InterPro: the protein sequence classification resource in 2025.

InterPro (https://www.ebi.ac.

The Pfam protein families database: embracing AI/ML.

The Pfam protein families database is a comprehensive collection of protein domains and families used for genome annotation and protein structure and function analysis (https://www.ebi.ac.

Investigation of protein family relationships with deep learning.

Motivation: In this article, we propose a method for finding similarities between Pfam families based on the pre-trained neural network ProtENN2. We use the model ProtENN2 per-residue embeddings to produce new high-dimensional per-family embeddings and develop an approach for calculating inter-family similarity scores based on these embeddings, and evaluate its predictions using structure comparison.

Results: We apply our method to Pfam annotation by refining clan membership for Pfam families, suggesting both new members of existing clans and potential new clans for future Pfam releases.

Machine learning designs new GCGR/GLP-1R dual agonists with enhanced biological potency.

Several peptide dual agonists of the human glucagon receptor (GCGR) and the glucagon-like peptide-1 receptor (GLP-1R) are in development for the treatment of type 2 diabetes, obesity and their associated complications. Candidates must have high potency at both receptors, but it is unclear whether the limited experimental data available can be used to train models that accurately predict the activity at both receptors of new peptide variants. Here we use peptide sequence data labelled with in vitro potency at human GCGR and GLP-1R to train several models, including a deep multi-task neural-network model using multiple loss optimization.

Predicting multiple conformations via sequence clustering and AlphaFold2.

AlphaFold2 (ref. ) has revolutionized structural biology by accurately predicting single structures of proteins. However, a protein's biological function often depends on multiple conformational substates, and disease-causing point mutations often cause population changes within these substates.

Mental health deserves better: Resisting the dilution of specialist pre-registration mental health nurse education in the United Kingdom.

This article aims to draw attention to increasing genericism in nurse education in the United Kingdom, which sees less specialist mental health education for mental health nursing students and offers opposition to such direction. In 2018, the Nursing and Midwifery Council produced the 'Future Nurse' standards which directed changes to pre-registration nurse education. This led to dissatisfaction from many mental health nurses, specifically regarding reduced mental health content for students studying mental health nursing.

Paracentral acute middle maculopathy immediately following internal carotid endovascular repair.

Paracentral acute middle maculopathy (PAMM) can result from decreased perfusion to retinal and choroidal circulations meeting in the inner nuclear layer (INL) and outer plexiform layer (OPL). Although ischemia in these layers is rare, certain procedures place patients at risk for possible events, such as cardiopulmonary bypass, coronary angiography, and endovascular coil embolization. We report the case of a 70-year-old woman with a sudden-onset left paracentral scotoma following internal carotid endovascular repair.

ProteInfer, deep neural networks for protein functional inference.

Predicting the function of a protein from its amino acid sequence is a long-standing challenge in bioinformatics. Traditional approaches use sequence alignment to compare a query sequence either to thousands of models of protein families or to large databases of individual protein sequences. Here we introduce ProteInfer, which instead employs deep convolutional neural networks to directly predict a variety of protein functions - Enzyme Commission (EC) numbers and Gene Ontology (GO) terms - directly from an unaligned amino acid sequence.

Nucleic Acid Structure Prediction Including Pseudoknots Through Direct Enumeration of States: A User's Guide to the LandscapeFold Algorithm.

Here we detail the LandscapeFold secondary structure prediction algorithm and how it is used. The algorithm was previously described and tested in (Kimchi O et al., Biophys J 117(3):520-532, 2019), though it was not named there.

MGnify: the microbiome sequence data analysis resource in 2023.

The MGnify platform (https://www.ebi.ac.

InterPro in 2022.

The InterPro database (https://www.ebi.ac.

A roadmap for the functional annotation of protein families: a community perspective.

Over the last 25 years, biology has entered the genomic era and is becoming a science of 'big data'. Most interpretations of genomic analyses rely on accurate functional annotations of the proteins encoded by more than 500 000 genomes sequenced to date. By different estimates, only half the predicted sequenced proteins carry an accurate functional annotation, and this percentage varies drastically between different organismal lineages.

