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Researchers trace genetic code’s origins to early protein structures

Genes are the building blocks of life, and the genetic code provides the instructions for the complex processes that make organisms function. But how and why did it come to be the way it is?

A recent study from the University of Illinois Urbana-Champaign sheds new light on the origin and evolution of the , providing valuable insights for genetic engineering and bioinformatics. The study is published in the Journal of Molecular Biology.

“We find the origin of the genetic code mysteriously linked to the dipeptide composition of a proteome, the collective of proteins in an organism,” said corresponding author Gustavo Caetano-Anollés, professor in the Department of Crop Sciences, the Carl R. Woese Institute for Genomic Biology, and Biomedical and Translation Sciences of Carle Illinois College of Medicine at U. of I.

‘Virtual clinical trials’ may predict success of heart failure drugs

Mayo Clinic researchers have developed a new way to predict whether existing drugs could be repurposed to treat heart failure, one of the world’s most pressing health challenges. By combining advanced computer modeling with real-world patient data, the team has created “virtual clinical trials” that may facilitate the discovery of effective therapies while reducing the time, cost, and risk of failed studies.

“We’ve shown that with our framework, we can predict the clinical effect of a drug without a . We can say with high confidence if a drug is likely to succeed or not,” says Nansu Zong, Ph.D., a biomedical informatician at Mayo Clinic and lead author of the study, which was published in npj Digital Medicine.

Machine learning unravels quantum atomic vibrations in materials

Caltech scientists have developed an artificial intelligence (AI)–based method that dramatically speeds up calculations of the quantum interactions that take place in materials. In new work, the group focuses on interactions among atomic vibrations, or phonons—interactions that govern a wide range of material properties, including heat transport, thermal expansion, and phase transitions. The new machine learning approach could be extended to compute all quantum interactions, potentially enabling encyclopedic knowledge about how particles and excitations behave in materials.

Scientists like Marco Bernardi, professor of applied physics, physics, and at Caltech, and his graduate student Yao Luo (MS ‘24) have been trying to find ways to speed up the gargantuan calculations required to understand such particle interactions from first principles in real materials—that is, beginning with only a material’s atomic structure and the laws of quantum mechanics.

Last year, Bernardi and Luo developed a data-driven method based on a technique called singular value decomposition (SVD) to simplify the enormous mathematical matrices scientists use to represent the interactions between electrons and phonons in a material.

Machine learning and quantum chemistry unite to simulate catalyst dynamics

Catalysts play an indispensable role in modern manufacturing. More than 80% of all manufactured products, from pharmaceuticals to plastics, rely on catalytic processes at some stage of production. Transition metals, in particular, stand out as highly effective catalysts because their partially filled d-orbitals allow them to easily exchange electrons with other molecules. This very property, however, makes them challenging to model accurately, requiring precise descriptions of their electronic structure.

Designing efficient transition-metal catalysts that can perform under realistic conditions requires more than a static snapshot of a reaction. Instead, we need to capture the dynamic picture—how molecules move and interact at different temperatures and pressures, where atomic motion fundamentally shapes catalytic performance.

To meet this challenge, the lab of Prof. Laura Gagliardi at the University of Chicago Pritzker School of Molecular Engineering (UChicago PME) and Chemistry Department has developed a powerful new tool that harnesses electronic structure theories and machine learning to simulate transition metal catalytic dynamics with both accuracy and speed.

New system dramatically speeds the search for polymer materials

MIT researchers developed a fully autonomous platform that can identify, mix, and characterize novel polymer blends until it finds the optimal blend. This system could streamline the design of new composite materials for sustainable biocatalysis, better batteries, cheaper solar panels, and safer drug-delivery materials.

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