Computer modeling of human angiogenin-dinucleotide substrate interaction

Structures of substrate bound human angiogenin complexes have been obtained for the first time by computer modeling. The dinucleotides CpA and UpA have been docked onto human angiogenin using a systematic grid search procedure in torsion and Eulerian angle space. The docking was guided throughout by the similarity of angiogenin-substrate interactions with interactions of RNase A and its substrate. The models were subjected to 1 nanosecond of molecular dynamics to access their stability. Structures extracted from MD simulations were refined by simulated annealing. Stable hydrogen bonds that bridged protein and ligand residues during the MD simulations were taken as restraints for simulated annealing. Our analysis on the MD structures and annealed models explains the substrate specificity of human angiogenin and is in agreement with experimental results. This study also predicts the B2 binding site residues of angiogenin, for which no experimental information is available so far. In the case of one of the substrates, CpA, we have also identified the presence of a water molecule that invariantly bridges the B2 base with the protein. We have compared our results to the RNase A-substrate complex and highlight the similarities and differences.     

Exploring structural change of protein bovine serum albumin by external perturbation using extrinsic fluorescence probe: spectroscopic measurement, molecular docking and molecular dynamics simulation

Structural modification through binding interaction of plasma protein bovine serum albumin (BSA) with an extrinsic charge transfer fluorophore 5-(4-dimethylamino-phenyl)-penta-2,4-dienoic acid (DMAPPDA) and its response to external perturbation due to interactions with surfactant sodium dodecyl sulphate (SDS) have been explored at physiological pH by steady state absorption, emission, fluorescence anisotropy, red edge excitation shift, far-UV circular dichroism and time resolved spectral measurements in combination with Molecular Docking and Molecular Dynamics (MD) simulation. Interaction of the probe with BSA is reflected by a small change in protein secondary structure with fluorescence enhancement and blue shift of probe emission. Molecular docking studies revealed that the probe binds to the hydrophobic cavity of sub-domain IIA of BSA. The distance for energy transfer from the tryptophan of BSA to the bound DMAPPDA measured by Fluorescence Resonance Energy Transfer is in good agreement with the molecular docking results. MD simulation predicts stabilization of the complex with respect to the bare molecule. Interaction of BSA and SDS with DMAPPDA supports the movement of the probe from hydrophilic free water region to a more restricted hydrophobic zone inside the protein.     

A leishmaniasis study: Structure-based screening and molecular dynamics mechanistic analysis for discovering potent inhibitors of spermidine synthase

Protozoa Leishmania donovani (Ld) is the main cause of the endemic disease leishmaniasis. Spermidine synthase (SS), an important enzyme in the synthetic pathway of polyamines in Ld, is an essential element for the survival of this protozoan. Targeting SS may provide an important aid for the development of drugs against Ld. However, absence of tertiary structure of spermidine synthase of Leishmania donovani (LSS) limits the possibilities of structure based drug designing. Presence of the same enzyme in the host itself further challenges the drug development process. We modeled the tertiary structure of LSS using homology modeling approach making use of homologous X-ray crystallographic structure of spermidine synthase of Trypanosoma cruzi (TSS) (2.5? resolution). The modeled structure was stabilized using Molecular Dynamics simulations. Based on active site structural differences between LSS and human spermidine synthase (HSS), we screened a large dataset of compounds against modeled protein using Glide virtual screen docking and selected two best inhibitors based on their docking scores (-10.04 and -13.11 respectively) with LSS and having least/no binding with the human enzyme. Finally Molecular Dynamics simulations were used to assess the dynamic stability of the ligand bound structures and to elaborate on the binding modes. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.     

Flexible relaxation of rigid-body docking solutions

Molecular Dynamics (MD) simulations have been performed on a set of rigid-body docking poses, carried out over 25 protein-protein complexes. The results show that fully flexible relaxation increases the fraction of native contacts (NC) by up to 70% for certain docking poses. The largest increase in the fraction of NC is observed for docking poses where anchor residues are able to sample their bound conformation. For each MD simulation, structural snap-shots were clustered and the centre of each cluster used as the MD-relaxed docking pose. A comparison between two energy-based scoring schemes, the first calculated for the MD-relaxed poses, the second for energy minimized poses, shows that the former are better in ranking complexes with large hydrophobic interfaces. Furthermore, complexes with large interfaces are generally ranked well, regardless of the type of relaxation method chosen, whereas complexes with small hydrophobic interfaces remain difficult to rank. In general, the results indicate that current force-fields are able to correctly describe direct intermolecular interactions between receptor and ligand molecules. However, these force-fields still fail in cases where protein-protein complexes are stabilized by subtle energy contributions.     

