A combined experimental and computational strategy to define protein interaction networks for peptide recognition modules

Competitive binding of actin and SH3 domains at proline-rich regions of Las17/WASP regulates actin polymerisation

Eukaryotic actin filaments bind factors that regulate their assembly and disassembly creating a self-organising system, the actin cytoskeleton. Despite extensive knowledge of signals that modulate actin organisation, significant gaps remain in our understanding of spatiotemporal regulation of de novo filament initiation. Yeast Las17/WASP is essential for actin polymerisation initiation supporting membrane invagination in Saccharomyces cerevisiae endocytosis and therefore its tight regulation is critical. The adaptor protein Sla1 inhibits Las17 but mechanisms underpinning Las17 activation remain elusive. Here we show that Las17 binding of tandem Sla1 SH3 domains is >100-fold stronger than single domains. Furthermore, SH3 domains directly compete with G-actin for binding in the Las17 polyproline region, thus rationalising how SH3 interactions can affect actin polymerisation despite their distance from C-terminal actin-binding and Arp2/3-interacting VCA domains. Our data and proposed model also highlight the likely importance of multiple weak interactions that together ensure spatial and temporal regulation of endocytosis.

Cryptic genetic variation shapes the fate of gene duplicates in a protein interaction network

Paralogous genes are often functionally redundant for long periods of time. While their functions are preserved, paralogs accumulate cryptic changes in sequence and expression, which could modulate the impact of future mutations through epistasis. We examine the impact of mutations on redundant myosin proteins that have maintained the same binding preference despite having accumulated differences in expression levels and amino acid substitutions in the last 100 million years. By quantifying the impact of all single-amino acid substitutions in their SH3 domains on the physical interaction with their interaction partners, we show that the same mutations in the paralogous SH3s change binding in a paralog-specific and interaction partner-specific manner. This contingency is explained by the difference in promoter strength of the two paralogous myosin genes and epistatic interactions between the mutations introduced and cryptic divergent sites within the SH3s. One significant consequence of this contingency is that while some mutations would be sufficient to nonfunctionalize one paralog, they would have minimal impact on the other. Our results reveal how cryptic divergence, which accumulates while maintaining functional redundancy in cellular networks, could bias gene duplicates to specific fates.

Protein-protein interaction detection using deep learning: A survey, comparative analysis, and experimental evaluation

This survey paper provides a comprehensive analysis of various Deep Learning (DL) techniques and algorithms for detecting protein-protein interactions (PPIs). It examines the scalability, interpretability, accuracy, and efficiency of each technique, offering a detailed empirical and experimental evaluation. Empirically, the techniques are assessed based on four key criteria, while experimentally, they are ranked by specific algorithms and broader methodological categories. Deep Neural Networks (DNNs) demonstrated high accuracy but faced limitations such as overfitting and low interpretability. Convolutional Neural Networks (CNNs) were highly efficient at extracting hierarchical features from biological sequences, while Generative Stochastic Networks (GSNs) excelled in handling uncertainty. Long Short-Term Memory (LSTM) networks effectively captured temporal dependencies within PPI sequences, though they presented scalability challenges. This paper concludes with insights into potential improvements and future directions for advancing DL techniques in PPI identification, highlighting areas where further optimization can enhance performance and applicability.

Mechanism of action of Salvia miltiorrhiza on avascular necrosis of the femoral head determined by integrated network pharmacology and molecular dynamics simulation

Avascular necrosis of the femoral head (ANFH) is a progressive, multifactorial, and challenging clinical condition that often leads to hip dysfunction and deterioration. The pathogenesis of ANFH is complex, and there is no foolproof treatment strategy. Although some pharmacologic and surgical treatments have been shown to improve ANFH, the associated side effects and poor prognosis are of concern. Therefore, there is an urgent need to explore therapeutic interventions with superior efficacy and safety to improve the quality of life of patients with ANFH. Salvia miltiorrhiza (SM), a traditional Chinese medicine with a long history, is widely used for the treatment of cardiovascular and musculoskeletal diseases due to its multiple pharmacological activities. However, the molecular mechanism of SM for the treatment of ANFH is still unclear. Therefore, this study aimed to explore the potential targets and mechanisms of SM for the treatment of ANFH using network pharmacology and molecular modeling techniques. By searching multiple databases, we screened 52 compounds and 42 common targets involved in ANFH therapy and identified dan-shexinkum d, cryptotanshinone, tanshinone iia, and dihydrotanshinlactone as key compounds. Based on the protein-protein interaction (PPI) network, TP53, AKT1, EGFR, STAT3, BCL2, IL6, and TNF were identified as core targets. Subsequent enrichment analysis revealed that these targets were mainly enriched in the AGE-RAGE, IL-17, and TNF pathways, which were mainly associated with inflammatory responses, apoptosis, and oxidative stress. In addition, molecular docking and 100 nanoseconds molecular dynamics (MD) simulations showed that the bioactive compounds of SM had excellent affinity and binding strength to the core targets. Among them, dan-shexinkum d possessed the lowest binding free energy (-215.874 kcal/mol and - 140.277 kcal/mol, respectively) for AKT1 and EGFR. These results demonstrated the multi-component, multi-target, and multi-pathway intervention mechanism of SM in the treatment of ANFH, which provided theoretical basis and clues for further experimental validation and development of anti-ANFH drugs.