Using sequence data to predict the self-assembly of supramolecular collagen structures.

Collagen fibrils are the major constituents of the extracellular matrix, which provides structural support to vertebrate connective tissues. It is widely assumed that the superstructure of collagen fibrils is encoded in the primary sequences of the molecular building blocks. However, the interplay between large-scale architecture and small-scale molecular interactions makes the ab initio prediction of collagen structure challenging.

Using deep learning to annotate the protein universe.

Understanding the relationship between amino acid sequence and protein function is a long-standing challenge with far-reaching scientific and translational implications. State-of-the-art alignment-based techniques cannot predict function for one-third of microbial protein sequences, hampering our ability to exploit data from diverse organisms. Here, we train deep learning models to accurately predict functional annotations for unaligned amino acid sequences across rigorous benchmark assessments built from the 17,929 families of the protein families database Pfam.

Minding the gaps: The importance of navigating holes in protein fitness landscapes.

Machine-learning-guided protein design is rapidly emerging as a strategy to find high-fitness multi-mutant variants. In this issue of Cell Systems, Wittman et al. analyze the impact of design decisions for machine-learning-assisted directed evolution (MLDE) on its ability to navigate a fitness landscape and reliably find global optima.

Association between ambient temperature, particulate air pollution and emergency room visits for conjunctivitis.

Background: Numerous studies have confirmed the association of ambient temperature and air pollution with a higher risk of morbidities, yet few have addressed their effect on the ocular system. The purpose of this study was to assess the association between temperature, air pollution, and emergency room visits for conjunctivitis.

Methods: In this case-crossover study, the records of all emergency room visits to Soroka University Medical Center (SUMC) from 2009 to 2014 were reviewed for patients with conjunctivitis.

Deep diversification of an AAV capsid protein by machine learning.

Modern experimental technologies can assay large numbers of biological sequences, but engineered protein libraries rarely exceed the sequence diversity of natural protein families. Machine learning (ML) models trained directly on experimental data without biophysical modeling provide one route to accessing the full potential diversity of engineered proteins. Here we apply deep learning to design highly diverse adeno-associated virus 2 (AAV2) capsid protein variants that remain viable for packaging of a DNA payload.

Rapid discovery and evolution of orthogonal aminoacyl-tRNA synthetase-tRNA pairs.

A central challenge in expanding the genetic code of cells to incorporate noncanonical amino acids into proteins is the scalable discovery of aminoacyl-tRNA synthetase (aaRS)-tRNA pairs that are orthogonal in their aminoacylation specificity. Here we computationally identify candidate orthogonal tRNAs from millions of sequences and develop a rapid, scalable approach-named tRNA Extension (tREX)-to determine the in vivo aminoacylation status of tRNAs. Using tREX, we test 243 candidate tRNAs in Escherichia coli and identify 71 orthogonal tRNAs, covering 16 isoacceptor classes, and 23 functional orthogonal tRNA-cognate aaRS pairs.

Glycation changes molecular organization and charge distribution in type I collagen fibrils.

Collagen fibrils are central to the molecular organization of the extracellular matrix (ECM) and to defining the cellular microenvironment. Glycation of collagen fibrils is known to impact on cell adhesion and migration in the context of cancer and in model studies, glycation of collagen molecules has been shown to affect the binding of other ECM components to collagen. Here we use TEM to show that ribose-5-phosphate (R5P) glycation of collagen fibrils - potentially important in the microenvironment of actively dividing cells, such as cancer cells - disrupts the longitudinal ordering of the molecules in collagen fibrils and, using KFM and FLiM, that R5P-glycated collagen fibrils have a more negative surface charge than unglycated fibrils.

Collagen-Inspired Self-Assembly of Twisted Filaments.

Collagen consists of three peptides twisted together through a periodic array of hydrogen bonds. Here we use this as inspiration to find design rules for programmed specific interactions for self-assembling synthetic collagenlike triple helices, starting from disordered configurations. The assembly generically nucleates defects in the triple helix, the characteristics of which can be manipulated by spatially varying the enthalpy of helix formation.