What Is AlphaFold? | NEJM

foreign [Music] researchers have been searching for a way to predict the 3D structure of proteins from the one-dimensional sequence of their amino acids from the central dogma we know that DNA is transcribed into RNA and then translated into an amino acid sequence which contains all the necessary information to reliably fold into a three-dimensional protein researchers have successfully determined protein structure experimentally using methods such as x-ray crystallography nuclear magnetic resonance spectroscopy and cryoelectron microscopy but these methods are time consuming and expensive the alpha fold artificial intelligence system developed by Google deepmind is the first non-experimental method

that can rapidly accomplish this with accuracy comparable to experiment but how does alpha fold 2 the 2021 iteration of alpha fold speed up the process of obtaining useful 3D protein structure models here is a simplified diagram of alpha fold 2's architecture we'll break it down into three sections and start here when an input sequence of residues or amino acids is entered Alpha fold Compares it to several databases of protein sequences to extract similar sequences from various organisms and tissues to generate a multiple sequence alignment or MSA and an initial MSA representation it also pairs the

input sequence and searches databases for templates of similar sequence proteins with experimentally determined structures this is then used to create an initial pair representation of the input sequence representing the relationship between every pair of residues within the target protein from here we can move on to section 2. the Evo former the evil former is a neural network unique to Alpha fold it consists of two towers that can communicate information to each other for the MSA representation the neural network prioritizes looking for row wise relationships between residue Pairs and the input sequence before considering column Wise

information that evaluates the importance of each residue in context of the other sequences the pair representation Tower evaluates the relationships between every two residues which can be thought of as nodes to refine the proximities or edges between the two it achieves this by triangulating the relationship of each node in a pair relative to a third node the goal of this process is to help the network satisfy the triangle inequality theorem where the sum of two edges on the triangle must be equal to or greater than the Third so how and where do the two towers

communicate before the pair representation calculates the triangulations on each Edge it considers the updated MSA residue relationships and updates each Edge accordingly the results of the pair representation data is then used in the msa's row wise weighting of pair relationships in the input sequence prior to its next round of row and column wise evaluations these individual and cross-communicated calculations happen 48 times in the evil former before creating the refined models of the initial MSA impair representations the final section involves another neural network called the structure module it takes the refined models and performs rotations and

translations on each amino acid revealing an initial guess of the 3D protein structure it also applies physical and chemical constraints dictated by atomic bonds angles and torsional angles the refined models as well as the output of the structure module is iterated back through the evil former and structure module process three more times for a total of four Cycles before it arrives at the final result predicted 3D Atomic coordinates for the proteins 3D structure it's important to note that experimentally determine 3D structures of proteins are almost always more accurate than predicted 3D structures and should be

used preferentially when available potential applications of predicted 3D Atomic coordinates from alpha fold include discovering drugs that bind tightly to protein pockets estimating the effect of genetic variants that change the amino acids of a protein on protein structure and function modeling interfaces of proteins that engage in protein protein interactions and Engineering proteins with new functions for medicine biotechnology Agriculture and the broader environment this video only touches on the concepts involved in Alpha fold 2's architecture to learn more read the original manuscript published in nature also be sure to check out the related article a Holy

Grail the prediction of protein structure by rusby Altman published in the New England Journal of Medicine [Music] foreign