Tumor-derived RHOA mutants interact with effectors in the GDP-bound state.

RHOA mutations are found at diverse residues in various cancer types, implying mutation- and cell-specific mechanisms of tumorigenesis. Here, we focus on the underlying mechanisms of two gain-of-function RHOA mutations, A161P and A161V, identified in adult T-cell leukemia/lymphoma. We find that RHOAA161P and RHOAA161V are both fast-cycling mutants with increased guanine nucleotide dissociation/association rates compared with RHOAWT and show reduced GTP-hydrolysis activity. Crystal structures reveal an altered nucleotide association in RHOAA161P and an open nucleotide pocket in RHOAA161V. Both mutations perturb the dynamic properties of RHOA switch regions and shift the conformational landscape important for RHOA activity, as shown by 31P NMR and molecular dynamics simulations. Interestingly, RHOAA161P and RHOAA161V can interact with effectors in the GDP-bound state. 1H-15N HSQC NMR spectra support the existence of an active population in RHOAA161V-GDP. The distinct interaction mechanisms resulting from the mutations likely favor an RHOAWT-like "ON" conformation, endowing GDP-bound state effector binding activity.

Dysbiosis in pregnant mice induced by transfer of human vaginal microbiota followed by reversal of pathological changes in the uterus and placenta via progesterone treatment.

OBJECTIVE: The vaginal microbiota dysbiosis induces inflammation in the uterus that triggers tissue damage and is associated with preterm birth. Progesterone is used to prevent labor in pregnant women at risk of preterm birth. However, the mechanism of action of progesterone still needs to be clarified. We aimed to show the immunomodulatory effect of progesterone on the inflammation of uterine tissue triggered by dysbiotic vaginal microbiota in a pregnant mouse model. METHODS: Healthy (n = 6) and dysbiotic (n = 7) vaginal microbiota samples isolated from pregnant women were transferred to control (n = 10) and dysbiotic (n = 14) pregnant mouse groups. The dysbiotic microbiota transferred group was treated with 1 mg progesterone (n = 7). Flow cytometry and immunohistochemistry analyses were used to evaluate inflammatory processes. Vaginal microbiota samples were analyzed by 16 S rRNA sequencing. RESULTS: Vaginal exposure to dysbiotic microbiota resulted in macrophage accumulation in the uterus and cellular damage in the placenta. Even though TNF and IL-6 elevations were not significant after dysbiotic microbiota transplantation, progesterone treatment decreased TNF and IL-6 expressions from 49.085 to 31.274% (p = 0.0313) and 29.279-21.216% (p = 0.0167), respectively. Besides, the macrophage density in the uterus was reduced, and less cellular damage in the placenta was observed. CONCLUSION: Analyzing the vaginal microbiota before or during pregnancy may support the decision for initiation of progesterone therapy. Our results also guide the development of new strategies for preventing preterm birth.

DiPPI: A Curated Data Set for Drug-like Molecules in Protein-Protein Interfaces.

Proteins interact through their interfaces, and dysfunction of protein-protein interactions (PPIs) has been associated with various diseases. Therefore, investigating the properties of the drug-modulated PPIs and interface-targeting drugs is critical. Here, we present a curated large data set for drug-like molecules in protein interfaces. We further introduce DiPPI (Drugs in Protein-Protein Interfaces), a two-module web site to facilitate the search for such molecules and their properties by exploiting our data set in drug repurposing studies. In the interface module of the web site, we present several properties, of interfaces, such as amino acid properties, hotspots, evolutionary conservation of drug-binding amino acids, and post-translational modifications of these residues. On the drug-like molecule side, we list drug-like small molecules and FDA-approved drugs from various databases and highlight those that bind to the interfaces. We further clustered the drugs based on their molecular fingerprints to confine the search for an alternative drug to a smaller space. Drug properties, including Lipinski's rules and various molecular descriptors, are also calculated and made available on the web site to guide the selection of drug molecules. Our data set contains 534,203 interfaces for 98,632 protein structures, of which 55,135 are detected to bind to a drug-like molecule. 2214 drug-like molecules are deposited on our web site, among which 335 are FDA-approved. DiPPI provides users with an easy-to-follow scheme for drug repurposing studies through its well-curated and clustered interface and drug data and is freely available at http://interactome.ku.edu.tr:8501.

