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Ion channels are integral membrane proteins mediating ion flow in response to changes in their environment. Among the different types of ion channels reported to date, the super-family of TRP channels stands out since its members have been linked to many pathophysiological processes. The family comprises 6 subfamilies and 28 members in mammals, which are widely distributed throughout most tissues and organs and have an important role in several aspects of cellular physiology. It has been evidenced that abnormal expression, post-translational modifications, and channel trafficking are associated with several pathologies, such as cancer, cardiovascular disease, diabetes, and brain disorders, among others. In this review, we present an updated summary of the mechanisms involved in the subcellular trafficking of TRP channels, with a special emphasis on whether different post-translational modifications and naturally occurring mutagenesis affect both expression and trafficking. Additionally, we describe how such changes have been associated with the development and progress of diverse pathologies associated with the gain or loss of functional phenotypes. The study of these processes will not only contribute to a better understanding the role of TRP channels in the different tissues but will also present novel possible therapeutic targets in diseases where their activity is dysregulated.

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Uncontrolled use of pesticides has caused a dramatic reduction in the number of pollinators, including bees. Studies on the effects of pesticides on bees have reported effects on both metabolic and neurological levels under chronic exposure. In this study, variations in the differential expression of head and thorax-abdomen proteins in Africanized A. mellifera bees treated acutely with sublethal doses of glyphosate and imidacloprid were studied using a proteomic approach. A total of 92 proteins were detected, 49 of which were differentially expressed compared to those in the control group (47 downregulated and 2 upregulated). Protein interaction networks with differential protein expression ratios suggested that acute exposure of A. mellifera to sublethal doses of glyphosate could cause head damage, which is mainly associated with behavior and metabolism. Simultaneously, imidacloprid can cause damage associated with metabolism as well as, neuronal damage, cellular stress, and impairment of the detoxification system. Regarding the thorax-abdomen fractions, glyphosate could lead to cytoskeleton reorganization and a reduction in defense mechanisms, whereas imidacloprid could affect the coordination and impairment of the oxidative stress response.

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Machine Learning (ML) algorithms have been important tools for the extraction of useful knowledge from biological sequences, particularly in healthcare, agriculture, and the environment. However, the categorical and unstructured nature of these sequences requiring usually additional feature engineering steps, before an ML algorithm can be efficiently applied. The addition of these steps to the ML algorithm creates a processing pipeline, known as end-to-end ML. Despite the excellent results obtained by applying end-to-end ML to biotechnology problems, the performance obtained depends on the expertise of the user in the components of the pipeline. In this work, we propose an end-to-end ML-based framework called BioPrediction-RPI, which can identify implicit interactions between sequences, such as pairs of non-coding RNA and proteins, without the need for specialized expertise in end-to-end ML. This framework applies feature engineering to represent each sequence by structural and topological features. These features are divided into feature groups and used to train partial models, whose partial decisions are combined into a final decision, which, provides insights to the user by giving an interpretability report. In our experiments, the developed framework was competitive when compared with various expert-created models. We assessed BioPrediction-RPI with 12 datasets when it presented equal or better performance than all tools in 40% to 100% of cases, depending on the experiment. Finally, BioPrediction-RPI can fine-tune models based on new data and perform at the same level as ML experts, democratizing end-to-end ML and increasing its access to those working in biological sciences.

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Sessile organisms, such as plants, developed various ways to sense and respond to external and internal stimuli to maximize their fitness through evolutionary time. Transcripts and protein regulation are, among many, the main mechanisms that plants use to respond to environmental changes. SKIP protein is one such, presenting an SNKW interacting domain, which is highly conserved among eukaryotes, where SKI interacting protein acts in regulating key processes. In the present work, many bioinformatics tools, such as phylogenetic relationships, gene structure, physical-chemical properties, conserved motifs, prediction of regulatory cis-elements, chromosomal localization, and protein-protein interaction network, were used to better understand the genome-wide SNW/SKIP domain-containing proteins. In total, 28 proteins containing the SNW/SKIP domain were identified in different plant species, including plants of agronomic interest. Two main protein clusters were formed in phylogenetic analysis, and gene structure analysis revealed that, in general, the coding region had no introns. Also, expression of these genes is possibly induced by abiotic stress stimuli. Primary structure analysis of the proteins revealed the existence of an evolutionarily conserved functional unit. But physicochemical properties show that proteins containing the SNW/SKIP domain are commonly unstable under in vivo conditions. In addition, the protein network, demonstrated that SKIP homologues could act by modulating plant fitness through gene expression regulation at the transcriptional and post-transcriptional levels. This could be corroborated by the expression number of gene copies of SKIP proteins in many species, highlighting it's crucial role in plant development and tolerance through the course of evolution.

