Chapter 4: Protein interactions and disease

Co-expression-wide association studies link genetically regulated interactions with complex traits

Transcriptome- and proteome-wide association studies (TWAS/PWAS) have proven successful in prioritizing genes and proteins whose genetically regulated expression modulates disease risk, but they ignore potential co-expression and interaction effects. To address this limitation, we introduce the co-expression-wide association study (COWAS) method, which can identify pairs of genes or proteins whose genetically regulated co-expression is associated with complex traits. COWAS first trains models to predict expression and co-expression conditional on genetic variation, and then tests for association between imputed co-expression and the trait of interest while also accounting for direct effects from each exposure. We applied our method to plasma proteomic concentrations from the UK Biobank, identifying dozens of interacting protein pairs associated with cholesterol levels, Alzheimer's disease, and Parkinson's disease. Notably, our results demonstrate that co-expression between proteins may affect complex traits even if neither protein is detected to influence the trait when considered on its own. We also show how COWAS can help disentangle direct and interaction effects, providing a richer picture of the molecular networks that mediate genetic effects on disease outcomes.

Classifying covalent protein binders by their targeted binding site

Covalent protein targeting represents a powerful tool for protein characterization, identification, and activity modulation. The safety of covalent therapeutics was questioned for many years due to the possibility of off-target binding and subsequent potential toxicity. Researchers have recently, however, demonstrated many covalent binders as safe, potent, and long-acting therapeutics. Moreover, they have achieved selective targeting among proteins with high structural similarities, overcome mutation-induced resistance, and obtained higher potency compared to non-covalent binders. In this review, we highlight the different classes of binding sites on a target protein that could be addressed by a covalent binder. Upon folding, proteins generate various concavities available for covalent modifications. Selective targeting to a specific site is driven by differences in the geometry and physicochemical properties of the binding pocket residues as well as the geometry and reactivity of the covalent modifier "warhead". According to the warhead reactivity and the nature of the binding region, covalent binders can alter or lock a targeted protein conformation and inhibit or enhance its activity. We survey these various modification sites using case studies of recently discovered covalent binders, bringing to the fore the versatile application of covalent protein binders with respect to drug discovery approaches.

Unveiling biomarker detection in Alzheimer's disease: a computational approach to microarray analysis

Alzheimer's disease (AD) is a major neurodegenerative condition that affects a significant number of people around the world, making understanding the underlying molecular mechanisms fundamental for identifying predictive biomarkers and therapeutic targets for treating AD. Analysis of the gene expression profile GSE5281, consisting of 161 samples (87 AD and 74 control samples) revealed differentially expressed genes (DEGs) used for KEGG screening to connect dysregulated genes to metabolic pathways or other neurological diseases including Parkinson's, prion, and Huntington's and construction of a protein interaction network. Protein-protein interaction (PPI) network and module analysis uncovered the hub genes ACTB, ACTG1, ATP5A1, CCT2, CDC42, EGFR, FN1, GAPDH, GFAP, GRIA1, HSP90AB1, MAPK1, PSMA3, PSMD14, SNAP25, SNCA, SOD1, SOX2, TPI1, and YWHAZ. The analysis revealed a link between dysregulated genes and processes in AD pathology, including the promotion of osteoporosis, an altered nucleotide metabolism, microtubule stability, and the dysfunctionality of the blood-brain barrier (BBB). These targets might be used as predictive biomarkers or to develop curative and preventive therapeutic approaches for treating AD.

Unlocking therapeutic frontiers: harnessing artificial intelligence in drug discovery for neurodegenerative diseases

Neurodegenerative diseases (NDs) pose serious healthcare challenges with limited therapeutic treatments and high social burdens. The integration of artificial intelligence (AI) into drug discovery has emerged as a promising approach to address these challenges. This review explores the application of AI techniques to unravel therapeutic frontiers for NDs. We examine the current landscape of AI-driven drug discovery and discuss the potentials of AI in accelerating the identification of novel therapeutic targets on ND research and drug development, optimization of drug candidates, and expediating personalized medicine approaches. Finally, we outline future directions and challenges in harnessing AI for the advancement of therapeutics in this critical area by emphasizing the importance of interdisciplinary collaboration and ethical considerations.

Studying protein-protein interactions: Latest and most popular approaches

PPIs, or protein-protein interactions, are essential for many biological processes. According to the findings, abnormal PPIs have been linked to several diseases, such as cancer and infectious and neurological disorders. Consequently, focusing on PPIs is a path toward disease treatment and a crucial tool for producing novel medications. Many methods exist to investigate PPIs, including low- and high-throughput studies. Since many PPIs have been discovered using in vitro and in vivo experimental approaches, the use of computational methods to predict PPIs has grown due to the expanding scale of PPI data and the intrinsic complexity of interacting mechanisms. Recognizing PPI networks offers a systematic means of predicting protein functions, and pathways that are included. These investigations can help uncover the underlying molecular mechanisms of complex phenotypes and clarify the biological processes related to health and diseases. Therefore, our goal in this study is to provide an overview of the latest and most popular approaches for investigating PPIs. We also overview some important clinical approaches based on the PPIs and how these interactions can be targeted.

Trends in co-fractionation mass spectrometry: A new gold-standard in global protein interaction network discovery

Co-fractionation mass spectrometry (CF-MS) uses biochemical fractionation to isolate and characterize macromolecular complexes from cellular lysates without the need for affinity tagging or capture. In recent years, this has emerged as a powerful technique for elucidating global protein-protein interaction networks in a wide variety of biospecimens. This review highlights the latest advancements in CF-MS experimental workflows including machine learning-guided analyses, for uncovering dynamic and high-resolution protein interaction landscapes with enhanced sensitivity, accuracy and throughput, enabling better biophysical characterization of endogenous protein complexes. By addressing challenges and emergent opportunities in the field, this review underscores the transformative potential of CF-MS in advancing our understanding of functional protein interaction networks in health and disease.

Integrating protein interaction and pathway crosstalk network reveals a promising therapeutic approach for psoriasis through apoptosis induction

Psoriasis is a complex inflammatory skin disease manifested by altered proliferation and differentiation of keratinocytes with dysfunctional apoptosis. This study aimed to identify regulatory factors and comprehend the underlying mechanisms of inefficient apoptosis to open up promising therapeutic approaches. Incorporating human protein interactions, apoptosis proteins, and physical relationships of psoriasis-apoptosis proteins helped us to generate a psoriasis-apoptosis interaction (SAI) network. Subsequently, topological and functional analyses of the SAI network revealed effective proteins, functional modules, hub motifs, dysregulated pathways and transcriptional gene regulatory factors. Network pharmacology, molecular docking and molecular dynamics simulation methods identified the potential drug-target interactions. RELA, MAPK1, MAPK3, MMP9, IL1B, AKT1 and STAT1 were revealed as effective proteins. The MAPK1-MAPK3-RELA motif was identified as a hub regulator in the crosstalk between 41 pathways. Among all pathways, "lipid and atherosclerosis" was found to be the predominant pathway. Acetylcysteine, arsenic-trioxide, β-elemene, bortezomib and curcumin were identified as potential drugs to inhibit pathway crosstalk. Experimental verifications were performed using the literature search, GSE13355 and GSE14905 microarray datasets. Drug-protein-pathway interactions associated with apoptosis were deciphered. These findings highlight the role of hub motif-mediated pathway-pathway crosstalk associated with apoptosis in the complexity of psoriasis and suggest crosstalk inhibition as an effective therapeutic approach.

