Burkhard Rost

Aldabra giant tortoises ( Aldabrachelys gigantea ) are exceptionally long-lived. We sequenced the genome and methylome of Jonathan, a 194-year-old Aldabra, to explore the molecular basis of his longevity. Relative to other giant tortoises ( A. gigantea and Chelonoidis abingdonii ), Jonathan has unique gene variants in most aging pathways. Moreover, Jonathan has substantial DNA methylation and methylation entropy changes compared to four other Aldabras ranging in age from a 5-year-old juvenile to older adults. Notably, we found that lower-entropy regions in Jonathan’s methylome were enriched for the promoters of genes involved in the mitochondrial electron transport chain, and RNA metabolism. This suggests that high-fidelity transcription of the genes in these pathways may be crucial for long-lived species. Our findings support a model for aging wherein the maintenance of low methylation entropy in gene promoters is coupled to efficient mitochondrial energy production, efficient RNA processing, and efficient genomic repair.

Recent progress in modeling techniques and high-throughput screening has significantly enhanced the accessibility of protein engineering. Nevertheless, further progress gets hindered by the lack of robust benchmarks that capture the practical challenges for real-world protein engineering. Here, we introduced DeePEn, a Depth-sensitive benchmark for Protein Engineering that quantifies model generalization to held-out variants at increasing mutational distance from the wild type. We defined distance as the number of simultaneous point mutations, i.e., single amino acid variants (SAVs), moving from wild-type to mutant (edit distance in computer science jargon). DeePEn retrospectively evaluates prediction and prioritization using experimentally labelled variants from deep mutational scanning (DMS) datasets; it does not benchmark prospective sequence generation or goal-directed optimization. Specifically selecting four DMS datasets with sufficient multi-mutation data points from ProteinGym, we assessed recent predictive models, including general and biophysics-informed protein Language Models (pLMs), and a non-transformer neural network. Our results highlight how the performance of all models deteriorates with increasing mutational distance and that no single metric sufficiently captures the diverse requirements of protein engineering. To overcome these shortcomings, DeePEn provides a readily available resource for multi-metric benchmarking that focuses on variant-effect prediction of variants distant from the wild type.

Protein Language Models (pLMs) have significantly advanced computational biology. Yet their scale and reliance on redundant training data raise a fundamental question: do pLMs generalize the statistical grammar of proteins, or do they simply memorize their training data? To investigate this, we used pseudoperplexity as a probe for sequence-level memorization, comparing ProtT5′s pseudoperplexity on a pre-training proxy dataset against a post-training holdout of genuinely novel sequences. To ensure a valid comparison, we matched the datasets by sequence length, cluster size, and taxonomic family. As a statistical baseline, we trained n -gram language models; analysis of higher-order n -gram composition and a statistically significant divergence in perplexity confirmed that the post-training sequences were genuinely novel at the local sequence level. ProtT5 showed a statistically significant difference in pseudoperplexity between seen and unseen sequences, though further analysis revealed this memorization signal to be modest. These findings suggest that ProtT5 exhibits detectable but limited memorization of its training data as measured by a pseudoperplexity-based probe.

Background: The Critical Assessment of Functional Annotation (CAFA) is a community effort held to understand the field of computational protein function prediction. Every three years, since 2010, the organizers initiate an experiment to collect function predictions on a large set of proteins and then evaluate the performance of predicting methods on a subset of proteins that have accumulated experimental annotations between the submission deadline and the evaluation time. CAFA provides an independent and rigorous assessment of the current state of the art, thus leveling the playing field, highlighting successes, revealing bottlenecks, and offering a forum for the exchange of ideas in protein science. Here, we report the results of the fourth CAFA experiment (CAFA4). Results: CAFA4 featured the participation of 148 methods from 70 research groups on a total of 46,205 unique proteins over a 5-year annotation accumulation phase, the longest in any CAFA. In a comparison across CAFA2-CAFA4 methods, the prediction of Gene Ontology (GO) terms has clearly improved across all three GO aspects and traditional evaluation settings. While not achieving the first rank, several CAFA2 and CAFA3 methods featured in the top ten methods in many evaluations, suggesting that earlier methods still hold relevance. The performance is weaker in the newly introduced “partial knowledge” evaluation category (proteins with experimental annotations before submission deadline that gained additional annotations in the same GO aspect during the annotation accumulation phase), highlighting the need for a new class of methods. The rankings of the methods were stable over the years in traditional evaluation settings, but less so in the new partial knowledge evaluation. Overall, the field continues to progress with some influx of new participants. Sustained efforts will be necessary to substantially advance it.

