The past week was a momentous occasion for protein structure prediction, structural biology at large, and in due time, may prove to be so for the whole of life sciences. CASP14, the conference for …

Just over a week ago the long-awaited AlphaFold2 (AF2) method paper and associated code finally came out, putting to rest questions that I and many others raised about public disclosure of AF2. Alr…

Machine learning biomolecules and their interactions en route to a bottom-up and emergent systems biology.

Our new AI model AlphaFold 3 can predict the structure and interactions of all life’s molecules with unprecedented accuracy.

DeepMind presented remarkably accurate protein structure predictions at the CASP14 conference. We explored network architectures incorporating related ideas and obtained the best performance with a...

Update: An updated version of this blogpost was published as a (peer-reviewed) Letter to the Editor at Bioinformatics, sans the “sociology” commentary. I just came back from CASP13, the…

AlphaFold2 and related systems use deep learning to predict protein structure from co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite dramatic, recent increases in...

Proteins power a vast array of functional processes in living cells. The capability to create new proteins with designed structures and functions would thus enable the engineering of cellular...

Nature - Google’s deep-learning program for determining the 3D shapes of proteins stands to transform biology, say scientists.

AlphaFold2 has been hailed as a breakthrough in protein folding. It can rapidly predict protein structures with lab-grade accuracy. However, its implementation does not include the necessary...

An algorithm that already predicts how proteins fold might also shed light on the physical principles that dictate this folding.

Nature Methods - Protein–peptide interactions that underpin cell signaling are accurately predicted by wedding the strengths of machine learning with the interpretability of biophysical...

Proteins are essential to life, supporting practically all its functions. They are large complex molecules, made up of chains of amino acids, and what a protein does largely depends on its unique...

Standardized data set for machine learning of protein structure - aqlaboratory/proteinnet

The problem of predicting a protein’s 3D structure from its primary amino acid sequence is a longstanding challenge in structural biology. Recently, approaches like AlphaFold have achieved remarkable...

Unsupervised protein language models trained across millions of diverse sequences learn structure and function of proteins. Protein language models studied to date have been trained to perform...

Although AlphaFold2 (AF2) and RoseTTAFold (RF) have transformed structural biology by enabling high-accuracy protein structure modeling, they are unable to model covalent modifications or interacti...

Deep neural networks have recently enabled spectacular progress in predicting protein structures, as demonstrated by DeepMind’s winning entry with Alphafold at the latest Critical Assessment of...

Large language models have recently been shown to develop emergent capabilities with scale, going beyond simple pattern matching to perform higher level reasoning and generate lifelike images and...

AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train...

Abstract. Summary: Computational prediction of protein structure from sequence is broadly viewed as a foundational problem of biochemistry and one of the m

The prediction of inter-residue contacts and distances from co-evolutionary data using deep learning has considerably advanced protein structure prediction. Here we build on these advances by...

The Program for Mathematical Genomics in the Department of Systems Biology invites candidates for faculty positions

March 6, 2019MIA MeetingMohammed AlQuraishiHMSEnd-to-end differentiable learning of protein structureAbstract: Predicting protein structure from sequence is ...

Recurrent Geometric Networks for end-to-end differentiable learning of protein structure - aqlaboratory/rgn

PyTorch library of layers acting on protein representations - lupoglaz/TorchProteinLibrary

For over a decade now I have been working, essentially off the grid, on protein folding. I started thinking about the problem during my undergraduate years and actively working on it from the very …