UniProt: a worldwide hub of protein knowledge

Exploring the Ocular Surface Microbiome and Tear Proteome in Glaucoma

Although glaucoma is a leading cause of irreversible blindness worldwide, its pathogenesis is incompletely understood, and intraocular pressure (IOP) is the only modifiable risk factor to target the disease. Several associations between the gut microbiome and glaucoma, including the IOP, have been suggested. There is growing evidence that interactions between microbes on the ocular surface, termed the ocular surface microbiome (OSM), and tear proteins, collectively called the tear proteome, may also play a role in ocular diseases such as glaucoma. This study aimed to find characteristic features of the OSM and tear proteins in patients with glaucoma. The whole-metagenome shotgun sequencing of 32 conjunctival swabs identified Actinobacteria, Firmicutes, and Proteobacteria as the dominant phyla in the cohort. The species Corynebacterium mastitidis was only found in healthy controls, and their conjunctival microbiomes may be enriched in genes of the phospholipase pathway compared to glaucoma patients. Despite these minor differences in the OSM, patients showed an enrichment of many tear proteins associated with the immune system compared to controls. In contrast to the OSM, this emphasizes the role of the proteome, with a potential involvement of immunological processes in glaucoma. These findings may contribute to the design of new therapeutic approaches targeting glaucoma and other associated diseases.

Vitamin D Relieves Epilepsy Symptoms and Neuroinflammation in Juvenile Mice by Activating the mTOR Signaling Pathway via RAF1: Insights from Network Pharmacology and Molecular Docking Studies

Epilepsy is a common neurological disorder, and the exploration of potential therapeutic drugs for its treatment is still ongoing. Vitamin D has emerged as a promising treatment due to its potential neuroprotective effects and anti-epileptic properties. This study aimed to investigate the effects of vitamin D on epilepsy and neuroinflammation in juvenile mice using network pharmacology and molecular docking, with a focus on the mammalian target of rapamycin (mTOR) signaling pathway. Experimental mouse models of epilepsy were established through intraperitoneal injection of pilocarpine, and in vitro injury models of hippocampal neurons were induced by glutamate (Glu) stimulation. The anti-epileptic effects of vitamin D were evaluated both in vivo and in vitro. Network pharmacology and molecular docking analysis were used to identify potential targets and regulatory pathways of vitamin D in epilepsy. The involvement of the mTOR signaling pathway in the regulation of mouse epilepsy by vitamin D was validated using rapamycin (RAPA). The levels of inflammatory cytokines (TNF-α, IL-1β, and IL-6) were assessed by enzyme-linked immunosorbent assay (ELISA). Gene and protein expressions were detected by quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot, respectively. The terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate nick end-labeling (TUNEL) staining was used to analyze the apoptosis of hippocampal neurons. In in vivo experiments, vitamin D reduced the Racine scores of epileptic mice, prolonged the latency of epilepsy, and inhibited the production of TNF-α, IL-1β, and IL-6 in the hippocampus. Furthermore, network pharmacology analysis identified RAF1 as a potential target of vitamin D in epilepsy, which was further confirmed by molecular docking analysis. Additionally, the mTOR signaling pathway was found to be involved in the regulation of mouse epilepsy by vitamin D. In in vitro experiments, Glu stimulation upregulated the expressions of RAF1 and LC3II/LC3I, inhibited mTOR phosphorylation, and induced neuronal apoptosis. Mechanistically, vitamin D activated the mTOR signaling pathway and alleviated mouse epilepsy via RAF1, while the use of the pathway inhibitor RAPA reversed this effect. Vitamin D alleviated epilepsy symptoms and neuroinflammation in juvenile mice by activating the mTOR signaling pathway via RAF1. These findings provided new insights into the molecular mechanisms underlying the anti-epileptic effects of vitamin D and further supported its use as an adjunctive therapy for existing anti-epileptic drugs.

A novel framework based on explainable AI and genetic algorithms for designing neurological medicines

The advent of the fourth industrial revolution, characterized by artificial intelligence (AI) as its central component, has resulted in the mechanization of numerous previously labor-intensive activities. The use of in silico tools has become prevalent in the design of biopharmaceuticals. Upon conducting a comprehensive analysis of the genomes of many organisms, it has been discovered that their tissues can generate specific peptides that confer protection against certain diseases. This study aims to identify a selected group of neuropeptides (NPs) possessing favorable characteristics that render them ideal for production as neurological biopharmaceuticals. Until now, the construction of NP classifiers has been the primary focus, neglecting to optimize these characteristics. Therefore, in this study, the task of creating ideal NPs has been formulated as a multi-objective optimization problem. The proposed framework, NPpred, comprises two distinct components: NSGA-NeuroPred and BERT-NeuroPred. The former employs the NSGA-II algorithm to explore and change a population of NPs, while the latter is an interpretable deep learning-based model. The utilization of explainable AI and motifs has led to the proposal of two novel operators, namely p-crossover and p-mutation. An online application has been deployed at https://neuropred.anvil.app for designing an ideal collection of synthesizable NPs from protein sequences.

A journey to your self: The vague definition of immune self and its practical implications

The identification of immunogenic peptides has become essential in an increasing number of fields in immunology, ranging from tumor immunotherapy to vaccine development. The nature of the adaptive immune response is shaped by the similarity between foreign and self-protein sequences, a concept extensively applied in numerous studies. Can we precisely define the degree of similarity to self? Furthermore, do we accurately define immune self? In the current work, we aim to unravel the conceptual and mechanistic vagueness hindering the assessment of self-similarity. Accordingly, we demonstrate the remarkably low consistency among commonly employed measures and highlight potential avenues for future research.

SAFPred: synteny-aware gene function prediction for bacteria using protein embeddings

MOTIVATION

Today, we know the function of only a small fraction of the protein sequences predicted from genomic data. This problem is even more salient for bacteria, which represent some of the most phylogenetically and metabolically diverse taxa on Earth. This low rate of bacterial gene annotation is compounded by the fact that most function prediction algorithms have focused on eukaryotes, and conventional annotation approaches rely on the presence of similar sequences in existing databases. However, often there are no such sequences for novel bacterial proteins. Thus, we need improved gene function prediction methods tailored for bacteria. Recently, transformer-based language models-adopted from the natural language processing field-have been used to obtain new representations of proteins, to replace amino acid sequences. These representations, referred to as protein embeddings, have shown promise for improving annotation of eukaryotes, but there have been only limited applications on bacterial genomes.

RESULTS

To predict gene functions in bacteria, we developed SAFPred, a novel synteny-aware gene function prediction tool based on protein embeddings from state-of-the-art protein language models. SAFpred also leverages the unique operon structure of bacteria through conserved synteny. SAFPred outperformed both conventional sequence-based annotation methods and state-of-the-art methods on multiple bacterial species, including for distant homolog detection, where the sequence similarity to the proteins in the training set was as low as 40%. Using SAFPred to identify gene functions across diverse enterococci, of which some species are major clinical threats, we identified 11 previously unrecognized putative novel toxins, with potential significance to human and animal health.

AVAILABILITY AND IMPLEMENTATION

https://github.com/AbeelLab/safpred.

Nitrate-dependent anaerobic methane oxidation coupled to Fe(III) reduction as a source of ammonium and nitrous oxide

'Candidatus Methanoperedens nitroreducens' is an archaeal methanotroph with global importance that links carbon and nitrogen cycles and great potential for sustainable operation of wastewater treatment. It has been reported to mediate the anaerobic oxidation of methane through a reverse methanogenesis pathway while reducing nitrate to nitrite. Here, we demonstrate that 'Ca. M. nitroreducens' reduces ferric iron forming ammonium (23.1 %) and nitrous oxide (N2O, 46.5 %) from nitrate. These results are supported with the upregulation of genes coding for proteins responsible for dissimilatory nitrate reduction to ammonium (nrfA), N2O formation (norV, cyt P460), and multiple multiheme c-type cytochromes for ferric iron reduction. Concomitantly, an increase in the N2O-reducing SJA-28 lineage and a decrease in the nitrite-reducing 'Candidatus Methylomirabilis oxyfera' are consistent with the changes in 'Ca. M. nitroreducens' end products. These findings demonstrate the highly flexible physiology of 'Ca. M. nitroreducens' in anaerobic ecosystems with diverse electron acceptor conditions, and further reveals its roles in linking methane oxidation to global biogeochemical cycles. 'Ca. M. nitroreducens' could significantly affect the bioavailability of nitrogen sources as well as the emission of greenhouse gas in natural ecosystems and wastewater treatment plants.

