DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation.

The Database of Intrinsically Disordered Proteins (DisProt, URL: https://disprot.org) is the major repository of manually curated annotations of intrinsically disordered proteins and regions from the literature. We report here recent updates of DisProt version 9, including a restyled web interface, refactored Intrinsically Disordered Proteins Ontology (IDPO), improvements in the curation process and significant content growth of around 30%. Higher quality and consistency of annotations is provided by a newly implemented reviewing process and training of curators. The increased curation capacity is fostered by the integration of DisProt with APICURON, a dedicated resource for the proper attribution and recognition of biocuration efforts. Better interoperability is provided through the adoption of the Minimum Information About Disorder (MIADE) standard, an active collaboration with the Gene Ontology (GO) and Evidence and Conclusion Ontology (ECO) consortia and the support of the ELIXIR infrastructure.

DisProt: intrinsic protein disorder annotation in 2020.

The Database of Protein Disorder (DisProt, URL: https://disprot.org) provides manually curated annotations of intrinsically disordered proteins from the literature. Here we report recent developments with DisProt (version 8), including the doubling of protein entries, a new disorder ontology, improvements of the annotation format and a completely new website. The website includes a redesigned graphical interface, a better search engine, a clearer API for programmatic access and a new annotation interface that integrates text mining technologies. The new entry format provides a greater flexibility, simplifies maintenance and allows the capture of more information from the literature. The new disorder ontology has been formalized and made interoperable by adopting the OWL format, as well as its structure and term definitions have been improved. The new annotation interface has made the curation process faster and more effective. We recently showed that new DisProt annotations can be effectively used to train and validate disorder predictors. We believe the growth of DisProt will accelerate, contributing to the improvement of function and disorder predictors and therefore to illuminate the 'dark' proteome.

Molecular Effects of Mutations in Human Genetic Diseases.

Next-generation sequencing (NGS) has enormously improved the identification of disease-candidate genetic variants [...].

A Missense De Novo Variant in the CASK-interactor KIRREL3 Gene Leading to Neurodevelopmental Disorder with Mild Cerebellar Hypoplasia.

KIRREL3 is a gene important for the central nervous system development-in particular for the process of neuronal migration, axonal fasciculation, and synaptogenesis-and colocalizes and cooperates in neurons with CASK gene. Alterations of KIRREL3 have been linked to neurodevelopmental disorders, ranging from developmental delay, to autism spectrum disorder, to attention deficit/hyperactivity disorder. The underlying mechanism is not yet fully understood, as it has been hypothesized a fully dominant effect, a risk factor role of KIRREL3 partially penetrating variants, and a recessive inheritance pattern. We report a novel and de novo KIRREL3 mutation in a child affected by severe neurodevelopmental disorder and with brain magnetic resonance imaging evidence of mega cisterna magna and mild cerebellar hypoplasia. This case strengthens the hypothesis that dominant KIRREL3 variants may lead to neurodevelopmental disruption; furthermore, given the strong interaction between KIRREL3 and CASK, we discuss as posterior fossa anomalies may also be part of the phenotype of KIRREL3-related syndrome.

Frequency of Usher gene mutations in non-syndromic hearing loss: higher variability of the Usher phenotype.

Non-syndromic hearing loss (NSHL) is characterized by a vast genetic heterogeneity; some syndromic forms as Usher syndrome (USH) have onset as isolated deafness and then evolve later in life. We developed an NGS targeted gene-panel containing 59 genes and a customized bioinformatic pipeline for the analysis of DNA samples from clinically highly selected subjects with sensorineural hearing loss, previously resulted negative for GJB2 mutations/GJB6 deletions. Among the 217 tested subjects, 24 (11.1%) were found to carry mutations in genes involved both in NSHL and USH. For 6 out of 24 patients a diagnosis of USH was performed. Eleven subjects out of 24 had hearing loss without vestibular or ocular dysfunction and, due to their young age, it was not possible to establish whether their phenotype could be NSHL or USH. Seven subjects were diagnosed with NSHL, due to their age and phenotype. A total of 41 likely pathogenic/pathogenic mutations were identified, among which 17 novel ones. We report a high frequency of mutations in genes involved both in NSHL and in USH in a cohort of individuals tested for seemingly isolated deafness. Our data also highlight a wider than expected phenotypic variability in the USH phenotype.

Assessment of patient clinical descriptions and pathogenic variants from gene panel sequences in the CAGI-5 intellectual disability challenge.

