ProteinPrompt: a webserver for predicting protein-protein interactions

The kinase domain of TRPM7 interacts with PAK1 and regulates pancreatic cancer cell epithelial-to-mesenchymal transition

Pancreatic ductal adenocarcinoma (PDAC) is the main and the deadliest form of pancreatic cancer. This is a major problem of public health since it will become the second leading cause of death by cancer in the next few years, mainly due to the lack of efficient therapies. Transient Receptor Potential Cation Channel Subfamily M Member 7 (TRPM7) protein, a cation channel fused with a serine/threonine kinase domain is overexpressed in PDAC and associated with a low survival. In this work, we aim to study the role of kinase domain on pancreatic cell fates by using a model of kinase domain deletion by CRISPR-Cas9. PANC-1 and MIA PaCa-2 PDAC cell lines were used and kinase domain was deleted by CRISPR-Cas9 strategy. Kinase domain deletion (ΔK) was validated by RT-qPCR and western blots. The effect of kinase domain deletion on channel function was studied by patch-clamp and Mn2+-quenching. The cell phenotype was studied by MTT and cell migration/invasion assays. Finally, the role of kinase domain was studied in vivo in xenografted mice. Here we show that TRPM7 kinase domain is required to maintain a mesenchymal phenotype in PDAC cells. We also demonstrated that TRPM7 and PAK1 interact in the same protein complexes. Moreover, TRPM7 kinase domain is required for carcinogenesis and cancer cell dissemination in vivo. Intriguingly, the role of TRPM7 kinase is cell specific and may depend on the KRAS oncogene mutation status. In conclusion, TRPM7 kinase domain is required to maintain a mesenchymal and aggressive phenotype in PDAC cells, and it could be a promising target against PDAC.

VHI-Pred: A Multi-Feature-Based Tool for Predicting Human-Virus Protein-Protein Interactions

Viral diseases pose a significant threat to public health, highlighting the importance of understanding protein-protein interactions between hosts and viruses for therapeutic development. However, this process is often expensive and time-consuming, especially given the rapid evolution of viruses. Machine learning algorithms and artificial intelligence have emerged as powerful tools for efficiently identifying these interactions. This study aims to develop a machine learning-based model to predict protein interactions between viral pathogens and human hosts while analyzing the factors influencing these interactions. The prediction model was constructed using three machine learning algorithms: Random Forest (RF), XGBoost (XGB), and Artificial Neural Networks (ANN). Each algorithm underwent rigorous testing. The modeling features included physicochemical properties, motifs, and amino acid sequences. Model performance was evaluated using fitness, accuracy, precision, sensitivity, and specificity metrics, with validation conducted via the K-fold method. The accuracy of the RF, XGB, and ANN models was 87%, 86%, and 86%, respectively. By integrating dimensionality reduction and clustering techniques, the accuracy of the RF model improved to 90%. Traditionally, studying host-pathogen interactions is labor intensive and costly. The integration of machine learning algorithms into this field significantly enhances the efficiency of analyzing viral pathogen-human host interactions. This study demonstrates the effectiveness of such an approach and provides valuable insights for future research. The results are accessible to researchers through a web application at http://vhi.sysbiomed.ir .

PPILS: Protein-protein interaction prediction with language of biological coding

Protein-protein interactions within a cell are essential for various fundamental biological processes. Computational techniques have arisen in bioinformatics due to the challenging and resource-intensive nature of experimental protein pair interaction studies. This research seeks to create a cutting-edge machine learning method for predicting protein pair interactions using carefully chosen input features and leveraging evolutionary data. PPILS leverages evolutionary knowledge from the protein language model. It develops an encoder-decoder architecture with light attention. The trained model obtains protein embeddings from a language model and employs a light attention-based encoder, where a single convolution operation generates attention. A subsequent convolution is applied to input features, creating a representative construct for the protein interaction prediction. These encoded representations are then channeled into the decoder to predict protein interactions. Our findings indicated that PPILS outperformed existing methods in PPI prediction. The proposed method could be essential in protein-protein interaction prediction, further accelerating the discovery of protein-based drugs.

