Yeast two-hybrid methods and their applications in drug discovery

The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges

Due to the transformation of artificial intelligence (AI) tools and technologies, AI-driven drug discovery has come to the forefront. It reduces the time and expenditure. Due to these advantages, pharmaceutical industries are concentrating on AI-driven drug discovery. Several drug molecules have been discovered using AI-based techniques and tools, and several newly AI-discovered drug molecules have already entered clinical trials. In this review, we first present the data and their resources in the pharmaceutical sector for AI-driven drug discovery and illustrated some significant algorithms or techniques used for AI and ML which are used in this field. We gave an overview of the deep neural network (NN) models and compared them with artificial NNs. Then, we illustrate the recent advancement of the landscape of drug discovery using AI to deep learning, such as the identification of drug targets, prediction of their structure, estimation of drug-target interaction, estimation of drug-target binding affinity, design of de novo drug, prediction of drug toxicity, estimation of absorption, distribution, metabolism, excretion, toxicity; and estimation of drug-drug interaction. Moreover, we highlighted the success stories of AI-driven drug discovery and discussed several collaboration and the challenges in this area. The discussions in the article will enrich the pharmaceutical industry.

Artificial intelligence for small molecule anticancer drug discovery

INTRODUCTION

The transition from conventional cytotoxic chemotherapy to targeted cancer therapy with small-molecule anticancer drugs has enhanced treatment outcomes. This approach, which now dominates cancer treatment, has its advantages. Despite the regulatory approval of several targeted molecules for clinical use, challenges such as low response rates and drug resistance still persist. Conventional drug discovery methods are costly and time-consuming, necessitating more efficient approaches. The rise of artificial intelligence (AI) and access to large-scale datasets have revolutionized the field of small-molecule cancer drug discovery. Machine learning (ML), particularly deep learning (DL) techniques, enables the rapid identification and development of novel anticancer agents by analyzing vast amounts of genomic, proteomic, and imaging data to uncover hidden patterns and relationships.

AREA COVERED

In this review, the authors explore the important landmarks in the history of AI-driven drug discovery. They also highlight various applications in small-molecule cancer drug discovery, outline the challenges faced, and provide insights for future research.

EXPERT OPINION

The advent of big data has allowed AI to penetrate and enable innovations in almost every stage of medicine discovery, transforming the landscape of oncology research through the development of state-of-the-art algorithms and models. Despite challenges in data quality, model interpretability, and technical limitations, advancements promise breakthroughs in personalized and precision oncology, revolutionizing future cancer management.

High throughput methods to study protein-protein interactions during host-pathogen interactions

The ability of a pathogen to survive and cause an infection is often determined by specific interactions between the host and pathogen proteins. Such interactions can be both intra- and extracellular and may define the outcome of an infection. There are a range of innovative biochemical, biophysical and bioinformatic techniques currently available to identify protein-protein interactions (PPI) between the host and the pathogen. However, the complexity and the diversity of host-pathogen PPIs has led to the development of several high throughput (HT) techniques that enable the study of multiple interactions at once and/or screen multiple samples at the same time, in an unbiased manner. We review here the major HT laboratory-based technologies employed for host-bacterial interaction studies.

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and expedient options to conventional approaches. The paper opens with an overview of well-known information sources for drug development. The discussion then moves on to molecular representation techniques that make it possible to convert data into representations that computers can understand. The paper also gives a general overview of the algorithms used in the creation of drug discovery models based on AI. In particular, the paper looks at how AI algorithms might be used to forecast drug toxicity, drug bioactivity, and drug physicochemical properties. De novo drug design, binding affinity prediction, and other AI-based models for drug-target interaction were covered in deeper detail. Modern applications of AI in nanomedicine design and pharmacological synergism/antagonism prediction were also covered. The potential advantages of AI in drug development are highlighted as the evaluation comes to a close. It underlines how AI may greatly speed up and improve the efficiency of drug discovery, resulting in the creation of new and better medicines. To fully realize the promise of AI in drug discovery, the review acknowledges the difficulties that come with its uses in this field and advocates for more study and development.

