Demo Data from EDBT 2026 / reviewers in the wild / expert
Motonori Ota
dblp:04/6142
· DBLP profile ↗
4ranked-venue papers
0as first author
0since 2021 · last 2018
0000-0001-9090-668XORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 4
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
3 papers |
Bioinformatics and computational biology · 100% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › protein structure analysis
protein structure alignment |
0.3 | 1 | 2018 | MICAN-SQ: a sequential protein structure alignment program that is applicable to monomers and all types of oligomers · Bioinform. 2018 |
Bioinformatics and computational biology
protein structure analysis |
0.3 | 1 | 2018 | MICAN-SQ: a sequential protein structure alignment program that is applicable to monomers and all types of oligomers · Bioinform. 2018 |
Bioinformatics and computational biology
structural bioinformatics |
0.1 | 1 | 2012 | SCPC: a method to structurally compare protein complexes · Bioinform. 2012 |
Bioinformatics and computational biology › protein function prediction › functional residue prediction
catalytic residue prediction |
0.1 | 1 | 2005 | P-cats: prediction of catalytic residues in proteins from their tertiary structures · Bioinform. 2005 |
Bioinformatics and computational biology
protein function prediction |
0.1 | 1 | 2005 | P-cats: prediction of catalytic residues in proteins from their tertiary structures · Bioinform. 2005 |
Bioinformatics and computational biology › protein analysis
protein-protein interaction |
0.0 | 1 | 2012 | SCPC: a method to structurally compare protein complexes · Bioinform. 2012 |
Methods — techniques the papers use, named apart from their topics
sequential alignment · 0.3dynamic programming · 0.3structural similarity scoring · 0.1secondary structure element alignment · 0.1structural analysis · 0.1multiple sequence alignment · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | MICAN-SQ: a sequential protein structure alignment program that is applicable to monomers and all types of oligomersabstractMotivation: Protein structure alignment is a significant tool to understand evolutionary processes and physicochemical properties of proteins. Important targets of structure alignment are not only monomeric but also oligomeric proteins that sometimes include domain swapping or fusions. Although various protein structural alignment programs have been developed, no method is applicable to any protein pair regardless of the number of chain components and oligomeric states with retaining sequential restrictions: structurally equivalent regions must be aligned in the same order along protein sequences. Results: In this paper, we introduced a new sequential protein structural alignment algorithm MICAN-SQ, which is applicable to protein structures in all oligomeric states. In particular, MICAN-SQ allows the complicated structural alignments of proteins with domain swapping or fusion regions. To validate MICAN-SQ, alignment accuracies were evaluated using curated alignments of monomers and examples of domain swapping, and compared with those of pre-existing protein structural alignment programs. The results of this study show that MICAN-SQ has superior accuracy and robustness in comparison with previous programs and offers limited computational times. We also demonstrate that MICAN-SQ correctly aligns very large complexes and fused proteins. The present computations warrant the consideration of MICAN-SQ for studies of evolutionary and physicochemical properties of monomeric structures and all oligomer types. Availability and implementation: The MICAN program was implemented in C. The source code and executable file can be freely downloaded from http://www.tbp.cse.nagoya-u.ac.jp/MICAN/. Supplementary information: Supplementary data are available at Bioinformatics online. Shintaro Minami, Kengo Sawada, Motonori Ota, George Chikenji |
Bioinform. | 3 |
| 2012 | SCPC: a method to structurally compare protein complexesabstractMOTIVATION: Protein-protein interactions play vital functional roles in various biological phenomena. Physical contacts between proteins have been revealed using experimental approaches that have solved the structures of protein complexes at atomic resolution. To examine the huge number of protein complexes available in the Protein Data Bank, an efficient automated method that compares protein complexes is required. RESULTS: We have developed Structural Comparison of Protein Complexes (SCPC), a novel method to structurally compare protein complexes. SCPC compares the spatial arrangements of subunits in a complex with those in another complex using secondary structure elements. Similar substructures are detected in two protein complexes and the similarity is scored. SCPC was applied to dimers, homo-oligomers and haemoglobins. SCPC properly estimated structural similarities between the dimers examined as well as an existing method, MM-align. Conserved substructures were detected in a homo-tetramer and a homo-hexamer composed of homologous proteins. Classification of quaternary structures of haemoglobins using SCPC was consistent with the conventional classification. The results demonstrate that SCPC is a valuable tool to investigate the structures of protein complexes. AVAILABILITY: SCPC is available at http://idp1.force.cs.is.nagoya-u.ac.jp/scpc/. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Ryotaro Koike, Motonori Ota |
Bioinform. | 2 |
| 2008 | An enhanced partial order curve comparison algorithm and its application to analyzing protein folding trajectoriesabstractBACKGROUND: Understanding how proteins fold is essential to our quest in discovering how life works at the molecular level. Current computation power enables researchers to produce a huge amount of folding simulation data. Hence there is a pressing need to be able to interpret and identify novel folding features from them. RESULTS: In this paper, we model each folding trajectory as a multi-dimensional curve. We then develop an effective multiple curve comparison (MCC) algorithm, called the enhanced partial order (EPO) algorithm, to extract features from a set of diverse folding trajectories, including both successful and unsuccessful simulation runs. The EPO algorithm addresses several new challenges presented by comparing high dimensional curves coming from folding trajectories. A detailed case study on miniprotein Trp-cage 1 demonstrates that our algorithm can detect similarities at rather low level, and extract biologically meaningful folding events. CONCLUSION: The EPO algorithm is general and applicable to a wide range of applications. We demonstrate its generality and effectiveness by applying it to aligning multiple protein structures with low similarities. For user's convenience, we provide a web server for the algorithm at http://db.cse.ohio-state.edu/EPO. Hakan Ferhatosmanoglu, Motonori Ota, Yusu Wang 0001 |
BMC Bioinform. | 3 |
| 2005 | P-cats: prediction of catalytic residues in proteins from their tertiary structuresabstractUNLABELLED: P-cats is a web server that predicts the catalytic residues in proteins from the atomic coordinates. P-cats receives a coordinate file of the tertiary structure and sends out analytical results via e-mail. The reply contains a summary and two URLs to allow the user to examine the conserved residues: one for interactive images of the prediction results and the other for a graphical view of the multiple sequence alignment. AVAILABILITY: P-cats is freely available at http://p-cats.hgc.jp/p-cats CONTACT: [email protected] Kengo Kinoshita, Motonori Ota |
Bioinform. | 2 |