An integrated suite of fast docking algorithms

Adaptive BP-Dock: An Induced Fit Docking Approach for Full Receptor Flexibility

We present an induced fit docking approach called Adaptive BP-Dock that integrates perturbation response scanning (PRS) with the flexible docking protocol of RosettaLigand in an adaptive manner. We first perturb the binding pocket residues of a receptor and obtain a new conformation based on the residue response fluctuation profile using PRS. Next, we dock a ligand to this new conformation by RosettaLigand, where we repeat these steps for several iterations. We test this approach on several protein test sets including difficult unbound docking cases such as HIV-1 reverse transcriptase and HIV-1 protease. Adaptive BP-Dock results show better correlation with experimental binding affinities compared to other docking protocols. Overall, the results imply that Adaptive BP-Dock can easily capture binding induced conformational changes by simultaneous sampling of protein and ligand conformations. This can provide faster and efficient docking of novel targets for rational drug design.

Anti-inflammatory effects of royal poinciana through inhibition of toll-like receptor 4 signaling pathway

Inflammation is part of the non-specific immune response that occurs in reaction to any type of bodily injury. In some disorders the inflammatory process, which under normal conditions is self-limiting, becomes continuous and chronic inflammatory diseases develop subsequently including cardiovascular diseases, diabetes, cancer etc. Barks of Delonix regia is used traditionally in the treatment of inflammatory diseases. Therefore, in this study we evaluated the therapeutic potential of D. regia ethanol extract and its active constituent β-Elemene with special interest in inflammation model using standard in vivo anti-inflammatory models: Carrageenan-induced paw edema, Cotton pellet granuloma, and Acetic acid-induced vascular permeability. To explicate the mechanism of action for the possible anti-inflammatory activity, we determined the level of major inflammatory mediators (NO, iNOS, COX-2-dependent prostaglandin E2 or PGE2), and pro-inflammatory cytokines (TNF-a, IL-1b, IL-6, and IL-12). Additionally, we determined the toll-like receptor 4 (TLR4), Myeloid differentiation primary response gene 88 (MyD88), by mRNA expression in drug treated LPS-induced murine macrophage model. To explore the mechanism of anti-inflammatory activity, we evaluated expression of c-Jun N-terminal kinases (JNK), nuclear factor kappa-B cells (NF-kB), and NF-kB inhibitor alpha (IK-Ba). Furthermore, we determined the acute and sub-acute toxicity of D. regia extract in BALB/c mice. This study established a significant anti-inflammatory activity of D. regia extract and β-Elemene along with the inhibition of TNF-a, IL-1b, IL-6 and IL-12 expressions. Further, the expression of TLR4, NF-kBp65, MyD88, iNOS and COX-2 molecules were reduced in drug-treated groups, but not in the LPS-stimulated untreated or control groups, Thus, our results collectively indicated that the D. regia extract and β-Elemene can efficiently inhibit inflammation.

Biophysical Characterization of Essential Phosphorylation at the Flexible C-Terminal Region of C-Raf with 14-3-3ζ Protein

Phosphorylation at the C-terminal flexible region of the C-Raf protein plays an important role in regulating its biological activity. Auto-phosphorylation at serine 621 (S621) in this region maintains C-Raf stability and activity. This phosphorylation mediates the interaction between C-Raf and scaffold protein 14-3-3ζ to activate the downstream MEK kinase pathway. In this study, we have defined the interaction of C-terminal peptide sequence of C-Raf with 14-3-3ζ protein and determined the possible structural adaptation of this region. Biophysical elucidation of the interaction was carried out using phosphopeptide (residue number 615–630) in the presence of 14-3-3ζ protein. Using isothermal titration calorimetry (ITC), a high binding affinity with micro-molar range was found to exist between the peptide and 14-3-3ζ protein, whereas the non-phosphorylated peptide did not show any appreciable binding affinity. Further interaction details were investigated using several biophysical techniques such as circular dichroism (CD), fluorescence, and nuclear magnetic resonance (NMR) spectroscopy, in addition to molecular modeling. This study provides the molecular basis for C-Raf C-terminal-derived phosphopeptide interaction with 14-3-3ζ protein as well as structural insights responsible for phosphorylated S621-mediated 14-3-3ζ binding at an atomic resolution.