Proteomic Profiling Identifies Predictive Signatures for Progression Risk in Patients with Advanced-Stage Follicular Lymphoma

Background: Follicular lymphoma (FL) is characterized by an indolent nature and generally favorable prognosis, yet poses a particular clinical challenge, since disease progression is observed in a notable subset of patients. Currently, it is not possible to anticipate which patients will be at risk of progression, highlighting the need for reliable predictive biomarkers that can be detected early in the disease. Methods: We applied tandem-mass-tag labelled nano-liquid chromatography tandem mass spectrometry (nLC-MS/MS) on 48 diagnostic formalin-fixed, paraffin-embedded tumor samples from patients with advanced-stage FL. Of these, 17 experienced subsequent progression (subsequently-progressing, sp-FL) while 31 did not (non-progressing, np-FL). Results: We identified 99 proteins that were significantly differentially expressed between sp-FL samples and np-FL samples (p < 0.05; log2-fold changes between 0.2 and -1.3). Based on this subset of proteins, we classified patients into high-risk and low-risk subgroups using unsupervised machine learning techniques. Pathway analyses of the identified proteins revealed aberrancies within the immune system and cellular energy metabolism. In addition, two proteins were selected for immunohistochemical evaluation, namely stimulator of interferon genes 1 (STING1) and isocitrate dehydrogenase 2 (IDH2). Notably, IDH2 retained significantly lower expression levels in sp-FL samples compared with np-FL samples (p = 0.034). Low IDH2 expression correlated with shorter progression-free survival (PFS, p = 0.020). Conclusions: This study provides evidence for some of the biological mechanisms likely to be involved in FL progression and, importantly, identifies potential predictive biomarkers for improvement of risk stratification up-front at time of FL diagnosis.

Identification of hub genes and potential networks by centrality network analysis of PCR amplified Fusarium oxysporum f. sp. lycopersici EF1α gene

BACKGROUND

Fusarium wilt is a devastating soil-borne fungal disease of tomato across the world. Conventional method of disease prevention including usage of common pesticides and methods like soil solarisation are usually ineffective in the treatment of this disease. Therefore, there is an urgent need to identify virulence related genes in the pathogen which can be targeted for fungicide development.

RESULTS

Pathogenicity testing and phylogenetic classification of the pathogen used in this study confirmed it as Fusarium oxysporum f. sp. lycopersici (Fol) strain. A recent discovery indicates that EF1α, a protein with conserved structural similarity across several fungal genera, has a role in the pathogenicity of Magnaporthe oryzae, the rice blast fungus. Therefore, in this study we have done structural and functional classification of EF1α to understand its role in pathogenicity of Fol. The protein model of Fol EF1α was created using the template crystal structure of the yeast elongation factor complex EEF1A:EEF1BA which showed maximum similarity with the target protein. Using the STRING online database, the interactive information among the hub genes of EF1α was identified and the protein-protein interaction network was recognized using the Cytoscape software. On combining the results of functional analysis, MCODE, CytoNCA and CytoHubba 4 hub genes including Fol EF1α were selected for further investigation. The three interactors of Fol EF1α showed maximum similarity with homologous proteins found in Neurospora crassa complexed with the known fungicide, cycloheximide. Through the sequence similarity and PDB database analysis, homologs of Fol EF1α were found: EEF1A:EEF1BA in complex with GDPNP in yeast and EF1α in complex with GDP in Sulfolobus solfataricus. The STITCH database analysis suggested that EF1α and its other interacting partners interact with guanosine diphosphate (GDPNP) and guanosine triphosphate (GTP).

CONCLUSIONS

Our study offers a framework for recognition of several hub genes network in Fusarium wilt that can be used as novel targets for fungicide development. The involvement of EF1α in nucleocytoplasmic transport pathway suggests that it plays role in GTP binding and thus apart from its use as a biomarker, it may be further exploited as an effective target for fungicide development. Since, the three other proteins that were found to be tightly associated Fol EF1α have shown maximum similarity with homologous proteins of Neurospora crassa that form complex with fungicide- Cycloheximide. Therefore, we suggest that cycloheximide can also be used against Fusarium wilt disease in tomato. The active site cavity of Fol EF1α can also be determined for computational screening of fungicides using the homologous proteins observed in yeast and Sulfolobus solfataricus. On this basis, we also suggest that the other closely associated genes that have been identified through STITCH analysis, they can also be targeted for fungicide development.

Nucleic acid-responsive smart systems for controlled cargo delivery

Stimulus-responsive delivery systems allow controlled, highly regulated, and efficient delivery of various cargos while minimizing side effects. Owing to the unique properties of nucleic acids, including the ability to adopt complex structures by base pairing, their easy synthesis, high specificity, shape memory, and configurability, they have been employed in autonomous molecular motors, logic circuits, reconfigurable nanoplatforms, and catalytic amplifiers. Moreover, the development of nucleic acid (NA)-responsive intelligent delivery vehicles is a rapidly growing field. These vehicles have attracted much attention in recent years due to their programmable, controllable, and reversible properties. In this work, we review several types of NA-responsive controlled delivery vehicles based on locks and keys, including DNA/RNA-responsive, aptamer-responsive, and CRISPR-responsive, and summarize their advantages and limitations.

Systematic computational strategies for identifying protein targets and lead discovery

Computational algorithms and tools have retrenched the drug discovery and development timeline. The applicability of computational approaches has gained immense relevance owing to the dramatic surge in the structural information of biomacromolecules and their heteromolecular complexes. Computational methods are now extensively used in identifying new protein targets, druggability assessment, pharmacophore mapping, molecular docking, the virtual screening of lead molecules, bioactivity prediction, molecular dynamics of protein-ligand complexes, affinity prediction, and for designing better ligands. Herein, we provide an overview of salient components of recently reported computational drug-discovery workflows that includes algorithms, tools, and databases for protein target identification and optimized ligand selection.

