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Allosteric regulation plays an important role in many biological processes, such as signal transduction, transcriptional regulation, and metabolism. Allostery is rooted in the fundamental physical properties of macromolecular systems, but its underlying mechanisms are still poorly understood. A collection of contributions to a recent interdisciplinary CECAM (Center Européen de Calcul Atomique et Moléculaire) workshop is used here to provide an overview of the progress and remaining limitations in the understanding of the mechanistic foundations of allostery gained from computational and experimental analyses of real protein systems and model systems. The main conceptual frameworks instrumental in driving the field are discussed. We illustrate the role of these frameworks in illuminating molecular mechanisms and explaining cellular processes, and describe some of their promising practical applications in engineering molecular sensors and informing drug design efforts., (Copyright © 2019. Published by Elsevier Ltd.)

Malaria, mainly caused by Plasmodium falciparum and Plasmodium vivax, has been a growing cause of morbidity and mortality. P. falciparum is more lethal than is P. vivax, but there is a vital need for effective drugs against both species. Geranylgeranyl diphosphate synthase (GGPPS) is an enzyme involved in the biosynthesis of quinones and in protein prenylation and has been proposed to be a malaria drug target. However, the structure of P. falciparumGGPPS (PfGGPPS) has not been determined, due to difficulties in crystallization. Here, we created a PfGGPPS model using the homologous P.vivaxGGPPS X-ray structure as a template. We simulated the modeled PfGGPPS as well as PvGGPPS using conventional and Gaussian accelerated molecular dynamics in both apo- and GGPP-bound states. The MD simulations revealed a striking similarity in the dynamics of both enzymes with loop 9-10 controlling access to the active site. We also found that GGPP stabilizes PfGGPPS and PvGGPPS into closed conformations and via similar mechanisms. Shape-based analysis of the binding sites throughout the simulations suggests that the two enzymes will be readily targeted by the same inhibitors. Finally, we produced three MD-validated conformations of PfGGPPS to be used in future virtual screenings for potential new antimalarial drugs acting on both PvGGPPS and PfGGPPS., (© 2018 John Wiley & Sons A/S.)

Potent and selective inhibitors for phospholipases A 2 (PLA 2 ) are useful for studying their intracellular functions. PLA 2 enzymes liberate arachidonic acid from phospholipids activating eicosanoid pathways that involve cyclooxygenase (COX) and lipoxygenase (LOX) leading to inflammation. Anti-inflammatory drugs target COX and LOX; thus, PLA 2 can also be targeted to diminish inflammation at an earlier stage in the process. This paper describes the employment of enzymatic assays, hydrogen/deuterium exchange mass spectrometry (DXMS) and computational chemistry to develop PLA 2 inhibitors. Beta-thioether trifluoromethylketones (TFKs) were screened against human GVIA calcium-independent, GIVA cytosolic and GV secreted PLA 2 s. These compounds exhibited inhibition toward Group VIA calcium-independent PLA 2 (GVIA iPLA 2 ), with the most potent and selective inhibitor 3 (OTFP) obtaining an X I (50) of 0.0002 mole fraction (IC 50 of 110nM). DXMS binding experiments in the presence of OTFP revealed the peptide regions of GVIA iPLA 2 that interact with the inhibitor. Molecular docking and dynamics simulations in the presence of a membrane were guided by the DXMS data in order to identify the binding mode of OTFP. Clustering analysis showed the binding mode of OTFP that occupied 70% of the binding modes occurring during the simulation. The resulted 3D complex was used for docking studies and a structure-activity relationship (SAR) was established. This paper describes a novel multidisciplinary approach in which a 3D complex of GVIA iPLA 2 with an inhibitor is reported and validated by experimental data. The SAR showed that the sulfur atom is vital for the potency of beta-thioether analogues, while the hydrophobic chain is important for selectivity. This work constitutes the foundation for further design, synthesis and inhibition studies in order to develop new beta-thioether analogues that are potent and selective for GVIA iPLA 2 exclusively., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

In the fight against heart failure, therapeutics that have the ability to increase the contractile power of the heart are urgently needed. One possible route of action to improve heart contractile power is increasing the calcium sensitivity of the thin filament. From a pharmaceutical standpoint, calcium sensitizers have the distinct advantage of not altering cardiomyocyte calcium levels and thus have lower potential for side-effects. Small chemical molecules have been shown to bind to the interface between cTnC and the cTnI switch peptide and exhibit calcium-sensitizing properties, possibly by stabilizing cTnC in an open conformation. Building on existing structural data of a known calcium sensitizer bound to cardiac troponin, we combined computational structure-based virtual screening drug discovery methods and solution NMR titration assays to identify a novel calcium sensitizer 4-(4-(2,5-dimethylphenyl)-1-piperazinyl)-3-pyridinamine (NSC147866) which binds to cTnC and the cTnC-cTnI147-163 complex. Its presence increases the affinity of switch peptide to cTnC by approximately a factor of two. This action is comparable to that of known levosimendan analogues., (© 2014 John Wiley & Sons A/S.)

About 8000 genes encode membrane proteins in the human genome. The information about their druggability will be very useful to facilitate drug discovery and development. The main problem, however, consists of limited structural and functional information about these proteins because they are difficult to produce biochemically and to study. In this paper we describe the strategy that combines Cell-free protein expression, NMR spectroscopy, and molecular DYnamics simulation (CNDY) techniques. Results of a pilot CNDY experiment provide us with a guiding light towards expedited identification of the hit compounds against a new uncharacterized membrane protein as a potentially druggable target. These hits can then be further characterized and optimized to develop the initial lead compound quicker. We illustrate such "omics" approach for drug discovery with the CNDY strategy applied to two example proteins: hypoxia-induced genes HIGD1A and HIGD1B., (Copyright © 2014 Elsevier Inc. All rights reserved.)

