Context-aware geometric deep learning for protein sequence design.

Protein design and engineering are evolving at an unprecedented pace leveraging the advances in deep learning. Current models nonetheless cannot natively consider non-protein entities within the design process. Here, we introduce a deep learning approach based solely on a geometric transformer of atomic coordinates and element names that predicts protein sequences from backbone scaffolds aware of the restraints imposed by diverse molecular environments.

PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces.

Proteins are essential molecular building blocks of life, responsible for most biological functions as a result of their specific molecular interactions. However, predicting their  binding  interfaces remains a challenge. In this study, we present a geometric transformer that acts directly on atomic coordinates labeled only with element names.

Visualization, Interactive Handling and Simulation of Molecules in Commodity Augmented Reality in Web Browsers Using moleculARweb's Virtual Modeling Kits.

moleculARweb (https://molecularweb.epfl.ch) began as a website for education and outreach in chemistry and structural biology through augmented reality (AR) content that runs in the web browsers of regular devices like smartphones, tablets, and computers.

Investigating Crosstalk Among PTMs Provides Novel Insight Into the Structural Basis Underlying the Differential Effects of Nt17 PTMs on Mutant Httex1 Aggregation.

Post-translational modifications (PTMs) within the first 17 amino acids (Nt17) of the Huntingtin protein (Htt) have been shown to inhibit the aggregation and attenuate the toxicity of mutant Htt proteins and in various models of Huntington's disease. Here, we expand on these studies by investigating the effect of methionine eight oxidation (oxM8) and its crosstalk with lysine 6 acetylation (AcK6) or threonine 3 phosphorylation (pT3) on the aggregation of mutant Httex1 (mHttex1). We show that M8 oxidation delays but does not inhibit the aggregation and has no effect on the final morphologies of mHttex1aggregates.

Aerolysin nanopores decode digital information stored in tailored macromolecular analytes.

Digital data storage is a growing need for our society and finding alternative solutions than those based on silicon or magnetic tapes is a challenge in the era of "big data." The recent development of polymers that can store information at the molecular level has opened up new opportunities for ultrahigh density data storage, long-term archival, anticounterfeiting systems, and molecular cryptography. However, synthetic informational polymers are so far only deciphered by tandem mass spectrometry.