Link prediction in protein-protein interaction network: A similarity multiplied similarity algorithm with paths of length three

Protein-protein interactions (PPIs) are crucial for various biological processes, and predicting PPIs is a major challenge. To solve this issue, the most common method is link prediction. Currently, the link prediction methods based on network Paths of Length Three (L3) have been proven to be highly effective. In this paper, we propose a novel link prediction algorithm, named SMS, which is based on L3 and protein similarities. We first design a mixed similarity that combines the topological structure and attribute features of nodes. Then, we compute the predicted value by summing the product of all similarities along the L3. Furthermore, we propose the Max Similarity Multiplied Similarity (maxSMS) algorithm from the perspective of maximum impact. Our computational prediction results show that on six datasets, including S. cerevisiae, H. sapiens, and others, the maxSMS algorithm improves the precision of the top 500, area under the precision-recall curve, and normalized discounted cumulative gain by an average of 26.99%, 53.67%, and 6.7%, respectively, compared to other optimal methods.

vCSF Danger-associated Molecular Patterns After Traumatic and Nontraumatic Acute Brain Injury: A Prospective Study

BACKGROUND

Danger-associated molecular patterns (DAMPs) may be implicated in the pathophysiological pathways associated with an unfavorable outcome after acute brain injury (ABI).

METHODS

We collected samples of ventricular cerebrospinal fluid (vCSF) for 5 days in 50 consecutive patients at risk of intracranial hypertension after traumatic and nontraumatic ABI. Differences in vCSF protein expression over time were evaluated using linear models and selected for functional network analysis using the PANTHER and STRING databases. The primary exposure of interest was the type of brain injury (traumatic vs. nontraumatic), and the primary outcome was the vCSF expression of DAMPs. Secondary exposures of interest included the occurrence of intracranial pressure ≥20 or ≥ 30 mm Hg during the 5 days post-ABI, intensive care unit (ICU) mortality, and neurological outcome (assessed using the Glasgow Outcome Score) at 3 months post-ICU discharge. Secondary outcomes included associations of these exposures with the vCSF expression of DAMPs.

RESULTS

A network of 6 DAMPs ( DAMP_trauma ; protein-protein interaction [PPI] P =0.04) was differentially expressed in patients with ABI of traumatic origin compared with those with nontraumatic ABI. ABI patients with intracranial pressure ≥30 mm Hg differentially expressed a set of 38 DAMPS ( DAMP_ICP30 ; PPI P < 0.001). Proteins in DAMP_ICP30 are involved in cellular proteolysis, complement pathway activation, and post-translational modifications. There were no relationships between DAMP expression and ICU mortality or unfavorable versus favorable outcomes.

CONCLUSIONS

Specific patterns of vCSF DAMP expression differentiated between traumatic and nontraumatic types of ABI and were associated with increased episodes of severe intracranial hypertension.

Modulators targeting protein-protein interactions in Mycobacterium tuberculosis

Tuberculosis (TB) is a chronic infectious disease caused by Mycobacterium tuberculosis (M. tuberculosis), mainly transmitted through droplets to infect the lungs, and seriously affecting patients' health and quality of life. Clinically, anti-TB drugs often entail side effects and lack efficacy against resistant strains. Thus, the exploration and development of novel targeted anti-TB medications are imperative. Currently, protein-protein interactions (PPIs) offer novel avenues for anti-TB drug development, and the study of targeted modulators of PPIs in M. tuberculosis has become a prominent research focus. Furthermore, a comprehensive PPI network has been constructed using computational methods and bioinformatics tools. This network allows for a more in-depth analysis of the structural biology of PPIs and furnishes essential insights for the development of targeted small-molecule modulators. Furthermore, this article provides a detailed overview of the research progress and regulatory mechanisms of PPI modulators in M. tuberculosis, the causative agent of TB. Additionally, it summarizes potential targets for anti-TB drugs and discusses the prospects of existing PPI modulators.

Deciphering proteomic mechanisms explaining the role of glutathione as an aid in improving plant fitness and tolerance against cadmium-toxicity in Brassica napus L

Cadmium (Cd) hazard is a serious limitation to plants, soils and environments. Cd-toxicity causes stunted growth, chlorosis, necrosis, and plant yield loss. Thus, ecofriendly strategies with understanding of molecular mechanisms of Cd-tolerance in plants is highly demandable. The Cd-toxicity caused plant growth retardation, leaf chlorosis and cellular damages, where the glutathione (GSH) enhanced plant fitness and Cd-toxicity in Brassica through Cd accumulation and antioxidant defense. A high-throughput proteome approach screened 4947 proteins, wherein 370 were differently abundant, 164 were upregulated and 206 were downregulated. These proteins involved in energy and carbohydrate metabolism, CO2 assimilation and photosynthesis, signal transduction and protein metabolism, antioxidant defense response, heavy metal detoxification, cytoskeleton and cell wall structure, and plant development in Brassica. Interestingly, several key proteins including glutathione S-transferase F9 (A0A078GBY1), ATP sulfurylase 2 (A0A078GW82), cystine lyase CORI3 (A0A078FC13), ferredoxin-dependent glutamate synthase 1 (A0A078HXC0), glutaredoxin-C5 (A0A078ILU9), glutaredoxin-C2 (A0A078HHH4) actively involved in antioxidant defense and sulfur assimilation-mediated Cd detoxification process confirmed by their interactome analyses. These candidate proteins shared common gene networks associated with plant fitness, Cd-detoxification and tolerance in Brassica. The proteome insights may encourage breeders for enhancing multi-omics assisted Cd-tolerance in Brassica, and GSH-mediated hazard free oil seed crop production for global food security.

Heliorhodopsin-mediated light-modulation of ABC transporter

Heliorhodopsins (HeRs) have been hypothesized to have widespread functions. Recently, the functions for few HeRs have been revealed; however, the hypothetical functions remain largely unknown. Herein, we investigate light-modulation of heterodimeric multidrug resistance ATP-binding cassette transporters (OmrDE) mediated by Omithinimicrobium cerasi HeR. In this study, we classifiy genes flanking the HeR-encoding genes and identify highly conservative residues for protein-protein interactions. Our results reveal that the interaction between OcHeR and OmrDE shows positive cooperatively sequential binding through thermodynamic parameters. Moreover, light-induced OcHeR upregulates OmrDE drug transportation. Hence, the binding may be crucial to drug resistance in O. cerasi as it survives in a drug-containing habitat. Overall, we unveil a function of HeR as regulatory rhodopsin for multidrug resistance. Our findings suggest potential applications in optogenetic technology.

Cold Spot SCANNER: Colab Notebook for predicting cold spots in protein-protein interfaces

BACKGROUND

Protein-protein interactions (PPIs) are conveyed through binding interfaces or surface patches on proteins that become buried upon binding. Structural and biophysical analysis of many protein-protein interfaces revealed certain unique features of these surfaces that determine the energetics of interactions and play a critical role in protein evolution. One of the significant aspects of binding interfaces is the presence of binding hot spots, where mutations are highly deleterious for binding. Conversely, binding cold spots are positions occupied by suboptimal amino acids and several mutations in such positions could lead to affinity enhancement. While there are many software programs for identification of hot spot positions, there is currently a lack of software for cold spot detection.

RESULTS

In this paper, we present Cold Spot SCANNER, a Colab Notebook, which scans a PPI binding interface and identifies cold spots resulting from cavities, unfavorable charge-charge, and unfavorable charge-hydrophobic interactions. The software offers a Py3DMOL-based interface that allows users to visualize cold spots in the context of the protein structure and generates a zip file containing the results for easy download.

CONCLUSIONS

Cold spot identification is of great importance to protein engineering studies and provides a useful insight into protein evolution. Cold Spot SCANNER is open to all users without login requirements and can be accessible at: https://colab.

RESEARCH

google.com/github/sagagugit/Cold-Spot-Scanner/blob/main/Cold_Spot_Scanner.ipynb .