TurboID-mediated proximity labeling for screening interacting proteins of FIP37 in Arabidopsis

Proximity labeling was recently developed to detect protein-protein interactions and members of subcellular multiprotein structures in living cells. Proximity labeling is conducted by fusing an engineered enzyme with catalytic activity, such as biotin ligase, to a protein of interest (bait protein) to biotinylate adjacent proteins. The biotinylated protein can be purified by streptavidin beads, and identified by mass spectrometry (MS). TurboID is an engineered biotin ligase with high catalytic efficiency, which is used for proximity labeling. Although TurboID-based proximity labeling technology has been successfully established in mammals, its application in plant systems is limited. Here, we report the usage of TurboID for proximity labeling of FIP37, a core member of m6A methyltransferase complex, to identify FIP37 interacting proteins in Arabidopsis thaliana. By analyzing the MS data, we found 214 proteins biotinylated by GFP-TurboID-FIP37 fusion, including five components of m6A methyltransferase complex that have been previously confirmed. Therefore, the identified proteins may include potential proteins directly involved in the m6A pathway or functionally related to m6A-coupled mRNA processing due to spatial proximity. Moreover, we demonstrated the feasibility of proximity labeling technology in plant epitranscriptomics study, thereby expanding the application of this technology to more subjects of plant research.

New cyclopentaquinoline and 3,5-dichlorobenzoic acid hybrids with neuroprotection against oxidative stress for the treatment of Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative brain disease. Thus, drugs including donepezil, rivastigmine, and galantamine are not entirely effective in the treatment of this multifactorial disease. The present study evaluates eight derivatives (3a-3h) as candidates with stronger anti-AD potential but with less side effects. Reactive oxygen species (ROS) assays were used to assess oxidative stress which involve in the neurodegeneration. The neuroprotective properties of 3e against oxidative stress were done in three experiments using MTT test. The anti-AD potential was determined based on their anticholinesterase inhibition ability, determined using Ellman's method, Aβ aggregation potential according to thioflavin (Th) fluorescence assay, and their antioxidative and anti-inflammatory activities. Compound 3e exhibited moderate cholinesterase inhibition activity (AChE, IC₅₀ = 0.131 µM; BuChE, IC₅₀ = 0.116 µM; SI = 1.13), significant inhibition of Aβ(1-42) aggregation (55.7%, at 5 µM) and acceptable neuroprotective activity. Extensive analysis of in vitro and in vivo assays indicates that new cyclopentaquinoline derivatives offer promise as candidates for new anti-AD drugs.

Improved in situ characterization of protein complex dynamics at scale with thermal proximity co-aggregation

Cellular activities are carried out vastly by protein complexes but large repertoire of protein complexes remains functionally uncharacterized which necessitate new strategies to delineate their roles in various cellular processes and diseases. Thermal proximity co-aggregation (TPCA) is readily deployable to characterize protein complex dynamics in situ and at scale. We develop a version termed Slim-TPCA that uses fewer temperatures increasing throughputs by over 3X, with new scoring metrics and statistical evaluation that result in minimal compromise in coverage and detect more relevant complexes. Less samples are needed, batch effects are minimized while statistical evaluation cost is reduced by two orders of magnitude. We applied Slim-TPCA to profile K562 cells under different duration of glucose deprivation. More protein complexes are found dissociated, in accordance with the expected downregulation of most cellular activities, that include 55S ribosome and respiratory complexes in mitochondria revealing the utility of TPCA to study protein complexes in organelles. Protein complexes in protein transport and degradation are found increasingly assembled unveiling their involvement in metabolic reprogramming during glucose deprivation. In summary, Slim-TPCA is an efficient strategy for characterization of protein complexes at scale across cellular conditions, and is available as Python package at https://pypi.org/project/Slim-TPCA/ .

