Pathway-based screening strategy for multitarget inhibitors of diverse proteins in metabolic pathways

Many virtual screening methods have been developed for identifying single-target inhibitors based on the strategy of “one–disease, one–target, one–drug”. The hit rates of these methods are often low because they cannot capture the features that play key roles in the biological functions of the target protein. Furthermore, single-target inhibitors are often susceptible to drug resistance and are ineffective for complex diseases such as cancers. Therefore, a new strategy is required for enriching the hit rate and identifying multitarget inhibitors. To address these issues, we propose the pathway-based screening strategy (called PathSiMMap) to derive binding mechanisms for increasing the hit rate and discovering multitarget inhibitors using site-moiety maps. This strategy simultaneously screens multiple target proteins in the same pathway; these proteins bind intermediates with common substructures. These proteins possess similar conserved binding environments (pathway anchors) when the product of one protein is the substrate of the next protein in the pathway despite their low sequence identity and structure similarity. We successfully discovered two multitarget inhibitors with IC₅₀ of <10 µM for shikimate dehydrogenase and shikimate kinase in the shikimate pathway of Helicobacter pylori. Furthermore, we found two selective inhibitors (IC₅₀ of <10 µM) for shikimate dehydrogenase using the specific anchors derived by our method. Our experimental results reveal that this strategy can enhance the hit rates and the pathway anchors are highly conserved and important for biological functions. We believe that our strategy provides a great value for elucidating protein binding mechanisms and discovering multitarget inhibitors.

Many drug development strategies focus on designing inhibitors for single targets. These inhibitors often lose potency owing to mutations in the protein binding sites and are ineffective for complex diseases. Multitarget inhibitors can decrease probability of drug resistance and enhance the therapeutic efficiency; however, identifying them is still a challenge because targets often have low sequence and structure similarities in their binding sites. Here we propose a pathway-based screening strategy that simultaneously screens proteins in a metabolic pathway for discovering multitarget inhibitors. Because these proteins interact with similar metabolites and modify them step-by-step, the proteins share similarities in binding sites. We developed pathway site-moiety maps that present the conserved binding environments of the proteins without relying on the sequence or structure alignment. Compounds that bind these conserved binding environments are often multitarget inhibitors. We applied this strategy to the shikimate pathway of Helicobacter pylori, and discovered two multitarget inhibitors (IC₅₀<10 µM) for shikimate dehydrogenase and shikimate kinase. In addition, we found two selective inhibitors based on specific binding environments for shikimate dehydrogenase. Thus the pathway-based screening strategy is useful for identifying multitarget inhibitors and elucidating protein-ligand binding mechanisms and has the potential to be applied to human diseases.

Parallel screening of wild-type and drug-resistant targets for anti-resistance neuraminidase inhibitors

Infection with influenza virus is a major public health problem, causing serious illness and death each year. Emergence of drug-resistant influenza virus strains limits the effectiveness of drug treatment. Importantly, a dual H275Y/I223R mutation detected in the pandemic influenza A 2009 virus strain results in multidrug resistance to current neuraminidase (NA) drugs. Therefore, discovery of new agents for treating multiple drug-resistant (MDR) influenza virus infections is important. Here, we propose a parallel screening strategy that simultaneously screens wild-type (WT) and MDR NAs, and identifies inhibitors matching the subsite characteristics of both NA-binding sites. These may maintain their potency when drug-resistant mutations arise. Initially, we analyzed the subsite of the dual H275Y/I223R NA mutant. Analysis of the site-moiety maps of NA protein structures show that the mutant subsite has a relatively small volume and is highly polar compared with the WT subsite. Moreover, the mutant subsite has a high preference for forming hydrogen-bonding interactions with polar moieties. These changes may drive multidrug resistance. Using this strategy, we identified a new inhibitor, Remazol Brilliant Blue R (RB19, an anthraquinone dye), which inhibited WT NA and MDR NA with IC₅₀ values of 3.4 and 4.5 µM, respectively. RB19 comprises a rigid core scaffold and a flexible chain with a large polar moiety. The former interacts with highly conserved residues, decreasing the probability of resistance. The latter forms van der Waals contacts with the WT subsite and yields hydrogen bonds with the mutant subsite by switching the orientation of its flexible side chain. Both scaffolds of RB19 are good starting points for lead optimization. The results reveal a parallel screening strategy for identifying resistance mechanisms and discovering anti-resistance neuraminidase inhibitors. We believe that this strategy may be applied to other diseases with high mutation rates, such as cancer and human immunodeficiency virus type 1.

