Thomas Litfin

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.

Sialic acid O -acetylation is implicated in the modulation of sialoglycan recognition and ganglioside biology. The sugar modification is catalyzed by CASD1, a Golgi membrane protein that encompasses a luminal catalytic domain and a multipass transmembrane domain. The mechanism of how acetyl-CoA is provided to the Golgi remains poorly understood. Here, we show that the acetyl-CoA transporter SLC33A1 provides acetyl-CoA to the luminal domain of CASD1 and that patient-derived SLC33A1 variants linked to inherited neurodevelopmental and neurodegenerative disorders impair ganglioside 9- O -acetylation. Under conditions that enable the formation of 7,9-di- O -acetylated sialoglycans, genetic inactivation of SLC33A1 impaired di- O -acetylation, but unexpectedly, still enabled mono- O -acetylation. Structure prediction and site-directed mutagenesis revealed a second active site in CASD1 that shares striking similarities with the catalytic acetyl-CoA binding transmembrane tunnel of the lysosomal acetyltransferase HGSNAT. Together, our data provide strong evidence that CASD1 has dual functionalities and catalyzes 7,9-di- O -acetylation through SLC33A1-dependent luminal acetylation and SLC33A1-independent transmembrane acetylation.

Proteins are the primary functional molecules of the cell, translating genetic information into biological function. This course provides a comprehensive introduction to the principles of protein architecture, from the chemical properties of amino acids to the assembly of multi-subunit complexes.

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 .

Motivation: The intricate network of RNA–RNA interactions, crucial for orchestrating essential cellular processes like transcriptional and translational regulations, has been unveiling through high-throughput techniques and computational predictions. As experimental determination of RNA–RNA interactions at the base-pair resolution remains challenging, a timely update for assessing complementary computational tools is necessary, particularly given the recent emergence of deep-learning-based methods. Results: Here, we employed base pairs derived from three-dimensional RNA complex structures as a gold standard benchmark to assess the performance of 23 different methods ranging from alignment-based methods, free-energy-based minimization to deep-learning techniques. The result indicates that a deep-learning-based method, 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. Availability and implementation: All data and codes are available at https://github.com/meilanglang/RNA-RNA-Interaction

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.

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 incorporating the non-coding RNA (ncRNA) sequences from RNAcentral, the transcriptome assembly and metagenome assembly from metagenomics RAST (MG-RAST), the genomic sequences from Genome Warehouse (GWH), and the genomic sequences from MGnify, in addition to the nucleotide (nt) database and its subsets in National Center of Biotechnology Information (NCBI). The resulting Master database of All possible RNA sequences (MARS) is 20-fold larger than NCBI’s 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 (MSAs) 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 ncRNAs and RNA language models based on MSAs. MARS is accessible at https://ngdc.cncb.ac.cn/omix/release/OMIX003037, and RNAcmap3 is accessible at http://zhouyq-lab.szbl.ac.cn/download/.

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 NCBI’s nucleotide database (nt) and its subsets. The resulting MARS database (Master database of All possible RNA sequences) is 20-fold larger than NCBI’s 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.

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.

Characterizing RNA structures and functions have mostly been focused on 2D, secondary and 3D, tertiary structures. Recent advances in experimental and computational techniques for probing or predicting RNA solvent accessibility make this 1D representation of tertiary structures an increasingly attractive feature to explore. Here, we provide a survey of these recent developments, which indicate the emergence of solvent accessibility as a simple 1D property, adding to secondary and tertiary structures for investigating complex structure–function relations of RNAs.