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Ermp1 is a putative metalloprotease from Schizosaccharomyces pombe and a member of the Fxna peptidases. Although their function is unknown, orthologous proteins from rats and humans have been associated with the maturation of ovarian follicles and increased ER stress. This study focuses on proposing the first prediction of PPI by comparison of the interologues between humans and yeasts, as well as the molecular docking and dynamics of the M28 domain of Ermp1 with possible target proteins. As results, 45 proteins are proposed that could interact with the metalloprotease. Most of these proteins are related to the transport of Ca2+ and the metabolism of amino acids and proteins. Docking and molecular dynamics suggest that the M28 domain of Ermp1 could hydrolyze leucine and methionine residues of Amk2, Ypt5 and Pex12. These results could support future experimental investigations of other Fxna peptidases, such as human ERMP1.

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The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a fast-spreading viral pathogen and poses a serious threat to human health. New SARS-CoV-2 variants have been arising worldwide; therefore, is necessary to explore more therapeutic options. The interaction of the viral spike (S) protein with the angiotensin-converting enzyme 2 (ACE2) host receptor is an attractive drug target to prevent the infection via the inhibition of virus cell entry. In this study, Ligand- and Structure-Based Virtual Screening (LBVS and SBVS) was performed to propose potential inhibitors capable of blocking the S receptor-binding domain (RBD) and ACE2 interaction. The best five lead compounds were confirmed as inhibitors through ELISA-based enzyme assays. The docking studies and molecular dynamic (MD) simulations of the selected compounds maintained the molecular interaction and stability (RMSD fluctuations less than 5 Å) with key residues of the S protein. The compounds DRI-1, DRI-2, DRI-3, DRI-4, and DRI-5 efficiently block the interaction between the SARS-CoV-2 spike protein and receptor ACE2 (from 69.90 to 99.65% of inhibition) at 50 µM. The most potent inhibitors were DRI-2 (IC50 = 8.8 µM) and DRI-3 (IC50 = 2.1 µM) and have an acceptable profile of cytotoxicity (CC50 > 90 µM). Therefore, these compounds could be good candidates for further SARS-CoV-2 preclinical experiments.

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The Sec61 translocon allows the translocation of secretory preproteins from the cytosol to the endoplasmic reticulum lumen during polypeptide biosynthesis. These proteins possess an N-terminal signal peptide (SP) which docks at the translocon. SP mutations can abolish translocation and cause diseases, suggesting an essential role for this SP/Sec61 interaction. However, a detailed biophysical characterization of this binding is still missing. Here, optical tweezers force spectroscopy was used to characterize the kinetic parameters of the dissociation process between Sec61 and the SP of prepro-alpha-factor. The unbinding parameters including off-rate constant and distance to the transition state were obtained by fitting rupture force data to Dudko-Hummer-Szabo models. Interestingly, the translocation inhibitor mycolactone increases the off-rate and accelerates the SP/Sec61 dissociation, while also weakening the interaction. Whereas the translocation deficient mutant containing a single point mutation in the SP abolished the specificity of the SP/Sec61 binding, resulting in an unstable interaction. In conclusion, we characterize quantitatively the dissociation process between the signal peptide and the translocon, and how the unbinding parameters are modified by a translocation inhibitor.

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Protein-Protein Interaction Networks aim to model the interactome, providing a powerful tool for understanding the complex relationships governing cellular processes. These networks have numerous applications, including functional enrichment, discovering cancer driver genes, identifying drug targets, and more. Various databases make protein-protein networks available for many species, including Homo sapiens. This work topologically compares four Homo sapiens networks using a coarse-to-fine approach, comparing global characteristics, sub-network topology, specific nodes centrality, and interaction significance. Results show that the four human protein networks share many common protein-encoding genes and some global measures, but significantly differ in the interactions and neighbourhood. Small sub-networks from cancer pathways performed better than the whole networks, indicating an improved topological consistency in functional pathways. The centrality analysis shows that the same genes play different roles in different networks. We discuss how studies and analyses that rely on protein-protein networks for humans should consider their similarities and distinctions.

