Tokyo Institute of Technology · Biochemistry, Genetics and Molecular Biology
Professor Yutaka Akiyama's research lab specializes in computational biology and bioinformatics, focusing on the development of advanced algorithms and simulation methods for drug discovery and systems biology. Key research directions include protein-protein interaction prediction, cyclic peptide drug design with an emphasis on membrane permeability, and high-throughput homology search for metagenomic data. The lab integrates computational techniques such as molecular dynamics simulations, machine learning, and structural bioinformatics to address challenges in drug target identification and virtual screening.
Figures are computed from collected data and may differ slightly.
An artificial neuron model, called the Gaussian machine, is introduced. Gaussian machines have graded output responses, as well as stochastic behavior caused by random noise added to the input of each neuron. The Gaussian machine model includes the McCulloch-Pitts model, the Hopfield machine, and the Boltzmann machine as special cases. To demonstrate the efficiency of Gaussian machines, a solution of the traveling salesperson problem (TSP) is presented. Gaussian machines show an ability to solve
DNA sequences are translated into protein coding sequences and then further assigned to protein families in metagenomic analyses, because of the need for sensitivity. However, huge amounts of sequence data create the problem that even general homology search analyses using BLASTX become difficult in terms of computational cost. We designed a new homology search algorithm that finds seed sequences based on the suffix arrays of a query and a database, and have implemented it as GHOSTX. GHOSTX achi
Membrane permeability is a significant obstacle facing the development of cyclic peptide drugs. However, membrane permeation mechanisms are poorly understood. To investigate common features of permeable (and nonpermeable) designs, it is necessary to reproduce the membrane permeation process of cyclic peptides through the lipid bilayer. We simulated the membrane permeation process of 100 six-residue cyclic peptides across the lipid bilayer based on steered molecular dynamics (MD) and replica-exch
The elucidation of protein-protein interaction (PPI) networks is important for understanding cellular structure and function and structure-based drug design. However, the development of an effective method to conduct exhaustive PPI screening represents a computational challenge. We have been investigating a protein docking approach based on shape complementarity and physicochemical properties. We describe here the development of the protein-protein docking software package "MEGADOCK" that sample
Recently, cyclic peptides have been considered breakthrough drugs because they can interact with "undruggable" targets such as intracellular protein-protein interactions. Membrane permeability is an essential indicator of oral bioavailability and intracellular targeting, and the development of membrane-permeable peptides is a bottleneck in cyclic peptide drug discovery. Although many experimental data on membrane permeability of cyclic peptides have been reported, a comprehensive database is not
The identification of comprehensive drug-target interactions is important in drug discovery. Although numerous computational methods have been developed over the years, a gold standard technique has not been established. Computational ligand docking and structure-based drug design allow researchers to predict the binding affinity between a compound and a target protein, and thus, they are often used to virtually screen compound libraries. In addition, docking techniques have also been applied to
Protein information analysis is widely regarded as a key technology in drug design, macromolecular engineering, and understanding genome sequences. Because vast amount of calculations are required, further speed-up for protein information analysis is very much in demand. We have implemented the PAPIA (PArallel Protein Information Analysis) system on the RWC PC cluster IIa (PAPIA cluster) which consists of 64 Pentium Pro 200MHz microprocessors. The PAPIA system performs fast parallel processing f
Techniques for predicting interactions between a drug and a target (protein) are useful for strategic drug repositioning. Neighborhood regularized logistic matrix factorization (NRLMF) is one of the state-of-the-art drug-target interaction prediction methods; it is based on a statistical model using the Bernoulli distribution. However, the prediction is not accurate when drug-target interaction pairs have less interaction information (e.g., the sum of the number of ligands for a target and the n
Cyclic peptides have attracted attention as a promising pharmaceutical modality due to their potential to selectively inhibit previously undruggable targets, such as intracellular protein-protein interactions. Poor membrane permeability is the biggest bottleneck hindering successful drug discovery based on cyclic peptides. Therefore, the development of computational methods that can predict membrane permeability and support elucidation of the membrane permeation mechanism of drug candidate pepti
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