Software for molecular docking: a review

Bioinformatics for Marine Products: An Overview of Resources, Bottlenecks, and Perspectives

The sea represents a major source of biodiversity. It exhibits many different ecosystems in a huge variety of environmental conditions where marine organisms have evolved with extensive diversification of structures and functions, making the marine environment a treasure trove of molecules with potential for biotechnological applications and innovation in many different areas. Rapid progress of the omics sciences has revealed novel opportunities to advance the knowledge of biological systems, paving the way for an unprecedented revolution in the field and expanding marine research from model organisms to an increasing number of marine species. Multi-level approaches based on molecular investigations at genomic, metagenomic, transcriptomic, metatranscriptomic, proteomic, and metabolomic levels are essential to discover marine resources and further explore key molecular processes involved in their production and action. As a consequence, omics approaches, accompanied by the associated bioinformatic resources and computational tools for molecular analyses and modeling, are boosting the rapid advancement of biotechnologies. In this review, we provide an overview of the most relevant bioinformatic resources and major approaches, highlighting perspectives and bottlenecks for an appropriate exploitation of these opportunities for biotechnology applications from marine resources.

In Silico and In Vitro Anti-Helicobacter Pylori Effects of Combinations of Phytochemicals and Antibiotics

Helicobacter pylori infection is a WHO class 1 carcinogenic factor of gastric adenocarcinoma. In the past decades, many studies have demonstrated the increasing trend of antibiotic resistance and pointed out the necessity of new effective treatment. This study was aimed at identifying phytochemicals that can inhibit H. pylori and possibly serve as adjuvant treatments. Here, in silico molecular docking and drug-like properties analyses were performed to identify potential inhibitors of urease, shikimate kinase and aspartate-semialdehyde dehydrogenase. These three enzymes are targets of the treatment of H. pylori. Susceptibility and synergistic testing were performed on the selected phytochemicals and the positive control antibiotic, amoxicillin. The in-silico study revealed that oroxindin, rosmarinic acid and verbascoside are inhibitors of urease, shikimate kinase and aspartate-semialdehyde dehydrogenase, respectively, in which, oroxindin has the highest potency against H. pylori, indicated by a minimum inhibitory concentration (MIC) value of 50 μg/mL. A combination of oroxindin and amoxicillin demonstrated additive effects against H. pylori, as indicated by a fractional inhibitory concentration (FIC) value of 0.75. This study identified phytochemicals that deserve further investigation for the development of adjuvant therapeutic agents to current antibiotics against H. pylori.

Supercomputer docking with a large number of degrees of freedom

Docking represents one of the most popular computational approaches in drug design. It has reached popularity owing to capability of identifying correct conformations of a ligand within an active site of the target-protein and of estimating the binding affinity of a ligand that is immensely helpful in prediction of compound activity. Despite many success stories, there are challenges, in particular, handling with a large number of degrees of freedom in solving the docking problem. Here, we show that SOL-P, the docking program based on the new Tensor Train algorithm, is capable to dock successfully oligopeptides having up to 25 torsions. To make the study comparative we have performed docking of the same oligopeptides with the SOL program which uses the same force field as that utilized by SOL-P and has common features of many docking programs: the genetic algorithm of the global optimization and the grid approximation. SOL has managed to dock only one oligopeptide. Moreover, we present the results of docking with SOL-P ligands into proteins with moveable atoms. Relying on visual observations we have determined the common protein atom groups displaced after docking which seem to be crucial for successful prediction of experimental conformations of ligands.

Logistic Regression Method for Ligand Discovery

Protein-based virtual screening is integral to the modern drug discovery process. Most protein-based virtual screening experiments are performed using docking programs. The accuracy of a docking program strongly relies on the incorporated scoring function used, which is based on various energy terms. The existing scoring functions deal with the energy terms that use the equal weight function or other weight functions, which do not depend on characteristics of the protein. To improve the existing methods, Lu and Wang proposed a protein-specific scoring function based on a regression analysis that was shown to have higher performance than the existing methods. In this study, we propose a protein-specific scoring approach to select potential ligands based on logistic regression analysis. The performance of our method was evaluated using the Directory of Useful Decoys docked data set, which contains 40 protein targets. The results showed that the proposed method can increase the enrichment factors for most of the 40 protein targets.

The perceptions of natural compounds against dipeptidyl peptidase 4 in diabetes: from in silico to in vivo

Dipeptidyl peptidase IV (DPP-4), an incretin glucagon-like peptide-1 (GLP-1) degrading enzyme, contains two forms and it can exert various physiological functions particular in controlling blood glucose through the action of GLP-1. In diabetic use, the DPP-4 inhibitor can block the DDP-4 to attenuate GLP-1 degradation and prolong GLP-1 its action and sensitize insulin activity for the purpose of lowering blood glucose. Nonetheless the adverse effects of DPP-4 inhibitors severely hinder their clinical applications, and notably there is a clinical demand for novel DPP-4 inhibitors from various sources including chemical synthesis, herbs, and plants with fewer side effects. In this review, we highlight various strategies, namely computational biology (in silico), in vitro enzymatic and cell assays, and in vivo animal tests, for seeking natural DPP-4 inhibitors from botanic sources including herbs and plants. The pros and cons of all approaches for new inhibitor candidates or hits will be under discussion.

