MoMA-LigPath: a web server to simulate protein-ligand unbinding

pyCaverDock: Python implementation of the popular tool for analysis of ligand transport with advanced caching and batch calculation support

SUMMARY

Access pathways in enzymes are crucial for the passage of substrates and products of catalysed reactions. The process can be studied by computational means with variable degrees of precision. Our in-house approximative method CaverDock provides a fast and easy way to set up and run ligand binding and unbinding calculations through protein tunnels and channels. Here we introduce pyCaverDock, a Python3 API designed to improve user experience with the tool and further facilitate the ligand transport analyses. The API enables users to simplify the steps needed to use CaverDock, from automatizing setup processes to designing screening pipelines.

AVAILABILITY AND IMPLEMENTATION

pyCaverDock API is implemented in Python 3 and is freely available with detailed documentation and practical examples at https://loschmidt.chemi.muni.cz/caverdock/.

Fast approximative methods for study of ligand transport and rational design of improved enzymes for biotechnologies

Acceleration of chemical reactions by the enzymes optimized using protein engineering represents one of the key pillars of the contribution of biotechnology towards sustainability. Tunnels and channels of enzymes with buried active sites enable the exchange of ligands, ions, and water molecules between the outer environment and active site pockets. The efficient exchange of ligands is a fundamental process of biocatalysis. Therefore, enzymes have evolved a wide range of mechanisms for repetitive conformational changes that enable periodic opening and closing. Protein-ligand interactions are traditionally studied by molecular docking, whereas molecular dynamics is the method of choice for studying conformational changes and ligand transport. However, computational demands make molecular dynamics impractical for screening purposes. Thus, several approximative methods have been recently developed to study interactions between a protein and ligand during the ligand transport process. Apart from identifying the best binding modes, these methods also provide information on the energetics of the transport and identify problematic regions limiting the ligand passage. These methods use approximations to simulate binding or unbinding events rapidly (calculation times from minutes to hours) and provide energy profiles that can be used to rank ligands or pathways. Here we provide a critical comparison of available methods, showcase their results on sample systems, discuss their practical applications in molecular biotechnologies and outline possible future developments.

Variability in the response of HBV D-subgenotypes to antiviral therapy: designing pan D-subgenotypic reverse transcriptase inhibitors

Nucleos(t)ide analogues entecavir (ETV) and tenofovir disoproxil fumarate (TDF) are recommended as first-line monotherapies for chronic hepatitis B (CHB). Multiple HBV genotypes/subgenotypes have been described, but their impact on treatment response remains largely elusive. We investigated the effectiveness of ETV/TDF on HBV/D-subgenotypes, D1/D2/D3/D5, studied the structural/functional differences in subgenotype-specific reverse transcriptase (RT) domains of viral polymerase and identified novel molecules with robust inhibitory activity on various D-subgenotypes. Transfection of Huh7 cells with full-length D1/D2/D3/D5 and in vitro TDF/ETV susceptibility assays demonstrated that D1/D2 had greater susceptibility to TDF/ETV while D3/D5 exhibited poorer response. Additionally, HBV load was substantially reduced in TDF-treated CHB patients carrying D1/D2 but minimally reduced in D3/D5-infected patients. Comparison of RT sequences of D-subgenotypes led to identification of unique subgenotype-specific residues and molecular modeling/docking/simulation studies depicted differential bindings of TDF/ETV to the active site of their respective RTs. Replacement of signature residues in D3/D5 HBV clones with corresponding amino acids seen in D1/D2 improved their susceptibility to TDF/ETV. Using high throughput virtual screening, we identified N(9)-[3-fluoro-2-(phosphonomethoxy)propyl] (FPMP) derivatives of purine bases, including N⁶-substituted (S)-FPMP derivative of 2,6-diaminopurine (DAP) (OB-123-VK), as potential binders of RT of different D-subgenotypes. We synthesized (S)-FPMPG prodrugs (FK-381-FEE/FK-381-SEE/FK-382) and tested their effectiveness along with OB-123-VK. Both OB-123-VK and FK-381-FEE exerted similar antiviral activities against all D-subgenotypes, although FK-381-FEE was more potent. Our study highlighted the natural variation in therapeutic response of D1/D2/D3/D5 and emphasized the need for HBV subgenotype determination before treatment. Novel molecules described here could benefit future design/discovery of pan-D-subgenotypic inhibitors. Importance: Current treatment of chronic hepatitis B relies heavily on nucleotide/nucleoside analogs in particular, tenofovir disoproxil fumarate (TDF) and entecavir (ETV) to keep HBV replication under control and prevent end-stage liver diseases. However, it was unclear whether the therapeutic effects of TDF/ETV differ among patients infected with different HBV genotypes and subgenotypes. HBV genotype D is the most widespread of all HBV genotypes and multiple D-subgenotypes have been described. We here report that different subgenotypes of HBV genotype-D exhibit variable response towards TDF and ETV and this could be attributed to naturally occurring amino acid changes in the reverse transcriptase domain of the subgenotype-specific polymerase. Further, we identified novel molecules and also synthesized prodrugs that are equally effective on different D-subgenotypes and could facilitate management of HBV/D-infected patients irrespective of D-subgenotype.