Protein-Protein Interaction Prediction via Structure-Based Deep Learning

Protein-protein interactions (PPIs) play an essential role in life activities. Many artificial intelligence algorithms based on protein sequence information have been developed to predict PPIs. However, these models have difficulty dealing with various sequence lengths and suffer from low generalization and prediction accuracy. In this study, we proposed a novel end-to-end deep learning framework, RSPPI, combining residual neural network (ResNet) and spatial pyramid pooling (SPP), to predict PPIs based on the protein sequence physicochemistry properties and spatial structural information. In the RSPPI model, ResNet was employed to extract the structural and physicochemical information from the protein three-dimensional structure and primary sequence; the SPP layer was used to transform feature maps to a single vector and avoid the fixed-length requirement. The RSPPI model possessed excellent cross-species performance and outperformed several state-of-the-art methods based either on protein sequence or gene ontology in most evaluation metrics. The RSPPI model provides a novel strategy to develop an AI PPI prediction algorithm.

Affinity purification mass spectrometry characterisation of the interactome of receptor tyrosine kinase proline-rich motifs in cancer

Receptor tyrosine kinase (RTK) overexpression is linked to the development and progression of multiple cancers. RTKs are classically considered to initiate cytoplasmic signalling pathways via ligand-induced tyrosine phosphorylation, however recent evidence points to a second tier of signalling contingent on interactions mediated by the proline-rich motif (PRM) regions of non-activated RTKs. The presence of PRMs on the C-termini of >40 % of all RTKs and the abundance of PRM-binding proteins encoded by the human genome suggests that there is likely to be a large number of previously unexplored interactions which add to the RTK intracellular interactome. Here, we explore the RTK PRM interactome and its potential significance using affinity purification mass spectrometry and in silico enrichment analyses. Peptides comprising PRM-containing C-terminal tail regions of EGFR, FGFR2 and HER2 were used as bait to affinity purify bound proteins from different cancer cell line lysates. 490 unique interactors were identified, amongst which proteins with metabolic, homeostatic and migratory functions were overrepresented. This suggests that PRMs from RTKs may sustain a diverse interactome in cancer cells. Since RTK overexpression is common in cancer, RTK PRM-derived signalling may be an important, but as yet underexplored, contributor to negative cancer outcomes including resistance to kinase inhibitors.

The receptor kinase RiSho1 in Rhizophagus irregularis regulates arbuscule development and drought tolerance during arbuscular mycorrhizal symbiosis

In terrestrial ecosystems, most plant species can form beneficial associations with arbuscular mycorrhizal (AM) fungi. Arbuscular mycorrhizal fungi benefit plant nutrient acquisition and enhance plant tolerance to drought. The high osmolarity glycerol 1 mitogen-activated protein kinase (HOG1-MAPK) cascade genes have been characterized in Rhizophagus irregularis. However, the upstream receptor of the HOG1-MAPK cascade remains to be investigated. We identify the receptor kinase RiSho1 from R. irregularis, containing four transmembrane domains and one Src homology 3 (SH3) domain, corresponding to the homologue of Saccharomyces cerevisiae. Higher expression levels of RiSho1 were detected during the in planta phase in response to drought. RiSho1 protein was localized in the plasma membrane of yeast, and interacted with the HOG1-MAPK module RiPbs2 directly by protein-protein interaction. RiSho1 complemented the growth defect of the yeast mutant ∆sho1 under sorbitol conditions. Knock-down of RiSho1 led to the decreased expression of downstream HOG1-MAPK cascade (RiSte11, RiPbs2, RiHog1) and drought-resistant genes (RiAQPs, RiTPSs, RiNTH1 and Ri14-3-3), hampered arbuscule development and decreased plants antioxidation ability under drought stress. Our study reveals the role of RiSho1 in regulating arbuscule development and drought-resistant genes via the HOG1-MAPK cascade. These findings provide new perspectives on the mechanisms by which AM fungi respond to drought.

Bsp1, a fungal CPI motif protein, regulates actin filament capping in endocytosis and cytokinesis

The capping of barbed filament ends is a fundamental mechanism for actin regulation. Capping protein controls filament growth and actin turnover in cells by binding to the barbed ends of the filaments with high affinity and slow off-rate. The interaction between capping protein and actin is regulated by capping protein interaction (CPI) motif proteins. We identified a novel CPI motif protein, Bsp1, which is involved in cytokinesis and endocytosis in budding yeast. We demonstrate that Bsp1 is an actin binding protein with a high affinity for capping protein via its CPI motif. In cells, Bsp1 regulates capping protein at endocytic sites and is a major recruiter of capping protein to the cytokinetic actin ring. Lastly, we define Bsp1-related proteins as a distinct fungi-specific CPI protein group. Our results suggest that Bsp1 promotes actin filament capping by the capping protein. This study establishes Bsp1 as a new capping protein regulator and promising candidate to regulate actin networks in fungi.

Network Pharmacology and Molecular Modeling Techniques in Unraveling the Underlying Mechanism of Citri Reticulatae Pericarpium aganist Type 2 Diabetic Osteoporosis

Type 2 diabetic osteoporosis (T2DOP) is a common complication in diabetic patients that seriously affects their health and quality of life. The pathogenesis of T2DOP is complex, and there are no targeted governance means in modern medicine. Citri Reticulatae Pericarpium (CRP) is a traditional Chinese medicine that has a long history and has been used in the treatment of osteoporosis diseases. However, the molecular mechanism for the CRP treatment of T2DOP is not clear. Therefore, this study aimed to explore the underlying mechanisms of CRP for the treatment of T2DOP by using network pharmacology and molecular modeling techniques. By retrieving multiple databases, we obtained 5 bioactive compounds and 63 common targets of bioactive compounds with T2DOP, and identified AKT 1, TP 53, JUN, BCL 2, MAPK 1, NFKB 1, and ESR 1 as the core targets of their PPI network. Enrichment analysis revealed that these targets were mainly enriched in the estrogen signaling pathway, TNF signaling pathway, and AGE-RAGE signaling pathway in diabetics, which were mainly related to oxidative stress and hormonal regulation. Molecular docking and molecular dynamics simulations have shown the excellent binding effect of the bioactive compounds of CRP and the core targets. These findings reveal that CRP may ameliorate T2DOP through multiple multicomponent and multitarget pathways.