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Protein-protein interaction is at the heart of most biological processes, and small peptides that bind to protein binding sites are resourceful tools to explore and understand the structural requirements for these interactions. In that sense, phage display is a well-suited technology to study protein-protein interactions, as it allows for unbiased screening of billions of peptides in search for those that interact with a protein binding domain. Here, we will illustrate how two distinct but complementary approaches, phage display and nuclear magnetic resonance (NMR), can be utilized to unveil structural details of peptide-protein interaction. Finally, knowledge derived from phage mutagenesis and NMR studies can be streamlined for quick peptidomimetic design and synthesis using the retroinversion approach to validate using in vitro and in vivo assays the therapeutic potential of peptides identified by phage display.

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Astringency corresponds to the sensation of dryness and roughness that is experienced in the oral cavity in association with the interaction between salivary proteins and food polyphenols. In this study, the phenolic composition of seven varietal wines, the intensity of astringency they evoke and the physicochemical reactivity of these wines with whole human saliva were evaluated. Phenolic composition of wines was characterized by spectrophotometry and HPLC chromatography. Intensity of astringency was evaluated by trained sensory panels. Saliva from a single volunteer subject was used to assess wine-saliva interactions. To this end, binary mixtures were produced at different v/v wine/saliva ratios and each of them assayed for the ability of the salivary protein to diffuse on a cellulose membrane (diffusion test) and to remain in solution (precipitation test). Physicochemical reactivities between wine components and the protein fraction of saliva were contrasted against the astringency and the phenolic profile of each varietal wine. The study supports the view that astringency depends on physicochemical interactions between two complex matrices -wine and saliva- and not between some of their particular components.

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Abstract Glioblastoma multiforme is a tumor of the central nervous system. Focal Adhesion Kinase (FAK) and αB-crystalline are two proteins involved in glioblastoma development. In this study, we investigated whether the FAK/αB-crystalline interaction is important for glioblastoma cells, we aimed to investigate the interaction of these two proteins in the glioblastoma multiforme cell line U87-MG. Two peptides named FP01 peptide (derived from αB-crystalline) and FP02 peptide (derived from FAK) were synthesized for this study. Treatment of U87-MG with the peptides FP01 and FP02 in the concentration at 50 µM reduced the viability cellular to around 41% and 51%, respectively. Morphological alterations in the cells treated with the peptides when compared to the control were observed. This study suggests that the interaction between FAK and αB-crystalline is important for the viability of glioblastoma cells

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Protein interactions are investigated under different conditions of lysozyme concentration, temperature and ionic strength by means of in-solution small angle X-Ray scattering (SAXS) experiments and Monte Carlo (MC) simulations. Initially, experimental data were analysed through a Hard-Sphere Double Yukawa (HSDY) model combined with Random Phase Approximation (RPA), a closure relationship commonly used in the literature for monodisperse systems. We realized by means of MC that the HSDY/RPA modelling fails to describe the protein-protein pair potential for moderated and dense systems at low ionic strength, mainly due to inherent distortions of the RPA approximation. Our SAXS/MC results thus show that lysozyme concentrations between 2 (diluted) and 20 mg/mL (not crowded) present similar protein-protein pair potential preserving the values of surface net charge around 7 e, protein diameter of 28 Å, decay range of attractive well potential of 3 Å and a depth of the well potential varying from 1 to 5 kBT depending on temperature and salt addition. Noteworthy, we here propose a novel method to analyse the SAXS data from interacting proteins through MC simulations, which overcomes the deficiencies presented by the use of a closure relationship. Furthermore, this new methodology of combining SAXS with MC simulations gives a step forward to investigate more complex systems as those composed of a mixture of proteins of distinct species presenting different molecular weights (and hence sizes) and surface net charges at low, moderate and very dense systems.

