Most proteins in cells are composed of multiple folding units (or domains) to perform
complex functions in a cooperative manner. Relative to the rapid progress in single-domain …

Recent developments in deep learning, coupled with an increasing number of sequenced
proteins, have led to a breakthrough in life science applications, in particular in protein …

Recent advances in machine learning have leveraged evolutionary information in multiple
sequence alignments to predict protein structure. We demonstrate direct inference of full …

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

ColabFold offers accelerated prediction of protein structures and complexes by combining
the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold's 40− 60 …

Protein structures can provide invaluable information, both for reasoning about biological
processes and for enabling interventions such as structure-based drug development or …

Proteins are essential to life, and understanding their structure can facilitate a mechanistic
understanding of their function. Through an enormous experimental effort 1, 2, 3, 4, the …

DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of
Structure Prediction (CASP14) conference. We explored network architectures that …

Modeling the effect of sequence variation on function is a fundamental problem for
understanding and designing proteins. Since evolution encodes information about function …

Large Language Models (LLMs) have emerged as a transformative power in enhancing
natural language comprehension, representing a significant stride toward artificial general …