Molecular modeling and molecular dynamics simulation studies of Delta-Notch complex

Notch is a single-pass transmembrane receptor protein which is composed of a short extracellular region, a single-pass transmembrane domain and a small intracellular region. Notch ligand like Delta, member of the DSL protein family, is also single-pass transmembrane protein. It has been demonstrated that of the 36 EGF repeats of Notch, 11th and 12th are sufficient to mediate interactions with Delta. Crystal structure of mammalian Notch extracellular ligand binding domain contains 11 and 12 EGF-like repeats. Here a portion of the Delta protein of Drosophila, known to interact with Notch extracellular domain (ECD) has been modeled using homology modeling. The structure of the Delta-Notch complex was subsequently modeled by protein docking method using GRAMM. MD simulations of the modeled structures were performed. The structure for Delta-Notch complex has been proposed based on interaction energy parameter and planarity studies.     

Multi‐scale characterization of the energy landscape of proteins with application to the C3D/Efb‐C complex

We present a novel multi‐level methodology to explore and characterize the low energy landscape and the thermodynamics of proteins. Traditional conformational search methods typically explore only a small portion of the conformational space of proteins and are hard to apply to large proteins due to the large amount of calculations required. In our multi‐scale approach, we first provide an initial characterization of the equilibrium state ensemble of a protein using an efficient computational conformational sampling method. We then enrich the obtained ensemble by performing short Molecular Dynamics (MD) simulations on selected conformations from the ensembles as starting points. To facilitate the analysis of the results, we project the resulting conformations on a low‐dimensional landscape to efficiently focus on important interactions and examine low energy regions. This methodology provides a more extensive sampling of the low energy landscape than an MD simulation starting from a single crystal structure as it explores multiple trajectories of the protein. This enables us to obtain a broader view of the dynamics of proteins and it can help in understanding complex binding, improving docking results and more. In this work, we apply the methodology to provide an extensive characterization of the bound complexes of the C3d fragment of human Complement component C3 and one of its powerful bacterial inhibitors, the inhibitory domain of Staphylococcus aureus extra‐cellular fibrinogen‐binding domain (Efb‐C) and two of its mutants. We characterize several important interactions along the binding interface and define low free energy regions in the three complexes. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Searching the conformational complexity and binding properties of HDAC6 through docking and molecular dynamic simulations

Histone deacetylases (HDACs) are a family of proteins involved in the deacetylation of histones and other non-histones substrates. HDAC6 belongs to class II and shares similar biological functions with others of its class. Nevertheless, its three-dimensional structure that involves the catalytic site remains unknown for exploring the ligand recognition properties. Therefore, in this contribution, homology modeling, 100-ns-long Molecular Dynamics (MD) simulation and docking calculations were combined to explore the conformational complexity and binding properties of the catalytic domain 2 from HDAC6 (DD2-HDAC6), for which activity and affinity toward five different ligands have been reported. Clustering analysis allowed identifying the most populated conformers present during the MD simulation, which were used as starting models to perform docking calculations with five DD2-HDAC6 inhibitors: Cay10603 (CAY), Rocilinostat (RCT), Tubastatin A (TBA), Tubacin (TBC), and Nexturastat (NXT), and then were also submitted to 100-ns-long MD simulations. Docking calculations revealed that the five inhibitors bind at the DD2-HDAC6 binding site with the lowest binding free energy, the same binding mode is maintained along the 100-ns-long MD simulations. Overall, our results provide structural information about the molecular flexibility of apo and holo DD2-HDAC6 states as well as insight of the map of interactions between DD2-HDAC6 and five well-known DD2-HDAC6 inhibitors allowing structural details to guide the drug design. Finally, we highlight the importance of combining different theoretical approaches to provide suitable structural models for structure-based drug design.     