Application of the SwissDrugDesign Online Resources in Virtual Screening

SwissDrugDesign is an important initiative led by the Molecular Modeling Group of the SIB Swiss Institute of Bioinformatics. This project provides a collection of freely available online tools for computer-aided drug design. Some of these web-based methods, i.e., SwissSimilarity and SwissTargetPrediction, were especially developed to perform virtual screening, while others such as SwissADME, SwissDock, SwissParam and SwissBioisostere can find applications in related activities. The present review aims at providing a short description of these methods together with examples of their application in virtual screening, where SwissDrugDesign tools successfully supported the discovery of bioactive small molecules.

Quantitative structure-activity relationship and molecular docking studies on human proteasome inhibitors for anticancer activity targeting NF-κB signaling pathway

The abnormal ubiquitin-proteasome is found as an important target in various human diseases, especially in cancer, and recently it has received prevalent attention as a challenging therapeutic target. The current work is designed to derive a predictive two-dimensional quantitative structure-activity relationship model for anticancer human proteasome target of NF-κB signaling pathway. The established 2 D-QSAR is dependent on multiple linear regression approach and validated through leave-One-Out and external test set prediction method. The robust QSAR model showed the r² of 0.83 and q² of 0.80 and pred_r² of 0.77. Three chemical properties, electronegativity count, average potential, and T_2_N_6 were identified as significant descriptors to predict the anticancer activities of the proteasome antagonists. Besides, the predicted top hit compounds were considered to check out the compliance with Rule of five and pharmacokinetic parameters for oral bioavailability in the human body. The molecular docking was accomplished to unravel the molecular mode of action of best-predicted compounds which was compatible with the standard drug. Following this approach, lastly two compounds NP and AP were recognized as the best candidates since these top compounds follow all the standard limit point of entire filters and indicated effective and decent docking score. The outcomes of the study sturdily suggested that the developed model and top hit compound's binding conformation are rational in the exploration of unknown antagonist's anticancer activity. The research would be of great support and is supposed to be of immense significance in the development and designing of drug-like candidates in preliminary drug discovery. Communicated by Ramaswamy H. Sarma.

From Target to Drug: Generative Modeling for the Multimodal Structure-Based Ligand Design

Chemical space is impractically large, and conventional structure-based virtual screening techniques cannot be used to simply search through the entire space to discover effective bioactive molecules. To address this shortcoming, we propose a generative adversarial network to generate, rather than search, diverse three-dimensional ligand shapes complementary to the pocket. Furthermore, we show that the generated molecule shapes can be decoded using a shape-captioning network into a sequence of SMILES enabling directly the structure-based de novo drug design. We evaluate the quality of the method by both structure- (docking) and ligand-based [quantitative structure-activity relationship (QSAR)] virtual screening methods. For both evaluation approaches, we observed enrichment compared to random sampling from initial chemical space of ZINC drug-like compounds.

3D-QSAR and docking studies on ursolic acid derivatives for anticancer activity based on bladder cell line T24 targeting NF-kB pathway inhibition

Bladder cancer is the common reason for mortality worldwide, and its increasing rate announces as a significant area of research in drug designing. The side effects and toxicity of existing drugs and the consequence of gradual cancer cell resistance against the available therapy make the treatment poor. Globally, there is a continuous high demand to develop new, more potent, and easily affordable drugs against cancer. The current research article illustrates the application of developed three-dimensional quantitative structure-activity relationship (3D-QSAR) based on human bladder cancer cell line T24 in vitro anticancer activity. The derived QSAR model has been used for prediction of natural compounds and analogs with 80% similarity of the most active compound of the dataset. The developed model describes the structure-activity relationship for terpenes and their derivatives at the molecular level. The developed comparative molecular field analysis (CoMFA) model shows a satisfactory cross-validation correlation coefficient (q2) of 0.54 and a regression correlation coefficient (r2) of 0.86. In order to evaluate the compliance with electronic pharmacokinetic parameters, Lipinski's rule of five filter, absorption, distribution, metabolism, and excretion (ADME) and toxicity of predicted compounds have been calculated. Furthermore, molecular-docking study has been performed to prioritize these predicted compounds based on their docking score and binding pocket similarity through the identified potential anticancer targets. Finally, two compounds T9 and B42 have been identified as the best hit because these two fall within the standard limits of all filters and show a good binding affinity. Conclusively, all satisfactory results strongly suggest that the derived 3D-QSAR model and obtained candidate's binding structures are reasonable in the prediction of a new antagonist's activity. The strategy adopted in the present research is expected to be of immense importance and a great support in the identification and optimization of lead in the early and advance drug discovery.

Fragment Binding Pose Predictions Using Unbiased Simulations and Markov-State Models

Predicting the costructure of small-molecule ligands and their respective target proteins has been a long-standing problem in drug discovery. For weak binding compounds typically identified in fragment-based screening (FBS) campaigns, determination of the correct binding site and correct binding mode is usually done experimentally via X-ray crystallography. For many targets of pharmaceutical interest, however, establishing an X-ray system which allows for sufficient throughput to support a drug discovery project is not possible. In this case, exploration of fragment hits becomes a very laborious and consequently slow process with the generation of protein/ligand cocrystal structures as the bottleneck of the entire process. In this work, we introduce a computational method which is able to reliably predict binding sites and binding modes of fragment-like small molecules using solely the structure of the apoprotein and the ligand's chemical structure as input information. The method is based on molecular dynamics simulations and Markov-state models and can be run as a fully automated protocol requiring minimal human intervention. We describe the application of the method to a representative subset of different target classes and fragments from historical FBS efforts at Boehringer Ingelheim and discuss its potential integration into the overall fragment-based drug discovery workflow.

