Ten rules for a structural bioinformatic analysis

Abstract

The Protein Data Bank (PDB) is one of the richest open‑source repositories in biology, housing over 242,000 macromolecular structural models alongside much of the experimental data that underpins these models. By systematically collecting, validating, and indexing these models, the PDB has accelerated structural biology discoveries, enabling researchers to compare new entries against a vast archive of solved structures and, more recently, powering protein structure prediction. Leveraging this wealth of data, structural bioinformatics has uncovered patterns, such as conserved protein folds, binding‑site features, or subtle conformational shifts among related proteins, that would be impossible to detect from any single structure. Through the democratization of structural data and open-source analytical tools, now amplified by the power of large language models, a broader community of researchers is equipped to drive new scientific discoveries using structural data. However, good structural bioinformatics requires understanding some of the nuances of the underlying experimental data, data encoding conventions, and quality control metrics that can affect a model's precision, fit‑to‑data, and comparability. This knowledge, combined with developing good controls, statistics, and connections to other databases, is essential for drawing accurate and reliable conclusions from PDB data. Here, we outline 10 recommendations for doing structural bioinformatic analyses crafted to pave the way for others to uncover exciting discoveries.