To request any journal reprints, please contact us.
POVME: An Algorithm for Measuring Binding-Pocket Volumes
Jacob D. Durrant, César Augusto F. de Oliveira, J. Andrew McCammon
Researchers engaged in computer-aided drug design often wish to measure the volume of a ligand-binding pocket in order to predict pharmacology. We have recently developed a simple algorithm, called POVME (POcket Volume MEasurer), for this purpose. POVME is Python implemented, fast, and freely available. To demonstrate its utility, we use the new algorithm to study three members of the matrix-metalloproteinase family of proteins. Despite the structural similarity of these proteins, differences in binding-pocket dynamics are easily identified.
Porous Protein Frameworks with Unsaturated Metal Centers in Sterically Encumbered Coordination Sites
Robert J. Radford, Morgan Lawrenz, Phuong C. Nguyen, J. Andrew McCammon, F. Akif Tezcan
Described is an engineered metal-binding protein, MBPPhen2, which forms porous crystalline frameworks that feature coordinatively unsaturated Zn- and Ni-centers.
From Zn to Mn: The Study of Novel Manganese-Binding Groups in the Search for New Drugs against Tuberculosis
Sarah L. Williams, César Augusto F. De Oliveira, H. Vazquez, J. Andrew McCammon
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 C5 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 (MtDXR). 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 MtDXR enzyme, as a promising starting point to the development of potent inhibitors.
Conformational Sampling and Nucleotide-Dependent Transitions of the GroEL Subunit Probed by Unbiased Molecular Dynamics Simulations
Lars Skjaerven, Barry Grant, Arturo Muga, Knut Teigen, J. Andrew McCammon, Nathalie Reuter, Aurora Martinez
GroEL is an ATP dependent molecular chaperone that promotes the folding of a large number of substrate proteins in E. coli. Large-scale conformational transitions occurring during the reaction cycle have been characterized from extensive crystallographic studies. However, the link between the observed conformations and the mechanisms involved in the allosteric response to ATP and the nucleotide-driven reaction cycle are not completely established. Here we describe extensive (in total 2.2 μs long) unbiased molecular dynamics (MD) simulations that probe the response of GroEL subunits to ATP binding. We observe nucleotide dependent conformational transitions, and show with multiple 100 ns long simulations that the ligand-induced shift in the conformational populations are intrinsically coded in the structure-dynamics relationship of the protein subunit. Thus, these simulations reveal a stabilization of the equatorial domain upon nucleotide binding and a concomitant “opening” of the subunit, which reaches a conformation close to that observed in the crystal structure of the subunits within the ADP-bound oligomer. Moreover, we identify changes in a set of unique intrasubunit interactions potentially important for the conformational transition.
New insights into the GABA(A) receptor structure and orthosteric ligand binding: Receptor modeling guided by experimental data
Tommy Sander, Bente Frølund, Anne Techau Bruun, Ivaylo Ivanov, J. Andrew McCammon, Thomas Balle
GABAA receptors (GABAARs) are ligand gated chloride ion channels that mediate overall inhibitory signaling in the CNS. A detailed understanding of their structure is important to gain insights in, e.g., ligand binding and functional properties of this pharmaceutically important target. Homology modeling is a necessary tool in this regard because experimentally determined structures are lacking. Here we present an exhaustive approach for creating a high quality model of the α1β2γ2 subtype of the GABAAR ligand binding domain, and we demonstrate its usefulness in understanding details of orthosteric ligand binding. The model was constructed by using multiple templates and by incorporation of knowledge from biochemical/pharmacological experiments. It was validated on the basis of objective energy functions, its ability to account for available residue specific information, and its stability in molecular dynamics (MD) compared with that of the two homologous crystal structures. We then combined the model with extensive structure-activity relationships available from two homologous series of orthosteric GABAAR antagonists to create a detailed hypothesis for their binding modes. Excellent agreement with key experimental data was found, including the ability of the model to accommodate and explain a previously developed pharmacophore model. A coupling to agonist binding was thereby established and discussed in relation to activation mechanisms. Our results highlight the importance of critical evaluation and optimization of each step in the homology modeling process. The approach taken here can greatly aid in increasing the understanding of GABAARs and related receptors where structural insight is limited and reliable models are difficult to obtain.
