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AlphaFold2 predicts protein structures from structural and functional knowledge. Alternatively, ESMFold does the same adopting protein language models. Here, we map available Pfam domains on pairs of models of the human reference proteome computed with both procedures and we compare the mapped regions relevant for functional annotation. We find that, rather irrespectively of the global superimposition of the pairwise models, Pfam-containing regions overlap with a TM-score above 0.8 and a predicted local distance difference test (pLDDT) which is higher than the rest of the modeled sequence. This indicates that both methods are similarly performing in modeled regions that overlap Pfam domains, carrying structural and functional information, with pLDDT values slightly higher for AlphaFold2. The mapping of 9834 Pfam domains also allows the location of 2578 active sites in 3382 enzymes of the human proteome, including 807 proteins for which the active site is not reported in UniProt.

Abstract Background A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. Methods We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. Results Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. Conclusions Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

Abstract Background Antimicrobial resistance has been identified as a major threat to global health. The pig food chain is considered an important source of antimicrobial resistance genes (ARGs). However, there is still a lack of knowledge on the dispersion of ARGs in pig production system, including the external environment. Results In the present study, we longitudinally followed one swine farm located in Italy from the weaning phase to the slaughterhouse to comprehensively assess the diversity of ARGs, their diffusion, and the bacteria associated with them. We obtained shotgun metagenomic sequences from 294 samples, including pig feces, farm environment, soil around the farm, wastewater, and slaughterhouse environment. We identified a total of 530 species-level genome bins (SGBs), which allowed us to assess the dispersion of microorganisms and their associated ARGs in the farm system. We identified 309 SGBs being shared between the animals gut microbiome, the internal and external farm environments. Specifically, these SGBs were characterized by a diverse and complex resistome, with ARGs active against 18 different classes of antibiotic compounds, well matching antibiotic use in the pig food chain in Europe. Conclusions Collectively, our results highlight the urgency to implement more effective countermeasures to limit the dispersion of ARGs in the pig food systems and the relevance of metagenomics-based approaches to monitor the spread of ARGs for the safety of the farm working environment and the surrounding ecosystems.

In order to shed light on the usage of protein language model-based alignment procedures, we attempted the classification of Glutathione S-transferases (GST; EC 2.5.1.18) and compared our results with the ARBA/UNI rule-based annotation in UniProt. GST is a protein superfamily involved in cellular detoxification from harmful xenobiotics and endobiotics, widely distributed in prokaryotes and eukaryotes. What is particularly interesting is that the superfamily is characterized by different classes, comprising proteins from different taxa that can act in different cell locations (cytosolic, mitochondrial and microsomal compartments) with different folds and different levels of sequence identity with remote homologs. For this reason, GST functional annotation in a specific class is problematic: unless a structure is released, the protein can be classified only on the basis of sequence similarity, which excludes the annotation of remote homologs. Here, we adopt an embedding-based alignment to classify 15,061 GST proteins automatically annotated by the UniProt-ARBA/UNI rules. Embedding is based on the Meta ESM2-15b protein language. The embedding-based alignment reaches more than a 99% rate of perfect matching with the UniProt automatic procedure. Data analysis indicates that 46% of the UniProt automatically classified proteins do not conserve the typical length of canonical GSTs, whose structure is known. Therefore, 46% of the classified proteins do not conserve the template/s structure required for their family classification. Our approach finds that 41% of 64,207 GST UniProt proteins not yet assigned to any class can be classified consistently with the structural template length.

Investigating marine species population structure in a multidisciplinary framework can reveal signatures of potential local adaptation and the consequences for management and conservation. In this study we delineate the population structure of common sole (Solea solea) in the Mediterranean Sea using genomic and otolith data, based on single nucleotide polymorphism (SNPs) markers, otolith shape and otolith trace element composition data. We correlated SNPs with environmental and spatial variables to evaluate the impact of the selected features on the actual population structure. Specifically, we used a seascape genetics approach with redundancy (RDA) and genetic-environmental association (GEA) analysis to identify loci potentially involved in local adaptation. Finally, putative functional annotation was investigated to detect genes associated with the detected patterns of neutral and adaptive genetic variation. Results from both genetic and otolith data suggested significant divergence among putative populations of common sole, confirming a clear separation between the Western and Eastern Mediterranean Sea, as well as a distinct genetic cluster corresponding to the Adriatic Sea. Evidence of fine-scale population structure in the Western Mediterranean Sea was observed at outlier loci level and further differentiation in the Adriatic. Longitude and salinity variation accounted for most of the wide and fine spatial structure. The GEA detected significant associated outlier loci potentially involved in local adaptation processes under highly structured differentiation. In the RDA both spatial distribution and environmental features could partially explain the genetic structure. Our study not only indicates that separation among Mediterranean sole population is led primarily by neutral processes because of low connectivity due to spatial segregation and limited dispersal, but it also suggests the presence of local adaptation. These results should be taken into account to support and optimize the assessment of stock units, including a review and possible redefinition of fishery management units.

