Genome informatics. International Conference on Genome Informatics最新文献

UGTs (UDP glycosyltransferase) are the largest glycosyltransferase gene family in higher plants, modifying secondary metabolites, hormones, and xenobiotics. This gene family plays an important role in the vast diversity of plant secondary metabolites specific to species. Experimental data of biochemical activities and physiological roles of plant UGTs are increasing but most UGTs are not still functionally characterized. To understand their catalytic specificity and function from sequence data, phylogenetic analyses have been achieved mainly in Arabidopsis, but massive and comprehensive approach covering various species has not been applied yet. In this study, we collected 733 UGT sequences derived from 96 plant species and 252 substrate specificity data. We constructed a phylogenetic tree and divided most part of these genes into nine sequence groups, which are characterized by biochemical specificity. Furthermore, we performed genome-wide analysis of seven plant species UGTs by mapping them into these groups. We propose this is the first step to understand whole glycosylated secondary metabolites of each plant species from its genome information.

Due to the large number of available protein structure alignment algorithms, a lot of effort has been made to define robust measures to evaluate their performances and the quality of generated alignments. Most quality measures involve the number of aligned residues and the RMSD. In this work, we analyze how these two properties are influenced by different residue assignment strategies as employed in common non-sequential structure alignment algorithms. Therefore, we implemented different residue assignment strategies into our non-sequential structure alignment algorithm GANGSTA+. We compared the resulting numbers of aligned residues and RMSDs for each residue assignment strategy and different alignment algorithms on a benchmark set of circular-permuted protein pairs. Unfortunately, differences in the residue assignment strategies are often ignored when comparing the performances of different algorithms. However, our results clearly show that this may strongly bias the observations. Bringing residue assignment strategies in line can explain observed performance differences between entirely different alignment algorithms. Our results suggest that performance comparison of non-sequential protein structure alignment algorithms should be based on the same residue assignment strategy.

Drug interactions which may cause harmful events are important for our health and new drag development. In the previous work, we extracted the drug interaction data from Japanese drug package inserts and generated the drug interaction network. The network contains a large number of drugs densely connected to each other, where drug targets and drug-metabolizing enzymes were shared in the drug interactions. In this study, we further analyzed the obtained drug interaction network by merging drugs into drug categories based on the Anatomical Therapeutic Chemical (ATC) classification. The merged data of drug interactions indicated drug properties that are related to drug interaction mechanisms or symptoms. We investigated the relationships between the drug groups and drug interaction mechanisms or symptoms.

Solved structures of protein-protein complexes give fundamental insights into protein function and molecular recognition. Although the determination of protein-protein complexes is generally more difficult than solving individual proteins, the number of experimentally determined complexes increased conspicuously during the last decade. Here, the interfaces of 750 transient protein-protein interactions as well as 2,000 interactions between domains of the same protein chain (obligate interactions) were analyzed to obtain a better understanding of molecular recognition and to identify features applicable for protein binding site prediction. Calculation of knowledge-based potentials showed a preference of contacts between amino acids having complementary physicochemical properties. The analysis of amino acid conservation of the entire interface area showed a weak but significant tendency to a higher evolutionary conservation of protein binding sites compared to surface areas that are permanently exposed to solvent. Remarkably, contact frequencies between outstandingly conserved residues are much higher than expected confirming the so-called "hot spot" theory. The comparisons between obligate and transient domain contacts reveal differences and point out that structural diversification and molecular recognition of protein-protein interactions are subjected to other evolutionary aspects than obligate domain-domain interactions.

Protein-Protein interactions play an important role in many cellular processes. However experimental determination of the protein complex structure is quite difficult and time consuming. Hence, there is need for fast and accurate in silico protein docking methods. These methods generally consist of two stages: (i) a sampling algorithm that generates a large number of candidate complex geometries (decoys), and (ii) a scoring function that ranks these decoys such that nearnative decoys are higher ranked than other decoys. We have recently developed a neural network based scoring function that performed better than other state-of-the-art scoring functions on a benchmark of 65 protein complexes. Here, we use similar ideas to develop a method that is based on linear scoring functions. We compare the linear scoring function of the present study with other knowledge-based scoring functions such as ZDOCK 3.0, ZRANK and the previously developed neural network. Despite its simplicity the linear scoring function performs as good as the compared state-of-the-art methods and predictions are simple and rapid to compute.