Protein Kinases: Phosphorylation Machines
Elaine E. Thompson, Susan S. Taylor, J. Andrew McCammon
Applying Molecular Dynamics Simulations to Identify Rarely Sampled Ligand-bound Conformational States of Undecaprenyl Pyrophosphate Synthase, an Antibacterial
William Sinko, César de Oliveira, Sarah Williams, Adam Van Wynsberghe, Jacob D. Durrant, Rong Cao, Eric Oldfield, J. Andrew McCammon
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.
Molecular Recognition in the Case of Flexible Targets
Anthony Ivetac, J. Andrew McCammon
A protein's flexibility is well recognized to underlie its capacity to engage in critical functions, such as signal transduction, biomolecular transport and biochemical reactivity. Molecular recognition is also tightly linked to the dynamics of the binding partners, yet protein flexibility has largely been ignored by the growing field of structure-based drug design (SBDD). In combination with experimentally determined structures, a number of computational methods have been proposed to model protein movements, which may be important for small molecule binding. Such techniques have the ability to expose new binding site conformations, which may in turn recognize and lead to the discovery of more potent and selective drugs through molecular docking. In this article, we discuss various methods and focus on the Relaxed Complex Scheme (RCS), which uses Molecular Dynamics (MD) simulations to model full protein flexibility and enhance virtual screening programmes. We review practical applications of the RCS and use a recent study of the HIV-1 reverse transcriptase to illustrate the various phases of the scheme. We also discuss some encouraging developments, aimed at addressing current weaknesses of the RCS.
Molecular Mimicry and Ligand Recognition in Binding and Catalysis by the Histone Demethylase LSD1-CoREST Complex
Riccardo Baron, Claudia Binda, Marcello Tortorici, J. Andrew McCammon, Andrea Mattevi
Histone demethylases LSD1 and LSD2 (KDM1A/B) catalyze the oxidative demethylation of Lys4 of histone H3. We used molecular dynamics simulations to probe the diffusion of the oxygen substrate. Oxygen can reach the catalytic center independently from the presence of a bound histone peptide, implying that LSD1 can complete subsequent demethylation cycles without detaching from the nucleosomal particle. The simulations highlight the role of a strictly conserved active-site Lys residue providing general insight into the enzymatic mechanism of oxygen-reacting flavoenzymes. The crystal structure of LSD1-CoREST bound to a peptide of the transcription factor SNAIL1 unravels a fascinating example of molecular mimicry. The SNAIL1 N-terminal residues bind to the enzyme active-site cleft, effectively mimicking the H3 tail. This finding predicts that other members of the SNAIL/Scratch transcription factor family might associate to LSD1/2. The combination of selective histone-modifying activity with the distinct recognition mechanisms underlies the biological complexity of LSD1/2.
BINANA: A Novel Algorithm for Ligand-Binding Characterization
Jacob D. Durrant, J. Andrew McCammon
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.
Implementation of Accelerated Molecular Dynamics in NAMD
Y. Wang, C. B. Harrison, K. Schulten, J. A. McCammon
Accelerated molecular dynamics (aMD) is an enhanced sampling method that improves the conformational space sampling by reducing energy barriers separating different states of a system. Here we present the implementation of aMD in the parallel simulation program NAMD. We show that aMD simulations performed with NAMD have only a small overhead compared with classical MD simulations. Through example applications to the alanine dipeptide, we discuss the choice of acceleration parameters, the interpretation of aMD results, as well as the advantages and limitations of the aMD method.
Gated Diffusion-controlled Reactions
J. Andrew McCammon
The binding and active sites of proteins are often dynamically occluded by motion of the nearby polypeptide. A variety of theoretical and computational methods have been developed to predict rates of ligand binding and reactivity in such cases. Two general approaches exist, "protein centric" approaches that explicitly treat only the protein target, and more detailed dynamical simulation approaches in which target and ligand are both treated explicitly. This mini-review describes recent work in this area and some of the biological implications.