Protein Docking Using a Single Representation for Protein Surface, Electrostatics, and Local Dynamics

Predicting the assembly of multiple proteins into specific complexes is critical to understanding their biological function in an organism and thus the design of drugs to address their malfunction. Proteins are flexible molecules, which inherently pose a problem to any protein docking computational method, where even a simple rearrangement of the side chain and backbone atoms at the interface of binding partners complicates the successful determination of the correct docked pose. Herein, we present a means of representing protein surface, electrostatics, and local dynamics within a single volumetric descriptor. We show that our representations can be physically related to the surface-accessible solvent area and mass of the protein. We then demonstrate that the application of this representation into a protein-protein docking scenario bypasses the need to compensate for, and predict, specific side chain packing at the interface of binding partners. This representation is leveraged in our de novo protein docking software, JabberDock, which can accurately and robustly predict difficult target complexes with an average success rate of >54%, which is comparable to or greater than the currently available methods.

Parallel cascade selection molecular dynamics to screen for protein complexes generated by rigid docking

We propose a flexible docking simulation based on parallel cascade selection molecular dynamics (PaCS-MD) as a post-processing treatment after a rigid docking simulation. PaCS-MD has been proposed as an enhanced sampling method for generating structural transition pathways from a given reactant to a product. The PaCS-MD cycle consists of the following two steps: (1) selections of important initial structures and (2) their conformational resampling from the selected initial structures. By repeating the conformational resampling from the important initial structures, structural transitions from the reactant to the product are gradually promoted. In the present flexible docking simulation, decoys (protein complexes) are generated by the rigid docking simulation a priori and employed as products of PaCS-MD. Then PaCS-MD is applied to reproduce association processes to the decoys from a reactant (completely separated proteins). To judge whether PaCS-MD found the association processes or not, the root-mean-square deviation measured from decoy (RMSD_decoy) was defined and monitored during the PaCS-MD cycles. By checking the RMSD_decoy values, a set of decoys is screened as a non-near native protein complex. In more detail, PaCS-MD detects near native protein complexes from the generated decoys by imposing a threshold (cutoff) for RMSD_decoy, i.e. RMSD_decoy < cutoff. As a demonstration, the present flexible docking addressed dimerization processes of K48-linked ubiquitin dimer without a covalent bond between its monomers. Finally, PaCS-MD screened out non-near native protein complexes from decoys generated by a rigid docking simulation.

Effects of cryopreservation on cAMP-dependent protein kinase and AMP-activated protein kinase in Atlantic salmon (Salmo salar) spermatozoa: Relation with post-thaw motility

Sperm motility in fish with external fertilization is critical for reproductive efficiency in aquaculture, especially in salmonids. Gamete preservation techniques, such as cryopreservation, however, reduce sperm motility and fertilizing capacity. Very few studies have addressed cryodamage from energetic and cell signalling approaches. In this study, cAMP-dependent protein kinase (PKA) and AMP-activated kinase (AMPK) activities were quantified in fresh and cryopreserved spermatozoa of Atlantic salmon (Salmo salar); and the relation with motility was analysed. Results indicate there was a decrease in membrane integrity and motility in post-thawed spermatozoa compared to fresh samples, however, there was about 30% of cells with intact plasma membrane but incapable of motility. The PKA and AMPK activities were less after cryopreservation, indicating that loss of motility may be related to alteration of these key enzymes. Furthermore, PKA and AMPK activities were positively correlated with each other and with motility; and inhibition decreased motility, indicating there is a functional relationship between PKA and AMPK. The PKA inhibition also decreased AMPK activity, but results from protein-protein docking analyses indicated AMPK activation directly by PKA is unlikely, thus an indirect mechanism may exist. There have been no previous reports of these kinase actions in fish spermatozoa, making these findings worthy of assessment when there are future studies being planned, and may serve as base knowledge for optimization of cryopreservation procedures and development of biotechnologies to improve reproduction efficiency in the aquaculture industry.

Getting Docking into Shape Using Negative Image-Based Rescoring

The failure of default scoring functions to ensure virtual screening enrichment is a persistent problem for the molecular docking algorithms used in structure-based drug discovery. To remedy this problem, elaborate rescoring and postprocessing schemes have been developed with a varying degree of success, specificity, and cost. The negative image-based rescoring (R-NiB) has been shown to improve the flexible docking performance markedly with a variety of drug targets. The yield improvement is achieved by comparing the alternative docking poses against the negative image of the target protein's ligand-binding cavity. In other words, the shape and electrostatics of the binding pocket is directly used in the similarity comparison to rank the explicit docking poses. Here, the PANTHER/ShaEP-based R-NiB methodology is tested with six popular docking softwares, including GLIDE, PLANTS, GOLD, DOCK, AUTODOCK, and AUTODOCK VINA, using five validated benchmark sets. Overall, the results indicate that R-NiB outperforms the default docking scoring consistently and inexpensively, demonstrating that the methodology is ready for wide-scale virtual screening usage.

