PRISM: a web server and repository for prediction of protein-protein interactions and modeling their 3D complexes

CSM-Potential2: A comprehensive deep learning platform for the analysis of protein interacting interfaces

Proteins are molecular machinery that participate in virtually all essential biological functions within the cell, which are tightly related to their 3D structure. The importance of understanding protein structure-function relationship is highlighted by the exponential growth of experimental structures, which has been greatly expanded by recent breakthroughs in protein structure prediction, most notably RosettaFold, and AlphaFold2. These advances have prompted the development of several computational approaches that leverage these data sources to explore potential biological interactions. However, most methods are generally limited to analysis of single types of interactions, such as protein-protein or protein-ligand interactions, and their complexity limits the usability to expert users. Here we report CSM-Potential2, a deep learning platform for the analysis of binding interfaces on protein structures. In addition to prediction of protein-protein interactions binding sites and classification of biological ligands, our new platform incorporates prediction of interactions with nucleic acids at the residue level and allows for ligand transplantation based on sequence and structure similarity to experimentally determined structures. We anticipate our platform to be a valuable resource that provides easy access to a range of state-of-the-art methods to expert and non-expert users for the study of biological interactions. Our tool is freely available as an easy-to-use web server and API available at https://biosig.lab.uq.edu.au/csm_potential.

Integrating multi-omics to unravel host-microbiome interactions in inflammatory bowel disease

The gut microbiome is crucial for nutrient metabolism, immune regulation, and intestinal homeostasis with changes in its composition linked to complex diseases like inflammatory bowel disease (IBD). Although the precise host-microbial mechanisms in disease pathogenesis remain unclear, high-throughput sequencing have opened new ways to unravel the role of interspecies interactions in IBD. Systems biology-a holistic computational framework for modeling complex biological systems-is critical for leveraging multi-omics datasets to identify disease mechanisms. This review highlights the significance of multi-omics data in IBD research and provides an overview of state-of-the-art systems biology resources and computational tools for data integration. We explore gaps, challenges, and future directions in the research field aiming to uncover novel biomarkers and therapeutic targets, ultimately advancing personalized treatment strategies. While focusing on IBD, the proposed approaches are applicable for other complex diseases, like cancer, and neurodegenerative diseases, where the microbiome has also been implicated.

Bag-1-mediated HSF1 phosphorylation regulates expression of heat shock proteins in breast cancer cells

According to the World Health Organization in 2022, 2.3 million women were diagnosed with breast cancer. Investigating the interaction networks between Bcl-2-associated athanogene (Bag)-1 and other chaperone proteins may further the current understanding of the regulation of protein homeostasis in breast cancer cells and contribute to the development of treatment options. The present study aimed to determine the interactions between Bag-1 and heat shock proteins (HSPs); namely, HSP90, HSP70 and HSP27, to elucidate their role in promoting heat shock factor-1 (HSF1)-dependent survival of breast cancer cells. HER2-negative (MCF-7) and HER2-positive (BT-474) cell lines were used to examine the impact of Bag-1 expression on HSF1 and HSPs. We demonstrated that Bag-1 overexpression promoted HER2 expression in breast cancer cells, thereby resulting in the concurrent constitutive activation of the HSF1-HSP axis. The activation of HSP results in the stabilization of several tumor-promoting HSP clients such as AKT, mTOR and HSF1 itself, which substantially accelerates tumor development. Our results suggest that Bag-1 can modulate the chaperone activity of HSPs, such as HSP27, by directly or indirectly regulating the phosphorylation of HSF1. This modulation of chaperone activity can influence the activation of genes involved in cellular homeostasis, thereby protecting cells against stress.

Interface-guided phenotyping of coding variants in the transcription factor RUNX1

Single-gene missense mutations remain challenging to interpret. Here, we deploy scalable functional screening by sequencing (SEUSS), a Perturb-seq method, to generate mutations at protein interfaces of RUNX1 and quantify their effect on activities of downstream cellular programs. We evaluate single-cell RNA profiles of 115 mutations in myelogenous leukemia cells and categorize them into three functionally distinct groups, wild-type (WT)-like, loss-of-function (LoF)-like, and hypomorphic, that we validate in orthogonal assays. LoF-like variants dominate the DNA-binding site and are recurrent in cancer; however, recurrence alone does not predict functional impact. Hypomorphic variants share characteristics with LoF-like but favor protein interactions, promoting gene expression indicative of nerve growth factor (NGF) response and cytokine recruitment of neutrophils. Accessible DNA near differentially expressed genes frequently contains RUNX1-binding motifs. Finally, we reclassify 16 variants of uncertain significance and train a classifier to predict 103 more. Our work demonstrates the potential of targeting protein interactions to better define the landscape of phenotypes reachable by missense mutations.

Computer modeling of the peculiarities in the interaction of IL-1 with its receptors in schizophrenia

One of the primary theories regarding the development of schizophrenia revolves around genetics, indicating the involvement of hereditary factors in various processes, including inflammation. Research has demonstrated that inflammatory reactions occurring in microglia can impact the progression of the disease. It has also been established that genetically determined changes in IL-1 can contribute to schizophrenia, thereby confirming the role of the IL-1 gene cluster in disease susceptibility. The aim of this study is a computer-based assessment of the structural interactions of IL-1 proteins with their receptors in schizophrenia. The study utilized the DisGeNET database, enabling the assessment of the reliability of identified IL-1 polymorphisms. Polymorphisms were also sought using NCBI PubMed. The NCBI Protein service was employed to search for and analyze the position of the identified polymorphisms on the chromosome. Structures for modeling were extracted from the Protein Data Bank database. Protein modeling was conducted using the SWISS-MODEL server, and protein interaction modeling was performed using PRISM. Notably, this study represents the first prediction of the interactions of IL-1α, IL-1β, and IL- 1RA proteins, taking into account the presence of single-nucleotide polymorphisms associated with schizophrenia in the sequence of the corresponding genes. The results indicate that the presence of SNP rs315952 in the IL-1RA protein gene, associated with schizophrenia, may lead to a weakening of the IL-1RA binding to receptors, potentially triggering the initiation of the IL-1 signaling pathway by disrupting or weakening the IL-1RA binding to receptors and facilitating the binding of IL-1 to them. Such alterations could potentially lead to a change in the immune response. The data obtained contribute theoretically to the development of ideas about the molecular mechanisms through which hereditary factors in schizophrenia influence the interactions of proteins of the IL-1 family, which play an important role in the processes of the immune system.

