In silico protein interaction analysis using the global proteome machine database

Experiments to probe for protein-protein interactions are the focus of functional proteomic studies, thus proteomic data repositories are increasingly likely to contain a large cross-section of such information. Here, we use the Global Proteome Machine database (GPMDB), which is the largest curated and publicly available proteomic data repository derived from tandem mass spectrometry, to develop an in silico protein interaction analysis tool. Using a human histone protein for method development, we positively identified an interaction partner from each histone protein family that forms the histone octameric complex. Moreover, this method, applied to the α subunits of the human proteasome, identified all of the subunits in the 20S core particle. Furthermore, we applied this approach to human integrin αIIb and integrin β3, a major receptor involved in the activation of platelets. We identified 28 proteins, including a protein network for integrin and platelet activation. In addition, proteins interacting with integrin β1 obtained using this method were validated by comparing them to those identified in a formaldehyde-supported coimmunoprecipitation experiment, protein-protein interaction databases and the literature. Our results demonstrate that in silico protein interaction analysis is a novel tool for identifying known/candidate protein-protein interactions and proteins with shared functions in a protein network.     

Molecular designing and in silico evaluation of darunavir derivatives as anticancer agents

Darunavir is a synthetic nonpeptidic protease inhibitor which has been tested for anticancer properties. To deduce and enhance the anticancer activity of the Darunavir, we have modified its reactive moiety in an effective way. We designed 9 analogues in ChemBioOffice 2010 and minimized using the LigPrep tool of Schrödinger 2011. These analogues can obstruct the activity of other signalling pathways which are implicated in many tumors. Results of the QikProp showed that all the analogues lied in the specified range of all the pharmacokinetic (ADMET) properties required to become the successful drug. Docking study was performed to test its anticancer activity against the biomarkers of the five main types of cancers i.e. bone, brain, breast, colon and skin cancer. Grid was generated for each oncoproteins by specifying the active site amino acids. The binding model of best scoring analogue with each protein was assessed from their G-scores and disclosed by docking analysis using the XP visualizer tool. An analysis of the receptor-ligand interaction studies revealed that these nine Darunavir analogues are active against all cancer biomarkers and have the features to prove themselves as anticancer drugs, further to be synthesized and tested against the cell lines.     

Small molecule inhibitor of the RPA70 N-terminal protein interaction domain discovered using in silico and in vitro methods

The pharmacological suppression of the DNA damage response and DNA repair can increase the therapeutic indices of conventional chemotherapeutics. Replication Protein A (RPA), the major single-stranded DNA binding protein in eukaryotes, is required for DNA replication, DNA repair, DNA recombination, and DNA damage response signaling. Through the use of high-throughput screening of 1500 compounds, we have identified a small molecule inhibitor, 15-carboxy-13-isopropylatis-13-ene-17,18-dioic acid (NSC15520), that inhibited both the binding of Rad9-GST and p53-GST fusion proteins to the RPA N-terminal DNA binding domain (DBD), interactions that are essential for robust DNA damage signaling. NSC15520 competitively inhibited the binding of p53-GST peptide with an IC(50) of 10 μM. NSC15520 also inhibited helix destabilization of a duplex DNA (dsDNA) oligonucleotide, an activity dependent on the N-terminal domain of RPA70. NSC15520 did not inhibit RPA from binding single-stranded oligonucleotides, suggesting that the action of this inhibitor is specific for the N-terminal DBD of RPA, and does not bind to DBDs essential for single-strand DNA binding. Computer modeling implicates direct competition between NSC15520 and Rad9 for the same binding surface on RPA. Inhibitors of protein-protein interactions within the N-terminus of RPA are predicted to act synergistically with DNA damaging agents and inhibitors of DNA repair. Novel compounds such as NSC15520 have the potential to serve as chemosensitizing agents.     

Predicting MoRFs in protein sequences using HMM profiles

Background

Intrinsically Disordered Proteins (IDPs) lack an ordered three-dimensional structure and are enriched in various biological processes. The Molecular Recognition Features (MoRFs) are functional regions within IDPs that undergo a disorder-to-order transition on binding to a partner protein. Identifying MoRFs in IDPs using computational methods is a challenging task.

Methods

In this study, we introduce hidden Markov model (HMM) profiles to accurately identify the location of MoRFs in disordered protein sequences. Using windowing technique, HMM profiles are utilised to extract features from protein sequences and support vector machines (SVM) are used to calculate a propensity score for each residue. Two different SVM kernels with high noise tolerance are evaluated with a varying window size and the scores of the SVM models are combined to generate the final propensity score to predict MoRF residues. The SVM models are designed to extract maximal information between MoRF residues, its neighboring regions (Flanks) and the remainder of the sequence (Others).

