also revealed that replacement of carboxyl to amide group at the C-terminal arginine of the peptide did not affect significantly the binding interaction to NRP-1. However, the molecular affinity study showed that these peptides have marked reduction in the activity against NRP-1. Pentapeptides having C-terminal arginine showed strong interaction and good inhibitory activity with NRP thus may be a good template for anti-angiogenic targeting agent.

Vinardo: A Scoring Function Based on Autodock Vina Improves Scoring, Docking, and Virtual Screening

Autodock Vina is a very popular, and highly cited, open source docking program. Here we present a scoring function which we call Vinardo (Vina RaDii Optimized). Vinardo is based on Vina, and was trained through a novel approach, on state of the art datasets. We show that the traditional approach to train empirical scoring functions, using linear regression to optimize the correlation of predicted and experimental binding affinities, does not result in a function with optimal docking capabilities. On the other hand, a combination of scoring, minimization, and re-docking on carefully curated training datasets allowed us to develop a simplified scoring function with optimum docking performance. This article provides an overview of the development of the Vinardo scoring function, highlights its differences with Vina, and compares the performance of the two scoring functions in scoring, docking and virtual screening applications. Vinardo outperforms Vina in all tests performed, for all datasets analyzed. The Vinardo scoring function is available as an option within Smina, a fork of Vina, which is freely available under the GNU Public License v2.0 from http://smina.sf.net. Precompiled binaries, source code, documentation and a tutorial for using Smina to run the Vinardo scoring function are available at the same address.

Computational Methods Applied to Rational Drug Design

Due to the synergic relationship between medical chemistry, bioinformatics and molecular simulation, the development of new accurate computational tools for small molecules drug design has been rising over the last years. The main result is the increased number of publications where computational techniques such as molecular docking, de novo design as well as virtual screening have been used to estimate the binding mode, site and energy of novel small molecules. In this work I review some tools, which enable the study of biological systems at the atomistic level, providing relevant information and thereby, enhancing the process of rational drug design.

Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics

Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.

Innovative computer-aided methods for the discovery of new kinase ligands

Recent evidence points to significant roles played by protein kinases in cell signaling and cellular proliferation. Faulty protein kinases are involved in cancer, diabetes and chronic inflammation. Efforts are continuously carried out to discover new inhibitors for selected protein kinases. In this review, we discuss two new computer-aided methodologies we developed to mine virtual databases for new bioactive compounds. One method is ligand-based exploration of the pharmacophoric space of inhibitors of any particular biotarget followed by quantitative structure-activity relationship-based selection of the best pharmacophore(s). The second approach is structure-based assuming that potent ligands come into contact with binding site spots distinct from those contacted by weakly potent ligands. Both approaches yield pharmacophores useful as 3D search queries for the discovery of new bioactive (kinase) inhibitors.

Adaptive BP-Dock: An Induced Fit Docking Approach for Full Receptor Flexibility

We present an induced fit docking approach called Adaptive BP-Dock that integrates perturbation response scanning (PRS) with the flexible docking protocol of RosettaLigand in an adaptive manner. We first perturb the binding pocket residues of a receptor and obtain a new conformation based on the residue response fluctuation profile using PRS. Next, we dock a ligand to this new conformation by RosettaLigand, where we repeat these steps for several iterations. We test this approach on several protein test sets including difficult unbound docking cases such as HIV-1 reverse transcriptase and HIV-1 protease. Adaptive BP-Dock results show better correlation with experimental binding affinities compared to other docking protocols. Overall, the results imply that Adaptive BP-Dock can easily capture binding induced conformational changes by simultaneous sampling of protein and ligand conformations. This can provide faster and efficient docking of novel targets for rational drug design.

1001 Ways to run AutoDock Vina for virtual screening

Large-scale computing technologies have enabled high-throughput virtual screening involving thousands to millions of drug candidates. It is not trivial, however, for biochemical scientists to evaluate the technical alternatives and their implications for running such large experiments. Besides experience with the molecular docking tool itself, the scientist needs to learn how to run it on high-performance computing (HPC) infrastructures, and understand the impact of the choices made. Here, we review such considerations for a specific tool, AutoDock Vina, and use experimental data to illustrate the following points: (1) an additional level of parallelization increases virtual screening throughput on a multi-core machine; (2) capturing of the random seed is not enough (though necessary) for reproducibility on heterogeneous distributed computing systems; (3) the overall time spent on the screening of a ligand library can be improved by analysis of factors affecting execution time per ligand, including number of active torsions, heavy atoms and exhaustiveness. We also illustrate differences among four common HPC infrastructures: grid, Hadoop, small cluster and multi-core (virtual machine on the cloud). Our analysis shows that these platforms are suitable for screening experiments of different sizes. These considerations can guide scientists when choosing the best computing platform and set-up for their future large virtual screening experiments.