PPInterface: A Comprehensive Dataset of 3D Protein-Protein Interface Structures.

The PPInterface dataset contains 815,082 interface structures, providing the most comprehensive structural information on protein-protein interfaces. This resource is extracted from over 215,000 three-dimensional protein structures stored in the Protein Data Bank (PDB). The dataset contains a wide range of protein complexes, providing a wealth of information for researchers investigating the structural properties of protein-protein interactions. The accompanying web server has a user-friendly interface that allows for efficient search and download functions. Researchers can access detailed information on protein interface structures, visualize them, and explore a variety of features, increasing the dataset's utility and accessibility. The dataset and web server can be found at https://3dpath.ku.edu.tr/PPInt/.

The vaginal microbiome composition during pregnancy in a region compromising different ethnic origins.

BACKGROUND: The vaginal microbiota plays a significant role in pregnancy outcomes and newborn health. Indeed, the composition and diversity of the vaginal microbiota can vary among different ethnic groups. Our study aimed to investigate the composition of the vaginal microbiome throughout the three trimesters of pregnancy and to identify any potential variations or patterns in the Turkish population compromising mixed ethnicities. METHOD: We conducted a longitudinal study to characterize the vaginal microbiota of pregnant women. The study included a total of 25 participants, and the samples were collected at each trimester: 11-13 weeks, 20-24 weeks and 28-34 weeks gestation. RESULTS: Lactobacillus species were consistently found to be dominant in the vaginal microbiota throughout all trimesters of pregnancy. Among Lactobacillus species, L. crispatus had the highest abundance in all trimesters (40.6%, 40.8% and 44.4%, respectively). L. iners was the second most prevalent species (28.5%, 31% and 25.04, respectively). Our findings reveal that the dominant composition of the vaginal microbiota aligns with the CST-type I, commonly observed in the European population. CONCLUSIONS: This suggests that there are shared mechanisms influencing the microbial communities in the vagina, which are likely influenced by factors such as genetics, lifestyle, and cultural behaviors rather than ethnicity alone. The complex interplay of these factors contributes to the establishment and maintenance of the vaginal microbiota during pregnancy. Understanding the underlying mechanisms and their impact on vaginal health across diverse populations is essential for improving pregnancy outcomes. The study was approved by the Koc University Ethical Committee (no:2019.093.IRB2.030) and registered at the clinical trials.

ProInterVal: Validation of Protein-Protein Interfaces through Learned Interface Representations.

Proteins are vital components of the biological world and serve a multitude of functions. They interact with other molecules through their interfaces and participate in crucial cellular processes. Disruption of these interactions can have negative effects on organisms, highlighting the importance of studying protein-protein interfaces for developing targeted therapies for diseases. Therefore, the development of a reliable method for investigating protein-protein interactions is of paramount importance. In this work, we present an approach for validating protein-protein interfaces using learned interface representations. The approach involves using a graph-based contrastive autoencoder architecture and a transformer to learn representations of protein-protein interaction interfaces from unlabeled data and then validating them through learned representations with a graph neural network. Our method achieves an accuracy of 0.91 for the test set, outperforming existing GNN-based methods. We demonstrate the effectiveness of our approach on a benchmark data set and show that it provides a promising solution for validating protein-protein interfaces.

Conformational diversity and protein-protein interfaces in drug repurposing in Ras signaling pathway.