Virtual screening of potential anticancer drugs based on microbial products

The development of microbial products for cancer treatment has been in the spotlight in recent years. In order to accelerate the lengthy and expensive drug development process, in silico screening tools are systematically employed, especially during the initial discovery phase. Moreover, considering the steadily increasing number of molecules approved by authorities for commercial use, there is a demand for faster methods to repurpose such drugs. Here we present a review on virtual screening web tools, such as publicly available databases of molecular targets and libraries of ligands, with the aim to facilitate the discovery of potential anticancer drugs based on microbial products. We provide an entry-level step-by-step description of the workflow for virtual screening of microbial metabolites with known protein targets, as well as two practical examples using freely available web tools. The first case presents a virtual screening study of drugs developed from microbial products using Caver Web, a web tool that performs docking along a tunnel. The second case comprises a comparative analysis between a wild type isocitrate dehydrogenase 1 and a mutant that results in cancer, using the recently developed web tool PredictSNP^Onco. In summary, this review provides the basic and essential background information necessary for virtual screening experiments, which may accelerate the discovery of novel anticancer drugs.

Computational design of enzymes for biotechnological applications

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.

Molecular interaction of anti-cancer ligands with human brain acetylcholinesterase

There are a significant number of cases whereby cancer patients belonging to the old age group additionally suffer from cognition decline (a hallmark feature of Alzheimer's disease). Hence, it is understandable that it would be a boon if certain drug molecules could provide health benefits to a patient suffering from cancer as well as Alzheimer's disease. The objective of the work was to identify anticancer molecule(s) whose chemical-skeleton could be used as 'seed' for future design of dual-acting drugs against Alzheimer's disease and cancer. The study employed criterion-based search, docking, SWISS-ADME-profiling, ▵ASA-calculations, molecular-overlay and 'MoMA'-simulation to query possible binding of selected anticancer molecules with human brain acetylcholinesterase (AChE). Molecular interactions of all of the top ranking ligands were analyzed. 'BOILED-egg' model was employed to query brain-penetration of the ligands. A detailed molecular-simulation-analysis was performed. Snapshots of different stages of dynamic molecular interactions (selected from 254 pdb files) were captured by MoMA LigPath, a robotics inspired simulation algorithm. The study concluded that chemical skeletons of 'Niraparib' and 'Ponatinib' might be used as 'seed(s)' for design of such drugs. If successfully materialized in future, this approach could decrease the total number of daily pills that an old patient needs to take. Furthermore, novel anticancer drugs could be synthesized that do not inhibit AChE (e.g. by removal/modification of moieties that are crucial to binding of anticancer drug to AChE) even if those happen to be 'Blood Brain Barrier'-permeable. Alternatively, fresh AChE-inhibitors could be designed based on the scaffolds of the aforementioned anticancer drugs.Communicated by Ramaswamy H. Sarma.

CaverDock: A Novel Method for the Fast Analysis of Ligand Transport

Here we present a novel method for the analysis of transport processes in proteins and its implementation called CaverDock. Our method is based on a modified molecular docking algorithm. It iteratively places the ligand along the access tunnel in such a way that the ligand movement is contiguous and the energy is minimized. The result of CaverDock calculation is a ligand trajectory and an energy profile of transport process. CaverDock uses the modified docking program Autodock Vina for molecular docking and implements a parallel heuristic algorithm for searching the space of possible trajectories. Our method lies in between the geometrical approaches and molecular dynamics simulations. Contrary to the geometrical methods, it provides an evaluation of chemical forces. However, it is far less computationally demanding and easier to set up compared to molecular dynamics simulations. CaverDock will find a broad use in the fields of computational enzymology, drug design, and protein engineering. The software is available free of charge to the academic users at https://loschmidt.chemi.muni.cz/caverdock/.