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Adaptation to different soil conditions is a well-regulated process vital for plant life. AtHB23 is a homeodomain-leucine zipper I transcription factor (TF) that was previously revealed as crucial for plant survival under salinity conditions. We wondered whether this TF has partners to perform this essential function. Therefore, TF cDNA library screening, yeast two-hybrid, bimolecular fluorescence complementation, and coimmunoprecipitation assays were complemented with expression analyses and phenotypic characterization of silenced, mutant, overexpression, and crossed plants in normal and salinity conditions. We revealed that AtHB23, AtPHL1, and AtMYB68 interact with each other, modulating root development and the salinity response. The encoding genes are coexpressed in specific root tissues and at specific developmental stages. In normal conditions, amiR68 silenced plants have fewer initiated roots, the opposite phenotype to that shown by amiR23 plants. AtMYB68 and AtPHL1 play opposite roles in lateral root elongation. Under salinity conditions, AtHB23 plays a crucial positive role in cooperating with AtMYB68, whereas AtPHL1 acts oppositely by obstructing the function of the former, impacting the plant's survival ability. Such interplay supports the complex interaction between these TF in primary and lateral roots. The root adaptation capability is associated with the amyloplast state. We identified new molecular players that through a complex relationship determine Arabidopsis root architecture and survival in salinity conditions.

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Acetylation signaling pathways in trypanosomatids, a group of early branching organisms, are poorly understood due to highly divergent protein sequences. To overcome this challenge, we used interactomic datasets and AlphaFold2 (AF2)-multimer to predict direct interactions and validated them using yeast two and three-hybrid assays. We focused on MORF4 related gene (MRG) domain-containing proteins and their interactions, typically found in histone acetyltransferase/deacetylase complexes. The results identified a structurally conserved complex, TcTINTIN, which is orthologous to human and yeast trimer independent of NuA4 for transcription interaction (TINTIN) complexes; and another trimeric complex involving an MRG domain, only seen in trypanosomatids. The identification of a key component of TcTINTIN, TcMRGBP, would not have been possible through traditional homology-based methods. We also conducted molecular dynamics simulations, revealing a conformational change that potentially affects its affinity for TcBDF6. The study also revealed a novel way in which an MRG domain participates in simultaneous interactions with two MRG binding proteins binding two different surfaces, a phenomenon not previously reported. Overall, this study demonstrates the potential of using AF2-processed interactomic datasets to identify protein complexes in deeply branched eukaryotes, which can be challenging to study based on sequence similarity. The findings provide new insights into the acetylation signaling pathways in trypanosomatids, specifically highlighting the importance of MRG domain-containing proteins in forming complexes, which may have important implications for understanding the biology of these organisms and developing new therapeutics. On the other hand, our validation of AF2 models for the determination of multiprotein complexes illuminates the power of using such artificial intelligence-derived tools in the future development of biology.

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Pathway analysis is an important step in the interpretation of single cell transcriptomic data, as it provides powerful information to detect which cellular processes are active in each individual cell. We have recently developed a protein-protein interaction network-based framework to quantify pluripotency associated pathways from scRNA-seq data. On this occasion, we extend this approach to quantify the activity of a pathway associated with any biological process, or even any list of genes. A systems-level characterization of pathway activities across multiple cell types provides a broadly applicable tool for the analysis of pathways in both healthy and disease conditions. Dysregulated cellular functions are a hallmark of a wide spectrum of human disorders, including cancer and autoimmune diseases. Here, we illustrate our method by analyzing various biological processes in healthy and cancer breast samples. Using this approach we found that tumor breast cells, even when they form a single group in the UMAP space, keep diverse biological programs active in a differentiated manner within the cluster.•We implement a protein-protein interaction network-based approach to quantify the activity of different biological processes.•The methodology can be used for cell annotation in scRNA-seq studies and is freely available as R package.

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Ionotropic receptors are ligand-gated ion channels triggering fast neurotransmitter responses. Among them, P2X and 5-HT3 receptors have been shown to physically interact each other and functionally inducing cross inhibitory responses. Nevertheless, despite the importance of P2X4 and 5-HT3A receptors that mediate for example neuropathic pain and psychosis respectively, complementary evidence has recently started to move forward in the understanding of this interaction. In this review, we discuss current evidence supporting the mechanism of crosstalking between both receptors, from the structural to the transduction pathway level. We expect this work may guide the design of further experiments to obtain a comprehensive view for the neuropharmacological role of these interacting receptors. This article is part of the Special Issue on "The receptor-receptor interaction as a new target for therapy".

