Biology subjects

Litfin, T.

Publications and source records attributed to Litfin, T..

10 recordsLinked to original sources

bioRxiv2026

Viruses, Proviruses and Satellites from Asgard Archaea Enrichments Reveal Complex Microbial Interactions

Asgard archaea are the closest known relatives of eukaryotes and are central to models of eukaryogenesis involving archaeal bacterial symbiosis, yet the contribution of viruses remains unexplored. Here, for the first time, we visualised unique viruses associated with Asgard archaeal cells. Additionally, we identified the first putative Asgard-archaeal virus satellite, which exhibited genomic interactions involving a bacterium, Stromatodesulfovibrio nilemahensis, providing evidence of a virus-mediated interaction between an Asgard archaeon and a bacterium. Additionally, novel proviruses of S. nilemahensis displayed distinct genomic features where predictions of alternate recombination sites suggested the acquisition of horizontally acquired genes associated with biofilm formation. Further, we comprehensively characterise (pro)viruses associated with this co-culture using high resolution cryo-electron tomography, proximity ligation (Hi-C), and metagenomics. Together, these findings expand the known diversity of Asgard archaeal viruses and establish a foundation for investigating the role of viruses in microbial symbiosis relevant to the emergence of eukaryotic life.

microbiology↗

bioRxiv2025

LMI4Boltz: Optimizing VRAM utilization to predict large macromolecular complexes with consumer grade hardware

AlphaFold2 has revolutionised structural biology by enabling the prediction of protein structures approaching experimental quality. AlphaFold3 extends this framework to support modelling broad biomolecular classes while also reducing the computational cost of prediction. However, AlphaFold3 is distributed with licence conditions which restrict general purpose use. Boltz is a permissive, open-source re-implementation of AlphaFold3, but it is bottlenecked by increased VRAM requirements and requires high-end GPU hardware to model large molecular systems. Here we introduce Low Memory Inference for Boltz (LMI4Boltz) which reduces VRAM requirements using in-place updates, offloading tensors to host memory, careful management of functional scope and aggressive chunking of key operations. Using these strategies, LMI4Boltz increases the token size limit of Boltz-2 by 66.7% without sacrificing prediction accuracy. These optimisations improve the accessibility of Boltz using consumer grade hardware and unlock the ability to model large molecular systems. LMI4Boltz is available at https://github.com/tlitfin/lmi4boltz.

bioinformatics↗

bioRxiv2025

Ultra-fast and highly sensitive protein structure alignment with segment-level representations and block-sparse optimization

Deep learning models for protein structure prediction have given rise to extreme growth in 3D structure data. As a result, traditional methods for geometric structure alignment are too slow to effectively search modern structure libraries. In this study we introduce SPfast - a fully geometric method for structure-based alignment which accelerates search by more than 2 orders of magnitude while increasing sensitivity by 21% and 5% compared with foldseek and TMalign respectively. Using the significant speed of SPfast to conduct more than 100B pairwise comparisons between bona fide uncharacterized proteins and a large-scale, annotated structure library uncovers new biological insights relating to type III secretion in pathogenic bacteria and identifies novel toxin-antitoxin systems. Putative SPfast-based functional assignments are supported by orthogonal evidence including shared genomic context and high-confidence AlphaFold3 complex modelling.

bioinformatics↗

bioRxiv2023

Deep learning models of RNA base-pairing structures generalize to unseen folds and make accurate zero-shot predictions of base-base interactions of RNA complexes

The intricate network of RNA-RNA interactions, crucial for orchestrating essential cellular processes like transcriptional and translational regulation, has been unveiling through high-throughput techniques and computational predictions. With the emergence of deep learning methodologies, the question arises: how do these cutting-edge techniques for base-pairing prediction compare to traditional free-energy-based approaches, particularly when applied to the challenging domain of interaction prediction via chain concatenation? In this study, we employ base pairs derived from three-dimensional RNA complex structures as the gold standard benchmark to assess the performance of 23 different methods, including recently developed deep learning models. Our results demonstrate that the deep-learning-based methods, SPOT-RNA can be generalized to make accurate zero-shot predictions of RNA-RNA interactions not only between previously unseen RNA structures but also between RNAs without monomeric structures. The finding underscores the potential of deep learning as a robust tool for advancing our understanding of these complex molecular interactions.

