• Diego Ulises Ferreiro

Natural protein molecules are amazing objects that somehow compute their structure, dynamics, and activities given a sequence of amino acids and an environment. In turn, protein evolution solves the problem of finding sequences that satisfy the constraints given by biological functions, closing an informational loop that relates an equilibrium thermodynamic system (protein folding) with a non-equilibrium information-gathering and -using system (protein evolution). I will present and discuss results from an information-theory perspective of protein folding, function, and evolution. I will also present extensions of the theory to other terrestrial biopolymers and potential extraterrestrial ones.

• David Moi, David Moi, University of Lausanne
• Christophe Dessimoz, Christophe Dessimoz, University of Lausanne

Inferring deep phylogenetic relationships between proteins requires methods that can capture the iterative optimisation of the final folded protein object through evolution. This entails considering both sequence and structure information. While sequence-based phylogenetics has long been the standard, recent progress in structure prediction and modeling has opened new opportunities to harness 3D structural information in tree reconstruction.
We recently introduced FoldTree, a practical framework for structure-based phylogenetics. Central to FoldTree is a robust benchmarking strategy that enables fair comparison between sequence and structure-based methods — a critical step given their fundamentally different inputs. Using local structural alphabets derived from protein geometry, FoldTree not only outperforms conventional sequence-based approaches for remote homologs but, surprisingly, also improves phylogenetic resolution among relatively close relatives. Its success has enabled novel evolutionary insights, such as clarifying the diversification of RRNPPA quorum-sensing receptors across bacteria, plasmids, and phages.
Building on this foundation, we now introduce a new generation of structural alphabets developed using graph neural networks (GNNs). In this approach, protein structures are represented as graphs where residues are nodes labeled with physicochemical and geometric features, and edges encode diverse relationships such as spatial proximity, hydrogen bonding, or allosteric coupling. These alphabets capture both local residue environments and the broader network of structural constraints, bridging the gap between sequence and structure information and enabling integrative phylogenetic inference from sequence and structure.
Together, these developments chart a path towards integrative sequence and structural phylogenetics, expanding the reach of evolutionary inference beyond the twilight zone of sequence similarity.

• Joel Roca Martinez, Joel Roca Martinez, UCL
• Hazel Leiva, Hazel Leiva, San Francisco State University
• Jialin Lin, Jialin Lin, Max Planck Institute
• Misty L Kuhn, Misty L Kuhn, San Francisco State University
• Christine Orengo, Christine Orengo, University College London

Spermine/spermidine acetyltransferases (SSATs) are members of the highly diverse Gcn5-related N-acetyltransferase (GNAT) superfamily, which ranks among the top 1% most structurally and sequence-diverse families in the CATH database. Prior studies have shown that while bacterial and eukaryotic SSAT enzymes catalyze the same reaction, they differ in residue conservation patterns, oligomeric states, and presence of allosteric sites. This raises the question of whether their functional similarity reflects convergent or divergent evolution. To investigate this, we utilized complementary in silico and in vitro experimental approaches. In silico experiments included analyzing ~37,000 GNAT sequences using AlphaFold2 modelling and additional sequence- and structure-based tools including FunFamer and FunTuner (in-house tools), Zebra3D, and IQ-TREE. A total of 71 SSAT enzymes were selected for in vitro experimental validation whereby substrate screening and enzyme kinetic assays showed distinct substrate preferences linked to specificity-determining residues and structural features. Our results support a model of convergent evolution between bacterial SpeG and human SSAT1 enzymes, with additional subfamilies showing divergent evolutionary paths. This work highlights the evolutionary plasticity of the GNAT fold and demonstrates how integrating computational and experimental strategies can uncover functional insights in large, diverse enzyme families.