Results

To evaluate the proposed method, its performance was compared to that of other MoRF predictors; MoRFpred and ANCHOR. The results show that the proposed method outperforms these two predictors.

Conclusions

Using HMM profile as a source of feature extraction, the proposed method indicates improvement in predicting MoRFs in disordered protein sequences.

     

Structure-based evaluation of in silico predictions of protein-protein interactions using Comparative Docking

MOTIVATION: Due to the limitations in experimental methods for determining binary interactions and structure determination of protein complexes, the need exists for computational models to fill the increasing gap between genome sequence information and protein annotation. Here we describe a novel method that uses structural models to reduce a large number of in silico predictions to a high confidence subset that is amenable to experimental validation. RESULTS: A two-stage evaluation procedure was developed, first, a sequence-based method assessed the conservation of protein interface patches used in the original in silico prediction method, both in terms of position within the primary sequence, and in terms of sequence conservation. When applying the most stringent conditions it was found that 20.5% of the data set being assessed passed this test. Secondly, a high-throughput structure-based docking evaluation procedure assessed the soundness of three dimensional models produced for the putative interactions. Of the data set being assessed, 8264 interactions or over 70% could be modelled in this way, and 27% of these can be considered 'valid' by the applied criteria. In all, 6.9% of the interactions passed both the tests and can be considered to be a high confidence set of predicted interactions, several of which are described. AVAILABILITY: http://bioinformatics.leeds.ac.uk/~bmb4sjc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Molecular inhibition of telomerase recruitment using designer peptides: an in silico approach

Telomere holds special mechanism for solving end repair problems and maintaining genomic stability. Protection of telomeres 1 (POT1) which belongs to shelterin family is identified as a key protein that recruits telomerase by interacting with telomere repeat binding factors (TRB1-3). Since, deciphering the mechanism through which POT assembles telomerase is of great interest, computational approaches have been undertaken to understand the mechanism in a well- developed model system – Arabidopsis thaliana. As a first step, an untraditional approach was mediated to locate the active site residues on modeled AtPOT1b protein by interaction studies using molecular docking. To keep in trend with the recent developments, peptide construction and validation was promoted as the next step via molecular dynamics simulation studies. Finally, the validated peptides based on propensity score was evaluated for its efficacy as a potent inhibitor for POT and TRB1-3 interactions. The best peptide, namely, (1-2-d) out of 30 designed peptides, was proved to be vital inhibitor by weakening the interacting complexes.     

The use of in vitro peptide binding profiles and in silico ligand-receptor interaction profiles to describe ligand-induced conformations of the retinoid X receptor alpha ligand-binding domain

It is hypothesized that different ligand-induced conformational changes can explain the different interactions of nuclear receptors with regulatory proteins, resulting in specific biological activities. Understanding the mechanism of how ligands regulate cofactor interaction facilitates drug design. To investigate these ligand-induced conformational changes at the surface of proteins, we performed a time-resolved fluorescence resonance energy transfer assay with 52 different cofactor peptides measuring the ligand-induced cofactor recruitment to the retinoid X receptor-alpha (RXRalpha) in the presence of 11 compounds. Simultaneously we analyzed the binding modes of these compounds by molecular docking. An automated method converted the complex three-dimensional data of ligand-protein interactions into two-dimensional fingerprints, the so-called ligand-receptor interaction profiles. For a subset of compounds the conformational changes at the surface, as measured by peptide recruitment, correlate well with the calculated binding modes, suggesting that clustering of ligand-receptor interaction profiles is a very useful tool to discriminate compounds that may induce different conformations and possibly different effects in a cellular environment. In addition, we successfully combined ligand-receptor interaction profiles and peptide recruitment data to reveal structural elements that are possibly involved in the ligand-induced conformations. Interestingly, we could predict a possible binding mode of LG100754, a homodimer antagonist that showed no effect on peptide recruitment. Finally, the extensive analysis of the peptide recruitment profiles provided novel insight in the potential cellular effect of the compound; for the first time, we showed that in addition to the induction of coactivator peptide binding, all well-known RXRalpha agonists also induce binding of corepressor peptides to RXRalpha.     

Molecular design of artificial ribonucleases using electrostatic interaction

A number of small organic ribonucleases have been synthesized with rigid polycationic structures containing an aromatic framework with two residues of bis-quaternary salts of 1,4-diazabicyclo[2.2.2]octane (DABCO) bearing various substituents. The compounds carrying positively charged groups connected via rigid linker are expected to bend the sugar-phosphate backbone and can stimulate the intramolecular phosphoester transfer reaction.     