Gossypium hirsutum calmodulin-like protein (CML 11) interaction with geminivirus encoded protein using bioinformatics and molecular techniques

Plant viruses are the most abundant destructive agents that exist in every ecosystem, causing severe diseases in multiple crops worldwide. Currently, a major gap is present in computational biology determining plant viruses interaction with its host. We lay out a strategy to extract virus-host protein interactions using various protein binding and interface methods for Geminiviridae, a second largest virus family. Using this approach, transcriptional activator protein (TrAP/C2) encoded by Cotton leaf curl Kokhran virus (CLCuKoV) and Cotton leaf curl Multan virus (CLCuMV) showed strong binding affinity with calmodulin-like (CML) protein of Gossypium hirsutum (Gh-CML11). Higher negative value for the change in Gibbs free energy between TrAP and Gh-CML11 indicated strong binding affinity. Consensus from gene ontology database and in-silico nuclear localization signal (NLS) tools identified subcellular localization of TrAP in the nucleus associated with Gh-CML11 for virus infection. Data based on interaction prediction and docking methods present evidences that full length and truncated C2 strongly binds with Gh-CML11. This computational data was further validated with molecular results collected from yeast two-hybrid, bimolecular fluorescence complementation system and pull down assay. In this work, we also show the outcomes of full length and truncated TrAP on plant machinery. This is a first extensive report to delineate a role of CML protein from cotton with begomoviruses encoded transcription activator protein.

Antibody design using deep learning: from sequence and structure design to affinity maturation

Deep learning has achieved impressive results in various fields such as computer vision and natural language processing, making it a powerful tool in biology. Its applications now encompass cellular image classification, genomic studies and drug discovery. While drug development traditionally focused deep learning applications on small molecules, recent innovations have incorporated it in the discovery and development of biological molecules, particularly antibodies. Researchers have devised novel techniques to streamline antibody development, combining in vitro and in silico methods. In particular, computational power expedites lead candidate generation, scaling and potential antibody development against complex antigens. This survey highlights significant advancements in protein design and optimization, specifically focusing on antibodies. This includes various aspects such as design, folding, antibody-antigen interactions docking and affinity maturation.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

Uncovering domain motif interactions using high-throughput protein-protein interaction detection methods

Protein-protein interactions (PPIs) are often mediated by short linear motifs (SLiMs) in one protein and domain in another, known as domain-motif interactions (DMIs). During the past decade, SLiMs have been studied to find their role in cellular functions such as post-translational modifications, regulatory processes, protein scaffolding, cell cycle progression, cell adhesion, cell signalling and substrate selection for proteasomal degradation. This review provides a comprehensive overview of the current PPI detection techniques and resources, focusing on their relevance to capturing interactions mediated by SLiMs. We also address the challenges associated with capturing DMIs. Moreover, a case study analysing the BioGrid database as a source of DMI prediction revealed significant known DMI enrichment in different PPI detection methods. Overall, it can be said that current high-throughput PPI detection methods can be a reliable source for predicting DMIs.

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.

Molecular Dynamics and Other HPC Simulations for Drug Discovery

High performance computing (HPC) is taking an increasingly important place in drug discovery. It makes possible the simulation of complex biochemical systems with high precision in a short time, thanks to the use of sophisticated algorithms. It promotes the advancement of knowledge in fields that are inaccessible or difficult to access through experimentation and it contributes to accelerating the discovery of drugs for unmet medical needs while reducing costs. Herein, we report how computational performance has evolved over the past years, and then we detail three domains where HPC is essential. Molecular dynamics (MD) is commonly used to explore the flexibility of proteins, thus generating a better understanding of different possible approaches to modulate their activity. Modeling and simulation of biopolymer complexes enables the study of protein-protein interactions (PPI) in healthy and disease states, thus helping the identification of targets of pharmacological interest. Virtual screening (VS) also benefits from HPC to predict in a short time, among millions or billions of virtual chemical compounds, the best potential ligands that will be tested in relevant assays to start a rational drug design process.

Distinct or Overlapping Areas of Mitochondrial Thioredoxin 2 May Be Used for Its Covalent and Strong Non-Covalent Interactions with Protein Ligands

In silico approaches were employed to examine the characteristics of interactions between human mitochondrial thioredoxin 2 (HsTrx2) and its 38 previously identified mitochondrial protein ligands. All interactions appeared driven mainly by electrostatic forces. The statistically significant residues of HsTrx2 for interactions were characterized as "contact hot spots". Since these were identical/adjacent to putative thermodynamic hot spots, an energy network approach identified their neighbors to highlight possible contact interfaces. Three distinct areas for binding emerged: (i) one around the active site for covalent interactions, (ii) another antipodal to the active site for strong non-covalent interactions, and (iii) a third area involved in both kinds of interactions. The contact interfaces of HsTrx2 were projected as respective interfaces for Escherichia coli Trx1 (EcoTrx1), 2, and HsTrx1. Comparison of the interfaces and contact hot spots of HsTrx2 to the contact residues of EcoTx1 and HsTrx1 from existing crystal complexes with protein ligands supported the hypothesis, except for a part of the cleft/groove adjacent to Trp30 preceding the active site. The outcomes of this study raise the possibility for the rational design of selective inhibitors for the interactions of HsTrx2 with specific protein ligands without affecting the entirety of the functions of the Trx system.