Tapioca: a platform for predicting de novo protein-protein interactions in dynamic contexts

Protein-protein interactions (PPIs) drive cellular processes and responses to environmental cues, reflecting the cellular state. Here we develop Tapioca, an ensemble machine learning framework for studying global PPIs in dynamic contexts. Tapioca predicts de novo interactions by integrating mass spectrometry interactome data from thermal/ion denaturation or cofractionation workflows with protein properties and tissue-specific functional networks. Focusing on the thermal proximity coaggregation method, we improved the experimental workflow. Finely tuned thermal denaturation afforded increased throughput, while cell lysis optimization enhanced protein detection from different subcellular compartments. The Tapioca workflow was next leveraged to investigate viral infection dynamics. Temporal PPIs were characterized during the reactivation from latency of the oncogenic Kaposi's sarcoma-associated herpesvirus. Together with functional assays, NUCKS was identified as a proviral hub protein, and a broader role was uncovered by integrating PPI networks from alpha- and betaherpesvirus infections. Altogether, Tapioca provides a web-accessible platform for predicting PPIs in dynamic contexts.

Towards superior biopolymer gels by enabling interpenetrating network structures: A review on types, applications, and gelation strategies

Gels derived from single networks of natural polymers (biopolymers) typically exhibit limited physical properties and thus have seen constrained applications in areas like food and medicine. In contrast, gels founded on a synergy of multiple biopolymers, specifically polysaccharides and proteins, with intricate interpenetrating polymer network (IPN) structures, represent a promising avenue for the creation of novel gel materials with significantly enhanced properties and combined advantages. This review begins with the scrutiny of newly devised IPN gels formed through a medley of polysaccharides and/or proteins, alongside an introduction of their practical applications in the realm of food, medicine, and environmentally friendly solutions. Finally, based on the fact that the IPN gelation process and mechanism are driven by different inducing factors entwined with a diverse amalgamation of polysaccharides and proteins, our survey underscores the potency of physical, chemical, and enzymatic triggers in orchestrating the construction of crosslinked networks within these biomacromolecules. In these mixed systems, each specific inducer aligns with distinct polysaccharides and proteins, culminating in the generation of semi-IPN or fully-IPN gels through the intricate interpenetration between single networks and polymer chains or between two networks, respectively. The resultant IPN gels stand as paragons of excellence, characterized by their homogeneity, dense network structures, superior textural properties (e.g., hardness, elasticity, adhesion, cohesion, and chewability), outstanding water-holding capacity, and heightened thermal stability, along with guaranteed biosafety (e.g., nontoxicity and biocompatibility) and biodegradability. Therefore, a judicious selection of polymer combinations allows for the development of IPN gels with customized functional properties, adept at meeting precise application requirements.

Multiplex, Quantitative, High-Resolution Imaging of Protein:Protein Complexes via Hybridization Chain Reaction

Signal amplification based on the mechanism of hybridization chain reaction (HCR) facilitates spatial exploration of gene regulatory networks by enabling multiplex, quantitative, high-resolution imaging of RNA and protein targets. Here, we extend these capabilities to the imaging of protein:protein complexes, using proximity-dependent cooperative probes to conditionally generate a single amplified signal if and only if two target proteins are colocalized within the sample. HCR probes and amplifiers combine to provide automatic background suppression throughout the protocol, ensuring that even if reagents bind nonspecifically in the sample, they will not generate amplified background. We demonstrate protein:protein imaging with a high signal-to-background ratio in human cells, mouse proT cells, and highly autofluorescent formalin-fixed paraffin-embedded (FFPE) human breast tissue sections. Further, we demonstrate multiplex imaging of three different protein:protein complexes simultaneously and validate that HCR enables accurate and precise relative quantitation of protein:protein complexes with subcellular resolution in an anatomical context. Moreover, we establish a unified framework for simultaneous multiplex, quantitative, high-resolution imaging of RNA, protein, and protein:protein targets, with one-step, isothermal, enzyme-free HCR signal amplification performed for all target classes simultaneously.

Detecting PTP Protein-Protein Interactions by Fluorescent Immunoprecipitation Analysis (FIPA)

Identifying protein-protein interactions is crucial for revealing protein functions and characterizing cellular processes. Manipulating PPIs has become widespread in treating human diseases such as cancer, autoimmunity, and infections. It has been recently applied to the regulation of protein tyrosine phosphatases (PTPs) previously considered undruggable. A broad panel of methods is available for studying PPIs. To complement the existing toolkit, we developed a simple method called fluorescent immunoprecipitation analysis (FIPA). This method is based on coimmunoprecipitation followed by protein gel electrophoresis and fluorescent imaging to visualize components of a protein complex simultaneously on a gel. The FIPA allows the detection of proteins expressed under native conditions and is compatible with mass spectrometry identification of protein bands.

Analyzing Fcγ-Receptor Interactions on Monocytes with the Proximity Ligation Assay (PLA)

Proximity ligation assays (PLA) enable the detection and characterization of protein interactions independent of protein abundance or genetic modifications. This technique exploits both antibody and DNA-binding features, providing high selectivity and sensitivity for protein recognition and visualization of single-protein molecules with high spatial accuracy. Here, we describe the general procedure for a direct PLA on splenic monocytes to analyze FcγRIIb homodimerization. However, this method can be applied to other cells and receptors of interest.

WLP3 Encodes the Ribosomal Protein L18 and Regulates Chloroplast Development in Rice

Plastid ribosomal proteins play a crucial role in the growth and development of plants, mainly in the gene expression and translation of key genes in chloroplasts. While some information is known about the regulatory processes of plastid ribosomal proteins in various plant species, there is limited knowledge about the underlying mechanisms in rice. In this study, ethyl methanesulfonate (EMS) mutagenesis was used to generate a new mutant called wlp3 (white leaf and panicle3), characterized by white or albino leaves and panicles, which exhibited this phenotype from the second leaf stage until tillering. Furthermore, after a certain period, the newly emerging leaves developed the same phenotype as the rice variety ZH11, while the albino leaves of wlp3 showed an incomplete chloroplast structure and significantly low chlorophyll content. A transition mutation (T to C) at position 380 was identified in the coding region of the LOC_Os03g61260 gene, resulting in the substitution of isoleucine by threonine during translation. WLP3 encodes the ribosomal L18 subunit, which is localized in the chloroplast. Complementation experiments confirmed that LOC_Os03g61260 was responsible for the albino phenotype in rice. WLP3 has high expression in the coleoptile, leaves at the three-leaf stage, and panicles at the heading stage. Compared to the wild-type (WT), wlp3 exhibited reduced chlorophyll synthesis and significantly decreased expression levels of genes associated with plastid development. Yeast two-hybrid (Y2H) analysis revealed that WLP3 interacts with other ribosomal subunits, to influence chloroplast development. These results contribute to a better understanding of the underlying molecular mechanisms of chloroplast development and plastid gene translation.

Exploration of key drug target proteins highlighting their related regulatory molecules, functional pathways and drug candidates associated with delirium: evidence from meta-data analyses

BACKGROUND

Delirium is a prevalent neuropsychiatric medical phenomenon that causes serious emergency outcomes, including mortality and morbidity. It also increases the suffering and the economic burden for families and carers. Unfortunately, the pathophysiology of delirium is still unknown, which is a major obstacle to therapeutic development. The modern network-based system biology and multi-omics analysis approach has been widely used to recover the key drug target biomolecules and signaling pathways associated with disease pathophysiology. This study aimed to identify the major drug target hub-proteins associated with delirium, their regulatory molecules with functional pathways, and repurposable drug candidates for delirium treatment.

METHODS

We used a comprehensive proteomic seed dataset derived from a systematic literature review and the Comparative Toxicogenomics Database (CTD). An integrated multi-omics network-based bioinformatics approach was utilized in this study. The STRING database was used to construct the protein-protein interaction (PPI) network. The gene set enrichment and signaling pathways analysis, the regulatory transcription factors and microRNAs were conducted using delirium-associated genes. Finally, hub-proteins associated repurposable drugs were retrieved from CMap database.

RESULTS

We have distinguished 11 drug targeted hub-proteins (MAPK1, MAPK3, TP53, JUN, STAT3, SRC, RELA, AKT1, MAPK14, HSP90AA1 and DLG4), 5 transcription factors (FOXC1, GATA2, YY1, TFAP2A and SREBF1) and 6 microRNA (miR-375, miR-17-5, miR-17-5p, miR-106a-5p, miR-125b-5p, and miR-125a-5p) associated with delirium. The functional enrichment and pathway analysis revealed the cytokines, inflammation, postoperative pain, oxidative stress-associated pathways, developmental biology, shigellosis and cellular senescence which are closely connected with delirium development and the hallmarks of aging. The hub-proteins associated computationally identified repurposable drugs were retrieved from database. The predicted drug molecules including aspirin, irbesartan, ephedrine-(racemic), nedocromil, and guanidine were characterized as anti-inflammatory, stimulating the central nervous system, neuroprotective medication based on the existing literatures. The drug molecules may play an important role for therapeutic development against delirium if they are investigated more extensively through clinical trials and various wet lab experiments.

