A startup is transforming polluted air into apparel. At least 15 major brands, including H&M and Walmart, are testing new technology for carbon neutral clothing. Breakthroughs, discoveries, and DIY tips sent six days a week. It might not seem like it when you nonchalantly click a Buy Now button while online shopping, but that new t-shirt is part of a complex global web of commerce taking a toll on the environment . Consulting giant McKinsey estimates that the fashion industry alone accounts for as much as 4 percent of total global climate emissions.

Enzymes are important proteins that catalyze chemical reactions. In recent years, machine learning methods have emerged to predict enzyme function from sequence; however, there are no standardized benchmarks to evaluate these methods. We introduce CARE, a benchmark and dataset suite for the Classification And Retrieval of Enzymes (CARE). CARE centers on two tasks: (1) classification of a protein sequence by its enzyme commission (EC) number and (2) retrieval of an EC number given a chemical reaction. For each task, we design train-test splits to evaluate different kinds of out-of-distribution generalization that are relevant to real use cases. For the classification task, we provide baselines for state-of-the-art methods. Because the retrieval task has not been previously formalized, we propose a method called Contrastive Reaction-EnzymE Pretraining (CREEP) as one of the first baselines for this task and compare it to the recent method, CLIPZyme. CARE is available at https://github.com/jsunn-y/CARE/.

IntroductionThe current methodologies for enzyme annotation primarily rely on established databases and classifications such as KEGG Orthology (KO), Enzyme Commission (EC) numbers, and Gene Ontology (GO) annotations, each with its specific focus and methodology. For instance, the EC system categorizes enzymes based on the chemical reactions they catalyze, providing a hierarchical numerical classification. KO links gene products to their functional orthologs across different species, whereas GO offers a broader ontology for describing the roles of genes and proteins in any organism. Despite their widespread use, these systems have notable limitations. The EC classification, while widely used, sometimes groups vastly different enzymes under the same category or subdivides similar ones excessively, based on the substrates they interact with--leading to ambiguities in enzyme function characterization.

Enzymes, as catalysts of biological systems, are the workhorses of various biological functions [35, 52, 13] (Figure 1a).

Chemist Omar Yaghi invented materials called MOFs, a few grams of which have the surface area of a football field. In school, we learn about the Stone Age, the Bronze Age - and we are currently in a silicon age characterised by computers and phones. What might define the next age? Omar Yaghi at the University of California, Berkeley, thinks a family of materials he helped pioneer in the 1990s has a good shot. They are metal-organic frameworks (MOFs), and working out how to make them earned him a share of the 2025 Nobel prize in chemistry .

Innovative research into the gene-editing tool targets influenza's ability to replicate--stopping it in its tracks. As he addressed an audience of virologists from China, Australia, and Singapore at October's Pandemic Research Alliance Symposium, Wei Zhao introduced an eye-catching idea. The gene-editing technology Crispr is best known for delivering groundbreaking new therapies for rare diseases, tweaking or knocking out rogue genes in conditions ranging from sickle cell disease to hemophilia . But Zhao and his colleagues at Melbourne's Peter Doherty Institute for Infection and Immunity have envisioned a new application. They believe Crispr could be tailored to create a next-generation treatment for influenza, whether that's the seasonal strains which plague both the northern and southern hemispheres on an annual basis, or the worrisome new variants in birds and other wildlife that might trigger the next pandemic.