Phosphatidylserine on sperm head interact with Annexin A5 on oviduct luminal cilia to form a sperm reservoir in pigs

After insemination, a subpopulation of sperm reaches the oviducts and binds to isthmic epithelial cells to form a "sperm reservoir". Our objective was to explore the role of annexin A5 (ANXA5), a protein that binds with high affinity to phosphatidylserine (PS), in the formation of the sperm reservoir in pigs. Phosphatidylserine was detected on the head of approximately 10 % of boar sperm at ejaculation. Porcine ANXA5 was immunodetected with a strong signal on luminal cilia in the isthmus and in derived isthmic epithelial spheroids (IES). Exogenous PS between 0.01 and 0.1 µg/mL and recombinant porcine ANXA5 (rpANXA5) above 0.1 µg/mL inhibited sperm binding to IES without reducing sperm motility. Pre-incubation of sperm, but not IES, with rpANXA5 inhibited sperm binding to IES. Under capacitating conditions, the proportion of live sperm with head PS exposure and the ability of sperm to bind to rpANXA5 and IES cilia increased within 30 min. Conversely, the acrosome reaction decreased the ability of sperm to bind rpANXA5 and prevented sperm binding to IES. In conclusion, sperm membrane remodelling during capacitation enhanced head PS exposure in motile sperm, resulting in increased interaction with ciliary ANXA5 on isthmic epithelial spheroids. These findings support a role for PS-ANXA5 interaction in the formation of the sperm reservoir in mammalian females.

Accurate Prediction of Protein-Binding Residues in Protein Sequences Using SCRIBER

Deciphering molecular-level mechanisms that govern protein-protein interactions (PPIs) relies in part on the accurate prediction of protein-binding partners and protein-binding residues. These predictions can be used to support a wide spectrum of applications that include development of PPI networks and protein docking programs, drug design studies, and investigations of molecular details that underlie certain diseases. Computational methods that predict protein-binding residues offer convenient, inexpensive, and relatively accurate data that can aid these efforts. We introduce and describe a user-friendly webserver for the SCRIBER method that conveniently provides state-of-the-art predictions of protein-binding residues and that minimizes cross-predictions, i.e., incorrect prediction of residues that bind other/non-protein ligands as protein binding. SCRIBER relies on a two-layer architecture that is specifically designed to reduce the cross-predictions. We motivate and explain this predictive architecture. We describe how to use the webserver, interact with its web interface, and collect, read, and understand results generated by SCRIBER. The SCRIBER webserver is available at http://biomine.cs.vcu.edu/servers/SCRIBER/ .

The power of computational proteomics platforms to decipher protein-protein interactions

Adopting computational tools for analyzing extensive biological datasets has profoundly transformed our understanding and interpretation of biological phenomena. Innovative platforms have emerged, providing automated analysis to unravel essential insights about proteins and the complexities of their interactions. These computational advancements align with traditional studies, which employ experimental techniques to discern and quantify physical and functional protein-protein interactions (PPIs). Among these techniques, tandem mass spectrometry is notably recognized for its precision and sensitivity in identifying PPIs. These approaches might serve as important information enabling the identification of PPIs with potential pharmacological significance. This review aims to convey our experience using computational tools for detecting PPI networks and offer an analysis of platforms that facilitate predictions derived from experimental data.

A Computational Approach in the Systematic Search of the Interaction Partners of Alternatively Spliced TREM2 Isoforms

Alzheimer's disease is the most common form of dementia, characterized by the pathological accumulation of amyloid-beta (Aβ) plaques and tau neurofibrillary tangles. Triggering receptor expressed on myeloid cells 2 (TREM2) is increasingly recognized as playing a central role in Aβ clearance and microglia activation in AD. The TREM2 gene transcriptional product is alternatively spliced to produce three different protein isoforms. The canonical TREM2 isoform binds to DAP12 to activate downstream pathways. However, little is known about the function or interaction partners of the alternative TREM2 isoforms. The present study utilized a computational approach in a systematic search for new interaction partners of the TREM2 isoforms by integrating several state-of-the-art structural bioinformatics tools from initial large-scale screening to one-on-one corroborative modeling and eventual all-atom visualization. CD9, a cell surface glycoprotein involved in cell-cell adhesion and migration, was identified as a new interaction partner for two TREM2 isoforms, and CALM, a calcium-binding protein involved in calcium signaling, was identified as an interaction partner for a third TREM2 isoform, highlighting the potential role of cell adhesion and calcium regulation in AD.