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.

Large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode

The soybean cyst nematode (SCN) [Heterodera glycines Ichinohe] is a devastating pathogen of soybean [Glycine max (L.) Merr.] that is rapidly becoming a global economic issue. Two loci conferring SCN resistance have been identified in soybean, Rhg1 and Rhg4; however, they offer declining protection. Therefore, it is imperative that we identify additional mechanisms for SCN resistance. In this paper, we develop a bioinformatics pipeline to identify protein-protein interactions related to SCN resistance by data mining massive-scale datasets. The pipeline combines two leading sequence-based protein-protein interaction predictors, the Protein-protein Interaction Prediction Engine (PIPE), PIPE4, and Scoring PRotein INTeractions (SPRINT) to predict high-confidence interactomes. First, we predicted the top soy interacting protein partners of the Rhg1 and Rhg4 proteins. Both PIPE4 and SPRINT overlap in their predictions with 58 soybean interacting partners, 19 of which had GO terms related to defense. Beginning with the top predicted interactors of Rhg1 and Rhg4, we implement a "guilt by association" in silico proteome-wide approach to identify novel soybean genes that may be involved in SCN resistance. This pipeline identified 1,082 candidate genes whose local interactomes overlap significantly with the Rhg1 and Rhg4 interactomes. Using GO enrichment tools, we highlighted many important genes including five genes with GO terms related to response to the nematode (GO:0009624), namely, Glyma.18G029000, Glyma.11G228300, Glyma.08G120500, Glyma.17G152300, and Glyma.08G265700. This study is the first of its kind to predict interacting partners of known resistance proteins Rhg1 and Rhg4, forming an analysis pipeline that enables researchers to focus their search on high-confidence targets to identify novel SCN resistance genes in soybean.

Machine Learning Advances in Predicting Peptide/Protein-Protein Interactions Based on Sequence Information for Lead Peptides Discovery

Peptides have shown increasing advantages and significant clinical value in drug discovery and development. With the development of high-throughput technologies and artificial intelligence (AI), machine learning (ML) methods for discovering new lead peptides have been expanded and incorporated into rational drug design. Predictions of peptide-protein interactions (PepPIs) and protein-protein interactions (PPIs) are both opportunities and challenges in computational biology, which will help to better understand the mechanisms of disease and provide the impetus for the discovery of lead peptides. This paper comprehensively reviews computational models for PepPI and PPI predictions. It begins with an introduction of various databases of peptide ligands and target proteins. Then it discusses data formats and feature representations for proteins and peptides. Furthermore, classical ML methods and emerging deep learning (DL) methods that can be used to train prediction models of PepPI and PPI are classified into four categories, and their advantages and disadvantages are analyzed. To assess the relative performance of different models, different validation protocols and evaluation indexes are discussed. The goal of this review is to help researchers quickly get started to develop computational frameworks using these integrated resources and eventually promote the discovery of lead peptides.

Peptides as multifunctional players in cancer therapy

Peptides exhibit lower affinity and a shorter half-life in the body than antibodies. Conversely, peptides demonstrate higher efficiency in tissue penetration and cell internalization than antibodies. Regardless of the pros and cons of peptides, they have been used as tumor-homing ligands for delivering carriers (such as nanoparticles, extracellular vesicles, and cells) and cargoes (such as cytotoxic peptides and radioisotopes) to tumors. Additionally, tumor-homing peptides have been conjugated with cargoes such as small-molecule or chemotherapeutic drugs via linkers to synthesize peptide-drug conjugates. In addition, peptides selectively bind to cell surface receptors and proteins, such as immune checkpoints, receptor kinases, and hormone receptors, subsequently blocking their biological activity or serving as hormone analogs. Furthermore, peptides internalized into cells bind to intracellular proteins and interfere with protein-protein interactions. Thus, peptides demonstrate great application potential as multifunctional players in cancer therapy.

PPISB: A Novel Network-Based Algorithm of Predicting Protein-Protein Interactions With Mixed Membership Stochastic Blockmodel

Protein-protein interactions (PPIs) play an essential role for most of biological processes in cells. Many computational algorithms have thus been proposed to predict PPIs. However, most of them heavily rest on the biological information of proteins while ignoring the latent structural features of proteins presented in a PPI network. In this paper, we propose an efficient network-based prediction algorithm, namely PPISB, based on a mixed membership stochastic blockmodel. By simulating the generative process of a PPI network, PPISB is able to capture the latent community structures. The inference procedure adopted by PPISB further optimizes the membership distributions of proteins over different complexes. After that, a distance measure is designed to compute the similarity between two proteins in terms of their likelihoods of being in the same complex, thus verifying whether they interact with each other or not. To evaluate the performance of PPISB, a series of extensive experiments have been conducted with five PPI networks collected from different species and the results demonstrate that PPISB has a promising performance when applied to predict PPIs in terms of several evaluation metrics. Hence, we reason that PPISB is preferred over state-of-the-art network-based prediction algorithms especially for predicting potential PPIs.