The proper understanding of biomolecular recognition mechanisms that take place in a drug target is of paramount importance to improve the efficiency of drug discovery and development. The intrinsic dynamic character of proteins has a strong influence on biomolecular recognition mechanisms and models such as conformational selection have been widely used to account for this dynamic association process. However, conformational changes occurring in the receptor prior and upon association with other molecules are diverse and not obvious to predict when only a few structures of the receptor are available. In view of the prominent role of protein flexibility in ligand binding and its implications for drug discovery, it is of great interest to identify receptor conformations that play a major role in biomolecular recognition before starting rational drug design efforts. In this review, we discuss a number of recent advances in computer-aided drug discovery techniques that have been proposed to incorporate receptor flexibility into structure-based drug design. The allowance for receptor flexibility provided by computational techniques such as molecular dynamics simulations or enhanced sampling techniques helps to improve the accuracy of methods used to estimate binding affinities and, thus, such methods can contribute to the discovery of novel drug leads., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Trypanosoma cruzi is the causative agent of Chagas disease, a neglected tropical disease that represents one of the major health challenges of the Latin American countries. Successful efforts were made during the last few decades to control the transmission of this disease, but there is still no treatment for the 10 million adults in the chronic phase of the disease. In T. cruzi, as well as in other pathogens, the flavoenzyme UDP-galactopyranose mutase (UGM) catalyzes the conversion of UDP-galactopyranose to UDP-galactofuranose, a precursor of the cell surface β-galactofuranose that is involved in the virulence of the pathogen. The fact that UGM is not present in humans makes inhibition of this enzyme a good approach in the design of new Chagas therapeutics. By performing a series of computer simulations of T. cruzi UGM in the presence or absence of an active site ligand, we address the molecular details of the mechanism that controls the uptake and retention of the substrate. The simulations suggest a modular mechanism in which each moiety of the substrate controls the flexibility of a different protein loop. Furthermore, the calculations indicate that interactions with the substrate diphosphate moiety are especially important for stabilizing the closed active site. This hypothesis is supported with kinetics measurements of site-directed mutants of T. cruzi UGM. Our results extend our knowledge of UGM dynamics and offer new alternatives for the prospective design of drugs., (© 2013 The Protein Society.)

We here present an improved version of AutoGrow (version 3.0), an evolutionary algorithm that works in conjunction with existing open-source software to automatically optimize candidate ligands for predicted binding affinity and other druglike properties. Though no substitute for the medicinal chemist, AutoGrow 3.0, unlike its predecessors, attempts to introduce some chemical intuition into the automated optimization process. AutoGrow 3.0 uses the rules of click chemistry to guide optimization, greatly enhancing synthesizability. Additionally, the program discards any growing ligand whose physical and chemical properties are not druglike. By carefully crafting chemically feasible druglike molecules, we hope that AutoGrow 3.0 will help supplement the chemist's efforts. To demonstrate the utility of the program, we use AutoGrow 3.0 to generate predicted inhibitors of three important drug targets: Trypanosoma brucei RNA editing ligase 1, peroxisome proliferator-activated receptor γ, and dihydrofolate reductase. In all cases, AutoGrow generates druglike molecules with high predicted binding affinities. AutoGrow 3.0 is available free of charge (http://autogrow.ucsd.edu) under the terms of the GNU General Public License and has been tested on Linux and Mac OS X., (Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.)

While it is well established that protonation and tautomeric states of ligands can significantly affect the results of virtual screening, such effects of ionizable residues of protein receptors are less well understood. In this study, we focus on histidine protonation and rotameric states and their impact on virtual screening of Mycobacterium tuberculosis enzyme RmlC. Depending on the net charge and the location of proton(s), a histidine can adopt three states: HIP (+1 charged, both δ- and ε-nitrogens protonated), HID (neutral, δ-nitrogen protonated), and HIE (neutral, ε-nitrogen protonated). Due to common ambiguities in X-ray crystal structures, a histidine may also be resolved as three additional states with its imidazole ring flipped. Here, we systematically investigate the predictive power of 36 receptor models with different protonation and rotameric states of two histidines in the RmlC active site by using results from a previous high-throughput screening. By measuring enrichment factors and area under the receiver operating characteristic curves, we show that virtual screening results vary depending on hydrogen bonding networks provided by the histidines, even in the cases where the ligand does not obviously interact with the side chain. Our results also suggest that, even with the help of widely used pKa prediction software, assigning histidine protonation and rotameric states for virtual screening can still be challenging and requires further examination and systematic characterization of the receptor-ligand complex.

Protein flexibility plays a major role in biomolecular recognition. In many cases, it is not obvious how molecular structure will change upon association with other molecules. In proteins, these changes can be major, with large deviations in overall backbone structure, or they can be more subtle as in a side-chain rotation. Either way the algorithms that predict the favorability of biomolecular association require relatively accurate predictions of the bound structure to give an accurate assessment of the energy involved in association. Here, we review a number of techniques that have been proposed to accommodate receptor flexibility in the simulation of small molecules binding to protein receptors. We investigate modifications to standard rigid receptor docking algorithms and also explore enhanced sampling techniques, and the combination of free energy calculations and enhanced sampling techniques. The understanding and allowance for receptor flexibility are helping to make computer simulations of ligand protein binding more accurate. These developments may help improve the efficiency of drug discovery and development. Efficiency will be essential as we begin to see personalized medicine tailored to individual patients, which means specific drugs are needed for each patient's genetic makeup., (© 2012 John Wiley & Sons A/S.)

