Scoring ensembles of docked protein:ligand interactions for virtual lead optimization

Structural systems pharmacology: A framework for integrating metabolic network and structure-based virtual screening for drug discovery against bacteria

Advances in genome-scale metabolic models (GEMs) and computational drug discovery have caused the identification of drug targets at the system-level and inhibitors to combat bacterial infection and drug resistance. Here we report a structural systems pharmacology framework that integrates the GEM and structure-based virtual screening (SBVS) method to identify drugs effective for Escherichia coli infection. The most complete genome-scale metabolic reconstruction integrated with protein structures (GEM-PRO) of E. coli, iML1515_GP, and FDA-approved drugs have been used. FBA was performed to predict drug targets in silico. The 195 essential genes were predicted in the rich medium. The subsystems in which a significant number of these genes are involved are cofactor, lipopolysaccharide (LPS) biosynthesis that are necessary for cell growth. Therefore, some proteins encoded by these genes are responsible for the biosynthesis and transport of LPS which is the first line of defense against threats. So, these proteins can be potential drug targets. The enzymes with experimental structure and cognate ligands were selected as final drug targets for performing the SBVS method. Finally, we have suggested those drugs that have good interaction with the selected proteins as drug repositioning cases. Also, the suggested molecules could be promising lead compounds. This framework may be helpful to fill the gap between genomics and drug discovery. Results show this framework suggests novel antibacterials that can be subjected to experimental testing soon and it can be suitable for other pathogens.

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.

Computational insight into the phthalocyanine-DNA binding via docking and molecular dynamics simulations

Phthalocyanines are considered as good DNA binders, which makes them promising anti-tumor drug leads. The purpose of this study is to investigate the interactions between DNA and quaternary metallophthalocyanine derivatives (Q-MPc) possessing varying metals (M = Zn, Ni, Cu, Fe, Mg and Ca) by molecular docking since there seems to be a lack of information in the literature regarding this issue. In this direction, Autodock Vina and Molegro Virtual Docker programs were employed. Autodock Vina results reveal that each Q-MPc derivative binds to DNA strongly with similar binding energies and almost identical binding modes. They bind to the grooves of DNA by constituting favorable interactions between phosphate groups of DNA and Q-MPcs. Although changing the metal has no significant effect on binding, presence of quaternary amine substituents increases the binding constant K_b by about 2-fold comparing to the core Pc (ZnPc). Contrary to Autodock Vina, the calculated Molegro Virtual Docker binding scores have been more diverse indicating that the scoring function of Molegro is better in differentiating these metals. Despite the fact that Molegro is superior to Autodock Vina in terms of metal characterization, Autodock Vina and Molegro exhibit similar binding sites for the studied metallophthalocyanines. We propose that Q-MPc derivatives designed in this study are promising anti-tumor lead compounds since they tightly bind to DNA with considerably high K_b values. Cationic substituents and presence of metal have both positive effects on DNA binding which is critical for designing DNA-active drugs. Additional calculations employing molecular dynamics (MD) simulations verified the stability of Q-MPc-DNA complexes which remained in contact after 20 ns via attractive interactions mainly between DNA backbone and the Pc metal center.

CDNs-STING Interaction Mechanism Investigations and Instructions on Design of CDN-Derivatives

Cyclic dinucleotides (CDNs) present thousand-fold differences of dissociation constants to STING, a pivotal protein in cytosolic dsDNA immunity. To understand how subtle chemical changes in CDNs lead to these substantial variances, a precise ranking of binding affinity is needed. However, the large size and flexibility of CDNs elevate the entropic effect and pose a challenge for this precise prediction. Therefore, in this paper, we developed a new protocol, a combination of selective-integrated tempering sampling of ligands and molecular docking, to take into account the entropic effects originating from extensive ligand configurational space and solvation on binding affinity evaluations. The calculated ranking orders of CDNs and CDN-derivatives to wild type STING and R232H mutant are in agreement with experimental measurements. Further molecular dynamics analysis revealed that the interaction between phosphonate groups and 232R differentiates the binding affinities. The 2'-5' linked phosphonate groups have a larger tendency to form hydrogen bonds with 232R than those with 3'-5' linkages. Moreover, the new protocol identified structural features that enhanced CDNs-STING binding, such as anti-glycosidic bonds and large pro-R distances, which explains the high binding affinity of dithio-RpRp-2'3'-CDA to STING and is expected to provide valuable guidance in the lead-drug optimization.

