Transcript BioE_CIT
Biological Signal Detection for Protein Function Prediction Sequences Investigators: Yang Dai Prime Grant Support: NSF Text File of Protein description Problem Statement and Motivation Coding Vector s MASVQLY ... …HKEPGV • High-throughput experiments generate new protein sequences with unknown function prediction •In silico protein function prediction is in need Machine Learner specific subcellular and subnuclear localization Technical Approach •Protein subcellular localization is a key element in understanding function •Such a prediction can be made based on protein sequences with machine learners •Feature extraction and scalability of learner are keys. Key Achievements and Future Goals • Use Fast Fourier Transform to capture long range correlation in protein sequence •Developed highly sophisticated sequence coding methods • Design a class of new kernels to capture subtle similarity between sequences •Developed an integrated multi-classification system for protein subcellular localization •Use domains and motifs of proteins as coding vectors •Developed a preliminary multi-classification system for subnuclear localization •Use multi-classification system based on deterministic machine learning approach, such as support vector machine • Use Bayesian probabilistic model • Will incorporate various knowledge from other databases into the current framework • Will design an integrative system for protein function prediction based on information of protein localizations, gene expression, and protein-protein interactions Computational Protein Topographics for Health Improvement Jie Liang, Ph.D. Bioengineering Prime Grant Support: National Science Foundation Career Award, National Institutes of Health R01, Office of Naval Research, and the Whitaker Foundation. Protein surface matching Problem Statement and Motivation • The structure of proteins provide rich information about how cells work. With the success of structural genomics, soon we will have all human proteins mapped to structures. • However, we need to develop computational tools to extract information from these structures to understand how cell works and how new diseases can be treated. •Therefore, the development of computational tools for surface matching and for function prediction will open the door for many new development for health improvement. Evolution of function Technical Approach Key Achievements and Future Goals • We use geometric models and fast algorithm to characterize surface properties of over thirty protein structures. • We have developed a web server CASTP (cast.engr. uic.edu) that identify and measures protein surfaces. It has been used by thousands of scientists world wide. • We develop evolutionary models to understand how proteins overall evolve to acquire different functions using different combination of surface textures. • We have built a protein surface library for >10,000 proteins, and have developed models to characterize cross reactivities of enzymes. • Efficient search methods and statistical models allow us to identify very similar surfaces on totally different proteins • We also developed methods for designing phage library for discovery of peptide drugs. • Probablistc models and sampling techniques help us to understand how protein works to perform their functions. • We have developed methods for predicting structures of beta-barrel membrane proteins. • Future: Understand how protein fold and assemble, and designing method for engineering better proteins and drugs. Structural Bioinformatics Study of Protein Interaction Network Investigators: Hui Lu, Bioengineering Prime Grant Support: NIH, DOL Protein-DNA complex: gene regulation DNA repair cancer treatment drug design gene therapy Problem Statement and Motivation • Protein interacts with other biomolecules to perform a function: DNA/RNA, ligands, drugs, membranes, and other proteins. • A high accuracy prediction of the protein interaction network will provide a global understanding of gene regulation, protein function annotation, and the signaling process. • The understanding and computation of protein-ligand binding have direct impact on drug design. Technical Approach • Data mining protein structures • Molecular Dynamics and Monte Carlo simulations • Machine learning • Phylogenetic analysis of interaction networks Key Achievements and Future Goals • Developed the DNA binding protein and binding site prediction protocols that have the best accuracy available. • Developed transcription factor binding site prediction. • Gene expression data analysis using clustering • Developed the only protocol that predicts the protein membrane binding behavior. • Binding affinity calculation using statistical physics • Will work on drug design based on structural binding. • Will work on the signaling protein binding mechanism. • Will build complete protein-DNA interaction prediction package and a Web server.