Induced pluripotent stem cell-derived hepatocytes reveal TCA cycle disruption and the potential basis for triheptanoin treatment for malate dehydrogenase 2 deficiency

Mitochondrial malate dehydrogenase 2 (MDH2) is crucial to cellular energy generation through direct participation in the tricarboxylic acid (TCA) cycle and the malate aspartate shuttle (MAS). Inherited MDH2 deficiency is an ultra-rare metabolic disease caused by bi-allelic pathogenic variants in the MDH2 gene, resulting in early-onset encephalopathy, psychomotor delay, muscular hypotonia and frequent seizures. Currently, there is no cure for this devastating disease. We recently reported symptomatic improvement of a three-year-old girl with MDH2 deficiency following treatment with the triglyceride triheptanoin. Here, we aimed to better characterize this disease and improve our understanding of the potential utility of triheptanoin treatment. Using fibroblasts derived from this patient, we generated induced pluripotent stem cells (hiPSCs) and differentiated them into hepatocytes (hiPSC-Heps). Characterization of patient-derived hiPSCs and hiPSC-Heps revealed significantly reduced MDH2 protein expression. Untargeted proteotyping of hiPSC-Heps revealed global dysregulation of mitochondrial proteins, including upregulation of TCA cycle and fatty acid oxidation enzymes. Metabolomic profiling confirmed TCA cycle and MAS dysregulation, and demonstrated normalization of malate, fumarate and aspartate following treatment with the triheptanoin components glycerol and heptanoate. Taken together, our results provide the first patient-derived hiPSC-Hep-based model of MDH2 deficiency, confirm altered TCA cycle function, and provide further evidence for the implementation of triheptanoin therapy for this ultra-rare disease.

Synopsis

This study reveals altered expression of mitochondrial pathways including the tricarboxylic acid cycle and changes in metabolite profiles in malate dehydrogenase 2 deficiency and provides the molecular basis for triheptanoin treatment in this ultra-rare disease.

Identification of type VI secretion system effector-immunity pairs using structural bioinformatics

The type VI secretion system (T6SS) is an important mediator of microbe-microbe and microbe-host interactions. Gram-negative bacteria use the T6SS to inject T6SS effectors (T6Es), which are usually proteins with toxic activity, into neighboring cells. Antibacterial effectors have cognate immunity proteins that neutralize self-intoxication. Here, we applied novel structural bioinformatic tools to perform systematic discovery and functional annotation of T6Es and their cognate immunity proteins from a dataset of 17,920 T6SS-encoding bacterial genomes. Using structural clustering, we identified 517 putative T6E families, outperforming sequence-based clustering. We developed a logistic regression model to reliably quantify protein-protein interaction of new T6E-immunity pairs, yielding candidate immunity proteins for 231 out of the 517 T6E families. We used sensitive structure-based annotation which yielded functional annotations for 51% of the T6E families, again outperforming sequence-based annotation. Next, we validated four novel T6E-immunity pairs using basic experiments in E. coli. In particular, we showed that the Pfam domain DUF3289 is a homolog of Colicin M and that DUF943 acts as its cognate immunity protein. Furthermore, we discovered a novel T6E that is a structural homolog of SleB, a lytic transglycosylase, and identified a specific glutamate that acts as its putative catalytic residue. Overall, this study applies novel structural bioinformatic tools to T6E-immunity pair discovery, and provides an extensive database of annotated T6E-immunity pairs.

Omics data analysis reveals the system-level constraint on cellular amino acid composition

Proteins play a pivotal role in coordinating the functions of organisms, essentially governing their traits, as the dynamic arrangement of diverse amino acids leads to a multitude of folded configurations within peptide chains. Despite dynamic changes in amino acid composition of an individual protein (referred to as AAP) and great variance in protein expression levels under different conditions, our study, utilizing transcriptomics data from four model organisms uncovers surprising stability in the overall amino acid composition of the total cellular proteins (referred to as AACell). Although this value may vary between different species, we observed no significant differences among distinct strains of the same species. This indicates that organisms enforce system-level constraints to maintain a consistent AACell, even amid fluctuations in AAP and protein expression. Further exploration of this phenomenon promises insights into the intricate mechanisms orchestrating cellular protein expression and adaptation to varying environmental challenges.

Engineering natural microbiomes toward enhanced bioremediation by microbiome modeling

Engineering natural microbiomes for biotechnological applications remains challenging, as metabolic interactions within microbiomes are largely unknown, and practical principles and tools for microbiome engineering are still lacking. Here, we present a combinatory top-down and bottom-up framework to engineer natural microbiomes for the construction of function-enhanced synthetic microbiomes. We show that application of herbicide and herbicide-degrader inoculation drives a convergent succession of different natural microbiomes toward functional microbiomes (e.g., enhanced bioremediation of herbicide-contaminated soils). We develop a metabolic modeling pipeline, SuperCC, that can be used to document metabolic interactions within microbiomes and to simulate the performances of different microbiomes. Using SuperCC, we construct bioremediation-enhanced synthetic microbiomes based on 18 keystone species identified from natural microbiomes. Our results highlight the importance of metabolic interactions in shaping microbiome functions and provide practical guidance for engineering natural microbiomes.

Investigation of the active ingredients and mechanism of Shuangling extract in dextran sulfate sodium salt induced ulcerative colitis mice based on network pharmacology and experimental verification

OBJECTIVE

To explore the pharmacodynamic effects and potential mechanisms of Shuangling extract against ulcerative colitis (UC).

METHODS

The bioinformatics method was used to predict the active ingredients and action targets of Shuangling extract against UC in mice. And the biological experiments such as serum biochemical indexes and histopathological staining were used to verify the pharmacological effect and mechanism of Shuangling extract against UC in mice.

RESULTS

The Shuangling extract reduced the levels of seruminterleukin-1β (IL-1β), tumor necrosis factor-α (TNF-N), interleukin-6 (IL-6) and other inflammatory factors in UC mice and inhibited the inflammatory response. AKT Serine/threonine Kinase 1 and IL-6 may be the main targets of the anti-UC action of Shuangling extract, and the TNF signaling pathway, Forkhead box O signaling pathway and T-cell receptor signaling pathway may be the main signaling pathways.

CONCLUSION

The Shuangling extract could inhibit the inflammatory response induced by UC and regulate intestinal immune function through multiple targets and multiple channels, which provided a new option and theoretical basis for anti-UC.

AMPActiPred: A three-stage framework for predicting antibacterial peptides and activity levels with deep forest

The emergence and spread of antibiotic-resistant bacteria pose a significant public health threat, necessitating the exploration of alternative antibacterial strategies. Antibacterial peptide (ABP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against bacteria infection, offering a promising avenue for developing novel therapeutic interventions. This study introduces AMPActiPred, a three-stage computational framework designed to identify ABPs, characterize their activity against diverse bacterial species, and predict their activity levels. AMPActiPred employed multiple effective peptide descriptors to effectively capture the compositional features and physicochemical properties of peptides. AMPActiPred utilized deep forest architecture, a cascading architecture similar to deep neural networks, capable of effectively processing and exploring original features to enhance predictive performance. In the first stage, AMPActiPred focuses on ABP identification, achieving an Accuracy of 87.6% and an MCC of 0.742 on an elaborate dataset, demonstrating state-of-the-art performance. In the second stage, AMPActiPred achieved an average GMean at 82.8% in identifying ABPs targeting 10 bacterial species, indicating AMPActiPred can achieve balanced predictions regarding the functional activity of ABP across this set of species. In the third stage, AMPActiPred demonstrates robust predictive capabilities for ABP activity levels with an average PCC of 0.722. Furthermore, AMPActiPred exhibits excellent interpretability, elucidating crucial features associated with antibacterial activity. AMPActiPred is the first computational framework capable of predicting targets and activity levels of ABPs. Finally, to facilitate the utilization of AMPActiPred, we have established a user-friendly web interface deployed at https://awi.cuhk.edu.cn/∼AMPActiPred/.