Computational approaches: discovery of GTPase HRas as prospective drug target for 1,3-diazine scaffolds

Heterocyclic 1,3-diazine nucleus is a valuable pharmacophore in the field of medicinal chemistry and exhibit a wide spectrum of biological activities. PharmMapper, a robust online tool used for establishing the target proteins based on reverse pharmacophore mapping. PharmMapper study is carried out to explore the pharmacological activity of 1,3-diazine derivatives using reverse docking program. PharmMapper, an open web server was used to recognize for all the feasible target proteins for the developed compounds through reverse pharmacophore mapping. The results were analyzed via molecular docking with maestro v11.5 (Schrodinger 2018-1) using GTPase HRas as possible target. The molecular docking studies displayed the binding behavior of 1,3-diazine within GTP binding pocket. From the docking study compounds s3 and s14 showed better docked score with anticancer potency against cancer cell line (HCT116). Hence, the GTPase HRas may be the possible target of 1,3-diazine derivatives for their anticancer activity where the retrieved information may be quite useful for developing rational drug designing. Furthermore the selected 1,3-diazine compounds were evaluated for their in vitro anticancer activity against murine macrophages cell line. 1,3-Diazine compounds exhibited good selectivity of the compounds towards the human colorectal carcinoma cell line instead of the murine macrophages. The toxicity study of the most active compounds was also performed on non cancerous HEK-293 cell line.

Applications of in Silico Methods for Design and Development of Drugs Targeting Protein-Protein Interactions

BACKGROUND

Identification of Protein-Protein Interactions (PPIs) is a major challenge in modern molecular biology and biochemistry research, due to the unquestionable role of proteins in cells, biological process and pathological states. Over the past decade, the PPIs have evolved from being considered a highly challenging field of research to being investigated and examined as targets for pharmacological intervention.

OBJECTIVE

Comprehension of protein interactions is crucial to known how proteins come together to build signalling pathways, to carry out their functions, or to cause diseases, when deregulated. Multiplicity and great amount of PPIs structures offer a huge number of new and potential targets for the treatment of different diseases.

METHODS

Computational techniques are becoming predominant in PPIs studies for their effectiveness, flexibility, accuracy and cost. As a matter of fact, there are effective in silico approaches which are able to identify PPIs and PPI site. Such methods for computational target prediction have been developed through molecular descriptors and data-mining procedures.

RESULTS

In this review, we present different types of interactions between protein-protein and the application of in silico methods for design and development of drugs targeting PPIs. We described computational approaches for the identification of possible targets on protein surface and to detect of stimulator/ inhibitor molecules.

CONCLUSION

A deeper study of the most recent bioinformatics methodologies for PPIs studies is vital for a better understanding of protein complexes and for discover new potential PPI modulators in therapeutic intervention.

Drug databases and their contributions to drug repurposing

Drug repurposing is an interesting field in the drug discovery scope because of reducing time and cost. It is also considered as an appropriate method for finding medications for orphan and rare diseases. Hence, many researchers have proposed novel methods based on databases which contain different information. Thus, a suitable organization of data which facilitates the repurposing applications and provides a tool or a web service can be beneficial. In this review, we categorize drug databases and discuss their advantages and disadvantages. Surprisingly, to the best of our knowledge, the importance and potential of databases in drug repurposing are yet to be emphasized. Indeed, the available databases can be divided into several groups based on data content, and different classes can be applied to find a new application of the existing drugs. Furthermore, we propose some suggestions for making databases more effective and popular in this field.

Fragment-Based Ligand-Protein Contact Statistics: Application to Docking Simulations

In this work, the information contained in the contacts between fragments of small-molecule ligands and protein residues has been collected and its exploitability has been verified by using the scoring of docking simulations as a test case for bringing about a proof of concept. Contact statistics between small-molecule fragments and binding site residues were collected and analyzed using a dataset composed of 200,000+ binding sites and associated ligands, derived from the database of the LIBRA ligand binding site recognition software, as a starting point. The fragments were generated by applying the decomposition algorithm implemented in BRICS. A simple "potential" based on the contact frequencies was tested against the CASF-2013 benchmark; its performance was then evaluated through the rescoring of docking poses generated for the DUD-E dataset. The results obtained indicate that this approach, its simplicity notwithstanding, yields promising results that are comparable, and in some cases, superior, to those obtained with other, more complex scoring functions.

Metadynamics simulations of ligand binding to GPCRs

Recent developments in metadynamics simulation techniques for ligand binding to Class A GPCRs are described and the results obtained elucidated. The computational protocol makes good use of modern massively parallel hardware, making simulations of the binding/unbinding process routine. The simulations reveal unprecedented details of the ligand-binding pathways, including multiple binding sites in many cases. Free energies of binding are reproduced very well and the simulations allow prediction of the efficacy (agonist, antagonist etc.) of ligands.

Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives

The discovery of new pharmaceutical drugs is one of the preeminent tasks-scientifically, economically, and socially-in biomedical research. Advances in informatics and computational biology have increased productivity at many stages of the drug discovery pipeline. Nevertheless, drug discovery has slowed, largely due to the reliance on small molecules as the primary source of novel hypotheses. Natural products (such as plant metabolites, animal toxins, and immunological components) comprise a vast and diverse source of bioactive compounds, some of which are supported by thousands of years of traditional medicine, and are largely disjoint from the set of small molecules used commonly for discovery. However, natural products possess unique characteristics that distinguish them from traditional small molecule drug candidates, requiring new methods and approaches for assessing their therapeutic potential. In this review, we investigate a number of state-of-the-art techniques in bioinformatics, cheminformatics, and knowledge engineering for data-driven drug discovery from natural products. We focus on methods that aim to bridge the gap between traditional small-molecule drug candidates and different classes of natural products. We also explore the current informatics knowledge gaps and other barriers that need to be overcome to fully leverage these compounds for drug discovery. Finally, we conclude with a "road map" of research priorities that seeks to realize this goal.