Screening of Tnfaip1-Interacting Proteins in Zebrafish Embryonic cDNA Libraries Using a Yeast Two-Hybrid System

TNFAIP1 regulates cellular biological functions, including DNA replication, DNA repair, and cell cycle, by binding to target proteins. Identification of Tnfaip1-interacting proteins contributes to the understanding of the molecular regulatory mechanisms of their biological functions. In this study, 48 hpf, 72 hpf, and 96 hpf wild-type zebrafish embryo mRNAs were used to construct yeast cDNA library. The library titer was 1.12 × 107 CFU/mL, the recombination rate was 100%, and the average length of the inserted fragments was greater than 1000 bp. A total of 43 potential interacting proteins of Tnfaip1 were identified using zebrafish Tnfaip1 as a bait protein. Utilizing GO functional annotation and KEGG signaling pathway analysis, we found that these interacting proteins are mainly involved in translation, protein catabolic process, ribosome assembly, cytoskeleton formation, amino acid metabolism, and PPAR signaling pathway. Further yeast spotting analyses identified four interacting proteins of Tnfaip1, namely, Ubxn7, Tubb4b, Rpl10, and Ybx1. The Tnfaip1-interacting proteins, screened from zebrafish embryo cDNA in this study, increased our understanding of the network of Tnfaip1-interacting proteins during the earliest embryo development and provided a molecular foundation for the future exploration of tnfaip1's biological functions.

A Transformer-Based Ensemble Framework for the Prediction of Protein-Protein Interaction Sites

The identification of protein-protein interaction (PPI) sites is essential in the research of protein function and the discovery of new drugs. So far, a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites. However, existing methods suffer from the low predictive accuracy or the limited scope of application. Specifically, some methods learned only global or local sequential features, leading to low predictive accuracy, while others achieved improved performance by extracting residue interactions from structures but were limited in their application scope for the serious dependence on precise structure information. There is an urgent need to develop a method that integrates comprehensive information to realize proteome-wide accurate profiling of PPI sites. Herein, a novel ensemble framework for PPI sites prediction, EnsemPPIS, was therefore proposed based on transformer and gated convolutional networks. EnsemPPIS can effectively capture not only global and local patterns but also residue interactions. Specifically, EnsemPPIS was unique in (a) extracting residue interactions from protein sequences with transformer and (b) further integrating global and local sequential features with the ensemble learning strategy. Compared with various existing methods, EnsemPPIS exhibited either superior performance or broader applicability on multiple PPI sites prediction tasks. Moreover, pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions within the local structure of PPI sites using only sequence information. The web server of EnsemPPIS is freely available at http://idrblab.org/ensemppis.

Targeting Peptides: The New Generation of Targeted Drug Delivery Systems

Peptides can act as targeting molecules, analogously to oligonucleotide aptamers and antibodies. They are particularly efficient in terms of production and stability in physiological environments; in recent years, they have been increasingly studied as targeting agents for several diseases, from tumors to central nervous system disorders, also thanks to the ability of some of them to cross the blood-brain barrier. In this review, we will describe the techniques employed for their experimental and in silico design, as well as their possible applications. We will also discuss advancements in their formulation and chemical modifications that make them even more stable and effective. Finally, we will discuss how their use could effectively help to overcome various physiological problems and improve existing treatments.

Artificial intelligence for drug discovery: Resources, methods, and applications

Conventional wet laboratory testing, validations, and synthetic procedures are costly and time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques have revolutionized their applications to drug discovery. Combined with accessible data resources, AI techniques are changing the landscape of drug discovery. In the past decades, a series of AI-based models have been developed for various steps of drug discovery. These models have been used as complements of conventional experiments and have accelerated the drug discovery process. In this review, we first introduced the widely used data resources in drug discovery, such as ChEMBL and DrugBank, followed by the molecular representation schemes that convert data into computer-readable formats. Meanwhile, we summarized the algorithms used to develop AI-based models for drug discovery. Subsequently, we discussed the applications of AI techniques in pharmaceutical analysis including predicting drug toxicity, drug bioactivity, and drug physicochemical property. Furthermore, we introduced the AI-based models for de novo drug design, drug-target structure prediction, drug-target interaction, and binding affinity prediction. Moreover, we also highlighted the advanced applications of AI in drug synergism/antagonism prediction and nanomedicine design. Finally, we discussed the challenges and future perspectives on the applications of AI to drug discovery.