MutationExplorer: a webserver for mutation of proteins and 3D visualization of energetic impacts

The possible effects of mutations on stability and function of a protein can only be understood in the context of protein 3D structure. The MutationExplorer webserver maps sequence changes onto protein structures and allows users to study variation by inputting sequence changes. As the user enters variants, the 3D model evolves, and estimated changes in energy are highlighted. In addition to a basic per-residue input format, MutationExplorer can also upload an entire replacement sequence. Previously the purview of desktop applications, such an upload can back-mutate PDB structures to wildtype sequence in a single step. Another supported variation source is human single nucelotide polymorphisms (SNPs), genomic coordinates input in VCF format. Structures are flexibly colorable, not only by energetic differences, but also by hydrophobicity, sequence conservation, or other biochemical profiling. Coloring by interface score reveals mutation impacts on binding surfaces. MutationExplorer strives for efficiency in user experience. For example, we have prepared 45 000 PDB depositions for instant retrieval and initial display. All modeling steps are performed by Rosetta. Visualizations leverage MDsrv/Mol*. MutationExplorer is available at: http://proteinformatics.org/mutation_explorer/.

A Non-Apoptotic Pattern of Caspase-9/Caspase-3 Activation During Differentiation of Human Embryonic Stem Cells into Cardiomyocytes

In vitro studies have demonstrated that the differentiation of embryonic stem cells (ESCs) into cardiomyocytes requires activation of caspases through the mitochondrial pathway. These studies have relied on synthetic substrates for activity measurements, which can be misleading due to potential none-specific hydrolysis of these substrates by proteases other than caspases. Hence, caspase-9 and caspase-3 activation are investigated during the differentiation of human ESCs (hESCs) by directly assessing caspase-9 and -3 cleavage. Western blot reveals the presence of the cleaved caspase-9 prior to and during the differentiation of human ESCs (hESCs) into cardiomyocytes at early stages, which diminishes as the differentiation progresses, without cleavage and activation of endogenous procaspase-3. Activation of exogenous procaspase-3 by endogenous caspase-9 and subsequent cleavage of chromogenic caspase-3 substrate i.e. DEVD-pNA during the course of differentiation confirmes that endogenous caspase-9 has the potency to recognize and activate procaspase-3, but for reasons that are unknown to us fails to do so. These observations suggest the existence of distinct mechanisms of caspase regulation in differentiation as compared to apoptosis. Bioinformatics analysis suggests the presence of caspase-9 regulators, which may influence proteolytic function under specific conditions.

MUTATIONEXPLORER- A WEBSERVER FOR MUTATION OF PROTEINS AND 3D VISUALIZATION OF ENERGETIC IMPACTS

The possible effects of mutations on stability and function of a protein can only be understood in the context of protein 3D structure. The MutationExplorer webserver maps sequence changes onto protein structures and allows users to study variation by inputting sequence changes. As the user enters variants, the 3D model evolves, and estimated changes in energy are highlighted. In addition to a basic per-residue input format, MutationExplorer can also upload an entire replacement sequence. Previously the purview of desktop applications, such an upload can back-mutate PDB structures to wildtype sequence in a single step. Another supported variation source is human single nucelotide polymorphisms (SNPs), genomic coordinates input in VCF format.

Methods for studying mammalian aquaporin biology

Aquaporins (AQPs), transmembrane water-conducting channels, have earned a great deal of scrutiny for their critical physiological roles in healthy and disease cell states, especially in the biomedical field. Numerous methods have been implemented to elucidate the involvement of AQP-mediated water transport and downstream signaling activation in eliciting whole cell, tissue, and organ functional responses. To modulate these responses, other methods have been employed to investigate AQP druggability. This review discusses standard in vitro, in vivo, and in silico methods for studying AQPs, especially for biomedical and mammalian cell biology applications. We also propose some new techniques and approaches for future AQP research to address current gaps in methodology.