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/.

Homologous recombination (HR) is a template-based DNA double-strand break repair pathway that functions to maintain genomic integrity. A vital component of the HR reaction is the identification of template DNA to be used during repair. This occurs through a mechanism known as the homology search. The homology search occurs in two steps: a collision step in which two pieces of DNA are forced to collide and a selection step that results in homologous pairing between matching DNA sequences. Selection of a homologous template is facilitated by recombinases of the RecA/Rad51 family of proteins in cooperation with helicases, translocases, and topoisomerases that determine the overall fidelity of the match. This menagerie of molecular machines acts to regulate critical intermediates during the homology search. These intermediates include recombinase filaments that probe for short stretches of homology and early strand invasion intermediates in the form of displacement loops (D-loops) that stabilize paired DNA. Here, we will discuss recent advances in understanding how these specific intermediates are regulated on the molecular level during the HR reaction. We will also discuss how the stability of these intermediates influences the ultimate outcomes of the HR reaction. Finally, we will discuss recent physiological models developed to explain how the homology search protects the genome.

BACKGROUND: Co-localized sets of genes that encode specialized functions are common across microbial genomes and occur in genomes of larger eukaryotes as well. Important examples include Biosynthetic Gene Clusters (BGCs) that produce specialized metabolites with medicinal, agricultural, and industrial value (e.g. antimicrobials). Comparative analysis of BGCs can aid in the discovery of novel metabolites by highlighting distribution and identifying variants in public genomes. Unfortunately, gene-cluster-level homology detection remains inaccessible, time-consuming and difficult to interpret.
RESULTS: The comparative gene cluster analysis toolbox (CAGECAT) is a rapid and user-friendly platform to mitigate difficulties in comparative analysis of whole gene clusters. The software provides homology searches and downstream analyses without the need for command-line or programming expertise. By leveraging remote BLAST databases, which always provide up-to-date results, CAGECAT can yield relevant matches that aid in the comparison, taxonomic distribution, or evolution of an unknown query. The service is extensible and interoperable and implements the cblaster and clinker pipelines to perform homology search, filtering, gene neighbourhood estimation, and dynamic visualisation of resulting variant BGCs. With the visualisation module, publication-quality figures can be customized directly from a web-browser, which greatly accelerates their interpretation via informative overlays to identify conserved genes in a BGC query.
CONCLUSIONS: Overall, CAGECAT is an extensible software that can be interfaced via a standard web-browser for whole region homology searches and comparison on continually updated genomes from NCBI. The public web server and installable docker image are open source and freely available without registration at: https://cagecat.bioinformatics.nl .

Meiosis is a highly conserved specialised cell division in sexual life cycles of eukaryotes, forming the base of gene reshuffling, biological diversity and evolution. Understanding meiotic machinery across different plant lineages is inevitable to understand the lineage-specific evolution of meiosis. Functional and cytogenetic studies of meiotic proteins from all plant lineage representatives are nearly impossible. So, we took advantage of the genomics revolution to search for core meiotic proteins in accumulating plant genomes by the highly sensitive homology search approaches, PSI-BLAST, HMMER and CLANS. We could find that most of the meiotic proteins are conserved in most of the lineages. Exceptionally, Arabidopsis thaliana ASY4, PHS1, PRD2, PRD3 orthologs were mostly not detected in some distant algal lineages suggesting their minimal conservation. Remarkably, an ancestral duplication of SPO11 to all eukaryotes could be confirmed. Loss of SPO11-1 in Chlorophyta and Charophyta is likely to have occurred, suggesting that SPO11-1 and SPO11-2 heterodimerisation may be a unique feature in land plants of Viridiplantae. The possible origin of the meiotic proteins described only in plants till now, DFO and HEIP1, could be traced and seems to occur in the ancestor of vascular plants and Streptophyta, respectively. Our comprehensive approach is an attempt to provide insights about meiotic core proteins and thus the conservation of meiotic pathways across plant kingdom. We hope that this will serve the meiotic community a basis for further characterisation of interesting candidates in future.

Since 1992, all state-of-the-art methods for fast and sensitive identification of evolutionary, structural, and functional relations between proteins (also referred to as \"homology detection\") use sequences and sequence-profiles (PSSMs). Protein Language Models (pLMs) generalize sequences, possibly capturing the same constraints as PSSMs, e.g., through embeddings. Here, we explored how to use such embeddings for nearest neighbor searches to identify relations between protein pairs with diverged sequences (remote homology detection for levels of <20% pairwise sequence identity, PIDE). While this approach excelled for proteins with single domains, we demonstrated the current challenges applying this to multi-domain proteins and presented some ideas how to overcome existing limitations, in principle. We observed that sufficiently challenging data set separations were crucial to provide deeply relevant insights into the behavior of nearest neighbor search when applied to the protein embedding space, and made all our methods readily available for others.