One common practice in drug discovery is to optimize known or suspected ligands in order to improve binding affinity. In performing these optimizations, it is useful to look at as many known inhibitors as possible for guidance. Medicinal chemists often seek to improve potency by altering certain chemical moieties of known/endogenous ligands while retaining those critical for binding. To our knowledge, no automated, ligand-based algorithm exists for systematically 'swapping' the chemical moieties of known ligands to generate novel ligands with potentially improved potency. To address this need, we have created a novel algorithm called 'LigMerge'. LigMerge identifies the maximum (largest) common substructure of two three-dimensional ligand models, superimposes these two substructures, and then systematically mixes and matches the distinct fragments attached to the common substructure at each common atom, thereby generating multiple compound models related to the known inhibitors that can be evaluated using computer docking prior to synthesis and experimental testing. To demonstrate the utility of LigMerge, we identify compounds predicted to inhibit peroxisome proliferator-activated receptor gamma, HIV reverse transcriptase, and dihydrofolate reductase with affinities higher than those of known ligands. We hope that LigMerge will be a helpful tool for the drug design community., (© 2012 John Wiley & Sons A/S.)

The method of accelerated molecular dynamics (aMD) has been shown to increase the rate of phase-space sampling in biomolecular simulations. In this chapter, we discuss the theory behind aMD and describe the implementation of two versions: dual-boost and selective aMD. Each method has its practical advantages: dual-boost aMD is useful for increasing sampling of global conformational motions while selective aMD can improve the rate of convergence of free energy calculations. Special emphasis is placed on the use of these methods in computer-aided drug design, and the example of oseltamivir binding to neuraminidase is highlighted for both cases.

We present a novel algorithm called CrystalDock that analyzes a molecular pocket of interest and identifies potential binding fragments. The program first identifies groups of pocket-lining receptor residues (i.e., microenvironments) and then searches for geometrically similar microenvironments present in publically available databases of ligand-bound experimental structures. Germane fragments from the crystallographic or NMR ligands are subsequently placed within the novel binding pocket. These positioned fragments can be linked together to produce ligands that are likely to be potent; alternatively, they can be joined to an inhibitor with a known or suspected binding pose to potentially improve binding affinity. To demonstrate the utility of the algorithm, CrystalDock is used to analyze the principal binding pockets of influenza neuraminidase and Trypanosoma brucei RNA editing ligase 1, validated drug targets in the fight against pandemic influenza and African sleeping sickness, respectively. In both cases, CrystalDock suggests modifications to known inhibitors that may improve binding affinity.

The relaxed complex scheme, a virtual-screening methodology that accounts for protein receptor flexibility, was used to identify a low-micromolar, non-bisphosphonate inhibitor of farnesyl diphosphate synthase. Serendipitously, we also found that several predicted farnesyl diphosphate synthase inhibitors were low-micromolar inhibitors of undecaprenyl diphosphate synthase. These results are of interest because farnesyl diphosphate synthase inhibitors are being pursued as both anti-infective and anticancer agents, and undecaprenyl diphosphate synthase inhibitors are antibacterial drug leads., (© 2011 John Wiley & Sons A/S.)

Aberrant Ras activity is a hallmark of diverse cancers and developmental diseases. Unfortunately, conventional efforts to develop effective small molecule Ras inhibitors have met with limited success. We have developed a novel multi-level computational approach to discover potential inhibitors of previously uncharacterized allosteric sites. Our approach couples bioinformatics analysis, advanced molecular simulations, ensemble docking and initial experimental testing of potential inhibitors. Molecular dynamics simulation highlighted conserved allosteric coupling of the nucleotide-binding switch region with distal regions, including loop 7 and helix 5. Bioinformatics methods identified novel transient small molecule binding pockets close to these regions and in the vicinity of the conformationally responsive switch region. Candidate binders for these pockets were selected through ensemble docking of ZINC and NCI compound libraries. Finally, cell-based assays confirmed our hypothesis that the chosen binders can inhibit the downstream signaling activity of Ras. We thus propose that the predicted allosteric sites are viable targets for the development and optimization of new drugs.

Protein flexibility plays a critical role in ligand binding to both orthosteric and allosteric sites. We here review some of the computer-aided drug-design techniques currently used to account for protein flexibility, ranging from methods that probe local receptor flexibility in the region of the protein immediately adjacent to the binding site, to those that account for general flexibility in all protein regions., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

As high-throughput biochemical screens are both expensive and labor intensive, researchers in academia and industry are turning increasingly to virtual-screening methodologies. Virtual screening relies on scoring functions to quickly assess ligand potency. Although useful for in silico ligand identification, these scoring functions generally give many false positives and negatives; indeed, a properly trained human being can often assess ligand potency by visual inspection with greater accuracy. Given the success of the human mind at protein-ligand complex characterization, we present here a scoring function based on a neural network, a computational model that attempts to simulate, albeit inadequately, the microscopic organization of the brain. Computer-aided drug design depends on fast and accurate scoring functions to aid in the identification of small-molecule ligands. The scoring function presented here, used either on its own or in conjunction with other more traditional functions, could prove useful in future drug-discovery efforts.

