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Ken Nishikawa

Publications and source records attributed to Ken Nishikawa.

9 recordsLinked to original sources
arXiv2017

Essential role of long non-coding RNAs in de novo chromatin modifications: The genomic address code hypothesis

The epigenome, i.e. the whole of chromatin modifications, is transferred from mother to daughter cells during cell differentiation. When de novo chromatin modifications (establishment or erasure of, respectively, new or pre-existing DNA methylations and/or histone modifications) are made in a daughter cell, however, it has a different epigenome than its mother cell. Although de novo chromatin modifications are an important event that comprises elementary processes of cell differentiation, its molecular mechanism remains poorly understood. We argue in this Letter that a key to solving this problem lies in understanding the role of long non-coding RNAs (lncRNAs)- a type of RNA that is becoming increasingly prominent in epigenetic studies. Many studies show that lncRNAs form ribonucleo-protein complexes in the nucleus and are involved in chromatin modifications. However, chromatin-modifying enzymes lack the information about genomic positions on which they act. It is known, on the other hand, that a single-stranded RNA in general can bind to a double-stranded DNA to form a triple helix. If each lncRNA forms a ribonucleo-protein complex with chromatin-modifying enzymes on one hand and, at the same time, a triple helix with a genomic region based on its specific nucleotide sequence on the other hand, it can induce de novo chromatin modifications at specific sites. Thus, the great variety of lncRNAs can be explained by the requirement for the diversity of "genomic address codes" specific to their cognate genomic regions where de novo chromatin modifications take place.

q-bio.GN↗
arXiv2013

Cooperation between genetic mutations and phenotypic plasticity can bypass the Weismann barrier: The cooperative model of evolution

The Weismann barrier, or the impossibility of inheritance of acquired traits, comprises a foundation of modern biology, and it has been a major obstacle in establishing the connection between evolution and ontogenesis. We propose the cooperative model based on the assumption that evolution is achieved by a cooperation between genetic mutations and acquired changes (phenotypic plasticity). It is also assumed in this model that natural selection operates on phenotypes, rather than genotypes, of individuals, and that the relationship between phenotypes and genotypes is one-to-many. In the simulations based on these assumptions, individuals exhibited phenotypic changes in response to an environmental change, corresponding multiple genetic mutations were increasingly accumulated in individuals in the population, and phenotypic plasticity was gradually replaced with genetic mutations. This result suggests that Lamarck's law of use and disuse can effectively hold without conflicting the Weismann barrier, and thus evolution can be logically connected with ontogenesis.

q-bio.PE↗
arXiv2007

Wang-Landau molecular dynamics technique to search for low-energy conformational space of proteins

Multicanonical molecular dynamics (MD) is a powerful technique for sampling conformations on rugged potential surfaces such as protein. However, it is notoriously difficult to estimate the multicanonical temperature effectively. Wang and Landau developed a convenient method for estimating the density of states based on a multicanonical Monte Carlo method. In their method, the density of states is calculated autonomously during a simulation. In this paper we develop a set of techniques to effectively apply the Wang-Landau method to MD simulations. In the multicanonical MD, the estimation of the derivative of the density of states is critical. In order to estimate it accurately, we devise two original improvements. First, the correction for the density of states is made smooth by using the Gaussian distribution obtained by a short canonical simulation. Second, an approximation is applied to the derivative, which is based on the Gaussian distribution and the multiple weighted histogram technique. A test of this method was performed with small polypeptides, Met-enkephalin and Trp-cage, and it is demonstrated that Wang-Landau MD is consistent with replica exchange MD but can sample much larger conformational space.

physics.comp-ph↗
arXiv2006

Gene cluster analysis method reliably identifies horizontally transferred genes and reveals their involvement in operon formation

The formation mechanism of operons remains controversial despite the proposal of many models. Although acquisition of genes from other species, horizontal gene transfer, is considered to occur, definitive concrete cases have been unavailable. It is desirable to select horizontally transferred genes reliably and examine their relationship to operons. We here developed a method to identify candidates of horizontally transferred genes based on minimization of gene cluster insertions/deletions. To select a benchmark set of positively horizontally transferred genes against which the candidate set can be appraised, we devised another procedure using intergenetic alignments. Comparison with the benchmark set of horizontally transferred genes demonstrated the absence of a significant number of false positives in the candidates, showing that the method identifies horizontally transferred genes with a high degree of confidence. Horizontally transferred genes constitute at least 5.5% of the genes in Escherichia, Shigella, and Salmonella and ~46% of which originate from other gamma-proteobacteria. Not only informational genes, but also operational genes (those involved in housekeeping) are horizontally transferred less frequently than expected. A gene-cluster analysis of Escherichia coli K-12 operons revealed that horizontal transfer produced four entire operons and expanded two operons, but deletion of intervening genes accounts for the formation of no operons. We propose that operons generally form by horizontal gene transfer. We further suggest that genes with related essential functions tend to reside in conserved operons, while genes in nonconserved operons generally confer slight advantage to the organisms and frequently undergo horizontal transfer and decay.

