Structural biology has been in existence since 1912, when German physicist Max Von Laue first directed X-rays at crystallized copper sulfate. He later won a Nobel Prize for this discovery, and other scientists built off his work, developing an X-ray crystallography technique that can illuminate the biological structures of proteins and other biological molecules. 

Today, structural biologists are not limited to X-ray crystallography; they use a variety of techniques to study biological 3D structures—and as a result they are revealing previously unknown molecular mechanisms that can support drug development.  

The Biotech Connector held on August 22 at the Frederick National Laboratory for Cancer Research (FNL) showcased the research of three investigators in the Frederick, Md., region who are working at the forefront of structural biology.

Tackling drug resistance 

Ruth Nussinov, Ph.D., senior investigator and head of FNL’s Computational Structural Biology Section, researches structural biology to improve cancer therapy. 

She explained that when cancer patients receive a single-drug therapy, they often develop resistance to it. Therefore, she advocated for a combination approach in which the patient receives two or more small-molecule drugs simultaneously. 

The combination of drugs can target the same protein, the same pathway, or alternate pathways. However, Nussinov said the best approach will target the proteins and pathways where the resistance mutations are most likely to arise. By using structural biology to understand the pathways where such mutations arise, investigators can select better combinations of therapies to use.  

“The oncologist sitting there in his office, what [drugs] should he use?” Nussinov asked at the event. This question is what her research ultimately looks to answer.  

Investigating disorder  

Carter Mitchell, Ph.D., chief scientific officer of Kemp Proteins, explained how a fascination with structure sparked his interest in the biological sciences, and how structural biology remains key to his current work elucidating protein functions. 

“We actually start every protein project thinking about protein structure,” he said.  

To investigate these structures, his team uses a machine learning pipeline, and he noted that proteins, although typically depicted as stationary circles or cartoons, are not at all static entities. They are always moving—which presents a challenge for the scientists trying to study them.  

Another challenge to understanding structure is disordered proteins, which are proteins that lack a fixed 3D structure. His machine learning modeling revealed some proteins that were fully disordered, meaning they had no fixed structure.  

Preventing toxicity  

Manu Kohli, Ph.D., principal scientific advisor at Charles River Laboratories, presented on how mass spectrometry techniques can support drug development.  

Mass spectrometry is an analytical laboratory technique to determine the structure, mass, and chemical properties of molecules. 

Kohli uses mass spectrometry to help identify small-molecule drug candidates that have favorable absorption, distribution, metabolism, and excretion (ADME) profiles.  

“When you are doing drug development, you want to have a good idea of what the ideal drug candidate should be,” he said.  

In particular, these ADME profiles are critical for ensuring a drug is not toxic and does not interact with other drugs. 

Kohli said the average oncology patient is on seven drugs, so identifying potential reactions between drugs is paramount. He also said these early mass spectrometry experiments are critical for identifying the optimal laboratory-based models that can model toxicology issues that may present in human patients.  

Join the next event 

The Biotech Connector is a quarterly networking and speaker series, hosted by the FNL and Frederick County Chamber of Commerce, to bring together life science professionals for an inside look at local advances and to network.  

Join us on November 21 for the next Biotech Connector, which will cover next-generation sequencing.

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