Computational Structural Biology Section

Overview

The Computational Structural Biology Section draws on multidisciplinary advances to decipher the mechanisms and pharmacology of oncogenic proteins and their signaling at the detailed molecular level.  

Our work is based on the concept of the free energy landscape. The landscape describes protein molecules as consisting of ensembles of conformations, whose relative populations determine function.  

The section pioneered the dynamic free energy landscape, conformational selection, and population shift as an alternative to the induced-fit textbook model to explain molecular recognition and allosteric regulation. Our ideas were validated by experiments. 

We extended the ensemble model to catalysis, oncogenic activation, and mechanisms of inhibition, contributing to extraordinary advancements in understanding structure, function, and gain-of-function. Our innovative perspective clarifies observations, and makes predictions, aiming to deepen understanding of experimental and clinical observations.  

The section focuses on Ras proteins, their  oncogenic activators, including tyrosine kinases, and downstream pathways, including PI3K, PTEN, and MAPK (B-Raf/KSR/MEK). 

Focus

Investigating protein structure, regulation and signaling 

  • Elucidate how same-allele mutations in oncogenic proteins can promote cancer and other diseases, such as developmental disorders. 
  • Refine theories of how protein mutations provoke disease. 
  • Evaluate data to expedite drug discovery and development. 
  • Model conformations in membranes, their activation and inhibition. 
  • Explore protein structure and dynamic behavior in activation and signaling. 
  • Decipher the physical and chemical behavior of molecules in cells. 
  • Predict protein-protein interactions between pathogen and human host proteins to explore pathogenesis and inhibition 
  • Construct a server for the community for the Human-pathogen protein-protein interaction prediction. 
  • Integrate computational and experimental approaches to advance biological and clinical applications. 

Decoding cancer drivers 

  • Decipher mechanisms in activation and signaling of oncogenic KRAS proteins and key proteins in their major signaling pathways. 
  • Study the signaling selectivity of RAS protein variants at the cell membrane. 
  • Decipher activation mechanisms of KRAS proteins regulators at the membrane. 
  • Determine the activation mechanisms of oncogenic PI3K, B-Raf, and PTEN and other key proteins in oncogenic Ras signaling and their complexes (e.g. B-Raf/MEK, B-Raf/KSR). 
  • Forecast parallel signaling pathways and key proteins that can be targeted to mitigate drug resistance. 
  • Resolve apparent experimental contradictions in Ras signaling. 
  • Investigate how wild-type KRAS proteins can inhibit their mutated cancerous variants. 

Devising a structure-based platform for precision medicine and emerging treatments 

  • Combine computational data, clinical observations, experimental approaches, genetics, biophysics, and protein structure data to alleviate treatment limitations. 
  • Consider the availability of each protein in each pathway in the specific cell, its activating mutations, and the chromatin accessibility of its encoding gene. 
  • Capitalize on new, protein structure-based concepts to develop and integrate computational algorithms and experimental approaches. 
  • Improve cancer treatment decisions by combining statistical analysis with a genetic and molecular structural basis. 
  • Integrate computational methods, functional assays, and conformational principles for interpreting cancer drivers. 
  • Develop a powerful deep learning computational methodology to accelerate the identification of novel drug-target interactions.