Adaptive Accelerated Molecular Dynamics (Ad-AMD) Revealing the Molecular Plasticity of P450cam
Phineus R. L. Markwick, Levi C. T. Pierce, David B. Goodin, J. Andrew McCammon
An extended accelerated molecular dynamics (AMD) methodology called adaptive AMD is presented. Adaptive AMD (Ad-AMD) is an efficient and robust conformational space sampling algorithm that is particularly-well suited to proteins with highly structured potential energy surfaces exhibiting complex, large-scale collective conformational transitions. Ad-AMD simulations of substrate-free P450cam reveal that this system exists in equilibrium between a fully and partially open conformational state. The mechanism for substrate binding depends on the size of the ligand. Larger ligands enter the P450cam binding pocket, and the resulting substrate-bound system is trapped in an open conformation via a population shift mechanism. Small ligands, which fully enter the binding pocket, cause an induced-fit mechanism, resulting in the formation of an energetically stable closed conformational state. These results are corroborated by recent experimental studies and potentially provide detailed insight into the functional dynamics and conformational behavior of the entire cytochrome-P450 superfamily.
Diffusion and Association Processes in Biological Systems: Theory, Computation and Experiments
Paolo Mereghetti, Daria Kokh, J. Andrew McCammon, Rebecca C. Wade
Macromolecular diffusion plays a fundamental role in biological processes. Here, we give an overview of recent methodological advances and some of the challenges for understanding how molecular diffusional properties influence biological function that were highlighted at a recent workshop, BDBDB2, the second Biological Diffusion and Brownian Dynamics Brainstorm.
Brownian dynamics study of the association between the 70S ribosome and the elongation factor G
Maciej Długosz, Gary A. Huber, J. Andrew McCammon, Joanna Trylska
Protein synthesis on the ribosome involves a number of external protein factors that bind at its functional sites. One key factor is the elongation factor G (EF-G) that facilitates the translocation of tRNAs between their binding sites, as well as advancement of the messenger RNA by one codon. The details of the EF-G/ribosome diffusional encounter and EF-G association pathway still remain unanswered. Here, we applied Brownian dynamics methodology to study bimolecular association in the bacterial EF-G/70S ribosome system. We estimated the EF-G association rate constants at 150mM and 300mM monovalent ionic strengths and obtained reasonable agreement with kinetic experiments. We have also elucidated the details of EF-G/ribosome association paths and found that positioning of the L11 protein of the large ribosomal subunit is likely crucial for EF-G entry to its binding site.
Accessing a Hidden Conformation of the Maltose Binding Protein Using Accelerated Molecular Dynamics Simulations
Denis Bucher, Barry J. Grant, Phineus R. Markwick, J. Andrew McCammon
Periplasmic binding proteins (PBPs) are a large family of molecular transporters that play a key role in nutrient uptake and chemotaxis in Gram-negative bacteria. All PBPs have characteristic two-domain architecture with a central interdomain ligand-binding cleft. Upon binding to their respective ligands, PBPs undergo a large conformational change that effectively closes the binding cleft. This conformational change is traditionally viewed as a ligand induced-fit process; however, the intrinsic dynamics of the protein may also be crucial for ligand recognition. Recent NMR paramagnetic relaxation enhancement (PRE) experiments have shown that the maltose binding protein (MBP) - a prototypical member of the PBP superfamily - exists in a rapidly exchanging (ns to μs regime) mixture comprising an open state (approx 95%), and a minor partially closed state (approx 5%). Here we describe accelerated MD simulations that provide a detailed picture of the transition between the open and partially closed states, and confirm the existence of a dynamical equilibrium between these two states in apo MBP. We find that a flexible part of the protein called the balancing interface motif (residues 175–184) is displaced during the transformation. Continuum electrostatic calculations indicate that the repacking of non-polar residues near the hinge region plays an important role in driving the conformational change. Oscillations between open and partially closed states create variations in the shape and size of the binding site. The study provides a detailed description of the conformational space available to ligand-free MBP, and has implications for understanding ligand recognition and allostery in related proteins.
Accelerating chemical reactions: Exploring reactive free-energy surfaces using accelerated ab initio molecular dynamics
Levi C. T. Pierce, Phineus R. L. Markwick, J. Andrew McCammon, Nikos L. Doltsinis
A biased potential molecular dynamics simulation approach, accelerated molecular dynamics (AMD), has been implemented in the framework of ab initio molecular dynamics for the study of chemical reactions. Using two examples, the double proton transfer reaction in formic acid dimer and the hypothetical adiabatic ring opening and subsequent rearrangement reactions in methylenecyclopropane, it is demonstrated that ab initio AMD can be readily employed to efficiently explore the reactive potential energy surface, allowing the prediction of chemical reactions and the identification of metastable states. An adaptive variant of the AMD method is developed, which additionally affords an accurate representation of both the free-energy surface and the mechanism associated with the chemical reaction of interest and can also provide an estimate of the reaction rate.
On the Use of Accelerated Molecular Dynamics to Enhance Configurational Sampling in Ab Initio Simulations
Denis Bucher, Levi C. T. Pierce, J. Andrew McCammon, Phineus R. L. Markwick
We have implemented the accelerated molecular dynamics approach (Hamelberg, D.; Mongan, J.; McCammon, J. A., J. Chem. Phys. 2004, 120 (24), 11919) in the framework of ab initio MD (AIMD). Using three simple examples, we demonstrate that accelerated AIMD (A-AIMD) can be used to accelerate solvent relaxation in AIMD simulations and facilitate the detection of reaction coordinates: (i) We show, for one cyclohexane molecule in the gas phase, that the method can be used to accelerate the rate of the chair-to-chair interconversion by a factor of ~ 1 × 105, while allowing for the reconstruction of the correct canonical distribution of low-energy states; (ii) We then show, for a water box of 64 H2O molecules, that A-AIMD can also be used in the condensed phase to accelerate the sampling of water conformations, without affecting the structural properties of the solvent; and (iii) The method is then used to compute the potential of mean force (PMF) for the dissociation of Na-Cl in water, accelerating the convergence by a factor of ~ 3-4 compared to conventional AIMD simulations. (2) These results suggest that A-AIMD is a useful addition to existing methods for enhanced conformational and phase-space sampling in solution. While the method does not make the use of collective variables superfluous, it also does not require the user to define a set of collective variables that can capture all the low-energy minima on the potential energy surface. This property may prove very useful when dealing with highly complex multidimensional systems that require a quantum mechanical treatment.
Predictive Power of Molecular Dynamics Receptor Structures in Virtual Screening
Sara Elizabeth Nichols, Riccardo Baron, Anthony David Ivetac, and J. Andrew McCammon
Molecular dynamics (MD) simulation is a well-established method for understanding protein dynamics. Conformations from unrestrained MD simulations have yet to be assessed for blind virtual screening (VS) by docking. This study presents a critical analysis of the predictive power of MD snapshots to this regard; evaluating two well-characterized systems of varying flexibility in ligand bound and unbound configurations. Results from such VS predictions are discussed with respect to experimentally determined structures. In all cases MD simulations provide snapshots that improve VS predictive power over known crystal structures, possibly due to sampling more relevant receptor conformations. Additionally, MD can move conformations previously not amenable to docking into the predictive range.
Understanding the Origins of Bacterial Resistance to Aminoglycosides through Molecular Dynamics Mutational Study of the Ribosomal A-Site
Julia Romanowska, J. Andrew McCammon, Joanna Trylska
Paromomycin is an aminoglycosidic antibiotic that targets the RNA of the bacterial small ribosomal subunit. It binds in the A-site, which is one of the three tRNA binding sites, and affects translational fidelity by stabilizing two adenines (A1492 and A1493) in the flipped-out state. Experiments have shown that various mutations in the A-site result in bacterial resistance to aminoglycosides. In this study, we performed multiple molecular dynamics simulations of the mutated A-site RNA fragment in explicit solvent to analyze changes in the physicochemical features of the A-site that were introduced by substitutions of specific bases. The simulations were conducted for free RNA and in complex with paromomycin. We found that the specific mutations affect the shape and dynamics of the binding cleft as well as significantly alter its electrostatic properties. The most pronounced changes were observed in the U1406C:U1495A mutant, where important hydrogen bonds between the RNA and paromomycin were disrupted. The present study aims to clarify the underlying physicochemical mechanisms of bacterial resistance to aminoglycosides due to target mutations.
Pyrone-Based Inhibitors of Metalloproteinases Types 2 and 3 May Work as Conformation-Selective Inhibitors
Jacob D. Durrant, César Augusto F. de Oliveira, J. Andrew McCammon
Matrix metalloproteinases (MMPs) are zinc-containing enzymes capable of degrading all components of the extracellular matrix. Due to their role in human disease, MMPs have been the subject of extensive study. A bioinorganic approach was recently used to identify novel inhibitors based on a maltol zinc-binding group, but accompanying molecular-docking studies failed to explain why one of these inhibitors, AM-6, had ~2500-fold selectivity for MMP-3 over MMP-2. A number of studies have suggested that the MMP active site is highly flexible, leading some to speculate that differences in active-site flexibility may explain inhibitor selectivity. In order to extend the bioinorganic approach in a way that accounts for MMP-2 and MMP-3 dynamics, we here investigate the predicted binding modes and energies of AM-6 docked into multiple structures extracted from MMP MD simulations. Our findings suggest that accounting for protein dynamics is essential for the accurate prediction of binding affinity and selectivity. Additionally, AM-6 and other similar inhibitors likely select for and stabilize only a subpopulation of all MMP conformations sampled by the apo protein. Consequently, when attempting to predict ligand affinity and selectivity using an ensemble of protein structures, it may be wise to disregard protein conformations that cannot accommodate the ligand.
Biological Diffusion and Brownian Dynamics
R.C. Wade, P. Mereghetti, J.A. McCammon (Eds.)
The Biological Diffusion and Brownian Dynamics thematic series of articles draws together the latest research from the second Biological Diffusion and Brownian Dynamics Brainstorm 2 (BDBDB2) workshop held in Heidelberg in October 2010. This collection of articles is guest edited by Rebecca Wade and Paolo Mereghetti (Heidelberg Institute for Theoretical Studies (HITS)) and Andy McCammon (UCSD), who provide an overview of the forum and topics under discussion in Brownian Dynamics simulations and related methodologies. The thematic series draws together the latest research in this rapidly advancing field as well as reviewing the current state of Brownian dynamics and diffusion.
Effects of Biomolecular Flexibility on Alchemical Calculations of Absolute Binding Free Energies
Morgan Lawrenz, Riccardo Baron, Yi Wang, and J. Andrew McCammon
The independent trajectory thermodynamic integration (IT-TI) approach (Lawrenz, M., et al. J. Chem. Theory. Comput. 2009, 5, 1106−1116) for free energy calculations with distributed computing is employed to study two distinct cases of protein–ligand binding: first, the influenza surface protein N1 neuraminidase bound to the inhibitor oseltamivir, and second, the Mycobacterium tuberculosis enzyme RmlC complexed with the molecule CID 77074. For both systems, finite molecular dynamics (MD) sampling and varied molecular flexibility give rise to IT-TI free energy distributions that are remarkably centered on the target experimental values, with a spread directly related to protein, ligand, and solvent dynamics. Using over 2 μs of total MD simulation, alternative protocols for the practical, general implementation of IT-TI are investigated, including the optimal use of distributed computing, the total number of alchemical intermediates, and the procedure to perturb electrostatics and van der Waals interactions. A protocol that maximizes predictive power and computational efficiency is proposed. IT-TI outperforms traditional TI predictions and allows a straightforward evaluation of the reliability of free energy estimates. Our study has broad implications for the use of distributed computing in free energy calculations of macromolecular systems.
