For over fifty years, protein crystallography has been the cornerstone technology of structural biology, unveiling the three-dimensional arrangement of atoms within proteins. Despite its unparalleled resolution, this technique yields static models, akin to isolated frames of a choreography never fully captured. The central question raised by the ISTA team is vital: How well do these crystallographic images represent the true dynamism of proteins functioning within living cells?

Lea Becker, the study’s first author and doctoral candidate at ISTA, highlights that proteins are perpetually engaged in complex conformational fluctuations — sometimes described as ‘breathing motions’ — whereby the molecule transiently opens and closes to enable interactions with other biomolecules. These motions, often concealed in standard crystallographic data due to molecular immobilization within the crystal lattice, are fundamental to protein functionality but remain challenging to capture experimentally.

To tackle this challenge, the team combined the strengths of several sophisticated techniques, leveraging X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and molecular dynamics simulations. This integrative approach has unveiled a more holistic portrait of protein behavior, overcoming the limitations of any single method. Their model system was GB1, a small protein known for its structural simplicity yet biological relevance, examined in complex with the IgG antibody both in solid crystalline states and in solution.

Particularly insightful was their focus on the behavior of aromatic rings within amino acid side chains. These rings, hydrophobic by nature, tend to be buried deeply within a protein’s core, away from aqueous environments. The ability of these rings to flip orientations — requiring substantial conformational shifts across the protein — serves as a sensitive molecular reporter for internal movements. By monitoring the kinetics and extent of these flips through state-of-the-art solid-state and solution NMR techniques, alongside enhanced-sampling molecular dynamics simulations, the researchers could infer the degree of flexibility and ‘breathing’ within both crystalline and solution states of GB1.

Their findings revealed that crystallization imposes constraints on protein dynamics, effectively damping the natural flexibility observed in solution. Nevertheless, the aromatic ring flips persisted, albeit at altered rates and amplitudes, indicating that proteins retain some dynamic capacity even within crystalline confines. This nuanced insight challenges previous assumptions that crystallography entirely freezes protein motion, demonstrating instead a reshaped, modulated dynamic landscape.

The implications extend far beyond mere academic curiosity. Understanding how proteins dynamically interact with substrates and binding partners underpins the evolutionary design of biological functions. The emergent field of de novo protein design — synthetic creation of proteins with desired structures and functions — still struggles to replicate the full spectrum of conformational flexibility found in nature. Most machine-designed proteins remain trapped in static conformations, which may underlie their limited functional success.

By elucidating the authentic dynamic behavior of proteins, studies like this lay the groundwork for designing proteins with tailored, functional flexibility, thereby enhancing the efficacy of biomolecular engineering. Furthermore, these insights stand to refine machine learning algorithms in structural biology, notably AlphaFold, which revolutionized protein structure prediction but currently models largely static structures. Incorporating dynamic data promises to bridge the gap between predicted structure and biological reality, accelerating drug discovery and deepening disease understanding.

This research was made possible through interdisciplinary collaboration among experimentalists and theoreticians, combining PhD student Lea Becker’s expertise in method development with Professor Paul Schanda’s long-standing fascination with protein dynamics. Contributions from international partners, including Christophe Chipot and Sylvain Engilberge, enriched the study through access to advanced facilities at the European Synchrotron Radiation Facility and expertise in computational modeling.

In sum, this study propels structural biology from static portraits into a dynamic cinema, capturing the invisible ‘breathing’ choreography of proteins that orchestrate life’s molecular symphony. Unmasking these motions not only deepens scientific understanding but also empowers the rational design of biomolecules harnessing nature’s own fluidity, promising a new chapter in biological innovation.

Subject of Research: Cells
Article Title: Aromatic Ring Flips Reveal Reshaping of Protein Dynamics in Crystals and Complexes
News Publication Date: 10-Jun-2026
Web References:

• DOI link to article
• AlphaFold
  References:
  Lea M. Becker, Haohao Fu, Ben P. Tatman, Matthias Dreydoppel, Anna Kapitonova, Ulrich Weininger, Sylvain Engilberge, Christophe Chipot, and Paul Schanda. 2026. Aromatic Ring Flips Reveal Reshaping of Protein Dynamics in Crystals and Complexes. Nature Chemistry. DOI: 10.1038/s41557-026-02155-0
  Image Credits: © ISTA

Keywords

Protein dynamics, Aromatic ring flips, Structural biology, X-ray crystallography, NMR spectroscopy, Molecular dynamics, Protein breathing motions, De novo protein design, Protein flexibility, AlphaFold, Protein-ligand binding, Molecular modeling

Intrinsically disordered proteins deviate from the traditional protein paradigm, where function is closely linked to a stable, folded structure. Instead, IDPs exist as dynamic ensembles of conformations, fluctuating constantly rather than settling into a singular architecture. This structural fluidity underpins their critical roles in biological processes such as molecular signaling, cross-linking, and environmental sensing, but it also presents a vexing obstacle for computational modeling. The transient and heterogeneous nature of IDPs means that conventional structure prediction algorithms, which rely on defined folding patterns, falter when applied to these proteins.