Uncovering the health implications of abandoned mines through protein profiling of local residents

Residents in areas with abandoned mines risk significant exposure to abundant heavy metals in the environment. However, current clinical indicators cannot fully reflect the health changes associated with abandoned mine exposure. The aim of this study was to identify biological changes in the residents of abandoned mine areas via proteomic analysis of their blood. Blood samples were collected from abandoned mine and control areas, and mass spectrometry was used for protein profiling. A total of 138 unique or common proteins that were differentially expressed in low-exposure abandoned mine area (LoAMA) or high-exposure abandoned mine area (HiAMA) compared to non-exposure control area (NEA) were analyzed, and identified 4 clusters based on functional similarity. Among the 10 proteins that showed specific change in LoAMA, 4 proteins(Apolipoprotein M, Apolipoprotein E, Apolipoprotein L1, and Cholesteryl ester transfer protein) were cluded in cluster 1(plasma lipoprotein remodeling), and linked to proteins that showed specific change in protein expression in HiAMA. Therefore, it is suggested that 4 proteins are changed at low exposure to an abandoned mine (or initial exposure), and then at high exposure, changes in various proteins involved in linked plasma lipoprotein remodeling are induced, which might triggered by the 4 proteins. Interestingly, in addition to plasma lipoprotein remodeling, proteins involved in other functional networks were changed in the high exposure group. These were all directly or indirectly linked to the 4 biomarkers(Apolipoprotein M, Apolipoprotein E, Apolipoprotein L1, and Cholesteryl ester transfer protein) that changed during low exposure. This suggests their potential utility in identifying areas impacted by abandoned mines. Especially, proteins involved in lipid metabolism and renal function-related diseases in individuals exposed to heavy metals in abandoned mine areas were correlated. Chronic kidney disease is predominantly instigated by cardiovascular disease and is commonly accompanied by dyslipidemia.

ProBASS - a language model with sequence and structural features for predicting the effect of mutations on binding affinity

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

What proteomics has taught us about honey bee (Apis mellifera) health and disease

The Western honey bee, Apis mellifera, is currently navigating a gauntlet of environmental pressures, including the persistent threat of parasites, pathogens, and climate change - all of which compromise the vitality of honey bee colonies. The repercussions of their declining health extend beyond the immediate concerns of apiarists, potentially imposing economic burdens on society through diminished agricultural productivity. Hence, there is an imperative to devise innovative monitoring techniques for assessing the health of honey bee populations. Proteomics, recognized for its proficiency in biomarker identification and protein-protein interactions, is poised to play a pivotal role in this regard. It offers a promising avenue for monitoring and enhancing the resilience of honey bee colonies, thereby contributing to the stability of global food supplies. This review delves into the recent proteomic studies of A. mellifera, highlighting specific proteins of interest and envisioning the potential of proteomics to improve sustainable beekeeping practices amidst the challenges of a changing planet.

Suppressing mitochondrial inner membrane protein (IMMT) inhibits the proliferation of breast cancer cells through mitochondrial remodeling and metabolic regulation

Metabolic reprogramming is widely recognized as a hallmark of malignant tumors, and the targeting of metabolism has emerged as an appealing approach for cancer treatment. Mitochondria, as pivotal organelles, play a crucial role in the metabolic regulation of tumor cells, and their morphological and functional alterations are intricately linked to the biological characteristics of tumors. As a key regulatory subunit of mitochondria, mitochondrial inner membrane protein (IMMT), plays a vital role in degenerative diseases, but its role in tumor is almost unknown. The objective of this research was to investigate the roles that IMMT play in the development and progression of breast cancer (BC), as well as to elucidate the underlying biological mechanisms that drive these effects. In this study, it was confirmed that the expression of IMMT in BC tissues was significantly higher than that in normal tissues. The analysis of The Cancer Genome Atlas (TCGA) database revealed that IMMT can serve as an independent prognostic factor for BC patients. Additionally, verification in clinical specimens of BC demonstrated a positive association between high IMMT expression and larger tumor size (> 2 cm), Ki-67 expression (> 15%), and HER-2 status. Furthermore, in vitro experiments have substantiated that the suppression of IMMT expression resulted in a reduction in cell proliferation and alterations in mitochondrial cristae, concomitant with the liberation of cytochrome c, but it did not elicit mitochondrial apoptosis. Through Gene Set Enrichment Analysis (GSEA) analysis, we have predicted the associated metabolic genes and discovered that IMMT potentially modulates the advancement of BC through its interaction with 16 metabolic-related genes, and the changes in glycolysis related pathways have been validated in BC cell lines after IMMT inhibition. Consequently, this investigation furnishes compelling evidence supporting the classification of IMMT as prognostic marker in BC, and underscoring its prospective utility as a novel target for metabolic therapy.

Protein mimetic 2D FAST rescues alpha synuclein aggregation mediated early and post disease Parkinson's phenotypes

Abberent protein-protein interactions potentiate many diseases and one example is the toxic, self-assembly of α-Synuclein in the dopaminergic neurons of patients with Parkinson's disease; therefore, a potential therapeutic strategy is the small molecule modulation of α-Synuclein aggregation. In this work, we develop an Oligopyridylamide based 2-dimensional Fragment-Assisted Structure-based Technique to identify antagonists of α-Synuclein aggregation. The technique utilizes a fragment-based screening of an extensive array of non-proteinogenic side chains in Oligopyridylamides, leading to the identification of NS132 as an antagonist of the multiple facets of α-Synuclein aggregation. We further identify a more cell permeable analog (NS163) without sacrificing activity. Oligopyridylamides rescue α-Synuclein aggregation mediated Parkinson's disease phenotypes in dopaminergic neurons in early and post disease Caenorhabditis elegans models. We forsee tremendous potential in our technique to identify lead therapeutics for Parkinson's disease and other diseases as it is expandable to other oligoamide scaffolds and a larger array of side chains.

Predictive Models and Impact of Interfacial Contacts and Amino Acids on Protein-Protein Binding Affinity

Protein-protein interactions (PPIs) play a central role in nearly all cellular processes. The strength of the binding in a PPI is characterized by the binding affinity (BA) and is a key factor in controlling protein-protein complex formation and defining the structure-function relationship. Despite advancements in understanding protein-protein binding, much remains unknown about the interfacial region and its association with BA. New models are needed to predict BA with improved accuracy for therapeutic design. Here, we use machine learning approaches to examine how well different types of interfacial contacts can be used to predict experimentally determined BA and to reveal the impact of the specific amino acids at the binding interface on BA. We create a series of multivariate linear regression models incorporating different contact features at both residue and atomic levels and examine how different methods of identifying and characterizing these properties impact the performance of these models. Particularly, we introduce a new and simple approach to predict BA based on the quantities of specific amino acids at the protein-protein interface. We found that the numbers of specific amino acids at the protein-protein interface were correlated with BA. We show that the interfacial numbers of amino acids can be used to produce models with consistently good performance across different data sets, indicating the importance of the identities of interfacial amino acids in underlying BA. When trained on a diverse set of complexes from two benchmark data sets, the best performing BA model was generated with an explicit linear equation involving six amino acids. Tyrosine, in particular, was identified as the key amino acid in controlling BA, as it had the strongest correlation with BA and was consistently identified as the most important amino acid in feature importance studies. Glycine and serine were identified as the next two most important amino acids in predicting BA. The results from this study further our understanding of PPIs and can be used to make improved predictions of BA, giving them implications for drug design and screening in the pharmaceutical industry.