Spatio-temporal regulation of endocytic protein assembly by SH3 domains in yeast

Clathrin-mediated endocytosis is a conserved eukaryotic membrane trafficking pathway that is driven by a sequentially assembled molecular machinery that contains over 60 different proteins. SH3 domains are the most abundant protein-protein interaction domain in this process, but the function of most SH3 domains in protein dynamics remains elusive. Using mutagenesis and live-cell fluorescence microscopy in the budding yeast Saccharomyces cerevisiae, we dissected SH3-mediated regulation of the endocytic pathway. Our data suggest that multiple SH3 domains regulate the actin nucleation-promoting Las17-Vrp1 complex, and that the network of SH3 interactions coordinates both Las17-Vrp1 assembly and dissociation. Furthermore, most endocytic SH3 domain proteins use the SH3 domain for their own recruitment, while a minority use the SH3 domain to recruit other proteins and not themselves. Our results provide a dynamic map of SH3 functions in yeast endocytosis and a framework for SH3 interaction network studies across biology.

Identifying Protein Complexes From Protein-Protein Interaction Networks Based on Fuzzy Clustering and GO Semantic Information

Protein complexes are of great significance to provide valuable insights into the mechanisms of biological processes of proteins. A variety of computational algorithms have thus been proposed to identify protein complexes in a protein-protein interaction network. However, few of them can perform their tasks by taking into account both network topology and protein attribute information in a unified fuzzy-based clustering framework. Since proteins in the same complex are similar in terms of their attribute information and the consideration of fuzzy clustering can also make it possible for us to identify overlapping complexes, we target to propose such a novel fuzzy-based clustering framework, namely FCAN-PCI, for an improved identification accuracy. To do so, the semantic similarity between the attribute information of proteins is calculated and we then integrate it into a well-established fuzzy clustering model together with the network topology. After that, a momentum method is adopted to accelerate the clustering procedure. FCAN-PCI finally applies a heuristical search strategy to identify overlapping protein complexes. A series of extensive experiments have been conducted to evaluate the performance of FCAN-PCI by comparing it with state-of-the-art identification algorithms and the results demonstrate the promising performance of FCAN-PCI.

Systematic analysis of olfactory protein-protein interactions network of fruitfly, Drosophila melanogaster

Olfaction is one of the physiological traits of insect behavior. Insects have evolved a sophisticated olfactory system and use a combined coding strategy to process general odor. Drosophila melanogaster is a powerful model to reveal the molecular and cellular mechanisms of odor detection. Identifying new olfactory targets through complex interactions will contribute to a better understanding of the functions, interactions, and signaling pathways of olfactory proteins. However, the mechanism of D. melanogaster olfaction is still unclear, and more olfactory proteins are required to be discovered. In this study, we tried to explore essential proteins in the olfactory system of D. melanogaster and conduct protein-protein interactions (PPIs) analysis. We constructed the PPIs network of the olfactory system of D. melanogaster, consisting of 863 proteins and 18,959 interactions. Various methods were used to perform functional enrichment analysis, topological analysis and cluster analysis. Our results confirmed that Class B scavenger receptors (SR-Bs), glutathione S-transferases (GSTs), and UDP-glycosyltransferases (UGTs) play an essential role in olfaction of D. melanogaster. The proteins obtained in this study can be used for subsequent functional identification in D. melanogaster olfactory.

SRC homology 3 domains: multifaceted binding modules

The assembly of complexes following the detection of extracellular signals is often controlled by signaling proteins comprising multiple peptide binding modules. The SRC homology (SH)3 family represents the archetypical modular protein interaction module, with ~300 annotated SH3 domains in humans that regulate an impressive array of signaling processes. We review recent findings regarding the allosteric contributions of SH3 domains host protein context, their phosphoregulation, and their roles in phase separation that challenge the simple model in which SH3s are considered to be portable domains binding to specific proline-rich peptide motifs.

Understanding binding affinity and specificity of modular protein domains: A focus in ligand design for the polyproline-binding families

Within the modular protein domains there are five families that recognize proline-rich sequences: SH3, WW, EVH1, GYF and UEV domains. This chapter reviews the main strategies developed for the design of ligands for these families, including peptides, peptidomimetics and drugs. We also describe some studies aimed to understand the molecular reasons responsible for the intrinsic affinity and specificity of these domains.

Interactome of human and SARS-CoV-2 proteins to identify human hub proteins associated with comorbidities

SARS-CoV-2 has a higher chance of progression in adults of any age with certain underlying health conditions or comorbidities like cancer, neurological diseases and in certain cases may even lead to death. Like other viruses, SARS-CoV-2 also interacts with host proteins to pave its entry into host cells. Therefore, to understand the behaviour of SARS-CoV-2 and design of effective antiviral drugs, host-virus protein-protein interactions (PPIs) can be very useful. In this regard, we have initially created a human-SARS-CoV-2 PPI database from existing works in the literature which has resulted in 7085 unique PPIs. Subsequently, we have identified at most 10 proteins with highest degrees viz. hub proteins from interacting human proteins for individual virus protein. The identification of these hub proteins is important as they are connected to most of the other human proteins. Consequently, when they get affected, the potential diseases are triggered in the corresponding pathways, thereby leading to comorbidities. Furthermore, the biological significance of the identified hub proteins is shown using KEGG pathway and GO enrichment analysis. KEGG pathway analysis is also essential for identifying the pathways leading to comorbidities. Among others, SARS-CoV-2 proteins viz. NSP2, NSP5, Envelope and ORF10 interacting with human hub proteins like COX4I1, COX5A, COX5B, NDUFS1, CANX, HSP90AA1 and TP53 lead to comorbidities. Such comorbidities are Alzheimer, Parkinson, Huntington, HTLV-1 infection, prostate cancer and viral carcinogenesis. Subsequently, using Enrichr tool possible repurposable drugs which target the human hub proteins are reported in this paper as well. Therefore, this work provides a consolidated study for human-SARS-CoV-2 protein interactions to understand the relationship between comorbidity and hub proteins so that it may pave the way for the development of anti-viral drugs.

