COVID 19 pandemic is still ongoing, having taken more than 6 million human lives with it, and it seems that the world will have to learn how to live with the virus around. In consequence, there is a need to develop different treatments against it, not only with vaccines, but also new medicines. To do this, human-virus protein-protein interactions (PPIs) play a key part in drug-target discovery, but finding them experimentally can be either costly or sometimes unreliable. Therefore, computational methods arose as a powerful alternative to predict these interactions, reducing costs and helping researchers confirm only certain interactions instead of trying all possible combinations in the laboratory. Malivhu is a tool that predicts human-virus PPIs through a 4-phase process using machine learning models, where phase 1 filters ssRNA(+) class virus proteins, phase 2 filters Coronaviridae family proteins and phase 3 filters severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) species proteins, and phase 4 predicts human-SARS-CoV/SARS-CoV-2/MERS protein-protein interactions. The performance of the models was measured with Matthews correlation coefficient, F1-score, specificity, sensitivity, and accuracy scores, getting accuracies of 99.07%, 99.83%, and 100% for the first 3 phases, respectively, and 94.24% for human-SARS-CoV PPI, 94.50% for human-SARS-CoV-2 PPI, and 95.45% for human-MERS PPI on independent testing. All the prediction models developed for each of the 4 phases were implemented as web server which is freely available at https://kaabil.net/malivhu/.

VIP1, an Arabidopsis thaliana basic leucine zipper transcription factor, and its close homologs are imported from the cytoplasm to the nucleus when cells are exposed to mechanical stress. They bind to AGCTG (G/T) and regulate mechanical stress responses in roots. However, their role in leaves is unclear. To clarify this, mutant lines (QM1 and QM2) that lack the functions of VIP1 and its close homologs (bZIP29, bZIP30 and PosF21) were generated. Brushing more severely damaged QM1 and QM2 leaves than wild-type leaves. Genes regulating stress responses and cell wall properties were downregulated in brushed QM2 leaves and upregulated in brushed VIP1-GFP-overexpressing (VIP1-GFPox) leaves compared to wild-type leaves in a transcriptome analysis. The VIP1-binding sequence AGCTG (G/T) was enriched in the promoters of genes downregulated in brushed QM2 leaves compared to wild-type leaves and in those upregulated in brushed VIP1-GFPox leaves. Calmodulin-binding transcription activators (CAMTAs) are known regulators of mechanical stress responses, and the CAMTA-binding sequence CGCGT was enriched in the promoters of genes upregulated in the brushed QM2 leaves and in those downregulated in the brushed VIP1-GFPox leaves. These findings suggest that VIP1 and its homologs upregulate genes via AGCTG (G/T) and influence CAMTA-dependent gene expression to enhance mechanical stress tolerance in leaves.

One third of all emerging infectious diseases are vector-borne, with no licensed antiviral therapies available against any vector-borne viruses. Zika virus and Usutu virus are two emerging flaviviruses transmitted primarily by mosquitoes. These viruses modulate different host pathways, including the PI3K/AKT/mTOR pathway. Here, we report the effect on ZIKV and USUV replication of two AKT inhibitors, Miransertib (ARQ-092, allosteric inhibitor) and Capivasertib (AZD5363, competitive inhibitor) in different mammalian and mosquito cell lines. Miransertib showed a stronger inhibitory effect against ZIKV and USUV than Capivasertib in mammalian cells, while Capivasertib showed a stronger effect in mosquito cells. These findings indicate that AKT plays a conserved role in flavivirus infection, in both the vertebrate host and invertebrate vector. Nevertheless, the specific function of AKT may vary depending on the host species. These findings indicate that AKT may be playing a conserved role in flavivirus infection in both, the vertebrate host and the invertebrate vector. However, the specific function of AKT may vary depending on the host species. A better understanding of virus-host interactions is therefore required to develop new treatments to prevent human disease and new approaches to control transmission by insect vectors.

Protein-protein interactions (PPIs) play fundamental roles in many biological processes including the functioning of glycosylation machineries present in the endoplasmic reticulum (ER) and Golgi apparatus of mammalian cells. For the last couple of years, we have been successfully employing the most advanced version of the split luciferase complementation assay, termed NanoBiT, to demonstrate PPIs between solute carrier 35 (SLC35) family members with nucleotide sugar transporting activity and functionally related glycosyltransferases. NanoBiT has several unmatched advantages as compared with other strategies for studying PPIs. Firstly, the tendency of the free luciferase fragments to spontaneously associate is strongly reduced. As a consequence, the fragments of the reconstituted luciferase may dissociate upon the disruption of the PPI of interest. Secondly, the recombinant fusion proteins are expressed at low (near-endogenous) levels. Both of these features significantly minimize the possibility of obtaining false positive results. In this study we pushed the boundaries of this already powerful technique even further by coupling it with bioluminescence imaging of PPIs. Specifically, we visualized homo- and heterologous complexes formed by MGAT1 and MGAT2 glycosylation enzymes tagged with NanoBiT fragments and demonstrated ER-to-Golgi transitions between enzyme homo- and heteromers.