Advancing Drug-Target Interaction prediction with BERT and subsequence embedding

Exploring the relationship between proteins and drugs plays a significant role in discovering new synthetic drugs. The Drug-Target Interaction (DTI) prediction is a fundamental task in the relationship between proteins and drugs. Unlike encoding proteins by amino acids, we use amino acid subsequence to encode proteins, which simulates the biological process of DTI better. For this research purpose, we proposed a novel deep learning framework based on Bidirectional Encoder Representation from Transformers (BERT), which integrates high-frequency subsequence embedding and transfer learning methods to complete the DTI prediction task. As the first key module, subsequence embedding allows to explore the functional interaction units from drug and protein sequences and then contribute to finding DTI modules. As the second key module, transfer learning promotes the model learn the common DTI features from protein and drug sequences in a large dataset. Overall, the BERT-based model can learn two kinds features through the multi-head self-attention mechanism: internal features of sequence and interaction features of both proteins and drugs, respectively. Compared with other methods, BERT-based methods enable more DTI-related features to be discovered by means of attention scores which associated with tokenized protein/drug subsequences. We conducted extensive experiments for the DTI prediction task on three different benchmark datasets. The experimental results show that the model achieves an average prediction metrics higher than most baseline methods. In order to verify the importance of transfer learning, we conducted an ablation study on datasets, and the results show the superiority of transfer learning. In addition, we test the scalability of the model on the dataset in unseen drugs and proteins, and the results of the experiments show that it is acceptable in scalability.

POLAT: Protein function prediction based on soft mask graph network and residue-Label ATtention

MOTIVATION

Elucidating protein function is a central problem in biochemistry, genetics, and molecular biology. Developing computational methods for protein function prediction is critical due to the significant gap between sequence and functional data. Recent advances in protein structure prediction, which strongly correlates with function, make it feasible to use structure to predict function. However, current structure-based methods overlook the fact that individual residues may contribute differently to the protein's function and do not take into account the correlation between protein residues and their functions. The challenge of effectively utilizing the relationship between protein residues and function-level information to predict protein function remains unsolved.

RESULT

We proposed a protein function prediction method based on Soft Mask Graph Networks and Residue-Label Attention (POLAT), which could combine sequence features, predicted structure features, and function-level information to get an accurate prediction. We use soft mask graph networks to adaptively extract the residues relevant to functions. A residue-label attention mechanism is adopted to obtain the protein-level encoded features of a protein, which are then concatenated with a protein-level embedding and fed into a dense classifier to determine the probabilities of each function. POLAT achieves 0.670, 0.515, 0.578 Fmax and 0.677, 0.409, 0.507 AUPR on the PDB cdhit test set for the MFO, BPO, and CCO domains, respectively, outperforming the existing structure-based SOTA method GAT-GO (Fmax 0.633, 0.492, 0.547; AUPR 0.660, 0.381, 0.479). POLAT is also competitive in extensive experiments among sequence-based and multimodal methods and achieves the SOTA performance in three out of six metrics.

ThermoFinder: A sequence-based thermophilic proteins prediction framework

Thermophilic proteins are important for academic research and industrial processes, and various computational methods have been developed to identify and screen them. However, their performance has been limited due to the lack of high-quality labeled data and efficient models for representing protein. Here, we proposed a novel sequence-based thermophilic proteins prediction framework, called ThermoFinder. The results demonstrated that ThermoFinder outperforms previous state-of-the-art tools on two benchmark datasets, and feature ablation experiments confirmed the effectiveness of our approach. Additionally, ThermoFinder exhibited exceptional performance and consistency across two newly constructed datasets, one of these was specifically constructed for the regression-based prediction of temperature optimum values directly derived from protein sequences. The feature importance analysis, using shapley additive explanations, further validated the advantages of ThermoFinder. We believe that ThermoFinder will be a valuable and comprehensive framework for predicting thermophilic proteins, and we have made our model open source and available on Github at https://github.com/Luo-SynBioLab/ThermoFinder.

Using EMBL-EBI Services via Web Interface and Programmatically via Web Services

The European Bioinformatics Institute (EMBL-EBI)'s Job Dispatcher framework provides access to a wide range of core databases and analysis tools that are of key importance in bioinformatics. As well as providing web interfaces to these resources, web services are available using REST and SOAP protocols that enable programmatic access and allow their integration into other applications and analytical workflows and pipelines. This article describes the various options available to researchers and bioinformaticians who would like to use our resources via the web interface employing RESTful web services clients provided in Perl, Python, and Java or who would like to use Docker containers to integrate the resources into analysis pipelines and workflows. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Retrieving data from EMBL-EBI using Dbfetch via the web interface Alternate Protocol 1: Retrieving data from EMBL-EBI using WSDbfetch via the REST interface Alternate Protocol 2: Retrieving data from EMBL-EBI using Dbfetch via RESTful web services with Python client Support Protocol 1: Installing Python REST web services clients Basic Protocol 2: Sequence similarity search using FASTA search via the web interface Alternate Protocol 3: Sequence similarity search using FASTA via RESTful web services with Perl client Support Protocol 2: Installing Perl REST web services clients Basic Protocol 3: Sequence similarity search using NCBI BLAST+ RESTful web services with Python client Basic Protocol 4: Sequence similarity search using HMMER3 phmmer REST web services with Perl client and Docker Support Protocol 3: Installing Docker and running the EMBL-EBI client container Basic Protocol 5: Protein functional analysis using InterProScan 5 RESTful web services with the Python client and Docker Alternate Protocol 4: Protein functional analysis using InterProScan 5 RESTful web services with the Java client Support Protocol 4: Installing Java web services clients Basic Protocol 6: Multiple sequence alignment using Clustal Omega via web interface Alternate Protocol 5: Multiple sequence alignment using Clustal Omega with Perl client and Docker Support Protocol 5: Exploring the RESTful API with OpenAPI User Inferface.

Network pharmacology prediction and experiment validation of anti-liver cancer activity of Curcumae Rhizoma and Hedyotis diffusa Willd

Objective

This study aims to elucidate anti-liver cancer components and potential mechanisms of Curcumae Rhizoma and Hedyotis diffusa Willd (CR-HDW).

Methods

Effective components and targets of CR-HDW were identified from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Liver cancer-related genes were collected from GeneCards, Gene-Disease Association (DisGeNET), and National Center for Biotechnology Information (NCBI). Protein-protein interaction networks, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were conducted to analyze the identified genes. Molecular docking was used to simulate binding of the active components and their target proteins. Cell activity assay, western blot, and senescence-associated β-galactosidase (SA-β-gal) experiments were conducted to validate core targets identified from molecular docking.

Results

Ten active compounds of CR-HDW were identified including quercetin, 3-epioleanic acid and hederagenin. The primary core proteins comprised Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), Protein Kinase B(AKT1), etc. The pathways for Phosphoinositide 3-kinase (PI3K)/ AKT, cellular senescence, Fork head boxO (FOXO) were revealed as important for anti-cancer activity of CR-HDW. Molecular docking demonstrated strong binding between liver cancer target proteins and major active components of CR-HDW. In-vitro experiments confirmed that hederagenin and 3-epioleolic acid inhibited HuH-7 cell growth, reduced expression of PI3K, AKT, and mechanistic target of rapamycin (mTOR) proteins. Hederagenin also induced HuH-7 senescence.

Conclusions

In summary, The authors' results suggest that the CR-HDW component (Hederagenin, 3-epoxy-olanolic acid) can inhibit the proliferation of HuH-7 cells by decreasing PI3K, AKT, and mTOR. Hederagenin also induced HuH-7 senescence.

Elucidating the molecular mechanisms of pterostilbene against cervical cancer through an integrated bioinformatics and network pharmacology approach

Pterostilbene (PTE), a natural phenolic compound, has exhibited promising anticancer properties in the preclinical treatment of cervical cancer (CC). This study aims to comprehensively investigate the potential targets and mechanisms underlying PTE's anticancer effects in CC, thereby providing a theoretical foundation for its future clinical application and development. To accomplish this, we employed a range of methodologies, including network pharmacology, bioinformatics, and computer simulation, with specific techniques such as WGCNA, PPI network construction, ROC curve analysis, KM survival analysis, GO functional enrichment, KEGG pathway enrichment, molecular docking, MDS, and single-gene GSEA. Utilizing eight drug target prediction databases, we have identified a total of 532 potential targets for PTE. By combining CC-related genes from the GeneCards disease database with significant genes derived from WGCNA analysis of the GSE63514 dataset, we obtained 7915 unique CC-related genes. By analyzing the intersection of the 7915 CC-related genes and the 2810 genes that impact overall survival time in CC, we identified 690 genes as crucial for CC. Through the use of a Venn diagram, we discovered 36 overlapping targets shared by PTE and CC. We have constructed a PPI network and identified 9 core candidate targets. ROC and KM curve analyses subsequently revealed IL1B, EGFR, IL1A, JUN, MYC, MMP1, MMP3, and ANXA5 as the key targets modulated by PTE in CC. GO and KEGG pathway enrichment analyses indicated significant enrichment of these key targets, primarily in the MAPK and IL-17 signaling pathways. Molecular docking analysis verified the effective binding of PTE to all nine key targets. MDS results showed that the protein-ligand complex between MMP1 and PTE was the most stable among the nine targets. Additionally, GSEA enrichment analysis suggested a potential link between elevated MMP1 expression and the activation of the IL-17 signaling pathway. In conclusion, our study has identified key targets and uncovered the molecular mechanism behind PTE's anticancer activity in CC, establishing a firm theoretical basis for further exploration of PTE's pharmacological effects in CC therapy.

