Aug 2026· Biochimica et Biophysica Acta - Proteins and Proteomics· Vol 1874, pp.
141170
· 0 citations· 61 references
Medicine
TL;DR
It is argued that incorporating frustration into computational and experimental strategies will be essential to move beyond purely stability-driven approaches toward the rational engineering of functional proteins.
Abstract
Proteins operate under competing demands imposed by stability, dynamics, and function, all of which are shaped by evolution. Local energetic frustration provides a quantitative framework to describe how these competing requirements are distributed within the native states of proteins, identifying regions where interactions are optimized and others where energetic conflicts are retained to enable functional behavior. In recent years, the study of local frustration has expanded significantly, driven by the integration of large-scale structural datasets and advances in artificial intelligence methods. Comparative analyses have shown that frustration patterns encode evolutionary pressures across protein families, with minimally frustrated interactions stabilizing structural cores and highly frustrated regions often associated with catalysis, binding, conformational transitions as well as pathogenic phenotypes. At the same time, modern protein language models and structure prediction methods seem to implicitly capture the statistical and structural features underlying frustration, enabling its prediction directly from sequence or structure at proteome scale. These developments suggest that local energetic frustration may be interpreted as an emergent property of the evolutionary information learned by AI models. Here, we review recent advances in the analysis and prediction of local frustration and discuss how this framework could provide mechanistic insights into protein evolution, conformational dynamics, and design. We further argue that incorporating frustration into computational and experimental strategies will be essential to move beyond purely stability-driven approaches toward the rational engineering of functional proteins.
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
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.
The (un)folding rates of natural proteins determine their native stability and functional homeostasis, making them important targets for protein engineering and design. From a prediction standpoint, the rates have been a long‐standing puzzle. We have known for decades that folding rates empirically correlate with properties of the native three dimensional (3D) structures and that both, folding and unfolding rates, scale with protein size. Whereas such rate correlations are too rough for being of practical use, no significant progress in prediction accuracy has occurred since then, despite many efforts even including machine learning approaches. Here, we retake on this challenge by expanding the simple one‐dimensional free energy surface (1D‐FES) model that originally led to demonstrate the size scaling of both rates, and a curated database with rates for 75 single‐domain proteins. We define the weighted sequence order (WSO) as a novel parameter that allows incorporating structural information into the 1D‐FES model explicitly. Via the WSO, we examine the role of global structural properties such as fold topology and core packing in defining the (un)folding rates within the context of a physics‐based model of protein folding. After introducing fold topology and packing at a coarse‐grained level, the model uses three floating parameters to predict the folding and unfolding rates within 6.5‐ and 10‐fold, respectively, resulting in ±6.5 kJ/mol accuracy in native stability, equivalent to the typical perturbation induced by one single‐point mutation. The net improvement over the 2‐parameter size‐only prediction is of 2.5‐fold. These new rate predictions are significantly closer to the threshold of usefulness for engineering and design. More importantly, this WSO‐modified 1D‐FES model can now directly accommodate atomistic, high‐resolution, force‐fields to further optimize the rate predictions, and/or to use rate information as a testbed for force‐field refinement. Finally, the WSO‐1D‐FES model could also serve as foundation for developing more complex models capable of dealing with multi‐domain proteins as well as with the evolutionary information cryptically encoded in natural protein sequences.
Mohammad Abdulqader, Victor Muñoz· Protein Science· 0 citations
It is demonstrated that BioEmu can generate plausible conformational ensembles for relatively large, six-and seven-pass membrane proteins, sampling rare states at a fraction of the computational cost of conventional MD simulations, suggesting that AI-based ensemble generation could provide an accessible approach for exploring membrane protein dynamics and complement conventional molecular modelling approaches.
B. Clifton, Adam G Grieve, Robin A. Corey· bioRxiv· 0 citations
The spatial and energetic encoding of allosteric regulatory sites remains a major challenge in structural biology, frequently representing a “blind spot” for sequence‐based artificial intelligence (AI) models. We present a protein language model (PLM)‐guided approach complemented by the energy landscape frustration analysis as a dual‐stream framework to investigate the relationship between AI prediction of binding sites and biophysical organization of regulatory pockets across the human kinome. By probing a fine‐tuned residue‐level PLM classifier across 453 kinase structures, a clear performance gap is discovered between highly predictable orthosteric pockets (Types I, I.5, and II) and poorly resolved distal allosteric sites (Type IV). Rather than attempting to interpret this blind spot through internal AI attributions alone, we use independent local frustration profiles to analyze the underlying physics of these sites. We determine that the detectability of orthosteric and allosteric binding sites reflects their energetic embedding within the protein energy landscape. Orthosteric catalytic sites reside within minimally frustrated, optimized energetic regions that are consistently detected with high confidence. In contrast, allosteric sites are enriched in neutrally frustrated zones, producing diffuse and context‐dependent predictions. We demonstrate that this neutral frustration of functional regions acts as a biophysical lubricant, facilitating the conformational plasticity required for regulatory transitions while simultaneously eroding the coevolutionary signals exploited by PLMs. Atomic‐resolution analysis of abelson murine leukemia (ABL) kinase spanning multiple conformational states and complexes bound to diverse ligands provides mechanistic validation of this principle. The myristoyl allosteric pocket in ABL remains neutrally frustrated across complexes with physiological ligands, chemically diverse modulators, from allosteric inhibitors to activators, and conformations engaged with SH2–SH3 regulatory domains. We propose that allosteric sites are encoded in persistent neutrally frustrated regions optimized for context‐dependent regulatory modulation. This study reveals how the organization of the protein energy landscape shapes universal “allosteric grammar” and algorithmic detectability of regulatory binding sites.
Will Gatlin, Max Ludwick, L. Turano et al.· Protein Science· 1 citation
Understanding how mutations combine to shape protein fitness remains a central challenge in biology, driven in part by the prevalence of highorder epistasis. Existing analyses of epistasis, however, implicitly define epistatic interactions under a uniform probability measure over sequence space, even though evolution constrains natural proteins to a highly structured, non-uniform distribution of sequences. Here, we show that the apparent complexity of protein epistasis depends fundamentally on the underlying evolutionary distribution of sequences. We develop an evolution-aware spectral framework that incorporates the evolutionary distribution of amino acids at each sequence position, inducing an orthogonal decomposition under the evolutionary measure while preserving efficient spectral algorithms for scalable analysis. Across diverse protein fitness landscapes, this framework consistently produces more compact spectral representations, explaining more phenotypic variation with fewer epistatic interactions while substantially reducing apparent high-order epistasis. It also enables more accurate recovery of fitness landscapes from limited experimental measurements and concentrates the remaining higher-order interactions into localized, structurally interpretable motifs. These results suggest that a substantial fraction of apparent high-order epistasis arises from defining epistatic interactions under a uniform measure over sequence space and can be resolved by aligning spectral analysis with evolutionary constraints.