Deep Learning Approaches for the Prediction of Protein Functional Sites.

Knowing which residues of a protein are important for its function is of paramount importance for understanding the molecular basis of this function and devising ways of modifying it for medical or biotechnological applications. Due to the difficulty in detecting these residues experimentally, prediction methods are essential to cope with the sequence deluge that is filling databases with uncharacterized protein sequences. Deep learning approaches are especially well suited for this task due to the large amounts of protein sequences for training them, the trivial codification of this sequence data to feed into these systems, and the intrinsic sequential nature of the data that makes them suitable for language models.

Differential expression and co-expression reveal cell types relevant to genetic disorder phenotypes.

Motivation: Knowledge of the specific cell types affected by genetic alterations in rare diseases is crucial for advancing diagnostics and treatments. Despite significant progress, the cell types involved in the majority of rare disease manifestations remain largely unknown. In this study, we integrated scRNA-seq data from non-diseased samples with known genetic disorder genes and phenotypic information to predict the specific cell types disrupted by pathogenic mutations for 482 disease phenotypes.

Advancing the allergenicity assessment of new proteins using a text mining resource.

With a society increasingly demanding alternative protein food sources, new strategies for evaluating protein safety issues, such as allergenic potential, are needed. Large-scale and systemic studies on allergenic proteins are hindered by the limited and non-harmonized clinical information available for these substances in dedicated databases. A missing key information is that representing the symptomatology of the allergens, especially given in terms of standard vocabularies, that would allow connecting with other biomedical resources to carry out different studies related to human health.

CoMentG: comprehensive retrieval of generic relationships between biomedical concepts from the scientific literature.

The CoMentG resource contains millions of relationships between terms of biomedical interest obtained from the scientific literature. At the core of the system is a methodology for detecting significant co-mentions of concepts in the entire PubMed corpus. That method was applied to nine sets of terms covering the most important classes of biomedical concepts: diseases, symptoms/clinical signs, molecular functions, biological processes, cellular compartments, anatomic parts, cell types, bacteria and chemical compounds.

Effects of Major Families of Modulators on Performances and Gastrointestinal Microbiota of Poultry, Pigs and Ruminants: A Systematic Approach.

Considering the ban on the use of antibiotics as growth stimulators in the livestock industry, the use of microbiota modulators appears to be an alternative solution to improve animal performance. This review aims to describe the effect of different families of modulators on the gastrointestinal microbiota of poultry, pigs and ruminants and their consequences on host physiology. To this end, 65, 32 and 4 controlled trials or systematic reviews were selected from PubMed for poultry, pigs and ruminants, respectively.

MBROLE3: improved functional enrichment of chemical compounds for metabolomics data analysis.

MBROLE (Metabolites Biological Role) facilitates the biological interpretation of metabolomics experiments. It performs enrichment analysis of a set of chemical compounds through statistical analysis of annotations from several databases. The original MBROLE server was released in 2011 and, since then, different groups worldwide have used it to analyze metabolomics experiments from a variety of organisms.

PMIDigest: Interactive Review of Large Collections of PubMed Entries to Distill Relevant Information.

Scientific knowledge is being accumulated in the biomedical literature at an unprecedented pace. The most widely used database with biomedicine-related article abstracts, PubMed, currently contains more than 36 million entries. Users performing searches in this database for a subject of interest face thousands of entries (articles) that are difficult to process manually.

Network-Based Methods for Approaching Human Pathologies from a Phenotypic Point of View.

Network and systemic approaches to studying human pathologies are helping us to gain insight into the molecular mechanisms of and potential therapeutic interventions for human diseases, especially for complex diseases where large numbers of genes are involved. The complex human pathological landscape is traditionally partitioned into discrete "diseases"; however, that partition is sometimes problematic, as diseases are highly heterogeneous and can differ greatly from one patient to another. Moreover, for many pathological states, the set of symptoms (phenotypes) manifested by the patient is not enough to diagnose a particular disease.

CoMent: Relationships Between Biomedical Concepts Inferred From the Scientific Literature.

The mining of the massive amounts of biomedical information is hindered by the still scarce representation of these data using formal vocabularies and ontologies, which is necessary for cross-linking conceptual entities between different resources and, in general, representing the information in a computer-tractable way. Basic things such as retrieving a comprehensive list of associations between complex diseases and their reported symptoms or underlying biological processes, given in terms of formal identifiers, are not trivial and, in many cases, these have to be generated by manual curation or inferred/predicted from indirect evidences. In this work, using a text-mining approach based on detecting significant co-mentions in the scientific literature, we generated a resource with millions of relationships between thousands of terms representing diseases, symptoms, biological processes, molecular functions and cellular compartments, all given in terms of formal identifiers of these terms in the main resources dealing with them.

Computational prediction of protein functional sites-Applications in biotechnology and biomedicine.

There are many computational approaches for predicting protein functional sites based on different sequence and structural features. These methods are essential to cope with the sequence deluge that is filling databases with uncharacterized protein sequences. They complement the more expensive and time-consuming experimental approaches by pointing them to possible candidate positions.

Prediction of Protein Sites and Physicochemical Properties Related to Functional Specificity.