Inferring homologous protein-protein interactions through pair position specific scoring matrix

Background

The protein-protein interaction (PPI) is one of the most important features to understand biological processes. For a PPI, the physical domain-domain interaction (DDI) plays the key role for biology functions. In the post-genomic era, to rapidly identify homologous PPIs for analyzing the contact residue pairs of their interfaces within DDIs on a genomic scale is essential to determine PPI networks and the PPI interface evolution across multiple species.

Results

In this study, we proposed "pair Position Specific Scoring Matrix (pairPSSM)" to identify homologous PPIs. The pairPSSM can successfully distinguish the true protein complexes from unreasonable protein pairs with about 90% accuracy. For the test set including 1,122 representative heterodimers and 2,708,746 non-interacting protein pairs, the mean average precision and mean false positive rate of pairPSSM were 0.42 and 0.31, respectively. Moreover, we applied pairPSSM to identify ~450,000 homologous PPIs with their interacting domains and residues in seven common organisms (e.g. Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Escherichia coli).

Conclusions

Our pairPSSM is able to provide statistical significance of residue pairs using evolutionary profiles and a scoring system for inferring homologous PPIs. According to our best knowledge, the pairPSSM is the first method for searching homologous PPIs across multiple species using pair position specific scoring matrix and a 3D dimer as the template to map interacting domain pairs of these PPIs. We believe that pairPSSM is able to provide valuable insights for the PPI evolution and networks across multiple species.

Space-related pharma-motifs for fast search of protein binding motifs and polypharmacological targets

Background

To discover a compound inhibiting multiple proteins (i.e. polypharmacological targets) is a new paradigm for the complex diseases (e.g. cancers and diabetes). In general, the polypharmacological proteins often share similar local binding environments and motifs. As the exponential growth of the number of protein structures, to find the similar structural binding motifs (pharma-motifs) is an emergency task for drug discovery (e.g. side effects and new uses for old drugs) and protein functions.

Results

We have developed a Space-Related Pharmamotifs (called SRPmotif) method to recognize the binding motifs by searching against protein structure database. SRPmotif is able to recognize conserved binding environments containing spatially discontinuous pharma-motifs which are often short conserved peptides with specific physico-chemical properties for protein functions. Among 356 pharma-motifs, 56.5% interacting residues are highly conserved. Experimental results indicate that 81.1% and 92.7% polypharmacological targets of each protein-ligand complex are annotated with same biological process (BP) and molecular function (MF) terms, respectively, based on Gene Ontology (GO). Our experimental results show that the identified pharma-motifs often consist of key residues in functional (active) sites and play the key roles for protein functions. The SRPmotif is available at http://gemdock.life.nctu.edu.tw/SRP/.

Conclusions

SRPmotif is able to identify similar pharma-interfaces and pharma-motifs sharing similar binding environments for polypharmacological targets by rapidly searching against the protein structure database. Pharma-motifs describe the conservations of binding environments for drug discovery and protein functions. Additionally, these pharma-motifs provide the clues for discovering new sequence-based motifs to predict protein functions from protein sequence databases. We believe that SRPmotif is useful for elucidating protein functions and drug discovery.

