Calculation of ligand-nucleic acid binding free energies with the generalized-born model in DOCK

RLDOCK method for predicting RNA-small molecule binding modes

RNA molecules play critical roles in cellular functions at the level of gene expression and regulation. The intricate 3D structures and the functional roles of RNAs make RNA molecules ideal targets for therapeutic drugs. The rational design of RNA-targeted drug requires accurate modeling of RNA-ligand interactions. Recently a new computational tool, RLDOCK, was developed to predict ligand binding sites and binding poses. Using an iterative multiscale sampling and search algorithm and a energy-based evaluation of ligand poses, the method enables efficient and accurate predictions for RNA-ligand interactions. Here we present a detailed illustration of the computational procedure for the practical implementation of the RLDOCK method. Using Flavin mononucleotide (FMN) docking to F. nucleatum FMN riboswitch as an example, we illustrate the computational protocol for RLDOCK-based prediction of RNA- ligand interactions. The RLDOCK software is freely accessible at http://https://github.com/Vfold-RNA/RLDOCK.

Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy

The binding of small-molecule ligands to protein or nucleic acid targets is important to numerous biological processes. Accurate prediction of the binding modes between a ligand and a macromolecule is of fundamental importance in structure-based structure-function exploration. When multiple ligands with different sizes are docked to a target receptor, it is reasonable to assume that the residues in the binding pocket may adopt alternative conformations upon interacting with the different ligands. In addition, it has been suggested that the entropic contribution to binding can be important. However, only a few attempts to include the side chain conformational entropy upon binding within the application of flexible receptor docking methodology exist. Here, we propose a new physics-based scoring function that includes both enthalpic and entropic contributions upon binding by considering the conformational variability of the flexible side chains within the ensemble of docked poses. We also describe a novel hybrid searching algorithm that combines both molecular dynamics (MD)-based simulated annealing and genetic algorithm crossovers to address the enhanced sampling of the increased search space. We demonstrate improved accuracy in flexible cross-docking experiments compared with rigid cross-docking. We test our developments by considering five protein targets, thrombin, dihydrofolate reductase(DHFR), T4 L99A, T4 L99A/M102Q, and PDE10A, which belong to different enzyme classes with different binding pocket environments, as a representative set of diverse ligands and receptors. Each target contains dozens of different ligands bound to the same binding pocket. We also demonstrate that this flexible docking algorithm may be applicable to RNA docking with a representative riboswitch example. Our findings show significant improvements in top ranking accuracy across this set, with the largest improvement relative to rigid, 23.64%, occurring for ligands binding to DHFR. We then evaluate the ability to identify lead compounds among a large chemical space for the proposed flexible receptor docking algorithm using a subset of the DUD-E containing receptor targets MCR, GCR, and ANDR. We demonstrate that our new algorithms show improved performance in modeling flexible binding site residues compared to DOCK. Finally, we select the T4 L99A and T4 L99A/M102Q decoy sets, containing dozens of binders and experimentally validated nonbinders, to test our approach in distinguishing binders from nonbinders. We illustrate that our new algorithms for searching and scoring have superior performance to rigid receptor CDOCKER as well as AutoDock Vina. Finally, we suggest that flexible CDOCKER is sufficiently fast to be utilized in high-throughput docking screens in the context of hierarchical approaches.

RLDOCK: A New Method for Predicting RNA-Ligand Interactions

The ability to accurately predict the binding site, binding pose, and binding affinity for ligand-RNA binding is important for RNA-targeted drug design. Here, we describe a new computational method, RLDOCK, for predicting the binding site and binding pose for ligand-RNA binding. By developing an energy-based scoring function, we sample exhaustively all of the possible binding sites with flexible ligand conformations for a ligand-RNA pair based on the geometric and energetic scores. The model distinguishes from other approaches in three notable features. First, the model enables exhaustive scanning of all of the possible binding sites, including multiple alternative or coexisting binding sites, for a given ligand-RNA pair. Second, the model is based on a new energy-based scoring function developed here. Third, the model employs a novel multistep screening algorithm to improve computational efficiency. Specifically, first, for each binding site, we use a gird-based energy map to rank the binding sites according to the minimum Lennard-Jones potential energy for the different ligand poses. Second, for a given selected binding site, we predict the ligand pose using a two-step algorithm. In the first step, we quickly identify the probable ligand poses using a coarse-grained simplified energy function. In the second step, for each of the probable ligand poses, we predict the ligand poses using a refined energy function. Tests of the RLDOCK for a set of 230 RNA-ligand-bound structures indicate that RLDOCK can successfully predict ligand poses for 27.8, 58.3, and 69.6% of all of the test cases with the root-mean-square deviation within 1.0, 2.0, and 3.0 Å, respectively, for the top three predicted docking poses. The computational method presented here may enable the development of a new, more comprehensive framework for the prediction of ligand-RNA binding with an ensemble of RNA conformations and the metal-ion effects.