• Wan-Chun Su, Wan-Chun Su, McGill University
• Yu Xia, Yu Xia, McGill University

The competitive nature of host-virus protein-protein interactions drives an ongoing evolutionary arms race between hosts and viruses. The surface regions on a host protein that interact with virus proteins (exogenous interfaces) frequently overlap with those that interact with other host proteins (endogenous interfaces), forming interfaces that are shared between virus and host protein partners (mimic-targeted interfaces). This phenomenon, referred to as interface mimicry, is a common strategy used by viruses to invade and exploit the cellular pathways of host organisms. Yet, the quantitative evolutionary consequences of interface mimicry on the host are not well-understood. Here, we integrate experimentally determined 3D structures and homology-based molecular templates of protein complexes with protein-protein interaction networks to construct a high-resolution human-virus structural interaction network. We perform rigorous site-specific evolutionary analyses on this structural interaction network and find that exogenous-specific interfaces evolve significantly faster than endogenous-specific interfaces. Surprisingly, mimic-targeted interfaces are as fast evolving as exogenous-specific interfaces, despite being targeted by both human and virus proteins. Moreover, we find that rapidly evolving mimic-targeted interfaces bound by human viruses are only visible in the mammalian lineage. Our findings suggest that virus targeting exerts an overwhelming influence on host interfacial evolution, within the context of domain-domain interactions, and that mimic-targeted interfaces on human proteins are the key battleground for a mammalian-specific host-virus evolutionary arms race. Overall, our study provides insights into the selective pressures that viruses impose on their hosts at the protein residue level, enabling a quantitative and systematic understanding of host-pathogen interaction and evolution.

• Jingi Yeo, Jingi Yeo, Seoul National University
• Yewon Han, Yewon Han, Seoul National University
• Nicola Bordin, Nicola Bordin, University College London
• Andy M. Lau, Andy M. Lau, InstaDeep
• Shaun Kandathil, Shaun Kandathil, University College London
• Hyunbin Kim, Hyunbin Kim, Seoul National University
• Milot Mirdita, Milot Mirdita, Seoul National University
• David Jones, David Jones, University College London
• Christine Orengo, Christine Orengo, University College London
• Martin Steinegger, Martin Steinegger, Seoul National University

Recent advances in protein structure prediction by AlphaFold2 and ESMFold have massively expanded the known protein structural landscape. The AlphaFold Protein Structure Database (AFDB) now contains over 200 million models, while ESMAtlas hosts more than 600 million predicted structures from metagenomic data in MGnify. These resources span diverse taxonomic groups, including many unculturable species, and reveal previously unknown evolutionary relationships and structural arrangements.
To harness this data, new computational strategies for classification and comparison are essential. We clustered the ESMatlas using Foldseek Cluster, identifying 72 million structure clusters and mapping their distribution across taxa. This uncovered novel evolutionary patterns, such as structural analogs in extreme environments and new domain combinations absent from PDB-based databases like CATH. In parallel, The Encyclopedia of Domains (TED) systematically classifies protein domains across the AFDB and reveals over 365 million domains—far surpassing traditional sequence-based methods. More than 100 million of these domains were previously undetected, underscoring the power of structure-based approaches in expanding known domain space.
Together, ESMAtlas and TED help chart uncharted structural territory. ESMatlas proteins enrich the known protein universe with unique domain architectures, while TED reveals thousands of putative new folds. These breakthroughs demonstrate how multidomain proteins evolve through novel fold combinations and packing geometries. Both efforts also reveal patterns of domain exclusivity, lineage-specific architectures, and structural convergence across the Tree of Life, suggesting environmental adaptations and ancient, conserved folds crucial for cellular function.
By uncovering new domain arrangements and interactions, we approach a comprehensive map of the protein universe.

• Hanne Zillmer, Hanne Zillmer, Max Planck Institute of Molecular Plant Physiology
• Dirk Walther, Dirk Walther, Max Planck Institute of Molecular Plant Physiology

With the availability of reliably predicted 3D-structures for essentially all known proteins, characterizing the entirety of protein - small-molecule interaction sites (binding pockets) has become a possibility. The aim of this study was to identify and analyze all compound-binding sites, i.e. the pocketomes, of eleven different species’ from different kingdoms of life to discern evolutionary trends as well as to arrive at a global cross-species view of the pocketome universe. All protein structures available in the AlphaFold database for each species were subjected to computational binding site predictions. The resulting set of potential binding sites was inspected for overlaps with known pockets and annotated with regard to the protein domains. 2D-projections of all pockets embedded in a 128-dimensional feature space and characterizing all pockets with regard to selected physicochemical properties, yielded informative, global pocketome maps that reveal differentiating features between pockets. By clustering all pockets within species, our study revealed a sub-linear scaling law of the number of unique binding sites relative to the number of unique protein structures per species. Thus, larger proteomes harbor less than proportionally more different binding sites than species with smaller proteomes. We discuss the significance of this finding as well as identify critical and unmet algorithmic challenges.