KIDFamMap: a database of kinase-inhibitor-disease family maps for kinase inhibitor selectivity and binding mechanisms

Kinases play central roles in signaling pathways and are promising therapeutic targets for many diseases. Designing selective kinase inhibitors is an emergent and challenging task, because kinases share an evolutionary conserved ATP-binding site. KIDFamMap (http://gemdock.life.nctu.edu.tw/KIDFamMap/) is the first database to explore kinase-inhibitor families (KIFs) and kinase-inhibitor-disease (KID) relationships for kinase inhibitor selectivity and mechanisms. This database includes 1208 KIFs, 962 KIDs, 55 603 kinase-inhibitor interactions (KIIs), 35 788 kinase inhibitors, 399 human protein kinases, 339 diseases and 638 disease allelic variants. Here, a KIF can be defined as follows: (i) the kinases in the KIF with significant sequence similarity, (ii) the inhibitors in the KIF with significant topology similarity and (iii) the KIIs in the KIF with significant interaction similarity. The KIIs within a KIF are often conserved on some consensus KIDFamMap anchors, which represent conserved interactions between the kinase subsites and consensus moieties of their inhibitors. Our experimental results reveal that the members of a KIF often possess similar inhibition profiles. The KIDFamMap anchors can reflect kinase conformations types, kinase functions and kinase inhibitor selectivity. We believe that KIDFamMap provides biological insights into kinase inhibitor selectivity and binding mechanisms.

Antigenic sites of H1N1 influenza virus hemagglutinin revealed by natural isolates and inhibition assays

The antigenic sites of hemagglutinin (HA) are crucial for understanding antigenic drift and vaccine strain selection for influenza viruses. In 1982, 32 epitope residues (called laboratory epitope residues) were proposed for antigenic sites of H1N1 HA based on the monoclonal antibody-selected variants. Interestingly, these laboratory epitope residues only cover 28% (23/83) mutation positions for 9 H1N1 vaccine strain comparisons (from 1977 to 2009). Here, we propose the entropy and likelihood ratio to model amino acid diversity and antigenic variant score for inferring 41 H1N1 HA epitope residues (called natural epitope residues) with statistically significant scores according to 1572 HA sequences and 197 pairs of HA sequences with hemagglutination inhibition (HI) assays of natural isolates. By combining both natural and laboratory epitope residues, we identified 62 (11 overlapped) residues clustered into five antigenic sites (i.e., A-E) which are highly correlated to the antigenic sites of H3N2 HA. Our method recognizes sites A, B and C as critical sites for escaping from neutralizing antibodies in H1N1 virus. Experimental results show that the accuracies of our models are 81.2% and 82.2% using 41 and 62 epitope residues, respectively, for predicting antigenic variants on 197 paring HA sequences. In addition, our model can detect the emergence of epidemic strains and reflect the genetic diversity and antigenic variant between the vaccine and circulating strains. Finally, our model is theoretically consistent with the evolution rates of H3N2 and H1N1 viruses and is often consistent to WHO vaccine strain selections. We believe that our models and the inferred antigenic sites of HA are useful for understanding the antigenic drift and evolution of influenza A H1N1 virus.

MiR-181a confers resistance of cervical cancer to radiation therapy through targeting the pro-apoptotic PRKCD gene