Pandemic (H1N1) influenza poses an imminent threat. Nations have stockpiled inhibitors of the influenza protein neuraminidase in hopes of protecting their citizens, but drug-resistant strains have already emerged, and novel therapeutics are urgently needed. In the current work, the computer program AutoGrow is used to generate novel predicted neuraminidase inhibitors. Given the great flexibility of the neuraminidase active site, protein dynamics are also incorporated into the computer-aided drug-design process. Several potential inhibitors are identified that are predicted to bind to neuraminidase better than currently approved drugs., (Copyright 2010 Elsevier Ltd. All rights reserved.)

Matrix metalloproteinases (MMPs) comprise a class of flexible proteins required for normal tissue remodeling. Overexpression of MMPs is associated with a wide range of pathophysiological processes, including vascular disease, multiple sclerosis, Alzheimer's disease, and cancer. Nearly all MMP inhibitors have failed in clinical trials, in part due to lack of specificity. Due to the highly dynamic molecular motions of the MMP-2 binding pockets, the rational drug design of MMP inhibitors has been very challenging. To address these challenges, in the current study we combine computer docking with molecular dynamics (MD) simulations in order to incorporate receptor-flexibility and induced-fit effects into the drug-design process. Our strategy identifies molecular fragments predicted to target multiple MMP-2 binding pockets., (2009 John Wiley & Sons, Ltd.)

Due in part to the increasing availability of crystallographic protein structures as well as rapid improvements in computing power, the past few decades have seen an explosion in the field of computer-based rational drug design. Several algorithms have been developed to identify or generate potential ligands in silico by optimizing the ligand-receptor hydrogen bond, electrostatic, and hydrophobic interactions. We here present AutoGrow, a novel computer-aided drug design algorithm that combines the strengths of both fragment-based growing and docking algorithms. To validate AutoGrow, we recreate three crystallographically resolved ligands from their constituent fragments.

The interactions among associating (macro)molecules are dynamic, which adds to the complexity of molecular recognition. While ligand flexibility is well accounted for in computational drug design, the effective inclusion of receptor flexibility remains an important challenge. The relaxed complex scheme (RCS) is a promising computational methodology that combines the advantages of docking algorithms with dynamic structural information provided by molecular dynamics (MD) simulations, therefore explicitly accounting for the flexibility of both the receptor and the docked ligands. Here, we briefly review the RCS and discuss new extensions and improvements of this methodology in the context of ligand binding to two example targets: kinetoplastid RNA editing ligase 1 and the W191G cavity mutant of cytochrome c peroxidase. The RCS improvements include its extension to virtual screening, more rigorous characterization of local and global binding effects, and methods to improve its computational efficiency by reducing the receptor ensemble to a representative set of configurations. The choice of receptor ensemble, its influence on the predictive power of RCS, and the current limitations for an accurate treatment of the solvent contributions are also briefly discussed. Finally, we outline potential methodological improvements that we anticipate will assist future development.

Matrix Metalloproteinases (MMPs) are zinc-containing proteinases that are responsible for the metabolism of extracellular matrix proteins. Overexpression of MMPs has been associated with a wide range of pathological diseases such as arthritis, cancer, multiple sclerosis and Alzheimer's disease. The excessive and unregulated activity of Matrix Metalloproteinases type 2 (MMP-2), also known as gelatinase A, has been identified in a numbers of cancer metastases. Several MMP inhibitors (MMPi) have been proposed in the literature aiming to interfere in the MMPs activity. In this work we performed long MD simulations in order to study the dynamical behavior of the binding pocket S1' in the apo forms of MMP type 2 and 3, and identify, at the molecular level, the structural properties relevant for the designing of specific inhibitor of MMP-2.

Multiscale simulations (coarse-grained Brownian dynamics simulations and all-atom molecular dynamics simulations in implicit solvent) were applied to reveal the binding processes of ligands as they enter the binding site of the HIV-1 protease. The initial structures used for the molecular dynamics simulations were generated based on the Brownian dynamics trajectories, and this is the first molecular dynamics simulation of modeling the association of a ligand with the protease. We found that a protease substrate successfully binds to the protein when the flaps are fully open. Surprisingly, a smaller cyclic urea inhibitor (XK263) can reach the binding site when the flaps are not fully open. However, if the flaps are nearly closed, the inhibitor must rearrange or binding can fail because the inhibitor cannot attain proper conformations to enter the binding site. Both the peptide substrate and XK263 can also affect the protein's internal motion, which may help the flaps to open. Simulations allow us to efficiently study the ligand binding processes and may help those who study drug discovery to find optimal association pathways and to design those ligands with the best binding kinetics.

To test the anticorrelated relationship that was recently displayed in conventional molecular dynamics (MD) simulations, several different restrained MD simulations on a wild type and on the V82F/I84V drug-resistant mutant of HIV-1 protease were performed. This anticorrelated relationship refers to the observation that compression of the peripheral ear-to-cheek region of HIV protease (i.e., the elbow of the flap to the fulcrum and the cantilever) occurred as the active site flaps were opening, and, conversely, expansion of that ear-to-cheek region occurred as both flaps were closing. An additional examination of this anticorrelated relationship was necessary to determine whether it can be harnessed in a useful manner. Consequently, six different MD experiments were performed that incorporated pairwise distance restraints in that ear-to-cheek region (i.e., the distance between the alpha-carbons of Gly40 and Gln61 was restrained to either 7.7 or 10.5 A, in both monomers). Pushing the backbones of the ear and the cheek regions away from each other slightly did force the flaps that guard the active site to remain closed in both the wild type and the mutant systems-even though there were no ligands in the active sites. Thus, these restrained MD simulations provided evidence that the anticorrelated relationship can be exploited to affect the dynamic behavior of the flaps that guard the active site of HIV-1 protease. These simulations supported our hypothesis of the mechanism governing flap motion, and they are the first step towards validating that peripheral surface as a new target for drug design.

