Kenji Mizuguchi

We describe a database of protein structure alignments for homologous families. The database HOMSTRAD presently contains 130 protein families and 590 aligned structures, which have been selected on the basis of quality of the X-ray analysis and accuracy of the structure. For each family, the database provides a structure-based alignment derived using COMPARER and annotated with JOY in a special format that represents the local structural environment of each amino acid residue. HOMSTRAD also prov

Abstract MOTIVATION: JOY is a program to annotate protein sequence alignments with three-dimensional (3D) structural features. It was developed to display 3D structural information in a sequence alignment and to help understand the conservation of amino acids in their specific local environments. RESULTS:: The JOY representation now constitutes an essential part of the two databases of protein structure alignments: HOMSTRAD (http://www-cryst.bioc.cam.ac.uk/homstrad ) and CAMPASS (http://www-crys

Predicting the fraction unbound in plasma provides a good understanding of the pharmacokinetic properties of a drug to assist candidate selection in the early stages of drug discovery. It is also an effective tool to mitigate the risk of late-stage attrition and to optimize further screening. In this study, we built in silico prediction models of fraction unbound in human plasma with freely available software, aiming specifically to improve the accuracy in the low value ranges. We employed sever

Prioritising candidate genes for further experimental characterisation is a non-trivial challenge in drug discovery and biomedical research in general. An integrated approach that combines results from multiple data types is best suited for optimal target selection. We developed TargetMine, a data warehouse for efficient target prioritisation. TargetMine utilises the InterMine framework, with new data models such as protein-DNA interactions integrated in a novel way. It enables complicated searc

We have developed a new method of protein structure comparison based on spatial arrangements of secondary structural elements (SSEs). Each SSE is represented by a single vector, and common spatial arrangements of vectors in a pair of proteins are detected. The method allows not only insertions and deletions of SSEs, but also topological permutations. It has a flexible target function that can be adjusted depending upon particular levels or definitions of structural similarity, and it is fast eno

OATP1B3 polymorphisms that result in altered expression, substrate specificity, and pH-dependent activity may be of potential relevance to hepatic clearance of substrate drugs in vivo.

Of the complementarity-determining regions (CDRs) of antibodies, H3 loops, with varying amino acid sequences and loop lengths, adopt particularly diverse loop conformations. The diversity of H3 conformations produces an array of antigen recognition patterns involving all the CDRs, in which the residue positions actually in contact with the antigen vary considerably. Therefore, for a deeper understanding of antigen recognition, it is necessary to relate the sequence and structural properties of e

The innate immune protein Stimulator of interferon genes (STING) promotes the induction of interferon beta (IFN-β) production via the phosphorylation of its C-terminal tail (CTT) by TANK-binding kinase 1 (TBK1). Potent ligands of STING are, therefore, promising candidates for novel anti-cancer drugs or vaccine adjuvants. However, the intrinsically flexible CTT poses serious problems in in silico drug discovery. Here, we performed molecular dynamics simulations of the STING fragment containing th

The guanidino-group modifying enzyme (GME) superfamily contains many drug targets, including metabolic enzymes from pathogenic microorganisms as well as key regulatory proteins from higher eukaryotes. These enzymes, despite their diverse sequences, adopt the common alpha/beta propeller fold and catalyze the modification of (methylated) guanidino groups. Our structural superposition and structure-based alignment for the GMEs have identified key residues that are involved in the catalysis and subs

Machine learning techniques are being increasingly used in the analysis of clinical and omics data. This increase is primarily due to the advancements in Artificial intelligence (AI) and the build-up of health-related big data. In this paper we have aimed at estimating the likelihood of adverse drug reactions or events (ADRs) in the course of drug discovery using various machine learning methods. We have also described a novel machine learning-based framework for predicting the likelihood of ADR

Hepatitis C virus (HCV) is a major cause of chronic liver disease worldwide. Here we attempt to further our understanding of the biological context of protein interactions in HCV pathogenesis, by investigating interactions between HCV proteins Core and NS4B and human host proteins. Using the yeast two-hybrid (Y2H) membrane protein system, eleven human host proteins interacting with Core and 45 interacting with NS4B were identified, most of which are novel. These interactions were used to infer o