The purpose of this study was to define the roles of miR-181a in determining sensitivity of cervical cancer to radiation therapy, to explore the underlying mechanism and to evaluate the potential of miR-181a as a biomarker for predicting radio-sensitivity. Tumor specimens from 18 patients with a histological diagnosis of squamous cervical carcinoma (stage IIIB) were used in the micro-RNA profiling and comparison. These patients never received any chemotherapy before radiation therapy. Human cervical cancer cell lines, SiHa and Me180, were used in vitro (cell culture) and in vivo (animal) studies. Transfection of tumor cells with the mimic or inhibitor of miR-181a, and reporter gene assay, were performed to investigate the role of miR-181a in determining radio-sensitivity and the target gene. Higher expression of miR-181a was observed in human cervical cancer specimens and cell lines that were insensitive to radiation therapy, as compared with sensitive cancer specimens and the cell lines. We also found that miR-181a negatively regulated the expression of PRKCD, a pro-apoptotic protein kinase, via targeting its 3'-untranslated region (UTR), thereby inhibiting irradiation-induced apoptosis and decreasing G2/M block. The role of miR-181a in conferring cellular resistance to radiation treatment was validated both in cell culture models and in mouse tumor xenograft models. The effect of miR-181a on radio-resistance was mediated through targeting the 3'-UTR of PRKCD gene. Thus, the expression level of miR-181a in cervical cancer may serve as a biomarker for sensitivity to radiation therapy, and targeting miR-181a may represent a new approach to sensitizing cervical cancer to radiation treatment.

Steric recognition of T-cell receptor contact residues is required to map mutant epitopes by immunoinformatical programmes

MHC class I-restricted CD8 T-lymphocyte epitopes comprise anchor motifs, T-cell receptor (TCR) contact residues and the peptide backbone. Serial variant epitopes with substitution of amino acids at either anchor motifs or TCR contact residues have been synthesized for specific interferon-γ responses to clarify the TCR recognition mechanism as well as to assess the epitope prediction capacity of immunoinformatical programmes. CD8 T lymphocytes recognise the steric configuration of functional groups at the TCR contact side chain with a parallel observation that peptide backbones of various epitopes adapt to the conserved conformation upon binding to the same MHC class I molecule. Variant epitopes with amino acid substitutions at the TCR contact site are not recognised by specific CD8 T lymphocytes without compromising their binding capacity to MHC class I molecules, which demonstrates two discrete antigen presentation events for the binding of peptides to MHC class I molecules and for TCR recognition. The predicted outcome of immunoinformatical programmes is not consistent with the results of epitope identification by laboratory experiments in the absence of information on the interaction with TCR contact residues. Immunoinformatical programmes based on the binding affinity to MHC class I molecules are not sufficient for the accurate prediction of CD8 T-lymphocyte epitopes. The predictive capacity is further improved to distinguish mutant epitopes from the non-mutated epitopes if the peptide-TCR interface is integrated into the computing simulation programme.

MoNetFamily: a web server to infer homologous modules and module-module interaction networks in vertebrates

A module is a fundamental unit forming with highly connected proteins and performs a certain kind of biological functions. Modules and module–module interaction (MMI) network are essential for understanding cellular processes and functions. The MoNetFamily web server can identify the modules, homologous modules (called module family) and MMI networks across multiple species for the query protein(s). This server first finds module candidates of the query by using BLASTP to search the module template database (1785 experimental and 1252 structural templates). MoNetFamily then infers the homologous modules of the selected module candidate using protein–protein interaction (PPI) families. According to homologous modules and PPIs, we statistically calculated MMIs and MMI networks across multiple species. For each module candidate, MoNetFamily identifies its neighboring modules and their MMIs in module networks of Homo sapiens, Mus musculus and Danio rerio. Finally, MoNetFamily shows the conserved proteins, PPI profiles and functional annotations of the module family. Our results indicate that the server can be useful for MMI network (e.g. 1818 modules and 9678 MMIs in H. sapiens) visualizations and query annotations using module families and neighboring modules. We believe that the server is able to provide valuable insights to determine homologous modules and MMI networks across multiple species for studying module evolution and cellular processes. The MoNetFamily sever is available at http://monetfamily.life.nctu.edu.tw.