The importance of dynamics in integrative modeling of supramolecular assemblies

Revealing the atomistic architecture of supramolecular complexes is a fundamental step toward a deeper understanding of cellular functioning. To date, this formidable task is facilitated by an emerging array of integrative modeling approaches that combine experimental data from different sources. One major challenge these methods have to face is the treatment of the dynamic rearrangements of the individual subunits upon assembly. While this flexibility can be sampled at different levels, integrating native dynamic determinants with available experimental inputs can provide an effective way to reveal the molecular recognition mechanisms at the basis of supramolecular assembly.

Evidence of colorectal cancer-associated mutation in MCAK: a computational report

Computational prediction of disease-associated non-synonymous polymorphism (nsSNP) has provided a significant platform to filter out the pathological mutations from large pool of SNP datasets at a very low cost input. Several methodologies and complementary protocols have been previously implemented and has provided significant prediction results. Although the previously implicated prediction methods were capable of investigating the most likely deleterious nsSNPs, but due to the lack of genotype-phenotype association analysis, the prediction results lacked in accuracy level. In this work we implemented the computational compilation of protein conformational changes as well as the probable disease-associated phenotypic outcomes. Our result suggested E403K mutation in mitotic centromere-associated kinesin protein as highly damaging and showed strong concordance to the previously observed colorectal cancer mutations aggregation tendency and energy value changes. Moreover, the molecular dynamics simulation results showed major loss in conformation and stability of mutant N-terminal kinesin-like domain structure. The result obtained in this study will provide future prospect of computational approaches in determining the SNPs that may affect the native conformation of protein structure and lead to cancer-associated disorders.

Scoring docking conformations using predicted protein interfaces

Background

Since proteins function by interacting with other molecules, analysis of protein-protein interactions is essential for comprehending biological processes. Whereas understanding of atomic interactions within a complex is especially useful for drug design, limitations of experimental techniques have restricted their practical use. Despite progress in docking predictions, there is still room for improvement. In this study, we contribute to this topic by proposing T-PioDock, a framework for detection of a native-like docked complex 3D structure. T-PioDock supports the identification of near-native conformations from 3D models that docking software produced by scoring those models using binding interfaces predicted by the interface predictor, Template based Protein Interface Prediction (T-PIP).

Results

First, exhaustive evaluation of interface predictors demonstrates that T-PIP, whose predictions are customised to target complexity, is a state-of-the-art method. Second, comparative study between T-PioDock and other state-of-the-art scoring methods establishes T-PioDock as the best performing approach. Moreover, there is good correlation between T-PioDock performance and quality of docking models, which suggests that progress in docking will lead to even better results at recognising near-native conformations.

Conclusion

Accurate identification of near-native conformations remains a challenging task. Although availability of 3D complexes will benefit from template-based methods such as T-PioDock, we have identified specific limitations which need to be addressed. First, docking software are still not able to produce native like models for every target. Second, current interface predictors do not explicitly consider pairwise residue interactions between proteins and their interacting partners which leaves ambiguity when assessing quality of complex conformations.

Prediction and redesign of protein-protein interactions

Understanding the molecular basis of protein function remains a central goal of biology, with the hope to elucidate the role of human genes in health and in disease, and to rationally design therapies through targeted molecular perturbations. We review here some of the computational techniques and resources available for characterizing a critical aspect of protein function - those mediated by protein-protein interactions (PPI). We describe several applications and recent successes of the Evolutionary Trace (ET) in identifying molecular events and shapes that underlie protein function and specificity in both eukaryotes and prokaryotes. ET is a part of analytical approaches based on the successes and failures of evolution that enable the rational control of PPI.

First comprehensive in silico analysis of the functional and structural consequences of SNPs in human GalNAc-T1 gene

GalNAc-T1, a key candidate of GalNac-transferases genes family that is involved in mucin-type O-linked glycosylation pathway, is expressed in most biological tissues and cell types. Despite the reported association of GalNAc-T1 gene mutations with human disease susceptibility, the comprehensive computational analysis of coding, noncoding and regulatory SNPs, and their functional impacts on protein level, still remains unknown. Therefore, sequence- and structure-based computational tools were employed to screen the entire listed coding SNPs of GalNAc-T1 gene in order to identify and characterize them. Our concordant in silico analysis by SIFT, PolyPhen-2, PANTHER-cSNP, and SNPeffect tools, identified the potential nsSNPs (S143P, G258V, and Y414D variants) from 18 nsSNPs of GalNAc-T1. Additionally, 2 regulatory SNPs (rs72964406 and #x26; rs34304568) were also identified in GalNAc-T1 by using FastSNP tool. Using multiple computational approaches, we have systematically classified the functional mutations in regulatory and coding regions that can modify expression and function of GalNAc-T1 enzyme. These genetic variants can further assist in better understanding the wide range of disease susceptibility associated with the mucin-based cell signalling and pathogenic binding, and may help to develop novel therapeutic elements for associated diseases.

