Comparative Analysis of Cyclization Techniques in Stapled Peptides: Structural Insights into Protein-Protein Interactions in a SARS-CoV-2 Spike RBD/hACE2 Model System

Medicinal chemistry is constantly searching for new approaches to develop more effective and targeted therapeutic molecules. The design of peptidomimetics is a promising emerging strategy that is aimed at developing peptides that mimic or modulate the biological activity of proteins. Among these, stapled peptides stand out for their unique ability to stabilize highly frequent helical motifs, but they have failed to be systematically reported. Here, we exploit chemically diverse helix-inducing i, i + 4 constraints-lactam, hydrocarbon, triazole, double triazole and thioether-on two distinct short sequences derived from the N-terminal peptidase domain of hACE2 upon structural characterization and in silico alanine scan. Our overall objective was to provide a sequence-independent comparison of α-helix-inducing staples using circular dichroism (CD) and nuclear magnetic resonance (NMR) spectroscopy. We identified a 9-mer lactam stapled peptide derived from the hACE2 sequence (His34-Gln42) capable of reaching its maximal helicity of 55% with antiviral activity in bioreporter- and pseudovirus-based inhibition assays. To the best of our knowledge, this study is the first comprehensive investigation comparing several cyclization methods with the goal of generating stapled peptides and correlating their secondary structures with PPI inhibitions using a highly topical model system (i.e., the interaction of SARS-CoV-2 Spike RBD with hACE2).

Identification of SH2 Domain-Containing Protein 3C as a Novel, Putative Interactor of Dipeptidyl Peptidase 3

Dipeptidyl peptidase 3 (DPP3) is a zinc-dependent exopeptidase with broad specificity for four to eight amino acid residue substrates. It has a role in the regulation of oxidative stress response NRF2-KEAP1 pathway through the interaction with KEAP1. We have conducted stable isotope labeling by amino acids in a cell culture coupled to mass spectrometry (SILAC-MS) interactome analysis of TRex HEK293T cells using DPP3 as bait and identified SH2 Domain-Containing Protein 3C (SH2D3C) as prey. SH2D3C is one of three members of a family of proteins that contain both the SH2 domain and a domain similar to guanine nucleotide exchange factor domains of Ras family GTPases (Ras GEF-like domain), named novel SH2-containing proteins (NSP). NSPs, including SH2D3C (NSP3), are adaptor proteins involved in the regulation of adhesion, migration, tissue organization, and immune response. We have shown that SH2D3C binds to DPP3 through its C-terminal Ras GEF-like domain, detected the colocalization of the proteins in living cells, and confirmed direct interaction in the cytosol and membrane ruffles. Computational analysis also confirmed the binding of the C-terminal domain of SH2D3C to DPP3, but the exact model could not be discerned. This is the first indication that DPP3 and SH2D3C are interacting partners, and further studies to elucidate the physiological significance of this interaction are on the way.

Design of Cyclic Peptides Targeting Protein-Protein Interactions Using AlphaFold

More than 930,000 protein-protein interactions (PPIs) have been identified in recent years, but their physicochemical properties differ from conventional drug targets, complicating the use of conventional small molecules as modalities. Cyclic peptides are a promising modality for targeting PPIs, but it is difficult to predict the structure of a target protein-cyclic peptide complex or to design a cyclic peptide sequence that binds to the target protein using computational methods. Recently, AlphaFold with a cyclic offset has enabled predicting the structure of cyclic peptides, thereby enabling de novo cyclic peptide designs. We developed a cyclic peptide complex offset to enable the structural prediction of target proteins and cyclic peptide complexes and found AlphaFold2 with a cyclic peptide complex offset can predict structures with high accuracy. We also applied the cyclic peptide complex offset to the binder hallucination protocol of AfDesign, a de novo protein design method using AlphaFold, and we could design a high predicted local-distance difference test and lower separated binding energy per unit interface area than the native MDM2/p53 structure. Furthermore, the method was applied to 12 other protein-peptide complexes and one protein-protein complex. Our approach shows that it is possible to design putative cyclic peptide sequences targeting PPI.

Bioinformatic analysis of the molecular mechanisms underlying the progression of bone defects

Background

The pathophysiology of bone defects (BDs) is complex, and the treatment for bone defects, in particular massive bone defects, remains a major clinical challenge. Our study was conducted to explore the molecular events related to the progression of bone defects a common clinical condition.