A molecular docking study of the interactions between human transferrin and seven metallocene dichlorides

Human Transferrin (hTf) is a metal-binding protein found in blood plasma and is well known for its role in iron delivery. With only a 30% of its capacity for Fe⁺³ binding, this protein has the potential ability to transport other metal ions or organometallic compounds from the blood stream to all cell tissues. In this perspective, recent studies have described seven metallocene dichlorides (Cp₂M(IV)Cl₂, M(IV)=V, Mo, W, Nb, Ti, Zr, Hf) suitable as anticancer drugs and less secondary effects than cisplatin. However, these studies have not provided enough data to clearly explain how hTf binds and transports these organometallic compounds into the cells. Thus, a computational docking study with native apo-hTf using Sybyl-X 2.0 program was conducted to explore the binding modes of these seven Cp₂M(IV)Cl₂ after their optimization and minimization using Gaussian 09. Our model showed that the first three Cp₂M(IV)Cl₂ (M(IV)=V, Mo, W) can interact with apo-hTf on a common binding site with the amino acid residues Leu-46, Ile-49, Arg-50, Leu-66, Asp-69, Ala-70, Leu-72, Ala-73, Pro-74 and Asn-75, while the next four Cp₂M(IV)Cl₂ (M(IV)=Nb, Ti, Zr, Hf) showed different binding sites, unknown until now. A decreasing order in the total score (equal to -log Kd) was observed from these docking studies: W (5.4356), Mo (5.2692), Nb (5.1672), V (4.5973), Ti (3.6529), Zr (2.0054) and Hf (1.8811). High and significant correlation between the affinity of these seven ligands (metallocenes) for apo-hTf and their bond angles CpMCp (r=0.94, p<0.01) and Cl-M-Cl (r=0.95, p<0.01) were observed, thus indicating the important role that these bond angles can play in ligand-protein interactions. Fluorescence spectra of apo-hTf, measured at pH 7.4, had a decrease in the fluorescence emission spectrum with increasing concentration of Cp₂M(IV)Cl₂. Experimental data has a good correlation between K_A (r=0.84, p=0.027) and K_d (r=0.94, p=0.0014) values and the calculated total scores obtained from our docking experiments. In conclusion, these results suggest that the seven Cp₂M(IV)Cl₂ used for this study can interact with apo-hTf, and their affinity was directly and inversely proportional to their bond angles CpMCp and ClMCl, respectively. Our docking studies also suggest that the binding of the first three Cp₂M(IV)Cl₂ (M(IV)=V, Mo, W) to hTf could abrogate the formation of the hTf-receptor complex, and as a consequence the metallocene-hTf complex might require another transport mechanism in order to get into the cell.

Cosolvent-Based Molecular Dynamics for Ensemble Docking: Practical Method for Generating Druggable Protein Conformations

Protein flexibility is a major hurdle in current structure-based virtual screening (VS). In spite of the recent advances in high-performance computing, protein-ligand docking methods still demand tremendous computational cost to take into account the full degree of protein flexibility. In this context, ensemble docking has proven its utility and efficiency for VS studies, but it still needs a rational and efficient method to select and/or generate multiple protein conformations. Molecular dynamics (MD) simulations are useful to produce distinct protein conformations without abundant experimental structures. In this study, we present a novel strategy that makes use of cosolvent-based molecular dynamics (CMD) simulations for ensemble docking. By mixing small organic molecules into a solvent, CMD can stimulate dynamic protein motions and induce partial conformational changes of binding pocket residues appropriate for the binding of diverse ligands. The present method has been applied to six diverse target proteins and assessed by VS experiments using many actives and decoys of DEKOIS 2.0. The simulation results have revealed that the CMD is beneficial for ensemble docking. Utilizing cosolvent simulation allows the generation of druggable protein conformations, improving the VS performance compared with the use of a single experimental structure or ensemble docking by standard MD with pure water as the solvent.

