Discovery of Rab1 binding sites using an ensemble of clustering methods.

Targeting non-native-ligand binding sites for potential investigative and therapeutic applications is an attractive strategy in proteins that share common native ligands, as in Rab1 protein. Rab1 is a subfamily member of Rab proteins, which are members of Ras GTPase superfamily. All Ras GTPase superfamily members bind to native ligands GTP and GDP, that switch on and off the proteins, respectively. Rab1 is physiologically essential for autophagy and transport between endoplasmic reticulum and Golgi apparatus. Pathologically, Rab1 is implicated in human cancers, a neurodegenerative disease, cardiomyopathy, and bacteria-caused infectious diseases. We have performed structural analyses on Rab1 protein using a unique ensemble of clustering methods, including multi-step principal component analysis, non-negative matrix factorization, and independent component analysis, to better identify representative Rab1 proteins than the application of a single clustering method alone does. We then used the identified representative Rab1 structures, resolved in multiple ligand states, to map their known and novel binding sites. We report here at least a novel binding site on Rab1, involving Rab1-specific residues that could be further explored for the rational design and development of investigative probes and/or therapeutic small molecules against the Rab1 protein. Proteins 2017; 85:859-871. © 2016 Wiley Periodicals, Inc.

Activity recognition using correlated pattern mining for people with dementia.

Due to the rapidly aging population around the world, senile dementia is growing into a prominent problem in many societies. To monitor the elderly dementia patients so as to assist them in carrying out their basic Activities of Daily Living (ADLs) independently, sensors are deployed in their homes. The sensors generate a stream of context information, i.e., snippets of the patient's current happenings, and pattern mining techniques can be applied to recognize the patient's activities based on these micro contexts. Most mining techniques aim to discover frequent patterns that correspond to certain activities. However, frequent patterns can be poor representations of activities. In this paper, instead of using frequent patterns, we propose using correlated patterns to represent activities. Using simulation data collected in a smart home testbed, our experimental results show that using correlated patterns rather than frequent ones improves the recognition performance by 35.5% on average.