- Research Overview
- Chemical Biology and Medicinal Chemistry
- Chemical Biology and Medicinal Chemistry Overview
- Discovering Enzyme Substrates and Functions
- Discovering Protein Ligands to Probe and Alter Function
- Discovering Enzyme Activators
- Analyzing Mechanisms of Drug Resistance via Chemical Biology
- Analyzing Enzyme Conformational Dynamics, Substrate Binding, and Catalysis
- Effective Drug Targeting of Pathogens via Medicinal Chemistry
- Computational Chemistry and Biology
- Computational Chemistry and Biology Overview
- Modeling protein regulation via allostery and post-translational modifications
- Visualizing and integrating bioinformatics and biomolecular data
- Modeling membrane permeation to optimize pharmacokinetics
- Determining enzyme function by predicting substrate specificity
- Physical Biology
- Protein and Cellular Engineering
- Protein and Cellular Engineering Overview
- Monitoring enzyme activity and disease biomarkers
- Generating human proteome antibodies via phage display and directed evolution
- Globally analyzing and dissecting apoptosis
- Proximity tagging of protein-protein interactions
- Investigating cellular interactions in tissues
- Creating fluorescent probes targeting the genome and key bio-pathways
- De novo design of catalytic and membrane proteins
- Probing and modulating membrane proteins
Computational Chemistry and Biology
Computational chemists develop and apply computer programs to answer key questions in biochemistry. They model, predict, visualize, and analyze the structures, functions, and interactions of biologically important molecules.
In structure-based drug discovery and rational design, researchers seek to find and create small molecules (ligands) that selectively bind to proteins involved in disease processes to therapeutically alter their activity.
Bioinformatics, visualization, and docking
Computation enables this effort to be faster, more efficient, and less expensive, starting with the search for biologically significant targets. Researchers use bioinformatics—the capability of computers to store, organize, and retrieve vast quantities of data—to map networks of protein interactions, discerning biochemical pathways amid complexity and determining key nodes for intervention.
When a target protein is discovered, computers help predict and refine details of its 3D structure (homology and loop modeling) as well as allowing researchers to visualize and interact with it, alone and in complexes, using programs pioneered and advanced by department scientists (e.g., UCSF Chimera).
Then molecular docking programs—also pioneered and advanced by department scientists (e.g., UCSF DOCK)—can virtually screen and rank potential ligands for the relative strength with which they bind to the targets.
Computer and “wet” complementary
Such programs guide the selection or synthesis of compounds for real-world testing (e.g., in the department’s Small Molecule Discovery Center (SMDC)). Indeed, computer (in silico) and “wet” experiments are complementary: While the former suggests lab-testable hypotheses (e.g., potential drug leads), the latter’s results can iteratively improve computer programs’ predictive accuracy.
In developing and refining their algorithms and mathematical functions, computational chemists combine empirical knowledge with biophysical laws and theory. A docking program, for example, might use knowledge about molecular shape complementarity, laws governing certain atomic-level interactions, and the theoretical effects of water on protein-ligand binding.
Simulation spans gaps
Simulation by computer programs helps to span gaps in empirical knowledge, including those due to the extremely small size and brief time scales of intra- and inter-molecular shape-shifting and interactions.
Such simulations can provide atomic-level details of molecular change over billionths of a second (nanoseconds) or less—levels of dynamic resolution that are difficult to attain by existing physical methods. These models can thus suggest the specific mechanisms underlying ligand-binding effects toward optimization of potential therapies.