Landscape of intrinsically disordered proteins in mental disorder diseases.

Disrupted genes linked to mental disorders sometimes exhibit characteristics of Intrinsically Disordered Proteins (IDPs). However, few studies have comprehensively explored the functional associations between protein disorder properties and different psychiatric disorders. In this study, we collected disrupted proteins for seven mental diseases (MDD, SCZ, BP, ID, AD, ADHD, ASD) and a control dataset from normal brains.

A comprehensive overview of recent advances in generative models for antibodies.

Therapeutic antibodies are an important class of biopharmaceuticals. With the rapid development of deep learning methods and the increasing amount of antibody data, antibody generative models have made great progress recently. They aim to solve the antibody space searching problems and are widely incorporated into the antibody development process.

MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods.

Major histocompatibility complex Class II (MHCII) proteins initiate and regulate immune responses by presentation of antigenic peptides to CD4+ T-cells and self-restriction. The interactions between MHCII and peptides determine the specificity of the immune response and are crucial in immunotherapy and cancer vaccine design. With the ever-increasing amount of MHCII-peptide binding data available, many computational approaches have been developed for MHCII-peptide interaction prediction over the last decade.

Macrophage AHR-TLR4 cross-talk drives p-STAT3 (Ser727)-mediated mitochondrial oxidative stress and upregulates IDO/ICAM-1 in the steatohepatitis induced by aflatoxin B.

Aflatoxin B (AFB) is a major mycotoxin contaminant showing in the environment and foods. In this study, the molecular initiating events (MIEs) of AFB-induced steatohepatitis were explored in mice and human cell model. We observed dose-dependent steatohepatitis in the AFB-treated mice, including triglyceride accumulation, fibrotic collagen secretion, enrichment of CD11b + and F4/80+ macrophages/Kupffer cells, cell death, lymphocytes clusters and remarkable atrophy areas.

Role of serum cytokines in the prediction of heart failure in patients with coronary artery disease.

Aims: Coronary artery disease (CAD) is the most common cause of heart failure (HF). This study aimed to identify cytokine biomarkers for predicting HF in patients with CAD.

Methods And Results: Twelve patients with CAD without HF (CAD-non HF), 12 patients with CAD complicated with HF (CAD-HF), and 12 healthy controls were enrolled for Human Cytokine Antibody Array, which were used as the training dataset.

Enzyme Stability-Activity Trade-Off: New Insights from Protein Stability Weaknesses and Evolutionary Conservation.

A general limitation of the use of enzymes in biotechnological processes under sometimes nonphysiological conditions is the complex interplay between two key quantities, enzyme activity and stability, where the increase of one is often associated with the decrease of the other. A precise stability-activity trade-off is necessary for the enzymes to be fully functional, but its weight in different protein regions and its dependence on environmental conditions is not yet elucidated. To advance this issue, we used the formalism that we have recently developed to effectively identify stability strength and weakness regions in protein structures and applied it to a large set of globular enzymes with known experimental structure and catalytic sites.

MPI-VGAE: protein-metabolite enzymatic reaction link learning by variational graph autoencoders.

Enzymatic reactions are crucial to explore the mechanistic function of metabolites and proteins in cellular processes and to understand the etiology of diseases. The increasing number of interconnected metabolic reactions allows the development of in silico deep learning-based methods to discover new enzymatic reaction links between metabolites and proteins to further expand the landscape of existing metabolite-protein interactome. Computational approaches to predict the enzymatic reaction link by metabolite-protein interaction (MPI) prediction are still very limited.

Genome-scale enzymatic reaction prediction by variational graph autoencoders.

Background: Enzymatic reaction networks are crucial to explore the mechanistic function of metabolites and proteins in biological systems and understanding the etiology of diseases and potential target for drug discovery. The increasing number of metabolic reactions allows the development of deep learning-based methods to discover new enzymatic reactions, which will expand the landscape of existing enzymatic reaction networks to investigate the disrupted metabolisms in diseases.

Results: In this study, we propose the MPI-VGAE framework to predict metabolite-protein interactions (MPI) in a genome-scale heterogeneous enzymatic reaction network across ten organisms with thousands of enzymatic reactions.

Ten quick tips for sequence-based prediction of protein properties using machine learning.

The ubiquitous availability of genome sequencing data explains the popularity of machine learning-based methods for the prediction of protein properties from their amino acid sequences. Over the years, while revising our own work, reading submitted manuscripts as well as published papers, we have noticed several recurring issues, which make some reported findings hard to understand and replicate. We suspect this may be due to biologists being unfamiliar with machine learning methodology, or conversely, machine learning experts may miss some of the knowledge needed to correctly apply their methods to proteins.

Using metagenomic data to boost protein structure prediction and discovery.

Over the past decade, metagenomic sequencing approaches have been providing an ever-increasing amount of protein sequence data at an astonishing rate. These constitute an invaluable source of information which has been exploited in various research fields such as the study of the role of the gut microbiota in human diseases and aging. However, only a small fraction of all metagenomic sequences collected have been functionally or structurally characterized, leaving much of them completely unexplored.

Parents' Decisions to Vaccinate Children against COVID-19: A Scoping Review.

Since 2019, the COVID-19 pandemic has resulted in sickness, hospitalizations, and deaths of the old and young and impacted global social and economy activities. Vaccination is one of the most important and efficient ways to protect against the COVID-19 virus. In a review of the literature on parents' decisions to vaccinate their children, we found that widespread vaccination was hampered by vaccine hesitancy, especially for children who play an important role in the coronavirus transmission in both family and school.

