Language：English   Publishing type：Research paper (scientific journal)  

Language：English  

DOI： 10.1103/PhysRevE.104.014404

Web of Science

Language：English  

DOI： 10.1038/s41598-019-55069-y

Web of Science

DOI： 10.1002/pro.3285

Web of Science

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1038/s41598-017-10275-4

Web of Science

Language：English   Publishing type：Research paper (scientific journal)  

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1038/srep17209

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1021/acs.jcim.5b00134

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1371/journal.pone.0107959

Language：English   Publishing type：Research paper (scientific journal)  

Background

Protein pairs that have the same secondary structure packing arrangement but have different topologies have attracted much attention in terms of both evolution and physical chemistry of protein structures. Further investigation of such protein relationships would give us a hint as to how proteins can change their fold in the course of evolution, as well as a insight into physico-chemical properties of secondary structure packing. For this purpose, highly accurate sequence order independent structure comparison methods are needed.
Results

We have developed a novel protein structure alignment algorithm, MICAN (a structure alignment algorithm that can handle Multiple-chain complexes, Inverse direction of secondary structures, Cα only models, Alternative alignments, and Non-sequential alignments). The algorithm was designed so as to identify the best structural alignment between protein pairs by disregarding the connectivity between secondary structure elements (SSE). One of the key feature of the algorithm is utilizing the multiple vector representation for each SSE, which enables us to correctly treat bent or twisted nature of long SSE. We compared MICAN with other 9 publicly available structure alignment programs, using both reference-dependent and reference-independent evaluation methods on a variety of benchmark test sets which include both sequential and non-sequential alignments. We show that MICAN outperforms the other existing methods for reproducing reference alignments of non-sequential test sets. Further, although MICAN does not specialize in sequential structure alignment, it showed the top level performance on the sequential test sets. We also show that MICAN program is the fastest non-sequential structure alignment program among all the programs we examined here.
Conclusions

MICAN is the fastest and the most accurate program among non-sequential alignment programs we examined here. These results suggest that MICAN is a highly effective tool for automatically detecting non-trivial structural relationships of proteins, such as circular permutations and segment-swapping, many of which have been identified manually by human experts so far. The source code of MICAN is freely download-able at http://www.tbp.cse.nagoya-u.ac.jp/MICAN.

DOI： 10.1186/1471-2105-14-24

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

The folding energy landscape of proteins has been suggested to be funnel-like with some degree of ruggedness on the slope. How complex the landscape, however, is still rather unclear. Many experiments for globular proteins suggested relative simplicity, whereas molecular simulations of shorter peptides implied more complexity. Here, by using complete conformational sampling of 2 globular proteins, protein G and src SH3 domain and 2 related random peptides, we investigated their energy landscapes, topological properties of folding networks, and folding dynamics. The projected energy surfaces of globular proteins were funneled in the vicinity of the native but also have other quite deep, accessible minima, whereas the randomized peptides have many local basins, including some leading to seriously misfolded forms. Dynamics in the denatured part of the network exhibited basin-hopping itinerancy among many conformations, whereas the protein reached relatively well-defined final stages that led to their native states. We also found that the folding network has the hierarchic nature characterized by the scale-free and the small-world properties.

Language：English   Publishing type：Research paper (scientific journal)  

Currently, one of the most serious problems in protein-folding simulations for de novo structure prediction is conformational sampling of medium-to-large proteins. In vivo, folding of these proteins is mediated by molecular chaperones. Inspired by the functions of chaperonins, we designed a simple chaperonin-like simulation protocol within the framework of the standard fragment assembly method: in our protocol, the strength of the hydrophobic interaction is periodically modulated to help the protein escape from misfolded structures. We tested this protocol for 38 proteins and found that, using a certain defined criterion of success, our method could successfully predict the native structures of 14 targets, whereas only those of 10 targets were successfully predicted using the standard protocol. In particular, for non-alpha-helical proteins, our method yielded significantly better predictions than the standard approach. This chaperonin-inspired protocol that enhanced de novo structure prediction using folding simulations may, in turn, provide new insights into the working principles underlying the chaperonin system.