The Critical Assessment of Genome Interpretation-5 intellectual disability challenge asked to use computational methods to predict patient clinical phenotypes and the causal variant(s) based on an analysis of their gene panel sequence data. Sequence data for 74 genes associated with intellectual disability (ID) and/or autism spectrum disorders (ASD) from a cohort of 150 patients with a range of neurodevelopmental manifestations (i.e. ID, autism, epilepsy, microcephaly, macrocephaly, hypotonia, ataxia) have been made available for this challenge. For each patient, predictors had to report the causative variants and which of the seven phenotypes were present. Since neurodevelopmental disorders are characterized by strong comorbidity, tested individuals often present more than one pathological condition. Considering the overall clinical manifestation of each patient, the correct phenotype has been predicted by at least one group for 93 individuals (62%). ID and ASD were the best predicted among the seven phenotypic traits. Also, causative or potentially pathogenic variants were predicted correctly by at least one group. However, the prediction of the correct causative variant seems to be insufficient to predict the correct phenotype. In some cases, the correct prediction has been supported by rare or common variants in genes different from the causative one.

Characterization of intellectual disability and autism comorbidity through gene panel sequencing.

Intellectual disability (ID) and autism spectrum disorder (ASD) are clinically and genetically heterogeneous diseases. Recent whole exome sequencing studies indicated that genes associated with different neurological diseases are shared across disorders and converge on common functional pathways. Using the Ion Torrent platform, we developed a low-cost next-generation sequencing gene panel that has been transferred into clinical practice, replacing single disease-gene analyses for the early diagnosis of individuals with ID/ASD. The gene panel was designed using an innovative in silico approach based on disease networks and mining data from public resources to score disease-gene associations. We analyzed 150 unrelated individuals with ID and/or ASD and a confident diagnosis has been reached in 26 cases (17%). Likely pathogenic mutations have been identified in another 15 patients, reaching a total diagnostic yield of 27%. Our data also support the pathogenic role of genes recently proposed to be involved in ASD. Although many of the identified variants need further investigation to be considered disease-causing, our results indicate the efficiency of the targeted gene panel on the identification of novel and rare variants in patients with ID and ASD.

Performance of computational methods for the evaluation of pericentriolar material 1 missense variants in CAGI-5.

The CAGI-5 pericentriolar material 1 (PCM1) challenge aimed to predict the effect of 38 transgenic human missense mutations in the PCM1 protein implicated in schizophrenia. Participants were provided with 16 benign variants (negative controls), 10 hypomorphic, and 12 loss of function variants. Six groups participated and were asked to predict the probability of effect and standard deviation associated to each mutation. Here, we present the challenge assessment. Prediction performance was evaluated using different measures to conclude in a final ranking which highlights the strengths and weaknesses of each group. The results show a great variety of predictions where some methods performed significantly better than others. Benign variants played an important role as negative controls, highlighting predictors biased to identify disease phenotypes. The best predictor, Bromberg lab, used a neural-network-based method able to discriminate between neutral and non-neutral single nucleotide polymorphisms. The CAGI-5 PCM1 challenge allowed us to evaluate the state of the art techniques for interpreting the effect of novel variants for a difficult target protein.

Assessing computational predictions of the phenotypic effect of cystathionine-beta-synthase variants.

Accurate prediction of the impact of genomic variation on phenotype is a major goal of computational biology and an important contributor to personalized medicine. Computational predictions can lead to a better understanding of the mechanisms underlying genetic diseases, including cancer, but their adoption requires thorough and unbiased assessment. Cystathionine-beta-synthase (CBS) is an enzyme that catalyzes the first step of the transsulfuration pathway, from homocysteine to cystathionine, and in which variations are associated with human hyperhomocysteinemia and homocystinuria. We have created a computational challenge under the CAGI framework to evaluate how well different methods can predict the phenotypic effect(s) of CBS single amino acid substitutions using a blinded experimental data set. CAGI participants were asked to predict yeast growth based on the identity of the mutations. The performance of the methods was evaluated using several metrics. The CBS challenge highlighted the difficulty of predicting the phenotype of an ex vivo system in a model organism when classification models were trained on human disease data. We also discuss the variations in difficulty of prediction for known benign and deleterious variants, as well as identify methodological and experimental constraints with lessons to be learned for future challenges.

DisProt 7.0: a major update of the database of disordered proteins.

The Database of Protein Disorder (DisProt, URL: www.disprot.org) has been significantly updated and upgraded since its last major renewal in 2007. The current release holds information on more than 800 entries of IDPs/IDRs, i.e. intrinsically disordered proteins or regions that exist and function without a well-defined three-dimensional structure. We have re-curated previous entries to purge DisProt from conflicting cases, and also upgraded the functional classification scheme to reflect continuous advance in the field in the past 10 years or so. We define IDPs as proteins that are disordered along their entire sequence, i.e. entirely lack structural elements, and IDRs as regions that are at least five consecutive residues without well-defined structure. We base our assessment of disorder strictly on experimental evidence, such as X-ray crystallography and nuclear magnetic resonance (primary techniques) and a broad range of other experimental approaches (secondary techniques). Confident and ambiguous annotations are highlighted separately. DisProt 7.0 presents classified knowledge regarding the experimental characterization and functional annotations of IDPs/IDRs, and is intended to provide an invaluable resource for the research community for a better understanding structural disorder and for developing better computational tools for studying disordered proteins.