Protein-Protein Docking: From Interaction to Interactome

The protein-protein docking problem is one of the focal points of activity in computational biophysics and structural biology. The three-dimensional structure of a protein-protein complex, generally, is more difficult to determine experimentally than the structure of an individual protein. Adequate computational techniques to model protein interactions are important because of the growing number of known protein structures, particularly in the context of structural genomics. Docking offers tools for fundamental studies of protein interactions and provides a structural basis for drug design. Protein-protein docking is the prediction of the structure of the complex, given the structures of the individual proteins. In the heart of the docking methodology is the notion of steric and physicochemical complementarity at the protein-protein interface. Originally, mostly high-resolution, experimentally determined (primarily by x-ray crystallography) protein structures were considered for docking. However, more recently, the focus has been shifting toward lower-resolution modeled structures. Docking approaches have to deal with the conformational changes between unbound and bound structures, as well as the inaccuracies of the interacting modeled structures, often in a high-throughput mode needed for modeling of large networks of protein interactions. The growing number of docking developers is engaged in the community-wide assessments of predictive methodologies. The development of more powerful and adequate docking approaches is facilitated by rapidly expanding information and data resources, growing computational capabilities, and a deeper understanding of the fundamental principles of protein interactions.     

Viper venom hyaluronidase and its potential inhibitor analysis: a multipronged computational investigation

Viper venom hyaluronidase (VV-HYA) inhibitors have long been used as therapeutic agents for arresting the local and systemic effects caused during its envenomation. Henceforth, to understand its structural features and also to identify the best potential inhibitor against it the present computational study was undertaken. Structure-based homology modeling of VV-HYA followed by its docking and free energy-based ranking analysis of ligand, the MD simulations of the lead complex was also performed. The sequence analysis and homology modeling of VV-HYA revealed a distorted (β/α)₈ folding as in the case of hydrolases family of proteins. Molecular docking of the resultant 3D structure of VV-HYA with known inhibitors (compounds 1–25) revealed the importance of molecular recognition of hotspot residues (Tyr 75, Arg 288, and Trp 321) other than that of the active site residues. It also revealed that Trp 321 of VV-HYA is highly important for mediating π–π interactions with ligands. In addition, the molecular docking and comparative free energy binding analysis was investigated for the VV-HYA inhibitors (compounds 1–25). Both molecular docking and relative free energy binding analysis clearly confirmed the identification of sodium chromoglycate (compound 1) as the best potential inhibitor against VV-HYA. Molecular dynamics simulations additionally confirmed the stability of their binding interactions. Further, the information obtained from this work is believed to serve as an impetus for future rational designing of new novel VV-HYA inhibitors with improved activity and selectivity.     

Protein-Protein Docking: From Interaction to Interactome

The protein-protein docking problem is one of the focal points of activity in computational biophysics and structural biology. The three-dimensional structure of a protein-protein complex, generally, is more difficult to determine experimentally than the structure of an individual protein. Adequate computational techniques to model protein interactions are important because of the growing number of known protein structures, particularly in the context of structural genomics. Docking offers tools for fundamental studies of protein interactions and provides a structural basis for drug design. Protein-protein docking is the prediction of the structure of the complex, given the structures of the individual proteins. In the heart of the docking methodology is the notion of steric and physicochemical complementarity at the protein-protein interface. Originally, mostly high-resolution, experimentally determined (primarily by x-ray crystallography) protein structures were considered for docking. However, more recently, the focus has been shifting toward lower-resolution modeled structures. Docking approaches have to deal with the conformational changes between unbound and bound structures, as well as the inaccuracies of the interacting modeled structures, often in a high-throughput mode needed for modeling of large networks of protein interactions. The growing number of docking developers is engaged in the community-wide assessments of predictive methodologies. The development of more powerful and adequate docking approaches is facilitated by rapidly expanding information and data resources, growing computational capabilities, and a deeper understanding of the fundamental principles of protein interactions.     