Induced-fit effects are well known in the binding of small molecules to proteins and other macromolecular targets. Among other targets, protein kinases are particularly flexible proteins, so that such effects should be considered in attempts at structure-based inhibitor design for kinase targets. This paper outlines some recent progress in methods for including target flexibility in computational studies of molecular recognition. A focus is the "relaxed complex method," in which ligands are docked to an ensemble of conformations of the target, and the best complexes are re-scored to provide predictions of optimal binding geometries. Early applications of this method have suggested a new approach to the development of inhibitors of HIV-1 Integrase.

Although computational techniques are increasingly being used in computer-aided drug design, the effects due to protein flexibility are still ignored in many applications. This review revisits rigorous statistical mechanical methods for predicting binding affinity, discusses some recent developments for improving their speed and reliability, and examines faster approximate models for facilitating virtual screening and lead optimization.

An extension of the new computational methodology for drug design, the "relaxed complex" method (J.-H. Lin, A. L. Perryman, J. R. Schames, and J. A. McCammon, Journal of the American Chemical Society, 2002, vol. 24, pp. 5632-5633), which accommodates receptor flexibility, is described. This relaxed complex method recognizes that ligand may bind to conformations that occur only rarely in the dynamics of the receptor. We have shown that the ligand-enzyme binding modes are very sensitive to the enzyme conformations, and our approach is capable of finding the best ligand enzyme complexes. Rapid docking serves as an efficient initial filtering method to screen a myriad of docking modes to a limited set, and it is then followed by more accurate scoring with the MM/PBSA (Molecular Mechanics/Poisson Boltzmann Surface Area) approach to find the best ligand-receptor complexes. The MM/PBSA scorings consistently indicate that the calculated binding modes that are most similar to those observed in the x-ray crystallographic complexes are the ones with the lowest free energies., (Copyright 2002 Wiley Periodicals, Inc. Biopolymers 68: 47-62, 2003)

A novel computational methodology for drug design that accommodates receptor flexibility is described. This "relaxed-complex" method recognizes that ligand may bind to conformations that occur only rarely in the dynamics of the receptor. We have shown that the ligand-enzyme binding modes are very sensitive to the enzyme conformations, and our approach is capable of finding the best ligand-enzyme complexes. This new method serves as the computational analog of the experimental "SAR by NMR" and "tether" methods, which permit a building block approach for constructing a very potent drug.

To maintain the lipid asymmetry of the cell envelope in Gram-negative bacteria, the MlaC protein serves as a lipid transfer factor and delivers phospholipids from the outer to the inner membrane. A strategy of antibiotic discovery is to design a proper compound that can tightly bind to the MlaC protein and inhibit the MlaC function. In this study, we performed virtual screening on multiple MlaC structures obtained from molecular dynamics simulations to identify potential MlaC binders. Our results suggested that clorobiocin is a compound that could bind to the MlaC protein. Through the comparison of the bound geometry between clorobiocin and novobiocin, we pointed out that the methyl-pyrrole group of the noviose sugar in clorobiocin forms hydrophobic interactions with amino acids in the phospholipid binding pocket, which allows the compound to bind deep in the active site. This also explains why clorobiocin shows a tighter binding affinity than novobiocin. Our study highlights a practical path of antibiotic development against Gram-negative bacteria.

Beta-secretase 1 (BACE-1) is an aspartyl protease implicated in the overproduction of β-amyloid fibrils responsible for Alzheimer disease. The process of β-amyloid genesis is known to be pH dependent, with an activity peak between solution pH of 3.5 and 5.5. We have studied the pH-dependent dynamics of BACE-1 to better understand the pH dependent mechanism. We have implemented support for graphics processor unit (GPU) accelerated constant pH molecular dynamics within the AMBER molecular dynamics software package and employed this to determine the relative population of different aspartyl dyad protonation states in the pH range of greatest β-amyloid production, followed by conventional molecular dynamics to explore the differences among the various aspartyl dyad protonation states. We observed a difference in dynamics between double-protonated, mono-protonated, and double-deprotonated states over the known pH range of higher activity. These differences include Tyr 71-aspartyl dyad proximity and active water lifetime. This work indicates that Tyr 71 stabilizes catalytic water in the aspartyl dyad active site, enabling BACE-1 activity.

Alchemical free energy (AFE) calculations based on molecular dynamics (MD) simulations are key tools in both improving our understanding of a wide variety of biological processes and accelerating the design and optimization of therapeutics for numerous diseases. Computing power and theory have, however, long been insufficient to enable AFE calculations to be routinely applied in early stage drug discovery. One of the major difficulties in performing AFE calculations is the length of time required for calculations to converge to an ensemble average. CPU implementations of MD-based free energy algorithms can effectively only reach tens of nanoseconds per day for systems on the order of 50,000 atoms, even running on massively parallel supercomputers. Therefore, converged free energy calculations on large numbers of potential lead compounds are often untenable, preventing researchers from gaining crucial insight into molecular recognition, potential druggability and other crucial areas of interest. Graphics Processing Units (GPUs) can help address this. We present here a seamless GPU implementation, within the PMEMD module of the AMBER molecular dynamics package, of thermodynamic integration (TI) capable of reaching speeds of >140 ns/day for a 44,907-atom system, with accuracy equivalent to the existing CPU implementation in AMBER. The implementation described here is currently part of the AMBER 18 beta code and will be an integral part of the upcoming version 18 release of AMBER. © 2018 Wiley Periodicals, Inc.