CONCLUSION

This study could possibly help future research on investigating the delirium-associated therapeutic target biomarker hub-proteins and repurposed drug compounds. These results will also aid understanding of the molecular mechanisms that underlie the pathophysiology of delirium onset and molecular function.

Pangenome diversification and resistance gene characterization in Salmonella Typhi prioritized RfaJ as a significant therapeutic marker

BACKGROUND

Salmonella Typhi stands as the etiological agent responsible for the onset of human typhoid fever. The pressing demand for innovative therapeutic targets against S. Typhi is underscored by the escalating prevalence of this pathogen and the severe nature of its infections. Consequently, this study employs pangenome analysis to scrutinize 119 S. Typhi-resistant strains, aiming to identify the most promising therapeutic targets originating from its core genome.

RESULTS

Subtractive genomics was employed to systematically eliminate non-homologous (n=1147), essential (n=551), drug-like (n=80), and pathogenicity-related (n=18) proteins from the initial pool of 3351 core genome proteins. Consequently, lipopolysaccharide 1,2-glucosyltransferase RfaJ was designated as the optimal pharmacological target due to its potential versatility. Furthermore, a compendium of 9000 FDA-approved compounds was repurposed for evaluation against the RfaJ drug target, with the specific intent of prioritizing novel, high-potency therapeutic candidates for combating S. Typhi. Ultimately, four compounds, namely DB00549 (Zafirlukast), DB15637 (Fluzoparib), DB15688 (Zavegepant), and DB12411 (Bemcentinib), were singled out as potential inhibitors based on the ligand-protein binding affinity (indicated by the lowest anticipated binding energy) and the overall stability of these compounds. Notably, molecular dynamics simulations, conducted over a 50 nanosecond interval, convincingly demonstrated the stability of these compounds in the context of the RfaJ protein.

CONCLUSION

In summary, the present findings hold significant promise as an initial stride in the broader drug discovery endeavor against S. Typhi infections. However, the experimental validation of the identified drug target and drug candidate is further required to increase the effectiveness of the applied methodology.

Proteome insights of citric acid-mediated cadmium toxicity tolerance in Brassica napus L

Cadmium (Cd) is a toxic substance that is uptake by plants from soils, Cd easily transfers into the food chain. Considering global food security, eco-friendly, cost-effective, and metal detoxification strategies are highly demandable for sustainable food crop production. The purpose of this study was to investigate how citric acid (CA) alleviates or tolerates Cd toxicity in Brassica using a proteome approach. In this study, the global proteome level was significantly altered under Cd toxicity with or without CA supplementation in Brassica. A total of 4947 proteins were identified using the gel-free proteome approach. Out of these, 476 proteins showed differential abundance between the treatment groups, wherein 316 were upregulated and 160 were downregulated. The gene ontology analysis reveals that differentially abundant proteins were involved in different biological processes including energy and carbohydrate metabolism, CO2 assimilation and photosynthesis, signal transduction and protein metabolism, antioxidant defense, heavy metal detoxification, plant development, and cytoskeleton and cell wall structure in Brassica leaves. Interestingly, several candidate proteins such as superoxide dismutase (A0A078GZ68) L-ascorbate peroxidase 3 (A0A078HSG4), glutamine synthetase (A0A078HLB2), glutathione S-transferase DHAR1 (A0A078HPN8), glutamine synthetase (A0A078HLB2), cysteine synthase (A0A078GAD3), S-adenosylmethionine synthase 2 (A0A078JDL6), and thiosulfate/3-mercaptopyruvate sulfur transferase 2 (A0A078H905) were involved in antioxidant defense system and sulfur assimilation-involving Cd-detoxification process in Brassica. These findings provide new proteome insights into CA-mediated Cd-toxicity alleviation in Brassica, which might be useful to oilseed crop breeders for enhancing heavy metal tolerance in Brassica using the breeding program, with sustainable and smart Brassica production in a metal-toxic environment.

Characterization of Two-Component System gene (TCS) in melatonin-treated common bean under salt and drought stress

The two-component system (TCS) generally consists of three elements, namely the histidine kinase (HK), response regulator (RR), and histidine phosphotransfer (HP) gene families. This study aimed to assess the expression of TCS genes in P. vulgaris leaf tissue under salt and drought stress and perform a genome-wide analysis of TCS gene family members using bioinformatics methods. This study identified 67 PvTCS genes, including 10 PvHP, 38 PvRR, and 19 PvHK, in the bean genome. PvHK2 had the maximum number of amino acids with 1261, whilst PvHP8 had the lowest number with 87. In addition, their theoretical isoelectric points were between 4.56 (PvHP8) and 9.15 (PvPRR10). The majority of PvTCS genes are unstable. Phylogenetic analysis of TCS genes in A. thaliana, G. max, and bean found that PvTCS genes had close phylogenetic relationships with the genes of other plants. Segmental and tandem duplicate gene pairs were detected among the TCS genes and TCS genes have been subjected to purifying selection pressure in the evolutionary process. Furthermore, the TCS gene family, which has an important role in abiotic stress and hormonal responses in plants, was characterized for the first time in beans, and its expression of TCS genes in bean leaves under salt and drought stress was established using RNAseq and qRT-PCR analyses. The findings of this study will aid future functional and genomic studies by providing essential information about the members of the TCS gene family in beans.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-023-01406-5.

Chromatin and non-chromatin immunoprecipitations to capture protein-protein and protein-nucleic acid interactions in living cells

Proteins are expressed from genes via sequential biological processes of transcription, mRNA processing, export and translation, and play their roles in maintaining cellular functions via interactions with proteins, DNAs or RNAs. Thus, it is important to study the protein interactions during biological processes in living cells towards understanding their mechanisms-of-action in real time. Methodologies have been developed over the years to study protein interactions in vivo. One state-of-the-art approach is formaldehyde crosslinking-based immuno- or chemi-precipitation to analyze selective as well as genome/proteome-wide interactions in living cells. It is a popular and widely used methodology for cellular analysis of the protein-protein and protein-nucleic acid interactions. Here, we describe this approach to analyze protein-protein/nucleic acid interactions in vivo.

MpbPPI: a multi-task pre-training-based equivariant approach for the prediction of the effect of amino acid mutations on protein-protein interactions

The accurate prediction of the effect of amino acid mutations for protein-protein interactions (PPI $\Delta \Delta G$) is a crucial task in protein engineering, as it provides insight into the relevant biological processes underpinning protein binding and provides a basis for further drug discovery. In this study, we propose MpbPPI, a novel multi-task pre-training-based geometric equivariance-preserving framework to predict PPI  $\Delta \Delta G$. Pre-training on a strictly screened pre-training dataset is employed to address the scarcity of protein-protein complex structures annotated with PPI $\Delta \Delta G$ values. MpbPPI employs a multi-task pre-training technique, forcing the framework to learn comprehensive backbone and side chain geometric regulations of protein-protein complexes at different scales. After pre-training, MpbPPI can generate high-quality representations capturing the effective geometric characteristics of labeled protein-protein complexes for downstream $\Delta \Delta G$ predictions. MpbPPI serves as a scalable framework supporting different sources of mutant-type (MT) protein-protein complexes for flexible application. Experimental results on four benchmark datasets demonstrate that MpbPPI is a state-of-the-art framework for PPI $\Delta \Delta G$ predictions. The data and source code are available at https://github.com/arantir123/MpbPPI.

Predator: Predicting the Impact of Cancer Somatic Mutations on Protein-Protein Interactions

Since many biological processes are governed by protein-protein interactions, understanding which mutations lead to a disruption in these interactions is profoundly important for cancer research. Most of the existing methods focus on the stability of the protein without considering the specific effects of a mutation on its interactions with other proteins. Here, we focus on somatic mutations that appear on the interface regions of the protein and predict the interactions that would be affected by a mutation of interest. We build an ensemble model, Predator, that classifies the interface mutations as disruptive or nondisruptive based on the predicted effects of mutations on specific protein-protein interactions. We show that Predator outperforms existing approaches in literature in terms of prediction accuracy. We then apply Predator on various TCGA cancer cohorts and perform comprehensive analysis at cohort level, patient level, and gene level in determining the genes whose interface mutations tend to yield a disruption in its interactions. The predictions obtained by Predator shed light on interesting patterns on several genes for each cohort regarding their potential as cancer drivers. Our analyses further reveal that the identified genes and their frequently disrupted partners exhibit patterns of mutually exclusivity across cancer cohorts under study.