PL-PatchSurfer2: Improved Local Surface Matching-Based Virtual Screening Method That Is Tolerant to Target and Ligand Structure Variation

Virtual screening has become an indispensable procedure in drug discovery. Virtual screening methods can be classified into two categories: ligand-based and structure-based. While the former have advantages, including being quick to compute, in general they are relatively weak at discovering novel active compounds because they use known actives as references. On the other hand, structure-based methods have higher potential to find novel compounds because they directly predict the binding affinity of a ligand in a target binding pocket, albeit with substantially lower speed than ligand-based methods. Here we report a novel structure-based virtual screening method, PL-PatchSurfer2. In PL-PatchSurfer2, protein and ligand surfaces are represented by a set of overlapping local patches, each of which is represented by three-dimensional Zernike descriptors (3DZDs). By means of 3DZDs, the shapes and physicochemical complementarities of local surface regions of a pocket surface and a ligand molecule can be concisely and effectively computed. Compared with the previous version of the program, the performance of PL-PatchSurfer2 is substantially improved by the addition of two more features, atom-based hydrophobicity and hydrogen-bond acceptors and donors. Benchmark studies showed that PL-PatchSurfer2 performed better than or comparable to popular existing methods. Particularly, PL-PatchSurfer2 significantly outperformed existing methods when apo-form or template-based protein models were used for queries. The computational time of PL-PatchSurfer2 is about 20 times shorter than those of conventional structure-based methods. The PL-PatchSurfer2 program is available at http://www.kiharalab.org/plps2/ .

A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism

Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase) and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants) in complex with their respective native metabolites or drug molecules. We find that changes in a protein's structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in metabolism.

Knowledge-Based Methods To Train and Optimize Virtual Screening Ensembles

Ensemble docking can be a successful virtual screening technique that addresses the innate conformational heterogeneity of macromolecular drug targets. Yet, lacking a method to identify a subset of conformational states that effectively segregates active and inactive small molecules, ensemble docking may result in the recommendation of a large number of false positives. Here, three knowledge-based methods that construct structural ensembles for virtual screening are presented. Each method selects ensembles by optimizing an objective function calculated using the receiver operating characteristic (ROC) curve: either the area under the ROC curve (AUC) or a ROC enrichment factor (EF). As the number of receptor conformations, N, becomes large, the methods differ in their asymptotic scaling. Given a set of small molecules with known activities and a collection of target conformations, the most resource intense method is guaranteed to find the optimal ensemble but scales as O(2^N). A recursive approximation to the optimal solution scales as O(N²), and a more severe approximation leads to a faster method that scales linearly, O(N). The techniques are generally applicable to any system, and we demonstrate their effectiveness on the androgen nuclear hormone receptor (AR), cyclin-dependent kinase 2 (CDK2), and the peroxisome proliferator-activated receptor δ (PPAR-δ) drug targets. Conformations that consisted of a crystal structure and molecular dynamics simulation cluster centroids were used to form AR and CDK2 ensembles. Multiple available crystal structures were used to form PPAR-δ ensembles. For each target, we show that the three methods perform similarly to one another on both the training and test sets.