Computational and Functional Characterization of Angiogenin Mutations,and Correlation with Amyotrophic Lateral Sclerosis

The Angiogenin (ANG) gene is frequently mutated in patients suffering from the neurodegenerative disease - amyotrophic lateral sclerosis (ALS). Most of the ALS-causing mutations in Angiogenin affect either its ribonucleolytic or nuclear translocation activity. Here we report the functional characterization of two previously uncharacterized missense mutations in Angiogenin - D22G and L35P. We predict the nature of loss-of-function(s) in these mutants through our previously established Molecular Dynamics (MD) simulation extended to 100 ns, and show that the predictions are entirely validated through biochemical studies with wild-type and mutated proteins. Based on our studies, we provide a biological explanation for the loss-of-function of D22G-Angiogenin leading to ALS, and suggest that the L35P-Angiogenin mutation would probably cause ALS symptoms in individuals harboring this mutation. Our study thus highlights the strength of MD simulation-based predictions, and suggests that this method can be used for correlating mutations in Angiogenin or other effector proteins with ALS symptoms.     

Molecular dynamics of a vasopressin V2 receptor in a phospholipid bilayer membrane

Molecular dynamics simulations were carried out for a V2 receptor (V2R) model embedded in a dimyristoylphosphatidylcholine (DMPC) bilayer. Both free and ligand-bound states of V2R were modeled. Our initial V2R model was obtained using a rule-based automated method for GPCR modeling and refined using constrained simulated annealing in vacuo. The docking site of the native vasopressin ligand was selected and justified upon consideration of ligand-receptor interactions and structure-activity data. The primary purpose of this work was to investigate the usefulness of MD simulation of an integral membrane protein like a GPCR receptor, upon inclusion of a carefully parameterized surrounding lipid membrane and water. Physical properties of the system were evaluated and compared with the fully hydrated pure DMPC bilayer membrane. The solvation interactions, individual lipid-protein interaction and fluctuations of the protein, the lipid, and water were analyzed in detail. As expected, the membrane-spanning helices of the protein fluctuate less than the peripheral loops do. The protein appears to disturb the local lipid structure. Simulations were carried out using AMBER 4.1 package upon constant number-pressure-temperature (NPT) conditions on massively parallel computers Cray T3E and IBM SP2.     

Molecular dynamics characterization of the active cavity of carboxypeptidase A and some of its inhibitor adducts.

Molecular dynamics (MD) calculations have been performed on carboxypeptidase A and on its adducts with inhibitors, such as d-phenylalanine (dPhe) and acetate. The catalytically essential zinc ion present in the protein was explicitly included in all the simulations. The simulation was carried out over a sphere of 15 A centered on the zinc ion. The crystallographic water molecules were explicitly taken into account; then the protein was solvated with a 18 A sphere of water molecules. MD calculations were carried out for 45-60 ps. There is no large deviation from the available X-ray structures of native and the dPhe adduct for the MD structures. Average MD structures were calculated starting from the X-ray structure of the dPhe adduct, and, from a structure obtained by docking the inhibitor in the native structure. Comparison between these two structures and with that of the native protein shows that some of the key variations produced by inhibitor binding are reproduced by MD calculations. Addition of acetate induces structural changes relevant for the understanding of the interaction network in the active cavity. The structural variations induced by different inhibitors are examined. The effects of these interactions on the catalytic mechanism and on the binding of substrate are discussed.     

Lipid a from lipopolysaccharide recognition: Structure,dynamics and cooperativity by molecular dynamics simulations

Molecular dynamics simulations of Lipid A and its natural precursor Lipid IVA from E.coli have been carried out free in solution, bound to the myeliod differentiation protein 2 (MD2) and in the complex of MD2 with the toll like receptor 4 (TLR4). In addition, simulations of the ligand free MD2 and MD2‐TLR4 complex were performed. A structural and energetic characterization of the bound and unbound states of Lipid A/IVA was generated. As the crystal structures depict, the main driving force for MD2‐Lipid A/IVA are the hydrophobic interactions between the aliphatic tails and the MD2 cavity. The charged phosphate groups do strongly interact with positively charged residues, located at the surface of MD2. However, they are not essential for keeping the lipids in the cavity, indicating a more prominent role in binding recognition and ionic interactions with TLR4 at the MD2/TLR4 interface. Interestingly, in the absence of any ligand MD2 rapidly closes, blocking the binding cavity. The presence of TLR4, though changing the dynamics, was not able to impede the aforementioned closing event. We hypothesize that fluctuations of the H1 region are essential for this phenomenon, and it is plausible that an equilibrium between the open and closed states exists, although the lengths of our simulations are not sufficient to encompass the reversible process. The MD2/Lipid A‐TLR4 complex simulations show that the presence of the ligand energetically stabilizes the complex relative to the ligand‐free structures, indicating cooperativity in the binding process. © Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Homology modeling,virtual screening and dynamics study of proteins involved in Pebrine - Serine protease inhibitor 106 and spore wall protein 26

