Introduction

Interdisciplinary science is beautiful science Biology has typically been the least quantitative of the sciences. However, over recent decades the surge in sequencing data, made possible by next generation sequencing, has facilitated the application of statistical and machine learning to biology. In this blog I will describe what first got me into computational biology and what helped power the first drastic improvements in protein structure prediction introduced by AlphaFold2[ref]. Specifically, I will discuss what is now called evolutionary or Direct Coupling Analysis (DCA) and focus on several key papers that formulated this fascinating application of statistical mechanical principles to biology.

Introduction

In the world of computational structural biology you might have heard of diffusion models as the current big thing in generative modelling. Diffusion models are great because primarily they look cool when you visualise the denoising process to generate a protein structure (checkout RFdiffusion Colab notebook), but also because they are state of the art at diverse and designable protein backbone structure generation.