ComDock: A novel approach for protein-protein docking with an efficient fusing strategy

Protein-protein interaction plays an important role in studying the mechanism of protein functions from the structural perspective. Molecular docking is a powerful approach to detect protein-protein complexes using computational tools, due to the high cost and time-consuming of the traditional experimental methods. Among existing technologies, the template-based method utilizes the structural information of known homologous 3D complexes as available and reliable templates to achieve high accuracy and low computational complexity. However, the performance of the template-based method depends on the quality and quantity of templates. When insufficient or even no templates, the ab initio docking method is necessary and largely enriches the docking conformations. Therefore, it's a feasible strategy to fuse the effectivity of the template-based model and the universality of ab initio model to improve the docking performance. In this study, we construct a new, diverse, comprehensive template library derived from PDB, containing 77,685 complexes. We propose a template-based method (named TemDock), which retrieves the evolutionary relationship between the target sequence and samples in the template library and transfers similar structural information. Then, the target structure is built by superposing on the homologous template complex with TM-align. Moreover, we develop a consensus-based method (named ComDock) to integrate our TemDock and an existing ab initio method (ZDOCK). On 105 targets with templates from Benchmark 5.0, the TemDock and ComDock achieve a success rate of 68.57 % and 71.43 % in the top 10 conformations, respectively. Compared with the HDOCK, ComDock obtains better I-RMSD of hit configurations on 9 targets and more hit models in the top 100 conformations. As an efficient method for protein-protein docking, the ComDock is expected to study protein-protein recognition and reveal the various biological passways that are critical for developing drug discovery. The final results are stored at https://github.com/guofei-tju/mqz_ComDock_docking.

Identifying Lymph Node Metastasis-Related Factors in Breast Cancer Using Differential Modular and Mutational Structural Analysis

Complex diseases are generally caused by disorders of biological networks and/or mutations in multiple genes. Comparisons of network topologies between different disease states can highlight key factors in their dynamic processes. Here, we propose a differential modular analysis approach that integrates protein-protein interactions with gene expression profiles for modular analysis, and introduces inter-modular edges and date hubs to identify the "core network module" that quantifies the significant phenotypic variation. Then, based on this core network module, key factors, including functional protein-protein interactions, pathways, and driver mutations, are predicted by the topological-functional connection score and structural modeling. We applied this approach to analyze the lymph node metastasis (LNM) process in breast cancer. The functional enrichment analysis showed that both inter-modular edges and date hubs play important roles in cancer metastasis and invasion, and in metastasis hallmarks. The structural mutation analysis suggested that the LNM of breast cancer may be the outcome of the dysfunction of rearranged during transfection (RET) proto-oncogene-related interactions and the non-canonical calcium signaling pathway via an allosteric mutation of RET. We believe that the proposed method can provide new insights into disease progression such as cancer metastasis.

Unraveling the Aurora kinase A and Epstein-Barr nuclear antigen 1 axis in Epstein Barr virus associated gastric cancer

Aurora kinase A (AURKA) is one of the crucial cell cycle regulators associated with gastric cancer. Here, we explored Epstein Barr Virus-induced gastric cancer progression through EBV protein EBNA1 with AURKA. We found that EBV infection enhanced cell proliferation and migration of AGS cells and upregulation of AURKA levels. AURKA knockdown markedly reduced the proliferation and migration of the AGS cells even with EBV infection. Moreover, MD-simulation data deciphered the probable connection between EBNA1 and AURKA. The in-vitro analysis through the transcript and protein expression showed that AURKA knockdown reduces the expression of EBNA1. Moreover, EBNA1 alone can enhance AURKA protein expression in AGS cells. Co-immunoprecipitation and NMR analysis between AURKA and EBNA1 depicts the interaction between two proteins. In addition, AURKA knockdown promotes apoptosis in EBV-infected AGS cells through cleavage of Caspase-3, -9, and PARP1. This study demonstrates that EBV oncogenic modulators EBNA1 possibly modulate AURKA in EBV-mediated gastric cancer progression.

Genome sequencing of cucumber mosaic virus (CMV) isolates infecting chilli and its interaction with host ferredoxin protein of different host for causing mosaic symptoms

Chilli (Capsicum annuum L.) is an important vegetable crop grown in the Indian sub-continent and is prone to viral infections under field conditions. During the field survey, leaf samples from chilli plants showing typical symptoms of disease caused by cucumber mosaic virus (CMV) such as mild mosaic, mottling and leaf distortion were collected. DAC-ELISA analysis confirmed the presence of CMV in 71 out of 100 samples, indicating its widespread prevalence in the region. Five CMV isolates, named Gu1, Gu2, BA, Ho, and Sal were mechanically inoculated onto cucumber and Nicotiana glutinosa plants to study their virulence. Inoculated plants expressed the characteristic symptoms of CMV such as chlorotic spots followed by mild mosaic and leaf distortion. Complete genomes of the five CMV isolates were amplified, cloned, and sequenced, revealing RNA1, RNA2, and RNA3 sequences with 3358, 3045, and 2220 nucleotides, respectively. Phylogenetic analysis classified the isolates as belonging to the CMV-IB subgroup, distinguishing them from subgroup IA and II CMV isolates. Recombination analysis showed intra and interspecific recombination in all the three RNA segments of these isolates. In silico protein-protein docking approach was used to decipher the mechanism behind the production of mosaic symptoms during the CMV-host interaction in 13 host plants. Analysis revealed that the production of mosaic symptoms could be due to the interaction between the coat protein (CP) of CMV and chloroplast ferredoxin proteins. Further, in silico prediction was validated in 13 host plants of CMV by mechanical sap inoculation. Twelve host plants produced systemic symptoms viz., chlorotic spot, chlorotic ringspot, chlorotic local lesion, mosaic and mild mosaic and one host plant, Solanum lycopersicum produced mosaic followed by shoestring symptoms.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03777-8.

PHB2 ameliorates Doxorubicin-induced cardiomyopathy through interaction with NDUFV2 and restoration of mitochondrial complex I function

BACKGROUND

Doxorubicin (DOX) is among the most widely employed antitumor agents, although its clinical applications have been largely hindered by severe cardiotoxicity. Earlier studies described an essential role of mitochondrial injury in the pathogenesis of DOX cardiomyopathy. PHB2 (Prohibitin 2) is perceived as an essential regulator for mitochondrial dynamics and oxidative phosphorylation (OXPHOS) although its involvement in DOX cardiomyopathy remains elusive.