Abstract

Pebrine is a microsporidian disease caused by Nosema bombycis in Bombyx mori (silk worm) which results in brown/black spots. The affected larvae either spin cocoons which are flimsy with low silk content or not spin a cocoon. It has been hypothesised that Serine Protease Inhibitor 106 (SPN106) is responsible for evasion of host immune system by inhibiting the melanization process in silkworms. Also, Spore Wall Protein 26 (SWP26) has been observed to bind with Ig- like protein Bombyx mori turtle-like protein (Bm-TLP) facilitating the attachment of the microsporidian to the host and contributing to infectivity. Till date, there is no crystal structure of the proteins SPN106, SWP26 and Bm-TLP available. In this study, we performed homology modeling of the three structures using Modeller v9.18 and the binding pockets were identified. Virtual screening was conducted using AutoDock Vina on a ligand library consisting of 28,870 lead-like molecules. The protein stability, compactness, fluctuations and protein-ligand interactions were investigated through Molecular Dynamics (MD) simulations studies using Desmond Maestro 11.3 and a potential lead molecule was identified.

Communicated by Ramaswamy H. Sarma     

Pre-existing soft modes of motion uniquely defined by native contact topology facilitate ligand binding to proteins

Modeling protein flexibility constitutes a major challenge in accurate prediction of protein-ligand and protein-protein interactions in docking simulations. The lack of a reliable method for predicting the conformational changes relevant to substrate binding prevents the productive application of computational docking to proteins that undergo large structural rearrangements. Here, we examine how coarse-grained normal mode analysis has been advantageously applied to modeling protein flexibility associated with ligand binding. First, we highlight recent studies that have shown that there is a close agreement between the large-scale collective motions of proteins predicted by elastic network models and the structural changes experimentally observed upon ligand binding. Then, we discuss studies that have exploited the predicted soft modes in docking simulations. Two general strategies are noted: pregeneration of conformational ensembles that are then utilized as input for standard fixed-backbone docking and protein structure deformation along normal modes concurrent to docking. These studies show that the structural changes apparently "induced" upon ligand binding occur selectively along the soft modes accessible to the protein prior to ligand binding. They further suggest that proteins offer suitable means of accommodating/facilitating the recognition and binding of their ligand, presumably acquired by evolutionary selection of the suitable three-dimensional structure.     

Assessment of CAPRI predictions in rounds 3-5 shows progress in docking procedures

The current status of docking procedures for predicting protein-protein interactions starting from their three-dimensional (3D) structure is reassessed by evaluating blind predictions, performed during 2003-2004 as part of Rounds 3-5 of the community-wide experiment on Critical Assessment of PRedicted Interactions (CAPRI). Ten newly determined structures of protein-protein complexes were used as targets for these rounds. They comprised 2 enzyme-inhibitor complexes, 2 antigen-antibody complexes, 2 complexes involved in cellular signaling, 2 homo-oligomers, and a complex between 2 components of the bacterial cellulosome. For most targets, the predictors were given the experimental structures of 1 unbound and 1 bound component, with the latter in a random orientation. For some, the structure of the free component was derived from that of a related protein, requiring the use of homology modeling. In some of the targets, significant differences in conformation were displayed between the bound and unbound components, representing a major challenge for the docking procedures. For 1 target, predictions could not go to completion. In total, 1866 predictions submitted by 30 groups were evaluated. Over one-third of these groups applied completely novel docking algorithms and scoring functions, with several of them specifically addressing the challenge of dealing with side-chain and backbone flexibility. The quality of the predicted interactions was evaluated by comparison to the experimental structures of the targets, made available for the evaluation, using the well-agreed-upon criteria used previously. Twenty-four groups, which for the first time included an automatic Web server, produced predictions ranking from acceptable to highly accurate for all targets, including those where the structures of the bound and unbound forms differed substantially. These results and a brief survey of the methods used by participants of CAPRI Rounds 3-5 suggest that genuine progress in the performance of docking methods is being achieved, with CAPRI acting as the catalyst.     