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

Predicting protein tertiary structure by folding-like simulations is one of the most stringent tests of how much we understand the principle of protein folding. Currently, the most successful method for folding-based structure prediction is the fragment assembly (FA) method. Here, we address why the FA method is so successful and its lesson for the folding problem. To do so, using the FA method, we designed a structure prediction test of "chimera proteins." In the chimera proteins, local structural preference is specific to the target sequences, whereas nonlocal interactions are only sequence-independent compaction forces. We find that these chimera proteins can find the native folds of the intact sequences with high probability indicating dominant roles of the local interactions. We further explore roles of local structural preference by exact calculation of the HP lattice model of proteins. From these results, we suggest principles of protein folding: For small proteins, compact structures that are fully compatible with local structural preference are few, one of which is the native fold. These local biases shape up the funnel-like energy landscape.

Language：English   Publishing type：Research paper (scientific journal)  

Predicting protein tertiary structures by in silico folding is still very difficult for proteins that have new folds. Here, we developed a coarse-grained energy function, SimFold, for de novo structure prediction, performed a benchmark test of prediction with fragment assembly simulations for 38 test proteins, and proposed consensus prediction with Rosetta. The SimFold energy consists of many terms that take into account solvent-induced effects on the basis of physicochemical consideration. In the benchmark test, SimFold succeeded in predicting native structures within 6.5 A for 12 of 38 proteins; this success rate was the same as that by the publicly available version of Rosetta (ab initio version 1.2) run with default parameters. We investigated which energy terms in SimFold contribute to structure prediction performance, finding that the hydrophobic interaction is the most crucial for the prediction, whereas other sequence-specific terms have weak but positive roles. In the benchmark, well-predicted proteins by SimFold and by Rosetta were not the same for 5 of 12 proteins, which led us to introduce consensus prediction. With combined decoys, we succeeded in prediction for 16 proteins, four more than SimFold or Rosetta separately. For each of 38 proteins, structural ensembles generated by SimFold and by Rosetta were qualitatively compared by mapping sampled structural space onto two dimensions. For proteins of which one of the two methods succeeded and the other failed in prediction, the former had a less scattered ensemble located around the native. For proteins of which both methods succeeded in prediction, often two ensembles were mixed up.

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

The global energy landscape of src SH3 domain is comprehensively explored and folding mechanisms are discussed by using a physico-chemical protein model and the reversible fragment assembly method. We found that the lowest energy structure found in simulations is quite similar to the native, an apparent free energy barrier exists between the denatured and native states, and the computed folding transition state ensemble is well consistent with experimental data. Interestingly, non-native alpha-helical contents are found at early stage of folding, which also seems to be consistent with a recent experiment. These results suggest that the fragment assembly method, originally developed for structure prediction, can be used for studying folding mechanisms as well.

Event date： 2016.6

Language：Japanese   Presentation type：Poster presentation  

Country：Japan  

Event date： 2015.6

Language：Japanese   Presentation type：Poster presentation  

Country：Japan  

Event date： 2014.6

Language：Japanese   Presentation type：Poster presentation  

Country：Japan  

Event date： 2013.6

Language：Japanese   Presentation type：Poster presentation  

Country：Japan  

Event date： 2012.6

Language：Japanese   Presentation type：Poster presentation  

Country：Japan  

Event date： 2011.6

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Country：Japan  

Event date： 2011.2 - 2011.3

Language：English   Presentation type：Oral presentation (invited, special)  

Country：Korea, Republic of  

Event date： 2010.6

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Country：Japan  

Event date： 2009.9

Language：English   Presentation type：Oral presentation (invited, special)  

Event date： 2009.7

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Country：Japan  

Event date： 2008.12

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Country：Japan  

Event date： 2008.11

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Country：Japan  

Event date： 2008.9

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan