Researchers have devised a faster, more efficient way to design custom peptides and perturb protein-protein interactions.
Image Credit: Page Lab
Research in Evolutionary and Computational Biology leverages large-scale genomic, transcriptomic and related data across diverse species to unlock the molecular mechanisms of life.
Researchers develop a method to investigate how bacteria respond to starvation and to identify which proteins bind to the "magic spot" - ppGpp.
New approach generates a wider variety of protein sequences optimized to bind to drug targets.
Designing synthetic proteins that can act as drugs for cancer or other diseases can be a tedious process: It generally involves creating a library of millions of proteins, then screening the library to find proteins that bind the correct target.
Thanks to continued advances in genetic sequencing, scientists have identified virtually every A, T, C, and G nucleotide in our genetic code. But to fully understand how the human genome encodes us, we need to go one step further, mapping the function of each base.
Student: Amanda Kedaigle
Title: Integrating Omics Data: A new Software Tool and its Use in Implicating Therapeutic Targets in Huntington's Disease
New discovery suggest that all life may share a common design principle.
Student: Vincent Xue
Title: Modeling and Designing Bcl-2 Family Protein Interactions Using High-Throughput Interaction Data
Drug that targets a key cancer protein could combat leukemia and other types of cancer.
MIT biologists have designed a new peptide that can disrupt a key protein that many types of cancers, including some forms of lymphoma, leukemia, and breast cancer, need to survive.
The new peptide targets a protein called Mcl-1, which helps cancer cells avoid the cellular suicide that is usually induced by DNA damage. By blocking Mcl-1, the peptide can force cancer cells to undergo programmed cell death.
Student: Peter Freese
Title: "Biochemical and Functional Characterization of Human RNA Binding Proteins"