Biology subjects

Kinoshita, K.

Publications and source records attributed to Kinoshita, K..

3 recordsLinked to original sources

bioRxiv2018

Structural basis of HEAT-kleisin interactions in the human condensin I subcomplex

ABSTRUCTCondensin I is a multi-protein complex that plays an essential role in mitotic chromosome assembly and segregation in eukaryotes. It is composed of five subunits: two SMC (SMC2 and SMC4), a kleisin (CAP-H) and two HEAT-repeat (CAP-D2 and -G) subunits. Although it has been shown that balancing acts of the two HEAT-repeat subunits enable this complex to support dynamic assembly of chromosomal axes in vertebrate cells, its underlying mechanisms remain poorly understood. Here, we report the crystal structure of a human condensin I subcomplex comprising hCAP-G and hCAP-H. hCAP-H binds to the concave surfaces of a harp-shaped HEAT repeat domain of hCAP-G. A physical interaction between hCAP-G and hCAP-H is indeed essential for mitotic chromosome assembly recapitulated in Xenopus egg cell-free extracts. Furthermore, this study reveals that the human CAP-G-H subcomplex has the ability to interact with not only a double-stranded DNA, but also a single-stranded DNA, implicating potential, functional divergence of the vertebrate condensin I complex in mitotic chromosome assembly.

cell biology↗

bioRxiv2018

Molecular mechanism of depolarization-dependent inactivation in W366F mutant of Kv1.2

Voltage-gated potassium channels play crucial roles in regulating membrane potential. They are activated by membrane depolarization, allowing the selective permeation of potassium ions across the plasma membrane, and enter a nonconducting state after lasting depolarization of membrane potential, a process known as inactivation. Inactivation in voltage-activated potassium channels occurs through two distinct mechanisms, N-type inactivation and C-type inactivation. C-type inactivation is caused by conformational changes in the extracellular mouth of the channel, while N-type inactivation is elicited by changes in the cytoplasmic mouth of the protein. The W434F-mutated Shaker channel is known as a nonconducting mutant and is in a C-type inactivation state at a depolarizing membrane potential. To clarify the structural properties of C-type inactivated protein, we performed molecular dynamics simulations of the wild-type and W366F (corresponding to W434F in Shaker) mutant of the Kv1.2-2.1 chimera channel. The W366F mutant was in a nearly nonconducting state with a depolarizing voltage and recovered from inactivation with a reverse voltage. Our simulations and 3D-RISM analysis suggested that structural changes in the selective filter upon membrane depolarization trap potassium ions around the entrance of the selectivity filter and prevent ion permeation. This pore restriction is involved in the molecular mechanism of C-type inactivation.

physiology↗

bioRxiv2017

De novo profile generation based on sequence context specificity with the long short-term memory network

Long short-term memory (LSTM) is one of the most attractive deep learning methods to learn time series or contexts of input data. Increasing studies, including biological sequence analyses in bioinformatics, utilize this architecture. Amino acid sequence profiles are widely used for bioinformatics studies, such as sequence similarity searches, multiple alignments, and evolutionary analyses. Currently, many biological sequences are becoming available, and the rapidly increasing amount of sequence data emphasizes the importance of scalable generators of amino acid sequence profiles. We employed the LSTM network and developed a novel profile generator to construct profiles without any assumptions, except for input sequence context. Our method could generate better profiles than existing de novo profile generators, including CSBuild and RPS-BLAST, on the basis of profile-sequence similarity search performance with linear calculation costs against input sequence size. In addition, we analyzed the effects of the memory power of LSTM and found that LSTM had high potential power to detect long-range interactions between amino acids, as in the case of beta-strand formation, which has been a difficult problem in protein bioinformatics using sequence information. We demonstrated the importance of sequence context and the feasibility of LSTM on biological sequence analyses. Our results demonstrated the effectiveness of memories in LSTM and showed that our de novo profile generator, SPBuild, achieved higher performance than that of existing methods for profile prediction of beta-strands, where long-range interactions of amino acids are important and are known to be difficult for the existing window-based prediction methods. Our findings will be useful for the development of other prediction methods related to biological sequences by machine learning methods.

bioinformatics↗