Induced Fit or Conformational Selection? The Role of the Semi-closed State in the Maltose Binding Protein
Denis Bucher, Barry J. Grant, J. Andrew McCammon
A full characterization of the thermodynamic forces underlying ligand-associated conformational changes in proteins is essential for understanding and manipulating diverse biological processes, including transport, signaling, and enzymatic activity. Recent experiments on the maltose binding protein (MBP) have provided valuable data about the different conformational states implicated in the ligand recognition process; however, a complete picture of the accessible pathways and the associated changes in free energy remains elusive. Here we describe results from advanced accelerated molecular dynamics (aMD) simulations, coupled with adaptively biased force (ABF) and thermodynamic integration (TI) free energy methods. The combination of approaches allows us to track the ligand recognition process on the microsecond time scale and provides a detailed characterization of the protein’s dynamic and the relative energy of stable states. We find that an induced-fit (IF) mechanism is most likely and that a mechanism involving both a conformational selection (CS) step and an IF step is also possible. The complete recognition process is best viewed as a “Pac Man” type action where the ligand is initially localized to one domain and naturally occurring hinge-bending vibrations in the protein are able to assist the recognition process by increasing the chances of a favorable encounter with side chains on the other domain, leading to a population shift. This interpretation is consistent with experiments and provides new insight into the complex recognition mechanism. The methods employed here are able to describe IF and CS effects and provide formally rigorous means of computing free energy changes. As such, they are superior to conventional MD and flexible docking alone and hold great promise for future development and applications to drug discovery.
Measuring the Successes and Deficiencies of Constant pH Molecular Dynamics: A Blind Prediction Study
Sarah L. Williams, Patrick G. Blachly, J. Andrew McCammon
A constant pH molecular dynamics method has been used in the blind prediction of pKa values of titratable residues in wild type and mutated structures of the Staphylococcal nuclease (SNase) protein. The predicted values have been subsequently compared to experimental values provided by the laboratory of GarcC-a-Moreno. CpHMD performs well in predicting the pKa of solvent-exposed residues. For residues in the protein interior, the CpHMD method encounters some difficulties in reaching convergence and predicting the pKa values for residues having strong interactions with neighboring residues. These results show the need to accurately and sufficiently sample conformational space in order to obtain pKa values consistent with experimental results.
Large-Scale Conformational Changes of Trypanosoma cruzi Proline Racemase Predicted by Accelerated Molecular Dynamics Simulation
César Augusto F. de Oliveira, Barry J. Grant, Michelle Zhou, J. Andrew McCammon
Chagas' disease, caused by the protozoan parasite Trypanosoma cruzi (T. cruzi), is a life-threatening illness affecting 11–18 million people. Currently available treatments are limited, with unacceptable efficacy and safety profiles. Recent studies have revealed an essential T. cruzi proline racemase enzyme (TcPR) as an attractive candidate for improved chemotherapeutic intervention. Conformational changes associated with substrate binding to TcPR are believed to expose critical residues that elicit a host mitogenic B-cell response, a process contributing to parasite persistence and immune system evasion. Characterization of the conformational states of TcPR requires access to long-time-scale motions that are currently inaccessible by standard molecular dynamics simulations. Here we describe advanced accelerated molecular dynamics that extend the effective simulation time and capture large-scale motions of functional relevance. Conservation and fragment mapping analyses identified potential conformational epitopes located in the vicinity of newly identified transient binding pockets. The newly identified open TcPR conformations revealed by this study along with knowledge of the closed to open interconversion mechanism advances our understanding of TcPR function. The results and the strategy adopted in this work constitute an important step toward the rationalization of the molecular basis behind the mitogenic B-cell response of TcPR and provide new insights for future structure-based drug discovery.
Non-Bisphosphonate Inhibitors of Isoprenoid Biosynthesis Identified via Computer-Aided Drug Design
Jacob D. Durrant, Rong Cao, Alemayehu A. Gorfe, Wei Zhu, Jikun Li, Anna Sankovsky, Eric Oldfield, J. Andrew McCammon
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.
