Deep embedding and alignment of protein sequences

Protein sequence alignment is a key component of most bioinformatics pipelines to study the structures and functions of proteins. Aligning highly divergent sequences remains, however, a difficult task that current algorithms often fail to perform accurately, leaving many proteins or open reading frames poorly annotated. Here, we leverage recent advances in deep learning for language modelling and differentiable programming to propose DEDAL, a flexible model to align protein sequences and detect homologs. DEDAL is a machine learning-based model that learns to align sequences by observing large datasets of raw protein sequences and of correct alignments. Once trained, we show that DEDAL improves by up to two- or three-fold the alignment correctness over existing methods on remote homologs, and better discriminates remote homologs from evolutionarily unrelated sequences, paving the way to improvements on many downstream tasks relying on sequence alignment in structural and functional genomics.

Rush Hour of LATs towards Their Transport Cycle

The mammalian SLC7 family comprises the L-amino acid transporters (LATs) and the cationic amino acid transporters (CATs). The relevance of these transporters is highlighted by their involvement in several human pathologies, including inherited rare diseases and acquired diseases, such as cancer. In the last four years, several crystal or cryo-EM structures of LATs and CATs have been solved. These structures have started to fill our knowledge gap that previously was based on the structural biology of remote homologs of the amino acid–polyamine–organocation (APC) transporters. This review recovers this structural and functional information to start generating the molecular bases of the transport cycle of LATs. Special attention is given to the known transporter conformations within the transport cycle and the molecular bases for substrate interaction and translocation, including the asymmetric interaction of substrates at both sides of the plasma membrane.

The evolution of autophagy proteins – diversification in eukaryotes and potential ancestors in prokaryotes

ABSTRACT Autophagy is a degradative pathway for cytoplasmic constituents, and is conserved across eukaryotes. Autophagy-related (ATG) genes have undergone extensive multiplications and losses in different eukaryotic lineages, resulting in functional diversification and specialization. Notably, even though bacteria and archaea do not possess an autophagy pathway, they do harbor some remote homologs of Atg proteins, suggesting that preexisting proteins were recruited when the autophagy pathway developed during eukaryogenesis. In this Review, we summarize our current knowledge on the distribution of Atg proteins within eukaryotes and outline the major multiplication and loss events within the eukaryotic tree. We also discuss the potential prokaryotic homologs of Atg proteins identified to date, emphasizing the evolutionary relationships and functional differences between prokaryotic and eukaryotic proteins.

Melbournevirus-encoded histone doublets are recruited to virus particles and form destabilized nucleosome-like structures

SummaryThe organization of genomic DNA into defined nucleosomes has long been viewed as a hallmark of eukaryotes. This paradigm has been challenged by the identification of ‘minimalist’ histones in archaea, and more recently by the discovery of genes that encode fused remote homologs of the four eukaryotic histones in Marseilleviridae, a subfamily of giant viruses that infect amoebae. We demonstrate that viral doublet histones localize to the cytoplasmic viral factories after virus infection, and ultimately to mature virions. CryoEM structures of viral nucleosome-like particles show strong similarities to eukaryotic nucleosomes, despite the limited sequence identify. The unique connectors that link the histone chains contribute to the observed instability of viral nucleosomes, and some histone tails assume structural roles. Our results further expand the range of ‘organisms’ that have nucleosomes and suggest a specialized function of histones in the biology of these unusual viruses.One Sentence SummarySome large DNA viruses encode fused histone doublets that are targeted to viral factories and assemble into open nucleosome-like structures.

Master Blaster: an approach to sensitive identification of remotely related proteins

Genome sequencing projects unearth sequences of all the protein sequences encoded in a genome. As the first step, homology detection is employed to obtain clues to structure and function of these proteins. However, high evolutionary divergence between homologous proteins challenges our ability to detect distant relationships. In the past, an approach involving multiple Position Specific Scoring Matrices (PSSMs) was found to be more effective than traditional single PSSMs. Cascaded search is another successful approach where hits of a search are queried to detect more homologues. We propose a protocol, ‘Master Blaster’, which combines the principles adopted in these two approaches to enhance our ability to detect remote homologues even further. Assessment of the approach was performed using known relationships available in the SCOP70 database, and the results were compared against that of PSI-BLAST and HHblits, a hidden Markov model-based method. Compared to PSI-BLAST, Master Blaster resulted in 10% improvement with respect to detection of cross superfamily connections, nearly 35% improvement in cross family and more than 80% improvement in intra family connections. From the results it was observed that HHblits is more sensitive in detecting remote homologues compared to Master Blaster. However, there are true hits from 46-folds for which Master Blaster reported homologs that are not reported by HHblits even using the optimal parameters indicating that for detecting remote homologues, use of multiple methods employing a combination of different approaches can be more effective in detecting remote homologs. Master Blaster stand-alone code is available for download in the supplementary archive.

Long-read metagenomics using PromethION uncovers oral bacteriophages and their interaction with host bacteria

Bacteriophages (phages), or bacterial viruses, are very diverse and highly abundant worldwide, including as a part of the human microbiomes. Although a few metagenomic studies have focused on oral phages, they relied on short-read sequencing. Here, we conduct a long-read metagenomic study of human saliva using PromethION. Our analyses, which integrate both PromethION and HiSeq data of >30 Gb per sample with low human DNA contamination, identify hundreds of viral contigs; 0–43.8% and 12.5–56.3% of the confidently predicted phages and prophages, respectively, do not cluster with those reported previously. Our analyses demonstrate enhanced scaffolding, and the ability to place a prophage in its host genomic context and enable its taxonomic classification. Our analyses also identify a Streptococcus phage/prophage group and nine jumbo phages/prophages. 86% of the phage/prophage group and 67% of the jumbo phages/prophages contain remote homologs of antimicrobial resistance genes. Pan-genome analysis of the phages/prophages reveals remarkable diversity, identifying 0.3% and 86.4% of the genes as core and singletons, respectively. Furthermore, our study suggests that oral phages present in human saliva are under selective pressure to escape CRISPR immunity. Our study demonstrates the power of long-read metagenomics utilizing PromethION in uncovering bacteriophages and their interaction with host bacteria.