METHODS

To decipher the possible role of PHB2 in DOX cardiomyopathy, tamoxifen-induced cardiac-specific PHB2 conditional knockout mice were generated and subjected to DOX challenge. Cardiac function and mitochondrial profiles were examined. Screening of downstream mediators of PHB2 was performed using proteomic profiling and bioinformatic analysis, and was further verified using co-immunoprecipitation and pulldown assays.

RESULTS

Our data revealed significantly downregulated PHB2 expression in DOX-challenged mouse hearts. PHB2CKO mice were more susceptible to DOX cardiotoxicity compared with PHB2flox/flox mice, as evidenced by more pronounced cardiac atrophy, interstitial fibrosis and decrease in left ventricular ejection fraction and fractional shortening. Mechanistically, PHB2 deficiency resulted in the impairment of mitochondrial bioenergetics and oxidative phosphorylation in DOX cardiotoxicity. Proteomic profiling and interactome analyses revealed that PHB2 interacted with NDUFV2 (NADH-ubiquinone oxidoreductase core subunit V2), a key subunit of mitochondrial respiratory Complex I to mediate regulatory property of PHB2 on mitochondrial metabolism. PHB2 governed the expression of NDUFV2 by promoting its stabilization, while PHB2 deficiency significantly downregulated NDUFV2 in DOX-challenged hearts. Cardiac overexpression of PHB2 alleviated mitochondrial defects in DOX cardiomyopathy both in vivo and in vitro.

CONCLUSIONS

Our study defined a novel role for PHB2 in mitochondrial dynamics and energetic metabolism through interacting with NDUFV2 in DOX-challenged hearts. Forced overexpression of PHB2 may be considered a promising therapeutic approach for patients with DOX cardiomyopathy.

Interface-guided phenotyping of coding variants in the transcription factor RUNX1 with SEUSS

Understanding the consequences of single amino acid substitutions in cancer driver genes remains an unmet need. Perturb-seq provides a tool to investigate the effects of individual mutations on cellular programs. Here we deploy SEUSS, a Perturb-seq like approach, to generate and assay mutations at physical interfaces of the RUNX1 Runt domain. We measured the impact of 115 mutations on RNA profiles in single myelogenous leukemia cells and used the profiles to categorize mutations into three functionally distinct groups: wild-type (WT)-like, loss-of-function (LOF)-like and hypomorphic. Notably, the largest concentration of functional mutations (non-WT-like) clustered at the DNA binding site and contained many of the more frequently observed mutations in human cancers. Hypomorphic variants shared characteristics with loss of function variants but had gene expression profiles indicative of response to neural growth factor and cytokine recruitment of neutrophils. Additionally, DNA accessibility changes upon perturbations were enriched for RUNX1 binding motifs, particularly near differentially expressed genes. Overall, our work demonstrates the potential of targeting protein interaction interfaces to better define the landscape of prospective phenotypes reachable by amino acid substitutions.

Narrow funnel-like interaction energy distribution is an indicator of specific protein interaction partner

Protein interaction networks underlie countless biological mechanisms. However, most protein interaction predictions are based on biological evidence that are biased to well-known protein interaction or physical evidence that exhibits low accuracy for weak interactions and requires high computational power. In this study, a novel method has been suggested to predict protein interaction partners by investigating narrow funnel-like interaction energy distribution. In this study, it was demonstrated that various protein interactions including kinases and E3 ubiquitin ligases have narrow funnel-like interaction energy distribution. To analyze protein interaction distribution, modified scores of iRMS and TM-score are introduced. Then, using these scores, algorithm and deep learning model for prediction of protein interaction partner and substrate of kinase and E3 ubiquitin ligase were developed. The prediction accuracy was similar to or even better than that of yeast two-hybrid screening. Ultimately, this knowledge-free protein interaction prediction method will broaden our understanding of protein interaction networks.

De novo design of protein interactions with learned surface fingerprints

Physical interactions between proteins are essential for most biological processes governing life1. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications2-9. Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein-protein interactions10. We hypothesized that these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof of principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4. Several designs were experimentally optimized, whereas others were generated purely in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

Fast Screening of Protein-Protein Interactions Using Förster Resonance Energy Transfer (FRET-) Based Fluorescence Plate Reader Assay in Live Cells

Protein-protein interactions (PPIs) have great importance for intracellular signal transduction and sustaining the homeostasis of an organism. Thus, the identification of PPIs is necessary to better understand the downstream signaling functions of the proteins in healthy and pathological conditions. Förster resonance energy transfer (FRET) between fluorescent proteins (FPs) is a powerful tool for detecting PPIs in living cells. In literature, FRET analysis methods such as donor photobleaching (FLIM), acceptor photobleaching, spectral imaging, and the three-filter cube method (sensitized emission) are abundantly applied to investigate PPIs; however, they require various expensive instrumentations, and their calculation methods are very time consuming. Since confocal microscopy applications and live cell-based techniques of FRET are very costly, scientists sometimes prefer plate readers for FRET experiments. However, plate reader applications also have many disadvantages and considerations compared to confocal fluorescence microscopy, and complex calculation procedures should be performed. To overcome these problems, we propose a FRET-based high-throughput assay method with a standard monochromator-based microplate reader, which is generally available in most biochemistry laboratories, and an alternative calculation procedure. This rapid, low cost, and effective analysis method enables the scientists to prescreen PPIs in living cells as a preliminary study and quick glance at the experiment before preparing the whole experimental setup with the expensive instrumentations. Additionally, the alternative calculation procedure provides the FRET area comparison without complex bleed-through calculations in a non-conventional manner by shortening the analysis processes with this quick and uncomplicated spectral representation.