Large-scale phosphomimetic screening identifies phospho-modulated motif-based protein interactions

Phosphorylation is a ubiquitous post-translation modification that regulates protein function by promoting, inhibiting or modulating protein-protein interactions. Hundreds of thousands of phosphosites have been identified but the vast majority have not been functionally characterised and it remains a challenge to decipher phosphorylation events modulating interactions. We generated a phosphomimetic proteomic peptide-phage display library to screen for phosphosites that modulate short linear motif-based interactions. The peptidome covers ~13,500 phospho-serine/threonine sites found in the intrinsically disordered regions of the human proteome. Each phosphosite is represented as wild-type and phosphomimetic variant. We screened 71 protein domains to identify 248 phosphosites that modulate motif-mediated interactions. Affinity measurements confirmed the phospho-modulation of 14 out of 18 tested interactions. We performed a detailed follow-up on a phospho-dependent interaction between clathrin and the mitotic spindle protein hepatoma-upregulated protein (HURP), demonstrating the essentiality of the phospho-dependency to the mitotic function of HURP. Structural characterisation of the clathrin-HURP complex elucidated the molecular basis for the phospho-dependency. Our work showcases the power of phosphomimetic ProP-PD to discover novel phospho-modulated interactions required for cellular function.

Chemokine Heteromers and Their Impact on Cellular Function-A Conceptual Framework

Chemoattractant cytokines or chemokines are proteins involved in numerous biological activities. Their essential role consists of the formation of gradient and (immune) cell recruitment. Chemokine biology and its related signaling system is more complex than simple ligand-receptor interactions. Beside interactions with their cognate and/or atypical chemokine receptors, and glycosaminoglycans (GAGs), chemokines form complexes with themselves as homo-oligomers, heteromers and also with other soluble effector proteins, including the atypical chemokine MIF, carbohydrate-binding proteins (galectins), damage-associated molecular patterns (DAMPs) or with chemokine-binding proteins such as evasins. Likewise, nucleic acids have been described as binding targets for the tetrameric form of CXCL4. The dynamic balance between monomeric and dimeric structures, as well as interactions with GAGs, modulate the concentrations of free chemokines available along with the nature of the gradient. Dimerization of chemokines changes the canonical monomeric fold into two main dimeric structures, namely CC- and CXC-type dimers. Recent studies highlighted that chemokine dimer formation is a frequent event that could occur under pathophysiological conditions. The structural changes dictated by chemokine dimerization confer additional biological activities, e.g., biased signaling. The present review will provide a short overview of the known functionality of chemokines together with the consequences of the interactions engaged by the chemokines with other proteins. Finally, we will present potential therapeutic tools targeting the chemokine multimeric structures that could modulate their biological functions.

Computational Modeling, High-Level Soluble Expression and In Vitro Cytotoxicity Assessment of Recombinant Pseudomonas aeruginosa Azurin: A Promising Anti-Cancer Therapeutic Candidate

Azurin is a natural protein produced by Pseudomonas aeruginosa that exhibits potential anti-tumor, anti-HIV, and anti-parasitic properties. The current study aimed to investigate the potential of azurin protein against breast cancer using both in silico and in vitro analyses. The amino acid sequence of Azurin was used to predict its secondary and tertiary structures, along with its physicochemical properties, using online software. The resulting structure was validated and confirmed using Ramachandran plots and ERRAT2. The mature azurin protein comprises 128 amino acids, and the top-ranked structure obtained from I-TASSER was shown to have a molecular weight of 14 kDa and a quality factor of 100% by ERRAT2, with 87.4% of residues in the favored region of the Ramachandran plot. Docking and simulation studies of azurin protein were conducted using HDOCK and Desmond servers, respectively. The resulting analysis revealed that Azurin docked against p53 and EphB2 receptors demonstrated maximum binding affinity, indicating its potential to cause apoptosis. The recombinant azurin gene was successfully cloned and expressed in a BL21 (DE3) strain using a pET20b expression vector under the control of the pelB ladder, followed by IPTG induction. The azurin protein was purified to high levels using affinity chromatography, yielding 70 mg/L. In vitro cytotoxicity assay was performed using MCF-7 cells, revealing the significant cytotoxicity of the azurin protein to be 105 µg/mL. These findings highlight the potential of azurin protein as an anticancer drug candidate.

Interactomics in plant defence: progress and opportunities

Interactomics is a branch of systems biology that deals with the study of protein-protein interactions and how these interactions influence phenotypes. Identifying the interactomes involved during host-pathogen interaction events may bring us a step closer to deciphering the molecular mechanisms underlying plant defence. Here, we conducted a systematic review of plant interactomics studies over the last two decades and found that while a substantial progress has been made in the field, plant-pathogen interactomics remains a less-travelled route. As an effort to facilitate the progress in this field, we provide here a comprehensive research pipeline for an in planta plant-pathogen interactomics study that encompasses the in silico prediction step to the validation step, unconfined to model plants. We also highlight four challenges in plant-pathogen interactomics with plausible solution(s) for each.

Drug Repurposing and Systems Biology approaches of Enzastaurin can target potential biomarkers and critical pathways in Colorectal Cancer

Colorectal cancer (CRC) is a severe health concern that results from a cocktail of genetic, epigenetic, and environmental abnormalities. Because it is the second most lethal malignancy in the world and the third-most common malignant tumor, but the treatment is unavailable. The goal of the current study was to use bioinformatics and systems biology techniques to determine the pharmacological mechanism underlying putative important genes and linked pathways in early-onset CRC. Computer-aided methods were used to uncover similar biological targets and signaling pathways associated with CRC, along with bioinformatics and network pharmacology techniques to assess the effects of enzastaurin on CRC. The KEGG and gene ontology (GO) pathway analysis revealed several significant pathways including in positive regulation of protein phosphorylation, negative regulation of the apoptotic process, nucleus, nucleoplasm, protein tyrosine kinase activity, PI3K-Akt signaling pathway, pathways in cancer, focal adhesion, HIF-1 signaling pathway, and Rap1 signaling pathway. Later, the hub protein module identified from the protein-protein interactions (PPIs) network, molecular docking and molecular dynamics simulation represented that enzastaurin showed strong binding interaction with two hub proteins including CASP3 (-8.6 kcal/mol), and MCL1 (-8.6 kcal/mol), which were strongly implicated in CRC management than other the five hub proteins. Moreover, the pharmacokinetic features of enzastaurin revealed that it is an effective therapeutic agent with minimal adverse effects. Enzastaurin may inhibit the potential biological targets that are thought to be responsible for the advancement of CRC and this study suggests a potential novel therapeutic target for CRC.

Integrating transcription and splicing into cell fate: Transcription factors on the block

Transcription factors (TFs) are present in all life forms and conserved across great evolutionary distances in eukaryotes. From yeast to complex multicellular organisms, they are pivotal players of cell fate decision by orchestrating gene expression at diverse molecular layers. Notably, TFs fine-tune gene expression by coordinating RNA fate at both the expression and splicing levels. They regulate alternative splicing, an essential mechanism for cell plasticity, allowing the production of many mRNA and protein isoforms in precise cell and tissue contexts. Despite this apparent role in splicing, how TFs integrate transcription and splicing to ultimately orchestrate diverse cell functions and cell fate decisions remains puzzling. We depict substantial studies in various model organisms underlining the key role of TFs in alternative splicing for promoting tissue-specific functions and cell fate. Furthermore, we emphasize recent advances describing the molecular link between the transcriptional and splicing activities of TFs. As TFs can bind both DNA and/or RNA to regulate transcription and splicing, we further discuss their flexibility and compatibility for DNA and RNA substrates. Finally, we propose several models integrating transcription and splicing activities of TFs in the coordination and diversification of cell and tissue identities. This article is categorized under: RNA Processing > Splicing Regulation/Alternative Splicing RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA Processing > Splicing Mechanisms.

Computational models for prediction of protein-protein interaction in rice and Magnaporthe grisea

INTRODUCTION

Plant-microbe interactions play a vital role in the development of strategies to manage pathogen-induced destructive diseases that cause enormous crop losses every year. Rice blast is one of the severe diseases to rice Oryza sativa (O. sativa) due to Magnaporthe grisea (M. grisea) fungus. Protein-protein interaction (PPI) between rice and fungus plays a key role in causing rice blast disease.

