E-pRSA: Embeddings Improve the Prediction of Residue Relative Solvent Accessibility in Protein Sequence.

Knowledge of the solvent accessibility of residues in a protein is essential for different applications, including the identification of interacting surfaces in protein-protein interactions and the characterization of variations. We describe E-pRSA, a novel web server to estimate Relative Solvent Accessibility values (RSAs) of residues directly from a protein sequence. The method exploits two complementary Protein Language Models to provide fast and accurate predictions. When benchmarked on different blind test sets, E-pRSA scores at the state-of-the-art, and outperforms a previous method we developed, DeepREx, which was based on sequence profiles after Multiple Sequence Alignments. The E-pRSA web server is freely available at https://e-prsa.biocomp.unibo.it/main/ where users can submit single-sequence and batch jobs.

Assessing predictions on fitness effects of missense variants in HMBS in CAGI6.

This paper presents an evaluation of predictions submitted for the "HMBS" challenge, a component of the sixth round of the Critical Assessment of Genome Interpretation held in 2021. The challenge required participants to predict the effects of missense variants of the human HMBS gene on yeast growth. The HMBS enzyme, critical for the biosynthesis of heme in eukaryotic cells, is highly conserved among eukaryotes. Despite the application of a variety of algorithms and methods, the performance of predictors was relatively similar, with Kendall's tau correlation coefficients between predictions and experimental scores around 0.3 for a majority of submissions. Notably, the median correlation (≥ 0.34) observed among these predictors, especially the top predictions from different groups, was greater than the correlation observed between their predictions and the actual experimental results. Most predictors were moderately successful in distinguishing between deleterious and benign variants, as evidenced by an area under the receiver operating characteristic (ROC) curve (AUC) of approximately 0.7 respectively. Compared with the recent two rounds of CAGI competitions, we noticed more predictors outperformed the baseline predictor, which is solely based on the amino acid frequencies. Nevertheless, the overall accuracy of predictions is still far short of positive control, which is derived from experimental scores, indicating the necessity for considerable improvements in the field. The most inaccurately predicted variants in this round were associated with the insertion loop, which is absent in many orthologs, suggesting the predictors still heavily rely on the information from multiple sequence alignment.

Evaluating predictors of kinase activity of STK11 variants identified in primary human non-small cell lung cancers.

Critical evaluation of computational tools for predicting variant effects is important considering their increased use in disease diagnosis and driving molecular discoveries. In the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, a dataset of 28 STK11 rare variants (27 missense, 1 single amino acid deletion), identified in primary non-small cell lung cancer biopsies, was experimentally assayed to characterize computational methods from four participating teams and five publicly available tools. Predictors demonstrated a high level of performance on key evaluation metrics, measuring correlation with the assay outputs and separating loss-of-function (LoF) variants from wildtype-like (WT-like) variants. The best participant model, 3Cnet, performed competitively with well-known tools. Unique to this challenge was that the functional data was generated with both biological and technical replicates, thus allowing the assessors to realistically establish maximum predictive performance based on experimental variability. Three out of the five publicly available tools and 3Cnet approached the performance of the assay replicates in separating LoF variants from WT-like variants. Surprisingly, REVEL, an often-used model, achieved a comparable correlation with the real-valued assay output as that seen for the experimental replicates. Performing variant interpretation by combining the new functional evidence with computational and population data evidence led to 16 new variants receiving a clinically actionable classification of likely pathogenic (LP) or likely benign (LB). Overall, the STK11 challenge highlights the utility of variant effect predictors in biomedical sciences and provides encouraging results for driving research in the field of computational genome interpretation.

Critical assessment of missense variant effect predictors on disease-relevant variant data.