While the molecular repertoire of the homologous recombination pathways is well studied, the search mechanism that enables recombination between distant homologous regions is poorly understood. Earlier work suggests that the recombinase RecA, an essential component for homology search, forms an elongated filament, nucleating at the break site. How this RecA structure carries out long-distance search remains unclear. Here, we follow the dynamics of RecA after induction of a single double-strand break on the Caulobacter chromosome. We find that the RecA-nucleoprotein filament, once formed, rapidly translocates in a directional manner in the cell, undergoing several pole-to-pole traversals, until homology search is complete. Concomitant with translocation, we observe dynamic variation in the length of the filament. Importantly in vivo, the RecA filament alone is incapable of such long-distance movement; both translocation and associated length variations are contingent on action of structural maintenance of chromosome (SMC)-like protein RecN, via its ATPase cycle. In summary, we have uncovered the three key elements of homology search driven by RecN: mobility of a finite segment of RecA, changes in filament length, and ability to conduct multiple pole-to-pole traversals, which together point to an optimal search strategy.

Trypanosomatids belong to a remarkable group of unicellular, parasitic organisms of the order Kinetoplastida, an early diverging branch of the phylogenetic tree of eukaryotes, exhibiting intriguing biological characteristics affecting gene expression (intronless polycistronic transcription, trans-splicing, and RNA editing), metabolism, surface molecules, and organelles (compartmentalization of glycolysis, variation of the surface molecules, and unique mitochondrial DNA), cell biology and life cycle (phagocytic vacuoles evasion and intricate patterns of cell morphogenesis). With numerous genomic-scale data of several trypanosomatids becoming available since 2005 (genomes, transcriptomes, and proteomes), the scientific community can further investigate the mechanisms underlying these unusual features and address other unexplored phenomena possibly revealing biological aspects of the early evolution of eukaryotes. One fundamental aspect comprises the processes and mechanisms involved in the acquisition and loss of genes throughout the evolutionary history of these primitive microorganisms. Here, we present a comprehensive in silico analysis of pseudogenes in three major representatives of this group: Leishmania major, Trypanosoma brucei, and Trypanosoma cruzi. Pseudogenes, DNA segments originating from altered genes that lost their original function, are genomic relics that can offer an essential record of the evolutionary history of functional genes, as well as clues about the dynamics and evolution of hosting genomes. Scanning these genomes with functional proteins as proxies to reveal intergenic regions with protein-coding features, relying on a customized threshold to distinguish statistically and biologically significant sequence similarities, and reassembling remnant sequences from their debris, we found thousands of pseudogenes and hundreds of open reading frames, with particular characteristics in each trypanosomatid: mutation profile, number, content, density, codon bias, average size, single- or multi-copy gene origin, number and type of mutations, putative primitive function, and transcriptional activity. These features suggest a common process of pseudogene formation, different patterns of pseudogene evolution and extant biological functions, and/or distinct genome organization undertaken by those parasites during evolution, as well as different evolutionary and/or selective pressures acting on distinct lineages.

In this chapter, we outline a pipeline for ortholog prediction and phylogenetic analysis in plants. This computational pipeline uses algorithms from different software to enable bioinformatic-beginner biologists to predict orthologs that can be shared with many distinct plant nonmodel and model species and identify gene loss events. Prediction of orthologs allows (1) investigation of the evolutionary relationships of plant genomes, (2) discovery of their origin, function, and (3) the impact of their adaptability to the environment.We developed a pipeline to fit, not only eukaryote but also prokaryote organisms, with small or large genomes. All results acquired from the orthologs predication will enable phylogenetic tree construction, using gene and species (phylogenomic) phylogeny approaches.

Enzymes catalyze a wide variety of reactions with exquisite precision under crowded conditions within cellular environments. When encountered with a choice of small molecules in their vicinity, even though most enzymes continue to be specific about the substrate they pick, some others are able to accept a range of substrates and subsequently produce a variety of products. The biosynthesis of Vitamin B12, an essential nutrient required by humans involves a multi-substrate α-phosphoribosyltransferase enzyme CobT that activates the lower ligand of B12. Vitamin B12 is a member of the cobamide family of cofactors which share a common tetrapyrrolic corrin scaffold with a centrally coordinated cobalt ion, and an upper and a lower ligand. The structural difference between B12 and other cobamides mainly arises from variations in the lower ligand, which is attached to the activated corrin ring by CobT and other downstream enzymes. In this chapter, we describe the steps involved in identifying and reconstituting the activity of new CobT homologs by deriving lessons from those previously characterized. We then highlight biochemical techniques to study the unique properties of these homologs. Finally, we describe a pairwise substrate competition assay to rank CobT substrate preference, a general method that can be applied for the study of other multi-substrate enzymes. Overall, the analysis with CobT provides insights into the range of cobamides that can be synthesized by an organism or a community, complementing efforts to predict cobamide diversity from complex metagenomic data.

Metagenomic analysis, a technique used to comprehensively analyze microorganisms present in the environment, requires performing high-precision homology searches on large amounts of sequencing data, the size of which has increased dramatically with the development of next-generation sequencing. NCBI BLAST is the most widely used software for performing homology searches, but its speed is insufficient for the throughput of current DNA sequencers. In this paper, we propose a new, high-performance homology search algorithm that employs a two-step seed search strategy using multiple reduced amino acid alphabets to identify highly similar subsequences. Additionally, we evaluated the validity of the proposed method against several existing tools. Our method was faster than any other existing program for ≤120,000 queries, while DIAMOND, an existing tool, was the fastest method for >120,000 queries.