[Docking with combined use of a force field and a quantum-chemical method]

The paper presents the results concerning the application of docking programs FLM to combined use of the MMFF94 force field and the semiempirical quantum-chemical method PM7 in the docking procedure. At the first step of this procedure a fairly wide range of low-energy minima of the protein-ligand complex is found in the frame of the MMFF94 force field using the FLM program. The energies of all these minima are recalculated using the PM7 method and the COSMO solvent continuum model at the second step. On the basis of these calculations the deepest minimum of the protein-ligand energy, calculated by the PM7 method with COSMO solvent, is determined, which gives the position of the ligand closest to its position in the crystal of the protein-ligand complex. It is shown that the first step of the combined procedure is performed more quickly and more efficiently in vacuum, rather than with a solvent model.

Turning to Computer-aided Drug Design in the Treatment of Diffuse Large B-cell Lymphoma: Has it been Helpful?

INTRODUCTION

Amidst the numerous effective therapeutic options available for the treatment of Diffuse Large B-cell Lymphoma (DLBCL), about 30-40% of patients treated with first-line chemoimmunotherapy still experience a relapse or refractory DLBCL. This has necessitated a continuous search for new therapeutic agents to augment the existing therapeutic arsenal.

METHODS

The dawn of Computer-Aided Drug Design (CADD) in the drug discovery process has accounted for persistency in the application of computational approaches either alone or in combinatorial strategies with experimental methods towards the identification of potential hit compounds with high therapeutic efficacy in abrogating DLBCL.

RESULTS

This review showcases the interventions of structure-based and ligand-based computational approaches which have led to the identification of numerous small molecule inhibitors against implicated targets in DLBCL therapy, even though many of these potential inhibitors are piled-up awaiting further experimental validation and exploration.

CONCLUSION

We conclude that a successful and a conscious amalgamation of CADD and experimental approaches could pave the way for the discovery of the next generation potential leads in DLBCL therapy with improved activities and minimal toxicities.

SNP2SIM: a modular workflow for standardizing molecular simulation and functional analysis of protein variants

Background

Molecular simulations are used to provide insight into protein structure and dynamics, and have the potential to provide important context when predicting the impact of sequence variation on protein function. In addition to understanding molecular mechanisms and interactions on the atomic scale, translational applications of those approaches include drug screening, development of novel molecular therapies, and targeted treatment planning. Supporting the continued development of these applications, we have developed the SNP2SIM workflow that generates reproducible molecular dynamics and molecular docking simulations for downstream functional variant analysis. The Python workflow utilizes molecular dynamics software (NAMD (Phillips et al., J Comput Chem 26(16):1781-802, 2005), VMD (Humphrey et al., J Mol Graph 14(1):33-8, 27-8, 1996)) to generate variant specific scaffolds for simulated small molecule docking (AutoDock Vina (Trott and Olson, J Comput Chem 31(2):455-61, 2010)).

Results

SNP2SIM is composed of three independent modules that can be used sequentially to generate the variant scaffolds of missense protein variants from the wildtype protein structure. The workflow first generates the mutant structure and configuration files required to execute molecular dynamics simulations of solvated protein variant structures. The resulting trajectories are clustered based on the structural diversity of residues involved in ligand binding to produce one or more variant scaffolds of the protein structure. Finally, these unique structural conformations are bound to small molecule ligand libraries to predict variant induced changes to drug binding relative to the wildtype protein structure.

Conclusions

SNP2SIM provides a platform to apply molecular simulation based functional analysis of sequence variation in the protein targets of small molecule therapies. In addition to simplifying the simulation of variant specific drug interactions, the workflow enables large scale computational mutagenesis by controlling the parameterization of molecular simulations across multiple users or distributed computing infrastructures. This enables the parallelization of the computationally intensive molecular simulations to be aggregated for downstream functional analysis, and facilitates comparing various simulation options, such as the specific residues used to define structural variant clusters. The Python scripts that implement the SNP2SIM workflow are available (SNP2SIM Repository. https://github.com/mccoymd/SNP2SIM, Accessed 2019 February ), and individual SNP2SIM modules are available as apps on the Seven Bridges Cancer Genomics Cloud (Lau et al., Cancer Res 77(21):e3-e6, 2017; Cancer Genomics Cloud [www.cancergenomicscloud.org; Accessed 2018 November]).

Can multiscale simulations unravel the function of metallo-enzymes to improve knowledge-based drug discovery?

Metallo-enzymes are a large class of biomolecules promoting specialized chemical reactions. Quantum-classical quantum mechanics/molecular mechanics molecular dynamics, describing the metal site at quantum mechanics level, while accounting for the rest of system at molecular mechanics level, has an accessible time-scale limited by its computational cost. Hence, it must be integrated with classical molecular dynamics and enhanced sampling simulations to disentangle the functions of metallo-enzymes. In this review, we provide an overview of these computational methods and their capabilities. In particular, we will focus on some systems such as CYP19A1 a Fe-dependent enzyme involved in estrogen biosynthesis, and on Mg2+-dependent DNA/RNA processing enzymes/ribozymes and the spliceosome, a protein-directed ribozyme. This information may guide the discovery of drug-like molecules and genetic manipulation tools.