• Miguel Fernandez-Martin, Miguel Fernandez-Martin, Barcelona Supercomputing Cente (BSC - CNS) - Life Sciences
• Nicola Bordin, Nicola Bordin, Institute of Structural and Molecular Biology
• Christine Orengo, Christine Orengo, Institute of Structural and Molecular Biology
• Alfonso Valencia, Alfonso Valencia, Barcelona Supercomputing Cente (BSC - CNS) - Life Sciences
• Gonzalo Parra, Gonzalo Parra, Barcelona Supercomputing Center (BSC - CNS) - Life Sciences

Understanding how protein families evolve and function remains a central question in molecular biophysics. By grouping evolutionarily related proteins into Functional Families (FunFams), CATH captures structural and functional conservation beyond sequence identity. By integrating AlphaFold2 models, CATH offers a representative view of the protein universe. Our group has developed a methodology to quantify local frustration conservation patterns in protein families, providing a biophysical interpretation of evolutionary constraints related to foldability, stability and function.
In this study, we scaled frustration conservation analysis to a representative portion of the protein universe. We have analyzed over 8,900 FunFams (2.2M sequences) from CATH and TED, and explored the frustration and aminoacid identities distributions across the 20 Foldseek’s 3Di tertiary neighborhoods. We investigated how these geometries influence conservation patterns and find that some aminoacid identities (e.g. C, V, L, F, I, M) are conserved in a minimally frustrated state, indicating their evolutionary importance as structural anchors. Other residues (e.g. T, S, H, G) tend to be conserved in a neutral state, historically overlooked, suggesting that neutral frustration is not just an energetic buffering state but an evolutionarily constrained one. Additionally, some residues (e.g. D, K, E, N, Q) exhibit high proportions of conserved high frustration, potentially relevant for function.
We present the first large-scale frustration survey of the protein universe, which allows us to distinguish whether sequence conservation reflects stability, neutrality or function. This framework offers a new way of interpreting conservation and lays the foundation for a biophysically informed understanding of protein evolution.

• Javier Sánchez Utgés, Javier Sánchez Utgés, University of Dundee
• Stuart MacGowan, Stuart MacGowan, University of Dundee
• Geoff Barton, Geoff Barton, University of Dundee

Fragment screening is used for hit identification in drug discovery, but it is often unclear which binding sites are functionally relevant. Here, data from 37 experiments is analysed. A method to group ligands by protein interactions is introduced and sites clustered by their solvent accessibility. This identified 293 ligand sites, grouped into four clusters. C1 includes buried, conserved, missense-depleted sites and is enriched in known functional sites. C4 comprises accessible, divergent, missense-enriched sites and is depleted in function.

This approach is extended to the entire PDB, resulting in the LIGYSIS dataset, accessible through a new web server. LIGYSIS-web hosts a database of 65,000 protein-ligand binding sites across 25,000 proteins. LIGYSIS sites are defined by aggregating unique relevant protein-ligand interfaces across multiple structures. Additionally, users can upload structures for analysis, results visualisation and download. Results are displayed in LIGYSIS-web, a Python Flask web application.

Finally, the human component of LIGYSIS, comprising 6800 binding sites across 2775 proteins, is employed to perform the largest benchmark of ligand site prediction to date. Thirteen canonical methods and fifteen novel variants are evaluated using fourteen metrics. Additionally, LIGYSIS is compared to datasets like PDBbind or MOAD and shown to be superior, since it considers non-redundant interfaces across biological assemblies. Re-scored fpocket predictions present the highest recall (60%). The detrimental effect in performance of redundant prediction, and the beneficial impact of stronger pocket scoring schemes is demonstrated. To conclude, top-N+2 recall is proposed as a robust benchmark metric and authors encouraged to share their benchmark code.