The binding of a series of metal-binding pharmacophores (MBPs) related to the ligand 1-hydroxypyridine-2-(1H)-thione (1,2-HOPTO) in the active site of human carbonic anhydrase II (hCAII) has been investigated. The presence and/or position of a single methyl substituent drastically alters inhibitor potency and can result in coordination modes not observed in small-molecule model complexes. It is shown that this unexpected binding mode is the result of a steric clash between the methyl group and a highly ordered water network in the active site that is further stabilized by the formation of a hydrogen bond and favorable hydrophobic contacts. The affinity of MBPs is dependent on a large number of factors including donor atom identity, orientation, electrostatics, and van der Waals interactions. These results suggest that metal coordination by metalloenzyme inhibitors is a malleable interaction and that it is thus more appropriate to consider the metal-binding motif of these inhibitors as a pharmacophore rather than a "chelator". The rational design of inhibitors targeting metalloenzymes will benefit greatly from a deeper understanding of the interplay between the variety of forces governing the binding of MBPs to active site metal ions.

The proper understanding of biomolecular recognition mechanisms that take place in a drug target is of paramount importance to improve the efficiency of drug discovery and development. The intrinsic dynamic character of proteins has a strong influence on biomolecular recognition mechanisms and models such as conformational selection have been widely used to account for this dynamic association process. However, conformational changes occurring in the receptor prior and upon association with other molecules are diverse and not obvious to predict when only a few structures of the receptor are available. In view of the prominent role of protein flexibility in ligand binding and its implications for drug discovery, it is of great interest to identify receptor conformations that play a major role in biomolecular recognition before starting rational drug design efforts. In this review, we discuss a number of recent advances in computer-aided drug discovery techniques that have been proposed to incorporate receptor flexibility into structure-based drug design. The allowance for receptor flexibility provided by computational techniques such as molecular dynamics simulations or enhanced sampling techniques helps to improve the accuracy of methods used to estimate binding affinities and, thus, such methods can contribute to the discovery of novel drug leads.

The nonmevalonate pathway is responsible for isoprenoid production in microbes, including H. pylori, M. tuberculosis and P. falciparum, but is nonexistent in humans, thus providing a desirable route for antibacterial and antimalarial drug discovery. We coordinate a structural study of IspH, a [4Fe-4S] protein responsible for converting HMBPP to IPP and DMAPP in the ultimate step in the nonmevalonate pathway. By performing accelerated molecular dynamics simulations on both substrate-free and HMBPP-bound [Fe4S4](2+) IspH, we elucidate how substrate binding alters the dynamics of the protein. Using principal component analysis, we note that while substrate-free IspH samples various open and closed conformations, the closed conformation observed experimentally for HMBPP-bound IspH is inaccessible in the absence of HMBPP. In contrast, simulations with HMBPP bound are restricted from accessing the open states sampled by the substrate-free simulations. Further investigation of the substrate-free simulations reveals large fluctuations in the HMBPP binding pocket, as well as allosteric pocket openings - both of which are achieved through the hinge motions of the individual domains in IspH. Coupling these findings with solvent mapping and various structural analyses reveals alternative druggable sites that may be exploited in future drug design efforts.

The protozoan parasite Trypanosoma cruzi, the etiological agent of Chagas' disease, affects millions of individuals and continues to be an important global health concern. The poor efficacy and unfavorable side effects of current treatments necessitate novel therapeutics. Cruzain, the major cysteine protease of T. cruzi, is one potential novel target. Recent advances in a class of vinyl sulfone inhibitors are encouraging; however, as most potential therapeutics fail in clinical trials and both disease progression and resistance call for combination therapy with several drugs, the identification of additional classes of inhibitory molecules is essential. Using an exhaustive virtual-screening and experimental validation approach, we identify several additional small-molecule cruzain inhibitors. Further optimization of these chemical scaffolds could lead to the development of novel drugs useful in the treatment of Chagas' disease.

In most eubacteria, apicomplexans, and most plants, including the causal agents for diseases such as malaria, leprosy, and tuberculosis, the methylerythritol phosphate pathway is the route for the biosynthesis of the C(5) precursors to the essential isoprenoid class of compounds. Owing to their absence in humans, the enzymes of the methylerythritol phosphate pathway have become attractive targets for drug discovery. This work investigates a new class of inhibitors against the second enzyme of the pathway, 1-deoxy-D-xylulose 5-phosphate reductoisomerase. Inhibition of this enzyme may involve the chelation of a crucial active site Mn ion, and the metal-chelating moieties studied here have previously been shown to be successful in application to the zinc-dependent metalloproteinases. Quantum mechanics and docking calculations presented in this work suggest the transferability of these metal-chelating compounds to Mn-containing 1-deoxy-D-xylulose 5-phosphate reductoisomerase enzyme, as a promising starting point to the development of potent inhibitors.

