esm3.intro.full8

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The proteins that exist today have developed into their present forms over the course of billions of years of natural evolution, passing through a vast evolutionary sieve

evolutionary sieve

In parallel experiments conducted over geological time, nature creates random mutations and applies selection, filtering proteins by their myriad sequences, structures, and functions

In parallel experiments conducted over geological time, nature creates random mutations and applies selection, filtering proteins by their myriad sequences, structures, and functions.

In this process, proteins are the key players, as they are responsible for carrying out most of the functions in living organisms. Proteins are made up of long chains of amino acids, which are linked together by peptide bonds. The sequence of amino acids in a protein determines its structure and function.

There are 20 different types of amino acids that can be used to build proteins, each with its own unique chemical properties. These properties influence the way that the protein folds into its final shape, which in turn determines how it interacts with other molecules in the cell.

The study of proteins and their functions is known as proteomics, and it is a rapidly growing field in biology. By understanding the roles that different proteins play in the body, researchers can gain insights into a wide range of biological processes, from cell signaling to metabolism.

One important area of proteomics research is the study of enzymes, which are proteins that catalyze chemical reactions in the body. Enzymes are involved in virtually every aspect of metabolism, from breaking down food molecules to synthesizing new ones.

Another area of interest in proteomics is the study of protein-protein interactions, which occur when two or more proteins come together to form a complex. These interactions are essential for many cellular processes, including gene expression and cell signaling.

Overall, the study of proteins and their functions is a fascinating and rapidly evolving field, with many exciting discoveries yet to be made.

As a result, the patterns in the proteins we observe reflect the action of the deep hidden variables of the biology that have shaped their evolution across time

proteins deep hidden variables biology evolution

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Gene sequencing surveys of Earth's natural diversity are cataloging the sequences $(1-3)$ and structures $(4,5)$ of proteins, containing billions of sequences and hundreds of millions of structures that illuminate patterns of variation across life

Gene sequencing surveys of Earth's natural diversity are cataloging the sequences $(1-3)$ and structures $(4,5)$ of proteins, containing billions of sequences and hundreds of millions of structures that illuminate patterns of variation across life.

A consensus is building that underlying these sequences is a fundamental language of protein biology that can be understood using large language models (6-10)

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A number of language models of protein sequences have now been developed and evaluated ( $9,11-14$ )

It has been found that the representations that emerge within language models reflect the biological structure and function of proteins $(6,15,16)$, and are learned without any supervision on those properties, improving with scale $(5,17,18)$

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In artificial intelligence, scaling laws have been found that predict the growth in capabilities with increasing scale, describing a frontier in compute, parameters and data (19-21)

artificial intelligence scaling laws compute parameters data

We present ESM3, a frontier multimodal generative model, that reasons over the sequences, structures, and functions of proteins

We present ESM3, a frontier multimodal generative model, that reasons over the sequences, structures, and functions of proteins.

ESM3 stands for "Evolutionary Scale Modeling 3", which is a type of generative model that can predict protein structures and functions based on their sequences. It is called "multimodal" because it can handle different types of data, such as sequences and structures, and "frontier" because it is a cutting-edge technology in the field of protein modeling.

Generative models are a type of machine learning algorithm that can create new data based on patterns learned from existing data. In the case of ESM3, it can generate new protein sequences and structures that are similar to those in the training data.

Protein sequences are the order of amino acids that make up a protein, while protein structures are the 3D shapes that proteins fold into. Understanding protein structures is important because it can help us design drugs and understand diseases.

ESM3 is a significant advancement in the field of protein modeling because it can predict protein structures and functions with high accuracy, even for proteins that have never been studied before. This has the potential to accelerate drug discovery and improve our understanding of diseases.

Overall, ESM3 is an exciting development in the field of protein modeling that has the potential to revolutionize our understanding of proteins and their functions.

ESM3 is trained as a generative masked language model over discrete tokens for each modality

sness@sness.net