METHODS

In this paper, four genomic information-based models such as (i) the interolog, (ii) the domain, (iii) the gene ontology, and (iv) the phylogenetic-based model are developed for predicting the interaction between O. sativa and M. grisea in a whole-genome scale.

RESULTS AND DISCUSSION

A total of 59,430 interacting pairs between 1,801 rice proteins and 135 blast fungus proteins are obtained from the four models. Furthermore, a machine learning model is developed to assess the predicted interactions. Using composition-based amino acid composition (AAC) and conjoint triad (CT) features, an accuracy of 88% and 89% is achieved, respectively. When tested on the experimental dataset, the CT feature provides the highest accuracy of 95%. Furthermore, the specificity of the model is verified with other pathogen-host datasets where less accuracy is obtained, which confirmed that the model is specific to O. sativa and M. grisea. Understanding the molecular processes behind rice resistance to blast fungus begins with the identification of PPIs, and these predicted PPIs will be useful for drug design in the plant science community.

Rapid Scan Electron Paramagnetic Resonance Spectroscopy Is a Suitable Tool to Study Intermolecular Interactions of Intrinsically Disordered Protein

Intrinsically disordered proteins (IDPs) are involved in most crucial cellular processes. However, they lack a well-defined fold hampering the investigation of their structural ensemble and interactions. Suitable biophysical methods able to manage their inherent flexibility and broad conformational ensemble are scarce. Here, we used rapid scan (RS) electron paramagnetic resonance (EPR) spectroscopy to study the intermolecular interactions of the IDP α-synuclein (aS). aS aggregation and fibril deposition is the hallmark of Parkinson's disease, and specific point mutations, among them A30P and A53T, were linked to the early onset of the disease. To understand the pathological processes, research intensively investigates aS aggregation kinetics, which was reported to be accelerated in the presence of ethanol. Conventional techniques fail to capture these fast processes due to their limited time resolution and, thus, lose kinetic information. We have demonstrated that RS EPR spectroscopy is suitable for studying aS aggregation by resolving underlying kinetics and highlighting differences in fibrillization behavior. RS EPR spectroscopy outperforms traditional EPR methods in terms of sensitivity by a factor of 5 in our case while significantly reducing data acquisition time. Thus, we were able to sample short time intervals capturing single events taking place during the aggregation process. Further studies will therefore be able to shed light on biological processes proceeding on fast time scales.

The Interactome of Protein, DNA, and RNA

Proteins participate in many processes of the organism and are very important for maintaining the health of the organism. However, proteins cannot function independently in the body. They must interact with proteins, DNA, RNA, and other substances to perform biological functions and maintain the body's health. At present, there are many experimental methods and software tools that can detect and predict the interaction between proteins and other substances. There are also many databases that record the interaction between proteins and other substances. This article mainly describes protein-protein, protein-DNA, and protein-RNA interactions in detail by introducing some commonly used experimental methods, the software tools produced with the accumulation of experimental data and the rapid development of machine learning, and the related databases that record the relationship between proteins and some substances. By this review, we hope that through the analysis and summary of various aspects, it will be convenient for researchers to conduct further research on protein interactions.

Recent developments of sequence-based prediction of protein-protein interactions

The identification of protein-protein interactions (PPIs) can lead to a better understanding of cellular functions and biological processes of proteins and contribute to the design of drugs to target disease-causing PPIs. In addition, targeting host-pathogen PPIs is useful for elucidating infection mechanisms. Although several experimental methods have been used to identify PPIs, these methods can yet to draw complete PPI networks. Hence, computational techniques are increasingly required for the prediction of potential PPIs, which have never been seen experimentally. Recent high-performance sequence-based methods have contributed to the construction of PPI networks and the elucidation of pathogenetic mechanisms in specific diseases. However, the usefulness of these methods depends on the quality and quantity of training data of PPIs. In this brief review, we introduce currently available PPI databases and recent sequence-based methods for predicting PPIs. Also, we discuss key issues in this field and present future perspectives of the sequence-based PPI predictions.

Therapeutic Target Identification and Inhibitor Screening against Riboflavin Synthase of Colorectal Cancer Associated Fusobacterium nucleatum

Colorectal cancer (CRC) ranks third among all cancers in terms of prevalence. There is growing evidence that gut microbiota has a role in the development of colorectal cancer. Fusobacterium nucleatum is overrepresented in the gastrointestinal tract and tumor microenvironment of patients with CRC. This suggests the role of F. nucleatum as a potential risk factor in the development of CRC. Hence, we aimed to explore whole genomes of F. nucleatum strains related to CRC to predict potential therapeutic markers through a pan-genome integrated subtractive genomics approach. In the current study, we identified 538 proteins as essential for F. nucleatum survival, 209 non-homologous to a human host, and 12 as drug targets. Eventually, riboflavin synthase (RiS) was selected as a therapeutic target for further processing. Three different inhibitor libraries of lead-like natural products, i.e., cyanobactins (n = 237), streptomycins (n = 607), and marine bacterial secondary metabolites (n = 1226) were screened against it. After the structure-based study, three compounds, i.e., CMNPD3609 (−7.63) > Malyngamide V (−7.03) > ZINC06804365 (−7.01) were prioritized as potential inhibitors of F. nucleatum. Additionally, the stability and flexibility of these compounds bound to RiS were determined via a molecular dynamics simulation of 50 ns. Results revealed the stability of these compounds within the binding pocket, after 5 ns. ADMET profiling showed compounds as drug-like, non-permeable to the blood brain barrier, non-toxic, and HIA permeable. Pan-genomics mediated drug target identification and the virtual screening of inhibitors is the preliminary step towards inhibition of this pathogenic oncobacterium and we suggest mouse model experiments to validate our findings.

Sportomics method to assess acute phase proteins in Olympic level athletes using dried blood spots and multiplex assay

Sportomics is a subject-centered holistic method similar to metabolomics focusing on sports as the metabolic challenge. Dried blood spot is emerging as a technique due to its simplicity and reproducibility. In addition, mass spectrometry and integrative computational biology enhance our ability to understand exercise-induced modifications. We studied inflammatory blood proteins (Alpha-1-acid glycoprotein-A1AG1; Albumin; Cystatin C; C-reactive protein-CRP; Hemoglobin-HBA; Haptoglobin-HPT; Insulin-like growth factor 1; Lipopolysaccharide binding protein-LBP; Mannose-binding lectin-MBL2; Myeloperoxidase-PERM and Serum amyloid A1-SAA1), in 687 samples from 97 World-class and Olympic athletes across 16 sports in nine states. Data were analyzed with Spearman's rank-order correlation. Major correlations with CRP, LBP; MBL2; A1AG1, and SAA1 were found. The pairs CRP-SAA1 and CRP-LBP appeared with a robust positive correlation. Other pairs, LBP-SAA1; A1AG1-CRP; A1AG1-SAA1; A1AG1-MBL, and A1AG1-LBP, showed a broader correlation across the sports. The protein-protein interaction map revealed 1500 interactions with 44 core proteins, 30 of them linked to immune system processing. We propose that the inflammation follow-up in exercise can provide knowledge for internal cargo management in training, competition, recovery, doping control, and a deeper understanding of health and disease.