bioinformatics↗

bioRxiv2023

Multiple sequence-alignment-based RNA language model and its application to structural inference

Compared to proteins, DNA and RNA are more difficult languages to interpret because 4-letter-coded DNA/RNA sequences have less information content than 20-letter-coded protein sequences. While BERT (Bidirectional Encoder Representations from Transformers)-like language models have been developed for RNA, they are ineffective at capturing the evolutionary information from homologous sequences because unlike proteins, RNA sequences are less conserved. Here, we have developed an unsupervised Multiple sequence-alignment-based RNA language model (RNA-MSM) by utilizing homologous sequences from an automatic pipeline, RNAcmap. The resulting unsupervised, two-dimensional attention maps and one-dimensional embeddings from RNA-MSM can be directly mapped with high accuracy to 2D base pairing probabilities and 1D solvent accessibilities, respectively. Further fine-tuning led to significantly improved performance on these two downstream tasks over existing state-of-the-art techniques. We anticipate that the pre-trained RNA-MSM model can be fine-tuned on many other tasks related to RNA structure and function.

bioinformatics↗

bioRxiv2023

The Master Database of All Possible RNA Sequences and Its Integration with RNAcmap for RNA Homology Search

Recent success of AlphaFold2 in protein structure prediction relied heavily on co-evolutionary information derived from homologous protein sequences found in the huge, integrated database of protein sequences (Big Fantastic Database). In contrast, the existing nucleotide databases were not consolidated to facilitate wider and deeper homology search. Here, we built a comprehensive database by including the noncoding RNA sequences from RNAcentral, the transcriptome assembly and metagenome assembly from MG-RAST, the genomic sequences from Genome Warehouse (GWH), and the genomic sequences from MGnify, in addition to NCBIs nucleotide database (nt) and its subsets. The resulting MARS database (Master database of All possible RNA sequences) is 20-fold larger than NCBIs nt database or 60-fold larger than RNAcentral. The new dataset along with a new split-search strategy allows a substantial improvement in homology search over existing state-of-the-art techniques. It also yields more accurate and more sensitive multiple sequence alignments (MSA) than manually curated MSAs from Rfam for the majority of structured RNAs mapped to Rfam. The results indicate that MARS coupled with the fully automatic homology search tool RNAcmap will be useful for improved structural and functional inference of noncoding RNAs.

bioinformatics↗

bioRxiv2022

Improved RNA homology detection and alignment by automatic iterative search in an expanded database

Unlike 20-letter-coded proteins, RNA homologous sequences are notoriously difficult to detect because their 4-letter-coded sequences can quickly lose their sequence identity. As a result, employing secondary structures has been found necessary to improve the sensitivity and the accuracy of homolog search. However, exact secondary structures often are not known. As a result, Rfam, the de facto gold-standard of RNA homologous families, has to rely on manual curation and experimental secondary structure if available. Here, we showed that using a combination of BLAST and iterative INFERNAL searches along with an expanded sequence database leads multiple sequence alignments (MSA) that are comparable to those provided by Rfam MSAs, according to secondary structure extracted from mutational coupling analysis and alignment accuracy when compared to structure alignment. The fully automatic tool (RNAcmap2) allows making homolog search, multiple sequence alignment, and mutational coupling analysis for any non-Rfam RNA sequences with Rfam-like performance.

bioinformatics↗

bioRxiv2021

SPOT-Contact-Single: Improving Single-Sequence-Based Prediction of Protein Contact Map using a Transformer Language Model, Large Training Set and Ensembled Deep Learning

MotivationAccurate prediction of protein contact-map is essential for accurate protein structure and function prediction. As a result, many methods have been developed for protein contact map prediction. However, most methods rely on protein-sequence-evolutionary information, which may not exist for many proteins due to lack of naturally occurring homologous sequences. Moreover, generating evolutionary profiles is computationally intensive. Here, we developed a contact-map predictor utilizing the output of a pre-trained language model ESM-1b as an input along with a large training set and an ensemble of residual neural networks. ResultsWe showed that the proposed method makes a significant improvement over a single-sequence-based predictor SSCpred with 15% improvement in the F1-score for the independent CASP14-FM test set. It also outperforms evolutionary-profile-based methods TrRosetta and SPOT-Contact with 48.7% and 48.5% respective improvement in the F1-score on the proteins without homologs (Neff=1) in the independent SPOT-2018 set. The new method provides a much faster and reasonably accurate alternative to evolution-based methods, useful for large-scale prediction. AvailabilityStand-alone-version of SPOT-Contact-Single is available at https://github.com/jas-preet/SPOT-Contact-Single. Direct prediction can also be made at https://sparks-lab.org/server/spot-contact-single. The datasets used in this research can also be downloaded from the GitHub. Contactjaspreetsingh2@griffithuni.edu.au, k.paliwal@griffith.edu.au, and zhouyq@szbl.ac.cn Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