The Critical Assessment of Functional Annotation (CAFA) is a long-standing community effort to independently assess computational methods for protein function prediction, to highlight well-performing methodologies, to identify bottlenecks in the field, and to provide a forum for the dissemination of results and exchange of ideas. In its fifth round (CAFA5) of triennial challenges, a partnership with Kaggle Inc. facilitated participation from a large community of data scientists and computational biologists through a competitive prospective challenge on the crowdsourcing platform. In this work, we present an in-depth analysis of the submitted predictions and report improvements in accuracy over all methods from the previous CAFA challenges. We further introduce a new evaluation setting for proteins with pre-existing (incomplete) annotations and identify the need for methods that better leverage existing annotations to predict those that will be discovered later. Finally, we characterize the prospective evaluation framework by examining performance on a strict set of unpublished annotations and across intermediate database releases. Our results indicate that recent developments in the field, such as the availability of protein language models and accurately predicted 3D structures, as well as the growth of experimental annotations through biocuration, have all contributed to performance improvements. ¹

Infectious diseases continue to be a leading cause of mortality and pose a significant global health threat. Thus, the development of tools for surveillance and early detection of emerging pathogens is needed. We introduce PathogenFinder2, a novel, alignment-free, taxonomy-agnostic model for predicting bacterial pathogenic capacity in humans using protein language models. It outperforms previous methods, particularly for novel taxa, and provides interpretable outputs by highlighting proteins most relevant to pathogenic potential. These insights aid the identification of virulence factors, vaccine targets, and infection-related metabolic pathways. Furthermore, we introduce the Bacterial Pathogenic Capacity Landscape, which reveals patterns linked to host condition, infection site, microbial antagonism, and environmental origin. The model is freely available online at https://genepi.dk/pathogenfinder2, or as a standalone program (https://github.com/genomicepidemiology/PathogenFinder2). Supplementary data are available at Bioinformatics online.

Understanding the relationships among amino acid sequences, structures and functions in proteins and how they evolve, remains a central challenge in molecular biology. It is still unclear which sequence elements differentially contribute to structural integrity or molecular function. Even more, there are ongoing debates on whether protein folds emerge as a result of evolution or as a consequence of physical laws. The energy landscapes theory states that proteins are minimally frustrated systems, i.e. they fold by minimising their energetic conflicts. However, some local frustration, believed to be selected for functional reasons, remains in the native state of proteins. Here, we combine reverse folding and structure prediction methods with sequence and local frustration analysis to address the aforementioned ideas. We found that reverse folding techniques are unable to erase evolutionary conserved frustration from certain residues, even when detrimental for structural integrity. We propose that certain frustration hotspots behave like architectural spandrels, not directly shaped by selection but emerging from physical constraints in protein folds which evolution can later co-opt for function. Our results provide a new perspective revealing how sequence variation and functional specificity could evolve from evolutionary, structural and biophysical constraints.

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 α -globin and β -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 .

Motivation: Regions in intrinsic disordered proteins (IDPs) constitute important continuous aspects of protein function. While their existence on a structural continuum is widely accepted, most computational predictions have, nevertheless, focused on binary classifications. Existing datasets are severely limited in size and experimental evidence for continuous disorder. Results: Building on recently released datasets of continuous protein disorder and flexibility, we introduce UdonPred, a lightweight neural network exclusively inputting embeddings from the protein Language Model (pLM) ProstT5 to predict per-residue protein disorder from sequence alone. Training and evaluating UdonPred on seven datasets with divergent definitions of disorder and flexibility suggests that not model capacity, but agreement and nuance of disorder annotations, remains the main driver of performance. Binary disorder annotations can be reliably predicted from a multitude of different disorder and flexibility datasets, but there is still room for improvement in predicting continuous disorder. Availability: All code and data used for training and evaluation is available under an open-source license at https://github.com/davidwagemann/udonpred . Contact: assistant@rostlab.org Supplementary information: Supplementary data are available at Journal Name online.