Integrated Bioinformatics-Based Subtractive Genomics Approach to Decipher the Therapeutic Drug Target and Its Possible Intervention against Brucellosis

Brucella suis, one of the causative agents of brucellosis, is Gram-negative intracellular bacteria that may be found all over the globe and it is a significant facultative zoonotic pathogen found in livestock. It may adapt to a phagocytic environment, reproduce, and develop resistance to harmful environments inside host cells, which is a crucial part of the Brucella life cycle making it a worldwide menace. The molecular underpinnings of Brucella pathogenicity have been substantially elucidated due to comprehensive methods such as proteomics. Therefore, we aim to explore the complete Brucella suis proteome to prioritize the novel proteins as drug targets via subtractive proteo-genomics analysis, an effort to conjecture the existence of distinct pathways in the development of brucellosis. Consequently, 38 unique metabolic pathways having 503 proteins were observed while among these 503 proteins, the non-homologs (n = 421), essential (n = 350), drug-like (n = 114), virulence (n = 45), resistance (n = 42), and unique to pathogen proteins were retrieved from Brucella suis. The applied subsequent hierarchical shortlisting resulted in a protein, i.e., isocitrate lyase, that may act as potential drug target, which was finalized after the extensive literature survey. The interacting partners for these shortlisted drug targets were identified through the STRING database. Moreover, structure-based studies were also performed on isocitrate lyase to further analyze its function. For that purpose, ~18,000 ZINC compounds were screened to identify new potent drug candidates against isocitrate lyase for brucellosis. It resulted in the shortlisting of six compounds, i.e., ZINC95543764, ZINC02688148, ZINC20115475, ZINC04232055, ZINC04231816, and ZINC04259566 that potentially inhibit isocitrate lyase. However, the ADMET profiling showed that all compounds fulfill ADMET properties except for ZINC20115475 showing positive Ames activity; whereas, ZINC02688148, ZINC04259566, ZINC04232055, and ZINC04231816 showed hepatoxicity while all compounds were observed to have no skin sensitization. In light of these parameters, we recommend ZINC95543764 compound for further experimental studies. According to the present research, which uses subtractive genomics, proteins that might serve as therapeutic targets and potential lead options for eradicating brucellosis have been narrowed down.

Subcellular Interactomes Revealed by Merging APEX with Cross-Linking Mass Spectrometry

Subcellular protein-protein interactions (PPIs) are essential to understanding the mechanism of diverse cellular signaling events and the pathogenesis of diseases. Herein, we report an integrated APEX proximity labeling and chemical cross-linking coupled with mass spectrometry (CXMS) platform named APEX-CXMS for spatially resolved subcellular interactome profiling in a high-throughput manner. APEX proximity labeling rapidly captures subcellular proteomes, and the highly reactive chemical cross-linkers can capture weak and dynamic interactions globally without extra genetic manipulation. APEX-CXMS was first applied to mitochondria and identified 653 pairs of interprotein cross-links. Six pairs of new interactions were selected and verified by coimmunoprecipitation, the mammalian two-hybrid system, and surface plasmon resonance method. Besides, our approach was further applied to the nucleus, capturing 336 pairs of interprotein cross-links with approximately 94% nuclear specificity. APEX-CXMS thus provides a simple, fast, and general alternative to map diverse subcellular PPIs.

Cold spots are universal in protein–protein interactions

Proteins interact with each other through binding interfaces that differ greatly in size and physico‐chemical properties. Within the binding interface, a few residues called hot spots contribute the majority of the binding free energy and are hence irreplaceable. In contrast, cold spots are occupied by suboptimal amino acids, providing possibility for affinity enhancement through mutations. In this study, we identify cold spots due to cavities and unfavorable charge interactions in multiple protein–protein interactions (PPIs). For our cold spot analysis, we first use a small affinity database of PPIs with known structures and affinities and then expand our search to nearly 4000 homo‐ and heterodimers in the Protein Data Bank (PDB). We observe that cold spots due to cavities are present in nearly all PPIs unrelated to their binding affinity, while unfavorable charge interactions are relatively rare. We also find that most cold spots are located in the periphery of the binding interface, with high‐affinity complexes showing fewer centrally located colds spots than low‐affinity complexes. A larger number of cold spots is also found in non‐cognate interactions compared to their cognate counterparts. Furthermore, our analysis reveals that cold spots are more frequent in homo‐dimeric complexes compared to hetero‐complexes, likely due to symmetry constraints imposed on sequences of homodimers. Finally, we find that glycines, glutamates, and arginines are the most frequent amino acids appearing at cold spot positions. Our analysis emphasizes the importance of cold spot positions to protein evolution and facilitates protein engineering studies directed at enhancing binding affinity and specificity in a wide range of applications.

FDX1 expression predicts favourable prognosis in clear cell renal cell carcinoma identified by bioinformatics and tissue microarray analysis

Ferredoxin 1 (FDX1), an iron-sulphur protein, is responsible for electron transfer in a range of metabolic redox reactions. Clear cell renal cell carcinoma (ccRCC) is an aggressive cancer characterised by metabolic reprogramming, and FDX1 is a critical regulator of cuproptosis. However, the expression profile and prognostic value of FDX1 associated with clinicopathological features in ccRCC remain largely unelucidated. In this study, we integrated a series of public bioinformatic analysis to explore the mRNA and protein profiles of FDX1 across human cancers and cell lines and validated its expression and prognostic value, especially in ccRCC. In this study, FDX1 mRNA and protein expression were aberrantly downregulated and associated with ccRCC grade, stage, and nodal metastasis, whereas in adjacent non-tumour kidney tissue, it was abundantly expressed and cytoplasmically localised in renal tubular epithelial cells. Multivariate analysis indicated that low FDX1 expression contributed to unfavourable overall and disease-free survival. The functional enrichment of FDX1 co-expressed genes in ccRCC involved mainly mitochondrial dysfunction in various metabolic processes and biological oxidation, besides iron-sulphur cluster biogenesis. Furthermore, FDX1 modulates immunological infiltration to affect prognosis. Thus, FDX1 downregulation is mechanistically because of ccRCC tumourigenesis and is a promising prognostic biomarker to stratify patients with ccRCC.

Determination of specific and non-specific protein-protein interactions for beta-lactoglobulin by analytical ultracentrifugation and membrane osmometry experiments

Protein-protein interactions are essential for the understanding of biological processes. Specific protein aggregation is an important aspect for many biological systems. In particular, electrostatic interactions play the key role for protein-protein interactions, as many amino acids have pH-dependent charge states. Moreover, protein dissociation is directly related to the solution pH, ionic strength, temperature and protein concentration. The subtle interplay between different specific and non-specific interactions is demonstrated for beta-lactoglobulin (BLG) with a focus on low salt concentrations, thus mimicking technically relevant processing conditions. BLG is a well-characterized model system, proven to attain its monomer-dimer equilibrium strongly dependent upon the pH of the solution. In this manuscript, we present a unique combination of analytical ultracentrifugation and membrane osmometry experiments, which quantifies specific and non-specific interactions, i.e. in terms of the dimer dissociation constants and the second osmotic virial coefficient, at pH 3 and 7 and sodium chloride concentrations of 10 mM and 100 mM. This provides direct insight to protein-protein interactions for a system with a concentration-dependent monomer-dimer equilibrium. Moreover, using a coarse-grained extended DLVO model in combination with molecular dynamics simulations, we quantify non-specific monomer-monomer, monomer-dimer and dimer-dimer interactions as well as the binding free energy of BLG dimerization from theoretical calculations. The experimentally determined interactions are shown to be mainly governed by electrostatic interactions and further agree with free energy calculations. Our experimental protocol aims to determine non-specific and specific interactions for a dynamically interacting system and provides an understanding of protein-protein interactions for BLG at low salt concentrations.

Struct2Graph: a graph attention network for structure based predictions of protein–protein interactions

Background

Development of new methods for analysis of protein–protein interactions (PPIs) at molecular and nanometer scales gives insights into intracellular signaling pathways and will improve understanding of protein functions, as well as other nanoscale structures of biological and abiological origins. Recent advances in computational tools, particularly the ones involving modern deep learning algorithms, have been shown to complement experimental approaches for describing and rationalizing PPIs. However, most of the existing works on PPI predictions use protein-sequence information, and thus have difficulties in accounting for the three-dimensional organization of the protein chains.

Results

In this study, we address this problem and describe a PPI analysis based on a graph attention network, named Struct2Graph, for identifying PPIs directly from the structural data of folded protein globules. Our method is capable of predicting the PPI with an accuracy of 98.89% on the balanced set consisting of an equal number of positive and negative pairs. On the unbalanced set with the ratio of 1:10 between positive and negative pairs, Struct2Graph achieves a fivefold cross validation average accuracy of 99.42%. Moreover, Struct2Graph can potentially identify residues that likely contribute to the formation of the protein–protein complex. The identification of important residues is tested for two different interaction types: (a) Proteins with multiple ligands competing for the same binding area, (b) Dynamic protein–protein adhesion interaction. Struct2Graph identifies interacting residues with 30% sensitivity, 89% specificity, and 87% accuracy.

Conclusions

In this manuscript, we address the problem of prediction of PPIs using a first of its kind, 3D-structure-based graph attention network (code available at https://github.com/baranwa2/Struct2Graph). Furthermore, the novel mutual attention mechanism provides insights into likely interaction sites through its unsupervised knowledge selection process. This study demonstrates that a relatively low-dimensional feature embedding learned from graph structures of individual proteins outperforms other modern machine learning classifiers based on global protein features. In addition, through the analysis of single amino acid variations, the attention mechanism shows preference for disease-causing residue variations over benign polymorphisms, demonstrating that it is not limited to interface residues.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04910-9.