Secretion-Positive LGI1 Mutations Linked to Lateral Temporal Epilepsy Impair Binding to ADAM22 and ADAM23 Receptors.

Autosomal dominant lateral temporal epilepsy (ADTLE) is a focal epilepsy syndrome caused by mutations in the LGI1 gene, which encodes a secreted protein. Most ADLTE-causing mutations inhibit LGI1 protein secretion, and only a few secretion-positive missense mutations have been reported. Here we describe the effects of four disease-causing nonsynonymous LGI1 mutations, T380A, R407C, S473L, and R474Q, on protein secretion and extracellular interactions. Expression of LGI1 mutant proteins in cultured cells shows that these mutations do not inhibit protein secretion. This finding likely results from the lack of effects of these mutations on LGI1 protein folding, as suggested by 3D protein modelling. In addition, immunofluorescence and co-immunoprecipitation experiments reveal that all four mutations significantly impair interaction of LGI1 with the ADAM22 and ADAM23 receptors on the cell surface. These results support the existence of a second mechanism, alternative to inhibition of protein secretion, by which ADLTE-causing LGI1 mutations exert their loss-of-function effect extracellularly, and suggest that interactions of LGI1 with both ADAM22 and ADAM23 play an important role in the molecular mechanisms leading to ADLTE.

Crohn disease risk prediction-Best practices and pitfalls with exome data.

The Critical Assessment of Genome Interpretation (CAGI) experiment is the first attempt to evaluate the state-of-the-art in genetic data interpretation. Among the proposed challenges, Crohn disease (CD) risk prediction has become the most classic problem spanning three editions. The scientific question is very hard: can anybody assess the risk to develop CD given the exome data alone? This is one of the ultimate goals of genetic analysis, which motivated most CAGI participants to look for powerful new methods. In the 2016 CD challenge, we implemented all the best methods proposed in the past editions. This resulted in 10 algorithms, which were evaluated fairly by CAGI organizers. We also used all the data available from CAGI 11 and 13 to maximize the amount of training samples. The most effective algorithms used known genes associated with CD from the literature. No method could evaluate effectively the importance of unannotated variants by using heuristics. As a downside, all CD datasets were strongly affected by sample stratification. This affected the performance reported by assessors. Therefore, we expect that future datasets will be normalized in order to remove population effects. This will improve methods comparison and promote algorithms focused on causal variants discovery.

Lessons from the CAGI-4 Hopkins clinical panel challenge.

The CAGI-4 Hopkins clinical panel challenge was an attempt to assess state-of-the-art methods for clinical phenotype prediction from DNA sequence. Participants were provided with exonic sequences of 83 genes for 106 patients from the Johns Hopkins DNA Diagnostic Laboratory. Five groups participated in the challenge, predicting both the probability that each patient had each of the 14 possible classes of disease, as well as one or more causal variants. In cases where the Hopkins laboratory reported a variant, at least one predictor correctly identified the disease class in 36 of the 43 patients (84%). Even in cases where the Hopkins laboratory did not find a variant, at least one predictor correctly identified the class in 39 of the 63 patients (62%). Each prediction group correctly diagnosed at least one patient that was not successfully diagnosed by any other group. We discuss the causal variant predictions by different groups and their implications for further development of methods to assess variants of unknown significance. Our results suggest that clinically relevant variants may be missed when physicians order small panels targeted on a specific phenotype. We also quantify the false-positive rate of DNA-guided analysis in the absence of prior phenotypic indication.

Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI.

Correct phenotypic interpretation of variants of unknown significance for cancer-associated genes is a diagnostic challenge as genetic screenings gain in popularity in the next-generation sequencing era. The Critical Assessment of Genome Interpretation (CAGI) experiment aims to test and define the state of the art of genotype-phenotype interpretation. Here, we present the assessment of the CAGI p16INK4a challenge. Participants were asked to predict the effect on cellular proliferation of 10 variants for the p16INK4a tumor suppressor, a cyclin-dependent kinase inhibitor encoded by the CDKN2A gene. Twenty-two pathogenicity predictors were assessed with a variety of accuracy measures for reliability in a medical context. Different assessment measures were combined in an overall ranking to provide more robust results. The R scripts used for assessment are publicly available from a GitHub repository for future use in similar assessment exercises. Despite a limited test-set size, our findings show a variety of results, with some methods performing significantly better. Methods combining different strategies frequently outperform simpler approaches. The best predictor, Yang&Zhou lab, uses a machine learning method combining an empirical energy function measuring protein stability with an evolutionary conservation term. The p16INK4a challenge highlights how subtle structural effects can neutralize otherwise deleterious variants.