q-bio.PE↗
arXiv2006

Recoverable One-dimensional Encoding of Three-dimensional Protein Structures

Protein one-dimensional (1D) structures such as secondary structure and contact number provide intuitive pictures to understand how the native three-dimensional (3D) structure of a protein is encoded in the amino acid sequence. However, it has not been clear whether a given set of 1D structures contains sufficient information for recovering the underlying 3D structure. Here we show that the 3D structure of a protein can be recovered from a set of three types of 1D structures, namely, secondary structure, contact number and residue-wise contact order which is introduced here for the first time. Using simulated annealing molecular dynamics simulations, the structures satisfying the given native 1D structural restraints were sought for 16 proteins of various structural classes and of sizes ranging from 56 to 146 residues. By selecting the structures best satisfying the restraints, all the proteins showed a coordinate RMS deviation of less than 4Å from the native structure, and for most of them, the deviation was even less than 2Å. The present result opens a new possibility to protein structure prediction and our understanding of the sequence-structure relationship.

q-bio.BM↗
arXiv2006

CRNPRED: Highly Accurate Prediction of One-dimensional Protein Structures by Large-scale Critical Random Networks

Background: One-dimensional protein structures such as secondary structures or contact numbers are useful for three-dimensional structure prediction and helpful for intuitive understanding of the sequence-structure relationship. Accurate prediction methods will serve as a basis for these and other purposes. Results: We implemented a program CRNPRED which predicts secondary structures, contact numbers and residue-wise contact orders. This program is based on a novel machine learning scheme called critical random networks. Unlike most conventional one-dimensional structure prediction methods which are based on local windows of an amino acid sequence, CRNPRED takes into account the whole sequence. CRNPRED achieves, on average per chain, Q3 = 81% for secondary structure prediction, and correlation coefficients of 0.75 and 0.61 for contact number and residue-wise contact order predictions, respectively. Conclusion: CRNPRED will be a useful tool for computational as well as experimental biologists who need accurate one-dimensional protein structure predictions.

q-bio.BM↗
arXiv2005

Predicting Secondary Structures, Contact Numbers, and Residue-wise Contact Orders of Native Protein Structure from Amino Acid Sequence by Critical Random Networks

Prediction of one-dimensional protein structures such as secondary structures and contact numbers is useful for the three-dimensional structure prediction and important for the understanding of sequence-structure relationship. Here we present a new machine-learning method, critical random networks (CRNs), for predicting one-dimensional structures, and apply it, with position-specific scoring matrices, to the prediction of secondary structures (SS), contact numbers (CN), and residue-wise contact orders (RWCO). The present method achieves, on average, $Q_3$ accuracy of 77.8% for SS, correlation coefficients of 0.726 and 0.601 for CN and RWCO, respectively. The accuracy of the SS prediction is comparable to other state-of-the-art methods, and that of the CN prediction is a significant improvement over previous methods. We give a detailed formulation of critical random networks-based prediction scheme, and examine the context-dependence of prediction accuracies. In order to study the nonlinear and multi-body effects, we compare the CRNs-based method with a purely linear method based on position-specific scoring matrices. Although not superior to the CRNs-based method, the surprisingly good accuracy achieved by the linear method highlights the difficulty in extracting structural features of higher order from amino acid sequence beyond that provided by the position-specific scoring matrices.

q-bio.BM↗
arXiv2005

Predicting Residue-wise Contact Orders of Native Protein Structure from Amino Acid Sequence

Residue-wise contact order (RWCO) is a new kind of one-dimensional protein structures which represents the extent of long-range contacts. We have recently shown that a set of three types of one-dimensional structures (secondary structure, contact number, and RWCO) contains sufficient information for reconstructing the three-dimensional structure of proteins. Currently, there exist prediction methods for secondary structure and contact number from amino acid sequence, but none exists for RWCO. Also, the properties of amino acids that affect RWCO is not clearly understood. Here, we present a linear regression-based method to predict RWCO from amino acid sequence, and analyze the regression parameters to identify the properties that correlates with the RWCO. The present method achieves the significant correlation of 0.59 between the native and predicted RWCOs on average. An unusual feature of the RWCO prediction is the remarkably large optimal half window size of 26 residues. The regression parameters for the central and near-central residues of the local sequence segment highly correlate with those of the contact number prediction, and hence with hydrophobicity.

q-bio.BM↗