Apoptosis Inhibitor 5: A Multifaceted Regulator of Cell Fate

Apoptosis, or programmed cell death, is a fundamental process that maintains tissue homeostasis, eliminates damaged or infected cells, and plays a crucial role in various biological phenomena. The deregulation of apoptosis is involved in many human diseases, including cancer. One of the emerging players in the intricate regulatory network of apoptosis is apoptosis inhibitor 5 (API5), also called AAC-11 (anti-apoptosis clone 11) or FIF (fibroblast growth factor-2 interacting factor). While it may not have yet the same level of notoriety as some other cancer-associated proteins, API5 has garnered increasing attention in the cancer field in recent years, as elevated API5 levels are often associated with aggressive tumor behavior, resistance to therapy, and poor patient prognosis. This review aims to shed light on the multifaceted functions and regulatory mechanisms of API5 in cell fate decisions as well as its interest as therapeutic target in cancer.

Detection of Biomarkers through Functionalized Polymers

Over the past decade, there has been a rising interest in utilizing functionalized porous polymers for sensor applications. By incorporating functional groups into nanostructured materials like hydrogels, nanosheets, and nanopores, exciting new opportunities have emerged for biomarker detection. The ability of functionalized polymers to undergo physical changes and deformations makes them perfect for modulating optical signals. This chemical mechanism enables the creation of biocompatible sensors for in situ biomarker measurement. Here a comprehensive overview of the current publication trends is provided in functionalized polymers, encompassing functional groups that can induce measurable physical deformations. It explores various materials categorized based on their detection targets, which include proteins, carbohydrates, ions, and deoxyribonucleic acid. As such, this work serves as a valuable reference for the development of functionalized polymer-based sensors.

Software for Predicting Binding Free Energy of Protein-Protein Complexes and Their Mutants

Protein-protein binding affinity prediction is important for understanding complex biochemical pathways and to uncover protein interaction networks. Quantitative estimation of the binding affinity changes caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses. The binding free energies of protein-protein complexes can be predicted using several computational tools. This chapter is a summary of software developed for the prediction of binding free energies for protein-protein complexes and their mutants.

Geometric Graph Learning to Predict Changes in Binding Free Energy and Protein Thermodynamic Stability upon Mutation

Accurate prediction of binding free energy changes upon mutations is vital for optimizing drugs, designing proteins, understanding genetic diseases, and cost-effective virtual screening. While machine learning methods show promise in this domain, achieving accuracy and generalization across diverse data sets remains a challenge. This study introduces Geometric Graph Learning for Protein-Protein Interactions (GGL-PPI), a novel approach integrating geometric graph representation and machine learning to forecast mutation-induced binding free energy changes. GGL-PPI leverages atom-level graph coloring and multiscale weighted colored geometric subgraphs to capture structural features of biomolecules, demonstrating superior performance on three standard data sets, namely, AB-Bind, SKEMPI 1.0, and SKEMPI 2.0 data sets. The model's efficacy extends to predicting protein thermodynamic stability in a blind test set, providing unbiased predictions for both direct and reverse mutations and showcasing notable generalization. GGL-PPI's precision in predicting changes in binding free energy and stability due to mutations enhances our comprehension of protein complexes, offering valuable insights for drug design endeavors.

TeM-DTBA: time-efficient drug target binding affinity prediction using multiple modalities with Lasso feature selection

Drug discovery, especially virtual screening and drug repositioning, can be accelerated through deeper understanding and prediction of Drug Target Interactions (DTIs). The advancement of deep learning as well as the time and financial costs associated with conventional wet-lab experiments have made computational methods for DTI prediction more popular. However, the majority of these computational methods handle the DTI problem as a binary classification task, ignoring the quantitative binding affinity that determines the drug efficacy to their target proteins. Moreover, computational space as well as execution time of the model is often ignored over accuracy. To address these challenges, we introduce a novel method, called Time-efficient Multimodal Drug Target Binding Affinity (TeM-DTBA), which predicts the binding affinity between drugs and targets by fusing different modalities based on compound structures and target sequences. We employ the Lasso feature selection method, which lowers the dimensionality of feature vectors and speeds up the proposed model training time by more than 50%. The results from two benchmark datasets demonstrate that our method outperforms state-of-the-art methods in terms of performance. The mean squared errors of 18.8% and 23.19%, achieved on the KIBA and Davis datasets, respectively, suggest that our method is more accurate in predicting drug-target binding affinity.

A computational biology approach to identify potential protein biomarkers and drug targets for sporadic amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by the loss of upper and lower motor neurons. The sporadic ALS (sALS) is a multigenic disorder and the complex mechanisms underlying its onset are still not fully delineated. Despite the recent scientific advancements, certain aspects of ALS pathogenic targets need to be yet clarified. The aim of the presented study is to identify potential genetic biomarkers and drug targets for sALS, by analysing gene expression profiles, presented in the publicly available GSE68605 dataset, of motor neurons cells obtained from sALS patients. We used different computational approaches including differential expression analysis, protein network mapping, candidate protein biomarker (CPB) identification, elucidation of the role of functional modules, and molecular docking analysis. The resultant top ten up- and downregulated genes were further used to construct protein-protein interaction network (PPIN). The PPIN analysis resulted in identifying four CPBs (namely RIOK2, AKT1, CTNNB1, and TNF) that commonly overlapped with one another in network parameters (degree, bottleneck and maximum neighbourhood component). The RIOK2 protein emerged as a potential mediator of top five functional modules that are associated with RNA binding, lipoprotein particle receptor binding in pre-ribosome, and interferon, cytokine-mediated signaling pathway. Furthermore, molecular docking analysis revealed that cyclosporine exhibited the highest binding affinity (-8.6 kJ/mol) with RIOK2, and surpassed the FDA-approved ALS drugs, such as riluzole and edaravone. This suggested that cyclosporine may serve as a promising candidate for targeting RIOK2 downregulation observed in sALS patients. In order to validate our computational results, it is suggested that in vitro and in vivo studies may be conducted in future to provide a more detailed understanding of ALS diagnosis, prognosis, and therapeutic intervention.

Comprehensive in silico analysis of the underutilized crop tef (Eragrostis tef (Zucc.) Trotter) genome reveals drought tolerance signatures

BACKGROUND

Tef (Eragrostis tef) is a C4 plant known for its tiny, nutritious, and gluten-free grains. It contains higher levels of protein, vitamins, and essential minerals like calcium (Ca), iron (Fe), copper (Cu), and zinc (Zn) than common cereals. Tef is cultivated in diverse ecological zones under diverse climatic conditions. Studies have shown that tef has great diversity in withstanding environmental challenges such as drought. Drought is a major abiotic stress severely affecting crop productivity and becoming a bottleneck to global food security. Here, we used in silico-based functional genomic analysis to identify drought-responsive genes in tef and validated their expression using quantitative RT-PCR.

RESULTS

We identified about 729 drought-responsive genes so far reported in six crop plants, including rice, wheat, maize, barley, sorghum, pearl millet, and the model plant Arabidopsis, and reported 20 genes having high-level of GO terms related to drought, and significantly enriched in several biological and molecular function categories. These genes were found to play diverse roles, including water and fluid transport, resistance to high salt, cold, and drought stress, abscisic acid (ABA) signaling, de novo DNA methylation, and transcriptional regulation in tef and other crops. Our analysis revealed substantial differences in the conserved domains of some tef genes from well-studied rice orthologs. We further analyzed the expression of sixteen tef orthologs using quantitative RT-PCR in response to PEG-induced osmotic stress.