An analysis of group IVA (GIVA) phospholipase A(2) (PLA(2)) inhibitor binding was conducted using a combination of deuterium exchange mass spectrometry (DXMS) and molecular dynamics (MD). Models of the GIVA PLA(2) inhibitors pyrrophenone and the 2-oxoamide AX007 docked into the protein were designed on the basis of deuterium exchange results, and extensive molecular dynamics simulations were run to determine protein-inhibitor contacts. The models show that both inhibitors interact with key residues that also exhibit changes in deuterium exchange upon inhibitor binding. Pyrrophenone is bound to the protein through numerous hydrophobic residues located distal from the active site, while the oxoamide is bound mainly through contacts near the active site. We also show differences in protein dynamics around the active site between the two inhibitor-bound complexes. This combination of computational and experimental methods is useful in defining more accurate inhibitor binding sites and can be used in the generation of better inhibitors against GIVA PLA(2).

G-protein-coupled receptors (GPCRs) mediate cellular responses to various hormones and neurotransmitters and are important targets for treating a wide spectrum of diseases. Although significant advances have been made in structural studies of GPCRs, details of their activation mechanism remain unclear. The X-ray crystal structure of the M2 muscarinic receptor, a key GPCR that regulates human heart rate and contractile forces of cardiomyocytes, was determined recently in an inactive antagonist-bound state. Here, activation of the M2 receptor is directly observed via accelerated molecular dynamics simulation, in contrast to previous microsecondtimescale conventional molecular dynamics simulations in which the receptor remained inactive. Receptor activation is characterized by formation of a Tyr2065.58-Tyr4407.53 hydrogen bond and ∼6-Å outward tilting of the cytoplasmic end of transmembrane a-helix 6, preceded by relocation of Trp4006.48 toward Phe1955.47 and Val1995.51 and flipping of Tyr4307.43 away from the ligand-binding cavity. Network analysis reveals that communication in the intracellular domains is greatly weakened during activation of the receptor. Together with the finding that residue motions in the ligand-binding and G-protein-coupling sites of the apo receptor are correlated, this result highlights a dynamic network for allostenc regulation of the M2 receptor activation. [ABSTRACT FROM AUTHOR]

Academic researchers and many in industry often lack the financial resources available to scientists working in "big pharma." High costs include those associated with high-throughput screening and chemical synthesis. In order to address these challenges, many researchers have in part turned to alternate methodologies. Virtual screening, for example, often substitutes for high-throughput screening, and click chemistry ensures that chemical synthesis is fast, cheap, and comparatively easy. Though both in silico screening and click chemistry seek to make drug discovery more feasible, it is not yet routine to couple these two methodologies. We here present a novel computer algorithm, called AutoClickChem, capable of performing many click-chemistry reactions in silico. AutoClickChem can be used to produce large combinatorial libraries of compound models for use in virtual screens. As the compounds of these libraries are constructed according to the reactions of click chemistry, they can be easily synthesized for subsequent testing in biochemical assays. Additionally, in silico modeling of click-chemistry products may prove useful in rational drug design and drug optimization. AutoClickChem is based on the pymolecule toolbox, a framework that may facilitate the development of future python-based programs that require the manipulation of molecular models. Both the pymolecule toolbox and AutoClickChem are released under the GNU General Public License version 3 and are available for download from http://autoclickchem.ucsd.edu. [ABSTRACT FROM AUTHOR]

Abstract: In the current work, we present a hydrogen-bond analysis of 2673 ligand–receptor complexes that suggests the total number of hydrogen bonds formed between a ligand and its receptor is a poor predictor of ligand potency; furthermore, even that poor prediction does not suggest a statistically significant correlation between hydrogen-bond formation and potency. While we are not the first to suggest that hydrogen bonds on average do not generally contribute to ligand binding affinities, this additional evidence is nevertheless interesting. The primary role of hydrogen bonds may instead be to ensure specificity, to correctly position the ligand within the active site, and to hold the protein active site in a ligand-friendly conformation. We also present a new computer program called HBonanza (hydrogen-bond analyzer) that aids the analysis and visualization of hydrogen-bond networks. HBonanza, which can be used to analyze single structures or the many structures of a molecular dynamics trajectory, is open source and python implemented, making it easily editable, customizable, and platform independent. Unlike many other freely available hydrogen-bond analysis tools, HBonanza provides not only a text-based table describing the hydrogen-bond network, but also a Tcl script to facilitate visualization in VMD, a popular molecular visualization program. Visualization in other programs is also possible. A copy of HBonanza can be obtained free of charge from http://www.nbcr.net/hbonanza. [Copyright &y& Elsevier]

Undecaprenyl pyrophosphate synthase is a cis-prenyltransferase enzyme, which is required for cell wall biosynthesis in bacteria. Undecaprenyl pyrophosphate synthase is an attractive target for antimicrobial therapy. We performed long molecular dynamics simulations and docking studies on undecaprenyl pyrophosphate synthase to investigate its dynamic behavior and the influence of protein flexibility on the design of undecaprenyl pyrophosphate synthase inhibitors. We also describe the first X-ray crystallographic structure of Escherichia coli apo-undecaprenyl pyrophosphate synthase. The molecular dynamics simulations indicate that undecaprenyl pyrophosphate synthase is a highly flexible protein, with mobile binding pockets in the active site. By carrying out docking studies with experimentally validated undecaprenyl pyrophosphate synthase inhibitors using high- and low-populated conformational states extracted from the molecular dynamics simulations, we show that structurally dissimilar compounds can bind preferentially to different and rarely sampled conformational states. By performing structural analyses on the newly obtained apo-undecaprenyl pyrophosphate synthase and other crystal structures previously published, we show that the changes observed during the molecular dynamics simulation are very similar to those seen in the crystal structures obtained in the presence or absence of ligands. We believe that this is the first time that a rare 'expanded pocket' state, key to drug design and verified by crystallography, has been extracted from a molecular dynamics simulation. Crystallography, molecular dynamics simulations, and structural analysis reveal a rare 'expanded pocket' state key to drug design for undecaprenyl pyrophosphate synthase, an attractive target for anti-microbial therapy [ABSTRACT FROM AUTHOR]

