김동섭 교수

Three-dimensional (3D) chromatin structure is an emerging paradigm for understanding gene regulation mechanisms. Hi-C (high-throughput chromatin conformation capture), a method to detect long-range chromatin interactions, allows extensive genome-wide investigation of 3D chromatin structure. However, broad application of Hi-C data have been hindered by the level of complexity in processing Hi-C data and the large size of raw sequencing data. In order to overcome these limitations, we constructed

There are several issues related to reverse docking methods such as target structure dataset construction, computational efficiency, how to include receptor flexibility, and most importantly, how to properly normalize the docking scores. In order for reverse docking to become a truly useful tool for the drug discovery, these issues need to be adequately resolved.

Prediction of protein-ligand interactions is a critical step during the initial phase of drug discovery. We propose a novel deep-learning-based prediction model based on a graph convolutional neural network, named GraphBAR, for protein-ligand binding affinity. Graph convolutional neural networks reduce the computational time and resources that are normally required by the traditional convolutional neural network models. In this technique, the structure of a protein-ligand complex is represented

Supplementary data are available at Bioinformatics online.

It is a common belief that some residues of a protein are more important than others. In some cases, point mutations of some residues make butterfly effect on the protein structure and function, but in other cases they do not. In addition, the residues important for the protein function tend to be not only conserved but also coevolved with other interacting residues in a protein. Motivated by these observations, the authors propose that there is a network composed of the residues, the residue-re

Topologically associated domains (TADs) are 3D genomic structures with high internal interactions that play important roles in genome compaction and gene regulation. Their genomic locations and their association with CCCTC-binding factor (CTCF)-binding sites and transcription start sites (TSSs) were recently reported. However, the relationship between TADs and other genomic elements has not been systematically evaluated. This was addressed in the present study, with a focus on the enrichment of

Knowing protein structure and inferring its function from the structure are one of the main issues of computational structural biology, and often the first step is studying protein secondary structure. There have been many attempts to predict protein secondary structure contents. Previous attempts assumed that the content of protein secondary structure can be predicted successfully using the information on the amino acid composition of a protein. Recent methods achieved remarkable prediction acc

Our method is remarkable in that it is powerful and intuitive approach without need of a sophisticated training algorithm. Moreover, our method is generally applicable to other types of PTMs.

In this study, we investigate what types of interactions are specific to their biological function, and what types of interactions are persistent regardless of their functional category in transient protein-protein heterocomplexes. This is the first approach to analyze protein-protein interfaces systematically at the molecular interaction level in the context of protein functions. We perform systematic analysis at the molecular interaction level using classification and feature subset selection

Vibrational line shapes for a hydrogen atom on an embedded atom model (EAM) of the Ni(111) surface are extracted from path integral Monte Carlo data. Maximum entropy methods are utilized to stabilize this inversion. Our results indicate that anharmonic effects are significant, particularly for vibrational motion parallel to the surface. Unlike their normal mode analogs, calculated quantum line shapes for the EAM potential predict the correct ordering of vibrational features corresponding to para

http://pbil.kaist.ac.kr

A new numerical procedure for the study of finite temperature quantum dynamics is developed. The method is based on the observation that the real and imaginary time dynamical data contain complementary types of information. Maximum entropy methods, based on a combination of real and imaginary time input data, are used to calculate the spectral densities associated with real time correlation functions. Model studies demonstrate that the inclusion of even modest amounts of short-time real time dat

We have investigated one of the strongest superacid systems, SbF5 in liquid HF, by ab initio molecular dynamics simulation. In dilute solution a barrierless, diffusion-limited fluorination reaction takes place to form the SbF6- anion and H2F+ cation. The initial contact ion pair evolves to become a fully separated ion pair by a series of stepwise, incoherent proton jumps. On average, the SbF6- anion had an octahedral structure with the average bond length of 1.9 Å. The cationic species is a prot

ADVERTISEMENT RETURN TO ISSUEPREVCommunicationNEXTLiquid Hydrogen Fluoride with an Excess Proton: Ab Initio Molecular Dynamics Study of a SuperacidDongsup Kim and Michael L. KleinView Author Information Center for Molecular Modeling and Department of Chemistry, University of Pennsylvania Philadelphia, Pennsylvania 19104-6323 Cite this: J. Am. Chem. Soc. 1999, 121, 48, 11251–11252Publication Date (Web):November 19, 1999Publication History Received26 August 1999Revised6 October 1999Published onlin