The answer lies in geometry: proteins with denser cores, larger size, and higher-order oligomeric assembly tolerate mutations more readily, occupy larger structural families, and support more versatile biological roles, reveals that protein size, shape, and self-assembly, not just sequence, are fundamental drivers of evolvability.
Abstract
A fundamental question in structural biology centres around understanding protein evolution. Key to this process is mutational robustness, defined as the protein fold’s ability to absorb sequence changes without collapsing its structure. Here, we show that robustness is systematically shaped by simple features such as protein size, geometry, and oligomeric state. We used Foldseek-identified (structural) homologs to quantify family size across monomers and higher homo-oligomers. We found that proteins in larger families are consistently larger in size, more compact in atomic density, and less exposed to solvent. Strikingly, homo-oligomers occupy systematically larger families than monomers, revealing quaternary structure itself as a driver of mutational tolerance, not merely a functional supplement. This signature of robustness can be further linked to increasing functional complexity in proteins; those with adaptive, multifaceted biological roles belong to larger structural families than those with specific roles, thereby linking structural flexibility directly to evolutionary versatility. In short, simple yet overlooked features of protein geometry can explain mutational robustness and evolvability, offering a structural rationale for why certain protein families have diversified extensively while others remain in evolutionary stasis. eTOC blurb (short summary) Why do some protein folds diversify into thousands of variants while others remain rare and rigid? This work shows that the answer lies in geometry: proteins with denser cores, larger size, and higher-order oligomeric assembly tolerate mutations more readily, occupy larger structural families, and support more versatile biological roles. This reveals that protein size, shape, and self-assembly, not just sequence, are fundamental drivers of evolvability.
Together, these results show that designed repeat-protein folding is governed by seed formation, interface propagation, and terminal boundary conditions, and establish intramolecular crosslinking as a strategy for rationally reshaping folding landscapes in designed proteins.
Melanie Weiß, Anna Lisa Heit, L. Milles et al.· bioRxiv· 0 citations
Together, these insights position conformational dynamics at the center of understanding and engineering the evolutionary logic of protein function, opening the door to study how proteins are tuned to operate under the nonequilibrium conditions of living cells.
Sixto M. Herrera, Elías Manríquez-Benítez, Exequiel Medina· Current Opinion in Structura...· 0 citations
Predicting protein dynamics is a long-standing problem in computational structural biology. Often, protein function critically depends on local directed motions, such as hinge movements, catalytic loop rearrangements and domain reorientations, which can be characterized by directional flexibility and correlated structural motions of the protein backbone. While Molecular Dynamics (MD) simulations provide an established but often prohibitively expensive approach, recent deep generative models aim to reduce this cost by directly predicting conformational ensembles, emulating MD. However, due to their large size and the need to generate several states until the derived dynamical properties converge, these models remain expensive. In this work, we propose BackFlip-2: a fast SE(3)-equivariant graph neural network trained to directly predict dynamical descriptors, such as directional backbone flexibility and pairwise dynamic correlations, from an equilibrium structure. In a series of experiments, we show that our model matches the accuracy of substantially larger ensemble generation models while being orders of magnitude faster, and demonstrate that the proposed equivariant architecture is especially well-suited for capturing anisotropic motions in proteins. BackFlip-2 model weights, training and inference code are available at https://github.com/graeter-group/backflip.
Vsevolod Viliuga, Leif Seute, Matteo Tadiello et al.· 0 citations
Raygun is introduced, a generative artificial intelligence framework that enables miniaturization, modification and augmentation of proteins, using a probabilistic encoding of protein sequences constructed from language model embeddings, enabling the kind of coordinated, large-scale sequence modifications that characterize natural protein evolution.
Kapil Devkota, Daichi Shonai, Joey Mao et al.· Nature· 1 citation
This work introduces mathematical topology metrics that quantify the entanglement complexity of a tertiary protein structure while respecting uncrossability constraints and results indicate that these metrics efficiently encode structural features linked to protein function and provide a more informative description than conventional metrics.
P. Malatesta, Roshita S. Chandnani, J. Yalim et al.· bioRxiv· 0 citations
An improved force field is developed, derived from its parent, Amber ff24EXP-GA, and its evaluation against Amber ff14SB and other contemporary force fields, such as CHARMM36m, in capturing the empirically determined conformational properties of unfolded systems: short peptides that serve as model systems for IDPs, and longer unfolded proteins.
Athul Suresh, B. Urbanc· 0 citations
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