HBonanza: A Computer Program for Molecular-Dynamics-Trajectory Hydrogen-Bond Analysis
Jacob D. Durrant, J. Andrew McCammon
In the current work, we present a hydrogen-bond analysis of 2,673 ligand-receptor complexes that suggests the total number of hydrogen bonds formed between a ligand and its protein 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.
Studying Functional Dynamics in Bio-molecules using Accelerated Molecular Dynamics
Phineus R. L. Markwick, J. Andrew McCammon
Many biologically important processes such as enzyme catalysis, signal transduction, ligand binding and allosteric regulation occur on the micro- to millisecond time-scale. Despite the sustained and rapid increase in available computational power and the development of efficient simulation algorithms, molecular dynamics (MD) simulations of proteins and bio-machines are generally limited to time-scales of tens to hundreds of nano-seconds. In this perspective article we present a comprehensive review of Accelerated Molecular Dynamics (AMD), an extended biased potential molecular dynamics approach that allows for the efficient study of bio-molecular systems up to time-scales several orders of magnitude greater than those accessible using standard classical MD methods, whilst still maintaining a fully atomistic representation of the system. Compared to many other approaches, AMD affords efficient enhanced conformational space sampling without any a priori understanding of the underlying free energy surface, nor does it require the specific prior definition of a reaction coordinate or a set of collective variables. Successful applications of the AMD method, including the study of slow time-scale functional dynamics in folded proteins and the conformational behavior of natively unstructured proteins are discussed and an outline of the different variants and extensions to the standard AMD approach is presented.
Enhanced lipid diffusion and mixing in accelerated molecular dynamics
Yi Wang, Phineus R. L. Markwick, César Augusto F. de Oliveira, J. Andrew McCammon
Accelerated molecular dynamics (aMD) is an enhanced sampling technique that expedites conformational space sampling by reducing the barriers separating various low-energy states of a system. Here, we present the first application of the aMD method on lipid membranes. Altogether, ~1.5 μs simulations were performed on three systems: a pure POPC bilayer, a pure DMPC bilayer, and a mixed POPC:DMPC bilayer. Overall, the aMD simulations are found to produce significant speedup in trans–gauche isomerization and lipid lateral diffusion versus those in conventional MD (cMD) simulations. Further comparison of a 70-ns aMD run and a 300-ns cMD run of the mixed POPC:DMPC bilayer shows that the two simulations yield similar lipid mixing behaviors, with aMD generating a 2–3-fold speedup compared to cMD. Our results demonstrate that the aMD method is an efficient approach for the study of bilayer structural and dynamic properties. On the basis of simulations of the three bilayer systems, we also discuss the impact of aMD parameters on various lipid properties, which can be used as a guideline for future aMD simulations of membrane systems.
Novel Allosteric Sites on Ras for Lead Generation
Barry J. Grant, Suryani Lukman, Harrison J. Hocker, Jaqueline Sayyah, Joan Heller Brown, J. Andrew McCammon, Alemayehu A. Gorfe
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.
Novel Inhibitors of Mycobacterium tuberculosis dTDP-6-deoxy-L-lyxo-4-hexulose Reductase (RmlD) Identified by Virtual Screening
Yi Wang, Tamara Noelle Hess, Victoria Jones, Joe Zhongxiang Zhou, Michael R. McNeil, J. Andrew McCammon
The complex and highly impermeable cell wall of Mycobacterium tuberculosis (Mtb) is largely responsible for the ability of the mycobacterium to resist the action of chemical therapeutics. An l-rhamnosyl residue, which occupies an important anchoring position in the Mtb cell wall, is an attractive target for novel anti-tuberculosis drugs. In this work, we report a virtual screening (VS) study targeting Mtb dTDP-deoxy-l-lyxo-4-hexulose reductase (RmlD), the last enzyme in the l-rhamnosyl synthesis pathway. Through two rounds of VS, we have identified four RmlD inhibitors with half inhibitory concentrations of 0.9–25 μM, and whole-cell minimum inhibitory concentrations of 20–200 μg/ml. Compared with our previous high throughput screening targeting another enzyme involved in l-rhamnosyl synthesis, virtual screening produced higher hit rates, supporting the use of computational methods in future anti-tuberculosis drug discovery efforts.
