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Reduced amino acid substitution matrices find traces of ancient coding alphabets in modern day proteins

All known living systems make proteins from the same twenty canonically-coded amino acids, but this was not always the case. Early genetic coding systems likely operated with a restricted pool of amino acid types and limited means to distinguish between them. Despite this, amino acid substitution models like LG and WAG all assume a constant coding alphabet over time. That makes them especially inappropriate for the aminoacyl-tRNA synthetases (aaRS) - the enzymes that govern translation. To address this limitation, we created a class of substitution models that accounts for evolutionary changes in the coding alphabet size by defining the transition from nineteen states in a past epoch to twenty now. We use a Bayesian phylogenetic framework to improve phylogeny estimation and testing of this two-alphabet hypothesis. The hypothesis was strongly rejected by datasets composed exclusively of "young" eukaryotic proteins. It was generally supported by "old" (aaRS and non-aaRS) proteins whose origins date from before the last universal common ancestor. Standard methods overestimate the divergence ages of proteins that originated under reduced coding alphabets in both simulated and aaRS alignments. The new model reduces this bias substantially. Our findings support the late incorporation of tryptophan into the genetic code (relative to tyrosine) and suggest that isoleucine and valine were once coded interchangeably, forming protein quasispecies. This work provides a robust, seamless framework for reconstructing phylogenies from ancient protein datasets and offers further insights into the dawn of molecular biology.

evolutionary biology↗

gyoza: a Snakemake workflow for modular analysis of deep-mutational scanning data

Deep-mutational scanning (DMS) is a powerful technique that allows screening large libraries of mutants at high throughput. It has been used in many applications, including to estimate the fitness impact of all single mutants of entire proteins, to catalog drug resistance mutations and even to predict protein structures. Here, we present gy[o]za, a Snakemake-based workflow to analyze DMS data. gy[o]za requires little programming knowledge and comes with comprehensive documentation to help the user go from raw sequencing data to functional impact scores. Complete with quality control and an automatically generated HTML report, this new pipeline should facilitate the analysis of time-series DMS experiments. gy[o]za is freely available on GitHub (https://github.com/durr1602/gyoza). Article summaryDeep-mutational scanning (DMS) refers to molecular biology methods used to generate many genetic variants and evaluate their effect on adaptive fitness. It is used both in fundamental and applied research, with implications in genomic medicine. Many data processing steps are needed to transform the output of DMS, high-throughput sequencing data, into scores that quantify the fitness effect of each variant. The analysis is tailored to the type of experimental design, which can vary a lot. To facilitate such analysis and make it accessible to people with limited knowledge in bioinformatics, we have developed gy[o]za, a free and easy-to-use program to analyze DMS data.

bioinformatics↗

Yeast HMGB protein Hmo1 is a multifaceted regulator of DNA damage tolerance

The chromosome architecture protein Hmo1 in Saccharomyces cerevisiae is categorized as an HMGB protein as it consists of two structure-specific DNA binding HMGB motifs. However, it deviates from canonical HMGB proteins that have acidic C-terminal domains (CTDs) by bearing a basic one. Hmo1 plays diverse functions in gene regulation and genome integrity. There is evidence implicating Hmo1 in DNA damage tolerance (DDT) that enables DNA replication to bypass lesions on the template. Hmo1 is believed to direct DNA lesions to the error-free template switching (TS) pathway of DDT and aids in the formation of the key TS intermediate sister chromatid junction (SCJ), but the underlying mechanism(s) has yet to be resolved. In this work, we took genetics and molecular biology approaches to further investigate the role of Hmo1 in DDT. We found extensive functional interactions of Hmo1 with components of the genome integrity network in cellular response to the genotoxin methyl methanesulfonate (MMS), implicating Hmo1 in multiple processes in the execution or regulation of homology-directed DNA repair, replication-coupled chromatin assembly, and cell cycle checkpoint. Notably, our data pointed to a role of Hmo1 in directing SJC to the nuclease-mediated resolution pathway instead of the helicase/topoisomerase mediated dissolution pathway for processing/removal. They also suggested that Hmo1 modulates both the recycling of parental histones and the deposition of newly synthesized histones at the replication fork to ensure proper chromatin formation on nascent DNA that is important for DDT. We found evidence that Hmo1 antagonizes the function of histone H2A variant H2A.Z (also known as Htz1 in yeast) in DDT. We also found that Hmo1 is required for DNA damage checkpoint signaling induced by MMS as wells as for DNA negative supercoiling as a proxy of chromatin structure. Moreover, we obtained evidence indicating that the CTD of Hmo1 may or may not be required for its function in DDT depending on the genetic background of the host. Taken together, our findings demonstrate that Hmo1 can contribute to, or regulate, multiple processes of DDT via different mechanisms.