Potential application of TurboID-based proximity labeling in studying the protein interaction network in plant response to abiotic stress

Abiotic stresses are major environmental conditions that reduce plant growth, productivity and quality. Protein-protein interaction (PPI) approaches can be used to screen stress-responsive proteins and reveal the mechanisms of protein response to various abiotic stresses. Biotin-based proximity labeling (PL) is a recently developed technique to label proximal proteins of a target protein. TurboID, a biotin ligase produced by directed evolution, has the advantages of non-toxicity, time-saving and high catalytic efficiency compared to other classic protein-labeling enzymes. TurboID-based PL has been successfully applied in animal, microorganism and plant systems, particularly to screen transient or weak protein interactions, and detect spatially or temporally restricted local proteomes in living cells. This review concludes classic PPI approaches in plant response to abiotic stresses and their limitations for identifying complex network of regulatory proteins of plant abiotic stresses, and introduces the working mechanism of TurboID-based PL, as well as its feasibility and advantages in plant abiotic stress research. We hope the information summarized in this article can serve as technical references for further understanding the regulation of plant adaptation to abiotic stress at the protein level.

Methods to characterize and discover molecular degraders in cells

Cells use many post-translational modifications (PTMs) to tailor proteins and transduce cellular signals. Recent years have witnessed the rapid growth of small molecule and enzymatic strategies to purposely manipulate one particular PTM, ubiquitination, on desired target proteins in cells. These approaches typically act by induced proximity between an E3 ligase and a target protein resulting in ubiquitination and degradation of the substrate in cells. In this review, we cover recent approaches to study molecular degraders and discover their induced substrates in vitro and in live cells. Methods that have been adapted and applied to the development of molecular degraders are described, including global proteomics, affinity-purification, chemical proteomics and enzymatic strategies. Extension of these strategies to edit additional PTMs in cells is also discussed. This review is intended to assist researchers who are interested in editing PTMs with new modalities to select suitable method(s) and guide their studies.

Utilizing graph machine learning within drug discovery and development

Graph machine learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst other data types. Herein, we present a multidisciplinary academic-industrial review of the topic within the context of drug discovery and development. After introducing key terms and modelling approaches, we move chronologically through the drug development pipeline to identify and summarize work incorporating: target identification, design of small molecules and biologics, and drug repurposing. Whilst the field is still emerging, key milestones including repurposed drugs entering in vivo studies, suggest GML will become a modelling framework of choice within biomedical machine learning.

Interfaces with Structure Dynamics of the Workhorses from Cells Revealed through Cross-Linking Mass Spectrometry (CLMS)

The fundamentals of how protein-protein/RNA/DNA interactions influence the structures and functions of the workhorses from the cells have been well documented in the 20th century. A diverse set of methods exist to determine such interactions between different components, particularly, the mass spectrometry (MS) methods, with its advanced instrumentation, has become a significant approach to analyze a diverse range of biomolecules, as well as bring insights to their biomolecular processes. This review highlights the principal role of chemistry in MS-based structural proteomics approaches, with a particular focus on the chemical cross-linking of protein-protein/DNA/RNA complexes. In addition, we discuss different methods to prepare the cross-linked samples for MS analysis and tools to identify cross-linked peptides. Cross-linking mass spectrometry (CLMS) holds promise to identify interaction sites in larger and more complex biological systems. The typical CLMS workflow allows for the measurement of the proximity in three-dimensional space of amino acids, identifying proteins in direct contact with DNA or RNA, and it provides information on the folds of proteins as well as their topology in the complexes. Principal CLMS applications, its notable successes, as well as common pipelines that bridge proteomics, molecular biology, structural systems biology, and interactomics are outlined.