Fluorescence resonance energy transfer in revealing protein-protein interactions in living cells

Genes are expressed to proteins for a wide variety of fundamental biological processes at the cellular and organismal levels. However, a protein rarely functions alone, but rather acts through interactions with other proteins to maintain normal cellular and organismal functions. Therefore, it is important to analyze the protein-protein interactions to determine functional mechanisms of proteins, which can also guide to develop therapeutic targets for treatment of diseases caused by altered protein-protein interactions leading to cellular/organismal dysfunctions. There is a large number of methodologies to study protein interactions in vitro, in vivo and in silico, which led to the development of many protein interaction databases, and thus, have enriched our knowledge about protein-protein interactions and functions. However, many of these interactions were identified in vitro, but need to be verified/validated in living cells. Furthermore, it is unclear whether these interactions are direct or mediated via other proteins. Moreover, these interactions are representative of cell- and time-average, but not a single cell in real time. Therefore, it is crucial to detect direct protein-protein interactions in a single cell during biological processes in vivo, towards understanding the functional mechanisms of proteins in living cells. Importantly, a fluorescence resonance energy transfer (FRET)-based methodology has emerged as a powerful technique to decipher direct protein-protein interactions at a single cell resolution in living cells, which is briefly described in a limited available space in this mini-review.

Exploiting Folding and Degradation Machineries To Target Undruggable Proteins: What Can a Computational Approach Tell Us?

Advances in genomics and proteomics have unveiled an ever-growing number of key proteins and provided mechanistic insights into the genesis of pathologies. This wealth of data showed that changes in expression levels of specific proteins, mutations, and post-translational modifications can result in (often subtle) perturbations of functional protein-protein interaction networks, which ultimately determine disease phenotypes. Although many such validated pathogenic proteins have emerged as ideal drug targets, there are also several that escape traditional pharmacological regulation; these proteins have thus been labeled "undruggable". The challenges posed by undruggable targets call for new sorts of molecular intervention. One fascinating solution is to perturb a pathogenic protein's expression levels, rather than blocking its activities. In this Concept paper, we shall discuss chemical interventions aimed at recruiting undruggable proteins to the ubiquitin proteasome system, or aimed at disrupting protein-protein interactions in the chaperone-mediated cellular folding machinery: both kinds of intervention lead to a decrease in the amount of active pathogenic protein expressed. Specifically, we shall discuss the role of computational strategies in understanding the molecular determinants characterizing the function of synthetic molecules typically designed for either type of intervention. Finally, we shall provide our perspectives and views on the current limitations and possibilities to expand the scope of rational approaches to the design of chemical regulators of protein levels.

Bacterial Protein Interaction Networks: Connectivity is Ruled by Gene Conservation, Essentiality and Function

BACKGROUND

Protein-protein interaction (PPI) networks are the backbone of all processes in living cells. In this work, we relate conservation, essentiality and functional repertoire of a gene to the connectivity k (i.e. the number of interactions, links) of the corresponding protein in the PPI network.

METHODS

On a set of 42 bacterial genomes of different sizes, and with reasonably separated evolutionary trajectories, we investigate three issues: i) whether the distribution of connectivities changes between PPI subnetworks of essential and nonessential genes; ii) how gene conservation, measured both by the evolutionary retention index (ERI) and by evolutionary pressures, is related to the connectivity of the corresponding protein; iii) how PPI connectivities are modulated by evolutionary and functional relationships, as represented by the Clusters of Orthologous Genes (COGs).

RESULTS

We show that conservation, essentiality and functional specialisation of genes constrain the connectivity of the corresponding proteins in bacterial PPI networks. In particular, we isolated a core of highly connected proteins (connectivities k≥40), which is ubiquitous among the species considered here, though mostly visible in the degree distributions of bacteria with small genomes (less than 1000 genes).

CONCLUSION

The genes that support this highly connected core are conserved, essential and, in most cases, belong to the COG cluster J, related to ribosomal functions and the processing of genetic information.

Identification of the anti-breast cancer targets of triterpenoids in Liquidambaris Fructus and the hints for its traditional applications

BACKGROUND

Liquidambaris Fructus is the infructescences of Liquidambar formosana Hance and it has been used to treat some breast disease in Traditional Chinese Medicine. In the previous study we found the anti-breast cancer effect of triterpenoid in Liquidambaris Fructus. This study is a further investigation of the triterpenoids in Liquidambaris Fructus and aims to identify their anti-breast cancer targets, meanwhile, to estimate the rationality of the traditional applications of Liquidambaris Fructus.

METHODS

Triterpenoids in Liquidambaris Fructus were isolated and their structures were identified by NMR spectrums. Potential targets of these triterpenoids were predicted using a reverse pharmacophore mapping strategy. Associations between these targets and the therapeutic targets of breast cancer were analyzed by constructing protein-protein interaction network, and targets played important roles in the network were identified using Molecular Complex Detection method. Binding affinity between the targets and triterpenoids was studied using molecular docking method. Gene ontology enrichment analysis was conducted to reveal the biological process and signaling pathways that the identified targets were involved in.

RESULTS

Thirteen triterpenoids were identified and 6 of them were the first time isolated from Liquidambaris Fructus. Predicted ADME properties revealed a good druggability of these triterpenoids. We identified 18 protein targets which were closely related to breast cancer progression, especially triple-negative, basal-like or advanced stage breast cancers. The triterpenoids could bind with these targets as their inhibitors: hydrophobic skeleton is a favorable factor for them to stabilize at binding site and polar C17- or C3- substituent was necessary for binding. GO enrichment analysis indicated that inhibition of protein tyrosine kinases autophosphorylation might be the primary mechanism for the anti-breast cancer effect of the triterpenoids, and ErbB4 and EGFR were the most relevant targets.