Implicit ligand theory: rigorous binding free energies and thermodynamic expectations from molecular docking

A rigorous formalism for estimating noncovalent binding free energies and thermodynamic expectations from calculations in which receptor configurations are sampled independently from the ligand is derived. Due to this separation, receptor configurations only need to be sampled once, facilitating the use of binding free energy calculations in virtual screening. Demonstrative calculations on a host-guest system yield good agreement with previous free energy calculations and isothermal titration calorimetry measurements. Implicit ligand theory provides guidance on how to improve existing molecular docking algorithms and insight into the concepts of induced fit and conformational selection in noncovalent macromolecular recognition.

Engineering protein therapeutics: predictive performances of a structure-based virtual affinity maturation protocol

The implementation of a structure based virtual affinity maturation protocol and evaluation of its predictivity are presented. The in silico protocol is based on conformational sampling of the interface residues (using the Dead End Elimination/A* algorithm), followed by the estimation of the change of free energy of binding due to a point mutation, applying MM/PBSA calculations. Several implementations of the protocol have been evaluated for 173 mutations in 7 different protein complexes for which experimental data were available: the use of the Boltzamnn averaged predictor based on the free energy of binding (ΔΔG(*)) combined with the one based on its polar component only (ΔΔE(pol*)) led to the proposal of a subset of mutations out of which 45% would have successfully enhanced the binding. When focusing on those mutations that are less likely to be introduced by natural in vivo maturation methods (99 mutations with at least two base changes in the codon), the success rate is increased to 63%. In another evaluation, focusing on 56 alanine scanning mutations, the in silico protocol was able to detect 89% of the hot-spots.

Potential and limitations of ensemble docking

A major problem in structure-based virtual screening applications is the appropriate selection of a single or even multiple protein structures to be used in the virtual screening process. A priori it is unknown which protein structure(s) will perform best in a virtual screening experiment. We investigated the performance of ensemble docking, as a function of ensemble size, for eight targets of pharmaceutical interest. Starting from single protein structure docking results, for each ensemble size up to 500,000 combinations of protein structures were generated, and, for each ensemble, pose prediction and virtual screening results were derived. Comparison of single to multiple protein structure results suggests improvements when looking at the performance of the worst and the average over all single protein structures to the performance of the worst and average over all protein ensembles of size two or greater, respectively. We identified several key factors affecting ensemble docking performance, including the sampling accuracy of the docking algorithm, the choice of the scoring function, and the similarity of database ligands to the cocrystallized ligands of ligand-bound protein structures in an ensemble. Due to these factors, the prospective selection of optimum ensembles is a challenging task, shown by a reassessment of published ensemble selection protocols.

Exploring c-Met kinase flexibility by sampling and clustering its conformational space

It is now widely recognized that the flexibility of both partners has to be considered in molecular docking studies. However, the question how to handle the best the huge computational complexity of exploring the protein binding site landscape is still a matter of debate. Here we investigate the flexibility of c-Met kinase as a test case for comparing several simulation methods. The c-Met kinase catalytic site is an interesting target for anticancer drug design. In particular, it harbors an unusual plasticity compared with other kinases ATP binding sites. Exploiting this feature may eventually lead to the discovery of new anticancer agents with exquisite specificity. We present in this article an extensive investigation of c-Met kinase conformational space using large-scale computational simulations in order to extend the knowledge already gathered from available X-ray structures. In the process, we compare the relevance of different strategies for modeling and injecting receptor flexibility information into early stage in silico structure-based drug discovery pipeline. The results presented here are currently being exploited in on-going virtual screening investigations on c-Met.

Molecular-level simulation of pandemic influenza glycoproteins

Computational simulation of pandemic diseases provides important insight into many disease features that may benefit public health. This is especially true for the influenza virus, a continuing global pandemic threat. Molecular or atomic-level investigation of influenza has predominantly focused on the two major virus glycoproteins, neuraminidase (NA) and hemagglutinin (HA). In this chapter, we walk the readers through major considerations for studying pandemic influenza glycoproteins, from choosing the most useful choice of system(s) to avoiding common pitfalls in experimental design and execution. While a brief discussion of several potential simulation and docking techniques is presented, we emphasize molecular dynamics (MD) and Brownian dynamics (BD) simulation techniques and molecular docking, within the context of biologically outstanding questions in influenza research.