BP-Dock: a flexible docking scheme for exploring protein-ligand interactions based on unbound structures

Molecular docking serves as an important tool in modeling protein-ligand interactions. However, it is still challenging to incorporate overall receptor flexibility, especially backbone flexibility, in docking due to the large conformational space that needs to be sampled. To overcome this problem, we developed a novel flexible docking approach, BP-Dock (Backbone Perturbation-Dock) that can integrate both backbone and side chain conformational changes induced by ligand binding through a multi-scale approach. In the BP-Dock method, we mimic the nature of binding-induced events as a first-order approximation by perturbing the residues along the protein chain with a small Brownian kick one at a time. The response fluctuation profile of the chain upon these perturbations is computed using the perturbation response scanning method. These response fluctuation profiles are then used to generate binding-induced multiple receptor conformations for ensemble docking. To evaluate the performance of BP-Dock, we applied our approach on a large and diverse data set using unbound structures as receptors. We also compared the BP-Dock results with bound and unbound docking, where overall receptor flexibility was not taken into account. Our results highlight the importance of modeling backbone flexibility in docking for recapitulating the experimental binding affinities, especially when an unbound structure is used. With BP-Dock, we can generate a wide range of binding site conformations realized in nature even in the absence of a ligand that can help us to improve the accuracy of unbound docking. We expect that our fast and efficient flexible docking approach may further aid in our understanding of protein-ligand interactions as well as virtual screening of novel targets for rational drug design.

Highly precise protein-protein interaction prediction based on consensus between template-based and de novo docking methods

Background

Elucidation of protein-protein interaction (PPI) networks is important for understanding disease mechanisms and for drug discovery. Tertiary-structure-based in silico PPI prediction methods have been developed with two typical approaches: a method based on template matching with known protein structures and a method based on de novo protein docking. However, the template-based method has a narrow applicable range because of its use of template information, and the de novo docking based method does not have good prediction performance. In addition, both of these in silico prediction methods have insufficient precision, and require validation of the predicted PPIs by biological experiments, leading to considerable expenditure; therefore, PPI prediction methods with greater precision are needed.

Results

We have proposed a new structure-based PPI prediction method by combining template-based prediction and de novo docking prediction. When we applied the method to the human apoptosis signaling pathway, we obtained a precision value of 0.333, which is higher than that achieved using conventional methods (0.231 for PRISM, a template-based method, and 0.145 for MEGADOCK, a non-template-based method), while maintaining an F-measure value (0.285) comparable to that obtained using conventional methods (0.296 for PRISM, and 0.220 for MEGADOCK).

Conclusions

Our consensus method successfully predicted a PPI network with greater precision than conventional template/non-template methods, which may thus reduce the cost of validation by laboratory experiments for confirming novel PPIs from predicted PPIs. Therefore, our method may serve as an aid for promoting interactome analysis.

Predicting protein-DNA interactions by full search computational docking

Protein-DNA interactions are essential for many biological processes. X-ray crystallography can provide high-resolution structures, but protein-DNA complexes are difficult to crystallize and typically contain only small DNA fragments. Thus, there is a need for computational methods that can provide useful predictions to give insights into mechanisms and guide the design of new experiments. We used the program DOT, which performs an exhaustive, rigid-body search between two macromolecules, to investigate four diverse protein-DNA interactions. Here, we compare our computational results with subsequent experimental data on related systems. In all cases, the experimental data strongly supported our structural hypotheses from the docking calculations: a mechanism for weak, nonsequence-specific DNA binding by a transcription factor, a large DNA-binding footprint on the surface of the DNA-repair enzyme uracil-DNA glycosylase (UNG), viral and host DNA-binding sites on the catalytic domain of HIV integrase, and a three-DNA-contact model of the linker histone bound to the nucleosome. In the case of UNG, the experimental design was based on the DNA-binding surface found by docking, rather than the much smaller surface observed in the crystallographic structure. These comparisons demonstrate that the DOT electrostatic energy gives a good representation of the distinctive electrostatic properties of DNA and DNA-binding proteins. The large, favourably ranked clusters resulting from the dockings identify active sites, map out large DNA-binding sites, and reveal multiple DNA contacts with a protein. Thus, computational docking can not only help to identify protein-DNA interactions in the absence of a crystal structure, but also expand structural understanding beyond known crystallographic structures.