Holo-like and Druggable Protein Conformations from Enhanced Sampling of Binding Pocket Volume and Shape

Understanding molecular recognition of small molecules by proteins in atomistic detail is key for drug design. Molecular docking is a widely used computational method to mimic ligand-protein association in silico. However, predicting conformational changes occurring in proteins upon ligand binding is still a major challenge. Ensemble docking approaches address this issue by considering a set of different conformations of the protein obtained either experimentally or from computer simulations, e.g., molecular dynamics. However, holo structures prone to host (the correct) ligands are generally poorly sampled by standard molecular dynamics simulations of the apo protein. In order to address this limitation, we introduce a computational approach based on metadynamics simulations called ensemble docking with enhanced sampling of pocket shape (EDES) that allows holo-like conformations of proteins to be generated by exploiting only their apo structures. This is achieved by defining a set of collective variables that effectively sample different shapes of the binding site, ultimately mimicking the steric effect due to the ligand. We assessed the method on three challenging proteins undergoing different extents of conformational changes upon ligand binding. In all cases our protocol generates a significant fraction of structures featuring a low RMSD from the experimental holo geometry. Moreover, ensemble docking calculations using those conformations yielded in all cases native-like poses among the top-ranked ones.

Grafted hyaluronic acid N-acetyl-l-methionine for targeting of LAT1 receptor: In-silico, synthesis and microscale thermophoresis studies

Neutral amino acids can be delivered into cells through the l-type amino acid transporter-1 (LAT1), which is a sodium independent transporter. The LAT1 protein is expressed in different tissues, including kidney, blood brain barrier and intestinal wall hence LAT1 can be used as a target in diseases associated with its overexpression. In-silico interactions between different ligands, including methionine (Met), N-acetyl-l-methionine (AcMet), hyaluronic acid (HA), grafted hyaluronic-acid l-methionine (HA-ADH-Met) and a novel grafted hyaluronic acid-N-acetyl-l-methionine (HA-ADH-AcMet), which are at the active site of the LAT1 transporter, were studied and the binding energies calculated. The HA-ADH-AcMet complex demonstrated binding energy and solvation energy of -74.84 and 81.46 kcal/mol, respectively, thus validating its potential to be synthesized. The structural conformation of the HA-ADH-AcMet was confirmed using ¹H NMR, FTIR, DSC and PXRD. Microscale thermophoresis was employed to study the binding affinity between the different ligands and LAT1. The binding affinity was expressed in terms of a dissociation constant (K_d), where that of HA-ADH-AcMet was found to be 408 nM which was considered the strongest among the different ligands tested. HA-ADH-AcMet can be used as a targeting moiety for development of medicines to treat different diseases and processes that express LAT1 protein.

Stalis: A Computational Method for Template-Based Ab Initio Ligand Design

Proteins interact with small molecules through specific molecular recognition, which is central to essential biological functions in living systems. Therefore, understanding such interactions is crucial for basic sciences and drug discovery. Here, we present Structure template-based ab initio ligand design solution (Stalis), a knowledge-based approach that uses structure templates from the Protein Data Bank libraries of whole ligands and their fragments and generates a set of molecules (virtual ligands) whose structures represent the pocket shape and chemical features of a given target binding site. Our benchmark performance evaluation shows that ligand structure-based virtual screening using virtual ligands from Stalis outperforms a receptor structure-based virtual screening using AutoDock Vina, demonstrating reliable overall screening performance applicable to computational high-throughput screening. However, virtual ligands from Stalis are worse in recognizing active compounds at the small fraction of a rank-ordered list of screened library compounds than crystal ligands, due to the low resolution of the virtual ligand structures. In conclusion, Stalis can facilitate drug discovery research by designing virtual ligands that can be used for fast ligand structure-based virtual screening. Moreover, Stalis provides actual three-dimensional ligand structures that likely bind to a target protein, enabling to gain structural insight into potential ligands. Stalis can be an efficient computational platform for high-throughput ligand design for fundamental biological study and drug discovery research at the proteomic level. © 2019 Wiley Periodicals, Inc.

Macrophage Migration Inhibitory Factor Acts as the Potential Target of a Newly Synthesized Compound, 1-(9'-methyl-3'-carbazole)-3, 4-dihydro-β-carboline

For a newly synthesized compound, identifying its target protein is a slow but pivotal step toward understand its pharmacologic mechanism. In this study, we systemically synthesized novel manzamine derivatives and chose 1-(9′-methyl-3′-carbazole)-3, 4-dihydro-β-carboline (MCDC) as an example to identify its target protein and function. MCDC had potent toxicity against several cancer cells. To identify its target protein, we first used a docking screen to predict macrophage migration inhibitory factor (MIF) as the potential target. Biochemical experiments, including mutation analysis and hydrogen-deuterium exchange assays, validated the binding of MCDC to MIF. Furthermore, MCDC was shown by microarrays to interfere with the cell cycle of breast cancer MCF7 cells. The activated signaling pathways included AKT phosphorylation and S phase-related proteins. Our results showed MIF as a potential direct target of a newly synthesized manzamine derivative, MCDC, and its pharmacologic mechanisms.

