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More evidence of Impact Factor Mania | Microbiology Spectrum journals.asm.org/doi/10.1128/sp…

Our work predicting multiple conformations by combining sequence clustering and AlphaFold2 is in press at nature! Thrilled to get to share - now includes experimental validation for several tests of AF-Cluster, led by the incredible Adedolapo M. Ojoawo! go.nature.com/49uCNxU

Machine learning for microbiologists nature.com/articles/s4157… In this Review, Segata, Waldron and co discuss key concepts of machine learning that are relevant to microbiologists and provide them with a set of tools to apply machine learning in microbiology research.

I’m deeply grateful to join the Zhulin Lab and have had an excellent mentor during my PhD! Excited to continue my scientific journey soon Cold Spring Harbor Laboratory

Yifei HUANG (Yifei Huang), our dear colleague, Asst Prof Penn State Biology, passed away from cancer in October at the age of 40. We are collecting donations for his daughter's education. All donations are tax-deductible. Penn State Research Please RT. gofund.me/ef82effe

Glad to share that my first first-author paper has been published online in Briefings in Bioinformatics journal! More to come in my Ph.D. time! Protein–DNA binding sites prediction based on pre-trained protein language model and contrastive learning academic.oup.com/bib/article/25…

Indigenous Andeans and Tibetans share a key adaptive trait to counter the effects of high-altitude hypoxia, according to a new Science Advances study. scim.ag/5T8

Nature research paper: Accurate structure prediction of biomolecular interactions with AlphaFold 3 go.nature.com/44AJ3Sz

In today's Cell, we propose a “developmental constraint” model whereby cancer cell state plasticity is restricted by the organism’s developmental map to access progenitor-like states and differentiated-like states of adjacent lineages.🧵⬇️ sciencedirect.com/science/articl… Ayushi Patel

AlphaFold2 structures guide prospective ligand discovery | Science science.org/doi/10.1126/sc…

Pan-cancer proteogenomics expands the landscape of therapeutic targets cell.com/cell/fulltext/… Mega merger of National Cancer Institute CPTAC with additional public datasets to identify actionable targets. Staggering amount of data in this resource paper Cell BCMHouston

We have trained ESM3 and we're excited to introduce EvolutionaryScale. ESM3 is a generative language model for programming biology. In experiments, we found ESM3 can simulate 500M years of evolution to generate new fluorescent proteins. Read more: evolutionaryscale.ai/blog/esm3-rele…

Three and a half years ago, artificial intelligence transformed the study of proteins. Prominent scientists in the field were left wondering: “What now?” It’s finally possible to start answering that question. Yasemin Saplakoglu reports in a new extended feature:quantamagazine.org/how-ai-revolut…

What if we could universally recombine, insert, delete, or invert any two pieces of DNA? In back-to-back nature papers, we report the discovery of bridge RNAs and 3 atomic structures of the first natural RNA-guided recombinase - a new mechanism for programmable genome design

Continuing an amazing collaboration with Krell Laboratory - a new story is about the bacterial purine sensor, led by Elizabet Monteagudo Cascales in Granada and Vadim Gumerov in Columbus, also Miguel_Matilla & Jose A. Gavira -Gavi-: nature.com/articles/s4146…

Nobel prize in physics: AI Nobel prize in chemistry: AI for biology

A new Science study presents “Evo”—a machine learning model capable of decoding and designing DNA, RNA, and protein sequences, from molecular to genome scale, with unparalleled accuracy. Evo’s ability to predict, generate, and engineer entire genomic sequences could change the

One day in Istanbul

Great collaboration during my PhD. Glad to contribute to this work!

Biology of Genomes 2025