CONCLUSIONS

The study revealed that triterpenoids from Liquidambaris Fructus might exert anti-breast cancer effect by directly inhibit multiple protein targets and signaling pathways, especially ErbB4 and EGFR and related pathways. This study also brings up another hint that the traditional applications of Liquidambaris Fructus on hypogalactia should be reassessed systematically because it might suppress rather than promote lactation by inhibiting the activity of ErbB4.

Comprehensive Analysis of G1 Cyclin Docking Motif Sequences that Control CDK Regulatory Potency In Vivo

Many protein-modifying enzymes recognize their substrates via docking motifs, but the range of functionally permissible motif sequences is often poorly defined. During eukaryotic cell division, cyclin-specific docking motifs help cyclin-dependent kinases (CDKs) phosphorylate different substrates at different stages, thus enforcing a temporally ordered series of events. In budding yeast, CDK substrates with Leu/Pro-rich (LP) docking motifs are recognized by Cln1/2 cyclins in late G1 phase, yet the key sequence features of these motifs were unknown. Here, we comprehensively analyze LP motif requirements in vivo by combining a competitive growth assay with deep mutational scanning. We quantified the effect of all single-residue replacements in five different LP motifs by using six distinct G1 cyclins from diverse fungi including medical and agricultural pathogens. The results uncover substantial tolerance for deviations from the consensus sequence, plus requirements at some positions that are contingent on the favorability of other motif residues. They also reveal the basis for variations in functional potency among wild-type motifs, and allow derivation of a quantitative matrix that predicts the strength of other candidate motif sequences. Finally, we find that variation in docking motif potency can advance or delay the time at which CDK substrate phosphorylation occurs, and thereby control the temporal ordering of cell cycle regulation. The overall results provide a general method for surveying viable docking motif sequences and quantifying their potency in vivo, and they reveal how variations in docking strength can tune the degree and timing of regulatory modifications.

Prediction of Protein-Protein Interactions with Local Weight-Sharing Mechanism in Deep Learning

Protein-protein interactions (PPIs) are important for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. The experimental methods for identifying PPIs are always time-consuming and expensive. Therefore, it is important to develop computational approaches for predicting PPIs. In this paper, an improved model is proposed to use a machine learning method in the study of protein-protein interactions. With the consideration of the factors affecting the prediction of the PPIs, a method of feature extraction and fusion is proposed to improve the variety of the features to be considered in the prediction. Besides, with the consideration of the effect affected by the different input order of the two proteins, we propose a "Y-type" Bi-RNN model and train the network by using a method which both needs backward and forward training. In order to insure the training time caused on the extra training either a backward one or a forward one, this paper proposes a weight-sharing policy to minimize the parameters in the training. The experimental results show that the proposed method can achieve an accuracy of 99.57%, recall of 99.36%, sensitivity of 99.76%, precision of 99.74%, MCC of 99.14%, and AUC of 99.56% under the benchmark dataset.

Exploring the computational methods for protein-ligand binding site prediction

Proteins participate in various essential processes in vivo via interactions with other molecules. Identifying the residues participating in these interactions not only provides biological insights for protein function studies but also has great significance for drug discoveries. Therefore, predicting protein-ligand binding sites has long been under intense research in the fields of bioinformatics and computer aided drug discovery. In this review, we first introduce the research background of predicting protein-ligand binding sites and then classify the methods into four categories, namely, 3D structure-based, template similarity-based, traditional machine learning-based and deep learning-based methods. We describe representative algorithms in each category and elaborate on machine learning and deep learning-based prediction methods in more detail. Finally, we discuss the trends and challenges of the current research such as molecular dynamics simulation based cryptic binding sites prediction, and highlight prospective directions for the near future.

Development and Application of Computational Methods in Phage Display Technology

BACKGROUND

Phage display is a powerful and versatile technology for the identification of peptide ligands binding to multiple targets, which has been successfully employed in various fields, such as diagnostics and therapeutics, drug-delivery and material science. The integration of next generation sequencing technology with phage display makes this methodology more productive. With the widespread use of this technique and the fast accumulation of phage display data, databases for these data and computational methods have become an indispensable part in this community. This review aims to summarize and discuss recent progress in the development and application of computational methods in the field of phage display.

METHODS

We undertook a comprehensive search of bioinformatics resources and computational methods for phage display data via Google Scholar and PubMed. The methods and tools were further divided into different categories according to their uses.

RESULTS

We described seven special or relevant databases for phage display data, which provided an evidence-based source for phage display researchers to clean their biopanning results. These databases can identify and report possible target-unrelated peptides (TUPs), thereby excluding false-positive data from peptides obtained from phage display screening experiments. More than 20 computational methods for analyzing biopanning data were also reviewed. These methods were classified into computational methods for reporting TUPs, for predicting epitopes and for analyzing next generation phage display data.

CONCLUSION

The current bioinformatics archives, methods and tools reviewed here have benefitted the biopanning community. To develop better or new computational tools, some promising directions are also discussed.