Coiled-coil domains (CCDs) are structural motifs observed in proteins in all organisms that perform several crucial functions. The computational identification of CCD segments over a protein sequence is of great importance for its functional characterization. This task can essentially be divided into three separate steps: the detection of segment boundaries, the annotation of the heptad repeat pattern along the segment, and the classification of its oligomerization state. Several methods have been proposed over the years addressing one or more of these predictive steps. In this protocol, we illustrate how to make use of CoCoNat, a novel approach based on protein language models, to characterize CCDs. CoCoNat is, at its release (August 2023), the state of the art for CCD detection. The web server allows users to submit input protein sequences and visualize the predicted domains after a few minutes. Optionally, precomputed segments can be provided to the model, which will predict the oligomerization state for each of them. CoCoNat can be easily integrated into biological pipelines by downloading the standalone version, which provides a single executable script to produce the output.Key features• Web server for the prediction of coiled-coil segments from a protein sequence.• Three different predictions from a single tool (segment position, heptad repeat annotation, oligomerization state).• Possibility to visualize the results online or to download the predictions in different formats for further processing.• Easy integration in automated pipelines with the local version of the tool.Graphical overview

Abstract According to databases such as OMIM, Humsavar, Clinvar and Monarch, 1494 human enzymes are presently associated to 2539 genetic diseases, 75% of which are rare (with an Orphanet code). The Mondo ontology initiative allows a standardization of the disease name into specific codes, making it possible a computational association between genes, variants, diseases, and their effects on biological processes. Here, we tackle the problem of which biological processes enzymes can affect when the protein variant is disease-associated. We adopt Reactome to describe human biological processes, and by mapping disease-associated enzymes in the Reactome pathways, we establish a Reactome-disease association. This allows a novel categorization of human monogenic and polygenic diseases based on Reactome pathways and reactions. Our analysis aims at dissecting the complexity of the human genetic disease universe, highlighting all the possible links within diseases and Reactome pathways. The novel mapping helps understanding the biochemical/molecular biology of the disease and allows a direct glimpse on the present knowledge of other molecules involved. This is useful for a complete overview of the disease molecular mechanism/s and for planning future investigations. Data are collected in DAR, a database that is free for search and available at https://dar.biocomp.unibo.it .

Collectively, rare genetic disorders affect a substantial portion of the world’s population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

Hox and ParaHox genes (HPHGs) are key developmental genes that pattern regional identity along the anterior–posterior body axis of most animals. Here, we identified HPHGs in tadpole shrimps (Pancrustacea, Branchiopoda, Notostraca), an iconic example of the so-called “living fossils” and performed a comparative genomics analysis of HPHGs and the Hox cluster among major branchiopod lineages. Notostraca possess the entire Hox complement, and the Hox cluster seems to be split into two different subclusters, although we were not able to support this finding with chromosome-level assemblies. However, the genomic structure of Hox genes in Notostraca appears more derived than that of Daphnia spp., which instead retains the plesiomorphic condition of a single compact cluster. Spinicaudata and Artemia franciscana show instead a Hox cluster subdivided across two or more genomic scaffolds with some orthologs either duplicated or missing. Yet, branchiopod HPHGs are similar among the various clades in terms of both intron length and number, as well as in their pattern of molecular evolution. Sequence substitution rates are in fact generally similar for most of the branchiopod Hox genes and the few differences we found cannot be traced back to natural selection, as they are not associated with any signals of diversifying selection or substantial switches in selective modes. Altogether, these findings do not support a significant stasis in the Notostraca Hox cluster and further confirm how morphological evolution is not tightly associated with genome dynamics.

Grouping residue variations in a protein according to their physicochemical properties allows a dimensionality reduction of all the possible substitutions in a variant with respect to the wild type. Here, by using a large dataset of proteins with disease-related and benign variations, as derived by merging Humsavar and ClinVar data, we investigate to which extent our physicochemical grouping procedure can help in determining whether patterns of variation types are related to specific groups of diseases and whether they occur in Pfam and/or InterPro gene domains. Here, we download 75,145 germline disease-related and benign variations of 3,605 genes, group them according to physicochemical categories and map them into Pfam and InterPro gene domains. Statistically validated analysis indicates that each cluster of genes associated to Mondo anatomical system categorizations is characterized by a specific variation pattern. Patterns identify specific Pfam and InterPro domain–Mondo category associations. Our data suggest that the association of variation patterns to Mondo categories is unique and may help in associating gene variants to genetic diseases. This work corroborates in a much larger data set previous observations from our group.

Abstract Disease lesion mimic (DLM) or necrotic mutants display necrotic lesions in the absence of pathogen infections. They can show improved resistance to some pathogens and their molecular dissection can contribute to revealing components of plant defense pathways. Although forward‐genetics strategies to find genes causal to mutant phenotypes are available in crops, these strategies require the production of experimental cross populations, mutagenesis, or gene editing and are time‐ and resource‐consuming or may have to deal with regulated plant materials. In this study, we described a collection of 34 DLM mutants in barley (Hordeum vulgare L.) and applied a novel method called complementation by sequencing (CBS), which enables the identification of the gene responsible for a mutant phenotype given the availability of two or more chemically mutagenized individuals showing the same phenotype. Complementation by sequencing relies on the feasibility to obtain all induced mutations present in chemical mutants and on the low probability that different individuals share the same mutated genes. By CBS, we identified a cytochrome P450 CYP71P1 gene as responsible for orange blotch DLM mutants, including the historical barley nec3 locus. By comparative phylogenetic analysis we showed that CYP71P1 gene family emerged early in angiosperm evolution but has been recurrently lost in some lineages including Arabidopsis thaliana (L.) Heynh. Complementation by sequencing is a straightforward cost‐effective approach to clone genes controlling phenotypes in a chemically mutagenized collection. The TILLMore (TM) collection will be instrumental for understanding the molecular basis of DLM phenotypes and to contribute knowledge about mechanisms of host–pathogen interaction.