• Alex Morehead, Alex Morehead, University of Missouri-Columbia
• Jianlin Cheng, Jianlin Cheng, University of Missouri-Columbia

Motivation: Powerful generative models of protein-ligand structure have recently been proposed, but few of these methods support both flexible protein-ligand docking and affinity estimation. Of those that do, none can directly model multiple binding ligands concurrently or have been rigorously benchmarked on pharmacologically relevant drug targets, hindering their widespread adoption in drug discovery efforts.

Results: In this work, we propose FlowDock, a deep geometric generative model based on conditional flow matching that learns to directly map unbound (apo) structures to their bound (holo) counterparts for an arbitrary number of binding ligands. Furthermore, FlowDock provides predicted structural confidence scores and binding affinity values with each of its generated protein-ligand complex structures, enabling fast virtual screening of new (multi-ligand) drug targets. For the commonly-used PoseBusters Benchmark dataset, FlowDock achieves a 51% blind docking success rate using unbound (apo) protein input structures and without any information derived from multiple sequence alignments, and for the challenging new DockGen-E dataset, FlowDock matches the performance of single-sequence Chai-1 for binding pocket generalization. Additionally, in the ligand category of the 16th community-wide Critical Assessment of Techniques for Structure Prediction (CASP16), FlowDock ranked among the top-5 methods for pharmacological binding affinity estimation across 140 protein-ligand complexes, demonstrating the efficacy of its learned representations in virtual screening.

Availability and implementation: Source code, data, and pre-trained models are available at https://github.com/BioinfoMachineLearning/FlowDock.

• Gordon Hanna, Gordon Hanna, Centre for Integrative Systems Biology and Bioinformatics
• Elbert Timothy, Elbert Timothy, Centre for Integrative Systems Biology and Bioinformatics
• Suhail A Islam, Suhail A Islam, Imperial College London
• Michael Sternberg, Michael Sternberg, Imperial College London
• Alessia David, Alessia David, Imperial College London

The deep learning algorithm AlphaFold has revolutionized the field of structural biology by producing highly accurate three-dimensional models of the proteome, thus providing a unique opportunity for atom-based analysis of human missense variants. Current variant prediction tools, such as REVEL, EVE and AlphaMissense, have significantly improved the prediction of damaging amino acid substitutions, but do not explain the mechanism by which these variants impact the phenotype, which, in most cases, remains elusive.
We developed a pipeline to automatically identify accurately modelled amino acid regions that can be used for variant characterization. The recommended AlphaFold pLDDT threshold for an accurately modelled residue is =>70. When using this threshold for the query residue, the accuracy of the atom-based predictions calculated using our in-house variant prediction algorithm Missense3D is 0.66, MCC 0.36, TPR/FPR 5.1. We show that, when the model accuracy of the environment surrounding the query residue (E-plDDT5A) is considered, an E-plDDT5A >=60 provides similar accuracy, MCC and TPR/FPR to that obtained using the plDDT threshold >=70 for the query residue alone, but increases the number of residues for which an atom-based analysis can be performed. When using this new E-plDDT 5A>=60 threshold, >68% of the human proteome and >4 million missense variants can be modelled with sufficient quality to allow an atom-based analysis.
In conclusion, AlphaFold 3D models offer a unique opportunity to understand the consequences of amino acid substitutions on protein structure, thus complementing existing evolutionary-based methods.