Elucidating the binding efficacy of β-galactosidase on graphene by docking approach and its potential application in galacto-oligosaccharide production

Herein, we propose the synthesis and characterization of graphene for the immobilization of β-galactosidase for improved galacto-oligosaccharide (GOS) production. The size of synthesized graphene was observed to be 25 nm by TEM analysis while interaction of enzyme with the nanosupport was observed by FTIR spectroscopy. Docking was obtained using molecular docking program Dock v.6.5 while the visual analyses and illustration of protein-ligand complex were investigated by utilizing chimera v.1.6.2 and PyMOL v.1.3 softwares. Immobilized β-galactosidase (IβG) showed improved stability against various physical and chemical denaturants. Km of IβG was increased to 6.41 mM as compared to 2.38 mM of soluble enzyme without bringing significant change in Vmax value. Maximum GOS content also registered an increase in lactose conversion. The maximum GOS production was achieved by immobilized enzyme at specific temperature and time. Hence, the developed nanosupport can be further exploited for developing a biosensor involving β-galactosidase or for immobilization of other industrially/therapeutically important enzymes.

Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape

Molecular docking is a computational approach for predicting the most probable position of ligands in the binding sites of macromolecules and constitutes the cornerstone of structure-based computer-aided drug design. Here, we present a new algorithm called Attracting Cavities that allows molecular docking to be performed by simple energy minimizations only. The approach consists in transiently replacing the rough potential energy hypersurface of the protein by a smooth attracting potential driving the ligands into protein cavities. The actual protein energy landscape is reintroduced in a second step to refine the ligand position. The scoring function of Attracting Cavities is based on the CHARMM force field and the FACTS solvation model. The approach was tested on the 85 experimental ligand-protein structures included in the Astex diverse set and achieved a success rate of 80% in reproducing the experimental binding mode starting from a completely randomized ligand conformer. The algorithm thus compares favorably with current state-of-the-art docking programs.

Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods

Molecular recognition, which is the process of biological macromolecules interacting with each other or various small molecules with a high specificity and affinity to form a specific complex, constitutes the basis of all processes in living organisms. Proteins, an important class of biological macromolecules, realize their functions through binding to themselves or other molecules. A detailed understanding of the protein–ligand interactions is therefore central to understanding biology at the molecular level. Moreover, knowledge of the mechanisms responsible for the protein-ligand recognition and binding will also facilitate the discovery, design, and development of drugs. In the present review, first, the physicochemical mechanisms underlying protein–ligand binding, including the binding kinetics, thermodynamic concepts and relationships, and binding driving forces, are introduced and rationalized. Next, three currently existing protein-ligand binding models—the “lock-and-key”, “induced fit”, and “conformational selection”—are described and their underlying thermodynamic mechanisms are discussed. Finally, the methods available for investigating protein–ligand binding affinity, including experimental and theoretical/computational approaches, are introduced, and their advantages, disadvantages, and challenges are discussed.

II. Dissociation free energies in drug–receptor systems via nonequilibrium alchemical simulations: application to the FK506-related immunophilin ligands

The fast switch double annihilation method (FS-DAM) provides an effective mean to the compute the binding free energies in drug-receptor systems. Here we present an application to the FK506-related ligands of the FKBP12 protein.

Predicting the binding properties of single walled carbon nanotubes (SWCNT) with an ADP/ATP mitochondrial carrier using molecular docking, chemoinformatics, and nano-QSBR perturbation theory

Interactions between single walled carbon nanotubes (SWCNT) family with mitochondrial ADP/ATP carrier (ANT-1) were evaluated using constitutional and electronic nanodescriptors defined by (n, m)-Hamada indexes (armchair, zig-zag and chiral).