Towards the Development of Novel Trypanosoma brucei RNA Editing Ligase 1 Inhibitors
J.D. Durrant, J.A. McCammon
Trypanosoma brucei (T. brucei) is an infectious agent for which drug development has been largely neglected. We here use a recently developed computer program called AutoGrow to add interacting molecular fragments to S5, a known inhibitor of the validated T. brucei drug target RNA editing ligase 1, in order to improve its predicted binding affinity. The proposed binding modes of the resulting compounds mimic that of ATP, the native substrate, and provide insights into novel protein-ligand interactions that may be exploited in future drug-discovery projects. We are hopeful that these new predicted inhibitors will aid medicinal chemists in developing novel therapeutics to fight human African trypanosomiasis.
CrystalDock: A Novel Approach to Fragment-Based Drug Design
Jacob D. Durrant, Aaron J. Friedman, J. Andrew McCammon
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.
Molecular Dynamics Simulations and Drug Discovery
Jacob D. Durrant, J. Andrew McCammon
This review discusses the many roles atomistic computer simulations of macromolecular (for example, protein) receptors and their associated small-molecule ligands can play in drug discovery, including the identification of cryptic or allosteric binding sites, the enhancement of traditional virtual-screening methodologies, and the direct prediction of small-molecule binding energies. The limitations of current simulation methodologies, including the high computational costs and approximations of molecular forces required, are also discussed. With constant improvements in both computer power and algorithm design, the future of computer-aided drug design is promising; molecular dynamics simulations are likely to play an increasingly important role.
Electrostatically Biased Binding of Kinesin to Microtubules
Barry J. Grant, Dana M. Gheorghe, Wenjun Zheng, Maria Alonso, Gary Huber, Maciej Dlugosz, J. Andrew McCammon, Robert A. Cross
The minimum motor domain of kinesin-1 is a single head. Recent evidence suggests that such minimal motor domains generate force by a biased binding mechanism, in which they preferentially select binding sites on the microtubule that lie ahead in the progress direction of the motor. A specific molecular mechanism for biased binding has, however, so far been lacking. Here we use atomistic Brownian dynamics simulations combined with experimental mutagenesis to show that incoming kinesin heads undergo electrostatically guided diffusion-to-capture by microtubules, and that this produces directionally biased binding. Kinesin-1 heads are initially rotated by the electrostatic field so that their tubulin-binding sites face inwards, and then steered towards a plus-endwards binding site. In tethered kinesin dimers, this bias is amplified. A 3-residue sequence (RAK) in kinesin helix alpha-6 is predicted to be important for electrostatic guidance. Real-world mutagenesis of this sequence powerfully influences kinesin-driven microtubule sliding, with one mutant producing a 5-fold acceleration over wild type. We conclude that electrostatic interactions play an important role in the kinesin stepping mechanism, by biasing the diffusional association of kinesin with microtubules.
NNScore 2.0: A Neural-Network Receptor-Ligand Scoring Function
Jacob D. Durrant, J. Andrew McCammon
NNScore is a neural-network-based scoring function designed to aid the computational identification of small-molecule ligands. While the test cases included in the original NNScore article demonstrated the utility of the program, the application examples were limited. The purpose of the current work is to further confirm that neural-network scoring functions are effective, even when compared to the scoring functions of state-of-the-art docking programs, such as AutoDock, the most commonly cited program, and AutoDock Vina, thought to be two orders of magnitude faster. Aside from providing additional validation of the original NNScore function, we here present a second neural-network scoring function, NNScore 2.0. NNScore 2.0 considers many more binding characteristics when predicting affinity than does the original NNScore. The network output of NNScore 2.0 also differs from that of NNScore 1.0; rather than a binary classification of ligand potency, NNScore 2.0 provides a single estimate of the pKd. To facilitate use, NNScore 2.0 has been implemented as an open-source python script. A copy can be obtained from http://www.nbcr.net/software/nnscore/.