Ligand Path: A Software Tool for Mapping Dynamic Ligand Migration Channel Networks

AbstractProteins are essential compositions of the living organisms and involved in the processes of different life events. Basically proteins are like amazing tiny bio-machines performing the functions in a stable and predictable manner and understanding the underline mechanisms can facilitate the pharmaceutical development. However, protein functions are not carried in a static style, so experimental observations of these dynamic movements of the drugs inside the proteins are difficult, so computational methods have an important and irreplaceable role.We developed a software tool called LigandPath for mapping the ligand migration channels in a constantly moving protein and this software can function with CADD (Computer aided drug design) software to map the possible migration pathways of candidate drugs inside a protein. Traditionally, biologists use MD (Molecular Dynamics) simulation to locate the ligand migration channels, but it takes long time for them to observe the complete migration paths. In order to overcome the limitations of the trajectory-based MD simulation, we adopt a computational method inspired from robotic motion planning called DyME (Dynamic Map Ensemble) and we develop the software tool LigandPath based on DyME. The software tool has already been successfully applied to map the potential migration channels of drugs candidates of three proteins, PPAR (peroxisome proliferator-activated receptors), UROD (uroporphyrinogen decarboxylase) and Sirt1 (silent information regulator 1) complexes in three publications

Statistical Machine Learning in Brain State Classification using EEG Data

In this article, we discuss how to use a variety of machine learning methods, e.g. tree bagging, random forest, boost, support vector machine, and Gaussian mixture model, for building classifiers for electroencephalogram (EEG) data, which is collected from different brain states on different subjects. Also, we discuss how training data size influences misclassification rate. Moreover, the number of subjects that contributes to the training data affects misclassification rate. Furthermore, we discuss how sample entropy contributes to building a classifier. Our results show that classification based on sample entropy give the smallest misclassification rate. Moreover, two data sets were collected from one channel and seven channels respectively. The classification results of each data set show that the more channels we use, the less misclassification we have. Our results show that it is promising to build a self-adaptive classification system by using EEG data to distinguish idle from active state

Multi-party quantum key agreement protocol with authentication

Utilizing the advantage of quantum entanglement swapping, a multi-party quantum key agreement protocol with authentication is proposed. In this protocol, a semi-trusted third party is introduced, who prepares Bell states, and sends one particle to multiple participants respectively. After that the participants can share a Greenberger-Horne-Zeilinger state by entanglement swapping. Finally, these participants measure the particles in their hands and obtain an agreement key. Here, classical hash function and Hadamard operation are utilized to authenticate the identity of participants. The correlations of GHZ states ensure the security of the proposed protocol. To illustrated it detailly, the security of this protocol against common attacks is analyzed, which shows that the proposed protocol is secure in theory