Challenges and current status of computational methods for docking small molecules to nucleic acids

Since the development of the first docking program in 1982, the use of docking-based in silico screening for potentially bioactive molecule discovery has become a common strategy in academia and pharmaceutical industry. Up until recently, application of docking programs has largely focused on drugs binding to proteins. However, with the discovery of promising drug targets in nucleic acids, including RNA riboswitches, DNA G-quadruplexes, and extended repeats in RNA, there has been greater interests in developing drugs for nucleic acids. However, due to major biochemical and physical differences in charges, binding pockets, and solvation, existing docking programs, developed for proteins, face difficulties when adopted directly for nucleic acids. In this review, we cover the current field of in silico docking to nucleic acids, available programs, as well as challenges faced in the field.

Role of Molecular Docking in Computer-Aided Drug Design and Development

Molecular Docking is widely used in CADD (Computer-Aided Drug Designing), SBDD (Structure-Based Drug Designing) and LBDD (Ligand-Based Drug Designing). It is a method used to predict the binding orientation of one molecule with the other and used for any kind of molecule based on the interaction like, small drug molecule with its protein target, protein – protein binding or a DNA – protein binding. Docking is very much popular technique due to its reliable prediction properties. This book chapter will provide an overview of diverse docking methodologies present that are used in drug design and development. There will be discussion on several case studies, pertaining to each method, followed by advantages and disadvantages of the discussed methodology. It will typically aim professionals in the field of cheminformatics and bioinformatics, both in academia and in industry and aspiring scientists and students who want to take up this as a profession in the near future. We will conclude with our opinion on the effectiveness of this technology in the future of pharmaceutical industry.

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.

In silico identification of novel ligands for G-quadruplex in the c-MYC promoter

G-quadruplex DNA formed in NHEIII1 region of oncogene promoter inhibits transcription of the genes. In this study, virtual screening combining pharmacophore-based search and structure-based docking screening was conducted to discover ligands binding to G-quadruplex in promoter region of c-MYC. Several hit ligands showed the selective PCR-arresting effects for oligonucleotide containing c-MYC G-quadruplex forming sequence. Among them, three hits selectively inhibited cell proliferation and decreased c-MYC mRNA level in Ramos cells, where NHEIII1 is included in translocated c-MYC gene for overexpression. Promoter assay using two kinds of constructs with wild-type and mutant sequences showed that interaction of these ligands with the G-quadruplex resulted in turning-off of the reporter gene. In conclusion, combined virtual screening methods were successfully used for discovery of selective c-MYC promoter G-quadruplex binders with anticancer activity.

Methods to enable the design of bioactive small molecules targeting RNA

RNA is an immensely important target for small molecule therapeutics or chemical probes of function. However, methods that identify, annotate, and optimize RNA-small molecule interactions that could enable the design of compounds that modulate RNA function are in their infancies. This review describes recent approaches that have been developed to understand and optimize RNA motif-small molecule interactions, including structure-activity relationships through sequencing (StARTS), quantitative structure-activity relationships (QSAR), chemical similarity searching, structure-based design and docking, and molecular dynamics (MD) simulations. Case studies described include the design of small molecules targeting RNA expansions, the bacterial A-site, viral RNAs, and telomerase RNA. These approaches can be combined to afford a synergistic method to exploit the myriad of RNA targets in the transcriptome.

Structure-based virtual screening for the identification of RNA-binding ligands

Structure-based virtual screening exploits the 3D structure of the target as a template for the discovery of new ligands. It is a key method for hit discovery and was originally developed for protein targets. Recently, this method has also been applied to RNA targets. This chapter gives an overview of this method and its application in the context of ligand discovery for RNA. In addition, it describes in detail how to conduct virtual screening for RNA targets, making use of software that is free for noncommercial use. Some advice on how to avoid common pitfalls in virtual screening is also given.