Globin phylogeny, evolution and function, the newest update

Our globin census update allows us to refine our vision of globin origin, evolution, and structure to function relationship in the context of the currently accepted tree of life. The modern globin domain originates as a single domain, three-over-three α-helical folded structure before the diversification of the kingdoms of life (Bacteria, Archaea, Eukarya). Together with the diversification of prokaryotes, three monophyletic globin families (M, S, and T) emerged, most likely in Proteobacteria and Actinobacteria, displaying specific sequence and structural features, and spread by vertical and horizontal gene transfer, most probably already present in the last universal common ancestor (LUCA). Non-globin domains were added, and eventually lost again, creating multi-domain structures in key branches of M- (FHb and Adgb) and the vast majority of S globins, which with their coevolved multi-domain architectures, have predominantly "sensor" functions. Single domain T-family globins diverged into four major groups and most likely display functions related to reactive nitrogen and oxygen species (RNOS) chemistry, as well as oxygen storage/transport which drives the evolution of its major branches with their characteristic key distal residues (B10, E11, E7, and G8). M-family evolution also lead to distinctive major types (FHb and Fgb, Ngb, Adgb, GbX vertebrate Gbs), and shows the shift from high oxygen affinity controlled by TyrB10-Gln/AsnE11 likely related to RNOS chemistry in microorganisms, to a moderate oxygen affinity storage/transport function controlled by hydrophobic B10/E11-HisE7 in multicellular animals.

Decoding the molecular interplay in the central dogma: An overview of mass spectrometry-based methods to investigate protein-metabolite interactions

With the emergence of next-generation nucleotide sequencing and mass spectrometry-based proteomics and metabolomics tools, we have comprehensive and scalable methods to analyze the genes, transcripts, proteins, and metabolites of a multitude of biological systems. Despite the fascinating new molecular insights at the genome, transcriptome, proteome and metabolome scale, we are still far from fully understanding cellular organization, cell cycles and biology at the molecular level. Significant advances in sensitivity and depth for both sequencing as well as mass spectrometry-based methods allow the analysis at the single cell and single molecule level. At the same time, new tools are emerging that enable the investigation of molecular interactions throughout the central dogma of molecular biology. In this review, we provide an overview of established and recently developed mass spectrometry-based tools to probe metabolite-protein interactions-from individual interaction pairs to interactions at the proteome-metabolome scale.

Genome resources for three modern cotton lines guide future breeding efforts

Cotton (Gossypium hirsutum L.) is the key renewable fibre crop worldwide, yet its yield and fibre quality show high variability due to genotype-specific traits and complex interactions among cultivars, management practices and environmental factors. Modern breeding practices may limit future yield gains due to a narrow founding gene pool. Precision breeding and biotechnological approaches offer potential solutions, contingent on accurate cultivar-specific data. Here we address this need by generating high-quality reference genomes for three modern cotton cultivars ('UGA230', 'UA48' and 'CSX8308') and updating the 'TM-1' cotton genetic standard reference. Despite hypothesized genetic uniformity, considerable sequence and structural variation was observed among the four genomes, which overlap with ancient and ongoing genomic introgressions from 'Pima' cotton, gene regulatory mechanisms and phenotypic trait divergence. Differentially expressed genes across fibre development correlate with fibre production, potentially contributing to the distinctive fibre quality traits observed in modern cotton cultivars. These genomes and comparative analyses provide a valuable foundation for future genetic endeavours to enhance global cotton yield and sustainability.

Protein Oxidative Modifications in Neurodegenerative Diseases: From Advances in Detection and Modelling to Their Use as Disease Biomarkers

Oxidation-reduction post-translational modifications (redox-PTMs) are chemical alterations to amino acids of proteins. Redox-PTMs participate in the regulation of protein conformation, localization and function, acting as signalling effectors that impact many essential biochemical processes in the cells. Crucially, the dysregulation of redox-PTMs of proteins has been implicated in the pathophysiology of numerous human diseases, including neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease. This review aims to highlight the current gaps in knowledge in the field of redox-PTMs biology and to explore new methodological advances in proteomics and computational modelling that will pave the way for a better understanding of the role and therapeutic potential of redox-PTMs of proteins in neurodegenerative diseases. Here, we summarize the main types of redox-PTMs of proteins while providing examples of their occurrence in neurodegenerative diseases and an overview of the state-of-the-art methods used for their detection. We explore the potential of novel computational modelling approaches as essential tools to obtain insights into the precise role of redox-PTMs in regulating protein structure and function. We also discuss the complex crosstalk between various PTMs that occur in living cells. Finally, we argue that redox-PTMs of proteins could be used in the future as diagnosis and prognosis biomarkers for neurodegenerative diseases.

Genetics of flight in spongy moths (Lymantria dispar ssp.): functionally integrated profiling of a complex invasive trait

BACKGROUND

Flight can drastically enhance dispersal capacity and is a key trait defining the potential of exotic insect species to spread and invade new habitats. The phytophagous European spongy moths (ESM, Lymantria dispar dispar) and Asian spongy moths (ASM; a multi-species group represented here by L. d. asiatica and L. d. japonica), are globally invasive species that vary in adult female flight capability-female ASM are typically flight capable, whereas female ESM are typically flightless. Genetic markers of flight capability would supply a powerful tool for flight profiling of these species at any intercepted life stage. To assess the functional complexity of spongy moth flight and to identify potential markers of flight capability, we used multiple genetic approaches aimed at capturing complementary signals of putative flight-relevant genetic divergence between ESM and ASM: reduced representation genome-wide association studies, whole genome sequence comparisons, and developmental transcriptomics. We then judged the candidacy of flight-associated genes through functional analyses aimed at addressing the proximate demands of flight and salient features of the ecological context of spongy moth flight evolution.

RESULTS

Candidate gene sets were typically non-overlapping across different genetic approaches, with only nine gene annotations shared between any pair of approaches. We detected an array of flight-relevant functional themes across gene sets that collectively suggest divergence in flight capability between European and Asian spongy moth lineages has coincided with evolutionary differentiation in multiple aspects of flight development, execution, and surrounding life history. Overall, our results indicate that spongy moth flight evolution has shaped or been influenced by a large and functionally broad network of traits.

CONCLUSIONS

Our study identified a suite of flight-associated genes in spongy moths suited to exploration of the genetic architecture and evolution of flight, or validation for flight profiling purposes. This work illustrates how complementary genetic approaches combined with phenotypically targeted functional analyses can help to characterize genetically complex traits.

Systematic proteomics analysis revealed different expression of laminin interaction proteins in breast cancer: lower in luminal subtype and higher in claudin-low subtype

Background

Breast cancer is a major public health concern. Proteomics enables identification of proteins with aberrant properties. Here, we identified proteins with abnormal expression levels in breast cancer tissues and systematically analyzed and validated the data to locate potential diagnostic and therapeutic targets.

Methods

Protein expression level in breast cancer tissues and para-carcinoma tissues were detected by Isobaric Tags for Relative and Absolute Quantification (iTRAQ) technology and further screened through Gene Expression Profiling Interactive Analysis (GEPIA) database. Cellular components, protein domain and Reactome pathway analysis were performed to screen functional targets. Abnormal expression levels of functional targets were validated by Oncomine database, quantitative real time polymerase chain reaction (qRT-PCR) and proteomics detection. Protein correlation analysis was performed to explain the abnormal expression levels of potential targets in breast cancer.

Results

Overall, 207 and 207 proteins were up- and down-regulated, respectively, in breast cancer tissues, and approximately 50% were also detected in the GEPIA database. The overlapping proteins were mainly extracellular proteins containing epidermal growth factor-like domain in leukocyte adhesion molecule (EGF-Lam) domain and enriched in laminin interaction pathway. Moreover, the downregulated laminin interaction proteins could be functional targets, which were also validated through Oncomine-Richardson and Oncomine-Curtis database. However, the lower expression level of laminin interaction proteins only fit for luminal breast cancer cells with no or low metastasis ability because the proteins achieved higher expression level in more invasive claudin-low breast cancer cells. In addition, when compared with corresponding in situ carcinoma tissues, above-mentioned proteins also showed higher expression levels in invasive carcinoma tissues. Finally, we have revealed the negative correlation between the laminin interaction proteins and the claudins.