Roles of amino acids in the Escherichia coli octaprenyl diphosphate synthase active site probed by structure-guided site-directed mutagenesis

Octaprenyl diphosphate synthase (OPPS) catalyzes consecutive condensation reactions of farnesyl diphosphate (FPP) with five molecules of isopentenyl diphosphates (IPP) to generate C(40) octaprenyl diphosphate, which constitutes the side chain of ubiquinone or menaquinone. To understand the roles of active site amino acids in substrate binding and catalysis, we conducted site-directed mutagenesis studies with Escherichia coli OPPS. In conclusion, D85 is the most important residue in the first DDXXD motif for both FPP and IPP binding through an H-bond network involving R93 and R94, respectively, whereas R94, K45, R48, and H77 are responsible for IPP binding by providing H-bonds and ionic interactions. K170 and T171 may stabilize the farnesyl carbocation intermediate to facilitate the reaction, whereas R93 and K225 may stabilize the catalytic base (MgPP(i)) for H(R) proton abstraction after IPP condensation. K225 and K235 in a flexible loop may interact with FPP when the enzyme becomes a closed conformation, which is therefore crucial for catalysis. Q208 is near the hydrophobic part of IPP and is important for IPP binding and catalysis.

GemAffinity: a scoring function for predicting binding affinity and virtual screening

Prediction of protein-ligand binding affinities plays an essential role for molecular recognition and virtual screening. We have developed a scoring function, namely GemAffinity, to predict binding affinities by using a stepwise regression method and 88 descriptors from 891 complex structures. GemAffinity consists of five descriptors, including van der Waals contacts; metal-ligand interactions; water effects; ligand deformation penalty; and conserved hydrogen-bonded residues. Experimental results indicate that GemAffinity is the best among 13 methods on a test set and can enrich screening accuracies on four sets. We believe that GemAffinity is useful for virtual screening and drug discovery.

Structures of Helicobacter pylori shikimate kinase reveal a selective inhibitor-induced-fit mechanism

Shikimate kinase (SK), which catalyzes the specific phosphorylation of the 3-hydroxyl group of shikimic acid in the presence of ATP, is the enzyme in the fifth step of the shikimate pathway for biosynthesis of aromatic amino acids. This pathway is present in bacteria, fungi, and plants but absent in mammals and therefore represents an attractive target pathway for the development of new antimicrobial agents, herbicides, and antiparasitic agents. Here we investigated the detailed structure–activity relationship of SK from Helicobacter pylori (HpSK). Site-directed mutagenesis and isothermal titration calorimetry studies revealed critical conserved residues (D33, F48, R57, R116, and R132) that interact with shikimate and are therefore involved in catalysis. Crystal structures of HpSK·SO₄, R57A, and HpSK•shikimate-3-phosphate•ADP show a characteristic three-layer architecture and a conformationally elastic region consisting of F48, R57, R116, and R132, occupied by shikimate. The structure of the inhibitor complex, E114A•162535, was also determined, which revealed a dramatic shift in the elastic LID region and resulted in conformational locking into a distinctive form. These results reveal considerable insight into the active-site chemistry of SKs and a selective inhibitor-induced-fit mechanism.

Core site-moiety maps reveal inhibitors and binding mechanisms of orthologous proteins by screening compound libraries

Members of protein families often share conserved structural subsites for interaction with chemically similar moieties despite low sequence identity. We propose a core site-moiety map of multiple proteins (called CoreSiMMap) to discover inhibitors and mechanisms by profiling subsite-moiety interactions of immense screening compounds. The consensus anchor, the subsite-moiety interactions with statistical significance, of a CoreSiMMap can be regarded as a "hot spot" that represents the conserved binding environments involved in biological functions. Here, we derive the CoreSiMMap with six consensus anchors and identify six inhibitors (IC(50)<8.0 µM) of shikimate kinases (SKs) of Mycobacterium tuberculosis and Helicobacter pylori from the NCI database (236,962 compounds). Studies of site-directed mutagenesis and analogues reveal that these conserved interacting residues and moieties contribute to pocket-moiety interaction spots and biological functions. These results reveal that our multi-target screening strategy and the CoreSiMMap can increase the accuracy of screening in the identification of novel inhibitors and subsite-moiety environments for elucidating the binding mechanisms of targets.