DOT2: Macromolecular docking with improved biophysical models

Computational docking is a useful tool for predicting macromolecular complexes, which are often difficult to determine experimentally. Here, we present the DOT2 software suite, an updated version of the DOT intermolecular docking program. DOT2 provides straightforward, automated construction of improved biophysical models based on molecular coordinates, offering checkpoints that guide the user to include critical features. DOT has been updated to run more quickly, allow flexibility in grid size and spacing, and generate an infinitive complete list of favorable candidate configurations. Output can be filtered by experimental data and rescored by the sum of electrostatic and atomic desolvation energies. We show that this rescoring method improves the ranking of correct complexes for a wide range of macromolecular interactions and demonstrate that biologically relevant models are essential for biologically relevant results. The flexibility and versatility of DOT2 accommodate realistic models of complex biological systems, improving the likelihood of a successful docking outcome.

Anti-HBV treatment induces novel reverse transcriptase mutations with reflective effect on HBV S antigen

INTRODUCTION

The identification of novel reverse-transcriptase (RT) drug-resistance mutations is critical in predicting the probability of success to anti-HBV treatment. Furthermore, due to HBV-RT/HBsAg gene-overlap, they can have an impact on HBsAg-detection and quantification.

METHODS

356 full-length HBV-RT sequences from 197 drug-naive patients and 159 patients experiencing virological-breakthrough to nucleoside/nucleotide-analogs (NUCs) were analyzed. Mutants and wild-type HBs-antigens were expressed in HuH7-hepatocytes and quantified in cell-supernatants and cell-lysates by Architect HBsAg-assay.

RESULTS

Ten novel RT-mutations (rtN53T-rtS78T-rtS85F-rtS135T-rtA181I-rtA200V-rtK212Q-rtL229V/F-rtM309K) correlated with specific NUC-treatments and classical drug-resistance mutations on divergent evolutionary pathways. Some of them reduced RT-binding affinity for anti-HBV drugs and altered S-antigen structure. Indeed, rtS78T (prevalence: 1.1% in drug-naïve and 12.2% in adefovir-failing patients) decreased the RT-affinity for adefovir more than the classical adefovir-resistance mutations rtA181 T/V (WT:-9.63 kcal/mol, rtA181T:-9.30 kcal/mol, rtA181V:-7.96 kcal/mol, rtS78T:-7.37 kcal/mol). Moreover, rtS78T introduced a stop-codon at HBsAg-position 69, and completely abrogated HBsAg-quantification in both supernatants and cell-lysates, indicating an impaired HBsAg-secretion/production. Furthermore, the HBsAg-mutation sP217L, silent in RT, significantly correlated with M204V/I-related virological-breakthrough and increased HBsAg-quantification in cell-lysate.

CONCLUSIONS

Mutations beyond those classically known can affect drug-binding affinity of mutated HBV-RT, and may have potential effects on HBsAg. Their cumulative effect on resistance and HBV-pathogenicity indicates the importance of preventing therapeutic failures.

Cancer associated E17K mutation causes rapid conformational drift in AKT1 pleckstrin homology (PH) domain

BACKGROUND

AKT1 (v-akt murine thymoma viral oncogene homologue 1) kinase is one of the most frequently activated proliferated and survival pathway of cancer. Recently it has been shown that E17K mutation in the Pleckstrin Homology (PH) domain of AKT1 protein leads to cancer by amplifying the phosphorylation and membrane localization of protein. The mutant has shown resistance to AKT1/2 inhibitor VIII drug molecule. In this study we have demonstrated the detailed structural and molecular consequences associated with the activity regulation of mutant protein.

METHODS

The docking score exhibited significant loss in the interaction affinity to AKT1/2 inhibitor VIII drug molecule. Furthermore, the molecular dynamics simulation studies presented an evidence of rapid conformational drift observed in mutant structure.