Using PlaPPISite to Predict and Analyze Plant Protein-Protein Interaction Sites

Proteome-wide characterization of protein-protein interactions (PPIs) is crucial to understand the functional roles of protein machinery within cells systematically. With the accumulation of PPI data in different plants, the interaction details of binary PPIs, such as the three-dimensional (3D) structural contexts of interaction sites/interfaces, are urgently demanded. To meet this requirement, we have developed a comprehensive and easy-to-use database called PlaPPISite ( http://zzdlab.com/plappisite/index.php ) to present interaction details for 13 plant interactomes. Here, we provide a clear guide on how to search and view protein interaction details through the PlaPPISite database. Firstly, the running environment of our database is introduced. Secondly, the input file format is briefly introduced. Moreover, we discussed which information related to interaction sites can be achieved through several examples. In addition, some notes about PlaPPISite are also provided. More importantly, we would like to emphasize the importance of interaction site information in plant systems biology through this user guide of PlaPPISite. In particular, the easily accessible 3D structures of PPIs in the coming post-AlphaFold2 era will definitely boost the application of plant interactome to decipher the molecular mechanisms of many fundamental biological issues.

Sibling rivalry among the ZBTB transcription factor family: homodimers versus heterodimers

BTB domains potentially can form homo- or heterodimers. The study examines the dimerization choice of several BTB domains and finds only one heterodimer, while all tested pairs can homodimerize.

The BTB domain is an oligomerization domain found in over 300 proteins encoded in the human genome. In the family of BTB domain and zinc finger–containing (ZBTB) transcription factors, 49 members share the same protein architecture. The N-terminal BTB domain is structurally conserved among the family members and serves as the dimerization site, whereas the C-terminal zinc finger motifs mediate DNA binding. The available BTB domain structures from this family reveal a natural inclination for homodimerization. In this study, we investigated the potential for heterodimer formation in the cellular environment. We selected five BTB homodimers and four heterodimer structures. We performed cell-based binding assays with fluorescent protein–BTB domain fusions to assess dimer formation. We tested the binding of several BTB pairs, and we were able to confirm the heterodimeric physical interaction between the BTB domains of PATZ1 and PATZ2, previously reported only in an interactome mapping experiment. We also found this pair to be co-expressed in several immune system cell types. Finally, we used the available structures of BTB domain dimers and newly constructed models in extended molecular dynamics simulations (500 ns) to understand the energetic determinants of homo- and heterodimer formation. We conclude that heterodimer formation, although frequently described as less preferred than homodimers, is a possible mechanism to increase the combinatorial specificity of this transcription factor family.

The human RNA polymerase I structure reveals an HMG-like docking domain specific to metazoans

We characterize the human RNA polymerase I by evolutionary biochemistry and cryo-EM revealing a built-in structural domain that apparently serves as transcription factor–binding platform in metazoans.

Transcription of the ribosomal RNA precursor by RNA polymerase (Pol) I is a major determinant of cellular growth, and dysregulation is observed in many cancer types. Here, we present the purification of human Pol I from cells carrying a genomic GFP fusion on the largest subunit allowing the structural and functional analysis of the enzyme across species. In contrast to yeast, human Pol I carries a single-subunit stalk, and in vitro transcription indicates a reduced proofreading activity. Determination of the human Pol I cryo-EM reconstruction in a close-to-native state rationalizes the effects of disease-associated mutations and uncovers an additional domain that is built into the sequence of Pol I subunit RPA1. This “dock II” domain resembles a truncated HMG box incapable of DNA binding which may serve as a downstream transcription factor–binding platform in metazoans. Biochemical analysis, in situ modelling, and ChIP data indicate that Topoisomerase 2a can be recruited to Pol I via the domain and cooperates with the HMG box domain–containing factor UBF. These adaptations of the metazoan Pol I transcription system may allow efficient release of positive DNA supercoils accumulating downstream of the transcription bubble.

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/.

Astrocytic NDRG2-PPM1A interaction exacerbates blood-brain barrier disruption after subarachnoid hemorrhage

Blood-brain barrier (BBB) injury critically exacerbates the poor prognosis of patients with subarachnoid hemorrhage (SAH). The massively increased matrix metalloproteinases 9 (MMP-9) plays a deleterious role in BBB. However, the main source and mechanism of MMP-9 production after SAH remain unclear. We reported that the increased MMP-9 was mainly derived from reactive astrocytes after SAH. Ndrg2 knockout in astrocytes inhibited MMP-9 expression after SAH and attenuated BBB damage. Astrocytic Ndrg2 knockout decreased the phosphorylation of Smad2/3 and the transcription of MMP-9. Notably, cytoplasmic NDRG2 bound to the protein phosphatase PPM1A and restricted the dephosphorylation of Smad2/3. Accordingly, TAT-QFNP12, a novel engineered peptide that could block the NDRG2-PPM1A binding and reduce Smad2/3 dephosphorylation, decreased astrocytic MMP-9 production and BBB disruption after SAH. In conclusion, this study identified NDRG2-PPM1A signaling in reactive astrocytes as a key switch for MMP-9 production and provided a novel therapeutic avenue for BBB protection after SAH.

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

PEPPI: Whole-proteome Protein-protein Interaction Prediction through Structure and Sequence Similarity, Functional Association, and Machine Learning

Proteome-wide identification of protein-protein interactions is a formidable task which has yet to be sufficiently addressed by experimental methodologies. Many computational methods have been developed to predict proteome-wide interaction networks, but few leverage both the sensitivity of structural information and the wide availability of sequence data. We present PEPPI, a pipeline which integrates structural similarity, sequence similarity, functional association data, and machine learning-based classification through a naïve Bayesian classifier model to accurately predict protein-protein interactions at a proteomic scale. Through benchmarking against a set of 798 ground truth interactions and an equal number of non-interactions, we have found that PEPPI attains 4.5% higher AUROC than the best of other state-of-the-art methods. As a proteomic-scale application, PEPPI was applied to model the interactions which occur between SARS-CoV-2 and human host cells during coronavirus infection, where 403 high-confidence interactions were identified with predictions covering 73% of a gold standard dataset from PSICQUIC and demonstrating significant complementarity with the most recent high-throughput experiments. PEPPI is available both as a webserver and in a standalone version and should be a powerful and generally applicable tool for computational screening of protein-protein interactions.

