ProFlex as a linguistic bridge for decoding protein dynamics in normal mode analysis – Damian J. Magill.

Executive Insight

In this work, we introduce ProFlex, a scalable and interpretable representation of protein flexibility. By compressing large scale normal mode analysis into a one-dimensional alphabet, ProFlex enables flexibility to be treated much like sequence or secondary structure data rendering it searchable, comparable, and compatible with existing bioinformatics and machine learning workflows. This makes it possible to integrate dynamic information into routine R&D activities, including candidate ranking, variant prioritization, structure function analysis, and cross family comparison, while remaining computationally tractable at industrial scale.

Why This Research Matters

Advances in protein structure prediction have dramatically expanded the available structural landscape, which creates many new opportunities for innovation in enzyme discovery, strain development, and functional protein optimization.
However, most workflows still struggle to fully leverage this information on a scale. Static structures alone rarely explain everything, with key functional traits determined by protein flexibility. Flexibility is often assessed indirectly, experimentally, or not at all, limiting its integration into high throughput screening and computational pipelines.

About the Authors

This research has been conducted by IFF’s Expert Contributor Damian Magill in collaboration with Dr Timofey Skvortsov from the School of Pharmacy at Queen’s University Belfast.

Read more on this research from Damian.This research has been conducted by IFF’s Expert Contributor Damian Magill in collaboration with Dr Timofey Skvortsov from the School of Pharmacy at Queen’s University Belfast.

Read more on this research from Damian.

How can ProFlex help researchers make better use of protein structures for functional insight?

Expert Contributor Dr. Damian Magill

Sr. Scientist II, R&D – H&B

AI tools such as AlphaFold have generated hundreds of thousands of high‑quality protein structures but translating this static structural information into functional insight remains a challenge. ProFlex addresses this gap by converting complex protein dynamics into a simple, interpretable alphabet that captures relative flexibility along the protein sequence. This enables rapid comparison, large‑scale screening, and sequence‑based analyses of protein motion without the need for computationally intensive molecular dynamics simulations. In practical terms, ProFlex can help individuals identify flexible or rigid regions linked to stability, activity, or regulation, prioritize protein variants, and integrate dynamic information into existing bioinformatics workflows. This makes protein dynamics accessible, scalable, and actionable for applied research and innovation.

Practical Applications in Food Biosciences

ProFlex enables the systematic exploitation of protein dynamics across multiple application domains, including:

• Application of conventional bioinformatics and sequence-based algorithms to protein flexibility data.
• Refinement of structural models, particularly in regions of low confidence or poor experimental support.
• Generation of novel, dynamics-informed features for machine learning and predictive modeling.
• Enhancement of structural phylogenetic analyses through the integration of protein dynamics information.

By compressing complex dynamic behavior into compact, interpretable representations, ProFlex allows organizations to extract greater value from existing protein structure data.

How This Research Was Conducted

Researchers conducted normal mode analysis of over 500,000 alphafold generated protein structures. These were used to empirically define an alphabet representing the relative flexibility of each structure known as ProFlex. Biological insights and applications of ProFlex were thoroughly evaluated.

Explore the Full Scientific Paper

Read the full peer‑reviewed publication in Nature Communications for detailed methods, data, and results.