genetics↗

Predicting the functional impact of single nucleotide variants in Drosophila melanogaster with FlyCADD

Understanding how genetic variants drive phenotypic differences is a major challenge in molecular biology. Single nucleotide polymorphisms form the vast majority of genetic variation and play critical roles in complex, polygenic phenotypes, yet their functional impact is poorly understood from traditional gene-level analyses. In-depth knowledge about the impact of single nucleotide polymorphisms has broad applications in health and disease, population genomic and evolution studies. The wealth of genomic data and available functional genetic tools make Drosophila melanogaster an ideal model species for studies at single nucleotide resolution. However, to leverage these resources for genotype-phenotype research and potentially combine it with the power of functional genetics, it is essential to develop techniques to predict functional impact and causality of single nucleotide variants. Here, we present FlyCADD, a functional impact prediction tool for single nucleotide variants in D. melanogaster. FlyCADD, based on the Combined Annotation-Dependent Depletion (CADD) framework, integrates over 650 genomic features - including conservation scores, GC content, and DNA secondary structure - into a single metric reflecting a variants predicted impact on evolutionary fitness. FlyCADD provides impact prediction scores for any single nucleotide variant on the D. melanogaster genome. We demonstrate the power of FlyCADD for typical applications, such as the ranking of phenotype-associated variants to prioritize variants for follow-up studies, evaluation of naturally occurring polymorphisms, and refining of CRISPR-Cas9 experimental design. FlyCADD provides a powerful framework for interpreting the functional impact of any single nucleotide variant in D. melanogaster, thereby improving our understanding of genotype-phenotype connections. Article summarySingle nucleotide polymorphisms (SNPs), the most common form of genomic variation, drive micro-evolution and adaptation. In Drosophila melanogaster, many SNPs are associated with phenotypes, yet functional validation is rare and experimentally challenging. FlyCADD is a new impact prediction tool that integrates D. melanogaster genome annotations into a single score predicting SNP impact. FlyCADD can be applied to distinguish causal from neutral variants, prioritize variants prior to functional studies, and to interpret natural variation, thereby improving understanding of genotype-phenotype relationships.

genomics↗

HydraRNA: a hybrid architecture based full-length RNA language model

RNA, an essential component of the central dogma of molecular biology, plays versatile roles in all cellular processes. RNA large language models (LLMs) are emerging as powerful methods in RNA research to decipher its intricate network of function and regulation. However, previous RNA LLMs were based on the transformer model and pre-trained on short segment of non-coding RNAs, which limits their general usability. Here we present the first full-length RNA foundation model, HydraRNA, which is based on a hybrid architecture of bidirectional state space model and multi-head attention mechanism, and is pre-trained on a large amount of both protein-coding and non-coding RNAs. Despite being pre-trained with the fewest parameters and the least GPU resources, HydraRNA learns better RNA representations and outperforms the existing foundation models on a variety of downstream tasks, including RNA classification, prediction of RNA secondary structure, RBP binding sites, mRNA stability and translation efficiency. Furthermore, HydraRNA can accurately predict the effect of mutations and estimate the relative contributions of different mRNA regions to the RNA stability and translation. We anticipate that HydraRNA will enable dissecting the diverse properties of RNA, accelerating the research of RNA regulation and facilitating the optimal design of RNA therapeutics.