Monoclonal antibody designed for SARS-nCoV-2 spike protein of receptor binding domain on antigenic targeted epitopes for inhibition to prevent viral entry

SARS, or severe acute respiratory syndrome, is caused by a novel coronavirus (COVID-19). This situation has compelled many pharmaceutical R&D companies and public health research sectors to focus their efforts on developing effective therapeutics. SARS-nCoV-2 was chosen as a protein spike to targeted monoclonal antibodies and therapeutics for prevention and treatment. Deep mutational scanning created a monoclonal antibody to characterize the effects of mutations in a variable antibody fragment based on its expression levels, specificity, stability, and affinity for specific antigenic conserved epitopes to the Spike-S-Receptor Binding Domain (RBD). Improved contacts between Fv light and heavy chains and the targeted antigens of RBD could result in a highly potent neutralizing antibody (NAbs) response as well as cross-protection against other SARS-nCoV-2 strains. It undergoes multipoint core mutations that combine enhancing mutations, resulting in increased binding affinity and significantly increased stability between RBD and antibody. In addition, we improved. Structures of variable fragment (Fv) complexed with the RBD of Spike protein were subjected to our established in-silico antibody-engineering platform to obtain enhanced binding affinity to SARS-nCoV-2 and develop ability profiling. We found that the size and three-dimensional shape of epitopes significantly impacted the activity of antibodies produced against the RBD of Spike protein. Overall, because of the conformational changes between RBD and hACE2, it prevents viral entry. As a result of this in-silico study, the designed antibody can be used as a promising therapeutic strategy to treat COVID-19.

CSM-Potential: mapping protein interactions and biological ligands in 3D space using geometric deep learning

Recent advances in protein structural modelling have enabled the accurate prediction of the holo 3D structures of almost any protein, however protein function is intrinsically linked to the interactions it makes. While a number of computational approaches have been proposed to explore potential biological interactions, they have been limited to specific interactions, and have not been readily accessible for non-experts or use in bioinformatics pipelines. Here we present CSM-Potential, a geometric deep learning approach to identify regions of a protein surface that are likely to mediate protein-protein and protein-ligand interactions in order to provide a link between 3D structure and biological function. Our method has shown robust performance, outperforming existing methods for both predictive tasks. By assessing the performance of CSM-Potential on independent blind tests, we show that our method was able to achieve ROC AUC values of up to 0.81 for the identification of potential protein-protein binding sites, and up to 0.96 accuracy on biological ligand classification. Our method is freely available as a user-friendly and easy-to-use web server and API at http://biosig.unimelb.edu.au/csm_potential.

Protein-protein interaction networks as miners of biological discovery

Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein-complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid, mass spectrometry, co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks. This article is protected by copyright. All rights reserved.

Unifying structural descriptors for biological and bioinspired nanoscale complexes

Biomimetic nanoparticles are known to serve as nanoscale adjuvants, enzyme mimics and amyloid fibrillation inhibitors. Their further development requires better understanding of their interactions with proteins. The abundant knowledge about protein-protein interactions can serve as a guide for designing protein-nanoparticle assemblies, but the chemical and biological inputs used in computational packages for protein-protein interactions are not applicable to inorganic nanoparticles. Analysing chemical, geometrical and graph-theoretical descriptors for protein complexes, we found that geometrical and graph-theoretical descriptors are uniformly applicable to biological and inorganic nanostructures and can predict interaction sites in protein pairs with accuracy >80% and classification probability ~90%. We extended the machine-learning algorithms trained on protein-protein interactions to inorganic nanoparticles and found a nearly exact match between experimental and predicted interaction sites with proteins. These findings can be extended to other organic and inorganic nanoparticles to predict their assemblies with biomolecules and other chemical structures forming lock-and-key complexes.

A Dual-Functional Orphan Response Regulator Negatively Controls the Differential Transcription of Duplicate groELs and Plays a Global Regulatory Role in Myxococcus

Differential transcription of functionally divergent duplicate genes is critical for bacterial cells to properly and competitively function in the environment, but the transcriptional regulation mechanisms remain in mystery. Myxococcus xanthus DK1622 possesses two duplicate groELs with divergent functions. Here, we report that MXAN_4468, an orphan gene located upstream of groEL2, encodes a response regulator (RR) and is responsible for the differential expression regulation of duplicate groELs. This RR protein realizes its negative regulatory role via a novel dual-mode functioning manner: binding to the transcription repressor HrcA to enhance its transcriptional inhibition of duplicate groELs and binding to the 3′ end of the MXAN_4468 sequence to specifically decrease the transcription of the following groEL2. Phosphorylation at the conserved 61^st aspartic acid is required to trigger the regulatory functions of MXAN_4468. Pull-down experiment and mutation demonstrated that two noncognate CheA proteins, respectively belonging to the Che8 and Che7 chemosensory pathways, are involved in the protein phosphorylation. A transcriptome analysis, as well as the pull-down experiment, suggested that MXAN_4468 plays a global negative regulatory role in M. xanthus. This study elucidates, for the first time, the regulatory mechanism of differential transcription of bacterial duplicate groELs and suggests a global regulatory role of a dual-functional orphan RR.

IMPORTANCE Multiply copied groELs require precise regulation of transcriptions for their divergent cellular functions. Here, we reported that an orphan response regulator (RR) tunes the transcriptional discrepancy of the duplicate groELs in Myxococcus xanthus DK1622 in a dual-functional mode. This RR protein has a conserved phosphorylation site, and the phosphorylation is required for the regulatory functions. Transcriptomic analysis, as well as a pull-down experiment, suggests that the RR plays a global regulatory role in M. xanthus. This study highlights that the dual-functional orphan RR might be involved in conducting the transcriptional symphony to stabilize the complex biological functions in cells.

