KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース)

Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N^2) space and O(N^2 L) time, but FastTree requires just O( NLa + N sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes of memory. In simulations, FastTree was slightly more accurate than neighbor joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.

/pdf/fast-tree-computing-large-minimum-evolution-trees-with-4grq0gwfpy.pdf

Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix

動注化学療法における pharmacokinetics と臨床

With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the ava...

RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview.

Since the discovery of AMP-activated protein kinase (AMPK) as a central regulator of energy homeostasis, many exciting insights into its structure, regulation and physiological roles have been revealed. While exercise, caloric restriction, metformin and many natural products increase AMPK activity and exert a multitude of health benefits, developing direct activators of AMPK to elicit beneficial effects has been challenging. However, in recent years, direct AMPK activators have been identified and tested in preclinical models, and a small number have entered clinical trials. Despite these advances, which disease(s) represent the best indications for therapeutic AMPK activation and the long-term safety of such approaches remain to be established.

/pdf/amp-activated-protein-kinase-the-current-landscape-for-drug-1tzru29iw4.pdf

AMP-activated protein kinase: the current landscape for drug development.

Characterization of native, intermediate, and denatured states is crucial for understanding the factors influencing the stability of proteins. We have carried out molecular dynamics simulations to study the unfolding of three peripheral subunit binding domains (PSBDs): E. coli BBL, Bacillus stearothermophilus E3BD, and human hbSBD, at three different temperatures: 300, 330, and 400 K, and in the presence of two solvents: water and 5 M guanidinium hydrochloride (GndCl) solution. These proteins share similar folds, with two parallel helices, maintained via a hydrophobic core comprising residues from their interconnecting loop. BBL is more sensitive to thermal and chemical denaturation in comparison to hbSBD, and E3BD is the most stable of all of the three proteins. The effect of temperature on the stability of these proteins is more pronounced in “water-only” simulations compared to that in the presence of guanidium hydrochloride in high concentrations. Our results show cooperative unfolding transitions of ...

Unfolding Transitions of Peripheral Subunit Binding Domains Show Cooperative Behavior.

Pathogens have specialized proteins to invade and infect the host by interacting with the host proteins. Despite several experimental studies, knowledge about host-pathogen interactions (HPIs) is limited. However, this knowledge is essential to understand disease mechanism and identify potential targets for disease prevention and intervention. In this study, we propose a pipeline for identifying host-pathogen protein-protein interactions. The pipeline consists of a biological knowledge-based filter, a domain-based statistical filter followed by a sequence-signature based machine learning method. Interspecies protein-protein interaction data between eukaryotic pathogens and human was used to build the domain-based statistical model. Known host-pathogen interactions of all eukaryotic pathogens from HPIDB and non-interacting protein data from Negatome were used as positive and negative training sets, respectively to train the machine learning model. We applied our pipeline to predict HPIs between human and malarial parasite, P. falciparum. Several biologically relevant features like tissue specificity, protein annotations and functions were used to construct a primary list of possible HPIs. Next, the statistical and machine learning based models were used as filters on the initial list to predict novel protein-protein interactions between human and P. falciparum during intra-erythrocytic stages. We have predicted several HPIs that are involved in host erythrocyte cytoskeleton remodeling, signaling and immune response. The proposed method can be used to find novel HPIs between human cells and any eukaryotic pathogens.

Gopalakrishnan Bulusu

Papers

Unfolding Transitions of Peripheral Subunit Binding Domains Show Cooperative Behavior.

Computational predictions of host-pathogen interactions using domain and sequence signature