bioinformatics↗

AGP-Net: A Universal Network for Gene Expression Prediction of Spatial Transcriptomics

In the era of high-throughput biology, molecular phenotypes have proven effective in predicting disease states and future trajectories. Transcriptomics, in particular, has enabled the dissection of complex diseases with heterogeneous genetic and environmental aetiology, both aiding diagnosis and augmenting treatment. As improving technology has led to measurements of gene expression at increasing granularity, it has become progressively feasible to resolve disease traits that present locally or with spatial heterogeneity. Principal among these are cancers in which tumour gene expression, while itself heterogenous, exhibits a distinct signature from that of the surrounding tissue. Identifying this signature through molecular phenotyping facilitates specific cancer diagnosis and treatment. Here, we introduce AGP-Net, a multi-modal foundation framework capable of predicting gene expression from histopathology images. Rather than produce an aggregated estimate of expression for each gene, AGP-Net disaggregates images into spots and attempts to resolve the variation in gene expression across them, thereby providing coarse spatial transcriptomic predictions across the tumour slice and surrounding region. The challenge in doing so is due to data sparsity relative to the dimensionality of the problem: the number of genes and their contextual heterogeneity within and between tissue and cancer types makes it difficult to train a model on the limited data available. The innovation of AGP-Net lies in borrowing strength across similar genes as defined by their textual language descriptions. Our AGP-Net supports datasets with varying gene coverage and facilitates the prediction of gene expression for previously unseen genes based on their textual descriptions. Trained on millions of spots from diverse dataset sources, AGP-Net establishes state-of-the-art performance in zero-shot spatial gene expression prediction, demonstrating its adaptability to generalize across novel scenarios.

pathology↗

CRISPR/Cas9-mediated editing of the ADE2 locus in Saccharomyces cerevisiae: targeted mutagenesis and homology-directed repair

The CRISPR/Cas9 system, derived from the adaptive immune defence of bacteria and archaea, has emerged as a powerful tool for genome engineering by enabling precise, site-specific modifications. Type II bacterial CRISPR systems recognize and cleave DNA in various microorganisms using the RNA-guided Cas9 endonuclease. We present a streamlined and efficient approach for genome editing in two strains of Saccharomyces cerevisiae, focused on targeted mutagenesis of the ADE2 gene of the BY4741 strain and homology-directed repair of the ade2 locus of the W303-1A strain, involving a single-step transformation strategy utilizing the pML104 plasmid, which encodes the Cas9 endonuclease and a user-defined single-guide RNA (gRNA) complementary to the target site. In the BY4741 (wildtype) strain, pML104-gRNA components achieved successful ADE2 disruption rates of 40% (95% CI: 0.10-0.70), confirmed by adenine auxotrophy screening and Sanger sequencing. Additionally, co-transformation of gRNA plasmid and donor repair templates in the W303-1A (ade2 mutant) strain also resulted in 40% recombination efficiency, restoring the function of the mutant gene to allow activation of de novo purine biosynthesis activity when cultured on SC-Ade medium. This optimized, cost-effective CRISPR/Cas9 method enhances editing efficiency in S. cerevisiae, providing a robust and practical platform for high-precision genetic modifications with broad applications in molecular biology and biotechnology.

genomics↗

Nanopore direct RNA sequencing (DRS) of MS2 bacteriophages in E.coli throughout its life cycle reveals a complex transcriptional activity to control and maintain its growth