C-phycocyanin decreases proliferation and migration of melanoma cells: In silico and in vitro evidences

The incidence and number of deaths caused by melanoma have been increasing in recent years, and the pigment C-phycocyanin (C-PC) appears as a possible alternative to treat this disease. So, the objective of this study was to combine in silico and in vitro analysis to understand the main anti-melanoma pathways exerted by C-PC. We evaluated the ability of C-PC to bind to the main cellular targets related in the progression of melanoma through molecular docking, and the reflection of this bind in the biological effects in the B16F10 cell line through in vitro analysis. Our results showed that C-PC was able to bind BRAF and MEK, which are related to the signal transduction pathway for proliferation and survival. There was also an interaction between C-PC and cyclin-dependent kinase 4 and 6. In vitro analysis demonstrated that C-PC decreased B16F10 cell proliferation, as observed by cell viability and mitotic index assays. C-PC also interacted with matrix metalloproteinase 2 and 9 and N-cadherin, which may have caused the decrease in cell migration observed in vitro. Besides that, C-PC interacts with VEGF, a factor responsible for regulating the proliferation and cellular invasion pathways. Finally, C-PC did not alter the cell viability of the non-tumoral melanocytes. Therefore, C-PC is a strong anti-tumor candidate for the treatment of melanoma, since it acts in different cellular pathways of melanoma, without causing damage to non-tumoral cells.

Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions

Protein-protein interactions play crucial and subtle roles in many biological processes and modifications of their fine mechanisms generally result in severe diseases. Peptide derivatives are very promising therapeutic agents for modulating protein-protein associations with sizes and specificities between those of small compounds and antibodies. For the same reasons, rational design of peptide-based inhibitors naturally borrows and combines computational methods from both protein-ligand and protein-protein research fields. In this chapter, we aim to provide an overview of computational tools and approaches used for identifying and optimizing peptides that target protein-protein interfaces with high affinity and specificity. We hope that this review will help to implement appropriate in silico strategies for peptide-based drug design that builds on available information for the systems of interest.

Dynamic disequilibrium-based pathogenicity model in mutated pyrin’s B30.2 domain—Casp1/p20 complex

Background

The B30.2 variants lead to most relevant severity forms of familial Mediterranean fever (FMF) manifestations. The B30.2 domain plays a key role in protein-protein interaction (PPI) of pyrin with other apoptosis proteins and in regulation the cascade of inflammatory reactions. Pyrin-casp1 interaction is mainly responsible for the dysregulation of the inflammatory responses in FMF. Lower binding affinity was observed between the mutant B30.2 pyrin and casp1 without the release of the complete pathogenicity mechanism. The aim of this study was to identify the possible effects of the interface pocked residues in B30.2/SPRY-Casp1/p20 complex using molecular mechanics simulation and in silico analysis.

Results

It was found that Lys671Met, Ser703Ile, and Ala744Ser variants led mainly to shift of the binding affinity (∆G), dissociation constant (K_d), and root mean square deviation (RMSD) in B30.2/SPRY-Casp1/p20 complex leading to dynamic disequilibrium of the p20-B30.2/SPRY complex toward its complex form. The current pathogenicity model and its predicted implementation in the relevant colchicine dosage were delineated.

Conclusion

The molecular mechanics analysis of B30.2/SPRY-p20 complex harboring Lys671Met, Ser703Ile, and Ala744Ser variants showed dynamic disequilibrium of B30.2/SPRY-casp1/p20complex in context of the studied variants that could be a new computational model for FMF pathogenicity. This study also highlighted the specific biochemical markers that could be useful to adjust the colchicine dose in FMF patients.

EGRET: edge aggregated graph attention networks and transfer learning improve protein-protein interaction site prediction

MOTIVATION

Protein-protein interactions (PPIs) are central to most biological processes. However, reliable identification of PPI sites using conventional experimental methods is slow and expensive. Therefore, great efforts are being put into computational methods to identify PPI sites.

RESULTS

We present Edge Aggregated GRaph Attention NETwork (EGRET), a highly accurate deep learning-based method for PPI site prediction, where we have used an edge aggregated graph attention network to effectively leverage the structural information. We, for the first time, have used transfer learning in PPI site prediction. Our proposed edge aggregated network, together with transfer learning, has achieved notable improvement over the best alternate methods. Furthermore, we systematically investigated EGRET's network behavior to provide insights about the causes of its decisions.

AVAILABILITY

EGRET is freely available as an open source project at https://github.com/Sazan-Mahbub/EGRET.

CONTACT

shams_bayzid@cse.buet.ac.bd.

Ras Multimers on the Membrane: Many Ways for a Heart-to-Heart Conversation

Formation of Ras multimers, including dimers and nanoclusters, has emerged as an exciting, new front of research in the 'old' field of Ras biomedicine. With significant advances made in the past few years, we are beginning to understand the structure of Ras multimers and, albeit preliminary, mechanisms that regulate their formation in vitro and in cells. Here we aim to synthesize the knowledge accrued thus far on Ras multimers, particularly the presence of multiple globular (G-) domain interfaces, and discuss how membrane nanodomain composition and structure would influence Ras multimer formation. We end with some general thoughts on the potential implications of Ras multimers in basic and translational biology.

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/.

Distance-based reconstruction of protein quaternary structures from inter-chain contacts

Predicting the quaternary structure of protein complex is an important problem. Inter‐chain residue‐residue contact prediction can provide useful information to guide the ab initio reconstruction of quaternary structures. However, few methods have been developed to build quaternary structures from predicted inter‐chain contacts. Here, we develop the first method based on gradient descent optimization (GD) to build quaternary structures of protein dimers utilizing inter‐chain contacts as distance restraints. We evaluate GD on several datasets of homodimers and heterodimers using true/predicted contacts and monomer structures as input. GD consistently performs better than both simulated annealing and Markov Chain Monte Carlo simulation. Starting from an arbitrarily quaternary structure randomly initialized from the tertiary structures of protein chains and using true inter‐chain contacts as input, GD can reconstruct high‐quality structural models for homodimers and heterodimers with average TM‐score ranging from 0.92 to 0.99 and average interface root mean square distance from 0.72 Å to 1.64 Å. On a dataset of 115 homodimers, using predicted inter‐chain contacts as restraints, the average TM‐score of the structural models built by GD is 0.76. For 46% of the homodimers, high‐quality structural models with TM‐score ≥ 0.9 are reconstructed from predicted contacts. There is a strong correlation between the quality of the reconstructed models and the precision and recall of predicted contacts. Only a moderate precision or recall of inter‐chain contact prediction is needed to build good structural models for most homodimers. Moreover, GD improves the quality of quaternary structures predicted by AlphaFold2 on a Critical Assessment of Techniques for Protein Structure Prediction–Critical Assessments of Predictions of Interactions dataset.