RESULTS

There was no stability loss in mutant as compared to native structure and the major cation-π interactions were also shown to be retained. Moreover, the active residues involved in membrane localization of protein exhibited significant rise in NHbonds formation in mutant. The rise in NHbond formation in active residues accounts for the 4-fold increase in the membrane localization potential of protein.

CONCLUSION

The overall result suggested that, although the mutation did not induce any stability loss in structure, the associated pathological consequences might have occurred due to the rapid conformational drifts observed in the mutant AKT1 PH domain.

GENERAL SIGNIFICANCE

The methodology implemented and the results obtained in this work will facilitate in determining the core molecular mechanisms of cancer-associated mutations and in designing their potential drug inhibitors.

Relationship between a point mutation S97C in CK1δ protein and its affect on ATP-binding affinity

CK1δ (Casein kinase I isoform delta) is a member of CK1 kinase family protein that mediates neurite outgrowth and the function as brain-specific microtubule-associated protein. ATP binding kinase domain of CK1δ is essential for regulating several key cell cycle signal transduction pathways. Mutation in CK1δ protein is reported to cause cancers and affects normal brain development. S97C mutation in kinase domain of CK1δ protein has been involved to induce breast cancer and ductal carcinoma. We performed molecular docking studies to examine the effect of this mutation on its ATP-binding affinity. Further, we conducted molecular dynamics simulations to understand the structural consequences of S97C mutation over the kinase domain of CK1δ protein. Docking results indicated the loss of ATP-binding affinity of mutant structure, which were further rationalized by molecular dynamics simulations, where a notable loss in 3-D conformation of CK1δ kinase domain was observed in mutant as compared to native. Our results explained the underlying molecular mechanism behind the observed cancer associated phenotype caused by S97C mutation in CK1δ protein.

Interactions of archaeal chromatin proteins Alba1 and Alba2 with nucleic acids

BACKGROUND

Architectural proteins have important roles in compacting and organising chromosomal DNA. There are two potential histone counterpart peptide sequences (Alba1 and Alba2) in the Aeropyrum pernix genome (APE1832.1 and APE1823).

METHODOLOGY/PRINCIPAL FINDINGS

THESE TWO PEPTIDES WERE EXPRESSED AND THEIR INTERACTIONS WITH VARIOUS DNAS WERE STUDIED USING A COMBINATION OF VARIOUS EXPERIMENTAL TECHNIQUES: surface plasmon resonance, UV spectrophotometry, circular dichroism-spectropolarimetry, gel-shift assays, and isothermal titration calorimetry.

CONCLUSIONS/SIGNIFICANCE

Our data indicate that there are significant differences in the properties of the Alba1 and Alba2 proteins. Both of these Alba proteins can thermally stabilise DNA polynucleotides, as seen from UV melting curves. Alba2 and equimolar mixtures of Alba1/Alba2 have greater effects on the thermal stability of poly(dA-dT).poly(dA-dT). Surface plasmon resonance sensorgrams for binding of Alba1, Alba2, and equimolar mixtures of Alba1/Alba2 to DNA oligonucleotides show different binding patterns. Circular dichroism indicates that Alba2 has a less-ordered secondary structure than Alba1. The secondary structures of the Alba proteins are not significantly influenced by DNA binding, even at high temperatures. Based on these data, we conclude that Alba1, Alba2, and equimolar mixtures of Alba1/Alba2 show different properties in their binding to various DNAs.

Structure-based in silico identification of ubiquitin-binding domains provides insights into the ALIX-V:ubiquitin complex and retrovirus budding

The ubiquitylation signal promotes trafficking of endogenous and retroviral transmembrane proteins. The signal is decoded by a large set of ubiquitin (Ub) receptors that tether Ub-binding domains (UBDs) to the trafficking machinery. We developed a structure-based procedure to scan the protein data bank for hidden UBDs. The screen retrieved many of the known UBDs. Intriguingly, new potential UBDs were identified, including the ALIX-V domain. Pull-down, cross-linking and E3-independent ubiquitylation assays biochemically corroborated the in silico findings. Guided by the output model, we designed mutations at the postulated ALIX-V:Ub interface. Biophysical affinity measurements using microscale-thermophoresis of wild-type and mutant proteins revealed some of the interacting residues of the complex. ALIX-V binds mono-Ub with a K(d) of 119 μM. We show that ALIX-V oligomerizes with a Hill coefficient of 5.4 and IC(50) of 27.6 μM and that mono-Ub induces ALIX-V oligomerization. Moreover, we show that ALIX-V preferentially binds K63 di-Ub compared with mono-Ub and K48 di-Ub. Finally, an in vivo functionality assay demonstrates the significance of ALIX-V:Ub interaction in equine infectious anaemia virus budding. These results not only validate the new procedure, but also demonstrate that ALIX-V directly interacts with Ub in vivo and that this interaction can influence retroviral budding.