The RNA bacteriophage MS2 is an RNA phage that infects the bacterium E. coli and is one of the most studied and prototypical model phages in molecular biology and microbiology. Previous research revealed complex translational control and fine-tuning for MS2 replication. However, the dynamics of its transcriptional activity and replication during the life cycles within the bacteria remain elusive. Here, we employed Nanopore Direct RNA sequencing (DRS) to investigate the transcriptome and epitranscriptome landscape of the MS2 in infected E. coli throughout multiple life cycles. We discovered that MS2 phages sustain a high level of transcriptional activity required for replication. We found large amounts of subgenomic small transcripts from RNA degradation, Nanopore DRS bias, and transcripts containing the coat-encoding region, required for virion assembly. We found hybrid reads due to the error-prone activity of the MS2 replicase complex by the template-switching mechanism. We evidenced that RNA modification is conserved throughout the entire life cycle in full-length transcripts without the acquisition of new modifications, whereas small transcripts did acquire newly modified sites. The conserved sequence and secondary structure (U-rich hairpin) of {Psi} installation sites were the most amenable to RNA modification, and one site aligned well with host RluA-mediated installation. Overall, our investigation reveals a more complex transcriptional dynamics of MS2 phages than anticipated within E. coli to maintain its growth and replicate under host pressure.

microbiology↗

SPDEv3.0: A Multidisciplinary Integrated Data Analysis Platform

Plant research encounters significant challenges in integrating multidisciplinary data and ensuring compatibility among diverse analytical methods. The limited automation in many existing tools further complicates data processing, thereby hindering efficiency and interdisciplinary collaboration. In response to these issues, we present SPDEv3.0, a multidisciplinary integrated data analysis platform specifically designed to tackle the complexities of data and the varied analytical requirements in plant sciences. Unlike traditional tools, SPDEv3.0 facilitates seamless cross-disciplinary data integration and automates analysis, featuring over 130 functions spanning plant breeding, molecular biology, genomics, and comparative genomics. The platform enables one-click operations for tasks ranging from data normalization to statistical analysis and visualization, significantly minimizing manual intervention and enhancing research efficiency. With its modular design and user-friendly interface, we believe that SPDEv3.0 will promote interdisciplinary collaboration and provides robust technical support for advancements in plant sciences.

bioinformatics↗

The photoreaction process of a large-Stokes-shift red fluorescence protein-LSSmCherry1

Large Stokes shift red fluorescent proteins are highly valued in fluorescence imaging due to their considerable spectral separation and minimal self-absorption. However, a notable gap remains in our understanding of these proteins excited-state dynamics. Unlocking this knowledge could potentially drive significant advancements in cellular and molecular biology. In this study, we systematically examine the excited-state dynamics of LSSmCherry1, a large Stokes shift red fluorescent protein, under varying isotopic compositions and temperatures. Our aim is to elucidate its distinctive spectral properties. Through steady fluorescence spectral experiments at different temperatures, we demonstrate that the large Stokes shift is significantly reduced by approximately 80 nm at low temperatures compared to room temperature. Using transient fluorescence and absorption spectroscopy, we dissect the excited-state dynamics of LSSmCherry1 in neutral environments. We also investigate the kinetic isotopic effect by comparing spectra in water and heavy water. These analyses allow us to delineate plausible photocyclic pathways. Our results reveal that the excited-state dynamics of LSSmCherry1 follow a model similar to that observed in other fluorescent proteins, such as Green Fluorescent Protein (GFP). Notably, we demonstrate that the excited-state proton transfer (ESPT) process is the primary origin of the large Stokes shift in LSSmCherry1. This ESPT process occurs rapidly, within approximately 365 fs after excitation in the neutral environment. This study provides crucial insights into the mechanisms underlying large Stokes shift fluorescent proteins, potentially paving the way for the development of improved fluorescent probes for biological imaging. TOC GraphicSome journals require a graphical entry for the Table of Contents. This should be laid out "print ready" so that the sizing of the text is correct. Inside the tocentry environment, the font used is Helvetica 8 pt, as required by Journal of the American Chemical Society. The surrounding frame is 9 cm by 3.5 cm, which is the maximum permitted for Journal of the American Chemical Society graphical table of content entries. The box will not resize if the content is too big: instead it will overflow the edge of the box. This box and the associated title will always be printed on a separate page at the end of the document.

