SEMINAR

A unified statistical model of protein multiple-sequence alignment integrating direct coupling and insertions

Abstract
The multiple-sequence alignment (MSA) of a protein family provides a wealth of information in terms of the conservation pattern of amino acid residues not only at each alignment site but also between distant sites. In order to statistically model the MSA incorporating both short-range and long-range correlations as well as insertions, I have derived a lattice gas model of the MSA based on the principle of maximum entropy. The partition function, obtained by the transfer matrix method with a mean-field approximation, accounts for all possible alignments with all possible sequences. The model parameters for short-range and long-range interactions were determined by a self-consistent condition and by a Gaussian approximation, respectively. Using this model with and without long-range interactions, I analyzed the globin and V-set domains by increasing the "temperature" and by "mutating" a site. The correlations between residue conservation and various measures of the system's stability indicate that the long-range interactions make the conservation pattern more specific to the structure, and increasingly stabilize better conserved residues.

BIOGRAPHY

2009-present: Associate Professor, Institute for Protein Research, Osaka University
2006-2008: Visiting Associate Professor, IPR, Osaka University
2003-2006: Assistant Professor, National Institute of Genetics, Japan
2001-2003: Postdoctoral fellow, Kobe University, Japan
2001: PhD, Department of Genetics, The Graduate University for Advanced Studies (SOKENDAI), Japan

Research Fields and Interests:
I am interested in relationships between protein sequences and structures and how they encode their functions. To that aim, I have been developing theoretical and computational methods as well as databases. For the last several years I was working on structure comparison of binding sites of proteins. More recently, I have been developing a theoretical model of protein sequences based on statistical physics to better exploit the rich information in multiple-sequence alignments.

Selected Publications:
1. A unified statistical model of protein multiple sequence alignment integrating direct coupling and insertions.
Kinjo AR.
(preprint) (2015) [arXiv:1506.02076]
2. GIRAF: a method for fast search and flexible alignment of ligand binding interfaces in proteins at atomic resolution.
Kinjo AR, Nakamura H.
BIOPHYSICS 8:79-94 (2012) [DOI:10.2142/biophysics.8.79]
3. Protein Data Bank Japan (PDBj): Maintaining a structural data archive and Resource Description Framework format.
Kinjo AR, Suzuki H, Yamashita R, Ikegawa Y, Kudo T, Igarashi R, Kengaku Y, Cho H, Standley DM, Nakagawa A, Nakamura H.
Nucleic Acids Research 40:D453-D460 (2012) [DOI:10.1093/nar/gkr811]
4. Comprehensive structural classification of ligand binding motifs in proteins.
Kinjo AR, Nakamura H.
Structure 17:234-246 (2009) [DOI:10.1016/j.str.2008.11.009]
5. On the optimal contact potential of proteins.
Kinjo AR, Miyazawa S.
Chemical Physics Letters 451:132-135 (2008) [DOI:10.1016/j.cplett.2007.12.005]