We focus on drug repurposing in the Ras signaling pathway, considering structural similarities of protein-protein interfaces. The interfaces formed by physically interacting proteins are found from PDB if available and via PRISM (PRotein Interaction by Structural Matching) otherwise. The structural coverage of these interactions has been increased from 21 to 92% using PRISM. Multiple conformations of each protein are used to include protein dynamics and diversity. Next, we find FDA-approved drugs bound to structurally similar protein-protein interfaces. The results suggest that HIV protease inhibitors tipranavir, indinavir, and saquinavir may bind to EGFR and ERBB3/HER3 interface. Tipranavir and indinavir may also bind to EGFR and ERBB2/HER2 interface. Additionally, a drug used in Alzheimer's disease can bind to RAF1 and BRAF interface. Hence, we propose a methodology to find drugs to be potentially used for cancer using a dataset of structurally similar protein-protein interface clusters rather than pockets in a systematic way.

Shared Proteins and Pathways of Cardiovascular and Cognitive Diseases: Relation to Vascular Cognitive Impairment.

One of the primary goals of systems medicine is the detection of putative proteins and pathways involved in disease progression and pathological phenotypes. Vascular cognitive impairment (VCI) is a heterogeneous condition manifesting as cognitive impairment resulting from vascular factors. The precise mechanisms underlying this relationship remain unclear, which poses challenges for experimental research. Here, we applied computational approaches like systems biology to unveil and select relevant proteins and pathways related to VCI by studying the crosstalk between cardiovascular and cognitive diseases. In addition, we specifically included signals related to oxidative stress, a common etiologic factor tightly linked to aging, a major determinant of VCI. Our results show that pathways associated with oxidative stress are quite relevant, as most of the prioritized vascular cognitive genes and proteins were enriched in these pathways. Our analysis provided a short list of proteins that could be contributing to VCI: DOLK, TSC1, ATP1A1, MAPK14, YWHAZ, CREB3, HSPB1, PRDX6, and LMNA. Moreover, our experimental results suggest a high implication of glycative stress, generating oxidative processes and post-translational protein modifications through advanced glycation end-products (AGEs). We propose that these products interact with their specific receptors (RAGE) and Notch signaling to contribute to the etiology of VCI.

A Genome-Wide Functional Screen Identifies Enhancer and Protective Genes for Amyloid Beta-Peptide Toxicity.

Alzheimer's disease (AD) is known to be caused by amyloid ß-peptide (Aß) misfolded into ß-sheets, but this knowledge has not yet led to treatments to prevent AD. To identify novel molecular players in Aß toxicity, we carried out a genome-wide screen in Saccharomyces cerevisiae, using a library of 5154 gene knock-out strains expressing Aß1-42. We identified 81 mammalian orthologue genes that enhance Aß1-42 toxicity, while 157 were protective. Next, we performed interactome and text-mining studies to increase the number of genes and to identify the main cellular functions affected by Aß oligomers (oAß). We found that the most affected cellular functions were calcium regulation, protein translation and mitochondrial activity. We focused on SURF4, a protein that regulates the store-operated calcium channel (SOCE). An in vitro analysis using human neuroblastoma cells showed that SURF4 silencing induced higher intracellular calcium levels, while its overexpression decreased calcium entry. Furthermore, SURF4 silencing produced a significant reduction in cell death when cells were challenged with oAß1-42, whereas SURF4 overexpression induced Aß1-42 cytotoxicity. In summary, we identified new enhancer and protective activities for Aß toxicity and showed that SURF4 contributes to oAß1-42 neurotoxicity by decreasing SOCE activity.

Alterations in vaginal microbiota among pregnant women with COVID-19.