To surmount this barrier, the team led by Harvard’s Paulson School of Engineering and Applied Sciences, along with collaborators at Northwestern, leveraged a sophisticated machine learning technique centered around automatic differentiation—a key computational concept widely utilized in deep learning. Automatic differentiation facilitates the calculation of exact derivatives of physical simulations in real-time, enabling precise optimization by highlighting how infinitesimal changes at the amino acid sequence level translate into modifications of ensemble behavior. Essentially, this approach enables a physics-based, gradient-driven search engine for protein sequences, identifying those with specific dynamic properties rather than fixed structures.

This departure from purely data-driven AI models represents a new paradigm: instead of training machine learning systems solely on empirical protein structures, the researchers integrated physics-based molecular dynamics simulations directly into the optimization loop. By doing so, they generated “differentiable” IDPs, whose properties are tethered to the fundamental laws governing molecular interactions and thermal fluctuations. This allows for a rational design of proteins tailored to functions spanning from molecular connectors that form loops to sensors that react to environmental changes.

Ryan Krueger, a graduate student at Harvard and one of the co-lead authors, explained the motivation behind this approach: “We wanted to avoid training models on vast datasets with limited applicability and instead utilize existing, validated simulations to generate new protein designs directly informed by physical reality.” This contrasts with prior strategies that often relied heavily on patterns mined from known protein structures, which are ill-equipped to capture the dynamic heterogeneity of disordered proteins.

The practical implications of this work are profound. IDPs have been implicated in a variety of diseases, notably neurodegenerative disorders such as Parkinson’s disease, where aberrant forms of alpha-synuclein—a prototypical intrinsically disordered protein—contribute to pathology. Being able to design IDPs with targeted functionalities and behaviors opens avenues for not only deeper mechanistic insights but also innovative therapeutic approaches that could modulate or mimic their activity.

From a technical standpoint, the research capitalized on gradient-based optimization methods, routinely used in neural network training, to iteratively refine protein sequences. These methods compute derivatives of objective functions concerning sequence parameters, enabling the algorithm to “climb” toward optimal configurations that exhibit the desired biophysical traits. Unlike heuristic or stochastic search techniques, this ensures computational efficiency and enhanced precision in navigating the vast combinatorial space of amino acid combinations.

Moreover, the team’s strategy integrates seamlessly with molecular dynamics, a computational method that simulates the physical motions of atoms and molecules over time. By coupling automatic differentiation algorithms with these physics-based simulations, the optimization process harnesses the rich dynamic profile of IDPs, including their transient interactions and conformational ensembles, to inform design decisions. This synergistic approach bridges the gap between theoretical modeling and functional protein engineering.

The study, published in the prestigious journal Nature Computational Science, signifies a critical step toward the rational design of biomolecules that elude conventional design frameworks. It comes at a pivotal moment when advances in artificial intelligence are rapidly reshaping biological research, yet intrinsic disorder remains a frontier. The work was co-led by Krishna Shrinivas, an assistant professor at Northwestern University and former NSF-Simons QuantBio Fellow, alongside Michael Brenner, the Catalyst Professor of Applied Mathematics and Applied Physics at Harvard SEAS.

Further supporting this multi-institutional collaboration were federal agencies including the National Science Foundation AI Institute of Dynamic Systems, the Office of Naval Research, and various Harvard-based research centers. The collective expertise spanned applied mathematics, computational physics, and molecular biology, underscoring the interdisciplinary nature essential for tackling such a complex problem.

Looking ahead, the implications of this method extend beyond natural protein systems. In synthetic biology, engineered IDPs designed with specified properties could serve as novel biomaterials, adaptable sensors, or dynamic scaffolds. The ability to computationally tune sequence-ensemble-function relationships with such granularity offers a powerful toolkit for biotechnologists and pharmaceutical developers alike.

In summary, the team’s innovative utilization of automatic differentiation within a physics-based simulation framework provides a robust, data-efficient pathway to unlocking the mysteries of intrinsically disordered proteins. By transcending the limitations of existing AI models, this research sets the stage for designing a previously inaccessible class of proteins with vast biological and clinical potential.

Subject of Research: Not applicable

Article Title: Generalized design of sequence–ensemble–function relationships for intrinsically disordered proteins

News Publication Date: 6-Oct-2025

Web References:

• Article DOI: 10.1038/s43588-025-00881-y
• Associated institutions: Harvard SEAS, Northwestern University

References:

• Original research published in Nature Computational Science

Image Credits: Ramanna Shrinivas

Keywords:
Protein folding, Protein expression, Protein stability, Proteins, Protein activity, Life sciences, Biochemistry, Biomolecules, Machine learning, Artificial neural networks, Deep learning, Computer science, Applied physics, Applied mathematics, Algorithms