Introduction
Dr. LEI Hongxing received his bachelor’s degree in biomedical engineering from Huazhong University of Science and Technology in 1991. After seven years of postgraduate research work at the Academy of Military Medical Sciences, he returned to school and completed his PhD education at Kansas State University in 2003. Since then, he worked in Dr. Yong Duan’s research group for five years as a postdoctoral research scientist and group manager, including one year at University of Delaware and four years at UC Davis. In June 2008, Dr. LEI joined BIG as a full professor.
Dr. LEI has been working in the field of computational biology since 1991. During the period 1991-1998, his research was focused on bioinformatics. While learning bioinformatics techniques such as sequence analysis and database management, he co-developed GOLDKEY, the first comprehensive bioinformatics software package in China. After 1998, his research interest shifted from bioinformatics to computational structural biology. Using molecular mechanics and molecular dynamics as the major approaches, his research covered a variety of subjects including protein folding, protein-protein docking, protein structure modeling, protein dynamics, and amyloid diseases induced by protein misfolding. In one of his many investigations on protein folding mechanism, an accurate folding to 0.5 angstrom was achieved for the first time in the field of computational protein folding.
Current research:
Computational structural biology
1) protein-protein docking
Most cellular processes are carried out by protein-protein interactions (PPIs). Predicting the 3D structures of protein-protein complexes starting from the individual structures of the constituent proteins (docking) can shed light on their functional mechanisms and roles in the cell. The structures of the complexes provide information regarding the interfaces of the proteins and assist in drug design. Docking can assist in predicting protein-protein interactions, in understanding signaling pathways and in evaluating the affinity of complexes. Prediction of PPIs is a hot spot in recent years. But there are still two difficulties need to be improved in the prediction technology. One is the scoring efficiency, the other is dispose of protein flexibility. The two difficulties have a common result in low veracity of the prediction of protein-protein docking. Our research aims at these two internationally recognized difficulties, and we are looking forward to improving the veracity of the prediction of protein-protein docking.
2) inhibition of A-beta aggregation
Most of neurodegenerative diseases such as Alzheimer's disease (AD) associate with amyloid β-peptide (A-beta) aggregation. One of the defining neuropathological features of AD is the occurrence of senile plaques containing insoluble, aggregated fibrous A-beta proteins. It aggregates into a common cross-β-sheet structure with the β-strands perpendicular to the fiber axis. It is also reported that the toxicity of these neurodegenerative diseases may also be caused by the soluble intermediate oligomers in addition to the mature fibrils. It is therefore important to inhibit the aggregation of A-beta at the early steps. We explored the inhibition ability of peptide-based aggregation inhibitors containing A-beta amino acid sequence (KLVFF) from part of the binding region responsible for A-beta self-association (A-beta 16-20)：RGKLVFFGR, named OR1 and RGKLVFFGR-NH2, named OR2, and compare them with KLVFF-NH2. By analyzing their free energy, determine their inhibition mechanism and identify a potential peptide inhibitor for further development as a novel therapy for AD.
3) protein folding
Protein folding is one of the most fundamental problems in biological sciences. We will tackle this problem by computer simulation in collaboration with Professor Yong Duan at UC Davis genome center. The grand challenge in the filed of computational protein folding is the consistent high-quality folding of a heterogeneous set of proteins including all-alpha, all-beta, and mixed alpha-beta proteins. Although limited success has been achieved in three proteins (HP35, protein A, and FSD), a much broader range of success will require extensive development, testing, and refinement of simulation force field. In another related direction, we will explore novel strategy to improve the sampling efficiency in folding simulation.
Computational systems biology
1) network modeling on lung cancer
Lung cancer is a complicated disease. We aim to construct a reliable network of lung cancer by combining datasets of expression profiles and protein-protein interaction. Then we will integrate all kinds of information about lung cancer based on the protein-protein interaction network, including sequencing, methylation and SEREX, to detect lung cancer related modules which can distinguish cancer and normal as well as NSCLC and SCLC. Since diagnosis of lung cancer at an early stage is helpful for the survival of the patients, we also use the network to analyze expression profiles of different stages of lung cancer to find biomarkers for early stage. As smoking is an important factor for lung cancer, we will explore the relationship between smoking and lung cancer. We believe there are some special modules linking smoking and lung cancer.
2) study of Alzheimer’s disease using systems biology approach
We construct network of Alzheimer’s disease with datasets of protein-protein interaction and expression profiles of Alzheimer’s disease and normal. Then we will compare Alzheimer’s disease and normal at the level of network, including topology characters and expression patterns. We wish to find special proteins and pathways which play important roles in the development of Alzheimer’s disease. And then we can find candidate for drug design which may reverse the development of Alzheimer’s disease but do not affect normal function of human. Drugs hitting a single target may be inadequate for the treatment of disease which involves multiple pathogenic factors. We will design new drugs which can interact with multi-target.
3) SNP and genetic diseases
Much effort in current human genomics, epidemiology and pharmacogenomics is focused on the identification of genetic variations that are responsible for common and complex diseases. Specifically, single nucleotide polymorphisms (SNPs), which are substitutions of a single nucleotide at a specific position on the genome, are in the forefront of such studies, as they form the majority of genetic variations in the human population. Reliable identification of disease-causing SNPs is expected to enable early diagnosis, personalized treatment and targeted drug design. Typically, SNPs occurring in functional genomic regions such as protein coding or regulatory regions are more likely to cause functional distortion and, as such, more likely to underlie disease-causing variations. We will use the bioinformatics approach to analysis the relationship between SNPs and disease. Current bioinformatics tools examine the functional effects of SNPs only with respect to a single biological function. Therefore, much time and effort is required from researchers to separately use multiple tools and interpret the predictions. We will provide a comprehensive collection of functional information about SNPs, using a large variety of publicly available tools and resources.
Contact
Email: leihx@big.ac.cn
Phone: 010-84097276