CaverDock: a molecular docking-based tool to analyse ligand transport through protein tunnels and channels

MOTIVATION

Protein tunnels and channels are key transport pathways that allow ligands to pass between proteins' external and internal environments. These functionally important structural features warrant detailed attention. It is difficult to study the ligand binding and unbinding processes experimentally, while molecular dynamics simulations can be time-consuming and computationally demanding.

RESULTS

CaverDock is a new software tool for analysing the ligand passage through the biomolecules. The method uses the optimized docking algorithm of AutoDock Vina for ligand placement docking and implements a parallel heuristic algorithm to search the space of possible trajectories. The duration of the simulations takes from minutes to a few hours. Here we describe the implementation of the method and demonstrate CaverDock's usability by: (i) comparison of the results with other available tools, (ii) determination of the robustness with large ensembles of ligands and (iii) the analysis and comparison of the ligand trajectories in engineered tunnels. Thorough testing confirms that CaverDock is applicable for the fast analysis of ligand binding and unbinding in fundamental enzymology and protein engineering.

AVAILABILITY AND IMPLEMENTATION

User guide and binaries for Ubuntu are freely available for non-commercial use at https://loschmidt.chemi.muni.cz/caverdock/. The web implementation is available at https://loschmidt.chemi.muni.cz/caverweb/. The source code is available upon request.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Relevance of Glutathione Reductase Inhibition by Fluoxetine to Human Health and Disease: Insights Derived from a Combined Kinetic and Docking Study

Glutathione reductase (GR) is a homodimeric enzyme playing an important role in the regeneration of the central antioxidant molecule reduced glutathione (GSH) from oxidized glutathione (GSSG) at the expense of a molecule of NADPH. GSH scavenges and eliminates superoxide and hydroxyl radicals non-enzymatically or serves as an electron donor for several enzymes. Fluoxetine (FLU), a selective serotonin reuptake inhibitor, is widely prescribed in the treatment of major depressive disorder. Here, using enzyme kinetic studies and molecular docking simulations, we aimed at disclosing the mechanistic and structural aspects of the interaction between GR and FLU. Affecting enzyme activity in a dose-dependent manner, FLU was shown to be a moderately potent (IC₅₀ = 0.88 mM) inhibitor of GR. When the variable substrate was GSSG, the type of inhibition was linear mixed-type competitive (K_i = 279 ± 32 μM; α = 5.48 ± 1.29). When the variable substrate was NADPH, however, the type of inhibition was non-competitive (K_i = 879 ± 82 μM). The observed difference in inhibition types was attributed to the binding of FLU in the large intermonomer cavity of GR, where it hampered catalysis and interfered with substrate binding. Overall, although it is anticipated that long-term use of FLU leads to acquired GR deficiency, the inhibitory action of FLU on GR may be therapeutically exploited in anti-cancer research.

A Robotics-Inspired Screening Algorithm for Molecular Caging Prediction

We define a molecular caging complex as a pair of molecules in which one molecule (the “host” or “cage”) possesses a cavity that can encapsulate the other molecule (the “guest”) and prevent it from escaping. Molecular caging complexes can be useful in applications such as molecular shape sorting, drug delivery, and molecular immobilization in materials science, to name just a few. However, the design and computational discovery of new caging complexes is a challenging task, as it is hard to predict whether one molecule can encapsulate another because their shapes can be quite complex. In this paper, we propose a computational screening method that predicts whether a given pair of molecules form a caging complex. Our method is based on a caging verification algorithm that was designed by our group for applications in robotic manipulation. We tested our algorithm on three pairs of molecules that were previously described in a pioneering work on molecular caging complexes and found that our results are fully consistent with the previously reported ones. Furthermore, we performed a screening experiment on a data set consisting of 46 hosts and four guests and used our algorithm to predict which pairs are likely to form caging complexes. Our method is computationally efficient and can be integrated into a screening pipeline to complement experimental techniques.