Detecting protein complexes from active protein interaction networks constructed with dynamic gene expression profiles

Background

Protein interaction networks (PINs) are known to be useful to detect protein complexes. However, most available PINs are static, which cannot reflect the dynamic changes in real networks. At present, some researchers have tried to construct dynamic networks by incorporating time-course (dynamic) gene expression data with PINs. However, the inevitable background noise exists in the gene expression array, which could degrade the quality of dynamic networkds. Therefore, it is needed to filter out contaminated gene expression data before further data integration and analysis.

Results

Firstly, we adopt a dynamic model-based method to filter noisy data from dynamic expression profiles. Then a new method is proposed for identifying active proteins from dynamic gene expression profiles. An active protein at a time point is defined as the protein the expression level of whose corresponding gene at that time point is higher than a threshold determined by a standard variance involved threshold function. Furthermore, a noise-filtered active protein interaction network (NF-APIN) is constructed. To demonstrate the efficiency of our method, we detect protein complexes from the NF-APIN, compared with those from other dynamic PINs.

Conclusion

A dynamic model based method can effectively filter out noises in dynamic gene expression data. Our method to compute a threshold for determining the active time points of noise-filtered genes can make the dynamic construction more accuracy and provide a high quality framework for network analysis, such as protein complex prediction.

     

In silico and in vivo evaluation of flavonoid extracts on CYP2D6-mediated herb-drug interaction

Flavonoid extracts are widely used for preventing and treating ischemic heart disease. However, because many flavonoid extracts have been verified to inhibit CYP2D6 the main metabolic enzyme for the majority of antiarrhythmics and beta-blockers, co-administration of flavonoid extracts with these drugs may cause adverse herb-drug interaction in clinic. Here, we evaluated 43 common flavonoids on CYP2D6 inhibition in sillico and four commercial flavonoid extracts in vivo on the pharmacokinetics and pharmacodynamics of metoprolol in rats. Surprisingly, we found that the core skeletons of flavonoids instead of their substituents determine the extent of inhibiting CYP2D6 by a flavonoid extract. Isoflavones are less likely to inhibit CYP2D6, compared with other categories of flavonoids. Consistently, co-administration of soy extract that mainly contains isoflavones did not significantly increase plasma concentration of metoprolol and alter the systolic blood pressure of rats. Our results have implication in rationally selecting flavonoid extracts for therapeutic application.     

Molecular recognition in bone morphogenetic protein (BMP)/receptor interaction

Bone morphogenetic proteins (BMPs) and other members of the TGF-beta superfamily are secreted signalling proteins determining the development, maintenance and regeneration of tissues and organs. These dimeric proteins bind, via multiple epitopes, two types of signalling receptor chains and numerous extracellular modulator proteins that stringently control their activity. Crystal structures of free ligands and of complexes with type I and type II receptor extracellular domains and with the modulator protein Noggin reveal structural epitopes that determine the affinity and specificity of the interactions. Modelling of a ternary complex BMP/(BMPR-IA(EC))2 / (ActR-II(EC))2 suggests a mechanism of receptor activation that does not rely on direct contacts between extracellular domains of the receptors. Mutational and interaction analyses indicate that the large hydrophobic core of the interface of BMP-2 (wrist epitope) with the type I receptor does not provide a hydrophobic hot spot for binding. Instead, main chain amide and carbonyl groups that are completely buried in the contact region represent major binding determinants. The affinity between ligand and receptor chains is probably strongly increased by two-fold interactions of the dimeric ligand and receptor chains that exist as homodimers in the membrane (avidity effects). BMP muteins with disrupted epitopes for receptor chains or modulator proteins provide clues for drug design and development.     

Molecular basis for evolving modularity in the yeast protein interaction network

Scale-free networks are generically defined by a power-law distribution of node connectivities. Vastly different graph topologies fit this law, ranging from the assortative, with frequent similar-degree node connections, to a modular structure. Using a metric to determine the extent of modularity, we examined the yeast protein network and found it to be significantly self-dissimilar. By orthologous node categorization, we established the evolutionary trend in the network, from an “emerging” assortative network to a present-day modular topology. The evolving topology fits a generic connectivity distribution but with a progressive enrichment in intramodule hubs that avoid each other. Primeval tolerance to random node failure is shown to evolve toward resilience to hub failure, thus removing the fragility often ascribed to scale-free networks. This trend is algorithmically reproduced by adopting a connectivity accretion law that disfavors like-degree connections for large-degree nodes. The selective advantage of this trend relates to the need to prevent a failed hub from inducing failure in an adjacent hub. The molecular basis for the evolutionary trend is likely rooted in the high-entropy penalty entailed in the association of two intramodular hubs.     

