---
title: "Highly accurate protein structure prediction with AlphaFold"
authors: ["Jumper J", "Evans R", "Pritzel A"]
year: 2021
journal: Nature
type: paper
tags: [protein-folding, deep-learning, structural-biology]
cited_by: 15000
impact: foundational
---

# Highly accurate protein structure prediction with [[AlphaFold]]

## [[Summary]]

AlphaFold 2 [[Achieved]] near-experimental accuracy in protein structure prediction [[Using]] [[Deep]] [[Learning]]. Revolutionary breakthrough combining attention mechanisms with evolutionary information.

## [[Key Findings]]

- **Accuracy**: Median GDT score of 92.4 on CASP14
- **Method**: Combines MSA [[Processing]] with structure module
- **Impact**: [[Solved 50]]-year protein folding problem

## Methodology

Uses [[Transformer Architecture]] for [[Multiple Sequence Alignment]] processing. Integrates [[Evolutionary Coupling]] signals with [[Spatial Graph Networks]].

## Relevance to My Work

Foundation for [[Protein-Ligand Docking]] [[Project]]. Methods directly applicable to [[Structure-Based Drug Design]].

## [[Related Papers]]

- Builds on [[Senior2020-AlphaFold1]]
- Enables [[Varadi2022-AlphaFold-Database]]
- Applied in [[Baek2021-RoseTTAFold]]