Sanjeevini: a freely accessible web-server for target directed lead molecule discovery

Background

Computational methods utilizing the structural and functional information help to understand specific molecular recognition events between the target biomolecule and candidate hits and make it possible to design improved lead molecules for the target.

Results

Sanjeevini represents a massive on-going scientific endeavor to provide to the user, a freely accessible state of the art software suite for protein and DNA targeted lead molecule discovery. It builds in several features, including automated detection of active sites, scanning against a million compound library for identifying hit molecules, all atom based docking and scoring and various other utilities to design molecules with desired affinity and specificity against biomolecular targets. Each of the modules is thoroughly validated on a large dataset of protein/DNA drug targets.

Conclusions

The article presents Sanjeevini, a freely accessible user friendly web-server, to aid in drug discovery. It is implemented on a tera flop cluster and made accessible via a web-interface at http://www.scfbio-iitd.res.in/sanjeevini/sanjeevini.jsp. A brief description of various modules, their scientific basis, validation, and how to use the server to develop in silico suggestions of lead molecules is provided.

Virtual Screening for RNA-Interacting Small Molecules

Computational virtual screening is useful and powerful strategy for rapid discovery of small biologically active molecules in the early stage of drug discovery. The discovery of a broad range of important biological processes involved by RNA has increased the importance of RNA as a new drug target. To apply structure-based virtual screening methods to the discovery of RNA-binding ligands, many RNA 3D structure prediction programs and optimized docking algorithms have been developed. In this chapter, a number of successful cases of virtual screening targeting RNA will be introduced.

AADS--an automated active site identification, docking, and scoring protocol for protein targets based on physicochemical descriptors

We report here a robust automated active site detection, docking, and scoring (AADS) protocol for proteins with known structures. The active site finder identifies all cavities in a protein and scores them based on the physicochemical properties of functional groups lining the cavities in the protein. The accuracy realized on 620 proteins with sizes ranging from 100 to 600 amino acids with known drug active sites is 100% when the top ten cavity points are considered. These top ten cavity points identified are then submitted for an automated docking of an input ligand/candidate molecule. The docking protocol uses an all atom energy based Monte Carlo method. Eight low energy docked structures corresponding to different locations and orientations of the candidate molecule are stored at each cavity point giving 80 docked structures overall which are then ranked using an effective free energy function and top five structures are selected. The predicted structure and energetics of the complexes agree quite well with experiment when tested on a data set of 170 protein-ligand complexes with known structures and binding affinities. The AADS methodology is implemented on an 80 processor cluster and presented as a freely accessible, easy to use tool at http://www.scfbio-iitd.res.in/dock/ActiveSite_new.jsp .

Novel ligands for a purine riboswitch discovered by RNA-ligand docking

The increasing number of RNA crystal structures enables a structure-based approach to the discovery of new RNA-binding ligands. To develop the poorly explored area of RNA-ligand docking, we have conducted a virtual screening exercise for a purine riboswitch to probe the strengths and weaknesses of RNA-ligand docking. Using a standard protein-ligand docking program with only minor modifications, four new ligands with binding affinities in the micromolar range were identified, including two compounds based on molecular scaffolds not resembling known ligands. RNA-ligand docking performed comparably to protein-ligand docking indicating that this approach is a promising option to explore the wealth of RNA structures for structure-based ligand design.

Target Flexibility in RNA-Ligand Docking Modeled by Elastic Potential Grids

The highly flexible nature of RNA provides a formidable challenge for structure-based drug design approaches that target RNA. We introduce an approach for modeling target conformational changes in RNA-ligand docking based on potential grids that are represented as elastic bodies using Navier's equation. This representation provides an accurate and efficient description of RNA-ligand interactions even in the case of a moving RNA structure. When applied to a data set of 17 RNA-ligand complexes, filtered out of the largest validation data set used for RNA-ligand docking so far, the approach is twice as successful as docking into an apo structure and still half as successful as redocking to the holo structure. The approach allows considering RNA movements of up to 6 Å rmsd and is based on a uniform and robust parametrization of the properties of the elastic potential grids, so that the approach is applicable to different RNA-ligand complex classes.