CONCLUSIONS

The findings showed differential regulation of some drought-responsive genes in shoots, roots, or both tissues. Hence, the genes identified in this study may be promising candidates for trait improvement in crops via transgenic or gene-editing technologies.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

A comprehensive protein interaction map and druggability investigation prioritized dengue virus NS1 protein as promising therapeutic candidate

Dengue Virus (DENV) is a serious threat to human life worldwide and is one of the most dangerous vector-borne diseases, causing thousands of deaths annually. We constructed a comprehensive PPI map of DENV with its host Homo sapiens and performed various bioinformatics analyses. We found 1195 interactions between 858 human and 10 DENV proteins. Pathway enrichment analysis was performed on the two sets of gene products, and the top 5 human proteins with the maximum number of interactions with dengue viral proteins revealed noticeable results. The non-structural protein NS1 in DENV had the maximum number of interactions with the host protein, followed by NS5 and NS3. Among the human proteins, HBA1 and UBE2I were associated with 7 viral proteins, and 3 human proteins (CSNK2A1, RRP12, and HSP90AB1) were found to interact with 6 viral proteins. Pharmacophore-based virtual screening of millions of compounds in the public databases was performed to identify potential DENV-NS1 inhibitors. The lead compounds were selected based on RMSD values, docking scores, and strong binding affinities. The top ten hit compounds were subjected to ADME profiling which identified compounds C2 (MolPort-044-180-163) and C6 (MolPort-001-742-737) as lead inhibitors against DENV-NS1. Molecular dynamics trajectory analysis and intermolecular interactions between NS1 and the ligands displayed the molecular stability of the complexes in the cellular environment. The in-silico approaches used in this study could pave the way for the development of potential specie-specific drugs and help in eliminating deadly viral infections. Therefore, experimental and clinical assays are required to validate the results of this study.

Glaucomatous optic neuropathy: Mitochondrial dynamics, dysfunction and protection in retinal ganglion cells

Glaucoma is a leading cause of irreversible blindness worldwide and is characterized by a slow, progressive, and multifactorial degeneration of retinal ganglion cells (RGCs) and their axons, resulting in vision loss. Despite its high prevalence in individuals 60 years of age and older, the causing factors contributing to glaucoma progression are currently not well characterized. Intraocular pressure (IOP) is the only proven treatable risk factor. However, lowering IOP is insufficient for preventing disease progression. One of the significant interests in glaucoma pathogenesis is understanding the structural and functional impairment of mitochondria in RGCs and their axons and synapses. Glaucomatous risk factors such as IOP elevation, aging, genetic variation, neuroinflammation, neurotrophic factor deprivation, and vascular dysregulation, are potential inducers for mitochondrial dysfunction in glaucoma. Because oxidative phosphorylation stress-mediated mitochondrial dysfunction is associated with structural and functional impairment of mitochondria in glaucomatous RGCs, understanding the underlying mechanisms and relationship between structural and functional alterations in mitochondria would be beneficial to developing mitochondria-related neuroprotection in RGCs and their axons and synapses against glaucomatous neurodegeneration. Here, we review the current studies focusing on mitochondrial dynamics-based structural and functional alterations in the mitochondria of glaucomatous RGCs and therapeutic strategies to protect RGCs against glaucomatous neurodegeneration.

Deep Local Analysis deconstructs protein-protein interfaces and accurately estimates binding affinity changes upon mutation

MOTIVATION

The spectacular recent advances in protein and protein complex structure prediction hold promise for reconstructing interactomes at large-scale and residue resolution. Beyond determining the 3D arrangement of interacting partners, modeling approaches should be able to unravel the impact of sequence variations on the strength of the association.

RESULTS

In this work, we report on Deep Local Analysis, a novel and efficient deep learning framework that relies on a strikingly simple deconstruction of protein interfaces into small locally oriented residue-centered cubes and on 3D convolutions recognizing patterns within cubes. Merely based on the two cubes associated with the wild-type and the mutant residues, DLA accurately estimates the binding affinity change for the associated complexes. It achieves a Pearson correlation coefficient of 0.735 on about 400 mutations on unseen complexes. Its generalization capability on blind datasets of complexes is higher than the state-of-the-art methods. We show that taking into account the evolutionary constraints on residues contributes to predictions. We also discuss the influence of conformational variability on performance. Beyond the predictive power on the effects of mutations, DLA is a general framework for transferring the knowledge gained from the available non-redundant set of complex protein structures to various tasks. For instance, given a single partially masked cube, it recovers the identity and physicochemical class of the central residue. Given an ensemble of cubes representing an interface, it predicts the function of the complex.

AVAILABILITY AND IMPLEMENTATION

Source code and models are available at http://gitlab.lcqb.upmc.fr/DLA/DLA.git.

A novel approach for the analysis of single-cell RNA sequencing identifies TMEM14B as a novel poor prognostic marker in hepatocellular carcinoma

A fundamental goal in cancer-associated genome sequencing is to identify the key genes. Protein-protein interactions (PPIs) play a crucially important role in this goal. Here, human reference interactome (HuRI) map was generated and 64,006 PPIs involving 9094 proteins were identified. Here, we developed a physical link and co-expression combinatory network construction (PLACE) method for genes of interest, which provides a rapid way to analyze genome sequencing datasets. Next, Kaplan‒Meier survival analysis, CCK8 assays, scratch wound assays and Transwell assays were applied to confirm the results. In this study, we selected single-cell sequencing data from patients with hepatocellular carcinoma (HCC) in GSE149614. The PLACE method constructs a protein connection network for genes of interest, and a large fraction (80%) of the genes (screened by the PLACE method) were associated with survival. Then, PLACE discovered that transmembrane protein 14B (TMEM14B) was the most significant prognostic key gene, and target genes of TMEM14B were predicted. The TMEM14B-target gene regulatory network was constructed by PLACE. We also detected that TMEM14B-knockdown inhibited proliferation and migration. The results demonstrate that we proposed a new effective method for identifying key genes. The PLACE method can be used widely and make outstanding contributions to the tumor research field.

"Common-Precursor" Protein Mimetic Approach to Rescue Aβ Aggregation-Mediated Alzheimer's Phenotypes

Abberent protein-protein interactions (aPPIs) are associated with an array of pathological conditions, which make them important therapeutic targets. The aPPIs are mediated via specific chemical interactions that spread over a large and hydrophobic surface. Therefore, ligands that can complement the surface topography and chemical fingerprints could manipulate aPPIs. Oligopyridylamides (OPs) are synthetic protein mimetics that have been shown to manipulate aPPIs. However, the previous OP library used to disrupt these aPPIs was moderate in number (∼30 OPs) with very limited chemical diversity. The onus is on the laborious and time-consuming synthetic pathways with multiple chromatography steps. We have developed a novel chromatography-free technique to synthesize a highly diverse chemical library of OPs using a "common-precursor" approach. We significantly expanded the chemical diversity of OPs using a chromatography-free high-yielding method. To validate our novel approach, we have synthesized an OP with identical chemical diversity to a pre-existing OP-based potent inhibitor of Aβ aggregation, a process central to Alzheimer's disease (AD). The newly synthesized OP ligand (RD242) was very potent in inhibiting Aβ aggregation and rescuing AD phenotypes in an in vivo model. Moreover, RD242 was very effective in rescuing AD phenotypes in a post-disease onset AD model. We envision that our "common-precursor" synthetic approach will have tremendous potential as it is expandable for other oligoamide scaffolds to enhance affinity for disease-relevant targets.