Conclusions

The laminin interaction protein, especially for laminins with β1 and γ1 subunits and their integrin receptors with α1 and α6 subunits, showed lower expression levels in luminal breast cancer with no or lower metastatic ability, but showed higher expression levels in claudin-low breast cancer with higher metastatic ability; and their higher expression could be related to the low claudin expression.

Ancient eukaryotic protein interactions illuminate modern genetic traits and disorders

All eukaryotes share a common ancestor from roughly 1.5 - 1.8 billion years ago, a single-celled, swimming microbe known as LECA, the Last Eukaryotic Common Ancestor. Nearly half of the genes in modern eukaryotes were present in LECA, and many current genetic diseases and traits stem from these ancient molecular systems. To better understand these systems, we compared genes across modern organisms and identified a core set of 10,092 shared protein-coding gene families likely present in LECA, a quarter of which are uncharacterized. We then integrated >26,000 mass spectrometry proteomics analyses from 31 species to infer how these proteins interact in higher-order complexes. The resulting interactome describes the biochemical organization of LECA, revealing both known and new assemblies. We analyzed these ancient protein interactions to find new human gene-disease relationships for bone density and congenital birth defects, demonstrating the value of ancestral protein interactions for guiding functional genetics today.

Atractylodes macrocephala Koidz Alleviates Symptoms in Zymosan-Induced Irritable Bowel Syndrome Mouse Model through TRPV1, NaV1.5, and NaV1.7 Channel Modulation

(1) Background: Irritable bowel syndrome (IBS) is a common disease in the gastrointestinal (GI) tract. Atractylodes macrocephala Koidz (AMK) is known as one of the traditional medicines that shows a good efficacy in the GI tract. (2) Methods: We investigated the effect of AMK in a network pharmacology and zymosan-induced IBS animal model. In addition, we performed electrophysiological experiments to confirm the regulatory mechanisms related to IBS. (3) Results: Various characteristics of AMK were investigated using TCMSP data and various analysis systems. AMK restored the macroscopic changes and weight to normal. Colonic mucosa and inflammatory factors were reduced. These effects were similar to those of amitriptyline and sulfasalazine. In addition, transient receptor potential (TRP) V1, voltage-gated Na+ (NaV) 1.5, and NaV1.7 channels were inhibited. (4) Conclusion: These results suggest that AMK may be a promising therapeutic candidate for IBS management through the regulation of ion channels.

Exploring cutting-edge strategies for drug repurposing in female cancers - An insight into the tools of the trade

Female cancers, which include breast and gynaecological cancers, represent a significant global health burden for women. Despite advancements in research pertinent to unearthing crucial pathological characteristics of these cancers, challenges persist in discovering potential therapeutic strategies. This is further exacerbated by economic burdens associated with de novo drug discovery and clinical intricacies such as development of drug resistance and metastasis. Drug repurposing, an innovative approach leveraging existing FDA-approved drugs for new indications, presents a promising avenue to expedite therapeutic development. Computational techniques, including virtual screening and analysis of drug-target-disease relationships, enable the identification of potential candidate drugs. Integration of diverse data types, such as omics and clinical information, enhances the precision and efficacy of drug repurposing strategies. Experimental approaches, including high-throughput screening assays, in vitro, and in vivo models, complement computational methods, facilitating the validation of repurposed drugs. This review highlights various target mining strategies based on analysis of differential gene expression, weighted gene co-expression, protein-protein interaction network, and host-pathogen interaction, among others. To unearth drug candidates, the technicalities of leveraging information from databases such as DrugBank, STITCH, LINCS, and ChEMBL, among others are discussed. Further in silico validation techniques encompassing molecular docking, pharmacophore modelling, molecular dynamic simulations, and ADMET analysis are elaborated. Overall, this review delves into the exploration of individual case studies to offer a wide perspective of the ever-evolving field of drug repurposing, emphasizing the multifaceted approaches and methodologies employed for the same to confront female cancers.

HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction

Drug synergy therapy is a promising strategy for cancer treatment. However, the extensive variety of available drugs and the time-intensive process of determining effective drug combinations through clinical trials pose significant challenges. It requires a reliable method for the rapid and precise selection of drug synergies. In response, various computational strategies have been developed for predicting drug synergies, yet the exploitation of heterogeneous biological network features remains underexplored. In this study, we construct a heterogeneous graph that encompasses diverse biological entities and interactions, utilizing rich data sets from sources, such as DrugCombDB, PubChem, UniProt, and cancer cell line encyclopedia (CCLE). We initialize node feature representations and introduce a novel virtual node to enhance drug representation. Our proposed method, the heterogeneous graph attention network for drug-drug synergy prediction (HANSynergy), has been experimentally validated to demonstrate that the heterogeneous graph attention network can extract key node features, efficiently harness the diversity of information, and further enhance network functionality through the incorporation of a multihead attention mechanism. In the comparative experiment, the highest accuracy (Acc) and area under the curve (AUC) are 0.877 and 0.947, respectively, in DrugCombDB_early data set, demonstrating the superiority of HANSynergy over the competing methods. Moreover, protein-protein interactions are important in understanding the mechanism of action of drugs. The heterogeneous attention mechanism facilitates protein-protein interaction analysis. By analyzing the changes of attention weight before and after heterogeneous network training, we investigated proteins that may be associated with drug combinations. Additionally, case studies align our findings with existing research, underscoring the potential of HANSynergy in drug synergy prediction. This advancement not only contributes to the burgeoning field of drug synergy prediction but also holds the potential to provide valuable insights and uncover new drug synergies for combating cancer.

Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning

Currently, antimicrobial resistance constitutes a serious threat to human health. Drugs based on antimicrobial peptides (AMPs) constitute one of the alternatives to address it. Shallow and deep learning (DL)-based models have mainly been built from amino acid sequences to predict AMPs. Recent advances in tertiary (3D) structure prediction have opened new opportunities in this field. In this sense, models based on graphs derived from predicted peptide structures have recently been proposed. However, these models are not in correspondence with state-of-the-art approaches to codify evolutionary information, and, in addition, they are memory- and time-consuming because depend on multiple sequence alignment. Herein, we presented a framework to create alignment-free models based on graph representations generated from ESMFold-predicted peptide structures, whose nodes are characterized with amino acid-level evolutionary information derived from the Evolutionary Scale Modeling (ESM-2) models. A graph attention network (GAT) was implemented to assess the usefulness of the framework in the AMP classification. To this end, a set comprised of 67,058 peptides was used. It was demonstrated that the proposed methodology allowed to build GAT models with generalization abilities consistently better than 20 state-of-the-art non-DL-based and DL-based models. The best GAT models were developed using evolutionary information derived from the 36- and 33-layer ESM-2 models. Similarity studies showed that the best-built GAT models codified different chemical spaces, and thus they were fused to significantly improve the classification. In general, the results suggest that esm-AxP-GDL is a promissory tool to develop good, structure-dependent, and alignment-free models that can be successfully applied in the screening of large data sets. This framework should not only be useful to classify AMPs but also for modeling other peptide and protein activities.

An In Silico Analysis of Genetic Variants and Structural Modeling of the Human Frataxin Protein in Friedreich's Ataxia

Friedreich's Ataxia (FRDA) stands out as the most prevalent form of hereditary ataxias, marked by progressive movement ataxia, loss of vibratory sensitivity, and skeletal deformities, severely affecting daily functioning. To date, the only medication available for treating FRDA is Omaveloxolone (Skyclarys®), recently approved by the FDA. Missense mutations within the human frataxin (FXN) gene, responsible for intracellular iron homeostasis regulation, are linked to FRDA development. These mutations induce FXN dysfunction, fostering mitochondrial iron accumulation and heightened oxidative stress, ultimately triggering neuronal cell death pathways. This study amalgamated 226 FXN genetic variants from the literature and database searches, with only 18 previously characterized. Predictive analyses revealed a notable prevalence of detrimental and destabilizing predictions for FXN mutations, predominantly impacting conserved residues crucial for protein function. Additionally, an accurate, comprehensive three-dimensional model of human FXN was constructed, serving as the basis for generating genetic variants I154F and W155R. These variants, selected for their severe clinical implications, underwent molecular dynamics (MD) simulations, unveiling flexibility and essential dynamic alterations in their N-terminal segments, encompassing FXN42, FXN56, and FXN78 domains pivotal for protein maturation. Thus, our findings indicate potential interaction profile disturbances in the FXN42, FXN56, and FXN78 domains induced by I154F and W155R mutations, aligning with the existing literature.