Identification of interaction partners using protein aggregation and NMR spectroscopy

The interaction among proteins is one of the most fundamental methods of information transfer in the living system. Many methods have been developed in order to identify the interaction pairs or groups either in vivo or in vitro. The in vitro pulldown/coprecipitation assay directly observes the protein that binds to the target. This method involves electrophoresis, which is a technique of a low resolution as well as a low throughput. As a better alternative, we wish to propose a new method that is based on the NMR spectroscopy. This method utilizes the aggregation of the target protein and the concomitant signal disappearance of the interacting partner. The aggregation is accomplished by the elastin-like polypeptide, which is fused to the target. If a protein binds to this supramolecular complex, its NMR signal then becomes too broadened in order to be observed, which is the basic phenomenon of the NMR spectroscopy. Thus, the protein that loses its signal is the one that binds to the target. A compound that interferes with these types of bindings among the proteins can be identified by observing the reappearance of the protein signals with the simultaneous disappearance of the signals of the compound. This technique will be applied in order to find an interaction pair in the information transfer pathway as well as a compound that disrupts it. This proposed method should be able to work with a mixture of proteins and provide a higher resolution in order to find the binding partner in a higher throughput fashion.

Multi-wavelength analytical ultracentrifugation of biopolymer mixtures and interactions

Multi-wavelength analytical ultracentrifugation (MW-AUC) is a recent development made possible by new analytical ultracentrifuge optical systems. MW-AUC extends the basic hydrodynamic information content of AUC and provides access to a wide range of new applications for biopolymer characterization, and is poised to become an essential analytical tool to study macromolecular interactions. It adds an orthogonal spectral dimension to the traditional hydrodynamic characterization by exploiting unique chromophores in analyte mixtures that may or may not interact. Here we illustrate the utility of MW-AUC for experimental investigations where the benefit of the added spectral dimension provides critical information that is not accessible, and impossible to resolve with traditional AUC methods. We demonstrate the improvements in resolution and information content obtained by this technique compared to traditional single- or dual-wavelength approaches, and discuss experimental design considerations and limitations of the method. We further address the advantages and disadvantages of the two MW optical systems available today, and the differences in data analysis strategies between the two systems.

Noncoding RNAs in liver physiology and metabolic diseases

The liver holds central roles in detoxification, energy metabolism and whole-body homeostasis but can develop malignant phenotypes when being chronically overwhelmed with fatty acids and glucose. The global rise of metabolic-associated fatty liver disease (MAFLD) is already affecting a quarter of the global population. Pharmaceutical treatment options against different stages of MAFLD do not yet exist and several clinical trials against hepatic transcription factors and other proteins have failed. However, emerging roles of noncoding RNAs, including long (lncRNA) and short noncoding RNAs (sRNA), in various cellular processes pose exciting new avenues for treatment interventions. Actions of noncoding RNAs mostly rely on interactions with proteins, whereby the noncoding RNA fine-tunes protein function in a process termed riboregulation. The developmental stage-, disease stage- and cell type-specific nature of noncoding RNAs harbors enormous potential to precisely target certain cellular pathways in a spatio-temporally defined manner. Proteins interacting with RNAs can be categorized into canonical or non-canonical RNA binding proteins (RBPs) depending on the existence of classical RNA binding domains. Both, RNA- and RBP-centric methods have generated new knowledge of the RNA-RBP interface and added an additional regulatory layer. In this review, we summarize recent advances of how of RBP-lncRNA interactions and various sRNAs shape cellular physiology and the development of liver diseases such as MAFLD and hepatocellular carcinoma.

Evidence of an intracellular interaction between the Escherichia coli enzymes EntC and EntB and identification of a potential electrostatic channeling surface

Siderophores are high-affinity small-molecule chelators employed by bacteria to acquire iron from the extracellular environment. The Gram-negative bacterium Escherichia coli synthesizes and secretes enterobactin, a tris-catechol siderophore. Enterobactin is synthesized by six cytoplasmic enzyme activities: EntC, EntB (isochorismatase (IC) domain), EntA, EntE, EntB (aryl carrier protein (ArCP) domain), and EntF. While various pairwise protein-protein interactions have been reported between EntB, EntA, EntE, and EntF, evidence for an interaction between EntC and EntB has remained elusive. We have employed bacterial two-hybrid assays and in vivo crosslinking to demonstrate an intracellular EntC-EntB interaction. A T18-EntC/T25-EntB co-transformant exhibited a positive two-hybrid signal compared to a control T18-EntC/T25 co-transformant. In vivo formaldehyde crosslinking of E. coli cells co-expressing HA-tagged EntB and H6-tagged EntC resulted in an observable ∼80 kDa band on Western blots that cross-reacted with anti-HA and anti-H6, corresponding to one HA-EntB monomer (33 kDa) crosslinked with one H6-EntC monomer (45 kDa). This band disappeared upon sample boiling, confirming it to be a formaldehyde-crosslinked species. Bands of molecular masses greater than 80 kDa that cross-reacted with both antibodies were also observed. Automated docking of the crystal structures of monomeric EntC and dimeric EntB resulted in a top-ranked candidate docked ensemble in which the active sites of EntC and EntB were oriented in apposition and connected by an electropositive surface potentially capable of channeling negatively charged isochorismate. These research outcomes provide the first reported evidence of an EntC-EntB interaction, as well as the first experimental evidence of higher-order complexes containing EntC and EntB.

Predicting Protein–Protein Interactions Based on Ensemble Learning-Based Model from Protein Sequence

Simple Summary

Due to most traditional high-throughput experiments are tedious and laborious in identifying potential protein–protein interaction. To better improve accuracy prediction in protein–protein interactions. We proposed a novel computational method that can identify unknown protein–protein interaction efficiently and hope this method can provide a helpful idea and tool for proteomics research.

Abstract

Protein–protein interactions (PPIs) play an essential role in many biological cellular functions. However, it is still tedious and time-consuming to identify protein–protein interactions through traditional experimental methods. For this reason, it is imperative and necessary to develop a computational method for predicting PPIs efficiently. This paper explores a novel computational method for detecting PPIs from protein sequence, the approach which mainly adopts the feature extraction method: Locality Preserving Projections (LPP) and classifier: Rotation Forest (RF). Specifically, we first employ the Position Specific Scoring Matrix (PSSM), which can remain evolutionary information of biological for representing protein sequence efficiently. Then, the LPP descriptor is applied to extract feature vectors from PSSM. The feature vectors are fed into the RF to obtain the final results. The proposed method is applied to two datasets: Yeast and H. pylori, and obtained an average accuracy of 92.81% and 92.56%, respectively. We also compare it with K nearest neighbors (KNN) and support vector machine (SVM) to better evaluate the performance of the proposed method. In summary, all experimental results indicate that the proposed approach is stable and robust for predicting PPIs and promising to be a useful tool for proteomics research.

Comparative Analysis of Structural Features in SLiMs from Eukaryotes, Bacteria, and Viruses with Importance for Host-Pathogen Interactions

Protein-protein interactions drive functions in eukaryotes that can be described by short linear motifs (SLiMs). Conservation of SLiMs help illuminate functional SLiMs in eukaryotic protein families. However, the simplicity of eukaryotic SLiMs makes them appear by chance due to mutational processes not only in eukaryotes but also in pathogenic bacteria and viruses. Further, functional eukaryotic SLiMs are often found in disordered regions. Although proteomes from pathogenic bacteria and viruses have less disorder than eukaryotic proteomes, their proteins can successfully mimic eukaryotic SLiMs and disrupt host cellular function. Identifying important SLiMs in pathogens is difficult but essential for understanding potential host-pathogen interactions. We performed a comparative analysis of structural features for experimentally verified SLiMs from the Eukaryotic Linear Motif (ELM) database across viruses, bacteria, and eukaryotes. Our results revealed that many viral SLiMs and specific motifs found across viruses and eukaryotes, such as some glycosylation motifs, have less disorder. Analyzing the disorder and coil properties of equivalent SLiMs from pathogens and eukaryotes revealed that some motifs are more structured in pathogens than their eukaryotic counterparts and vice versa. These results support a varying mechanism of interaction between pathogens and their eukaryotic hosts for some of the same motifs.

