Quantifying the Role of OpenFold Components in Protein Structure Prediction

Fuente: arXiv
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Hauptverfasser: Hayes, Tyler L., Krishnan, Giri P.
Format: Preprint
Veröffentlicht: 2025
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author Hayes, Tyler L.
Krishnan, Giri P.
author_facet Hayes, Tyler L.
Krishnan, Giri P.
contents Models such as AlphaFold2 and OpenFold have transformed protein structure prediction, yet their inner workings remain poorly understood. We present a methodology to systematically evaluate the contribution of individual OpenFold components to structure prediction accuracy. We identify several components that are critical for most proteins, while others vary in importance across proteins. We further show that the contribution of several components is correlated with protein length. These findings provide insight into how OpenFold achieves accurate predictions and highlight directions for interpreting protein prediction networks more broadly.
format Preprint
id arxiv_https___arxiv_org_abs_2511_14781
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Quantifying the Role of OpenFold Components in Protein Structure Prediction
Hayes, Tyler L.
Krishnan, Giri P.
Biomolecules
Artificial Intelligence
Models such as AlphaFold2 and OpenFold have transformed protein structure prediction, yet their inner workings remain poorly understood. We present a methodology to systematically evaluate the contribution of individual OpenFold components to structure prediction accuracy. We identify several components that are critical for most proteins, while others vary in importance across proteins. We further show that the contribution of several components is correlated with protein length. These findings provide insight into how OpenFold achieves accurate predictions and highlight directions for interpreting protein prediction networks more broadly.
title Quantifying the Role of OpenFold Components in Protein Structure Prediction
topic Biomolecules
Artificial Intelligence
url https://arxiv.org/abs/2511.14781