The Verticillium dahliae Sho1-MAPK pathway regulates melanin biosynthesis and is required for cotton infection

Verticillium dahliae is a soil‐borne fungus that causes vascular wilt on numerous plants worldwide. The fungus survives in the soil for up to 14 years by producing melanized microsclerotia. The protective function of melanin in abiotic stresses is well documented. Here, we found that the V. dahliae tetraspan transmembrane protein VdSho1, a homolog of the Saccharomyces cerevisiae Sho1, acts as an osmosensor, and is required for plant penetration and melanin biosynthesis. The deletion mutant ΔSho1 was incubated on a cellophane membrane substrate that mimics the plant epidermis, revealing that the penetration of ΔSho1 strain was reduced compared to the wild‐type strain. Furthermore, VdSho1 regulates melanin biosynthesis by a signalling mechanism requiring a kinase‐kinase signalling module of Vst50‐Vst11‐Vst7. Strains, ΔVst50, ΔVst7 and ΔVst11 also displayed defective penetration and melanin production like the ΔSho1 strain. Defects in penetration and melanin production in ΔSho1 were restored by overexpression of Vst50, suggesting that Vst50 lies downstream of VdSho1 in the regulatory pathway governing penetration and melanin biosynthesis. Data analyses revealed that the transmembrane portion of VdSho1 was essential for both membrane penetration and melanin production. This study demonstrates that Vst50‐Vst11‐Vst7 module regulates VdSho1‐mediated plant penetration and melanin production in V. dahliae, contributing to virulence.

Efficiently Detecting Protein Complexes from Protein Interaction Networks via Alternating Direction Method of Multipliers

Protein complexes are crucial in improving our understanding of the mechanisms employed by proteins. Various computational algorithms have thus been proposed to detect protein complexes from protein interaction networks. However, given massive protein interactome data obtained by high-throughput technologies, existing algorithms, especially those with additionally consideration of biological information of proteins, either have low efficiency in performing their tasks or suffer from limited effectiveness. For addressing this issue, this work proposes to detect protein complexes from a protein interaction network with high efficiency and effectiveness. To do so, the original detection task is first formulated into an optimization problem according to the intuitive properties of protein complexes. After that, the framework of alternating direction method of multipliers is applied to decompose this optimization problem into several subtasks, which can be subsequently solved in a separate and parallel manner. An algorithm for implementing this solution is then developed. Experimental results on five large protein interaction networks demonstrated that compared to state-of-the-art protein complex detection algorithms, our algorithm outperformed them in terms of both effectiveness and efficiency. Moreover, as number of parallel processes increases, one can expect an even higher computational efficiency for the proposed algorithm with no compromise on effectiveness.

Identifying protein complexes based on an edge weight algorithm and core-attachment structure

Background

Protein complex identification from protein-protein interaction (PPI) networks is crucial for understanding cellular organization principles and functional mechanisms. In recent decades, numerous computational methods have been proposed to identify protein complexes. However, most of the current state-of-the-art studies still have some challenges to resolve, including their high false-positives rates, incapability of identifying overlapping complexes, lack of consideration for the inherent organization within protein complexes, and absence of some biological attachment proteins.

Results

In this paper, to overcome these limitations, we present a protein complex identification method based on an edge weight method and core-attachment structure (EWCA) which consists of a complex core and some sparse attachment proteins. First, we propose a new weighting method to assess the reliability of interactions. Second, we identify protein complex cores by using the structural similarity between a seed and its direct neighbors. Third, we introduce a new method to detect attachment proteins that is able to distinguish and identify peripheral proteins and overlapping proteins. Finally, we bind attachment proteins to their corresponding complex cores to form protein complexes and discard redundant protein complexes. The experimental results indicate that EWCA outperforms existing state-of-the-art methods in terms of both accuracy and p-value. Furthermore, EWCA could identify many more protein complexes with statistical significance. Additionally, EWCA could have better balance accuracy and efficiency than some state-of-the-art methods with high accuracy.

Conclusions

In summary, EWCA has better performance for protein complex identification by a comprehensive comparison with twelve algorithms in terms of different evaluation metrics. The datasets and software are freely available for academic research at https://github.com/RongquanWang/EWCA.

The S. pombe adaptor protein Bbc1 regulates localization of Wsp1 and Vrp1 during endocytic actin patch assembly

Arp2/3 complex-nucleated branched actin networks provide the key force necessary for endocytosis. The Arp2/3 complex is activated by nucleation-promoting factors including the Schizosaccharomyces pombe Wiskott-Aldrich syndrome protein (Wsp1) and myosin-1 (Myo1). There are >40 known yeast endocytic proteins with distinct spatial and temporal localizations and functions; however, it is still unclear how these proteins work together to drive endocytosis. Here, we used quantitative live-cell imaging to determine the function of the uncharacterized S. pombe protein Bbc1. We discovered that Myo1 interacts with and recruits Bbc1 to sites of endocytosis. Bbc1 competes with the verprolin Vrp1 for localization to patches and association with Myo1, thus releasing Vrp1 and its binding partner Wsp1 from Myo1. Normally Myo1 remains at the base of the endocytic invagination and Vrp1-Wsp1 internalizes with the endocytic vesicle. However, in the absence of Bbc1, a portion of Vrp1-Wsp1 remains with Myo1 at the base of the invagination, and endocytic structures internalize twice as far. We propose that Bbc1 disrupts a transient interaction of Myo1 with Vrp1 and Wsp1 and thereby limits Arp2/3 complex-mediated nucleation of actin branches at the plasma membrane.This article has an associated First Person interview with the first author of the paper.

A novel matrix of sequence descriptors for predicting protein-protein interactions from amino acid sequences

Protein-protein interactions (PPIs) play an important role in the life activities of organisms. With the availability of large amounts of protein sequence data, PPIs prediction methods have attracted increasing attention. A variety of protein sequence coding methods have emerged, but the training of these methods is particularly time consuming. To solve this issue, we have proposed a novel matrix sequence coding method. Based on deep neural network (DNN) and a novel matrix protein sequence descriptor, we constructed a protein interaction prediction model for predicting PPIs. When performed on human PPIs data, the method achieved an accuracy of 94.34%, a recall of 98.28%, an area under the curve (AUC) of 97.79% and a loss of 23.25%. A non-redundant dataset was used to evaluate this prediction model, and the prediction accuracy is 88.29%. These results indicate that the matrix of sequence (MOS) descriptor can enhance the predictive power of PPIs and reduce training time, which can be a useful complement for future proteomics research. The experimental code and experimental results can be found at https://github.com/smalltalkman/hppi-tensorflow.