Specificity Determining Positions (SDPs) are protein sites responsible for functional specificity within a family of homologous proteins. These positions are extracted from a family's multiple sequence alignment and complement the fully conserved positions as predictors of functional sites. SDP analysis is now routinely used for locating these specificity-related sites in families of proteins of biomedical or biotechnological interest with the aim of mutating them to switch specificities or design new ones.

Panorama of the Intracellular Molecular Concert Orchestrated by Actinoporins, Pore-Forming Toxins from Sea Anemones.

Actinoporins (APs) are soluble pore-forming proteins secreted by sea anemones that experience conformational changes originating in pores in the membranes that can lead to cell death. The processes involved in the binding and pore-formation of members of this protein family have been deeply examined in recent years; however, the intracellular responses to APs are only beginning to be understood. Unlike pore formers of bacterial origin, whose intracellular impact has been studied in more detail, currently, we only have knowledge of a few poorly integrated elements of the APs' intracellular action.

Predicting biological pathways of chemical compounds with a profile-inspired approach.

Background: Assignment of chemical compounds to biological pathways is a crucial step to understand the relationship between the chemical repertory of an organism and its biology. Protein sequence profiles are very successful in capturing the main structural and functional features of a protein family, and can be used to assign new members to it based on matching of their sequences against these profiles. In this work, we extend this idea to chemical compounds, constructing a profile-inspired model for a set of related metabolites (those in the same biological pathway), based on a fragment-based vectorial representation of their chemical structures.

Sticholysins, pore-forming proteins from a marine anemone can induce maturation of dendritic cells through a TLR4 dependent-pathway.

Sticholysins (Sts) I and II (StI and StII) are pore-forming proteins (PFPs), purified from the Caribbean Sea anemone Stichodactyla helianthus. StII encapsulated into liposomes induces a robust antigen-specific cytotoxic CD8 T lymphocytes (CTL) response and in its free form the maturation of bone marrow-derived dendritic cells (BM-DCs). It is probable that the latter is partially supporting in part the immunomodulatory effect on the CTL response induced by StII-containing liposomes.

Concomitant prediction of environmental fate and toxicity of chemical compounds.

The environmental fate of many functional molecules that are produced on a large scale as precursors or as additives to specialty goods (plastics, fibers, construction materials, etc.), let alone those synthesized by the pharmaceutical industry, is generally unknown. Assessing their environmental fate is crucial when taking decisions on the manufacturing, handling, usage, and release of these substances, as is the evaluation of their toxicity in humans and other higher organisms.

Protein residues determining interaction specificity in paralogous families.

Motivation: Predicting the residues controlling a protein's interaction specificity is important not only to better understand its interactions but also to design mutations aimed at fine-tuning or swapping them as well.

Results: In this work, we present a methodology that combines sequence information (in the form of multiple sequence alignments) with interactome information to detect that kind of residues in paralogous families of proteins. The interactome is used to define pairwise similarities of interaction contexts for the proteins in the alignment.

Phenotype-genotype comorbidity analysis of patients with rare disorders provides insight into their pathological and molecular bases.

Genetic and molecular analysis of rare disease is made difficult by the small numbers of affected patients. Phenotypic comorbidity analysis can help rectify this by combining information from individuals with similar phenotypes and looking for overlap in terms of shared genes and underlying functional systems. However, few studies have combined comorbidity analysis with genomic data.

Range of adiposity and cardiorenal syndrome.

Obesity and obesity-related co-morbidities, diabetes mellitus, and hypertension are among the fastest-growing risk factors of heart failure and kidney disease worldwide. Obesity, which is not a unitary concept, or a static process, ranges from alterations in distribution to the amount of adiposity. Visceral adiposity, which includes intraabdominal visceral fat mass and ectopic fat deposition such as hepatic, cardiac, or renal, was robustly associated with a greater risk for cardiorenal morbidity than subcutaneous adiposity.

Applications of molecular networks in biomedicine.

Due to the large interdependence between the molecular components of living systems, many phenomena, including those related to pathologies, cannot be explained in terms of a single gene or a small number of genes. Molecular networks, representing different types of relationships between molecular entities, embody these large sets of interdependences in a framework that allow their mining from a systemic point of view to obtain information. These networks, often generated from high-throughput omics datasets, are used to study the complex phenomena of human pathologies from a systemic point of view.

Determination of serum free light chains as a marker of systemic lupus flare.

Introduction: The aim of this study was to explore the usefulness of the determination of free light chains (FLC) in serum as a biomarker of flare in patients with systemic lupus erythematosus (SLE) and to analyze the differences in their discriminatory capacity with complement C3 and C4.

Methods: This was a prospective cohort study. The definition of flare was based on the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) Flare Index.

WeReview: CRISPR Tools-Live Repository of Computational Tools for Assisting CRISPR/Cas Experiments.

Computational tools are essential in the process of designing a CRISPR/Cas experiment for the targeted modification of an organism's genome. Among other functionalities, these tools facilitate the design of a guide-RNA (gRNA) for a given nuclease that maximizes its binding to the intended genomic site, while avoiding binding to undesired sites with similar sequences in the genome of the organism of interest (off-targets). Due to the popularity of this methodology and the rapid pace at which it evolves and changes, new computational tools show up constantly.