Exploring the roles of ribosomal peptides in prokaryote-phage interactions through deep learning-enabled metagenome mining

BACKGROUND

Microbial secondary metabolites play a crucial role in the intricate interactions within the natural environment. Among these metabolites, ribosomally synthesized and post-translationally modified peptides (RiPPs) are becoming a promising source of therapeutic agents due to their structural diversity and functional versatility. However, their biosynthetic capacity and ecological functions remain largely underexplored.

RESULTS

Here, we aim to explore the biosynthetic profile of RiPPs and their potential roles in the interactions between microbes and viruses in the ocean, which encompasses a vast diversity of unique biomes that are rich in interactions and remains chemically underexplored. We first developed TrRiPP to identify RiPPs from ocean metagenomes, a deep learning method that detects RiPP precursors in a hallmark gene-independent manner to overcome the limitations of classic methods in processing highly fragmented metagenomic data. Applying this method to metagenomes from the global ocean microbiome, we uncover a diverse array of previously uncharacterized putative RiPP families with great novelty and diversity. Through correlation analysis based on metatranscriptomic data, we observed a high prevalence of antiphage defense-related and phage-related protein families that were co-expressed with RiPP families. Based on this putative association between RiPPs and phage infection, we constructed an Ocean Virus Database (OVD) and established a RiPP-involving host-phage interaction network through host prediction and co-expression analysis, revealing complex connectivities linking RiPP-encoding prokaryotes, RiPP families, viral protein families, and phages. These findings highlight the potential of RiPP families involved in prokaryote-phage interactions and coevolution, providing insights into their ecological functions in the ocean microbiome.

CONCLUSIONS

This study provides a systematic investigation of the biosynthetic potential of RiPPs from the ocean microbiome at a global scale, shedding light on the essential insights into the ecological functions of RiPPs in prokaryote-phage interactions through the integration of deep learning approaches, metatranscriptomic data, and host-phage connectivity. This study serves as a valuable example of exploring the ecological functions of bacterial secondary metabolites, particularly their associations with unexplored microbial interactions. Video Abstract.

Generative design of compounds with desired potency from target protein sequences using a multimodal biochemical language model

Deep learning models adapted from natural language processing offer new opportunities for the prediction of active compounds via machine translation of sequential molecular data representations. For example, chemical language models are often derived for compound string transformation. Moreover, given the principal versatility of language models for translating different types of textual representations, off-the-beaten-path design tasks might be explored. In this work, we have investigated generative design of active compounds with desired potency from target sequence embeddings, representing a rather provoking prediction task. Therefore, a dual-component conditional language model was designed for learning from multimodal data. It comprised a protein language model component for generating target sequence embeddings and a conditional transformer for predicting new active compounds with desired potency. To this end, the designated "biochemical" language model was trained to learn mappings of combined protein sequence and compound potency value embeddings to corresponding compounds, fine-tuned on individual activity classes not encountered during model derivation, and evaluated on compound test sets that were structurally distinct from training sets. The biochemical language model correctly reproduced known compounds with different potency for all activity classes, providing proof-of-concept for the approach. Furthermore, the conditional model consistently reproduced larger numbers of known compounds as well as more potent compounds than an unconditional model, revealing a substantial effect of potency conditioning. The biochemical language model also generated structurally diverse candidate compounds departing from both fine-tuning and test compounds. Overall, generative compound design based on potency value-conditioned target sequence embeddings yielded promising results, rendering the approach attractive for further exploration and practical applications. SCIENTIFIC CONTRIBUTION: The approach introduced herein combines protein language model and chemical language model components, representing an advanced architecture, and is the first methodology for predicting compounds with desired potency from conditioned protein sequence data.

Bilateral Uveal Melanoma: An Insight into Genetic Predisposition in Four New Unrelated Patients and Review of Published Cases

Background: Primary bilateral uveal melanoma (BUM) is an exceptionally rare form of uveal melanoma (UM). This study aimed to explore the potential existence of a genetic predisposition towards the development of BUM. Methods: We employed an exome sequencing approach on germline DNA from four unrelated patients diagnosed with BUM, seeking pathogenic or likely pathogenic variants indicative of a genetic predisposition to UM. Results: None of the patients exhibited pathogenic variants in the BAP1 gene. However, loss-of-function (LoF) variants in the TERF2IP and BAX genes were identified in two of the BUM patients. For patients BUM1 and BUM2, no pathogenic/likely pathogenic variants of significant clinical relevance to BUM were found to warrant inclusion in this report. Conclusions: Our findings suggest the presence of yet-to-be-discovered genes that may contribute to UM predisposition, as evidenced by the absence of pathogenic variants in known UM predisposition genes among the four BUM patients studied. The TERF2IP and BAX genes emerge as noteworthy candidates for further investigation regarding their role in genetic predisposition to UM. Specifically, the potential role of UM as a candidate cancer within the spectrum of cancers linked to pathogenic variants in the TERF2IP gene and other genes associated with the shelterin complex warrants further examination. Additional functional studies are necessary to support or challenge this hypothesis.

Chromosome fusion and programmed DNA elimination shape karyotypes of nematodes

An increasing number of metazoans undergo programmed DNA elimination (PDE), where a significant amount of DNA is selectively lost from the somatic genome during development. In some nematodes, PDE leads to the removal and remodeling of the ends of all germline chromosomes. In several species, PDE also generates internal breaks that lead to sequence loss and increased numbers of somatic chromosomes. The biological significance of these karyotype changes associated with PDE and the origin and evolution of nematode PDE remain largely unknown. Here, we assembled the single germline chromosome of the nematode Parascaris univalens and compared the karyotypes, chromosomal gene organization, and PDE features among other nematodes. We show that PDE in Parascaris converts an XX/XY sex-determination system in the germline into an XX/XO system in the somatic cells. Comparisons of Ascaris, Parascaris, and Baylisascaris ascarid chromosomes suggest that PDE existed in the ancestor of these nematodes, and their current distinct germline karyotypes were derived from fusion events of smaller ancestral chromosomes. The DNA breaks involved in PDE resolve these fused germline chromosomes into their pre-fusion karyotypes. These karyotype changes may lead to alterations in genome architecture and gene expression in the somatic cells. Cytological and genomic analyses further suggest that satellite DNA and the heterochromatic chromosome arms are dynamic and may play a role during meiosis. Overall, our results show that chromosome fusion and PDE have been harnessed in these ascarids to sculpt their karyotypes, altering the genome organization and serving specific functions in the germline and somatic cells.

SOS1 tonoplast neo-localization and the RGG protein SALTY are important in the extreme salinity tolerance of Salicornia bigelovii

The identification of genes involved in salinity tolerance has primarily focused on model plants and crops. However, plants naturally adapted to highly saline environments offer valuable insights into tolerance to extreme salinity. Salicornia plants grow in coastal salt marshes, stimulated by NaCl. To understand this tolerance, we generated genome sequences of two Salicornia species and analyzed the transcriptomic and proteomic responses of Salicornia bigelovii to NaCl. Subcellular membrane proteomes reveal that SbiSOS1, a homolog of the well-known SALT-OVERLY-SENSITIVE 1 (SOS1) protein, appears to localize to the tonoplast, consistent with subcellular localization assays in tobacco. This neo-localized protein can pump Na+ into the vacuole, preventing toxicity in the cytosol. We further identify 11 proteins of interest, of which SbiSALTY, substantially improves yeast growth on saline media. Structural characterization using NMR identified it as an intrinsically disordered protein, localizing to the endoplasmic reticulum in planta, where it can interact with ribosomes and RNA, stabilizing or protecting them during salt stress.

De Novo Antimicrobial Peptide Design with Feedback Generative Adversarial Networks

Antimicrobial peptides (AMPs) are promising candidates for new antibiotics due to their broad-spectrum activity against pathogens and reduced susceptibility to resistance development. Deep-learning techniques, such as deep generative models, offer a promising avenue to expedite the discovery and optimization of AMPs. A remarkable example is the Feedback Generative Adversarial Network (FBGAN), a deep generative model that incorporates a classifier during its training phase. Our study aims to explore the impact of enhanced classifiers on the generative capabilities of FBGAN. To this end, we introduce two alternative classifiers for the FBGAN framework, both surpassing the accuracy of the original classifier. The first classifier utilizes the k-mers technique, while the second applies transfer learning from the large protein language model Evolutionary Scale Modeling 2 (ESM2). Integrating these classifiers into FBGAN not only yields notable performance enhancements compared to the original FBGAN but also enables the proposed generative models to achieve comparable or even superior performance to established methods such as AMPGAN and HydrAMP. This achievement underscores the effectiveness of leveraging advanced classifiers within the FBGAN framework, enhancing its computational robustness for AMP de novo design and making it comparable to existing literature.