SeRenDIP-CE: sequence-based interface prediction for conformational epitopes.

Motivation: Antibodies play an important role in clinical research and biotechnology, with their specificity determined by the interaction with the antigen's epitope region, as a special type of protein-protein interaction (PPI) interface. The ubiquitous availability of sequence data, allows us to predict epitopes from sequence in order to focus time-consuming wet-lab experiments toward the most promising epitope regions. Here, we extend our previously developed sequence-based predictors for homodimer and heterodimer PPI interfaces to predict epitope residues that have the potential to bind an antibody.

SWOTein: a structure-based approach to predict stability Strengths and Weaknesses of prOTEINs.

Motivation: Although structured proteins adopt their lowest free energy conformation in physiological conditions, the individual residues are generally not in their lowest free energy conformation. Residues that are stability weaknesses are often involved in functional regions, whereas stability strengths ensure local structural stability. The detection of strengths and weaknesses provides key information to guide protein engineering experiments aiming to modulate folding and various functional processes.

SOLart: a structure-based method to predict protein solubility and aggregation.

Motivation: The solubility of a protein is often decisive for its proper functioning. Lack of solubility is a major bottleneck in high-throughput structural genomic studies and in high-concentration protein production, and the formation of protein aggregates causes a wide variety of diseases. Since solubility measurements are time-consuming and expensive, there is a strong need for solubility prediction tools.

A comprehensive computational study of amino acid interactions in membrane proteins.

Transmembrane proteins play a fundamental role in a wide series of biological processes but, despite their importance, they are less studied than globular proteins, essentially because their embedding in lipid membranes hampers their experimental characterization. In this paper, we improved our understanding of their structural stability through the development of new knowledge-based energy functions describing amino acid pair interactions that prevail in the transmembrane and extramembrane regions of membrane proteins. The comparison of these potentials and those derived from globular proteins yields an objective view of the relative strength of amino acid interactions in the different protein environments, and their role in protein stabilization.

SeRenDIP: SEquential REmasteriNg to DerIve profiles for fast and accurate predictions of PPI interface positions.

Motivation: Interpretation of ubiquitous protein sequence data has become a bottleneck in biomolecular research, due to a lack of structural and other experimental annotation data for these proteins. Prediction of protein interaction sites from sequence may be a viable substitute. We therefore recently developed a sequence-based random forest method for protein-protein interface prediction, which yielded a significantly increased performance than other methods on both homomeric and heteromeric protein-protein interactions.

Computational analysis of the amino acid interactions that promote or decrease protein solubility.

The solubility of globular proteins is a basic biophysical property that is usually a prerequisite for their functioning. In this study, we probed the solubility of globular proteins with the help of the statistical potential formalism, in view of objectifying the connection of solubility with structural and energetic properties and of the solubility-dependence of specific amino acid interactions. We started by setting up two independent datasets containing either soluble or aggregation-prone proteins with known structures.

Seeing the trees through the forest: sequence-based homo- and heteromeric protein-protein interaction sites prediction using random forest.

Motivation: Genome sequencing is producing an ever-increasing amount of associated protein sequences. Few of these sequences have experimentally validated annotations, however, and computational predictions are becoming increasingly successful in producing such annotations. One key challenge remains the prediction of the amino acids in a given protein sequence that are involved in protein-protein interactions.

CLUB-MARTINI: Selecting Favourable Interactions amongst Available Candidates, a Coarse-Grained Simulation Approach to Scoring Docking Decoys.

Large-scale identification of native binding orientations is crucial for understanding the role of protein-protein interactions in their biological context. Measuring binding free energy is the method of choice to estimate binding strength and reveal the relevance of particular conformations in which proteins interact. In a recent study, we successfully applied coarse-grained molecular dynamics simulations to measure binding free energy for two protein complexes with similar accuracy to full-atomistic simulation, but 500-fold less time consuming.

Sequence specificity between interacting and non-interacting homologs identifies interface residues--a homodimer and monomer use case.

Background: Protein families participating in protein-protein interactions may contain sub-families that have different binding characteristics, ranging from right binding to showing no interaction at all. Composition differences at the sequence level in these sub-families are often decisive to their differential functional interaction. Methods to predict interface sites from protein sequences typically exploit conservation as a signal.

[Association of T393C single nucleotide polymorphism of GNAS1 gene with non-valvular atrial fibrillation].

Objective: To analyze the association between T393C single nucleotide polymorphism (SNP) of GNAS1 gene and non-valvular atrial fibrillation (AF) in Chinese Han patients.

Methods: Ninety patients with non-valvular AF and 90 healthy subjects were examined for T393C SNP of GNAS1 gene using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). The allele genotypes and the distribution of allele frequencies were analyzed and compared between the two groups.

[Structural and functional changes of the carotid artery and their relationship with subclinical inflammation in patients with H-type hypertension].

Objective: To investigate the changes in the structure and function of the carotid artery and their relationship with subclinical inflammation in patients with H-type hypertension.

Methods: Sixty patients with H-type hypertension and 49 with non-H-type hypertension were enrolled in this study, with 20 healthy volunteers as the control group. All the subjects underwent color Doppler ultrasound examination of the carotid artery, and their blood levels of hyper-sensitive C-reactive protein (hs-CRP), fibrinogen (FIB), and tumor necrosis factor-α (TNF-α) were measured to investigate the correlation between the structural and functional changes of the carotid artery and the inflammatory factors.