On the role of electrostatics in protein-protein interactions

The role of electrostatics in protein-protein interactions and binding is reviewed in this paper. A brief outline of the computational modeling, in the framework of continuum electrostatics, is presented and the basic electrostatic effects occurring upon the formation of the complex are discussed. The effect of the salt concentration and pH of the water phase on protein-protein binding free energy is demonstrated which indicates that the increase of the salt concentration tends to weaken the binding, an observation that is attributed to the optimization of the charge-charge interactions across the interface. It is pointed out that the pH-optimum (pH of optimal binding affinity) varies among the protein-protein complexes, and perhaps is a result of their adaptation to particular subcellular compartments. The similarities and differences between hetero- and homo-complexes are outlined and discussed with respect to the binding mode and charge complementarity.

Binding-interaction prediction of RNA-binding ligands

RNA molecules are involved in a wide range of biological processes and have been recognized as very important therapeutic targets. Mainly owing to the scarcity of information and experimental studies, the application of computational approaches and, in particular, of docking methodologies in the RNA field has developed slowly. However, in recent years the docking of RNA-binding ligands has experienced significant expansion. This article focuses attention on the docking of RNA-binding ligands, analyzing the development of RNA-docking approaches, the reliability of the docking methods and, finally, evaluating the results of docking-based virtual screening studies reported in the literature.

Molecular recognition of RNA: challenges for modelling interactions and plasticity

There is growing interest in molecular recognition processes of RNA because of RNA's widespread involvement in biological processes. Computational approaches are increasingly used for analysing and predicting binding to RNA, fuelled by encouraging progress in developing simulation, free energy and docking methods for nucleic acids. These developments take into account challenges regarding the energetics of RNA-ligand binding, RNA plasticity, and the presence of water molecules and ions in the binding interface. Accordingly, we will detail advances in force field and scoring function development for molecular dynamics (MD) simulations, free energy computations and docking calculations of nucleic acid complexes. Furthermore, we present methods that can detect moving parts within RNA structures based on graph-theoretical approaches or normal mode analysis (NMA). As an example of the successful use of these developments, we will discuss recent structure-based drug design approaches that focus on the bacterial ribosomal A-site RNA as a drug target.

Search for novel aminoglycosides by combining fragment-based virtual screening and 3D-QSAR scoring

Aminoglycosides are antibiotics targeting the 16S RNA A site of the bacterial ribosome. There have been many efforts directed toward design of their synthetic derivatives, however with only few successes. As RNA binders, aminoglycosides are also a difficult target for computational drug design, since most of the existing methods were developed for protein ligands. Here, we present an approach that allows for evading the problems related to still poorly developed RNA docking and scoring algorithms. It is aimed at identification of new molecular scaffolds potentially binding to the A site. The considered molecules are based on the neamine core, which is common for all aminoglycosides and provides specificity toward the binding site, linked with diverse molecular fragments via its O5 or O6 oxygen atom. Suitable fragments are selected with the use of 3D searches of molecular fragments library against two distinct pharmacophores designed on the basis of available structural data for aminoglycoside-RNA complexes. The compounds resulting from fragments assembly with neamine are then scored with a 3D-QSAR model developed using the biological data for known aminoglycoside derivatives. Twenty-one new potential ligands are obtained, four of which have predicted activities comparable to less potent aminoglycoside antibiotics.

Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go

Accelerating the drug discovery process requires predictive computational protocols capable of reducing or simplifying the synthetic and/or combinatorial challenge. Docking-based virtual screening methods have been developed and successfully applied to a number of pharmaceutical targets. In this review, we first present the current status of docking and scoring methods, with exhaustive lists of these. We next discuss reported comparative studies, outlining criteria for their interpretation. In the final section, we describe some of the remaining developments that would potentially lead to a universally applicable docking/scoring method.

DrugScoreRNA--knowledge-based scoring function to predict RNA-ligand interactions