Abstract: Computational chemists and structural biologists are often interested in characterizing ligand–receptor complexes for hydrogen-bond, hydrophobic, salt-bridge, van der Waals, and other interactions in order to assess ligand binding. When done by hand, this characterization can become tedious, especially when many complexes need be analyzed. In order to facilitate the characterization of ligand binding, we here present a novel Python-implemented computer algorithm called BINANA (BINding ANAlyzer), which is freely available for download at http://www.nbcr.net/binana/. To demonstrate the utility of the new algorithm, we use BINANA to confirm that the number of hydrophobic contacts between a ligand and its protein receptor is positively correlated with ligand potency. Additionally, we show how BINANA can be used to search through a large ligand–receptor database to identify those complexes that are remarkable for selected binding features, and to identify lead candidates from a virtual screen with specific, desirable binding characteristics. We are hopeful that BINANA will be useful to computational chemists and structural biologists who wish to automatically characterize many ligand–receptor complexes for key binding characteristics. [Copyright &y& Elsevier]

To address the problem of specificity in G-protein coupled receptor (GPCR) drug discovery, there has been tremendous recent interest in allosteric drugs that bind at sites topographically distinct from the orthosteric site. Unfortunately, structure-based drug design of allosteric GPCR ligands has been frustrated by the paucity of structural data for allosteric binding sites, making a strong case for predictive computational methods. In this work, we map the surfaces of the β1 (β1AR) and β2 (β2AR) adrenergic receptor structures to detect a series of five potentially druggable allosteric sites. We employ the FTMAP algorithm to identify ‘hot spots’ with affinity for a variety of organic probe molecules corresponding to drug fragments. Our work is distinguished by an ensemble-based approach, whereby we map diverse receptor conformations taken from molecular dynamics (MD) simulations totaling approximately 0.5 μs. Our results reveal distinct pockets formed at both solvent-exposed and lipid-exposed cavities, which we interpret in light of experimental data and which may constitute novel targets for GPCR drug discovery. This mapping data can now serve to drive a combination of fragment-based and virtual screening approaches for the discovery of small molecules that bind at these sites and which may offer highly selective therapies. [ABSTRACT FROM AUTHOR]

Chagas disease, caused by the unicellular parasite Trypanosoma cruzi, claims 50,000 lives annually and is the leading cause of infectious myocarditis in the world. As current antichagastic therapies like nifurtimox and benznidazole are highly toxic, ineffective at parasite eradication, and subject to increasing resistance, novel therapeutics are urgently needed. Cruzain, the major cysteine protease of Trypanosoma cruzi, is one attractive drug target. In the current work, molecular dynamics simulations and a sequence alignment of a non-redundant, unbiased set of peptidase C1 family members are used to identify uncharacterized cruzain binding sites. The two sites identified may serve as targets for future pharmacological intervention. Author Summary: Chagas disease, an infection that afflicts millions of people in Central and South America, is caused by the unicellular parasite Trypanosoma cruzi. In the chronic stage of the disease, patients' hearts are adversely affected. Chagas is the leading cause of infectious heart disease in the world. The current drugs used to treat Chagas disease are highly toxic, unable to eradiate the parasite, and subject to increasing drug resistance. Consequently, researchers are actively looking for new treatments. One attractive drug target is a Chagas protein called cruzain, which is required for the parasite's survival. Drugs that can inhibit the correct functioning of cruzain within the parasite may one day serve as powerful treatments in the fight against this devastating tropical disease. To design drugs that will be effective against cruzain, we need to know what portions of the protein are crucial for its functionality. For example, portions of the protein that bind to other proteins or to small molecules are likely to be critical. These regions are called "binding sites." In the current work, we identify two uncharacterized cruzain binding sites. With this knowledge in hand, future researchers may be able to design drugs that target these sites. [ABSTRACT FROM AUTHOR]

The Relaxed Complex method, an approach to structure-based drug design that incorporates the flexibilities of both the ligand and target protein, was applied to the immunodeficiency virus protease system. The control cases used AutoDock3.0.5 to dock a fully flexible version of the prospective drug JE-2147 (aka SM-319777 or KNI-764) to large ensembles of conformations extracted from conventional, all atom, explicitly solvated molecular dynamic simulations of the wild type, and the V82F/I84V drug-resistant mutant of HIV-1 protease. The best set of run parameters from the control cases produced robust results when used against 2200 different conformations of the wild-type HIV-1 protease or against 2200 conformations of the mutant. The results of the control cases, the published advice from experts, and structural intuition were used to design a new series of 23 potential active site inhibitors. The compounds were evaluated by docking them against 700 different conformations of the V82F/I84V mutant. The results of this first round of lead optimization were quite promising. Approximately one-third of that series performed at least slightly better than the parent compound, and four of those compounds displayed significantly better binding affinities against that drug-resistant mutant (within our computational model). [ABSTRACT FROM AUTHOR]

Presents information on structure-based computational methods utilized to enhance progress in the discovery and refinement of therapeutic agents. Description of methods and applications; Discussion on related issues; Strengths and weaknesses of the various methods for drug design.