Functional metabolomics innovates therapeutic discovery of traditional Chinese medicine derived functional compounds

Traditional Chinese medicines (TCMs) produce chemically diverse functional compounds that are importantly chemical resource for facilitating new drug discovery and development against a diversity of diseases. However, modern exploration of TCM derived functional compounds is significantly hindered by the inefficient elucidation of pharmacological functions over past decades, because conventional research methods are incapable of efficiently elucidating therapeutic potential of TCM conferred by multiple functional compounds. Functional metabolomics has the priority-capacity to characterize systems therapeutic actions of TCM by precisely capturing molecular interactions between disease response metabolite biomarkers (DRMB) and functional compounds (secondary metabolites), which underline pharmacological efficiency and associated therapeutic mechanisms. In this critical review, we innovatively summarize systems therapeutic feature of TCM derived functional compounds from a functional-metabolism perspective, then systems metabolic targets (SMT) identified by functional metabolomics method are strategically proposed to better understanding of therapeutic discovery of TCM derived functional compounds. In addition, we propose the perspective strategy as Spatial Temporal Operative Real Metabolomics (STORM) to considerably improve analytical capacity of functional metabolomics method by selectively incorporating the cutting edge technologies of mass spectrometry imaging, isotope-metabolic fluxomics, synthetic and biosynthetic chemistry, which could considerably enhance the precision and resolution of elucidating pharmacological efficiency and associated therapeutic mechanisms of TCM derived functional compounds. Collectively, such critical review is expected to provide novel perspective-strategy that could significantly improve modern exploration and exploitation of TCM derived functional compounds that further promote new drug discovery and development against the complex diseases.

Insulin's Discovery: New Insights on Its Hundredth Birthday: From Insulin Action and Clearance to Sweet Networks

In 2021, the 100th anniversary of the isolation of insulin and the rescue of a child with type 1 diabetes from death will be marked. In this review, we highlight advances since the ingenious work of the four discoverers, Frederick Grant Banting, John James Rickard Macleod, James Bertram Collip and Charles Herbert Best. Macleoad closed his Nobel Lecture speech by raising the question of the mechanism of insulin action in the body. This challenge attracted many investigators, and the question remained unanswered until the third part of the 20th century. We summarize what has been learned, from the discovery of cell surface receptors, insulin action, and clearance, to network and precision medicine.

Proteomics of host-bacterial interactions: new insights from dual perspectives

Mass-spectrometry (MS)-based proteomics is a powerful and robust platform for studying the interactions between biological systems during health and disease. Bacterial infections represent a significant threat to global health and drive the pursuit of novel therapeutic strategies to combat emerging and resistant pathogens. During infection, the interplay between a host and pathogen determines the ability of the microbe to survive in a hostile environment and promotes an immune response by the host as a protective measure. It is the protein-level changes from either biological system that define the outcome of infection, and MS-based proteomics provides a rapid and effective platform to identify such changes. In particular, proteomics detects alterations in protein abundance, quantifies protein secretion and (or) release, measures an array of post-translational modifications that influence signaling cascades, and profiles protein-protein interactions through protein complex and (or) network formation. Such information provides new insight into the role of known and novel bacterial effectors, as well as the outcome of host cell activation. In this Review, we highlight the diverse applications of MS-based proteomics in profiling the relationship between bacterial pathogens and the host. Our work identifies a plethora of strategies for exploring mechanisms of infection from dual perspectives (i.e., host and pathogen), and we suggest opportunities to extrapolate the current knowledgebase to other biological systems for applications in therapeutic discovery.

Cyclin-dependent kinases and rare developmental disorders

Extensive studies in the past 30 years have established that cyclin-dependent kinases (CDKs) exert many diverse, important functions in a number of molecular and cellular processes that are at play during development. Not surprisingly, mutations affecting CDKs or their activating cyclin subunits have been involved in a variety of rare human developmental disorders. These recent findings are reviewed herein, giving a particular attention to the discovered mutations and their demonstrated or hypothesized functional consequences, which can account for pathological human phenotypes. The review highlights novel, important CDK or cyclin functions that were unveiled by their association with human disorders, and it discusses the shortcomings of mouse models to reveal some of these functions. It explains how human genetics can be used in combination with proteome-scale interaction databases to loom regulatory networks around CDKs and cyclins. Finally, it advocates the use of these networks to profile pathogenic CDK or cyclin variants, in order to gain knowledge on protein function and on pathogenic mechanisms.