Development of 2-(5,6,7-Trifluoro-1H-Indol-3-yl)-quinoline-5-carboxamide as a Potent, Selective, and Orally Available Inhibitor of Human Androgen Receptor Targeting Its Binding Function-3 for the Treatment of Castration-Resistant Prostate Cancer

Prostate cancer (PCa) patients undergoing androgen deprivation therapy almost invariably develop castration-resistant prostate cancer (CRPC). Targeting the androgen receptor (AR) Binding Function-3 (BF3) site offers a promising option to treat CRPC. However, BF3 inhibitors have been limited by poor potency or inadequate metabolic stability. Through extensive medicinal chemistry, molecular modeling, and biochemistry, we identified 2-(5,6,7-trifluoro-1H-Indol-3-yl)-quinoline-5-carboxamide (VPC-13789), a potent AR BF3 antagonist with markedly improved pharmacokinetic properties. We demonstrate that VPC-13789 suppresses AR-mediated transcription, chromatin binding, and recruitment of coregulatory proteins. This novel AR antagonist selectively reduces the growth of both androgen-dependent and enzalutamide-resistant PCa cell lines. Having demonstrated in vitro efficacy, we developed an orally bioavailable prodrug that reduced PSA production and tumor volume in animal models of CRPC with no observed toxicity. VPC-13789 is a potent, selective, and orally bioavailable antiandrogen with a distinct mode of action that has a potential as novel CRPC therapeutics.

Getting to know each other: PPIMem, a novel approach for predicting transmembrane protein-protein complexes

Because of their considerable number and diversity, membrane proteins and their macromolecular complexes represent the functional units of cells. Their quaternary structure may be stabilized by interactions between the α-helices of different proteins in the hydrophobic region of the cell membrane. Membrane proteins equally represent potential pharmacological targets par excellence for various diseases. Unfortunately, their experimental 3D structure and that of their complexes with other intramembrane protein partners are scarce due to technical difficulties. To overcome this key problem, we devised PPIMem, a computational approach for the specific prediction of higher-order structures of α-helical transmembrane proteins. The novel approach involves proper identification of the amino acid residues at the interface of molecular complexes with a 3D structure. The identified residues compose then nonlinear interaction motifs that are conveniently expressed as mathematical regular expressions. These are efficiently implemented for motif search in amino acid sequence databases, and for the accurate prediction of intramembrane protein-protein complexes. Our template interface-based approach predicted 21,544 binary complexes between 1,504 eukaryotic plasma membrane proteins across 39 species. We compare our predictions to experimental datasets of protein-protein interactions as a first validation method. The online database that results from the PPIMem algorithm with the annotated predicted interactions are implemented as a web server and can be accessed directly at https://transint.univ-evry.fr.

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.

Relation between ABCB1 overexpression and COX2 and ALOX5 genes in human erythroleukemia cell lines

This study aimed to characterize the relationship between the COX2 and ALOX5 genes, as well as their link with the multidrug resistance (MDR) phenotype in sensitive (K562) and MDR (K562-Lucena and FEPS) erythroleukemia cells. For this, the inhibitors of 5-LOX (zileuton) and COX-2 (acetylsalicylic acid-ASA) and cells with the silenced ABCB1 gene were used. The treatment with ASA caused an increase in the gene expression of COX2 and ABCB1 in both MDR cell lines, and a decrease in the expression of ALOX5 in the FEPS cells. Silencing the ABCB1 gene induced a decrease in COX2 expression and an increase in the ALOX5 gene. Treatment with zileuton did not alter the expression of COX2 and ABCB1. Cytometry data showed that there was an increase in ABCB1 protein expression after exposure to ASA. In addition, the increased activity of ABCB1 in the K562-Lucena cell line indicates that ASA may be a substrate for this efflux pump, corroborating the molecular docking that showed that ASA can bind to ABCB1. Regardless of the genetic alteration in COX2 and ABCB1, the direct relationship between these genes and the inverse relationship with ALOX5 remained in the MDR cell lines. We assume that ABCB1 can play a regulatory role in COX2 and ALOX5 during the transformation of the parental cell line K562, explaining the increased gene expression of COX2 and decreased ALOX5 in the MDR cell lines.

Mig6 not only inhibits EGFR and HER2 but also targets HER3 and HER4 in a differential specificity: Implications for targeted esophageal cancer therapy

The human EGF receptor family plays pivotal roles in physiology and cancer, which contains four closely-related members: HER1/EGFR, HER2, HER3 and HER4. Previously, it was found that the mitogen-inducible gene 6 (Mig6) protein is a negative regulator of EGFR and HER2 by using its S1 segment to bind at the kinase dimerization interface. However, it is still unclear whether the S1 segment can also effectively target HER3 and HER4? Here, we performed a systematic investigation to address this issue. The segment can bind to all the four HER kinases with a varying affinity and moderate selectivity; breaking of the segment into shorter hotspot peptides would largely impair the affinity and selectivity, indicating that the full-length sequence is required for the effective binding of S1 to these kinases. The hs2 peptide, which corresponds to the middle hotspot region of S1 segment, can partially retain the affinity to HER kinases, can moderately compete with S1 segment at the dimerization interfaces, and can mimic the biological function of Mig6 protein to suppress HER4+ esophageal cancer at cellular level. In addition, we also analyzed the binding potency of S1 segment and hs2 peptide to the kinase domains of other five widely documented growth factor receptors (GFRs). It was showed that both the S1 and hs2 cannot effectively interact with these receptors. Overall, the Mig6 is suggested as a specific pan-HER inhibitor, which can target and suppress HER family members with a broad selectivity, but exhibits weak or no activity towards other GFRs.