RNAcmap: A Fully Automatic Method for Predicting Contact Maps of RNAs by Evolutionary Coupling Analysis

MotivationThe accuracy of RNA secondary and tertiary structure prediction can be significantly improved by using structural restraints derived from evolutionary or direct coupling analysis. Currently, these coupling analyses relied on manually curated multiple sequence alignments collected in the Rfam database, which contains 3016 families. By comparison, millions of non-coding RNA sequences are known. Here, we established RNAcmap, a fully automatic method that enables evolutionary coupling analysis for any RNA sequences. The homology search was based on the covariance model built by Infernal according to two secondary structure predictors: a folding-based algorithm RNAfold and the latest deep-learning method SPOT-RNA. ResultsWe show that the performance of RNAcmap is less dependent on the specific evolutionary coupling tool but is more dependent on the accuracy of secondary structure predictor with the best performance given by RNAcmap (SPOT-RNA). The performance of RNAcmap (SPOT-RNA) is comparable to that based on Rfam-supplied alignment and consistent for those sequences that are not in Rfam collections. Further improvement can be made with a simple meta predictor RNAcmap (SPOT-RNA/RNAfold) depending on which secondary structure predictor can find more homologous sequences. Reliable base-pairing information generated from RNAcmap, for RNAs with high effective homologous sequences, in particular, will be useful for aiding RNA structure prediction. Availability and implementationRNAcmap is available as a web server at https://sparks-lab.org/server/rnacmap/) and as a standalone application along with the datasets at https://github.com/sparks-lab-org/RNAcmap.

bioinformatics↗

bioRxiv2020

Systematic analysis of REBASE identifies numerous Type I restriction-modification systems that contain duplicated, variable hsdS specificity genes that randomly switch methyltransferase specificity by recombination.

N6-adenine DNA methyltransferases associated with some Type I and Type III restriction-modification (R-M) systems are able to randomly switch expression by variation in the length of locus-encoded simple sequence repeats (SSRs). SSR tract-length variation causes ON/OFF switching of methyltransferase expression, resulting in genome-wide methylation differences, and global changes in gene expression. These epigenetic regulatory systems are called phasevarions, phase-variable regulons, and are widespread in bacteria. A distinct switching system has also been described in Type I R-M systems, based on recombination-driven changes in hsdS genes, which dictate the DNA target site. In order to determine the prevalence of recombination-driven phasevarions, we generated a program called RecombinationRepeatSearch to interrogate REBASE and identify the presence and number of inverted repeats of hsdS downstream of Type I R-M loci. We report that 5.9% of Type I R-M systems have duplicated variable hsdS genes containing inverted repeats capable of phase-variation. We report the presence of these systems in the major pathogens Enterococcus faecalis and Listeria monocytogenes, which will have important implications for pathogenesis and vaccine development. These data suggest that in addition to SSR-driven phasevarions, many bacteria have independently evolved phase-variable Type I R-M systems via recombination between multiple, variable hsdS genes. ImportanceMany bacterial species contain DNA methyltransferases that have random on/off switching of expression. These systems called phasevarions (phase-variable regulons) control the expression of multiple genes by global methylation changes. In every previously characterised phasevarion, genes involved in pathobiology, antibiotic resistance, and potential vaccine candidates are randomly varied in their expression, commensurate with methyltransferase switching. A systematic study to determine the extent of phasevarions controlled by invertible Type I R-M systems has never before been performed. Understanding how bacteria regulate genes is key to the study of physiology, virulence, and vaccine development; therefore it is critical to identify and characterize phase-variable methyltransferases controlling phasevarions.

microbiology↗