Matching phenotypes to whole genomes: Lessons learned from four iterations of the personal genome project community challenges.

The advent of next-generation sequencing has dramatically decreased the cost for whole-genome sequencing and increased the viability for its application in research and clinical care. The Personal Genome Project (PGP) provides unrestricted access to genomes of individuals and their associated phenotypes. This resource enabled the Critical Assessment of Genome Interpretation (CAGI) to create a community challenge to assess the bioinformatics community's ability to predict traits from whole genomes. In the CAGI PGP challenge, researchers were asked to predict whether an individual had a particular trait or profile based on their whole genome. Several approaches were used to assess submissions, including ROC AUC (area under receiver operating characteristic curve), probability rankings, the number of correct predictions, and statistical significance simulations. Overall, we found that prediction of individual traits is difficult, relying on a strong knowledge of trait frequency within the general population, whereas matching genomes to trait profiles relies heavily upon a small number of common traits including ancestry, blood type, and eye color. When a rare genetic disorder is present, profiles can be matched when one or more pathogenic variants are identified. Prediction accuracy has improved substantially over the last 6 years due to improved methodology and a better understanding of features.

Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges.

Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype-phenotype relationships.

INGA: protein function prediction combining interaction networks, domain assignments and sequence similarity.

Identifying protein functions can be useful for numerous applications in biology. The prediction of gene ontology (GO) functional terms from sequence remains however a challenging task, as shown by the recent CAFA experiments. Here we present INGA, a web server developed to predict protein function from a combination of three orthogonal approaches. Sequence similarity and domain architecture searches are combined with protein-protein interaction network data to derive consensus predictions for GO terms using functional enrichment. The INGA server can be queried both programmatically through RESTful services and through a web interface designed for usability. The latter provides output supporting the GO term predictions with the annotating sequences. INGA is validated on the CAFA-1 data set and was recently shown to perform consistently well in the CAFA-2 blind test. The INGA web server is available from URL: http://protein.bio.unipd.it/inga.

Effect of treatment with myo-inositol on semen parameters of patients undergoing an IVF cycle: in vivo study.

INTRODUCTION: Myo-inositol (MI) is a precursor for the synthesis of phosphatidylinositol polyphosphates (PIPs). The aim of the study is to evaluate the effect of its administration on semen parameters of male patients undergoing an in vitro fertilization cycles. METHODS: In vivo study. Samples were semen of 62 patients divided into three different groups: healthy fertile patients (Group A); patients with oligoasthenospermia (OA) (Group B); control group (CTR). The collected samples were analyzed by optic microscopy in order to evaluate semen's volume, spermatozoa's number and motility before and after density-gradient separation method. These parameters were evaluated before and after administration of 4000 mg/die of MI and 400 µg of folic acid for 2 months. The results were analyzed statistically with Student's t-test. RESULTS: After treatment there was a significant increase of basal and after density-gradient separation method spermatozoa concentration in Group B, and a significant increase of spermatozoa count after density-gradient separation method in Group A. The motility values were higher in healthy men than patients with OA before treatment, but there was no improvement in both groups after treatment. CONCLUSIONS: Exogenous administration of MI significantly improves semen's parameters both in patients with OA and in normal fertile men.

Fly cryptochrome and the visual system.

Cryptochromes are flavoproteins, structurally and evolutionarily related to photolyases, that are involved in the development, magnetoreception, and temporal organization of a variety of organisms. Drosophila CRYPTOCHROME (dCRY) is involved in light synchronization of the master circadian clock, and its C terminus plays an important role in modulating light sensitivity and activity of the protein. The activation of dCRY by light requires a conformational change, but it has been suggested that activation could be mediated also by specific "regulators" that bind the C terminus of the protein. This C-terminal region harbors several protein-protein interaction motifs, likely relevant for signal transduction regulation. Here, we show that some functional linear motifs are evolutionarily conserved in the C terminus of cryptochromes and that class III PDZ-binding sites are selectively maintained in animals. A coimmunoprecipitation assay followed by mass spectrometry analysis revealed that dCRY interacts with Retinal Degeneration A (RDGA) and with Neither Inactivation Nor Afterpotential C (NINAC) proteins. Both proteins belong to a multiprotein complex (the Signalplex) that includes visual-signaling molecules. Using bioinformatic and molecular approaches, dCRY was found to interact with Neither Inactivation Nor Afterpotential C through Inactivation No Afterpotential D (INAD) in a light-dependent manner and that the CRY-Inactivation No Afterpotential D interaction is mediated by specific domains of the two proteins and involves the CRY C terminus. Moreover, an impairment of the visual behavior was observed in fly mutants for dCRY, indicative of a role, direct or indirect, for this photoreceptor in fly vision.