Structure-based functional design of drugs: from target to lead compound

Proteomic and genomic discoveries have identified vast numbers of new drug targets for investigation. In the quest to discover drugs that modulate the function of these targets, identification of small-molecule drug leads is one of the earliest steps. Structure-based drug design has emerged as a valuable, inexpensive, and rapid computational resource that identifies lead compounds that are complementary to the structure of the target. Leads identified through this process are biologically evaluated and "hit compounds" with affinity and activity are further optimized. This chapter introduces the process of structure-based drug design, including preparation of the ligand database, preparation of the target structure, docking and scoring, and evaluation.

Rational prediction with molecular dynamics for hit identification

Although the motions of proteins are fundamental for their function, for pragmatic reasons, the consideration of protein elasticity has traditionally been neglected in drug discovery and design. This review details protein motion, its relevance to biomolecular interactions and how it can be sampled using molecular dynamics simulations. Within this context, two major areas of research in structure-based prediction that can benefit from considering protein flexibility, binding site detection and molecular docking, are discussed. Basic classification metrics and statistical analysis techniques, which can facilitate performance analysis, are also reviewed. With hardware and software advances, molecular dynamics in combination with traditional structure-based prediction methods can potentially reduce the time and costs involved in the hit identification pipeline.

Multiple-targeting and conformational selection in the estrogen receptor: computation and experiment

Conformational selection is a primary mechanism in biomolecular recognition. The conformational ensemble may determine the ability of a drug to compete with a native ligand for a receptor target. Traditional docking procedures which use one or few protein structures are limited and may not be able to represent a complex competition among closely related protein receptors in agonist and antagonist ensembles. Here, we test a protocol aimed at selecting a drug candidate based on its ability to synergistically bind to distinct conformational states. We demonstrate, for the case of estrogen receptor α (ERα) and estrogen receptor β (ERβ), that the functional outcome of ligand binding can be inferred from its ability to simultaneously bind both ERα and ERβ in agonist and antagonist conformations as calculated docking scores. Combining a conformational selection method with an experimental reporter gene system in yeast, we propose that several phytoestrogens can be novel estrogen receptor β selective agonists. Our work proposes a computational protocol to select estrogen receptor subtype selective agonists. Compared with other models, present method gives the best prediction in ligands' function.

VoteDock: consensus docking method for prediction of protein-ligand interactions

Molecular recognition plays a fundamental role in all biological processes, and that is why great efforts have been made to understand and predict protein-ligand interactions. Finding a molecule that can potentially bind to a target protein is particularly essential in drug discovery and still remains an expensive and time-consuming task. In silico, tools are frequently used to screen molecular libraries to identify new lead compounds, and if protein structure is known, various protein-ligand docking programs can be used. The aim of docking procedure is to predict correct poses of ligand in the binding site of the protein as well as to score them according to the strength of interaction in a reasonable time frame. The purpose of our studies was to present the novel consensus approach to predict both protein-ligand complex structure and its corresponding binding affinity. Our method used as the input the results from seven docking programs (Surflex, LigandFit, Glide, GOLD, FlexX, eHiTS, and AutoDock) that are widely used for docking of ligands. We evaluated it on the extensive benchmark dataset of 1300 protein-ligands pairs from refined PDBbind database for which the structural and affinity data was available. We compared independently its ability of proper scoring and posing to the previously proposed methods. In most cases, our method is able to dock properly approximately 20% of pairs more than docking methods on average, and over 10% of pairs more than the best single program. The RMSD value of the predicted complex conformation versus its native one is reduced by a factor of 0.5 Å. Finally, we were able to increase the Pearson correlation of the predicted binding affinity in comparison with the experimental value up to 0.5.