What is the proper role of AI ‘assistance’ in graduate student writing? It depends on what you mean by ‘graduate’.

The PRIME model improves protein mutants’ key properties with a 30% success rate, using machine learning without prior data.

Despite recent advances in the computational design of protein binders, designing proteins that bind with high affinity to polar protein targets remains an outstanding problem. Here we show that RFdif...

A deep learning approach enables accurate computational design of soluble and functional analogues of membrane proteins, expanding the soluble protein fold space and facilitating new approac...

Official repository for the Boltz-1 biomolecular interaction model - jwohlwend/boltz

Summary: Solutions to improve the accessibility of scientific conferences are proposed, including designated accessibility officers, accessibility checklists and measures to avoid personal disclosures...

We present the NetSci program–an open-source scientific software package designed for estimating mutual information (MI) between data sets using GPU acceleration and a k-nearest-neighbor algorithm. This approach significantly enhances calculation speed, achieving improvements of several orders of magnitude over traditional CPU-based methods, with data set size limits dictated only by available hardware. To validate NetSci, we accurately compute MI for an analytically verifiable two-dimensional Gaussian distribution and replicate the generalized correlation (GC) analysis previously conducted on the B1 domain of protein G. We also apply NetSci to molecular dynamics simulations of the Sarcoendoplasmic Reticulum Calcium-ATPase (SERCA) pump, exploring the allosteric mechanisms and pathways influenced by ATP and 2′-deoxy-ATP (dATP) binding. Our analysis reveals distinct allosteric effects induced by ATP compared to dATP, with predicted information pathways from the bound nucleotide to the calcium-binding domain differing based on the nucleotide involved. NetSci proves to be a valuable tool for estimating MI and GC in various data sets and is particularly effective for analyzing intraprotein communication and information transfer.