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

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

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

ART-RRT: As-Rigid-As-Possible search for protein conformational transition paths

The possible functions of a protein are strongly related to its structural rearrangements in the presence of other molecules or environmental changes. Hence, the evaluation of transition paths of proteins, which encodes conformational changes between stable states, is important since it may reveal the underlying mechanisms of the biochemical processes related to these motions. During the last few decades, different geometry-based methods have been proposed to predict such transition paths. However, in the cases where the solution requires complex motions, these methods, which typically constrain only locally the molecular structures, could produce physically irrelevant solutions involving self-intersection. Recently, we have proposed ART-RRT, an efficient method for finding ligand-unbinding pathways. It relies on the exploration of energy valleys in low-dimensional spaces, taking advantage of some mechanisms inspired from computer graphics to ensure the consistency of molecular structures. This article extends ART-RRT to the problem of finding probable conformational transition between two stable states for proteins. It relies on a bidirectional exploration rooted on the two end states and introduces an original strategy to attempt connections between the explored regions. The resulting method is able to produce at low computational cost biologically realistic paths free from self-intersection. These paths can serve as valuable input to other advanced methods for the study of proteins. A better understanding of conformational changes of proteins is important since it may reveal the underlying mechanisms of the biochemical processes related to such motions. Recently, the ART-RRT method has been introduced for finding ligand-unbinding pathways. This article presents an adaptation of the method for finding probable conformational transition between two stable states of a protein. The method is not only computationally cost-effective but also able to produce biologically realistic paths which are free from self-intersection.

GPathFinder: Identification of Ligand-Binding Pathways by a Multi-Objective Genetic Algorithm

Protein-ligand docking is a widely used method to generate solutions for the binding of a small molecule with its target in a short amount of time. However, these methods provide identification of physically sound protein-ligand complexes without a complete view of the binding process dynamics, which has been recognized to be a major discriminant in binding affinity and ligand selectivity. In this paper, a novel piece of open-source software to approach this problem is presented, called GPathFinder. It is built as an extension of the modular GaudiMM platform and is able to simulate ligand diffusion pathways at atomistic level. The method has been benchmarked on a set of 20 systems whose ligand-binding routes were studied by other computational tools or suggested from experimental "snapshots". In all of this set, GPathFinder identifies those channels that were already reported in the literature. Interestingly, the low-energy pathways in some cases indicate novel possible binding routes. To show the usefulness of GPathFinder, the analysis of three case systems is reported. We believe that GPathFinder is a software solution with a good balance between accuracy and computational cost, and represents a step forward in extending protein-ligand docking capacities, with implications in several fields such as drug or enzyme design.

Engineering enzyme access tunnels

Enzymes are efficient and specific catalysts for many essential reactions in biotechnological and pharmaceutical industries. Many times, the natural enzymes do not display the catalytic efficiency, stability or specificity required for these industrial processes. The current enzyme engineering methods offer solutions to this problem, but they mainly target the buried active site where the chemical reaction takes place. Despite being many times ignored, the tunnels and channels connecting the environment with the active site are equally important for the catalytic properties of enzymes. Changes in the enzymatic tunnels and channels affect enzyme activity, specificity, promiscuity, enantioselectivity and stability. This review provides an overview of the emerging field of enzyme access tunnel engineering with case studies describing design of all the aforementioned properties. The software tools for the analysis of geometry and function of the enzymatic tunnels and channels and for the rational design of tunnel modifications will also be discussed. The combination of new software tools and enzyme engineering strategies will provide enzymes with access tunnels and channels specifically tailored for individual industrial processes.

Advances in distributed computing with modern drug discovery

INTRODUCTION

Computational chemistry dramatically accelerates the drug discovery process and high-performance computing (HPC) can be used to speed up the most expensive calculations. Supporting a local HPC infrastructure is both costly and time-consuming, and, therefore, many research groups are moving from in-house solutions to remote-distributed computing platforms. Areas covered: The authors focus on the use of distributed technologies, solutions, and infrastructures to gain access to HPC capabilities, software tools, and datasets to run the complex simulations required in computational drug discovery (CDD). Expert opinion: The use of computational tools can decrease the time to market of new drugs. HPC has a crucial role in handling the complex algorithms and large volumes of data required to achieve specificity and avoid undesirable side-effects. Distributed computing environments have clear advantages over in-house solutions in terms of cost and sustainability. The use of infrastructures relying on virtualization reduces set-up costs. Distributed computing resources can be difficult to access, although web-based solutions are becoming increasingly available. There is a trade-off between cost-effectiveness and accessibility in using on-demand computing resources rather than free/academic resources. Graphics processing unit computing, with its outstanding parallel computing power, is becoming increasingly important.