Computational Analysis of miR-140 and miR-135 as Potential Targets to Develop Combinatorial Therapeutics for Degenerative Tendinopathy

Background

Degenerative tendinopathy, a condition causing movement restriction due to high pain, highly impacts productivity and quality of life. The healing process is a complex phenomenon and involves a series of intra-cellular and inter-cellular processes. Proliferation and differentiation of the tenocyte is a major and essential process to heal degenerative tendinopathy. The recent development in microRNA (miRNA)-mediated reprogramming of the cellular function through specific pathways opened door for the development of new regenerative therapeutics. Based on information about gene expression and regulation of tendon injury and healing, we attempted to evaluate the combinatorial effect of selected miRNAs for better healing of degenerative tendinopathy.

Methods

The present study was designed to evaluate the combinatorial effect of two miRNAs (has-miR-140 and has-miR-135) in the healing process of the tendon. Publicly available information/data were retrieved from appropriate platforms such as PubMed. Only molecular data, directly associated with tendinopathies, including genes/proteins and miRNAs, were used in this study. The miRNAs involved in tendinopathy were analyzed by a Bioinformatics tools (e.g., TargetScan, miRDB, and the RNA22v2). Interactive involvement of the miRNAs with key proteins involved in tendinopathy was predicted by the Insilco approach.

Results

Based on information available in the public domain, tendon healing-associated miRNAs were predicted to explore their therapeutic potentials. Based on computation analysis, focusing on the potential regulatory effect on tendon healing, the miR-135 and miR-140 were selected for this study. These miRNAs were found as key players in tendon healing through Rho-associated coiled-coil containing protein kinase 1 (ROCK1), IGF-1/PI3K/Akt, PIN, and Wnt signaling pathways. It was also predicted that these miRNAs may reprogram the cells to induce proliferation and differentiation activity. Many miRNAs are likely to regulate genes important for the tendinopathy healing process, and the result of this study allows an approach for miRNA-mediated regeneration of the tenocyte for tendon healing. Based on computational analysis, the role of these miRNAs in different pathways was established, and the results provided insights into the combinatorial approach of miRNA-mediated cell reprogramming.

Conclusions

In this study, the association between miRNAs and the disease was evaluated to correlate the tendinopathy genes and the relevant role of different miRNAs in their regulation. Through this study, it was established that the synergistic effect of more than one miRNA on directed reprogramming of the cell could be helpful in the regeneration of damaged tissue. It is anticipated that this study will be helpful for the design of miRNA cocktails for the orchestration of cellular reprogramming events.

Integrated Bioinformatics Analysis Identifies Crucial Biochemical Processes Shared between Pancreatitis and Pancreatic Ductal Adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy associated with rapid progression and an abysmal prognosis. Previous research has shown that chronic pancreatitis can significantly increase the risk of developing PDAC. The overarching hypothesis is that some of the biological processes disrupted during the inflammatory stage tend to show significant dysregulation, even in cancer. This might explain why chronic inflammation increases the risk of carcinogenesis and uncontrolled proliferation. Here, we try to pinpoint such complex processes by comparing the expression profiles of pancreatitis and PDAC tissues.

METHODS

We analyzed a total of six gene expression datasets retrieved from the EMBL-EBI ArrayExpress and NCBI GEO databases, which included 306 PDAC, 68 pancreatitis and 172 normal pancreatic samples. The disrupted genes identified were used to perform downstream analysis for ontology, interaction, enriched pathways, potential druggability, promoter methylation, and the associated prognostic value. Further, we performed expression analysis based on gender, patient's drinking habit, race, and pancreatitis status.

RESULTS

Our study identified 45 genes with altered expression levels shared between PDAC and pancreatitis. Over-representation analysis revealed that protein digestion and absorption, ECM-receptor interaction, PI3k-Akt signaling, and proteoglycans in cancer pathways as significantly enriched. Module analysis identified 15 hub genes, of which 14 were found to be in the druggable genome category.

CONCLUSION

In summary, we have identified critical genes and various biochemical processes disrupted at a molecular level. These results can provide valuable insights into certain events leading to carcinogenesis, and therefore help identify novel therapeutic targets to improve PDAC treatment in the future.

Stretching Peptides to Generate Small Molecule β-Strand Mimics

Advances in the modulation of protein-protein interactions (PPIs) enable both characterization of PPI networks that govern diseases and design of therapeutics and probes. The shallow protein surfaces that dominate PPIs are challenging to target using standard methods, and approaches for accessing extended backbone structures are limited. Here, we incorporate a rigid, linear, diyne brace between side chains at the i to i+2 positions to generate a family of low-molecular-weight, extended-backbone peptide macrocycles. NMR and density functional theory studies show that these stretched peptides adopt stable, rigid conformations in solution and can be tuned to explore extended peptide conformational space. The diyne brace is formed in excellent conversions (>95%) and amenable to high-throughput synthesis. The minimalist structure-inducing tripeptide core (<300 Da) is amenable to further synthetic elaboration. Diyne-braced inhibitors of bacterial type 1 signal peptidase demonstrate the utility of the technique.

Characterization of huntingtin interactomes and their dynamic responses in living cells by proximity proteomics

Huntingtin (Htt) is a large protein without clearly defined molecular functions. Mutation in this protein causes Huntington's disease (HD), a fatal inherited neurodegenerative disorder. Identification of Htt-interacting proteins by the traditional approaches including yeast two-hybrid systems and affinity purifications has greatly facilitated the understanding of Htt function. However, these methods eliminated the intracellular spatial information of the Htt interactome during sample preparations. Moreover, the temporal changes of the Htt interactome in response to acute cellular stresses cannot be easily resolved with these approaches. Ascorbate peroxidase (APEX2)-based proximity labeling has been used to spatiotemporally investigate protein-protein interactions in living cells. In this study, we generated stable human SH-SY5Y cell lines expressing full-length Htt23Q and Htt145Q with N-terminus tagged Flag-APEX2 to quantitatively map the spatiotemporal changes of Htt interactome to a mild acute proteotoxic stress. Our data revealed that normal and mutant Htt (muHtt) are associated with distinct intracellular microenvironments. Specifically, mutant Htt is preferentially associated with intermediate filaments and myosin complexes. Furthermore, the dynamic changes of Htt interactomes in response to stress are different between normal and mutant Htt. Vimentin is identified as one of the most significant proteins that preferentially interacts with muHtt in situ. Further functional studies demonstrated that mutant Htt affects the vimentin's function of regulating proteostasis in healthy and HD human neural stem cells. Taken together, our data offer important insights into the molecular functions of normal and mutant Htt by providing a list of Htt-interacting proteins in their natural cellular context for further studies in different HD models.