SCLpred-ECL: Subcellular Localization Prediction by Deep N-to-1 Convolutional Neural Networks

The subcellular location of a protein provides valuable insights to bioinformaticians in terms of drug designs and discovery, genomics, and various other aspects of medical research. Experimental methods for protein subcellular localization determination are time-consuming and expensive, whereas computational methods, if accurate, would represent a much more efficient alternative. This article introduces an ab initio protein subcellular localization predictor based on an ensemble of Deep N-to-1 Convolutional Neural Networks. Our predictor is trained and tested on strict redundancy-reduced datasets and achieves 63% accuracy for the diverse number of classes. This predictor is a step towards bridging the gap between a protein sequence and the protein's function. It can potentially provide information about protein-protein interaction to facilitate drug design and processes like vaccine production that are essential to disease prevention.

In Silico Search for Drug Candidates Targeting the PAX8-PPARγ Fusion Protein in Thyroid Cancer

The PAX8/PPARγ rearrangement, producing the PAX8-PPARγ fusion protein (PPFP), is thought to play an essential role in the oncogenesis of thyroid follicular tumors. To identify PPFP-targeted drug candidates and establish an early standard of care for thyroid tumors, we performed ensemble-docking-based compound screening. Specifically, we investigated the pocket structure that should be adopted to search for a promising ligand compound for the PPFP; the position of the ligand-binding pocket on the PPARγ side of the PPFP is similar to that of PPARγ; however, the shape is slightly different between them due to environmental factors. We developed a method for selecting a PPFP structure with a relevant pocket and high prediction accuracy for ligand binding. This method was validated using PPARγ, whose structure and activity values are known for many compounds. Then, we performed docking calculations to the PPFP for 97 drug or drug-like compounds registered in the DrugBank database with a thiazolidine backbone, which is one of the characteristics of ligands that bind well to PPARγ. Furthermore, the binding affinities of promising ligand candidates were estimated more reliably using the molecular mechanics Poisson-Boltzmann surface area method. Thus, we propose promising drug candidates for the PPFP with a thiazolidine backbone.

Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning

Estimating protein targets of compounds based on the similarity principle-similar molecules are likely to show comparable bioactivity-is a long-standing strategy in drug research. Having previously quantified this principle, we present here a large-scale evaluation of its predictive power for inferring macromolecular targets by reverse screening an unprecedented vast external test set of more than 300,000 active small molecules against another bioactivity set of more than 500,000 compounds. We show that machine-learning can predict the correct targets, with the highest probability among 2069 proteins, for more than 51% of the external molecules. The strong enrichment thus obtained demonstrates its usefulness in supporting phenotypic screens, polypharmacology, or repurposing. Moreover, we quantified the impact of the bioactivity knowledge available for proteins in terms of number and diversity of actives. Finally, we advise that developers of such approaches follow an application-oriented benchmarking strategy and use large, high-quality, non-overlapping datasets as provided here.

A phase-separated protein hub modulates resistance to Fusarium head blight in wheat

Fusarium head blight (FHB) is a devastating wheat disease. Fhb1, the most widely applied genetic locus for FHB resistance, is conferred by TaHRC of an unknown mode of action. Here, we show that TaHRC alleles distinctly drive liquid-liquid phase separation (LLPS) within a proteinaceous complex, determining FHB susceptibility or resistance. TaHRC-S (susceptible) exhibits stronger LLPS ability than TaHRC-R (resistant), and this distinction is further intensified by fungal mycotoxin deoxynivalenol, leading to opposing FHB symptoms. TaHRC recruits a protein class with intrinsic LLPS potentials, referred to as an "HRC-containing hub." TaHRC-S drives condensation of hub components, while TaHRC-R comparatively suppresses hub condensate formation. The function of TaSR45a splicing factor, a hub member, depends on TaHRC-driven condensate state, which in turn differentially directs alternative splicing, switching between susceptibility and resistance to wheat FHB. These findings reveal a mechanism for FHB spread within a spike and shed light on the roles of complex condensates in controlling plant disease.

Leveraging machine learning models for peptide-protein interaction prediction

Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as docking and molecular dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.

Small protein blockers of human IL-6 receptor alpha inhibit proliferation and migration of cancer cells

BACKGROUND

Interleukin-6 (IL-6) is a multifunctional cytokine that controls the immune response, and its role has been described in the development of autoimmune diseases. Signaling via its cognate IL-6 receptor (IL-6R) complex is critical in tumor progression and, therefore, IL-6R represents an important therapeutic target.

METHODS

An albumin-binding domain-derived highly complex combinatorial library was used to select IL-6R alpha (IL-6Rα)-targeted small protein binders using ribosome display. Large-scale screening of bacterial lysates of individual clones was performed using ELISA, and their IL-6Rα blocking potential was verified by competition ELISA. The binding of proteins to cells was monitored by flow cytometry and confocal microscopy on HEK293T-transfected cells, and inhibition of signaling function was examined using HEK-Blue IL-6 reporter cells. Protein binding kinetics to living cells was measured by LigandTracer, cell proliferation and toxicity by iCELLigence and Incucyte, cell migration by the scratch wound healing assay, and prediction of binding poses using molecular modeling by docking.

RESULTS

We demonstrated a collection of protein variants called NEF ligands, selected from an albumin-binding domain scaffold-derived combinatorial library, and showed their binding specificity to human IL-6Rα and antagonistic effect in HEK-Blue IL-6 reporter cells. The three most promising NEF108, NEF163, and NEF172 variants inhibited cell proliferation of malignant melanoma (G361 and A2058) and pancreatic (PaTu and MiaPaCa) cancer cells, and suppressed migration of malignant melanoma (A2058), pancreatic carcinoma (PaTu), and glioblastoma (GAMG) cells in vitro. The NEF binders also recognized maturation-induced IL-6Rα expression and interfered with IL-6-induced differentiation in primary human B cells.

CONCLUSION

We report on the generation of small protein blockers of human IL-6Rα using directed evolution. NEF proteins represent a promising class of non-toxic anti-tumor agents with migrastatic potential.

Engineering PD-1-targeted small protein variants for in vitro diagnostics and in vivo PET imaging

BACKGROUND

Programmed cell death 1 (PD-1) belongs to immune checkpoint proteins ensuring negative regulation of the immune response. In non-small cell lung cancer (NSCLC), the sensitivity to treatment with anti-PD-1 therapeutics, and its efficacy, mostly correlated with the increase of tumor infiltrating PD-1+ lymphocytes. Due to solid tumor heterogeneity of PD-1+ populations, novel low molecular weight anti-PD-1 high-affinity diagnostic probes can increase the reliability of expression profiling of PD-1+ tumor infiltrating lymphocytes (TILs) in tumor tissue biopsies and in vivo mapping efficiency using immune-PET imaging.

METHODS

We designed a 13 kDa β-sheet Myomedin scaffold combinatorial library by randomization of 12 mutable residues, and in combination with ribosome display, we identified anti-PD-1 Myomedin variants (MBA ligands) that specifically bound to human and murine PD-1-transfected HEK293T cells and human SUP-T1 cells spontaneously overexpressing cell surface PD-1.

RESULTS

Binding affinity to cell-surface expressed human and murine PD-1 on transfected HEK293T cells was measured by fluorescence with LigandTracer and resulted in the selection of most promising variants MBA066 (hPD-1 KD = 6.9 nM; mPD-1 KD = 40.5 nM), MBA197 (hPD-1 KD = 29.7 nM; mPD-1 KD = 21.4 nM) and MBA414 (hPD-1 KD = 8.6 nM; mPD-1 KD = 2.4 nM). The potential of MBA proteins for imaging of PD-1+ populations in vivo was demonstrated using deferoxamine-conjugated MBA labeled with 68Galium isotope. Radiochemical purity of 68Ga-MBA proteins reached values 94.7-99.3% and in vitro stability in human serum after 120 min was in the range 94.6-98.2%. The distribution of 68Ga-MBA proteins in mice was monitored using whole-body positron emission tomography combined with computerized tomography (PET/CT) imaging up to 90 min post-injection and post mortem examined in 12 mouse organs. The specificity of MBA proteins was proven by co-staining frozen sections of human tonsils and NSCLC tissue biopsies with anti-PD-1 antibody, and demonstrated their potential for mapping PD-1+ populations in solid tumors.