Native State of Complement Protein C3d Analysed via Hydrogen Exchange and Conformational Sampling

Hydrogen/deuterium exchange detected by mass spectrometry (HDXMS) provides valuable information on protein structure and dynamics. Although HDX-MS data is often interpreted using crystal structures, it was suggested that conformational ensembles produced by molecular dynamics simulations yield more accurate interpretations. In this paper, we analyse the complement protein C3d by performing an HDX-MS experiment, and evaluate several interpretation methodologies using an existing prediction model to derive HDX-MS data from protein structure. To interpret and refine C3d's HDX-MS data, we look for a conformation (or conformational ensemble) of C3d that allows computationally replicating this data. We confirm that crystal structures are not a good choice and suggest that conformational ensembles produced by molecular dynamics simulations might not always be satisfactory either. Finally, we show that coarse-grained conformational sampling of C3d produces a conformation from which its HDX-MS data can be replicated and refined.

Radiation damage at the active site of human alanine:glyoxylate aminotransferase reveals that the cofactor position is finely tuned during catalysis

The alanine:glyoxylate aminotransferase (AGT), a hepatocyte-specific pyridoxal-5'-phosphate (PLP) dependent enzyme, transaminates L-alanine and glyoxylate to glycine and pyruvate, thus detoxifying glyoxylate and preventing pathological oxalate precipitation in tissues. In the widely accepted catalytic mechanism of the aminotransferase family, the lysine binding to PLP acts as a catalyst in the stepwise 1,3-proton transfer, interconverting the external aldimine to ketimine. This step requires protonation by a conserved aspartate of the pyridine nitrogen of PLP to enhance its ability to stabilize the carbanionic intermediate. The aspartate residue is also responsible for a significant geometrical distortion of the internal aldimine, crucial for catalysis. We present the structure of human AGT in which complete X-ray photoreduction of the Schiff base has occurred. This result, together with two crystal structures of the conserved aspartate pathogenic variant (D183N) and the molecular modeling of the transaldimination step, led us to propose that an interplay of opposite forces, which we named spring mechanism, finely tunes PLP geometry during catalysis and is essential to move the external aldimine in the correct position in order for the 1,3-proton transfer to occur.

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

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

Molecular Interaction of Anti-Diabetic Drugs With Acetylcholinesterase and Sodium Glucose Co-Transporter 2

Type 2 Diabetes Mellitus (T2DM) and Alzheimer's disease (AD) are the two disorders which are known to share pertinent pathological and therapeutic links. Sodium glucose co-transporter-2 (SGLT2) and Acetylcholinesterase (AChE) are established inhibition targets for T2DM and AD treatments, respectively. Reports suggest that anti-diabetic drugs could be used for AD treatment also. The present study used molecular docking by Autodock4.2 using our "Click-By-Click"-protocol, Ligplot1.4.3 and "change in accessible surface area (ΔASA)-calculations" to investigate the binding of two investigational anti-diabetic drugs, Ertugliflozin and Sotagliflozin to an established target (SGLT2) and a research target (human brain AChE). Sotagliflozin appeared more promising for SGLT2 as well as AChE-inhibition with reference to ΔG and Ki values in comparison to Ertugliflozin. The ΔG and Ki values for "Sotagliflozin:AChE-binding" were -7.16 kcal/mol and 5.6 μM, respectively while the same were found to be -8.47 kcal/mol and 0.62 μM, respectively for its interaction with SGLT2. Furthermore, "Sotagliflozin:SGLT2-interaction" was subjected to (un)binding simulation analyses by "Molecular-Motion-Algorithms." This information is significant as the exact binding mode, interacting amino acid residues and simulation results for the said interaction have not been described yet. Also no X-ray crystal is available for the same. Finally, the results described herein indicate that Sotagliflozin could have an edge over Ertugliflozin for treatment of Type 2 diabetes. Future design of drugs based on Sotagliflozin scaffolds for treatment of Type 2 and/or Type 3 diabetes are highly recommended. As these drugs are still in late phases of clinical trials, the results described herein appear timely. J. Cell. Biochem. 118: 3855-3865, 2017. © 2017 Wiley Periodicals, Inc.