biochemistry↗

A High-Performance and Recoverable DNA Origami Rotary Motor

Inspired by biological molecular machines, we developed a highly processive DNA origami rotary motor. The rotor consists of two disk-shaped DNA origamis that are connected by a single-stranded swivel that allows free rotation but prevents rotor dissociation in the event of operational error. The rotor is propelled by two bipedal walkers that stride on a circular DNA track propelled by a previously optimized DNA propulsion mechanism called fuel before antifuel. The DNA fuel and antifuel strands are delivered by a microfluidic device. The rotation is monitored by a single-molecule, light scattering, defocused imaging technique; light scattered from a gold nanorod attached to the rotor enables high angular and temporal resolution analyses of rotor orientation. Imaging movies show individual rotors performing 96 individual steps, corresponding to 8 full rotor revolutions, with direction determined by the fuel and antifuel sequence. In cases of operational errors, which result in free Brownian rotation, the rotors were able to recover and continue rotating as commanded. Our origami-based rotary motor design, microfluidics-based control system, and high-resolution monitoring of rotor orientation will facilitate the development of DNA-based machines driven by autonomous propulsion.

biophysics↗

The global proteome of Streptococcus pneumoniae EF3030 under nutrient-defined in vitro conditions

Streptococcus pneumoniae is a pathobiont that colonises the upper respiratory tract of humans without causing symptoms but can cause a range of life-threatening diseases including pneumonia, sepsis, and meningitis. It also causes less severe, non-invasive infections such as otitis media and sinusitis. This bacterium thrives in the nasopharynx, where nutrient availability is limited, and has adapted to this environment by developing mechanisms to survive host stress and regulate protein abundance. To study the molecular biology of S. pneumoniae under in vitro and infection-related conditions, a suitable cultivation medium is essential for reproducible experiments. In this study, we optimized a chemically-defined minimal medium that mimics the in vivo nutrient-limited condition and used it for proteome analysis. This optimized medium not only shortened the lag phase but also improved the growth of S. pneumoniae clinical isolates and other streptococcal species. We applied this medium to analyse the global proteome of the pneumococcal colonising strain EF3030, focusing on the transition from the early to late log phase. Our proteomic analysis revealed distinct patterns of protein abundance in different functional categories including metabolism, amino acid synthesis, natural competence, RNA synthesis, cell wall synthesis, protein degradation, and stress responses. Notably, choline-binding protein CbpD, competence factors ComGA and ComEA as well as proteins involved in processing internalized single-stranded DNA (ssDNA) such as Dpr and DprA were higher in abundance in the late log phase. This proteomic profiling provides valuable insights into the pathophysiology of strain S. pneumoniae EF3030 under defined nutrient conditions.

microbiology↗

FlatProt: 2D visualization eases protein structure comparison

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSUnderstanding and comparing three-dimensional (3D) structures of proteins can advance bioinformatics, molecular biology, and drug discovery. While 3D models offer detailed insights, comparing multiple structures simultaneously remains challenging, especially on two-dimensional (2D) displays. Existing 2D visualization tools lack standardized approaches for pipelined inspection of large protein sets, limiting their utility in large-scale pre-filtering. ResultsWe introduce FlatProt, a tool designed to complement 3D viewers by enabling standardized 2D visualization of individual protein structures or large sets thereof. By including Foldseek-based family rotation alignment or an inertia-based fallback, FlatProt creates consistent and scalable visual representations for user-defined protein structures. It supports domain-aware decomposition, family-level overlays, and lightweight visual abstraction of secondary structures. FlatProt processes proteins efficiently, as showcased on a subset of the human-proteome. ConclusionFlatProt provides clear, consistent, user-friendly visualizations that support rapid, comparative inspection of protein structures at scale. By bridging the gap between interactive 3D tools and static visual summaries, it enables users to explore conserved features, detect outliers, and prioritize structures for further analysis. AvailabilityGitHub (https://github.com/t03i/FlatProt); Zenodo (https://doi.org/10.5281/zenodo.15697296).