Methods

First, microarray data of GSE20980 were obtained from the Gene Expression Omnibus (GEO) database, where 33 samples in total were used to analyze the molecular biological processes related to bone defects. Next, the original data were normalized and differentially expressed genes (DEGs) were identified. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted. Finally, a protein-protein interaction (PPI) network was constructed and the trends of the different genes were confirmed.

Results

Compared with the samples of non-critical size defects (NCSD), the samples of critical size defects (CSD) had 2057, 827, and 1,024 DEGs at 7, 14, and 21 days post injury, respectively. At day 7, the DEGs were significantly enriched in metabolic pathways, at day 14 the DEGs were predominantly enriched in G-protein coupled signaling pathways and the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) signaling pathway, and at day 21 the DEGs were mainly enriched in circadian entrainment and synaptic-related functions. The PPI network showed similar results. Quantitative real-time PCR (qRT-PCR) and western blot (WB) were performed to validate the partial results of sequencing.

Conclusion

This study provides some clues about the molecular mechanism behind bone defects, which should contribute to scientific research and clinical treatment of this condition.

Recent advances in predicting and modeling protein-protein interactions

Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.

DLiP-PPI library: An integrated chemical database of small-to-medium-sized molecules targeting protein-protein interactions

Protein-protein interactions (PPIs) are recognized as important targets in drug discovery. The characteristics of molecules that inhibit PPIs differ from those of small-molecule compounds. We developed a novel chemical library database system (DLiP) to design PPI inhibitors. A total of 32,647 PPI-related compounds are registered in the DLiP. It contains 15,214 newly synthesized compounds, with molecular weight ranging from 450 to 650, and 17,433 active and inactive compounds registered by extracting and integrating known compound data related to 105 PPI targets from public databases and published literature. Our analysis revealed that the compounds in this database contain unique chemical structures and have physicochemical properties suitable for binding to the protein-protein interface. In addition, advanced functions have been integrated with the web interface, which allows users to search for potential PPI inhibitor compounds based on types of protein-protein interfaces, filter results by drug-likeness indicators important for PPI targeting such as rule-of-4, and display known active and inactive compounds for each PPI target. The DLiP aids the search for new candidate molecules for PPI drug discovery and is available online (https://skb-insilico.com/dlip).

The Emerging Roles of Protein Interactions with O-GlcNAc Cycling Enzymes in Cancer

The dynamic O-GlcNAc modification of intracellular proteins is an important nutrient sensor for integrating metabolic signals into vast networks of highly coordinated cellular activities. Dysregulation of the sole enzymes responsible for O-GlcNAc cycling, O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA), and the associated cellular O-GlcNAc profile is a common feature across nearly every cancer type. Many studies have investigated the effects of aberrant OGT/OGA expression on global O-GlcNAcylation activity in cancer cells. However, recent studies have begun to elucidate the roles of protein-protein interactions (PPIs), potentially through regions outside of the immediate catalytic site of OGT/OGA, that regulate greater protein networks to facilitate substrate-specific modification, protein translocalization, and the assembly of larger biomolecular complexes. Perturbation of OGT/OGA PPI networks makes profound changes in the cell and may directly contribute to cancer malignancies. Herein, we highlight recent studies on the structural features of OGT and OGA, as well as the emerging roles and molecular mechanisms of their aberrant PPIs in rewiring cancer networks. By integrating complementary approaches, the research in this area will aid in the identification of key protein contacts and functional modules derived from OGT/OGA that drive oncogenesis and will illuminate new directions for anti-cancer drug development.

Visualization of in vivo protein-protein interactions in plants

Molecular processes depend on the concerted and dynamic interactions of proteins, either by one-on-one interactions of the same or different proteins or by the assembly of larger protein complexes consisting of many different proteins. Here, not only the protein-protein interaction (PPI) itself, but also the localization and activity of the protein of interest (POI) within the cell is essential. Therefore, in all cell biological experiments, preserving the spatio-temporal state of one POI relative to another is key to understanding the underlying complex and dynamic regulatory mechanisms in vivo. In this review, we examine some of the applicable techniques to measure PPIs in planta as well as recent combinatorial advances of PPI methods to measure the formation of higher order complexes with an emphasis on in vivo imaging techniques. We compare the different methods and discuss their benefits and potential pitfalls to facilitate the selection of appropriate techniques by providing a comprehensive overview of how to measure in vivo PPIs in plants.

Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases

Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.

Design, Synthesis, Chemical and Biochemical Insights Into Novel Hybrid Spirooxindole-Based p53-MDM2 Inhibitors With Potential Bcl2 Signaling Attenuation

The tumor resistance to p53 activators posed a clinical challenge. Combination studies disclosed that concomitant administration of Bcl2 inhibitors can sensitize the tumor cells and induce apoptosis. In this study, we utilized a rapid synthetic route to synthesize two novel hybrid spirooxindole-based p53-MDM2 inhibitors endowed with Bcl2 signaling attenuation. The adducts mimic the thematic features of the chemically stable potent spiro [3H-indole-3,2'-pyrrolidin]-2(1H)-ones p53-MDM2 inhibitors, while installing a pyrrole ring via a carbonyl spacer inspired by the natural marine or synthetic products that efficiently inhibit Bcl2 family functions. A chemical insight into the two synthesized spirooxindoles including single crystal x-ray diffraction analysis unambiguously confirmed their structures. The synthesized spirooxindoles 2a and 2b were preliminarily tested for cytotoxic activities against normal cells, MDA-MB 231, HepG-2, and Caco-2 via MTT assay. 2b was superior to 5-fluorouracil. Mechanistically, 2b induced apoptosis-dependent anticancer effect (43%) higher than that of 5-fluorouracil (34.95%) in three studied cancer cell lines, activated p53 (47%), downregulated the Bcl2 gene (1.25-fold), and upregulated p21 (2-fold) in the treated cancer cells. Docking simulations declared the possible binding modes of the synthesized compounds within MDM2.

MicroRNA-Target Interaction Regulatory Network in Alzheimer's Disease

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia; however, early diagnosis of the disease is challenging. Research suggests that biomarkers found in blood, such as microRNAs (miRNA), may be promising for AD diagnostics. Experimental data on miRNA–target interactions (MTI) associated with AD are scattered across databases and publications, thus making the identification of promising miRNA biomarkers for AD difficult. In response to this, a list of experimentally validated AD-associated MTIs was obtained from miRTarBase. Cytoscape was used to create a visual MTI network. STRING software was used for protein–protein interaction analysis and mirPath was used for pathway enrichment analysis. Several targets regulated by multiple miRNAs were identified, including: BACE1, APP, NCSTN, SP1, SIRT1, and PTEN. The miRNA with the highest numbers of interactions in the network were: miR-9, miR-16, miR-34a, miR-106a, miR-107, miR-125b, miR-146, and miR-181c. The analysis revealed seven subnetworks, representing disease modules which have a potential for further biomarker development. The obtained MTI network is not yet complete, and additional studies are needed for the comprehensive understanding of the AD-associated miRNA targetome.

Comprehensive Analysis of Five Phyllostachys edulis SQUA-like Genes and Their Potential Functions in Flower Development

Bamboo is one of the most important non-timber forest resources worldwide. It has considerable economic value and unique flowering characteristics. The long juvenile phase in bamboo and unpredictable flowering time limit breeding and genetic improvement and seriously affect the productivity and application of bamboo forests. Members of SQUA-like subfamily genes play an essential role in controlling flowering time and floral organ identity. A comprehensive study was conducted to explain the functions of five SQUA-like subfamily genes in Phyllostachys edulis. Expression analysis revealed that all PeSQUAs have higher transcript levels in the reproductive period than in the juvenile phase. However, PeSQUAs showed divergent expression patterns during inflorescence development. The protein–protein interaction (PPI) patterns among PeSQUAs and other MADS-box members were analyzed by yeast two-hybrid (Y2H) experiments. Consistent with amino acid sequence similarity and phylogenetic analysis, the PPI patterns clustered into two groups. PeMADS2, 13, and 41 interacted with multiple PeMADS proteins, whereas PeMADS3 and 28 hardly interacted with other proteins. Based on our results, PeSQUA might possess different functions by forming protein complexes with other MADS-box proteins at different flowering stages. Furthermore, we chose PeMADS2 for functional analysis. Ectopic expression of PeMADS2 in Arabidopsis and rice caused early flowering, and abnormal phenotype was observed in transgenic Arabidopsis lines. RNA-seq analysis indicated that PeMADS2 integrated multiple pathways regulating floral transition to trigger early flowering time in rice. This function might be due to the interaction between PeMADS2 and homologous in rice. Therefore, we concluded that the five SQUA-like genes showed functional conservation and divergence based on sequence differences and were involved in floral transitions by forming protein complexes in P. edulis. The MADS-box protein complex model obtained in the current study will provide crucial insights into the molecular mechanisms of bamboo’s unique flowering characteristics.