Regular, systematic, and independent assessment of computational tools used to predict the pathogenicity of missense variants is necessary to evaluate their clinical and research utility and suggest directions for future improvement. Here, as part of the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, we assess missense variant effect predictors (or variant impact predictors) on an evaluation dataset of rare missense variants from disease-relevant databases. Our assessment evaluates predictors submitted to the CAGI6 Annotate-All-Missense challenge, predictors commonly used by the clinical genetics community, and recently developed deep learning methods for variant effect prediction. To explore a variety of settings that are relevant for different clinical and research applications, we assess performance within different subsets of the evaluation data and within high-specificity and high-sensitivity regimes. We find strong performance of many predictors across multiple settings. Meta-predictors tend to outperform their constituent individual predictors; however, several individual predictors have performance similar to that of commonly used meta-predictors. The relative performance of predictors differs in high-specificity and high-sensitivity regimes, suggesting that different methods may be best suited to different use cases. We also characterize two potential sources of bias. Predictors that incorporate allele frequency as a predictive feature tend to have reduced performance when distinguishing pathogenic variants from very rare benign variants, and predictors supervised on pathogenicity labels from curated variant databases often learn label imbalances within genes. Overall, we find notable advances over the oldest and most cited missense variant effect predictors and continued improvements among the most recently developed tools, and the CAGI Annotate-All-Missense challenge (also termed the Missense Marathon) will continue to assess state-of-the-art methods as the field progresses. Together, our results help illuminate the current clinical and research utility of missense variant effect predictors and identify potential areas for future development.

Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A.

Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the Arylsulfatase A (ARSA) gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models. Notably, a model developed by participants of a genetics and coding bootcamp, trained with standard machine-learning tools in Python, demonstrated superior performance among submissions. Furthermore, the study observed that state-of-the-art deep learning methods provided small but statistically significant improvement in predictive performance compared to less elaborate techniques. These findings underscore the utility of variant effect prediction, and the potential for models trained with modest resources to accurately classify VUS in genetic and clinical research.

Alpha&ESMhFolds: A Web Server for Comparing AlphaFold2 and ESMFold Models of the Human Reference Proteome.

We develop a novel database Alpha&ESMhFolds which allows the direct comparison of AlphaFold2 and ESMFold predicted models for 42,942 proteins of the Reference Human Proteome, and when available, their comparison with 2,900 directly associated PDB structures with at least a structure to sequence coverage of 70%. Statistics indicate that good quality models tend to overlap with a TM-score >0.6 as long as some PDB structural information is available. As expected, a direct model superimposition to the PDB structure highlights that AlphaFold2 models are slightly superior to ESMFold ones. However, some 55% of the database is endowed with models overlapping with TM-score <0.6. This highlights the different outputs of the two methods. The database is freely available for usage at https://alpha-esmhfolds.biocomp.unibo.it/.

Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project.

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.

Dispersion of antimicrobial resistant bacteria in pig farms and in the surrounding environment.

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.

CoCoNat: A Deep Learning-Based Tool for the Prediction of Coiled-coil Domains in Protein Sequences.

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.

MultifacetedProtDB: a database of human proteins with multiple functions.

MultifacetedProtDB is a database of multifunctional human proteins deriving information from other databases, including UniProt, GeneCards, Human Protein Atlas (HPA), Human Phenotype Ontology (HPO) and MONDO. It collects under the label 'multifaceted' multitasking proteins addressed in literature as pleiotropic, multidomain, promiscuous (in relation to enzymes catalysing multiple substrates) and moonlighting (with two or more molecular functions), and difficult to be retrieved with a direct search in existing non-specific databases. The study of multifunctional proteins is an expanding research area aiming to elucidate the complexities of biological processes, particularly in humans, where multifunctional proteins play roles in various processes, including signal transduction, metabolism, gene regulation and cellular communication, and are often involved in disease insurgence and progression. The webserver allows searching by gene, protein and any associated structural and functional information, like available structures from PDB, structural models and interactors, using multiple filters. Protein entries are supplemented with comprehensive annotations including EC number, GO terms (biological pathways, molecular functions, and cellular components), pathways from Reactome, subcellular localization from UniProt, tissue and cell type expression from HPA, and associated diseases following MONDO, Orphanet and OMIM classification. MultiFacetedProtDB is freely available as a web server at: https://multifacetedprotdb.biocomp.unibo.it/.

Routes of dispersion of antibiotic resistance genes from the poultry farm system.