Adjuvant transcatheter arterial chemoembolization for intrahepatic cholangiocarcinoma after curative surgery: retrospective control study

BACKGROUND

Effects of adjuvant transcatheter arterial chemoembolization (TACE) for intrahepatic cholangiocarcinoma (ICC) radical surgery have never been evaluated.

METHODS

A retrospective analysis was conducted on 125 ICC patients who had undergone operations with curative intent in Shanghai Eastern Hepatobiliary Surgery Hospital from July 2002 to December 2003. Of these patients, 53 underwent adjuvant TACE (TACE group) and 72 did not (non-TACE group). Adjuvant TACE was performed one time 1.5-2.0 months after the operation.

RESULTS

Follow-up was performed at a median of 18 months (range 3-96 months). There was no significant recurrence-free survival (RFS) difference between the TACE and non-TACE groups (P = 0.659). The 1-, 3-, and 5-year overall survival (OS) rates were 69.8, 37.7, and 28.3%, respectively, for the TACE group and 54.2, 25.0, and 20.8%, respectively, for the non-TACE group (P = 0.045). Among 54 patients with a recurrence time of ≤ 3 months, the OS rate of the TACE group was better than that of the non-TACE group (P < 0.001). For 59 patients with a recurrence time later than the median RFS, no significant RFS difference was found between the TACE and non-TACE groups (P = 0.681). These results indicate that TACE could not delay recurrence but could prolong the OS of patients with early recurrence.

CONCLUSIONS

Adjuvant TACE after radical surgery was associated with better survival among the ICC patients with early recurrence.

LigSeeSVM: ligand-based virtual screening using support vector machines and data fusion

Ligand-based in silico drug screening is useful for lead discovery, in particular for those targets without structures. Here, we have developed LigSeeSVM, a ligand-based screening tool using data fusion and Support Vector Machines (SVMs). We used Atom Pair (AP) structure descriptors and Physicochemical (PC) descriptors of compounds to generate SVM-AP and SVM-PC models. Sequentially, the two models were combined using rank-based data fusion to create LigSeeSVM model. LigSeeSVM was evaluated on five data sets. Experimental results show that the performance of LigSeeSVM is better than other ligand-based virtual screening approaches. We believe that LigSeeSVM is useful for lead compounds.

PAComplex: a web server to infer peptide antigen families and binding models from TCR-pMHC complexes

One of the most adaptive immune responses is triggered by specific T-cell receptors (TCR) binding to peptide-major histocompatibility complexes (pMHC). Despite the availability of many prediction servers to identify peptides binding to MHC, these servers are often lacking in peptide-TCR interactions and detailed atomic interacting models. PAComplex is the first web server investigating both pMHC and peptide-TCR interfaces to infer peptide antigens and homologous peptide antigens of a query. This server first identifies significantly similar TCR-pMHC templates (joint Z-value ≥ 4.0) of the query by using antibody-antigen and protein-protein interacting scoring matrices for peptide-TCR and pMHC interfaces, respectively. PAComplex then identifies the homologous peptide antigens of these hit templates from complete pathogen genome databases (≥10(8) peptide candidates from 864,628 protein sequences of 389 pathogens) and experimental peptide databases (80,057 peptides in 2287 species). Finally, the server outputs peptide antigens and homologous peptide antigens of the query and displays detailed interacting models (e.g. hydrogen bonds and steric interactions in two interfaces) of hitTCR-pMHC templates. Experimental results demonstrate that the proposed server can achieve high prediction accuracy and offer potential peptide antigens across pathogens. We believe that the server is able to provide valuable insights for the peptide vaccine and MHC restriction. The PAComplex sever is available at http://PAcomplex.life.nctu.edu.tw.

Clopidogrel 150 vs. 75 mg day(-1) in patients undergoing percutaneous coronary intervention: a meta-analysis

BACKGROUND

Whether an increase in the daily oral maintenance dose of clopidogrel may improve clinical outcomes in patients undergoing percutaneous coronary intervention (PCI) is still debated.