Recent advances in cellular biosensor technology to investigate tau oligomerization

Tau is a microtubule binding protein which plays an important role in physiological functions but it is also involved in the pathogenesis of Alzheimer's disease and related tauopathies. While insoluble and β-sheet containing tau neurofibrillary tangles have been the histopathological hallmark of these diseases, recent studies suggest that soluble tau oligomers, which are formed prior to fibrils, are the primary toxic species. Substantial efforts have been made to generate tau oligomers using purified recombinant protein strategies to study oligomer conformations as well as their toxicity. However, no specific toxic tau species has been identified to date, potentially due to the lack of cellular environment. Hence, there is a need for cell-based models for direct monitoring of tau oligomerization and aggregation. This review will summarize the recent advances in the cellular biosensor technology, with a focus on fluorescence resonance energy transfer, bimolecular fluorescence complementation, and split luciferase complementation approaches, to monitor formation of tau oligomers and aggregates in living cells. We will discuss the applications of the cellular biosensors in examining the heterogeneous tau conformational ensembles and factors affecting tau self-assembly, as well as detecting cell-to-cell propagation of tau pathology. We will also compare the advantages and limitations of each type of tau biosensors, and highlight their translational applications in biomarker development and therapeutic discovery.

Chronological Gene Expression of Human Gingival Fibroblasts with Low Reactive Level Laser (LLL) Irradiation

Though previously studies have reported that Low reactive Level Laser Therapy (LLLT) promotes wound healing, molecular level evidence was uncleared. The purpose of this study is to examine the temporal molecular processes of human immortalized gingival fibroblasts (HGF) by LLLT by the comprehensive analysis of gene expression. HGF was seeded, cultured for 24 h, and then irradiated with a Nd: YAG laser at 0.5 W for 30 s. After that, gene differential expression analysis and functional analysis were performed with DNA microarray at 1, 3, 6 and 12 h after the irradiation. The number of genes with up- and downregulated differentially expression genes (DEGs) compared to the nonirradiated group was large at 6 and 12 h after the irradiation. From the functional analysis results of DEGs, Biological Process (BP) based Gene Ontology (GO), BP ‘the defense response’ is considered to be an important process with DAVID. Additionally, the results of PPI analysis of DEGs involved in the defense response with STRING, we found that the upregulated DEGs such as CXCL8 and NFKB1, and the downregulated DEGs such as NFKBIA and STAT1 were correlated with multiple genes. We estimate that these genes are key genes on the defense response after LLLT.

Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein–protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors–genes interaction, protein–drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.

In Silico Insights into Protein-protein Interaction Disruptive Mutations in the PCSK9-LDLR complex

Gain-of-function mutations in PCSK9 (proprotein convertase subtilisin/kexin type 9) lead to reduced uptake of LDL (low density lipoprotein) cholesterol and, therefore, increased plasma LDL levels. However, the mechanism by which these mutants reduce LDL reuptake is not fully understood. Here, we have used molecular dynamics simulations, MM/PBSA (Molecular Mechanics/Poisson-Boltzmann Surface Area) binding affinity calculations, and residue interaction networks, to investigate the protein-protein interaction (PPI) disruptive effects of two of PCSK9's gain-of-function mutations, Ser127Arg and Asp374Tyr on the PCSK9 and LDL receptor complex. In addition to these PPI disruptive mutants, a third, non-interface mutation (Arg496Trp) is included as a positive control. Our results indicate that Ser127Arg and Asp374Tyr confer significantly improved binding affinity, as well as different binding modes, when compared to the wild-type. These PPI disruptive mutations lie between the EGF(A) (epidermal growth factor precursor homology domain A) of the LDL receptor and the catalytic domain of PCSK9 (Asp374Tyr) and between the prodomain of PCSK9 and the β-propeller of the LDL receptor (Ser127Arg). The interactions involved in these two interfaces result in an LDL receptor that is sterically inhibited from entering its closed conformation. This could potentially implicate the prodomain as a target for small molecule inhibitors.