Poultry farms are hotspots for the development and spread of antibiotic resistance genes (ARGs), due to high stocking densities and extensive use of antibiotics, posing a threat of spread and contagion to workers and the external environment. Here, we applied shotgun metagenome sequencing to characterize the gut microbiome and resistome of poultry, workers and their households - also including microbiomes from the internal and external farm environment - in three different farms in Italy during a complete rearing cycle. Our results highlighted a relevant overlap among the microbiomes of poultry, workers, and their families (gut and skin), with clinically relevant ARGs and associated mobile elements shared in both poultry and human samples. On a finer scale, the reconstruction of species-level genome bins (SGBs) allowed us to delineate the dynamics of microorganism and ARGs dispersion from farm systems. We found the associations with worker microbiomes representing the main route of ARGs dispersion from poultry to human populations. Collectively, our findings clearly demonstrate the urgent need to implement more effective procedures to counteract ARGs dispersion from poultry food systems and the relevance of metagenomics-based metacommunity approaches to monitor the ARGs dispersion process for the safety of the working environment on farms.

Critical assessment of variant prioritization methods for rare disease diagnosis within the Rare Genomes Project.

Background: A major obstacle faced by rare disease families is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years, and causal variants are identified in under 50%. The Rare Genomes Project (RGP) is a direct-to-participant research study on the utility of genome sequencing (GS) for diagnosis and gene discovery. Families are consented for sharing of sequence and phenotype data with researchers, allowing development of a Critical Assessment of Genome Interpretation (CAGI) community challenge, placing variant prioritization models head-to-head in a real-life clinical diagnostic setting. Methods: Predictors were provided a dataset of phenotype terms and variant calls from GS of 175 RGP individuals (65 families), including 35 solved training set families, with causal variants specified, and 30 test set families (14 solved, 16 unsolved). The challenge tasked teams with identifying the causal variants in as many test set families as possible. Ranked variant predictions were submitted with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on rank position of true positive causal variants and maximum F-measure, based on precision and recall of causal variants across EPCR thresholds. Results: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performing teams recalled the causal variants in up to 13 of 14 solved families by prioritizing high quality variant calls that were rare, predicted deleterious, segregating correctly, and consistent with reported phenotype. In unsolved families, newly discovered diagnostic variants were returned to two families following confirmatory RNA sequencing, and two prioritized 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 phenotype overlap with asparagine synthetase deficiency. Conclusions: By objective assessment of variant predictions, we provide insights into current state-of-the-art algorithms and platforms for genome sequencing analysis for rare disease diagnosis and explore areas for future optimization. Identification of diagnostic variants in unsolved families promotes synergy between researchers with clinical and computational expertise as a means of advancing the field of clinical genome interpretation.

CoCoNat: a novel method based on deep learning for coiled-coil prediction.

MOTIVATION: Coiled-coil domains (CCD) are widespread in all organisms and perform several crucial functions. Given their relevance, the computational detection of CCD is very important for protein functional annotation. State-of-the-art prediction methods include the precise identification of CCD boundaries, the annotation of the typical heptad repeat pattern along the coiled-coil helices as well as the prediction of the oligomerization state. RESULTS: In this article, we describe CoCoNat, a novel method for predicting coiled-coil helix boundaries, residue-level register annotation, and oligomerization state. Our method encodes sequences with the combination of two state-of-the-art protein language models and implements a three-step deep learning procedure concatenated with a Grammatical-Restrained Hidden Conditional Random Field for CCD identification and refinement. A final neural network predicts the oligomerization state. When tested on a blind test set routinely adopted, CoCoNat obtains a performance superior to the current state-of-the-art both for residue-level and segment-level CCD. CoCoNat significantly outperforms the most recent state-of-the-art methods on register annotation and prediction of oligomerization states. AVAILABILITY AND IMPLEMENTATION: CoCoNat web server is available at https://coconat.biocomp.unibo.it. Standalone version is available on GitHub at https://github.com/BolognaBiocomp/coconat.

Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study.

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

Finding functional motifs in protein sequences with deep learning and natural language models.