The maintenance of vaginal microbiota is an important factor to achieve optimum pregnancy outcomes. The study aims to describe the alterations in the composition of vaginal microbiota in pregnant women with coronavirus disease 2019 (COVID-19). This was a prospective case-control study. Vaginal swabs were collected from uninfected pregnant women (n = 28) and pregnant women with COVID-19 (n = 19) during the active phase of infection and within a month after recovering from infection. The vaginal microbiota on the swabs was examined by 16S rRNA gene sequencing. Shannon index indicates that alpha diversity is significantly higher in women with COVID-19 (p = 0.012). There was a significant decrease in Firmicutes (p = 0.014) with an increase in Bacteroidota (p = 0.018) phyla and a decrease in Lactobacillus (p = 0.007) genus in women with COVID-19 than those of uninfected pregnant women. The relative abundance of L. crispatus, L. iners, L. gasseri, and L. jensenii were lower in the COVID-19 group than in uninfected pregnant women. In subgroup analysis, the amount of Ureaplasma spp. was higher in women with moderate/severe than those of asymptomatic/mild disease (p = 0.036). The study revealed that vaginal dysbiosis with low abundance of Lactobacillus species occurred in pregnant women infected with severe acute respiratory syndrome coronavirus-2. These findings may lead to new studies to elucidate the risk of pregnancy adverse outcomes related to COVID-19.

Structural coverage of the human interactome.

Complex biological processes in cells are embedded in the interactome, representing the complete set of protein-protein interactions. Mapping and analyzing the protein structures are essential to fully comprehending these processes' molecular details. Therefore, knowing the structural coverage of the interactome is important to show the current limitations. Structural modeling of protein-protein interactions requires accurate protein structures. In this study, we mapped all experimental structures to the reference human proteome. Later, we found the enrichment in structural coverage when complementary methods such as homology modeling and deep learning (AlphaFold) were included. We then collected the interactions from the literature and databases to form the reference human interactome, resulting in 117 897 non-redundant interactions. When we analyzed the structural coverage of the interactome, we found that the number of experimentally determined protein complex structures is scarce, corresponding to 3.95% of all binary interactions. We also analyzed known and modeled structures to potentially construct the structural interactome with a docking method. Our analysis showed that 12.97% of the interactions from HuRI and 73.62% and 32.94% from the filtered versions of STRING and HIPPIE could potentially be modeled with high structural coverage or accuracy, respectively. Overall, this paper provides an overview of the current state of structural coverage of the human proteome and interactome.

HMI-PRED 2.0: a biologist-oriented web application for prediction of host-microbe protein-protein interaction by interface mimicry.

SUMMARY: HMI-PRED 2.0 is a publicly available web service for the prediction of host-microbe protein-protein interaction by interface mimicry that is intended to be used without extensive computational experience. A microbial protein structure is screened against a database covering the entire available structural space of complexes of known human proteins. AVAILABILITY AND IMPLEMENTATION: HMI-PRED 2.0 provides user-friendly graphic interfaces for predicting, visualizing and analyzing host-microbe interactions. HMI-PRED 2.0 is available at https://hmipred.org/.

Is Persistent Post-COVID Headache Associated With Protein-Protein Interactions Between Antibodies Against Viral Spike Protein and CGRP Receptor?: A Case Report.

Background: After the acute pandemic of coronavirus disease 2019 (COVID-19), a wide variety of symptoms are identified under the term post-COVID syndrome, such as persistent headache. Post-COVID headache can be presented in a broad spectrum like headache attributed to systemic infection, chronification of already existing primary headache, or long-lasting, and also late-onset new daily persistent headache. Still, little is known about the pathophysiology of post-COVID headache, but activation of the trigeminovascular system may be one of the players. Case Report: Here, we present a case with a severe, long-lasting post-COVID headache and its sudden cessation with calcitonin gene-related peptide (CGRP) monoclonal antibody treatment. Conclusion: In our previous protein mimicry study, we have pointed at mimicry of virus spike protein and CGRP receptors. This mechanism may enlighten the current, common, and yet unsolved post-COVID headache cases.

Artificial intelligence approaches to human-microbiome protein-protein interactions.

Host-microbiome interactions play significant roles in human health and disease. Artificial intelligence approaches have been developed to better understand and predict the molecular interplay between the host and its microbiome. Here, we review recent advancements in computational methods to predict microbial effects on human cells with a special focus on protein-protein interactions. We categorize recent methods from traditional ones to more recent deep learning methods, followed by several challenges and potential solutions in structure-based approaches. This review serves as a brief guide to the current status and future directions in the field.