bioinformatics↗

GeneFix-AI: AI-Powered CRISPR-Cas9 System for Real-Time Detection and Correction of Mutations in Non-Human Species

The evolution of genome engineering technologies has transformed biomedical research, enabling precise and efficient modification of genetic material Doudna and Charpentier, 2014. Among these, CRISPR-Cas9 stands out as a revolutionary gene-editing tool, though it often requires extensive expertise and technical knowledge Cong et al., 2013; J. G. Doench et al., 2016. We propose GeneFix-AI, an Artificial Intelligence (AI)-driven platform for real-time prediction and correction of genetic mutations in non-human species. Developed using cutting-edge models inspired by recent advances at Harvard and Peking University Chen et al., 2021; Wu et al., 2020, GeneFix-AI integrates machine learning to predict mutations, design optimal guide RNAs, and evaluate editing outcomes. This system aims to automate the CRISPR-Cas9 workflow, making high-precision gene editing more accessible to researchers without extensive molecular biology backgrounds Liu et al., 2019. We present the system architecture, training methodology, and potential impact of GeneFix-AI in democratizing genome editing and accelerating discoveries in genetics.

bioinformatics↗

Integrating the ENCODE blocklist for machine learning quality control of ChIP-seq with seqQscorer

MotivationQuality assessment of next-generation sequencing data is a complex but important task to ensure correct conclusions from experiments in molecular biology, biomedicine, and biotechnology. We previously introduced seqQscorer, a quality assessment tool using machine learning to support this process. To improve seqQscorer in terms of accuracy and processing time, we integrated the ENCODE blocklist* to derive a new type of quality-related features, supposed to be more informative and faster in generation than those conventionally used by seqQscorer. ResultsThe novel seqQscorer extension, called seqBLQ, allows us to improve the quality assessment for ChIP-seq data derived from human tissues and cell lines. Furthermore, seqBLQ enhances the usability of the tool by simplifying the installation procedure and reducing the computational resources required for feature generation. Availability and implementationhttps://github.com/salbrec/seqQscorer

bioinformatics↗

Massively parallel reporter assay for mapping gene-specific regulatory regions at single nucleotide resolution

Precise gene regulation is essential for the development and function of complex tissues, yet comprehensive mapping of cis-regulatory modules (CRMs) remains challenging due to limitations in throughput, resolution, and the ability to assay within specific cell types. Here, we introduce two complementary approaches--a locus-specific massively parallel reporter assay (LS-MPRA) and a degenerate MPRA (d-MPRA)--specifically designed to address some of these shortcomings. LS-MPRA leverages bacterial artificial chromosomes (BACs) to generate high-complexity libraries spanning large genomic regions, enabling unbiased interrogation of millions of DNA fragments potentially relevant for the regulation of a specific gene or set of genes. The d-MPRA employs systematic mutagenesis to resolve the functional architecture of CRMs at nucleotide resolution, thereby nominating critical nucleotides as potential TF binding sites, or for other regulatory roles. We applied these methods to retinal genes that are stably expressed in differentiated cells of the retina, in rod photoreceptors and in subsets of bipolar interneurons, using both in vivo and ex vivo preparations of mouse tissue. LS-MPRA recapitulated some of the known CRMs for these genes--such as the proximal promoter region of Rho--and identified potentially novel CRMs, including those located within neighboring genes. The method was then applied to a gene that is dynamically expressed in subsets of retinal progenitor cells, Olig2, where it identified three distinct CRM regions (Olig2-NR1, NR2, and NR3). D-MPRA and subsequent motif analyses nominated critical TF binding sites within these regions. CUT&RUN experiments confirmed direct binding of these candidates. Moreover, extending LS-MPRA to chick retina and spinal cord demonstrated the applicability of these methods across species and tissues. Together, the integrated LS-MPRA and d-MPRA strategies provide a robust, high-resolution platform for discovery of the cis-regulatory code underlying tissue-specific gene expression. It does not require prior knowledge of potential CRMs, and is quite rapid and straightforward to deploy, using typical molecular biology methods. The fragment size can be scaled to create short CRMs, e.g. for cell type-specific expression within viral vectors. It should enable CRM discovery at a scale and affordability for laboratories wishing to focus on a particular locus or set of loci.