[Budding yeast, a model and a tool… also for biomedical research]

Yeast has been used for thousands of years as a leavening agent and for alcoholic fermentation, but it is only in 1857 that Louis Pasteur described the microorganism at the basis of these two tremendously important economic activities. From there, yeast strains could be selected and modified on a rational basis to optimize these uses, thereby also allowing the development of yeast as a popular eukaryotic model system. This model led to a cornucopia of seminal discoveries in cell biology. For about two decades yeast has also been used as a model and a tool for therapeutic research, from the production of therapeutics and the development of diagnostic tools to the identification of new therapeutic targets, drug candidates and chemical probes. These diverse chemobiological applications of yeast are presented and discussed in the present review article.

New Tetrahydroacridine Hybrids with Dichlorobenzoic Acid Moiety Demonstrating Multifunctional Potential for the Treatment of Alzheimer's Disease

A series of new tetrahydroacridine and 3,5-dichlorobenzoic acid hybrids with different spacers were designed, synthesized, and evaluated for their ability to inhibit both cholinesterase enzymes. Compounds 3a, 3b, 3f, and 3g exhibited selective butyrylcholinesterase (EqBuChE) inhibition with IC₅₀ values ranging from 24 to 607 nM. Among them, compound 3b was the most active (IC₅₀ = 24 nM). Additionally, 3c (IC₅₀ for EeAChE = 25 nM and IC₅₀ for EqBuChE = 123 nM) displayed dual cholinesterase inhibitory activity and was the most active compound against acetylcholinesterase (AChE). Active compound 3c was also tested for the ability to inhibit Aβ aggregation. Theoretical physicochemical properties of the compounds were calculated using ACD Labs Percepta and Chemaxon. A Lineweaver–Burk plot and docking study showed that 3c targeted both the catalytic active site (CAS) and the peripheral anionic site (PAS) of AChE. Moreover, 3c appears to possess neuroprotective activity and could be considered a free-radical scavenger. In addition, 3c did not cause DNA damage and was found to be less toxic than tacrine after oral administration; it also demonstrated little inhibitory activity towards hyaluronidase (HYAL), which may indicate that it possesses anti-inflammatory properties. The screening for new in vivo interactions between 3c and known receptors was realized by yeast three-hybrid technology (Y3H).

Screening and production of an affibody inhibiting the interaction of the PD-1/PD-L1 immune checkpoint

An affibody is a 58 amino acids peptide derived from the Z domain of staphylococcal protein A and generally applied in areas such as imaging diagnosis, clinical therapeutics and biotechnology research. To screen for an affibody targeting the immune checkpoint PD-L1, a combinatorial affibody library was generated in yeast using degenerate overlap PCR primers and In-fusion technology. Z-j1 and Z-j2 affibodies targeting the Ig-like V domain of PD-L1 were screened and identified from this combinatorial library using the yeast two hybrid system. The Z-j1 and Z-j2 recombinant affibody proteins were over produced in E.coli and purified. ELISA and GST pull-down assays showed that recombinant Z-j1 and Z-j2 affibody proteins bound with high affinity to PD-L1 and inhibited the interaction of PD-1/PD-L1. Thus, novel affibodies targeting the immune checkpoint PD-1/PD-L1 were identified and produced in this study and have the potential to be used in cancer immunotherapy.