CONCLUSIONS

Using directed evolution, we developed a unique set of small binding proteins that can improve PD-1 diagnostics in vitro as well as in vivo using PET/CT imaging.

CASTpFold: Computed Atlas of Surface Topography of the universe of protein Folds

Geometric and topological properties of protein structures, including surface pockets, interior cavities, and cross channels, are of fundamental importance for proteins to carry out their functions. Computed Atlas of Surface Topography of proteins (CASTp) is a widely used web server for locating, delineating, and measuring these geometric and topological properties of protein structures. Recent developments in AI-based protein structure prediction such as AlphaFold2 (AF2) have significantly expanded our knowledge on protein structures. Here we present CASTpFold, a continuation of CASTp that provides accurate and comprehensive identifications and quantifications of protein topography. It now provides (i) results on an expanded database of proteins, including the Protein Data Bank (PDB) and non-singleton representative structures of AlphaFold2 structures, covering 183 million AF2 structures; (ii) functional pockets prediction with corresponding Gene Ontology (GO) terms or Enzyme Commission (EC) numbers for AF2-predicted structures; and (iii) pocket similarity search function for surface and protein-protein interface pockets. The CASTpFold web server is freely accessible at https://cfold.bme.uic.edu/castpfold/.

Changing selection on amino acid substitutions in Gag protein between major HIV-1 subtypes

Amino acid preferences at a protein site depend on the role of this site in protein function and structure as well as on external constraints. All these factors can change in the course of evolution, making amino acid propensities of a site time-dependent. When viral subtypes divergently evolve in different host subpopulations, such changes may depend on genetic, medical, and sociocultural differences between these subpopulations. Here, using our previously developed phylogenetic approach, we describe sixty-nine amino acid sites of the Gag protein of human immunodeficiency virus type 1 (HIV-1) where amino acids have different impact on viral fitness in six major subtypes of the type M. These changes in preferences trigger adaptive evolution; indeed, 32 (46 per cent) of these sites experienced strong positive selection at least in one of the subtypes. At some of the sites, changes in amino acid preferences may be associated with differences in immune escape between subtypes. The prevalence of an amino acid in a protein site within a subtype is only a poor predictor for whether this amino acid is preferred in this subtype according to the phylogenetic analysis. Therefore, attempts to identify the factors of viral evolution from comparative genomics data should integrate across multiple sources of information.

Potential mechanisms of traditional Chinese medicine in treating insomnia: A network pharmacology, GEO validation, and molecular-docking study

The purpose of this study is to investigate the potential mechanisms of Chinese herbs for the treatment of insomnia using a combination of data mining, network pharmacology, and molecular-docking validation. All the prescriptions for insomnia treated by the academician Qi Wang from 2020 to 2022 were collected. The Ancient and Modern Medical Case Cloud Platform v2.3 was used to identify high-frequency Chinese medicinal herbs and the core prescription. The Traditional Chinese Medicine Systems Pharmacology and UniProt databases were utilized to predict the effective active components and targets of the core herbs. Insomnia-related targets were collected from 4 databases. The intersecting targets were utilized to build a protein-protein interaction network and conduct gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis using the STRING database, Cytoscape software, and clusterProfiler package. Gene chip data (GSE208668) were obtained from the Gene Expression Omnibus database. The limma package was applied to identify differentially expressed genes (DEGs) between insomnia patients and healthy controls. To create a "transcription factor (TF)-miRNA-mRNA" network, the differentially expressed miRNAs were entered into the TransmiR, FunRich, Targetscan, and miRDB databases. Subsequently, the overlapping targets were validated using the DEGs, and further validations were conducted through molecular docking and molecular dynamics simulations. Among the 117 prescriptions, 65 herbs and a core prescription were identified. Network pharmacology and bioinformatics analysis revealed that active components such as β-sitosterol, stigmasterol, and canadine acted on hub targets, including interleukin-6, caspase-3, and hypoxia-inducible factor-1α. In GSE208668, 6417 DEGs and 7 differentially expressed miRNAs were identified. A "TF-miRNA-mRNA" network was constructed by 4 "TF-miRNA" interaction pairs and 66 "miRNA-mRNA" interaction pairs. Downstream mRNAs exert therapeutic effects on insomnia by regulating circadian rhythm. Molecular-docking analyses demonstrated good docking between core components and hub targets. Molecular dynamics simulation displayed the strong stability of the complex formed by small molecule and target. The core prescription by the academician Qi Wang for treating insomnia, which involves multiple components, targets, and pathways, showed the potential to improve sleep, providing a basis for clinical treatment of insomnia.

Integrated convolution and self-attention for improving peptide toxicity prediction

MOTIVATION

Peptides are promising agents for the treatment of a variety of diseases due to their specificity and efficacy. However, the development of peptide-based drugs is often hindered by the potential toxicity of peptides, which poses a significant barrier to their clinical application. Traditional experimental methods for evaluating peptide toxicity are time-consuming and costly, making the development process inefficient. Therefore, there is an urgent need for computational tools specifically designed to predict peptide toxicity accurately and rapidly, facilitating the identification of safe peptide candidates for drug development.

RESULTS

We provide here a novel computational approach, CAPTP, which leverages the power of convolutional and self-attention to enhance the prediction of peptide toxicity from amino acid sequences. CAPTP demonstrates outstanding performance, achieving a Matthews correlation coefficient of approximately 0.82 in both cross-validation settings and on independent test datasets. This performance surpasses that of existing state-of-the-art peptide toxicity predictors. Importantly, CAPTP maintains its robustness and generalizability even when dealing with data imbalances. Further analysis by CAPTP reveals that certain sequential patterns, particularly in the head and central regions of peptides, are crucial in determining their toxicity. This insight can significantly inform and guide the design of safer peptide drugs.

AVAILABILITY AND IMPLEMENTATION

The source code for CAPTP is freely available at https://github.com/jiaoshihu/CAPTP.

A proteomics-based survey reveals thrombospondin-4 as a ligand regulated by the mannose receptor in the injured lung

Receptor-mediated cellular uptake of specific ligands constitutes an important step in the dynamic regulation of individual protein levels in extracellular fluids. With a focus on the inflammatory lung, we here performed a proteomics-based search for novel ligands regulated by the mannose receptor (MR), a macrophage-expressed endocytic receptor. WT and MR-deficient mice were exposed to lipopolysaccharide, after which the protein content in their lung epithelial lining fluid was compared by tandem mass tag-based mass spectrometry. More than 1200 proteins were identified in the epithelial lining fluid using this unbiased approach, but only six showed a statistically different abundance. Among these, an unexpected potential new ligand, thrombospondin-4 (TSP-4), displayed a striking 17-fold increased abundance in the MR-deficient mice. Experiments using exogenous addition of TSP-4 to MR-transfected CHO cells or MR-positive alveolar macrophages confirmed that TSP-4 is a ligand for MR-dependent endocytosis. Similar studies revealed that the molecular interaction with TSP-4 depends on both the lectin activity and the fibronectin type-II domain of MR and that a closely related member of the TSP family, TSP-5, is also efficiently internalized by the receptor. This was unlike the other members of this protein family, including TSPs -1 and -2, which are ligands for a close MR homologue known as urokinase plasminogen activator receptor-associated protein. Our study shows that MR takes part in the regulation of TSP-4, an important inflammatory component in the injured lung, and that two closely related endocytic receptors, expressed on different cell types, undertake the selective endocytosis of distinct members of the TSP family.

An Integer Linear Programming Model to Optimize Coding DNA Sequences By Joint Control of Transcript Indicators

A Coding DNA Sequence (CDS) is a fraction of DNA whose nucleotides are grouped into consecutive triplets called codons, each one encoding an amino acid. Because most amino acids can be encoded by more than one codon, the same amino acid chain can be obtained by a very large number of different CDSs. These synonymous CDSs show different features that, also depending on the organism the transcript is expressed in, could affect translational efficiency and yield. The identification of optimal CDSs with respect to given transcript indicators is in general a challenging task, but it has been observed in recent literature that integer linear programming (ILP) can be a very flexible and efficient way to achieve it. In this article, we add evidence to this observation by proposing a new ILP model that simultaneously optimizes different well-grounded indicators. With this model, we efficiently find solutions that dominate those returned by six existing codon optimization heuristics.