Liver cancer is a complex disease that involves various oncoproteins and the inactivation of tumor suppressor proteins (TSPs). Gankyrin is one such oncoprotein, first identified in human hepatocellular carcinoma, that is known to inactivate multiple TSPs, leading to proliferation and metastasis of tumor cells. Despite this, there has been limited development of small molecule gankyrin binders for the treatment of liver cancer. In this study, we are reporting the structure-based design of gankyrin-binding small molecules which inhibit the proliferation of HuH6 and HepG2 cells while also increasing the levels of certain TSPs, such as Rb and p53. Interestingly the first molecule to exhibit inhibition by 3D structure stabilization is seen. These results suggest a possible mechanism for small-molecule inhibition of gankyrin and demonstrate that gankyrin is a viable therapeutic target for the treatment of liver cancer.

Nucleotide excision repair (NER) clears genomes of DNA adducts formed by UV light, environmental agents, and antitumor drugs. Gene mutations that lead to defects in the core NER reaction cause the skin cancer-prone disease xeroderma pigmentosum. In NER, DNA lesions are excised within an oligonucleotide of 25-30 residues via a complex, multi-step reaction that is regulated by protein-protein interactions. These interactions were first characterized in the 1990s using pull-down, co-IP and yeast two-hybrid assays. More recently, high-resolution structures and detailed functional studies have started to yield detailed pictures of the progression along the NER reaction coordinate. In this review, we highlight how the study of interactions among proteins by structural and/or functional studies have provided insights into the mechanisms by which the NER machinery recognizes and excises DNA lesions. Furthermore, we identify reported, but poorly characterized or unsubstantiated interactions in need of further validation.

Multicellularity was accompanied by the emergence of new classes of cell surface and secreted proteins. The nematode C. elegans is a favorable model to study cell surface interactomes, given its well-defined and stereotyped cell types and intercellular contacts. Here we report our C. elegans extracellular interactome dataset, the largest yet for an invertebrate. Most of these interactions were unknown, despite recent datasets for flies and humans, as our collection contains a larger selection of protein families. We uncover new interactions for all four major axon guidance pathways, including ectodomain interactions between three of the pathways. We demonstrate that a protein family known to maintain axon locations are secreted receptors for insulins. We reveal novel interactions of cystine-knot proteins with putative signaling receptors, which may extend the study of neurotrophins and growth-factor-mediated functions to nematodes. Finally, our dataset provides insights into human disease mechanisms and how extracellular interactions may help establish connectomes.

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.

The interactions of G-protein-coupled receptors (GPCRs) with other proteins are critical in several cellular processes but resolving their structural dynamics remains challenging. An increasing number of GPCR complexes have been experimentally resolved but others including receptor variants are yet to be characterized, necessitating computational predictions of their interactions. Although integrative approaches with multi-scale simulations would provide rigorous estimates of their conformational dynamics, protein-protein docking remains a first tool of choice of many researchers due to the availability of open-source programs and easy to use web servers with reasonable predictive power. Protein-protein docking algorithms have limited ability to consider protein flexibility, environment effects, and entropy contributions and are usually a first step towards more integrative approaches. The two critical steps of docking: the sampling and scoring algorithms have improved considerably and their performance has been validated against experimental data. In this chapter, we provide an overview and generalized protocol of a few docking protocols using GPCRs as test cases. In particular, we demonstrate the interactions of GPCRs with extracellular protein ligands and an intracellular protein effectors (G-protein) predicted from docking approaches and test their limitations. The current chapter will help researchers critically assess docking protocols and predict experimentally testable structures of GPCR complexes.

Protein-protein interactions (PPIs) govern virtually all cellular processes. Even a single mutation within PPI can significantly influence overall protein functionality and potentially lead to various types of diseases. To date, numerous approaches have emerged for predicting the change in free energy of binding (ΔΔGbind) resulting from mutations, yet the majority of these methods lack precision. In recent years, protein language models (PLMs) have been developed and shown powerful predictive capabilities by leveraging both sequence and structural data from protein-protein complexes. Yet, PLMs have not been optimized specifically for predicting ΔΔGbind. We developed an approach to predict effects of mutations on PPI binding affinity based on two most advanced protein language models ESM2 and ESM-IF1 that incorporate PPI sequence and structural features, respectively. We used the two models to generate embeddings for each PPI mutant and subsequently fine-tuned our model by training on a large dataset of experimental ΔΔGbind values. Our model, ProBASS (Protein Binding Affinity from Structure and Sequence) achieved a correlation with experimental ΔΔGbind values of 0.83 ± 0.05 for single mutations and 0.69 ± 0.04 for double mutations when model training and testing was done on the same PDB. Moreover, ProBASS exhibited very high correlation (0.81 ± 0.02) between prediction and experiment when training and testing was performed on a dataset containing 2325 single mutations in 132 PPIs. ProBASS surpasses the state-of-the-art methods in correlation with experimental data and could be further trained as more experimental data becomes available. Our results demonstrate that the integration of extensive datasets containing ΔΔGbind values across multiple PPIs to refine the pre-trained PLMs represents a successful approach for achieving a precise and broadly applicable model for ΔΔGbind prediction, greatly facilitating future protein engineering and design studies.