Kentaro Onizuka

HMM with protein structure grammar

The authors propose a structure-prediction framework for proteins that uses hidden Markov models (HMM) with a protein structure grammar. By adopting a protein structure grammar, the HMM makes it possible to treat global interactions, the interaction between two secondary structures which are apart in the sequence. In this framework, prediction of local and global structures are totally treated through global and local interactions which are expressed by the protein sequence grammar. The relations between some of the previous methods for secondary structure prediction and HMMs are discussed. Some experimental results on secondary structure prediction are included. The learning algorithms for the HMMs are presented.<<ETX>>

Quick selection of representative protein chain sets based on customizable requirements

MOTIVATION Protein structure classification has been recognized as one of the most important research issues in protein structure analysis. A substantial number of methods for the classification have been proposed, and several databases have been constructed using these methods. Since some proteins with very similar sequences may exhibit structural diversities, we have proposed PDB-REPRDB: a database of representative protein chains from the Protein Data Bank (PDB), which strategy of selection is based not only on sequence similarity but also on structural similarity. Forty-eight representative sets whose similarity criteria were predetermined were made available over the World Wide Web (WWW). However, the sets were insufficient in number to satisfy users researching protein structures by various methods. RESULT We have improved the system for PDB-REPRDB so that the user may obtain a quick selection of representative chains from PDB. The selection of representative chains can be dynamically configured according to the users requirement. The WWW interface provides a large degree of freedom in setting parameters, such as cut-off scores of sequence and structural similarity. This paper describes the method we use to classify chains and select the representatives in the system. We also describe the interface used to set the parameters.

Using data compression for multidimensional distribution analysis [molecular biology]

A new method for multi-dimensional distribution analysis using a data compression technique applied to the knowledge-based mean-force potentials between residues for the analysis of protein sequence-structure compatibility performs much better than that of conventional 1D distance-based potentials derived from binned distributions.

Rapid protein fragment search using hash functions based on the Fourier transform

MOTIVATION Since the protein structure database has been growing very rapidly in recent years, the development of efficient methods for searching for similar structures is very important. RESULTS This paper presents a novel method for searching for similar fragments of proteins. In this method, a hash vector (a vector of real numbers) is associated with each fixed-length fragment of three-dimensional protein structure. Each vector consists of low-frequency components of the Fourier-like spectrum for the distances between C alpha atoms and the centroid. Then, we can analyze the similarity between fragments by evaluating the difference between hash vectors. The novel aspect of the method is that the following property is proved theoretically: if the root mean square distance between two fragments is small, then the distance between the hash vectors is small. Several variants of this method were compared with a naive method and a previous method using PDB data. The results show that the fastest one among the variants is 18-80 times faster than the naive method, and 3-10 times faster than the previous method.

New hashing techniques and their application to a protein structure database system

We have devised novel methods to evaluate the structural similarity of proteins and we compare them. In each method, a hash vector is associated with each fixed length fragment of three dimensional protein structure. Then, we analyze the similarity between fragments by evaluating the difference between true hash vectors. The novel aspect of the methods is that the following property is proved theoretically: the root mean square deviation between two fragments is small, so the distance between the hash vectors associated with the fragments is small. The methods were compared with the previous methods using PDB data, and were shown to be much faster. One of the new hashing methods is already included in PROTEIX, a database management system for protein structures. The features of PROTEIX are described.<<ETX>>

Biological- and Chemical- Parallel Applications on a PC Cluster

We describe the development of biological and chemical parallel applications running on a PC cluster. Our parallel cluster consists of 64 Pentium Pro 200MHz microprocessors. We describe one biological parallel application on our integrated parallel cluster system, called PAPIA. This system is dedicated to protein information analysis. The PAPIA system enables very fast protein database searches (3-D structure matching and sequence, homology search) by fully utilizing the power of the 64 local hard-disks, as well as fast parallel calculations supported by the high-bandwidth and low-latency communication driver developed by RWCP. We also present a web service (http://www.rwcp.or.jp/papia/) so any biologist can easily submit jobs to the PAPIA system through a web browser. This service has been accessed from 58 countries in the world to date.

Parallel PDB Data Retriever "PDB Diving Booster"

A powerful PDB-data-retrieval system “PDB Diving Booster,” which is a collection of functions/methods for data-retrieval and datatransmission, will enhances high-performance parallel-distributed programming in the field of structural biology. This system provides with 1) functions for reading whatever irregular data in PDB file, 2) methods to access and calculate elementary data in the structured object representing a protein structure, and 3) powerful data-transmission functions/methods for a parallel distributed environment. High performance parallel analysis of protein structures is realized only by writing a simple and short program in C++ and linking over libraries.

Development of biological and chemical applications on a 64-node PC cluster

We describe the development of two parallel applications running on our PC cluster which consists of 64 Pentium Pro 200MHz microprocessors. One is an integrated parallel calculation system, called the PAPIA system, dedicated to protein information analysis. The PAPIA system enables very fast protein database searches (3-0 structure matching and sequence homology search) by fully utilizing the power of the 64 local hard-disks, as well as fast parallel calculations supported by the high-bandwidth and low-latency communication driver developed by RWCR We have starteda WWWsewice (http://www. rwcp.oKjp/papid) so any biologist can easily submit jobs to the PAPIA system through a WWW browse,: The service has been accessed from 55 countries in the world. The other application is parallel molecular dynamics simulation. We have eficiently ported the AMBER program to the PC cluster; and then appended an optional functionality of the Barnes-Hut tree code for quick and accurate calculation of Coulomb potentials without a cut-off approximation (PPPC method). The RWC PC Cluster shows good scalability in these two real parallel applications.

Parallelization of Space Plasma Particle Simulation

This paper describes parallelization of the space plasma particle simulation program “KEMPOI” and shows its performance on five different platforms. One of our goals is to solve the Electrostatic Solitary Wave (ESW) problem by intensive computer simulations, which previously took about 1 month for a single experiment (107 particles, 104 time steps). The parallelized version performs the same calculation in 3 hours and a bigger one (2.7 x 108 particles, 1.6 x 104 time steps) in about 8 hours on our 256-processor Hitachi SR2201 parallel computer. It has made systematic real-world space plasma particle simulations feasible.