• Guanglei Yu, Guanglei Yu, Central South University
• Xuehua Bi, Xuehua Bi, Xinjiang Medical University
• Teng Ma, Teng Ma, Central South University
• Yaohang Li, Yaohang Li, Old Dominion University
• Jianxin Wang, Jianxin Wang, Central South University

Motivation: Protein-protein interactions (PPIs) are fundamental aspects in understanding biological processes. Accurately predicting the effects of mutations on PPIs remains a critical requirement for drug design and disease mechanistic studies. Recently, deep learning models using protein 3D structures have become predominant for predicting mutation effects. However, significant challenges remain in practical applications, in part due to the considerable disparity in generalization capabilities between easy and hard mutations. Specifically, a hard mutation is defined as one with its maximum TM-score < 0.6 when compared to the training set. Additionally, compared to physics-based approaches, deep learning models may overestimate performance due to potential data leakage.
Results:We propose new training/test splits that mitigate data leakage according to the CATH homologous superfamily. Under the constraints of physical energy, protein 3D structures and CATH domain objectives, we employ a hybrid noise strategy as data augmentation and present a geometric encoder scenario, named CATH-ddG, to represent the mutational microenvironment differences between wild-type and mutated protein complexes. Additionally, we fine-tune ESM2 representations by incorporating a lightweight nonlinear module to achieve the transferability of sequence co-evolutionary information. Finally, our study demonstrates that CATH-ddG framework provides enhanced generalization by outperforming other baselines on non-superfamily leakage splits, which plays a crucial role in exploring robust mutation effect regression prediction. Independent case studies demonstrate successful enhancement of binding affinity on 419 antibody variants to human epidermal growth factor receptor 2 (HER2) and 285 variants in the receptor-binding domain (RBD) of SARS-CoV-2 to angiotensin-converting enzyme 2 (ACE2) receptor.

• Raymund Hackett, Raymund Hackett, Leiden University Medical Center
• Martin Larralde, Martin Larralde, Leiden University Medical Center
• Ioannis Riziotis, Ioannis Riziotis, European Bioinformatics Institute (EMBL-EBI)
• Janet Thornton, Janet Thornton, European Bioinformatics Institute (EMBL-EBI)
• Georg Zeller, Georg Zeller, Leiden University Medical Center

Detecting catalytic features in protein structures can provide important hints about enzyme function and mechanism. Keeping pace with the rapidly growing universe of predicted protein structures requires computationally fast and scalable but interpretable tools. A library of 3D coordinates describing enzyme catalytic sites, referred to as templates, has been collected from manually curated and literature annotated examples of enzyme catalytic mechanisms described in the Mechanism and Catalytic Site Atlas. We provide this library of templates and a fast and modular python tool implementing the geometric matching algorithm Jess to identify matching catalytic sites in both experimental and predicted protein structures. We implement stringent match filtering to reduce the number of false matches occurring by chance. We validated this method against a non-redundant set of high quality experimental and predicted enzyme structures with well annotated catalytic sites. Geometric, knowledge based criteria are used to differentiate catalytically informative matches from spurious ones. We show that structurally matching catalytic templates is more sensitive than sequence based and even some structure based approaches in identifying homology between extremely distant enzymes. Since geometric matching does not depend on conserved sequence motifs or even common evolutionary history, we are able to identify examples of structural active site similarity in divergent and possibly convergent enzymes. Such examples make interesting case studies into the ancestral evolution of enzyme function. While insufficient for detecting and characterising substrate specific binding sites, this methodology could be suitable for expanding the annotation of enzyme active sites across proteomes.

• Gyorgy Abrusan, Gyorgy Abrusan, King's College London
• Aleksej Zelezniak, Aleksej Zelezniak, King's College London

The main forces driving protein complex evolution are currently not well understood, especially in homomers, where quaternary structure might frequently evolve neutrally. Here we examine the factors determining oligomerisation by analysing the evolution of enzymes in circumstances where homomers rarely evolve. We show that 1) In extracellular environments, most enzymes with known structure are monomers, while in the cytoplasm homomers, indicating that the evolution of oligomers is cellular environment dependent; 2) The evolution of quaternary structure within protein orthogroups is more consistent with the predictions of constructive neutral evolution than an adaptive process: quaternary structure is gained easier than it is lost, and most extracellular monomers evolved from proteins that were monomers also in their ancestral state, without the loss of interfaces. Our results indicate that oligomerisation is context-dependent, and even when adaptive, in many cases it is probably not driven by the intrinsic properties of enzymes, like their biochemical function, but rather the properties of the environment where the enzyme is active. These factors might be macromolecular crowding and excluded volume effects facilitating the evolution of interfaces, and the maintenance of cellular homeostasis through shaping cytoplasm fluidity, protein degradation, or diffusion rates.