Coarse-Grained Conformational Sampling of Protein Structure Improves the Fit to Experimental Hydrogen-Exchange Data

Monitoring hydrogen/deuterium exchange (HDX) undergone by a protein in solution produces experimental data that translates into valuable information about the protein's structure. Data produced by HDX experiments is often interpreted using a crystal structure of the protein, when available. However, it has been shown that the correspondence between experimental HDX data and crystal structures is often not satisfactory. This creates difficulties when trying to perform a structural analysis of the HDX data. In this paper, we evaluate several strategies to obtain a conformation providing a good fit to the experimental HDX data, which is a premise of an accurate structural analysis. We show that performing molecular dynamics simulations can be inadequate to obtain such conformations, and we propose a novel methodology involving a coarse-grained conformational sampling approach instead. By extensively exploring the intrinsic flexibility of a protein with this approach, we produce a conformational ensemble from which we extract a single conformation providing a good fit to the experimental HDX data. We successfully demonstrate the applicability of our method to four small and medium-sized proteins.

Interactive exploration of ligand transportation through protein tunnels

BACKGROUND

Protein structures and their interaction with ligands have been in the focus of biochemistry and structural biology research for decades. The transportation of ligand into the protein active site is often complex process, driven by geometric and physico-chemical properties, which renders the ligand path full of jitter and impasses. This prevents understanding of the ligand transportation and reasoning behind its behavior along the path.

RESULTS

To address the needs of the domain experts we design an explorative visualization solution based on a multi-scale simplification model. It helps to navigate the user to the most interesting parts of the ligand trajectory by exploring different attributes of the ligand and its movement, such as its distance to the active site, changes of amino acids lining the ligand, or ligand "stuckness". The process is supported by three linked views - 3D representation of the simplified trajectory, scatterplot matrix, and bar charts with line representation of ligand-lining amino acids.

CONCLUSIONS

The usage of our tool is demonstrated on molecular dynamics simulations provided by the domain experts. The tool was tested by the domain experts from protein engineering and the results confirm that it helps to navigate the user to the most interesting parts of the ligand trajectory and to understand the ligand behavior.

Defining Low-Dimensional Projections to Guide Protein Conformational Sampling

Exploring the conformational space of proteins is critical to characterize their functions. Numerous methods have been proposed to sample a protein's conformational space, including techniques developed in the field of robotics and known as sampling-based motion-planning algorithms (or sampling-based planners). However, these algorithms suffer from the curse of dimensionality when applied to large proteins. Many sampling-based planners attempt to mitigate this issue by keeping track of sampling density to guide conformational sampling toward unexplored regions of the conformational space. This is often done using low-dimensional projections as an indirect way to reduce the dimensionality of the exploration problem. However, how to choose an appropriate projection and how much it influences the planner's performance are still poorly understood issues. In this article, we introduce two methodologies defining low-dimensional projections that can be used by sampling-based planners for protein conformational sampling. The first method leverages information about a protein's flexibility to construct projections that can efficiently guide conformational sampling, when expert knowledge is available. The second method builds similar projections automatically, without expert intervention. We evaluate the projections produced by both methodologies on two conformational search problems involving three middle-size proteins. Our experiments demonstrate that (i) defining projections based on expert knowledge can benefit conformational sampling and (ii) automatically constructing such projections is a reasonable alternative.

