Structural biology meets data science: Does anything change?

TL;DR

This paper explores how data science and structural biology can mutually enhance each other, emphasizing machine learning's role in understanding biological relationships at scale.

Contribution

It highlights the potential for deep integration of data science techniques into structural biology, focusing on five key pillars and emphasizing machine learning applications.

Findings

Data science accelerates biological discovery.

Machine learning reveals complex biological relationships.

Interdisciplinary approaches can transform structural biology.

Abstract

Data science has emerged from the proliferation of digital data, coupled with advances in algorithms, software and hardware (e.g., GPU computing). Innovations in structural biology have been driven by similar factors, spurring us to ask: can these two fields impact one another in deep and hitherto unforeseen ways? We posit that the answer is yes. New biological knowledge lies in the relationships between sequence, structure, function and disease, all of which play out on the stage of evolution, and data science enables us to elucidate these relationships at scale. Here, we consider the above question from the five key pillars of data science: acquisition, engineering, analytics, visualization and policy, with an emphasis on machine learning as the premier analytics approach.