OBJECTIVES

This meta-analysis aimed to estimate the relative effect of a 150- vs. 75-mg daily maintenance dosage of clopidogrel on clinical and laboratory end-points in patients undergoing PCI.

METHODS

We searched electronic and printed sources (up to 14 December 2010) for both randomized control trials and observational studies satisfying the predefined inclusion criteria.

RESULTS

We retrieved 12 reports of studies including a total of 23,814 patients. Clopidogrel, 150 mg day(-1), was associated with significant reductions in major adverse cardiac and/or cerebrovascular events (odds ratio [OR], 0.67; 95% confidence interval [CI], 0.48-0.94), myocardial infarction (OR, 0.72; 95% CI, 0.60-0.86), target vessel revascularization (OR, 0.27; 95% CI, 0.12-0.62) and stent thrombosis (OR, 0.64; 95% CI, 0.53-0.77) and decreased adenosine diphosphate-induced maximal platelet aggregation. However, as compared with 75 mg day(-1), the 150-mg daily maintenance dosage significantly increased the risk of minor bleeding (OR, 1.21; 95% CI, 1.08-1.36).

CONCLUSION

As compared with the currently recommended 75-mg day(-1) maintenance dosage of clopidogrel, the 150-mg day(-1) dosage can reduce major adverse cardiac and/or cerebrovascular events but may increase the risk of minor bleeding.

iGEMDOCK: a graphical environment of enhancing GEMDOCK using pharmacological interactions and post-screening analysis

BACKGROUND

Pharmacological interactions are useful for understanding ligand binding mechanisms of a therapeutic target. These interactions are often inferred from a set of active compounds that were acquired experimentally. Moreover, most docking programs loosely coupled the stages (binding-site and ligand preparations, virtual screening, and post-screening analysis) of structure-based virtual screening (VS). An integrated VS environment, which provides the friendly interface to seamlessly combine these VS stages and to identify the pharmacological interactions directly from screening compounds, is valuable for drug discovery.

RESULTS

We developed an easy-to-use graphic environment, iGEMDOCK, integrating VS stages (from preparations to post-screening analysis). For post-screening analysis, iGEMDOCK provides biological insights by deriving the pharmacological interactions from screening compounds without relying on the experimental data of active compounds. The pharmacological interactions represent conserved interacting residues, which often form binding pockets with specific physico-chemical properties, to play the essential functions of a target protein. Our experimental results show that the pharmacological interactions derived by iGEMDOCK are often hot spots involving in the biological functions. In addition, iGEMDOCK provides the visualizations of the protein-compound interaction profiles and the hierarchical clustering dendrogram of the compounds for post-screening analysis.

CONCLUSIONS

We have developed iGEMDOCK to facilitate steps from preparations of target proteins and ligand libraries toward post-screening analysis. iGEMDOCK is especially useful for post-screening analysis and inferring pharmacological interactions from screening compounds. We believe that iGEMDOCK is useful for understanding the ligand binding mechanisms and discovering lead compounds. iGEMDOCK is available at http://gemdock.life.nctu.edu.tw/dock/igemdock.php.

Changed epitopes drive the antigenic drift for influenza A (H3N2) viruses

Background

In circulating influenza viruses, gradually accumulated mutations on the glycoprotein hemagglutinin (HA), which interacts with infectivity-neutralizing antibodies, lead to the escape of immune system (called antigenic drift). The antibody recognition is highly correlated to the conformation change on the antigenic sites (epitopes), which locate on HA surface. To quantify a changed epitope for escaping from neutralizing antibodies is the basis for the antigenic drift and vaccine development.