Thiol-ene-based microfluidic chips for glycopeptide enrichment and online digestion of inflammation-related proteins osteopontin and immunoglobulin G

Proteins, and more specifically glycoproteins, have been widely used as biomarkers, e.g., to monitor disease states. Bottom-up approaches based on mass spectrometry (MS) are techniques commonly utilized in glycoproteomics, involving protein digestion and glycopeptide enrichment. Here, a dual function polymeric thiol-ene-based microfluidic chip (TE microchip) was applied for the analysis of the proteins osteopontin (OPN) and immunoglobulin G (IgG), which have important roles in autoimmune diseases, in inflammatory diseases, and in coronavirus disease 2019 (COVID-19). TE microchips with larger internal surface features immobilized with trypsin were successfully utilized for OPN digestion, providing rapid and efficient digestion with a residence time of a few seconds. Furthermore, TE microchips surface-modified with ascorbic acid linker (TEA microchip) have been successfully utilized for IgG glycopeptide enrichment. To illustrate the use of the chips for more complex samples, they were applied to enrich IgG glycopeptides from human serum samples with antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The dual functional TE microchips could provide high throughput for online protein digestion and glycopeptide enrichment, showing great promise for future extended applications in proteomics and the study of related diseases.

Biomolecular Topology: Modelling and Analysis

With the great advancement of experimental tools, a tremendous amount of biomolecular data has been generated and accumulated in various databases. The high dimensionality, structural complexity, the nonlinearity, and entanglements of biomolecular data, ranging from DNA knots, RNA secondary structures, protein folding configurations, chromosomes, DNA origami, molecular assembly, to others at the macromolecular level, pose a severe challenge in their analysis and characterization. In the past few decades, mathematical concepts, models, algorithms, and tools from algebraic topology, combinatorial topology, computational topology, and topological data analysis, have demonstrated great power and begun to play an essential role in tackling the biomolecular data challenge. In this work, we introduce biomolecular topology, which concerns the topological problems and models originated from the biomolecular systems. More specifically, the biomolecular topology encompasses topological structures, properties and relations that are emerged from biomolecular structures, dynamics, interactions, and functions. We discuss the various types of biomolecular topology from structures (of proteins, DNAs, and RNAs), protein folding, and protein assembly. A brief discussion of databanks (and databases), theoretical models, and computational algorithms, is presented. Further, we systematically review related topological models, including graphs, simplicial complexes, persistent homology, persistent Laplacians, de Rham-Hodge theory, Yau-Hausdorff distance, and the topology-based machine learning models.

Deciphering the Basis of Molecular Biology of Selected Cardiovascular Diseases: A View on Network Medicine

Cardiovascular diseases are the leading cause of death across the world. For decades, researchers have been studying the causes of cardiovascular disease, yet many of them remain undiscovered or poorly understood. Network medicine is a recently expanding, integrative field that attempts to elucidate this issue by conceiving of disease as the result of disruptive links between multiple interconnected biological components. Still in its nascent stages, this revolutionary application of network science facilitated a number of important discoveries in complex disease mechanisms. As methodologies become more advanced, network medicine harbors the potential to expound on the molecular and genetic complexities of disease to differentiate how these intricacies govern disease manifestations, prognosis, and therapy. This is of paramount importance for confronting the incredible challenges of current and future cardiovascular disease research. In this review, we summarize the principal molecular and genetic mechanisms of common cardiac pathophysiologies as well as discuss the existing knowledge on therapeutic strategies to prevent, halt, or reverse these pathologies.

Hom-Complex-Based Machine Learning (HCML) for the Prediction of Protein-Protein Binding Affinity Changes upon Mutation

Protein-protein interactions (PPIs) are involved in almost all biological processes in the cell. Understanding protein-protein interactions holds the key for the understanding of biological functions, diseases and the development of therapeutics. Recently, artificial intelligence (AI) models have demonstrated great power in PPIs. However, a key issue for all AI-based PPI models is efficient molecular representations and featurization. Here, we propose Hom-complex-based PPI representation, and Hom-complex-based machine learning models for the prediction of PPI binding affinity changes upon mutation, for the first time. In our model, various Hom complexes Hom(G₁, G) can be generated for the graph representation G of protein-protein complex by using different graphs G₁, which reveal G₁-related inner connections within the graph representation G of protein-protein complex. Further, for a specific graph G₁, a series of nested Hom complexes are generated to give a multiscale characterization of the PPIs. Its persistent homology and persistent Euler characteristic are used as molecular descriptors and further combined with the machine learning model, in particular, gradient boosting tree (GBT). We systematically test our model on the two most-commonly used data sets, that is, SKEMPI and AB-Bind. It has been found that our model outperforms all the existing models as far as we know, which demonstrates the great potential of our model for the analysis of PPIs. Our model can be used for the analysis and design of efficient antibodies for SARS-CoV-2.

Short Arrestin-3-Derived Peptides Activate JNK3 in Cells

Arrestins were first discovered as suppressors of G protein-mediated signaling by G protein-coupled receptors. It was later demonstrated that arrestins also initiate several signaling branches, including mitogen-activated protein kinase cascades. Arrestin-3-dependent activation of the JNK family can be recapitulated with peptide fragments, which are monofunctional elements distilled from this multi-functional arrestin protein. Here, we use maltose-binding protein fusions of arrestin-3-derived peptides to identify arrestin elements that bind kinases of the ASK1-MKK4/7-JNK3 cascade and the shortest peptide facilitating JNK signaling. We identified a 16-residue arrestin-3-derived peptide expressed as a Venus fusion that leads to activation of JNK3α2 in cells. The strength of the binding to the kinases does not correlate with peptide activity. The ASK1-MKK4/7-JNK3 cascade has been implicated in neuronal apoptosis. While inhibitors of MAP kinases exist, short peptides are the first small molecule tools that can activate MAP kinases.

CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention

Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug–target affinity is crucial. The proposed model, the prediction of drug–target affinity using a convolution model with self-attention (CSatDTA), applies convolution-based self-attention mechanisms to the molecular drug and target sequences to predict drug–target affinity (DTA) effectively, unlike previous convolution methods, which exhibit significant limitations related to this aspect. The convolutional neural network (CNN) only works on a particular region of information, excluding comprehensive details. Self-attention, on the other hand, is a relatively recent technique for capturing long-range interactions that has been used primarily in sequence modeling tasks. The results of comparative experiments show that CSatDTA surpasses previous sequence-based or other approaches and has outstanding retention abilities.