SARS-CoV-2 Interactome 3D: A Web interface for 3D visualization and analysis of SARS-CoV-2-human mimicry and interactions.

SUMMARY: We present a web-based server for navigating and visualizing possible interactions between SARS-CoV-2 and human host proteins. The interactions are obtained from HMI_Pred which relies on the rationale that virus proteins mimic host proteins. The structural alignment of the viral protein with one side of the human protein-protein interface determines the mimicry. The mimicked human proteins and predicted interactions, and the binding sites are presented. The user can choose one of the 18 SARS-CoV-2 protein structures and visualize the potential 3D complexes it forms with human proteins. The mimicked interface is also provided. The user can superimpose two interacting human proteins in order to see whether they bind to the same site or different sites on the viral protein. The server also tabulates all available mimicked interactions together with their match scores and number of aligned residues. This is the first server listing and cataloging all interactions between SARS-CoV-2 and human protein structures, enabled by our innovative interface mimicry strategy. AVAILABILITY AND IMPLEMENTATION: The server is available at https://interactome.ku.edu.tr/sars/.

Artificial intelligence based methods for hot spot prediction.

Proteins interact through their interfaces to fulfill essential functions in the cell. They bind to their partners in a highly specific manner and form complexes that have a profound effect on understanding the biological pathways they are involved in. Any abnormal interactions may cause diseases. Therefore, the identification of small molecules which modulate protein interactions through their interfaces has high therapeutic potential. However, discovering such molecules is challenging. Most protein-protein binding affinity is attributed to a small set of amino acids found in protein interfaces known as hot spots. Recent studies demonstrate that drug-like small molecules specifically may bind to hot spots. Therefore, hot spot prediction is crucial. As experimental data accumulates, artificial intelligence begins to be used for computational hot spot prediction. First, we review machine learning and deep learning for computational hot spot prediction and then explain the significance of hot spots toward drug design.

Interactome analysis of Bag-1 isoforms reveals novel interaction partners in endoplasmic reticulum-associated degradation.

Bag-1 is a multifunctional protein that regulates Hsp70 chaperone activity, apoptosis, and proliferation. The three major Bag-1 isoforms have different subcellular localizations and partly non-overlapping functions. To identify the detailed interaction network of each isoform, we utilized mass spectrometry-based proteomics and found that interactomes of Bag-1 isoforms contained many common proteins, with variations in their abundances. Bag-1 interactomes were enriched with proteins involved in protein processing and degradation pathways. Novel interaction partners included VCP/p97; a transitional ER ATPase, Rad23B; a shuttling factor for ubiquitinated proteins, proteasome components, and ER-resident proteins, suggesting a role for Bag-1 also in ER-associated protein degradation (ERAD). Bag-1 pull-down from cells and tissues from breast cancer patients validated these interactions and showed cancer-related prominence. Using in silico predictions we detected hotspot residues of Bag-1. Mutations of these residues caused loss of binding to protein quality control elements and impaired proteasomal activity in MCF-7 cells. Following CD147 glycosylation pattern, we showed that Bag-1 downregulated VCP/p97-dependent ERAD. Overall, our data extends the interaction map of Bag-1, and broadens its role in protein homeostasis. Targeting the interaction surfaces revealed in this study might be an effective strategy in the treatment of cancer.

Normal Mode Analysis of KRas4B Reveals Partner Specific Dynamics.

Ras GTPase interacts with its regulators and downstream effectors for its critical function in cellular signaling. Targeting the disrupted mechanisms in Ras-related human cancers requires understanding the distinct dynamics of these protein-protein interactions. We performed normal mode analysis (NMA) of KRas4B in wild-type or mutant monomeric and neurofibromin-1 (NF1), Son of Sevenless 1 (SOS1) or Raf-1 bound dimeric conformational states to reveal partner-specific dynamics of the protein. Gaussian network model (GNM) analysis showed that the known KRas4B lobes further partition into subdomains upon binding to its partners. Furthermore, KRas4B interactions with different partners suppress the flexibility in not only their binding sites but also distant residues in the allosteric lobe in a partner-specific way. The conformational changes can be driven by intrinsic residue fluctuations of the open state KRas4B-GDP, as we illustrated with anisotropic network model (ANM) analysis. The allosteric paths connecting the nucleotide binding residues to the allosteric site at α3-L7 portray differences in the inactive and active states. These findings help in understanding the partner-specific KRas4B dynamics, which could be utilized for therapeutic targeting.