Results

We have developed an epitope-based method to identify the antigenic drift of influenza A utilizing the conformation changes on epitopes. A changed epitope, an antigenic site on HA with an accumulated conformation change to escape from neutralizing antibody, can be considered as a "key feature" for representing the antigenic drift. According to hemagglutination inhibition (HI) assays and HA/antibody complex structures, we statistically measured the conformation change of an epitope by considering the number of critical position mutations with high genetic diversity and antigenic scores. Experimental results show that two critical position mutations can induce the conformation change of an epitope to escape from the antibody recognition. Among five epitopes of HA, epitopes A and B, which are near to the receptor binding site, play a key role for neutralizing antibodies. In addition, two changed epitopes often drive the antigenic drift and can explain the selections of 24 WHO vaccine strains.

Conclusions

Our method is able to quantify the changed epitopes on HA for predicting the antigenic variants and providing biological insights to the vaccine updates. We believe that our method is robust and useful for studying influenza virus evolution and vaccine development.

TSCC: Two-Stage Combinatorial Clustering for virtual screening using protein-ligand interactions and physicochemical features

BACKGROUND

The increasing numbers of 3D compounds and protein complexes stored in databases contribute greatly to current advances in biotechnology, being employed in several pharmaceutical and industrial applications. However, screening and retrieving appropriate candidates as well as handling false positives presents a challenge for all post-screening analysis methods employed in retrieving therapeutic and industrial targets.

RESULTS

Using the TSCC method, virtually screened compounds were clustered based on their protein-ligand interactions, followed by structure clustering employing physicochemical features, to retrieve the final compounds. Based on the protein-ligand interaction profile (first stage), docked compounds can be clustered into groups with distinct binding interactions. Structure clustering (second stage) grouped similar compounds obtained from the first stage into clusters of similar structures; the lowest energy compound from each cluster being selected as a final candidate.

CONCLUSION

By representing interactions at the atomic-level and including measures of interaction strength, better descriptions of protein-ligand interactions and a more specific analysis of virtual screening was achieved. The two-stage clustering approach enhanced our post-screening analysis resulting in accurate performances in clustering, mining and visualizing compound candidates, thus, improving virtual screening enrichment.

3D-interologs: an evolution database of physical protein- protein interactions across multiple genomes

Background

Comprehensive exploration of protein-protein interactions is a challenging route to understand biological processes. For efficiently enlarging protein interactions annotated with residue-based binding models, we proposed a new concept "3D-domain interolog mapping" with a scoring system to explore all possible protein pairs between the two homolog families, derived from a known 3D-structure dimmer (template), across multiple species. Each family consists of homologous proteins which have interacting domains of the template for studying domain interface evolution of two interacting homolog families.

Results

The 3D-interologs database records the evolution of protein-protein interactions database across multiple species. Based on "3D-domain interolog mapping" and a new scoring function, we infer 173,294 protein-protein interactions by using 1,895 three-dimensional (3D) structure heterodimers to search the UniProt database (4,826,134 protein sequences). The 3D- interologs database comprises 15,124 species and 283,980 protein-protein interactions, including 173,294 interactions (61%) and 110,686 interactions (39%) summarized from the IntAct database. For a protein-protein interaction, the 3D-interologs database shows functional annotations (e.g. Gene Ontology), interacting domains and binding models (e.g. hydrogen-bond interactions and conserved residues). Additionally, this database provides couple-conserved residues and the interacting evolution by exploring the interologs across multiple species. Experimental results reveal that the proposed scoring function obtains good agreement for the binding affinity of 275 mutated residues from the ASEdb. The precision and recall of our method are 0.52 and 0.34, respectively, by using 563 non-redundant heterodimers to search on the Integr8 database (549 complete genomes).

Conclusions

Experimental results demonstrate that the proposed method can infer reliable physical protein-protein interactions and be useful for studying the protein-protein interaction evolution across multiple species. In addition, the top-ranked strategy and template interface score are able to significantly improve the accuracies of identifying protein-protein interactions in a complete genome. The 3D-interologs database is available at http://3D- interologs.life.nctu.edu.tw.