An investigation into non-covalent functionalization of a single-walled carbon nanotube and a graphene sheet with protein G:A combined experimental and molecular dynamics study

Investigation of non-covalent interaction of hydrophobic surfaces with the protein G (PrG) is necessary due to their frequent utilization in immunosensors and ELISA. It has been confirmed that surfaces, including carbonous-nanostructures (CNS) could orient proteins for a better activation. Herein, PrG interaction with single-walled carbon nanotube (SWCNT) and graphene (Gra) nanostructures was studied by employing experimental and MD simulation techniques. It is confirmed that the PrG could adequately interact with both SWCNT and Gra and therefore fine dispersion for them was achieved in the media. Results indicated that even though SWCNT was loaded with more content of PrG in comparison with the Gra, the adsorption of the PrG on Gra did not induce significant changes in the IgG tendency. Several orientations of the PrG were adopted in the presence of SWCNT or Gra; however, SWCNT could block the PrG-FcR. Moreover, it was confirmed that SWCNT reduced the α-helical structure content in the PrG. Reduction of α-helical structure of the PrG and improper orientation of the PrG-SWCNT could remarkably decrease the PrG tendency to the Fc of the IgG. Importantly, the Gra could appropriately orient the PrG by both exposing the PrG-FcR and also by blocking the fragment of the PrG that had tendency to interact with Fab in IgG.

Benchmarking of different molecular docking methods for protein-peptide docking

Background

Molecular docking studies on protein-peptide interactions are a challenging and time-consuming task because peptides are generally more flexible than proteins and tend to adopt numerous conformations. There are several benchmarking studies on protein-protein, protein-ligand and nucleic acid-ligand docking interactions. However, a series of docking methods is not rigorously validated for protein-peptide complexes in the literature. Considering the importance and wide application of peptide docking, we describe benchmarking of 6 docking methods on 133 protein-peptide complexes having peptide length between 9 to 15 residues. The performance of docking methods was evaluated using CAPRI parameters like FNAT, I-RMSD, L-RMSD.

Result

Firstly, we performed blind docking and evaluate the performance of the top docking pose of each method. It was observed that FRODOCK performed better than other methods with average L-RMSD of 12.46 Å. The performance of all methods improved significantly for their best docking pose and achieved highest average L-RMSD of 3.72 Å in case of FRODOCK. Similarly, we performed re-docking and evaluated the performance of the top and best docking pose of each method. We achieved the best performance in case of ZDOCK with average L-RMSD 8.60 Å and 2.88 Å for the top and best docking pose respectively. Methods were also evaluated on 40 protein-peptide complexes used in the previous benchmarking study, where peptide have length up to 5 residues. In case of best docking pose, we achieved the highest average L-RMSD of 4.45 Å and 2.09 Å for the blind docking using FRODOCK and re-docking using AutoDock Vina respectively.

Conclusion

The study shows that FRODOCK performed best in case of blind docking and ZDOCK in case of re-docking. There is a need to improve the ranking of docking pose generated by different methods, as the present ranking scheme is not satisfactory. To facilitate the scientific community for calculating CAPRI parameters between native and docked complexes, we developed a web-based service named PPDbench (http://webs.iiitd.edu.in/raghava/ppdbench/).

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2449-y) contains supplementary material, which is available to authorized users.

Assessing the Quality of in Silico Produced Biomolecules: The Discovery of a New Conformer

The computational procedures for predicting the 3D structure of aptamers interacting with different biological molecules have gained increasing attention in recent years. The information acquired through these methods represents a crucial input for research, especially when relevant crystallographic data are not available. A number of software programs able to perform macromolecular docking are currently accessible, leading to the prediction of the quaternary structure of complexes formed by two or more interacting biological macromolecules. Nevertheless, the scoring protocols employed for ranking the candidate structures do not always produce satisfactory results, making difficult the identification of structures that are most likely to occur in nature. In this paper, we propose a novel procedure to improve the predictive performances of computational scoring protocols, using a maximum likelihood estimate based on topological and electrical properties of interacting biomolecules. The reliability of the new computational approach, enabling the ranking of aptamer-protein configurations produced by an open source docking program, has been assessed by its successful application to a set of antiangiopoietin aptamers, for which experimental data highlighting the sequence-dependent affinity toward the target protein are available. The procedure led to the identification of two main types of aptamer conformers involved in angiopoietin binding. Interestingly, one of these reproduces the arrangement of angiopoietin with its natural target, tyrosine kinase, while the other one is completely unexpected. The possible scenarios related to these results have been discussed. The methodology here described can be used to refine the outcomes of different computational procedures and can be applied to a wide range of biological molecules, thus representing a new tool for guiding the design of bioinspired sensors with enhanced selectivity.

Microswitches for the Activation of the Nociceptin Receptor Induced by Cebranopadol: Hints from Microsecond Molecular Dynamics