• Zhongyao Zhang, Zhongyao Zhang, VIB - KULeuven
• Rob Van der Kant, Rob Van der Kant, VIB - KULeuven
• Iva Marković, Iva Marković, VIB - Ghent University
• David Vizarraga, David Vizarraga, The Barcelona Institute of Science and Technology
• Teresa Garcia, Teresa Garcia, VIB - KULeuven
• Katerina Maragkou, Katerina Maragkou, VIB - KULeuven
• Javier Delgado Blanco, Javier Delgado Blanco, The Barcelona Institute of Science and Technology
• Damiano Cianferoni, Damiano Cianferoni, The Barcelona Institute of Science and Technology
• Gabriele Orlando, Gabriele Orlando, VIB - KULeuven
• Gabriel Cia, Gabriel Cia, VIB - KULeuven
• Nick Geukens, Nick Geukens, PharmAbs - KULeuven
• Carlo Carolis, Carlo Carolis, The Barcelona Institute of Science and Technology
• Alexander N. Volkov, Alexander N. Volkov, VIB - VUB
• Savvas N. Savvides, Savvas N. Savvides, VIB - Ghent University
• Maarten Dewilde, Maarten Dewilde, PharmAbs - KULeuven
• Joost Schymkowitz, Joost Schymkowitz, VIB - KULeuven
• Luis Serrano Pubul, Luis Serrano Pubul

Monoclonal antibodies are rapidly becoming a standard drug format in the pharmaceutical industry, but current immunization-based methods for antibody discovery often present limitations in terms of developability, binding affinity, cross-reactivity and, importantly, selectively targeting a prespecified epitope. Given these limitations, rational optimization and de novo design of antibodies with computational methods is becoming an attractive alternative to traditional antibody development methods. While recent deep learning methods have shown tremendous progress for protein design, their application to therapeutic antibody formats remains one of the major open challenges in the field. Here, we present EvolveX, a structure-based computational pipeline for antibody optimization and de novo design. EvolveX is a multi-objective optimization algorithm incorporating CDR modeling, biophysical parameters from the FoldX empirical force field and developability features into a single unified antibody design pipeline. We experimentally validated the ability of EvolveX to optimize the affinity and stability of a nanobody targeting mouse Vsig4 and, more challengingly, its ability to redesign the nanobody to bind the human Vsig4 ortholog with very high affinity compared to the wildtype nanobody, resulting in a 1000-fold improved Kd. Structural analyses by X-ray crystallography and NMR confirmed the accuracy of the predicted designs, which display optimized interactions with the antigen. Collectively, our study highlights EvolveX’s potential to overcome current limitations in antibody design, offering a powerful tool for the development of next-generation therapeutics with enhanced specificity, stability, and efficacy.

• Rakshanda Sajeed, Rakshanda Sajeed, TCS Research
• Swatantra Pradhan, Swatantra Pradhan, TCS Research
• Rajgopal Srinivasan, Rajgopal Srinivasan, TCS Research
• Sadhna Rana, Sadhna Rana, TCS Research

The recognition of B epitopes by B cells of the immune system initiates immune response that leads to production of antibodies to combat bacterial and viral infections. The development of computational methods for predicting the epitopes on antigens has shown promising results in the development of subunit vaccines and therapeutics. Recently, the use of protein language models (pLMs) for epitope prediction has led to substantial increase in the prediction accuracies. However, precision needs to be improved greatly to gain significance in practical application. Here, we develop and evaluate a series of models using different combinations of features and feature fusion techniques on a curated independent test set. Our results show that the models that use both protein embeddings along with structural features perform better at predicting B epitopes as compared to the baseline model that uses only protein embeddings as features. We also show from the attention analysis of B and T epitopes, that the evolutionary scale model, ESM-2 captures T-B reciprocity implicitly in the model as a large fraction of high scoring B epitopes are highly attended by the T epitopes.