Fusion proteins mediate alternation of protein interaction networks in cancers

Fusions of two different genes could lead to the production of chimeric RNAs, which could be translated into novel fusion (or chimeric) proteins. Fusion proteins often act as oncoproteins and drive cancer development, particularly in leukemia and lymphomas. Fusion proteins modify the existing protein-protein interaction (PPI) networks, which could eliminate some PPIs by removing protein domains in such fusions. This alternation of protein interaction networks could impact the signaling pathways and switch on the cancer-promoting activity that could drive the generation of cancer phenotypes and/or loss of controlled apoptosis. Thus, knowledge of the fusion proteins and their protein interaction networks could facilitate a deeper molecular understanding of cancer development, which could help to design new approaches for cancer therapies. Here, we discuss the structural features of fusion proteins and how they impact the PPI networks in cancers. Further, we discuss how to analyze the fusion protein-mediated alternation of PPI networks in cancers.

Structure-based assessment and druggability classification of protein-protein interaction sites

The featureless interface formed by protein–protein interactions (PPIs) is notorious for being considered a difficult and poorly druggable target. However, recent advances have shown PPIs to be druggable, with the discovery of potent inhibitors and stabilizers, some of which are currently being clinically tested and approved for medical use. In this study, we assess the druggability of 12 commonly targeted PPIs using the computational tool, SiteMap. After evaluating 320 crystal structures, we find that the PPI binding sites have a wide range of druggability scores. This can be attributed to the unique structural and physiochemical features that influence their ligand binding and concomitantly, their druggability predictions. We then use these features to propose a specific classification system suitable for assessing PPI targets based on their druggability scores and measured binding-affinity. Interestingly, this system was able to distinguish between different PPIs and correctly categorize them into four classes (i.e. very druggable, druggable, moderately druggable, and difficult). We also studied the effects of protein flexibility on the computed druggability scores and found that protein conformational changes accompanying ligand binding in ligand-bound structures result in higher protein druggability scores due to more favorable structural features. Finally, the drug-likeness of many published PPI inhibitors was studied where it was found that the vast majority of the 221 ligands considered here, including orally tested/marketed drugs, violate the currently acceptable limits of compound size and hydrophobicity parameters. This outcome, combined with the lack of correlation observed between druggability and drug-likeness, reinforces the need to redefine drug-likeness for PPI drugs. This work proposes a PPI-specific classification scheme that will assist researchers in assessing the druggability and identifying inhibitors of the PPI interface.

In silico analysis highlighting the prevalence of BCL2L1 gene and its correlation to miRNA in human coronavirus (HCoV) genetic makeup

The ongoing pandemic that resulted from coronavirus disease (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), had been spiraling out of control with no known antiviral drugs or vaccines. Due to the extremely serious nature of the disease, it has claimed many lives, with a mortality rate of 3.4% declared by the World Health Organization (WHO) on March 3, 2020. The aim of this study is to gain an understanding of the regulatory nature of the proteins involved in COVID-19 and to explore the possibility that microRNA (miRNA) could become a major component in the decoding of the virus. In the study, we were able to correlate the host protein gene BCL2L1 with miRNA miR-23b via network analysis. MiRNAs have previously been associated with the antiviral properties of various viral diseases, such as enterovirus 71 and hepatitis. They have been reported to act as antiviral regulators, since they are an integral component in the direct regulation of viral genes. MiRNAs are also capable of enabling the virus to avoid the host immune response by suppressing the IFN-α/β signaling pathway or increasing the production of IFN-α/β and as a result, inhibiting the viral infection. Here, we explain and shed light on the various correlations in the miRNA-gene-disease association that are seen in the host proteins of COVID-19.

Gene and Protein Interaction Network Analysis in Epithelial-Mesenchymal Transition of Hertwig's Epithelial Root Sheath reveals periodontal regenerative drug targets - An in silico study

Background and aim

Hertwig’s Εpithelial Root Sheath (HΕRS) has a major function in the developing tooth roots. Earlier research revealed that it undergoes epithelial–mesenchymal transition, a vital process for the morphogenesis and complete development of the tooth and its surrounding periodontium. Few studies have demonstrated the role of HERS in cementogenesis through ΕMΤ. The background of this in-silico system biology approach is to find a hub protein and gene involved in the EMT of HERS that may uncover novel insights in periodontal regenerative drug targets.

Materials and methods

The protein and gene list involved in epithelial–mesenchymal transition were obtained from literature sources. The protein interaction was constructed using STRING software and the protein interaction network was analyzed. Molecular docking simulation checks the binding energy and stability of protein-ligand complex.

Results

Results revealed the hub gene to be DYRK1A(Hepcidin), and the ligand was identified as isoetharine. SΤRIΝG results showed a confidence cutoff of 0.9 in sensitivity analysis with a condensed protein interaction network. Overall, 98 nodes from 163 nodes of expected edges were found with an average node degree of 11.9. Docking results show binding energy of −4.70, and simulation results show an RMSD value of 5.6 Å at 50 ns.

Conclusion

Isoetharine could be a potential drug for periodontal regeneration.

Deciphering the Host-Pathogen Interactome of the Wheat-Common Bunt System: A Step towards Enhanced Resilience in Next Generation Wheat

Common bunt, caused by two fungal species, Tilletia caries and Tilletia laevis, is one of the most potentially destructive diseases of wheat. Despite the availability of synthetic chemicals against the disease, organic agriculture relies greatly on resistant cultivars. Using two computational approaches—interolog and domain-based methods—a total of approximately 58 M and 56 M probable PPIs were predicted in T. aestivum–T. caries and T. aestivum–T. laevis interactomes, respectively. We also identified 648 and 575 effectors in the interactions from T. caries and T. laevis, respectively. The major host hubs belonged to the serine/threonine protein kinase, hsp70, and mitogen-activated protein kinase families, which are actively involved in plant immune signaling during stress conditions. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the host proteins revealed significant GO terms (O-methyltransferase activity, regulation of response to stimulus, and plastid envelope) and pathways (NF-kappa B signaling and the MAPK signaling pathway) related to plant defense against pathogens. Subcellular localization suggested that most of the pathogen proteins target the host in the plastid. Furthermore, a comparison between unique T. caries and T. laevis proteins was carried out. We also identified novel host candidates that are resistant to disease. Additionally, the host proteins that serve as transcription factors were also predicted.

Persistent spectral based ensemble learning (PerSpect-EL) for protein-protein binding affinity prediction

Protein-protein interactions (PPIs) play a significant role in nearly all cellular and biological activities. Data-driven machine learning models have demonstrated great power in PPIs. However, the design of efficient molecular featurization poses a great challenge for all learning models for PPIs. Here, we propose persistent spectral (PerSpect) based PPI representation and featurization, and PerSpect-based ensemble learning (PerSpect-EL) models for PPI binding affinity prediction, for the first time. In our model, a sequence of Hodge (or combinatorial) Laplacian (HL) matrices at various different scales are generated from a specially designed filtration process. PerSpect attributes, which are statistical and combinatorial properties of spectrum information from these HL matrices, are used as features for PPI characterization. Each PerSpect attribute is input into a 1D convolutional neural network (CNN), and these CNN networks are stacked together in our PerSpect-based ensemble learning models. We systematically test our model on the two most commonly used datasets, i.e. SKEMPI and AB-Bind. It has been found that our model can achieve state-of-the-art results and outperform all existing models to the best of our knowledge.