SP-dock: protein-protein docking using shape and physicochemical complementarity

In this paper, a framework for protein-protein docking is proposed, which exploits both shape and physicochemical complementarity to generate improved docking predictions. Shape complementarity is achieved by matching local surface patches. However, unlike existing approaches, which are based on single-patch or two-patch matching, we developed a new algorithm that compares simultaneously, groups of neighboring patches from the receptor with groups of neighboring patches from the ligand. Taking into account the fact that shape complementarity in protein surfaces is mostly approximate rather than exact, the proposed group-based matching algorithm fits perfectly to the nature of protein surfaces. This is demonstrated by the high performance that our method achieves especially in the case where the unbound structures of the proteins are considered. Additionally, several physicochemical factors, such as desolvation energy, electrostatic complementarity (EC), hydrophobicity (HP), Coulomb potential (CP), and Lennard-Jones potential are integrated using an optimized scoring function, improving geometric ranking in more than 60 percent of the complexes of Docking Benchmark 2.4.

A method for integrative structure determination of protein-protein complexes

MOTIVATION

Structural characterization of protein interactions is necessary for understanding and modulating biological processes. On one hand, X-ray crystallography or NMR spectroscopy provide atomic resolution structures but the data collection process is typically long and the success rate is low. On the other hand, computational methods for modeling assembly structures from individual components frequently suffer from high false-positive rate, rarely resulting in a unique solution.

RESULTS

Here, we present a combined approach that computationally integrates data from a variety of fast and accessible experimental techniques for rapid and accurate structure determination of protein-protein complexes. The integrative method uses atomistic models of two interacting proteins and one or more datasets from five accessible experimental techniques: a small-angle X-ray scattering (SAXS) profile, 2D class average images from negative-stain electron microscopy micrographs (EM), a 3D density map from single-particle negative-stain EM, residue type content of the protein-protein interface from NMR spectroscopy and chemical cross-linking detected by mass spectrometry. The method is tested on a docking benchmark consisting of 176 known complex structures and simulated experimental data. The near-native model is the top scoring one for up to 61% of benchmark cases depending on the included experimental datasets; in comparison to 10% for standard computational docking. We also collected SAXS, 2D class average images and 3D density map from negative-stain EM to model the PCSK9 antigen-J16 Fab antibody complex, followed by validation of the model by a subsequently available X-ray crystallographic structure.

Computational screening and molecular dynamics simulation of disease associated nsSNPs in CENP-E

Aneuploidy and chromosomal instability (CIN) are hallmarks of most solid tumors. Mutations in centroemere proteins have been observed in promoting aneuploidy and tumorigenesis. Recent studies reported that Centromere-associated protein-E (CENP-E) is involved in inducing cancers. In this study we investigated the pathogenic effect of 132 nsSNPs reported in CENP-E using computational platform. Y63H point mutation found to be associated with cancer using SIFT, Polyphen, PhD-SNP, MutPred, CanPredict and Dr. Cancer tools. Further we investigated the binding affinity of ATP molecule to the CENP-E motor domain. Complementarity scores obtained from docking studies showed significant loss in ATP binding affinity of mutant structure. Molecular dynamics simulation was carried to examine the structural consequences of Y63H mutation. Root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (R(g)), solvent accessibility surface area (SASA), energy value, hydrogen bond (NH Bond), eigenvector projection, trace of covariance matrix and atom density analysis results showed notable loss in stability for mutant structure. Y63H mutation was also shown to disrupt the native conformation of ATP binding region in CENP-E motor domain. Docking studies for remaining 18 mutations at 63rd residue position as well as other two computationally predicted disease associated mutations S22L and P69S were also carried to investigate their affect on ATP binding affinity of CENP-E motor domain. Our study provided a promising computational methodology to study the tumorigenic consequences of nsSNPs that have not been characterized and clear clue to the wet lab scientist.