There is growing interest in RNA as a drug target due to its widespread involvement in biological processes. To exploit the power of structure-based drug-design approaches, novel scoring and docking tools need to be developed that can efficiently and reliably predict binding modes and binding affinities of RNA ligands. We report for the first time the development of a knowledge-based scoring function to predict RNA-ligand interactions (DrugScoreRNA). Based on the formalism of the DrugScore approach, distance-dependent pair potentials are derived from 670 crystallographically determined nucleic acid-ligand and -protein complexes. These potentials display quantitative differences compared to those of DrugScore (derived from protein-ligand complexes) and DrugScoreCSD (derived from small-molecule crystal data). When used as an objective function for docking 31 RNA-ligand complexes, DrugScoreRNA generates "good" binding geometries (rmsd (root mean-square deviation) < 2 A) in 42% of all cases on the first scoring rank. This is an improvement of 44% to 120% when compared to DrugScore, DrugScoreCSD, and an RNA-adapted AutoDock scoring function. Encouragingly, good docking results are also obtained for a subset of 20 NMR structures not contained in the knowledge-base to derive the potentials. This clearly demonstrates the robustness of the potentials. Binding free energy landscapes generated by DrugScoreRNA show a pronounced funnel shape in almost 3/4 of all cases, indicating the reduced steepness of the knowledge-based potentials. Docking with DrugScoreRNA can thus be expected to converge fast to the global minimum. Finally, binding affinities were predicted for 15 RNA-ligand complexes with DrugScoreRNA. A fair correlation between experimental and computed values is found (RS = 0.61), which suffices to distinguish weak from strong binders, as is required in virtual screening applications. DrugScoreRNA again shows superior predictive power when compared to DrugScore, DrugScoreCSD, and an RNA-adapted AutoDock scoring function.

DNA minor groove pharmacophores describing sequence specific properties

The more that is known about human and other genome sequences and the correlation between gene expression and the course of a disease, the more evident it seems to be that DNA is chosen as a drug target instead of proteins which are built with the information encoded by DNA. According to this approach, small minor groove binding molecules have been designed to bind the DNA sequence specifically and thereby downregulate genes. Because of their lack of druglikeness, we plan to use them as templates for forthcoming virtual screening experiments to discover molecules with the same bioactivity and a different scaffold. In this proof of principle study, carried out with the software tool Catalyst, we present a model work for description of a ligand-DNA complex with the aid of pharmacophore modeling methods. The successful reproduction of sequence specificity of a polyamidic minor groove binding ligand is the precondition for later model application to virtual screening.

Virtual screening of DNA minor groove binders

The utility of the two docking programs DOCK and AutoDock in studying the binding of small molecules to the minor groove of B-DNA is examined. The AutoDock program is found to be more effective in both pose prediction and ranking of known binders over random compounds, and this superior performance is shown to be because of the scoring functions rather than the sampling algorithms.

Docking of aminoglycosides to hydrated and flexible RNA

Although much effort has been devoted to the development of programs suited for the docking of ligands to proteins, much less progress has been achieved in the nucleic acid field. We have developed a unique approach for docking aminoglycosides to RNA considering the flexibility of these macromolecules using conformational ensembles and accounting for the role of the first hydration shell. This concept, successfully implemented in AutoDock, relies on the computation of the intermolecular interaction energy that accounts for the presence of dynamically bound water molecules to the RNA. As an application, a set of 11 aminoglycosides was docked with an average root-mean-square deviation (RMSD) of 1.41 A to be compared with an average RMSD of 3.25 A when the original AutoDock protocol was used.

Targeting the DNA minor groove with fused ring dicationic compounds: comparison of in silico screening and a high-resolution crystal structure

The crystal structure of the DNA minor groove biphenyl benzimidazole diamidine ligand DB819 has been determined, bound to the DNA sequence d(CGCGAATTCGCG)(2), at a resolution of 1.36 Angstrom. Conditions for reliable in silico docking that reproduce the observed position of the ligand in the minor groove have been determined.

Chemical approaches to the discovery and development of cancer therapies

The chemical sciences are essential for the process of anticancer-drug discovery, and a range of chemical research techniques is needed to develop clinically effective drugs. Improved understanding of the cellular, molecular and genetic basis of cancer has increased the number of drug targets available. What chemical approaches are used to develop agents that target specific features of cancer cells and make these therapeutics more effective? We outline the roles that chemical synthesis and understanding of drug uptake have had in drug discovery over the past 100 years, as well as the chemical insights derived from knowledge of the three-dimensional structure of targets.

Simulation and modeling of nucleic acid structure, dynamics and interactions

In moving towards the simulation of larger nucleic acid assemblies over longer timescales that include more accurate representations of the environment, we are nearing the end of an era characterized by single nanosecond molecular dynamics simulation of nucleic acids. We are excited by the promise and predictability of the modeling methods, yet remain prudently cautious of sampling and force field limitations. Highlights include the accurate representation of subtle drug-DNA interactions, the detailed study of modified and unusual nucleic acid structures, insight into the influence of dynamics on the structure of DNA, and exploration of the interaction of solvent and ions with nucleic acids.