根据基因表达谱筛选间接互作蛋白质进行蛋白质功能预测

利用基因表达谱数据,通过计算互作蛋白质的表达相关系数,来筛选、优化蛋白质互作网络。结果显示,利用经过筛选的互作数据,根据邻居计数法和卡方法进行功能预测的预测效果明显提高,距离待测蛋白质较远的邻居也包含着与待测蛋白质功能一致的信息。     

Molecular distinction amongst varieties of Mulberry using RAPD and DAMD profiles

Background  

Mulberry trees are the most important host for rearing mulberry silkworms in sericulture. Improved varieties of mulberry tree have been developed through traditional breeding procedures. Not much work, however, has been carried out on the molecular characterization of these varieties. Random Amplified Polymorphic DNA (RAPD) and Directed Amplification of Minisatellite DNA (DAMD) methods based on Polymerase Chain Reaction are important tools to analyze genetic diversity of mulberries. These have been used to determine variation amongst nine varieties of Morus spp. maintained at Banthra Research Station of National Botanical Research Institute, Lucknow.     

In silico screening and biological evaluation of inhibitors of Src-SH3 domain interaction with a proline-rich ligand

Src signalling and transduction are directly involved in cell growth, cell cycle, malignant transformation and cell migration, providing therapeutic opportunities through inhibition of Src. Here we report virtual screening for novel compounds that inhibit the Src-SH3 protein-protein interaction with a proline-rich peptide ligand. Computational docking of the ZINC compound database was performed using GOLD. Top-scoring compounds were assayed using a fluorescence polarization-based assay. A benzoquinoline derivative showed micromolar inhibition of binding between Src-SH3 and the proline-rich peptide. Several analogues were subsequently assayed showing the requirement of a linker between the benzoquinoline and phenyl rings, and electron donating substituents on the phenyl ring.     

Elucidating the molecular interaction of Zebrafish (Danio rerio) peptidoglycan recognition protein 2 with diaminopimelic acid and lysine type peptidoglycans using in silico approaches

Abstract

Peptidoglycan recognition proteins (PGRPs) belong to the family of pattern recognition receptor, represent the major constituent of innate immunity. Although PGRPs are structurally conserved through evolution, their involvement in innate immunity is different in vertebrates and invertebrates. They are highly specific towards recognition of ligands and can hydrolyze bacterial peptidoglycans (PGNs). Zebrafish PGRPs (zPGRPs) have both peptidoglycans lytic amidase activity and broad-spectrum bactericidal activity, but far less is known about how these receptors recognize these microbial ligands. Such studies are hindered due to lack of structural and functional configuration of zPGRPs. Therefore, in this study, we predicted the three-dimensional structure of zPGRP2 through theoretical modeling, investigated the conformational and dynamic properties through molecular dynamics simulations. Molecular docking study revealed the microbial ligands, that is, muramyl pentapeptide–DAP , muramyl pentapeptide–LYS, muramyl tripeptide–DAP, muramyl tripeptide–Lys, muramyl tetrapeptide–DAP, muramyl tetrapeptide–LYS and tracheal cytotoxin interacts with the conserved amino acids of the ligand recognition site comprised of β1, α2, α4, β4 and loops connecting β1 ? α2, α2 ? β2, β3 ? β4 and α4 ? α5. Conserved His31, His32, Ala34, Ile35, Pro36, Lys38, Asp60, Trp61, Trp63, Ala89, His90, Asp106, His143 and Arg144 are predicted to essential for binding and provides stability to these zPGRP–PGN complexes. Our study provides basic molecular information for further research on the immune mechanisms of PGRP’s in Zebrafish. The plasticity of the zPGRP’s binding site revealed by these microbial ligands suggests an intrinsic capacity of the innate immune system to rapidly evolve specificities to meet new microbial challenges in the future.

Communicated by Ramaswamy H. Sarma     

Improving protein template recognition by using small-angle x-ray scattering profiles

Small-angle x-ray scattering (SAXS) is able to extract low-resolution protein shape information without requiring a specific crystal formation. However, it has found little use in atomic-level protein structure determination due to the uncertainty of residue-level structural assignment. We developed a new algorithm, SAXSTER, to couple the raw SAXS data with protein-fold-recognition algorithms and thus improve template-based protein-structure predictions. We designed nine different matching scoring functions of template and experimental SAXS profiles. The logarithm of the integrated correlation score showed the best template recognition ability and had the highest correlation with the true template modeling (TM)-score of the target structures. We tested the method in large-scale protein-fold-recognition experiments and achieved significant improvements in prioritizing the best template structures. When SAXSTER was applied to the proteins of asymmetric SAXS profile distributions, the average TM-score of the top-ranking templates increased by 18% after homologous templates were excluded, which corresponds to a p-value < 10⁻⁹ in Student's t-test. These data demonstrate a promising use of SAXS data to facilitate computational protein structure modeling, which is expected to work most efficiently for proteins of irregular global shape and/or multiple-domain protein complexes.