developmental biology↗

Importin α/β1 dependent nuclear import of Black Sea Bass Polyomavirus Large Tumor Antigen is mediated by a classical NLS located downstream of the SF3 helicase domain

Polyomaviruses (PyVs) are small dsDNA viruses that replicate in the host cell nucleus, primarily relying on the viral encoded large tumor antigen (LTA). Aided by recent advances in molecular biology techniques, the list of known PyVs is rapidly growing, revealing unexpected broad sequence, and host heterogenicity. Given their dependence on nuclear localization, large tumor antigens represent an attractive model for studying the nuclear transport process. A comprehensive analysis of the evolution of classical nuclear localization signals (cNLSs) within LTAs encoded by PyVs infecting mammals highlighted strong positional conservation of cNLSs between the LXCXE motif and the origin-binding domain (OBD). Here we extend such analysis to PyVs infecting non-mammalian hosts. We combined biochemical, structural and functional assays to demonstrate that Black Sea Bass (BSB) PyV-LTA is transported into the nucleus by the Importin (IMP)/{beta}1 heterodimer thanks to the recognition of a bipartite cNLS located downstream of the SF3 helicase domain, rather than between the LXCXE motif and the OBD. Such cNLS binds with high affinity to several IMP isoforms by simultaneously interacting with the minor and major binding sites. Substitution of NLS key basic residues abrogating binding to IMP, or co-expression with the well characterized IMP/{beta}1 inhibitor Bimax2 suppressed nuclear localization. Intriguingly a cNLS could be identified in a similar position in LTAs from other PyVs infecting ray-finned fishes, but not cartilaginous fishes, birds or scorpions, where cNLSs were predicted elsewhere. Our study suggests that LTAs from PyVs infecting different non-mammalian hosts might bear cNLS in distinctive positions, possibly reflecting processes of virus-host adaptation.

microbiology↗

pLM-SAV: A Δ-Embedding Approach for Predicting Pathogenic Single Amino Acid Variants

Predicting whether single amino acid variants (SAVs) in proteins lead to pathogenic outcomes is a critical challenge in molecular biology and precision medicine. Experimental determination of all possible mutation effects is infeasible, and while state-of-the-art tools such as AlphaMissense show promise, their diagnostic performance is insufficient and they are often difficult to run locally. We developed pLM-SAV, a simple yet effective predictor that leverages protein language models (pLMs). {Delta}-embeddings, computed as the difference between wild-type and mutant sequence embeddings, are used as input for a convolutional neural network. To prevent data leakage, we trained our model on a well-characterized, labeled set of Eff10k and evaluated it on a non-homologous subset of ClinVar data. Our results demonstrate that this approach performs exceptionally well on the Eff10k test folds and reasonably on ClinVar test sets. Notably, pLM-SAV excels in resolving ambiguous predictions by AlphaMissense. We also found that an ensemble method, REVEL, outperforms both AlphaMissense and pLM-SAV, thus, we integrated these REVEL- enhanced predictions into our widely used AlphaMissense web application. Our results demonstrate that an SAV predictor trained on labeled data can achieve high predictive performance. Unlike previous methods such as VESPA, pLM-SAV uses no handcrafted features or substitution matrices, relying solely on pLM-derived representations. We anticipate that incorporating delta-embeddings into other mutation effect predictors or mutant structure prediction methods will further enhance their accuracy and utility in diverse biological contexts. Availability and ImplementationFreely available at https://doi.org/10.5281/zenodo.15502498 and https://alphamissense.hegelab.org.

bioinformatics↗