报告人：Chunli Yan, PhD
Dept. of Chemistry, Georgia State Univ., Atlanta, GA USA30302
High-resolution structural information is needed in order to unveil the underlying mechanistic of biomolecular function. Due to the technical limitations or the nature of the underlying complexes, acquiring atomic resolution information is difficult for many challenging systems, while, often, low-resolution biochemical or biophysical data can still be obtained. To make best use of all the available information and shed light on these challenging systems, integrative computational tools are required that can judiciously combine and accurately translate sparse experimental data into structural information. We will integrate the computational methods that generate models of assembly structures with various experimental data. Such data include atomic and residue positions from X-ray crystallography and NMR spectroscopy, shape and density for an assembly from ElectronMicroscopy (EM) and small angle x-ray scattering (SAXS).
Virtual screening (VS) holds great promise as an inexpensive and fast alternative to high-throughput screening (HTS) to discover useful starting points for drug discovery projects.A hybrid ligand-based virtual screening protocol that incorporates elements from both shape-based and electrostatic-based techniques was used for the identification of prospective small molecule inhibitors. The targets consist ofPRMT (Protein arginine methyltransferases) and JNK (c-Jun N-terminal kinases).