Recently, prediction of structural/functional motifs in protein sequences takes advantage of powerful machine learning based approaches. Protein encoding adopts protein language models overpassing standard procedures. Different combinations of machine learning and encoding schemas are available for predicting different structural/functional motifs. Particularly interesting is the adoption of protein language models to encode proteins in addition to evolution information and physicochemical parameters. A thorough analysis of recent predictors developed for annotating transmembrane regions, sorting signals, lipidation and phosphorylation sites allows to investigate the state-of-the-art focusing on the relevance of protein language models for the different tasks. This highlights that more experimental data are necessary to exploit available powerful machine learning methods.

ISPRED-SEQ: Deep Neural Networks and Embeddings for Predicting Interaction Sites in Protein Sequences.

The knowledge of protein-protein interaction sites (PPIs) is crucial for protein functional annotation. Here we address the problem focusing on the prediction of putative PPIs considering as input protein sequences. The issue is important given the huge volume of protein sequences compared to experimental and/or computed structures. Taking advantage of protein language models, recently developed, and Deep Neural networks, here we describe ISPRED-SEQ, which overpasses state-of-the-art predictors addressing the same problem. ISPRED-SEQ is freely available for testing at https://ispredws.biocomp.unibo.it.

Resources and tools for rare disease variant interpretation.

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.

Wood feeding and social living: Draft genome of the subterranean termite Reticulitermes lucifugus (Blattodea; Termitoidae).

Termites (Insecta, Blattodea, Termitoidae) are a widespread and diverse group of eusocial insects known for their ability to digest wood matter. Herein, we report the draft genome of the subterranean termite Reticulitermes lucifugus, an economically important species and among the most studied taxa with respect to eusocial organization and mating system. The final assembly (~813 Mb) covered up to 88% of the estimated genome size and, in agreement with the Asexual Queen Succession Mating System, it was found completely homozygous. We predicted 16,349 highly supported gene models and 42% of repetitive DNA content. Transposable elements of R. lucifugus show similar evolutionary dynamics compared to that of other termites, with two main peaks of activity localized at 25% and 8% of Kimura divergence driven by DNA, LINE and SINE elements. Gene family turnover analyses identified multiple instances of gene duplication associated with R. lucifugus diversification, with significant lineage-specific gene family expansions related to development, perception and nutrient metabolism pathways. Finally, we analysed P450 and odourant receptor gene repertoires in detail, highlighting the large diversity and dynamical evolutionary history of these proteins in the R. lucifugus genome. This newly assembled genome will provide a valuable resource for further understanding the molecular basis of termites biology as well as for pest control.

Pathogenic variation types in human genes relate to diseases through Pfam and InterPro mapping.

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.

E-SNPs&GO: embedding of protein sequence and function improves the annotation of human pathogenic variants.

MOTIVATION: The advent of massive DNA sequencing technologies is producing a huge number of human single-nucleotide polymorphisms occurring in protein-coding regions and possibly changing their sequences. Discriminating harmful protein variations from neutral ones is one of the crucial challenges in precision medicine. Computational tools based on artificial intelligence provide models for protein sequence encoding, bypassing database searches for evolutionary information. We leverage the new encoding schemes for an efficient annotation of protein variants. RESULTS: E-SNPs&GO is a novel method that, given an input protein sequence and a single amino acid variation, can predict whether the variation is related to diseases or not. The proposed method adopts an input encoding completely based on protein language models and embedding techniques, specifically devised to encode protein sequences and GO functional annotations. We trained our model on a newly generated dataset of 101 146 human protein single amino acid variants in 13 661 proteins, derived from public resources. When tested on a blind set comprising 10 266 variants, our method well compares to recent approaches released in literature for the same task, reaching a Matthews Correlation Coefficient score of 0.72. We propose E-SNPs&GO as a suitable, efficient and accurate large-scale annotator of protein variant datasets. AVAILABILITY AND IMPLEMENTATION: The method is available as a webserver at https://esnpsandgo.biocomp.unibo.it. Datasets and predictions are available at https://esnpsandgo.biocomp.unibo.it/datasets. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.