New Models Help Predict Protein Dynamic Signatures

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This breakthrough in accurately predicting protein crystallization propensity is vital for developing drugs and understanding diseases

A new computational model and tool developed at UConn uses advanced techniques to analyze protein dynamics and predict their crystallization propensity accurately. (Christopher LaRosa/UConn Photo)

To the average person, knowing how a protein wiggles might not seem that exciting or pertinent, but then again, most people aren’t fascinated by the natural movements and fluctuations of proteins and their functional properties. If, however, you were interested in designing new drugs, better understanding how diseases can be eradicated or enhancing biotechnology for industrial and therapeutic applications, you might be on the edge of your seat waiting to see what a new study on protein sequencing and crystallization has to offer.

An article about that study, authored by Anna Tarakanova, assistant professor in the School of Mechanical, Aerospace, and Manufacturing Engineering at UConn’s College of Engineering, has just appeared in a prominent monthly scientific journal, Matter, which focuses on the general field of materials science. The study examines how the natural movements and fluctuations of proteins – the protein’s “wiggles” – can help predict their functional properties. Tarakanova was assisted by Mohammad Madani, a Mechanical Engineering graduate student and first author of the study.

Read more in the UConn Today article