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Zika virus (ZIKV) is an arbovirus of the Flaviviridae genus that has rapidly disseminated from across the Pacific to the Americas. Robust evidence has indicated a crucial role of ZIKV in congenital virus syndrome, including neonatal microcephaly. Moreover, emerging evidence suggests an association between ZIKV infection and the development of an extensive spectrum of central nervous system inflammatory demyelinating diseases (CNS IDD), such as multiple sclerosis-like clinical phenotypes. However, the underlying mechanisms of host-pathogen neuro-immune interactions remain to be elucidated. This study aimed to identify common transcriptional signatures between multiple sclerosis (MS) and ZIKV infection to generate molecular interaction networks, thereby leading to the identification of deregulated processes and pathways, which could give an insight of these underlying molecular mechanisms. Our investigation included publicly available transcriptomic data from MS patients in either relapse or remission (RR-MS) and datasets of subjects acutely infected by ZIKV for both immune peripheral cells and central nervous system cells. The protein-protein interaction (PPI) analysis showed upregulated AP-1 transcription factors (JUN and FOS) among the top hub and bottleneck genes in RR-MS and ZIKV data. Gene enrichment analysis retrieved a remarkable presence of ontologies and pathways linked to oxidative stress responses, immune cell function, inflammation, interleukin signaling, cell division, and transcriptional regulation commonly enriched in both scenarios. Considering the recent findings concerning AP-1 function in immunological tolerance breakdown, regulation of inflammation, and its function as an oxidative stress sensor, we postulate that the ZIKV trigger may contribute as a boost for the activation of such AP-1-regulated mechanisms that could favor the development of MS-like phenotypes following ZIKV infection in a genetically susceptible individual.

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The combination of a tumor-penetrating peptide (TPP) with a peptide able to interfere with a given protein-protein interaction (IP) is a promising strategy with potential clinical application. Little is known about the impact of fusing a TPP with an IP, both in terms of internalization and functional effect. Here, we analyze these aspects in the context of breast cancer, targeting PP2A/SET interaction, using both in silico and in vivo approaches. Our results support the fact that state-of-the-art deep learning approaches developed for protein-peptide interaction modeling can reliably identify good candidate poses for the IP-TPP in interaction with the Neuropilin-1 receptor. The association of the IP with the TPP does not seem to affect the ability of the TPP to bind to Neuropilin-1. Molecular simulation results suggest that peptide IP-GG-LinTT1 in a cleaved form interacts with Neuropilin-1 in a more stable manner and has a more helical secondary structure than the cleaved IP-GG-iRGD. Surprisingly, in silico investigations also suggest that the non-cleaved TPPs can bind the Neuropilin-1 in a stable manner. The in vivo results using xenografts models show that both bifunctional peptides resulting from the combination of the IP and either LinTT1 or iRGD are effective against tumoral growth. The peptide iRGD-IP shows the highest stability to serum proteases degradation while having the same antitumoral effect as Lin TT1-IP, which is more sensitive to proteases degradation. Our results support the development of the TPP-IP strategy as therapeutic peptides against cancer.

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Toxoplasma gondii is an obligate intracellular apicomplexan that causes toxoplasmosis in humans and animals. Central to its dissemination and pathogenicity is the ability to rapidly divide in the tachyzoite stage and infect any type of nucleated cell. Adaptation to different cell contexts requires high plasticity in which heat shock proteins (Hsps) could play a fundamental role. Tgj1 is a type I Hsp40 of T. gondii, an ortholog of the DNAJA1 group, which is essential during the tachyzoite lytic cycle. Tgj1 consists of a J-domain, ZFD, and DNAJ_C domains with a CRQQ C-terminal motif, which is usually prone to lipidation. Tgj1 presented a mostly cytosolic subcellular localization overlapping partially with endoplasmic reticulum. Protein-protein Interaction (PPI) analysis showed that Tgj1 could be implicated in various biological pathways, mainly translation, protein folding, energy metabolism, membrane transport and protein translocation, invasion/pathogenesis, cell signaling, chromatin and transcription regulation, and cell redox homeostasis among others. The combination of Tgj1 and Hsp90 PPIs retrieved only 70 interactors linked to the Tgj1-Hsp90 axis, suggesting that Tgj1 would present specific functions in addition to those of the Hsp70/Hsp90 cycle, standing out invasion/pathogenesis, cell shape motility, and energy pathway. Within the Hsp70/Hsp90 cycle, translation-associated pathways, cell redox homeostasis, and protein folding were highly enriched in the Tgj1-Hsp90 axis. In conclusion, Tgj1 would interact with a wide range of proteins from different biological pathways, which could suggest a relevant role in them.