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.

Identification of host transcriptome-guided repurposable drugs for SARS-CoV-1 infections and their validation with SARS-CoV-2 infections by using the integrated bioinformatics approaches

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is one of the most severe global pandemic due to its high pathogenicity and death rate starting from the end of 2019. Though there are some vaccines available against SAER-CoV-2 infections, we are worried about their effectiveness, due to its unstable sequence patterns. Therefore, beside vaccines, globally effective supporting drugs are also required for the treatment against SARS-CoV-2 infection. To explore commonly effective repurposable drugs for the treatment against different variants of coronavirus infections, in this article, an attempt was made to explore host genomic biomarkers guided repurposable drugs for SARS-CoV-1 infections and their validation with SARS-CoV-2 infections by using the integrated bioinformatics approaches. At first, we identified 138 differentially expressed genes (DEGs) between SARS-CoV-1 infected and control samples by analyzing high throughput gene-expression profiles to select drug target key receptors. Then we identified top-ranked 11 key DEGs (SMAD4, GSK3B, SIRT1, ATM, RIPK1, PRKACB, MED17, CCT2, BIRC3, ETS1 and TXN) as hub genes (HubGs) by protein-protein interaction (PPI) network analysis of DEGs highlighting their functions, pathways, regulators and linkage with other disease risks that may influence SARS-CoV-1 infections. The DEGs-set enrichment analysis significantly detected some crucial biological processes (immune response, regulation of angiogenesis, apoptotic process, cytokine production and programmed cell death, response to hypoxia and oxidative stress), molecular functions (transcription factor binding and oxidoreductase activity) and pathways (transcriptional mis-regulation in cancer, pathways in cancer, chemokine signaling pathway) that are associated with SARS-CoV-1 infections as well as SARS-CoV-2 infections by involving HubGs. The gene regulatory network (GRN) analysis detected some transcription factors (FOXC1, GATA2, YY1, FOXL1, TP53 and SRF) and micro-RNAs (hsa-mir-92a-3p, hsa-mir-155-5p, hsa-mir-106b-5p, hsa-mir-34a-5p and hsa-mir-19b-3p) as the key transcriptional and post- transcriptional regulators of HubGs, respectively. We also detected some chemicals (Valproic Acid, Cyclosporine, Copper Sulfate and arsenic trioxide) that may regulates HubGs. The disease-HubGs interaction analysis showed that our predicted HubGs are also associated with several other diseases including different types of lung diseases. Then we considered 11 HubGs mediated proteins and their regulatory 6 key TFs proteins as the drug target proteins (receptors) and performed their docking analysis with the SARS-CoV-2 3CL protease-guided top listed 90 anti-viral drugs out of 3410. We found Rapamycin, Tacrolimus, Torin-2, Radotinib, Danoprevir, Ivermectin and Daclatasvir as the top-ranked 7 candidate-drugs with respect to our proposed target proteins for the treatment against SARS-CoV-1 infections. Then, we validated these 7 candidate-drugs against the already published top-ranked 11 target proteins associated with SARS-CoV-2 infections by molecular docking simulation and found their significant binding affinity scores with our proposed candidate-drugs. Finally, we validated all of our findings by the literature review. Therefore, the proposed candidate-drugs might play a vital role for the treatment against different variants of SARS-CoV-2 infections with comorbidities, since the proposed HubGs are also associated with several comorbidities.

Modeling the Dynamics of Protein–Protein Interfaces, How and Why?

Protein–protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein–protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein–protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein–protein interface.

Computational identification of host genomic biomarkers highlighting their functions, pathways and regulators that influence SARS-CoV-2 infections and drug repurposing

The pandemic threat of COVID-19 has severely destroyed human life as well as the economy around the world. Although, the vaccination has reduced the outspread, but people are still suffering due to the unstable RNA sequence patterns of SARS-CoV-2 which demands supplementary drugs. To explore novel drug target proteins, in this study, a transcriptomics RNA-Seq data generated from SARS-CoV-2 infection and control samples were analyzed. We identified 109 differentially expressed genes (DEGs) that were utilized to identify 10 hub-genes/proteins (TLR2, USP53, GUCY1A2, SNRPD2, NEDD9, IGF2, CXCL2, KLF6, PAG1 and ZFP36) by the protein–protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of hub-DEGs revealed some important functions and signaling pathways that are significantly associated with SARS-CoV-2 infections. The interaction network analysis identified 5 TFs proteins and 6 miRNAs as the key regulators of hub-DEGs. Considering 10 hub-proteins and 5 key TFs-proteins as drug target receptors, we performed their docking analysis with the SARS-CoV-2 3CL protease-guided top listed 90 FDA approved drugs. We found Torin-2, Rapamycin, Radotinib, Ivermectin, Thiostrepton, Tacrolimus and Daclatasvir as the top ranked seven candidate drugs. We investigated their resistance performance against the already published COVID-19 causing top-ranked 11 independent and 8 protonated receptor proteins by molecular docking analysis and found their strong binding affinities, which indicates that the proposed drugs are effective against the state-of-the-arts alternatives independent receptor proteins also. Finally, we investigated the stability of top three drugs (Torin-2, Rapamycin and Radotinib) by using 100 ns MD-based MM-PBSA simulations with the two top-ranked proposed receptors (TLR2, USP53) and independent receptors (IRF7, STAT1), and observed their stable performance. Therefore, the proposed drugs might play a vital role for the treatment against different variants of SARS-CoV-2 infections.

Interactome of Arabidopsis Thaliana

More than 95,000 protein–protein interactions of Arabidopsis thaliana have been published and deposited in databases. This dataset was supplemented by approximately 900 additional interactions, which were identified in the literature from the years 2002–2021. These protein–protein interactions were used as the basis for a Cytoscape network and were supplemented with data on subcellular localization, gene ontologies, biochemical properties and co-expression. The resulting network has been exemplarily applied in unraveling the PPI-network of the plant vacuolar proton-translocating ATPase (V-ATPase), which was selected due to its central importance for the plant cell. In particular, it is involved in cellular pH homeostasis, providing proton motive force necessary for transport processes, trafficking of proteins and, thereby, cell wall synthesis. The data points to regulation taking place on multiple levels: (a) a phosphorylation-dependent regulation by 14-3-3 proteins and by kinases such as WNK8 and NDPK1a, (b) an energy-dependent regulation via HXK1 and the glucose receptor RGS1 and (c) a Ca2+-dependent regulation by SOS2 and IDQ6. The known importance of V-ATPase for cell wall synthesis is supported by its interactions with several proteins involved in cell wall synthesis. The resulting network was further analyzed for (experimental) biases and was found to be enriched in nuclear, cytosolic and plasma membrane proteins but depleted in extracellular and mitochondrial proteins, in comparison to the entity of protein-coding genes. Among the processes and functions, proteins involved in transcription were highly abundant in the network. Subnetworks were extracted for organelles, processes and protein families. The degree of representation of organelles and processes reveals limitations and advantages in the current knowledge of protein–protein interactions, which have been mainly caused by a high number of database entries being contributed by only a few publications with highly specific motivations and methodologies that favor, for instance, interactions in the cytosol and the nucleus.

A Photosensitized Singlet Oxygen (1O2) Toolbox for Bio-Organic Applications: Tailoring 1O2 Generation for DNA and Protein Labelling, Targeting and Biosensing

Singlet oxygen (¹O₂) is the excited state of ground, triplet state, molecular oxygen (O₂). Photosensitized ¹O₂ has been extensively studied as one of the reactive oxygen species (ROS), responsible for damage of cellular components (protein, DNA, lipids). On the other hand, its generation has been exploited in organic synthesis, as well as in photodynamic therapy for the treatment of various forms of cancer. The aim of this review is to highlight the versatility of ¹O_2, discussing the main bioorganic applications reported over the past decades, which rely on its production. After a brief introduction on the photosensitized production of ¹O₂, we will describe the main aspects involving the biologically relevant damage that can accompany an uncontrolled, aspecific generation of this ROS. We then discuss in more detail a series of biological applications featuring ¹O₂ generation, including protein and DNA labelling, cross-linking and biosensing. Finally, we will highlight the methodologies available to tailor ¹O₂ generation, in order to accomplish the proposed bioorganic transformations while avoiding, at the same time, collateral damage related to an untamed production of this reactive species.