Homology modeling and docking studies of FabH (β-ketoacyl-ACP synthase III) enzyme involved in type II fatty acid biosynthesis of Chlorella variabilis: a potential algal feedstock for biofuel production

The concept of using microalgae as an alternative renewable source of biofuel has gained much importance in recent years. However, its commercial feasibility is still an area of concern for researchers. Unraveling the fatty acid metabolic pathway and understanding structural features of various key enzymes regulating the process will provide valuable insights to target microalgae for augmented oil content. FabH (β-ketoacyl-acyl carrier protein synthase; KAS III) is a condensing enzyme catalyzing the initial elongation step of type II fatty acid biosynthetic process and acyl carrier protein (ACP) facilitates the shuttling of the fatty acyl intermediates to the active site of the respective enzymes in the pathway. In the present study, a reliable three-dimensional structure of FabH from Chlorella variabilis, an oleaginous green microalga was modeled and subsequently the key residues involved in substrate binding were determined by employing protein-protein docking and molecular dynamics (MD) simulation protocols. The FabH-ACP complex having the lowest docking energy score showed the binding of ACP to the electropositive FabH surface with strong hydrogen bond interactions. The MD simulation results indicated that the substrate-complexed FabH adopted a more stable conformation than the free enzyme. Further, the FabH structure retained its stability throughout the simulation although noticeable displacements were observed in the loop regions. Molecular simulation studies suggested the importance of crucial hydrogen bonding of the conserved Arg(91) of FabH with Glu(53) and Asp(56) of ACP for exhibiting high affinity between the enzyme and substrate. The molecular modeling results are consistent with available experimental results on the flexibility of FabH and the present study provides first in silico insights into the structural and dynamical aspect of catalytic mechanism of FabH, which could be used for further site-specific mutagenic experiments to develop engineered high oil-yielding microalgal strains for biofuel production.

Expanding the conformational selection paradigm in protein-ligand docking

Conformational selection emerges as a theme in macromolecular interactions. Data validate it as a prevailing mechanism in protein-protein, protein-DNA, protein-RNA, and protein-small molecule drug recognition. This raises the question of whether this fundamental biomolecular binding mechanism can be used to improve drug docking and discovery. Actually, in practice this has already been taking place for some years in increasing numbers. Essentially, it argues for using not a single conformer, but an ensemble. The paradigm of conformational selection holds that because the ensemble is heterogeneous, within it there will be states whose conformation matches that of the ligand. Even if the population of this state is low, since it is favorable for binding the ligand, it will bind to it with a subsequent population shift toward this conformer. Here we suggest expanding it by first modeling all protein interactions in the cell by using Prism, an efficient motif-based protein-protein interaction modeling strategy, followed by ensemble generation. Such a strategy could be particularly useful for signaling proteins, which are major targets in drug discovery and bind multiple partners through a shared binding site, each with some-minor or major-conformational change.

Normal mode-based approaches in receptor ensemble docking

Explicitly accounting for target flexibility in docking still constitutes a difficult challenge due to the high dimensionality of the conformational space to be sampled. This especially applies to the high-throughput scenario, where the screening of hundreds of thousands compounds takes place. The use of multiple receptor conformations (MRCs) to perform ensemble docking in a sequential fashion is a simple but powerful approach that allows to incorporate binding site structural diversity in the docking process. Whenever enough experimental structures to build a diverse ensemble are not available, normal mode analysis provides an appealing and efficient approach to in silico generate MRCs by distortion along few low-frequency modes that represent collective mid- and large-scale displacements. In this way, the dimension of the conformational space to be sampled is heavily reduced. This methodology is especially suited to incorporate target flexibility at the backbone level. In this chapter, the main components of normal mode-based approaches in the context of ensemble docking are presented and explained, including the theoretical and practical considerations needed for the successful development and implementation of this methodology.