Neuropsychiatric Symptoms of COVID-19 Explained by SARS-CoV-2 Proteins' Mimicry of Human Protein Interactions.

The first clinical symptoms focused on the presentation of coronavirus disease 2019 (COVID-19) have been respiratory failure, however, accumulating evidence also points to its presentation with neuropsychiatric symptoms, the exact mechanisms of which are not well known. By using a computational methodology, we aimed to explain the molecular paths of COVID-19 associated neuropsychiatric symptoms, based on the mimicry of the human protein interactions with SARS-CoV-2 proteins. Methods: Available 11 of the 29 SARS-CoV-2 proteins' structures have been extracted from Protein Data Bank. HMI-PRED (Host-Microbe Interaction PREDiction), a recently developed web server for structural PREDiction of protein-protein interactions (PPIs) between host and any microbial species, was used to find the "interface mimicry" through which the microbial proteins hijack host binding surfaces. Classification of the found interactions was conducted using the PANTHER Classification System. Results: Predicted Human-SARS-CoV-2 protein interactions have been extensively compared with the literature. Based on the analysis of the molecular functions, cellular localizations and pathways related to human proteins, SARS-CoV-2 proteins are found to possibly interact with human proteins linked to synaptic vesicle trafficking, endocytosis, axonal transport, neurotransmission, growth factors, mitochondrial and blood-brain barrier elements, in addition to its peripheral interactions with proteins linked to thrombosis, inflammation and metabolic control. Conclusion: SARS-CoV-2-human protein interactions may lead to the development of delirium, psychosis, seizures, encephalitis, stroke, sensory impairments, peripheral nerve diseases, and autoimmune disorders. Our findings are also supported by the previous in vivo and in vitro studies from other viruses. Further in vivo and in vitro studies using the proteins that are pointed here, could pave new targets both for avoiding and reversing neuropsychiatric presentations.

Mechanistic Differences of Activation of Rac1P29S and Rac1A159V.

Rac1 is a small GTPase that plays key roles in actin reorganization, cell motility, and cell survival/growth as well as in various cancer types and neurodegenerative diseases. Similar to other Ras superfamily GTPases, Rac1 switches between active GTP-bound and inactive GDP-bound states. Switch I and II regions open and close during GDP/GTP exchange. P29S and A159V (paralogous to K-RasA146) mutations are the two most common somatic mutations of Rac1. Rac1P29S is a known hotspot for melanoma, whereas Rac1A159V most commonly occurs in head and neck cancer. To investigate how these substitutions induce the Rac1 dynamics, we used atomistic molecular dynamics simulations on the wild-type Rac1 and two mutant systems (P29S and A159V) in the GTP bound state, and on the wild-type Rac1 and P29S mutated system in the GDP bound state. Here, we show that P29S and A159V mutations activate Rac1 with different mechanisms. In Rac1P29S-GTP, the substitution increases the flexibility of Switch I based on RMSF and dihedral angle calculations and leads to an open conformation. We propose that the open Switch I conformation is one of the underlying reasons for rapid GDP/GTP exchange of Rac1P29S. On the other hand, in Rac1A159V-GTP, some of the contacts of the guanosine ring of GTP with Rac1 are temporarily lost, enabling the guanosine ring to move toward Switch I and subsequently close the switch. Rac1A159V-GTP adopts a Ras state 2 like conformation, where both switch regions are in closed conformation and Thr35 forms a hydrogen bond with the nucleotide.