Detection of Protein Complexes Based on Penalized Matrix Decomposition in a Sparse Protein⁻Protein Interaction Network

High-throughput technology has generated large-scale protein interaction data, which is crucial in our understanding of biological organisms. Many complex identification algorithms have been developed to determine protein complexes. However, these methods are only suitable for dense protein interaction networks, because their capabilities decrease rapidly when applied to sparse protein–protein interaction (PPI) networks. In this study, based on penalized matrix decomposition (PMD), a novel method of penalized matrix decomposition for the identification of protein complexes (i.e., PMD_pc) was developed to detect protein complexes in the human protein interaction network. This method mainly consists of three steps. First, the adjacent matrix of the protein interaction network is normalized. Second, the normalized matrix is decomposed into three factor matrices. The PMD_pc method can detect protein complexes in sparse PPI networks by imposing appropriate constraints on factor matrices. Finally, the results of our method are compared with those of other methods in human PPI network. Experimental results show that our method can not only outperform classical algorithms, such as CFinder, ClusterONE, RRW, HC-PIN, and PCE-FR, but can also achieve an ideal overall performance in terms of a composite score consisting of F-measure, accuracy (ACC), and the maximum matching ratio (MMR).

Intrinsic Disorder and Posttranslational Modifications: The Darker Side of the Biological Dark Matter

Intrinsically disordered proteins (IDPs) and intrinsically disordered protein regions (IDPRs) are functional proteins and domains that devoid stable secondary and/or tertiary structure. IDPs/IDPRs are abundantly present in various proteomes, where they are involved in regulation, signaling, and control, thereby serving as crucial regulators of various cellular processes. Various mechanisms are utilized to tightly regulate and modulate biological functions, structural properties, cellular levels, and localization of these important controllers. Among these regulatory mechanisms are precisely controlled degradation and different posttranslational modifications (PTMs). Many normal cellular processes are associated with the presence of the right amounts of precisely activated IDPs at right places and in right time. However, wrecked regulation of IDPs/IDPRs might be associated with various human maladies, ranging from cancer and neurodegeneration to cardiovascular disease and diabetes. Pathogenic transformations of IDPs/IDPRs are often triggered by altered PTMs. This review considers some of the aspects of IDPs/IDPRs and their normal and aberrant regulation by PTMs.

The SSU processome interactome in Saccharomyces cerevisiae reveals novel protein subcomplexes

Ribosome assembly is an evolutionarily conserved and energy intensive process required for cellular growth, proliferation, and maintenance. In yeast, assembly of the small ribosomal subunit (SSU) requires approximately 75 assembly factors that act in coordination to form the SSU processome, a 6 MDa ribonucleoprotein complex. The SSU processome is required for processing, modifying, and folding the preribosomal RNA (rRNA) to prepare it for incorporation into the mature SSU. Although the protein composition of the SSU processome has been known for some time, the interaction network of the proteins required for its assembly has remained poorly defined. Here, we have used a semi-high-throughput yeast two-hybrid (Y2H) assay and coimmunoprecipitation validation method to produce a high-confidence interactome of SSU processome assembly factors (SPAFs), providing essential insight into SSU assembly and ribosome biogenesis. Further, we used glycerol density-gradient sedimentation to reveal the presence of protein subcomplexes that have not previously been observed. Our work not only provides essential insight into SSU assembly and ribosome biogenesis, but also serves as an important resource for future investigations into how defects in biogenesis and assembly cause congenital disorders of ribosomes known as ribosomopathies.

Physical Studies of P450-P450 Interactions: Predicting Quaternary Structures of P450 Complexes in Membranes from Their X-ray Crystal Structures

Cytochrome P450 enzymes, which catalyze oxygenation reactions of both exogenous and endogenous chemicals, are membrane bound proteins that require interaction with their redox partners in order to function. Those responsible for drug and foreign compound metabolism are localized primarily in the endoplasmic reticulum of liver, lung, intestine, and other tissues. More recently, the potential for P450 enzymes to exist as supramolecular complexes has been shown by the demonstration of both homomeric and heteromeric complexes. The P450 units in these complexes are heterogeneous with respect to their distribution and function, and the interaction of different P450s can influence P450-specific metabolism. The goal of this review is to examine the evidence supporting the existence of physical complexes among P450 enzymes. Additionally, the review examines the crystal lattices of different P450 enzymes derived from X-ray diffraction data to make assumptions regarding possible quaternary structures in membranes and in turn, to predict how the quaternary structures could influence metabolism and explain the functional effects of specific P450-P450 interactions.