Sequencing the whole genome of various species has many applications, not only in understanding biological systems, but also in medicine, pharmacy, and agriculture. In recent years, the emergence of high-throughput next generation sequencing technologies has dramatically reduced the time and costs for whole genome sequencing. These new technologies provide ultrahigh throughput with a lower per-unit data cost. However, the data are generated from very short fragments of DNA. Thus, it is very important to develop algorithms for merging these fragments. One method of merging these fragments without using a reference dataset is called de novo assembly. Many algorithms for de novo assembly have been proposed in recent years. Velvet and SOAPdenovo2 are well-known assembly algorithms, which have good performance in terms of memory and time consumption. However, memory consumption increases dramatically when the size of input fragments is larger. Therefore, it is necessary to develop an alternative algorithm with low memory usage. In this paper, we propose an algorithm for de novo assembly with lower memory. In our experiments using E.coli K-12 strain MG 1655 and human chromosome 14, the memory consumption of our proposed algorithm was less than that of other popular assemblers.

View full abstract

We developed a new application to quantitatively evaluate the sequence conservation of ligand-binding sites by integrating information pertaining to protein structures, ligand-binding sites, and amino acid sequences. These data are visualized onto protein structures via a Jmol or PyMOL interface. The visualization is very important for structure-based drug design (SBDD). Key features of this application are the visualization of slight differences in specific ligand-binding sites and ConservationScore comparable among ligand-binding sites. Furthermore, we conducted an experiment to visualize the calculation and comparison of the ConservationScore of four viral proteins as well as an experiment to visualize the differences between proteins belonging to the human β adrenergic receptor family. This application is available at http://www.bio.gsic.titech.ac.jp/visco.html.

View full abstract

Background: Spleen tyrosine kinase (SYK) is a protein related to various diseases. Aberrant SYK expression often causes the progression and initiation of several diseases including cancer and autoimmune diseases. Despite the importance of inhibiting SYK and identifying candidate inhibitors, no clinically effective inhibitors have been reported to date. Therefore, there is a need for novel SYK inhibitors. Results: Candidate compounds were investigated using in silico screening by chooseLD, which simulates ligand docking to proteins. Using this system, known inhibitors were correctly recognized as compounds with high affinity to SYK. Furthermore, many compounds in the DrugBank database were newly identified as having high affinity to the ATP-binding sites in the kinase domain with a similar affinity to previously reported inhibitors. Conclusions: Many drug candidate compounds from the DrugBank database were newly identified as inhibitors of SYK. Because compounds registered in the DrugBank are expected to have fewer side effects than currently available compounds, these newly identified compounds may be clinically useful inhibitors of SYK for the treatment of various diseases.

View full abstract

It is necessary to confirm that a new drug can be appropriately cleared from the human body. However, checking the clearance pathway of a drug in the human body requires clinical trials, and therefore requires large cost. Thus, computational methods for drug clearance pathway prediction have been studied. The proposed prediction methods developed previously were based on a supervised learning algorithm, which requires clearance pathway information for all drugs in a training set as input labels. However, these data are often insufficient in its numbers because of the high cost of their acquisition. In this paper, we propose a new drug clearance pathway prediction method based on semi-supervised learning, which can use not only labeled data but also unlabeled data. We evaluated the effectiveness of our method, focusing on the cytochrome P₄₅₀ 2C19 enzyme, which is involved in one of the major clearance pathways.

View full abstract