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.

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.

Co-evolution positions and rules for antigenic variants of human influenza A/H3N2 viruses.

BACKGROUND: In pandemic and epidemic forms, avian and human influenza viruses often cause significant damage to human society and economics. Gradually accumulated mutations on hemagglutinin (HA) cause immunologically distinct circulating strains, which lead to the antigenic drift (named as antigenic variants). The "antigenic variants" often requires a new vaccine to be formulated before each annual epidemic. Mapping the genetic evolution to the antigenic drift of influenza viruses is an emergent issue to public health and vaccine development RESULTS: We developed a method for identifying antigenic critical amino acid positions, rules, and co-mutated positions for antigenic variants. The information gain (IG) and the entropy are used to measure the score of an amino acid position on hemagglutinin (HA) for discriminating between antigenic variants and similar viruses. A position with high IG and entropy implied that this position is highly correlated to an antigenic drift. Nineteen positions with high IG and high genetic diversity are identified as antigenic critical positions on the HA proteins. Most of these antigenic critical positions are located on five epitopes or on the surface based on the HA structure. Based on IG values and entropies of these 19 positions on the HA, the decision tree was applied to create a rule-based model and to identify rules for predicting antigenic variants of a given two HA sequences which are often a vaccine strain and a circulating strain. The predicting accuracies of this model on two sets, which consist of a training set (181 hemagglutination inhibition (HI) assays) and an independent test set (31,878 HI assays), are 91.2% and 96.2% respectively. CONCLUSION: Our method is able to identify critical positions, rules, and co-mutated positions on HA for predicting the antigenic variants. The information gains and the entropies of HA positions provide insight to the antigenic drift and co-evolution positions for influenza seasons. We believe that our method is robust and is potential useful for studying influenza virus evolution and vaccine development.

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.

Kappa-alpha plot derived structural alphabet and BLOSUM-like substitution matrix for rapid search of protein structure database.

We present a novel protein structure database search tool, 3D-BLAST, that is useful for analyzing novel structures and can return a ranked list of alignments. This tool has the features of BLAST (for example, robust statistical basis, and effective and reliable search capabilities) and employs a kappa-alpha (kappa, alpha) plot derived structural alphabet and a new substitution matrix. 3D-BLAST searches more than 12,000 protein structures in 1.2 s and yields good results in zones with low sequence similarity.