SymmRef: a flexible refinement method for symmetric multimers

Symmetric protein complexes are abundant in the living cell. Predicting their atomic structure can shed light on the mechanism of many important biological processes. Symmetric docking methods aim to predict the structure of these complexes given the unbound structure of a single monomer, or its model. Symmetry constraints reduce the search-space of these methods and make the prediction easier compared to asymmetric protein-protein docking. However, the challenge of modeling the conformational changes that the monomer might undergo is a major obstacle. In this article, we present SymmRef, a novel method for refinement and reranking of symmetric docking solutions. The method models backbone and side-chain movements and optimizes the rigid-body orientations of the monomers. The backbone movements are modeled by normal modes minimization and the conformations of the side-chains are modeled by selecting optimal rotamers. Since solved structures of symmetric multimers show asymmetric side-chain conformations, we do not use symmetry constraints in the side-chain optimization procedure. The refined models are re-ranked according to an energy score. We tested the method on a benchmark of unbound docking challenges. The results show that the method significantly improves the accuracy and the ranking of symmetric rigid docking solutions. SymmRef is available for download at http:// bioinfo3d.cs.tau.ac.il/SymmRef/download.html.

Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM

Prediction of protein-protein interactions at the structural level on the proteome scale is important because it allows prediction of protein function, helps drug discovery and takes steps toward genome-wide structural systems biology. We provide a protocol (termed PRISM, protein interactions by structural matching) for large-scale prediction of protein-protein interactions and assembly of protein complex structures. The method consists of two components: rigid-body structural comparisons of target proteins to known template protein-protein interfaces and flexible refinement using a docking energy function. The PRISM rationale follows our observation that globally different protein structures can interact via similar architectural motifs. PRISM predicts binding residues by using structural similarity and evolutionary conservation of putative binding residue 'hot spots'. Ultimately, PRISM could help to construct cellular pathways and functional, proteome-scale annotation. PRISM is implemented in Python and runs in a UNIX environment. The program accepts Protein Data Bank-formatted protein structures and is available at http://prism.ccbb.ku.edu.tr/prism_protocol/.

On the role of electrostatics in protein-protein interactions

The role of electrostatics in protein-protein interactions and binding is reviewed in this paper. A brief outline of the computational modeling, in the framework of continuum electrostatics, is presented and the basic electrostatic effects occurring upon the formation of the complex are discussed. The effect of the salt concentration and pH of the water phase on protein-protein binding free energy is demonstrated which indicates that the increase of the salt concentration tends to weaken the binding, an observation that is attributed to the optimization of the charge-charge interactions across the interface. It is pointed out that the pH-optimum (pH of optimal binding affinity) varies among the protein-protein complexes, and perhaps is a result of their adaptation to particular subcellular compartments. The similarities and differences between hetero- and homo-complexes are outlined and discussed with respect to the binding mode and charge complementarity.

Prediction of protein-protein interactions: unifying evolution and structure at protein interfaces

The vast majority of the chores in the living cell involve protein-protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein-protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations.

Structural and functional similarities between osmotin from Nicotiana tabacum seeds and human adiponectin

Osmotin, a plant protein, specifically binds a seven transmembrane domain receptor-like protein to exert its biological activity via a RAS2/cAMP signaling pathway. The receptor protein is encoded in the gene ORE20/PHO36 and the mammalian homolog of PHO36 is a receptor for the human hormone adiponectin (ADIPOR1). Moreover it is known that the osmotin domain I can be overlapped to the β-barrel domain of adiponectin. Therefore, these observations and some already existing structural and biological data open a window on a possible use of the osmotin or of its derivative as adiponectin agonist. We have modelled the three-dimensional structure of the adiponectin trimer (ADIPOQ), and two ADIPOR1 and PHO36 receptors. Moreover, we have also modelled the following complexes: ADIPOQ/ADIPOR1, osmotin/PHO36 and osmotin/ADIPOR1. We have then shown the structural determinants of these interactions and their physico-chemical features and analyzed the related interaction residues involved in the formation of the complexes. The stability of the modelled structures and their complexes was always evaluated and controlled by molecular dynamics. On the basis of these results a 9 residues osmotin peptide was selected and its interaction with ADIPOR1 and PHO36 was modelled and analysed in term of energetic stability by molecular dynamics. To confirm in vivo the molecular modelling data, osmotin has been purified from nicotiana tabacum seeds and its nine residues peptide synthesized. We have used cultured human synovial fibroblasts that respond to adiponectin by increasing the expression of IL-6, TNF-alpha and IL-1beta via ADIPOR1. The biological effect on fibroblasts of osmotin and its peptide derivative has been found similar to that of adiponectin confirming the results found in silico.