A structural insight into the inhibitory mechanism of an orally active PI3K/mTOR dual inhibitor, PKI-179 using computational approaches

The PI3K/AKT/mTOR signaling pathway has been identified as an important target for cancer therapy. Attempts are increasingly made to design the inhibitors against the key proteins of this pathway for anti-cancer therapy. The PI3K/mTOR dual inhibitors have proved more effective than the inhibitors against only single protein targets. Recently discovered PKI-179, an orally effective compound, is one such dual inhibitor targeting both PI3K and mTOR. This anti-cancer compound is efficacious both in vitro and in vivo. However, the binding mechanisms and the molecular interactions of PKI-179 with PI3K and mTOR are not yet available. The current study investigated the exact binding mode and the molecular interactions of PKI-179 with PI3Kγ and mTOR using molecular docking and (un)binding simulation analyses. The study identified PKI-179 interacting residues of both the proteins and their importance in binding was ranked by the loss in accessible surface area, number of molecular interactions of the residue, and consistent appearance of the residue in (un)binding simulation analysis. The key residues involved in binding of PKI-179 were Ala-805 in PI3Kγ and Ile-2163 in mTOR as they have lost maximum accessible surface area due to binding. In addition, the residues which played a role in binding of the drug but were away from the catalytic site were also identified using (un)binding simulation analyses. Finally, comparison of the interacting residues in the respective catalytic sites was done for the difference in the binding of the drug to the two proteins. Thus, the pairs of the residues falling at the similar location with respect to the docked drug were identified. The striking similarity in the interacting residues of the catalytic site explains the concomitant inhibition of both proteins by a number of inhibitors. In conclusion, the docking and (un)binding simulation analyses of dual inhibitor PKI-179 with PI3K and mTOR will provide a suitable multi-target model for studying drug-protein interactions and thus help in designing the novel drugs with higher potency.

Computational insights into the inhibitory mechanism of human AKT1 by an orally active inhibitor, MK-2206

The AKT signaling pathway has been identified as an important target for cancer therapy. Among small-molecule inhibitors of AKT that have shown tremendous potential in inhibiting cancer, MK-2206 is a highly potent, selective and orally active allosteric inhibitor. Promising preclinical anticancer results have led to entry of MK-2206 into Phase I/II clinical trials. Despite such importance, the exact binding mechanism and the molecular interactions of MK-2206 with human AKT are not available. The current study investigated the exact binding mode and the molecular interactions of MK-2206 with human AKT isoforms using molecular docking and (un)binding simulation analyses. The study also involved the docking analyses of the structural analogs of MK-2206 to AKT1 and proposed one as better inhibitor. The Dock was used for docking simulations of MK-2206 into the allosteric site of AKT isoforms. The Ligplot+ was used for analyses of polar and hydrophobic interactions between AKT isoforms and the ligands. The MoMa-LigPath web server was used to simulate the ligand (un)binding from the binding site to the surface of the protein. In the docking and (un)binding simulation analyses of MK-2206 with human AKT1, the Trp-80 was the key residue and showed highest decrease in the solvent accessibility, highest number of hydrophobic interactions, and the most consistent involvement in all (un)binding simulation phases. The number of molecular interactions identified and calculated binding energies and dissociation constants from the co-complex structures of these isoforms, clearly explained the varying affinity of MK-2206 towards these isoforms. The (un)binding simulation analyses identified various additional residues which despite being away from the binding site, play important role in initial binding of the ligand. Thus, the docking and (un)binding simulation analyses of MK-2206 with AKT isoforms and its structure analogs will provide a suitable model for studying drug-protein interaction and will help in designing better drugs.

Anticancer compound plumbagin and its molecular targets: a structural insight into the inhibitory mechanisms using computational approaches

Plumbagin (5-hydroxy-2-methyl-1,4-naphthoquinone) is a naphthoquinone derivative from the roots of plant Plumbago zeylanica and belongs to one of the largest and diverse groups of plant metabolites. The anticancer and antiproliferative activities of plumbagin have been observed in animal models as well as in cell cultures. Plumbagin exerts inhibitory effects on multiple cancer-signaling proteins, however, the binding mode and the molecular interactions have not yet been elucidated for most of these protein targets. The present study is the first attempt to provide structural insights into the binding mode of plumbagin to five cancer signaling proteins viz. PI3Kγ, AKT1/PKBα, Bcl-2, NF-κB, and Stat3 using molecular docking and (un)binding simulation analysis. We validated plumbagin docking to these targets with previously known important residues. The study also identified and characterized various novel interacting residues of these targets which mediate the binding of plumbagin. Moreover, the exact modes of inhibition when multiple mode of inhibition existed was also shown. Results indicated that the engaging of these important interacting residues in plumbagin binding leads to inhibition of these cancer-signaling proteins which are key players in the pathogenesis of cancer and thereby ceases the progression of the disease.