Computational approaches for protein function prediction: A combined strategy from multiple sequence alignment to molecular docking-based virtual screening

The functional characterization of proteins represents a daily challenge for biochemical, medical and computational sciences. Although finally proved on the bench, the function of a protein can be successfully predicted by computational approaches that drive the further experimental assays. Current methods for comparative modeling allow the construction of accurate 3D models for proteins of unknown structure, provided that a crystal structure of a homologous protein is available. Binding regions can be proposed by using binding site predictors, data inferred from homologous crystal structures, and data provided from a careful interpretation of the multiple sequence alignment of the investigated protein and its homologs. Once the location of a binding site has been proposed, chemical ligands that have a high likelihood of binding can be identified by using ligand docking and structure-based virtual screening of chemical libraries. Most docking algorithms allow building a list sorted by energy of the lowest energy docking configuration for each ligand of the library. In this review the state-of-the-art of computational approaches in 3D protein comparative modeling and in the study of protein–ligand interactions is provided. Furthermore a possible combined/concerted multistep strategy for protein function prediction, based on multiple sequence alignment, comparative modeling, binding region prediction, and structure-based virtual screening of chemical libraries, is described by using suitable examples. As practical examples, Abl-kinase molecular modeling studies, HPV-E6 protein multiple sequence alignment analysis, and some other model docking-based characterization reports are briefly described to highlight the importance of computational approaches in protein function prediction.     

Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment

The community-wide GPCR Dock assessment is conducted to evaluate the status of molecular modeling and ligand docking for human G protein-coupled receptors. The present round of the assessment was based on the recent structures of dopamine D3 and CXCR4 chemokine receptors bound to small molecule antagonists and CXCR4 with?a synthetic cyclopeptide. Thirty-five groups submitted their receptor-ligand complex structure predictions prior to the release of the crystallographic coordinates. With closely related homology modeling templates, as for dopamine D3 receptor, and with incorporation of biochemical and QSAR data, modern computational techniques predicted complex details with accuracy approaching experimental. In contrast,?CXCR4 complexes that had less-characterized interactions and only distant homology to the known GPCR structures still remained very challenging. The assessment results provide guidance for modeling and crystallographic communities in method development and target selection for further expansion of the structural coverage of the GPCR universe.     

Molecular docking of balanol to dynamics snapshots of protein kinase A

Even if the structure of a receptor has been determined experimentally, it may not be a conformation to which a ligand would bind when induced fit effects are significant. Molecular docking using such a receptor structure may thus fail to recognize a ligand to which the receptor can bind with reasonable affinity. Here, we examine one way to alleviate this problem by using an ensemble of receptor conformations generated from a molecular dynamics simulation for molecular docking. Two molecular dynamics simulations were conducted to generate snapshots for protein kinase A: one with the ligand bound, the other without. The ligand, balanol, was then docked to conformations of the receptors presented by these trajectories. The Lamarckian genetic algorithm in Autodock [Goodsell et al. J Mol Recognit 1996;9(1):1-5; Morris et al. J Comput Chem 1998;19(14):1639-1662] was used in the docking. Three ligand models were used: rigid, flexible, and flexible with torsional potentials. When the snapshots were taken from the molecular dynamics simulation of the protein-ligand complex, the correct docking structure could be recovered easily by the docking algorithm in all cases. This was an easier case for challenging the docking algorithm because, by using the structure of the protein in a protein-ligand complex, one essentially assumed that the protein already had a pocket to which the ligand can fit well. However, when the snapshots were taken from the ligand-free protein simulation, which is more useful for a practical application when the structure of the protein-ligand complex is not known, several clusters of structures were found. Of the 10 docking runs for each snapshot, at least one structure was close to the correctly docked structure when the flexible-ligand models were used. We found that a useful way to identify the correctly docked structure was to locate the structure that appeared most frequently as the lowest energy structure in the docking experiments to different snapshots.