Cebranopadol (CBP) is a novel analgesic acting as agonist at the nociceptin (NOP) and μ-opioid (MOP) receptors, exhibiting high potency and efficacy as an antinociceptive and antihypersensitive drug. The binding conformation and the dynamical interactions of CBP with the NOP receptor have been investigated by molecular docking, molecular dynamics (MD) in the microsecond time scale, and hybrid quantum mechanics/molecular mechanics (QM/MM). CBP binds to the NOP receptor as a bidentate ligand of the aspartate D130^3,32 by means of both its fluoroindole and dimethyl nitrogens. Starting from the known crystal structure of the inactive state of the receptor, in complex with the antagonist compound-24 (NOP-C24) the comparative analysis of 1 μs MD trajectories of the NOP-C24 complex itself and the NOP_free and NOP-CBP complexes provides new insights on the already known microswitches related to receptor activation, in the frame of the extended ternary complex model. The agonist acts by destabilizing the inactive conformation of the NOP receptor, by inducing a conformational change of M134^3,36, which allows W276^6,48 to flip around its χ₂ dihedral, going in close proximity to the receptor hydrophobic core (T138^3,40, P227^5,50, F272^6,44), which is known to be fundamental for the activation of the opioid receptors. A complete rational picture is also provided for the role of N133^3,35 and W276^6,48 undergoing critical conformational changes related to an anticooperativity effect, i.e. the well-known role of sodium as negative modulator of agonist binding. Finally, the movement of residue Y319^7,53 belonging to the NPxxY motif is also induced by the binding of the agonist in the inactive state, opening a gate for a water channel just as upon receptor activation.

Approaches to the Structure-Based Design of Antivirulence Drugs: Therapeutics for the Post-Antibiotic Era

The alarming rise of multidrug-resistant bacterial strains, coupled with decades of stagnation in the field of antibiotic development, necessitates exploration of new therapeutic approaches to treat bacterial infections. Targeting bacterial virulence is an attractive alternative to traditional antibiotics in that this approach disarms pathogens that cause human diseases, without placing immediate selective pressure on the target bacterium or harming commensal species. The growing number of validated virulence protein targets for which structural information has been obtained, along with advances in computational power and screening algorithms, make the rational design of antivirulence drugs a promising avenue to explore. Here, we review the principles of structure-based drug design and the exciting opportunities this technique presents for antivirulence drug discovery.

Insights into the substrate binding specificity of quorum-quenching acylase PvdQ

Quorum sensing is a cell to cell signaling mechanism that enables them to coordinate their behaviors in a density-dependent manner mediated by small diffusible signaling molecules, which can control the virulence and biofilm gene expression in many Gram-negative and positive bacteria. N-acyl homoserine lactone acylase PvdQ from human opportunistic pathogen Pseudomonas aeruginosa is a quorum-quenching enzyme that can hydrolyze the amide bond of the quorum signaling N-acyl homoserine lactones (AHLs) thereby degrading the signaling molecules, turning off the biofilm phenotype and resulting in a reduction of bacterial virulence. Previous studies demonstrated that PvdQ has different preferences for N-acyl substrates with different acyl chain lengths and substituents. However, the substrate binding specificity determinants of the quorum-quenching enzyme PvdQ with the different bacterial ligands are unknown and unintuitive. Further, elucidation of these determinants can lead to mutants with efficiency and broader substrate promiscuity. To investigate this question, a computational study was carried out combining multiple molecular docking methods, molecular dynamics simulations, residue interaction network analysis, and binding free energy calculations. The main findings are: firstly, the results from pKa predictions support that the pKa of the N-terminus of Serβ1 was depressed due to the surrounding residues. Multiple molecular docking studies provide useful information about the detailed binding modes and binding affinities. Secondly, 300 ns molecular dynamics simulations were carried out to analyze the overall molecular motions of substrate-bound and substrate-free PvdQ. The specific interactions between the active site of PvdQ and different ligands revealed the determinants for the preference among the ligands. A systematic comparison and analysis of the protein dynamic fingerprint of each complex demonstrated that binding of the most favorable ligand, C12-homoserine lactone (C12-HSL), reduced the global motions of the complex and maintained the correct arrangement of the catalytic site. Further, the residue interaction network analysis of each system illustrated that there are more communication contacts and pathways between the residues in the C12-HSL complex as compared to complexes with the other ligands. The binding of the C12-HSL ligand facilitates structural communication between the two knobs and the active site. While the binding of the other ligands tend to impair specific communication pathways between the two knobs and the active site, and lead to a catalytically inefficient state. Finally, simulation results from free energy landscape and binding free energy analysis revealed that the C12-HSL ligand has the lowest binding free energy and greater stability than the less favored ligands. Each of the following residues: Serβ1, Hisβ23, Pheβ24, Metβ30, Pheβ32, Leuβ50, Asnβ57, Thrβ69, Valβ70, Trpβ162, Trpβ186, Asnβ269, Argβ297 and Leuα146, play different roles in substrate binding specificity. This is the first computational study that provides molecular information for structure-dynamic-function relationships of PvdQ with different ligands and demonstrates determinants of bacterial substrate binding specificity.

Open-Access Activity Prediction Tools for Natural Products. Case Study: hERG Blockers

Interference with the hERG potassium ion channel may cause cardiac arrhythmia and can even lead to death. Over the last few decades, several drugs, already on the market, and many more investigational drugs in various development stages, have had to be discontinued because of their hERG-associated toxicity. To recognize potential hERG activity in the early stages of drug development, a wide array of computational tools, based on different principles, such as 3D QSAR, 2D and 3D similarity, and machine learning, have been developed and are reviewed in this chapter. The various available prediction tools Similarity Ensemble Approach, SuperPred, SwissTargetPrediction, HitPick, admetSAR, PASSonline, Pred-hERG, and VirtualToxLab™ were used to screen a dataset of known hERG synthetic and natural product actives and inactives to quantify and compare their predictive power. This contribution will allow the reader to evaluate the suitability of these computational methods for their own related projects. There is an unmet need for natural product-specific prediction tools in this field.