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Fernandez-Martin, M.

Publications and source records attributed to Fernandez-Martin, M..

4 recordsLinked to original sources

Adaptive and Spandrel-like Constraints at Functional Sites in Protein Folds

How new molecular functions emerge during protein evolution remains a fundamental question in molecular biology. The energy landscape theory states that proteins are minimally frustrated, i.e. they have minimized their internal conflicts, to allow robust folding. Yet, not all energetic conflicts are eliminated, with functional regions such as catalytic residues and ligand-binding sites being often enriched in frustrated interactions, trading localized stability for biological activity. However, it is still uncertain whether this functional frustration is an evolutionary adaptation, positively selected despite its energetic cost or an inevitable physical byproduct of the fold architecture. Here, we combine reverse folding, structure prediction, and sequence analysis with local frustration profiling to address this long-standing question. Unexpectedly, we found that reverse folding algorithms are unable to energetically minimize evolutionary conserved frustration at specific residues, even when detrimental to overall structural stability. We propose that these frustration hotspots act as architectural spandrels, inherent physical constraints of the fold that evolution subsequently co-opts for function. Our findings connect biophysical constraints and evolutionary selection, providing a new framework to understand how functional specificity emerges in protein landscapes.

bioinformatics↗

FrustrAI-Seq: Scaling Local Energetic Frustration to the Protein Sequence Space

Proteins fold into their native three-dimensional (3D) structures by navigating complex energy landscapes shaped by the biophysical and biochemical properties of their sequence. Once folded, some sequence positions (dubbed residues) remain locally frustrated, reflecting functional constraints incompatible with optimal packing. This local energetic frustration provides important insights into protein function and dynamics, but its analysis typically relies on structure-based energy calculations and remains energetically costly at scale. Here, we introduce an ultra-fast sequence-based prediction of local energetic frustration directly from protein sequences using embeddings from protein language models (pLMs). Our method, coined FrustrAI-Seq, enables proteome-wide frustration profiling in minutes (17 minutes for the entire human proteome on a single Nvidia H100 GPU) while retaining biologically relevant performance as shown for the alpha-globin and beta-lactamase family. By eliminating the need for explicit structural or evolutionary information, this approach expands frustration analysis to protein regions and classes that were previously inaccessible, including intrinsically disordered regions and high-throughput de novo designed protein datasets. To support reproducibility and large-scale applications, we provide the largest freely available resource of precomputed local frustration scores to date (10^6 proteins), along with model weights and complete training and inference code at: github.com/leuschjanphilipp/FrustrAI-Seq.

bioinformatics↗

Sequence-based coevolutionary prediction of species-specific interactomes

Evolutionarily conserved protein-protein interactions (PPIs) reveal fundamental biological processes. However, predicting protein function solely from these interactions provides an incomplete picture of biological systems. Computational methods for predicting PPIs often struggle due to limited functional annotations in databases, making it difficult to fully understand unique biological systems.. This study introduces ContextMirror2.0 (CM2.0), a coevolution-based method designed to address these limitations. Coevolutionary approaches, unlike supervised machine learning methods, do not rely on labelled datasets, proving valuable in addressing the scarcity of data for species-specific PPIs. While around 40% of CM2.0s top-1000 predicted PPIs for E. coli, S. enterica, and S. aureus are present in experimental PPI databases, a comparative analysis of predicted functional communities revealed highly conserved interaction patterns and species-specific interactions. CM2.0 leverages coevolutionary information to explore protein interaction dynamics and understand the functional consequences of species-specific variations. This information can inform further studies on the emergence of novel functions, adaptation to specific environments, and the development of targeted therapies.

bioinformatics↗

Frustraevo: A Web Server To Localize And Quantify The Conservation Of Local Energetic Frustration In Protein Families

According to the Principle of Minimal Frustration, folded proteins can have only a minimal number of strong energetic conflicts in their native states. However, not all interactions are energetically optimized for folding but some remain in energetic conflict, i.e. they are highly frustrated. This remaining local energetic frustration has been shown to be statistically correlated with distinct functional aspects such as protein-protein interaction sites, allosterism and catalysis. Fuelled by the recent breakthroughs in efficient protein structure prediction that have made available good quality models for most proteins, we have developed a strategy to calculate local energetic frustration within large protein families and quantify its conservation over evolutionary time. Based on this evolutionary information we can identify how stability and functional constraints have appeared at the common ancestor of the family and have been maintained over the course of evolution. Here, we present FrustraEvo, a web server tool to calculate and quantify the conservation of local energetic frustration in protein families. The webserver is freely available at URL: https://frustraevo.qb.fcen.uba.ar

bioinformatics↗