Phage Display Technology and its Applications in Cancer Immunotherapy

Background:

Phage display is an effective technology for generation and selection targeting protein for a variety of purpose, which is based on a direct linkage between the displayed protein and the DNA sequence encoding it and utilized in selecting peptides, improving peptides affinity and indicating protein-protein interactions. Phage particles displaying peptide have the potential to apply in the identification of cell-specific targeting molecules, identification of cancer cell surface biomarkers, identification anti-cancer peptide, and the design of peptide-based anticancer therapy.

Method/Results:

Literature searches, reviews and assessments about Phage were performed in this review from PubMed and Medline databases.

Conclusion:

The phage display technology is an inexpensive method for expressing exogenous peptides, generating unique peptides that bind any given target and investigating protein-protein interactions. Due to the powerful ability to insert exogenous gene and display exogenous peptides on the surface, phages may represent a powerful peptide delivery system that can be utilized to develop rapid, efficient, safe and inexpensive cancer therapy methods.

A novel conjoint triad auto covariance (CTAC) coding method for predicting protein-protein interaction based on amino acid sequence

Protein-protein interactions (PPIs) play a crucial role in the life-sustaining activities of organisms. Although various methods for the prediction of PPIs have been developed in the past decades, their robustness and prediction accuracy need to be improved. Therefore, it is necessary to develop an effective and accurate method to predict PPIs. Aiming at making sure that PPIs can be predicted effectively, in this paper, we propose a new sequence-based approach based on deep neural network (DNN) and conjoint triad auto covariance (CTAC) to improve the effectiveness of predicting PPIs. The coding method of CTAC combines the advantages of conjoint triad and auto covariance. Therefore, the CTAC can obtain more PPIs information from the amino acid sequence. The model of DNNCTAC achieved an accuracy of 98.37%, recall of 99.41%, area under the curve (AUC) of 99.24% and loss of 22.7%, respectively, on human dataset. These results indicate that DNNCTAC can enhance the predictive power of PPIs and can significantly enhance the accuracy of the prediction. And, it has proved to be a useful complement to future proteomics research. The source codes and all datasets are available at https://github.com/smalltalkman/hppi-tensorflow.

DNN-PPI: A LARGE-SCALE PREDICTION OF PROTEIN–PROTEIN INTERACTIONS BASED ON DEEP NEURAL NETWORKS

Protein–protein interaction (PPI) is very important for various biological processes and has given rise to a series of prediction-computing methods. In spite of different computing methods in relation to PPI prediction, PPI network projects fail to perform on a large scale. Aiming at ensuring that PPI can be predicted effectively, we used a deep neural network (DNN) for the study of PPI prediction that is based on an amino acid sequence. We present a novel DNN-PPI model with an auto covariance (AC) descriptor and a conjoint triad (CT) descriptor for the prediction of PPI that is based only on the protein sequence information. The 10-fold cross-validation indicated that the best DNN-PPI model with CT achieved 97.65% accuracy, 98.96% recall and a 98.51% area under the curve (AUC). The model exhibits a prediction accuracy of 94.20–97.10% for other external datasets. All of these suggest the high validity of the proposed algorithm in relation to various species.

A Novel Core-Attachment-Based Method to Identify Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks

Cellular functions are always performed by protein complexes. At present, many approaches have been proposed to identify protein complexes from protein-protein interaction (PPI) networks. Some approaches focus on detecting local dense subgraphs in PPI networks which are regarded as protein-complex cores, then identify protein complexes by including local neighbors. However, from gene expression profiles at different time points or tissues it is known that proteins are dynamic. Therefore, identifying dynamic protein complexes should become very important and meaningful. In this study, a novel core-attachment-based method named CO-DPC to detect dynamic protein complexes is presented. First, CO-DPC selects active proteins according to gene expression profiles and the 3-sigma principle, and constructs dynamic PPI networks based on the co-expression principle and PPI networks. Second, CO-DPC detects local dense subgraphs as the cores of protein complexes and then attach close neighbors of these cores to form protein complexes. In order to evaluate the method, the method and the existing algorithms are applied to yeast PPI networks. The experimental results show that CO-DPC performs much better than the existing methods. In addition, the identified dynamic protein complexes can match very well and thus become more meaningful for future biological study.

Screening and Identification of PLK1-Polo Box Binding Peptides by High-Throughput Sequencing of Phage-Selected Libraries

BACKGROUND

Human proteome contains a plethora of short linear peptide motifs that is crucial for signaling and other cellular processes. These motifs are difficult to identify due to lack of systematic approach for their detection.

OBJECTIVES

Here we demonstrate the use of peptide phage display in combination with high throughput next generation sequencing to identify enriched peptide sequences through biopanning process against polo box domain (PBD) of mitotic polo like kinase 1 (Plk1).

METHODS

Purified recombinant Plk1 and two unrelated controls namely B-lymphocyte antigen (CD20) and fluorescent protein (mCherry) were subjected to peptide phage display analysis. Bacterially-propagated phage DNA was amplified by PCR using triplet bar coded primers to tag the pool from each amplicon.

RESULTS

Proteomic peptide phage display along with next generation sequencing and Bioinformatics analysis demonstrated several known and putative novel interactions which were potentially related to Plk1-PBD. With our strategy, we were able to identify and characterize several Plk1-PBD binding peptides, as well as define more precisely, consensus sequences.

CONCLUSION

We believe that this information could provide valuable tools for exploring novel interaction involved in Plk1 signaling as well as to choose peptides for Plk1 specific drug development.