Protein Complex Identification and quantitative complexome by CN-PAGE

The majority of cellular processes are carried out by protein complexes. Various size fractionation methods have previously been combined with mass spectrometry to identify protein complexes. However, most of these approaches lack the quantitative information which is required to understand how changes of protein complex abundance and composition affect metabolic fluxes. In this paper we present a proof of concept approach to quantitatively study the complexome in the model plant Arabidopsis thaliana at the end of the day (ED) and the end of the night (EN). We show that size-fractionation of native protein complexes by Clear-Native-PAGE (CN-PAGE), coupled with mass spectrometry can be used to establish abundance profiles along the molecular weight gradient. Furthermore, by deconvoluting complex protein abundance profiles, we were able to drastically improve the clustering of protein profiles. To identify putative interaction partners, and ultimately protein complexes, our approach calculates the Euclidian distance between protein profile pairs. Acceptable threshold values are based on a cut-off that is optimized by a receiver-operator characteristic (ROC) curve analysis. Our approach shows low technical variation and can easily be adapted to study in the complexome in any biological system.

Protein interactions study through proximity-labeling

Introduction: The proteome is a dynamic system in which protein-protein interactions play a crucial part in shaping the cell phenotype. However, given the current limitations of available technologies to describe the dynamic nature of these interactions, the identification of protein-protein interactions has long been a major challenge in proteomics. In recent years, the development of BioID and APEX, two proximity-tagging technologies, have opened-up new perspectives and have already started to change our conception of protein-protein interactions, and more generally, of the proteome. With a broad range of application encompassing health, these new technologies are currently setting milestones crucial to understand fine cellular mechanisms. Area covered: In this article, we describe both the recent and the more conventional available tools to study protein-protein interactions, compare the advantages and the limitations of these techniques, and discuss the recent advancements led by the proximity tagging techniques to refine our conception of the proteome. Expert opinion: The recent development of proximity labeling techniques emphasizes the growing importance of such technologies to decipher cellular mechanism. Although several challenges still need to be addressed, many fields can benefit from these tools and notably the detection of new therapeutic targets for patient care.

Automated feature engineering improves prediction of protein-protein interactions

Over the last decade, various machine learning (ML) and statistical approaches for protein-protein interaction (PPI) predictions have been developed to help annotating functional interactions among proteins, essential for our system-level understanding of life. Efficient ML approaches require informative and non-redundant features. In this paper, we introduce novel types of expert-crafted sequence, evolutionary and graph features and apply automatic feature engineering to further expand feature space to improve predictive modeling. The two-step automatic feature-engineering process encompasses the hybrid method for feature generation and unsupervised feature selection, followed by supervised feature selection through a genetic algorithm (GA). The optimization of both steps allows the feature-engineering procedure to operate on a large transformed feature space with no considerable computational cost and to efficiently provide newly engineered features. Based on GA and correlation filtering, we developed a stacking algorithm GA-STACK for automatic ensembling of different ML algorithms to improve prediction performance. We introduced a unified method, HP-GAS, for the prediction of human PPIs, which incorporates GA-STACK and rests on both expert-crafted and 40% of newly engineered features. The extensive cross validation and comparison with the state-of-the-art methods showed that HP-GAS represents currently the most efficient method for proteome-wide forecasting of protein interactions, with prediction efficacy of 0.93 AUC and 0.85 accuracy. We implemented the HP-GAS method as a free standalone application which is a time-efficient and easy-to-use tool. HP-GAS software with supplementary data can be downloaded from: http://www.vinca.rs/180/tools/HP-GAS.php .

2HybridTools, a handy software to facilitate clone identification and mutation mapping from yeast two-hybrid screening

Yeast Two-Hybrid (Y2H) and reverse Two-Hybrid (RY2H) are powerful protein–protein interaction screening methods that rely on the interaction of bait and prey proteins fused to DNA binding (DB) and activation domains (AD), respectively. Y2H allows identification of protein interaction partners using screening libraries, while RY2H is used to determine residues critical to a given protein–protein interaction by exploiting site-directed mutagenesis. Currently, both these techniques still rely on sequencing of positive clones using conventional Sanger sequencing. For Y2H, a screen can yield several positives; the identification of such clones is further complicated by the fact that sequencing products usually contain vector sequence. For RY2H, obtaining a complete sequence is required to identify the full range of residues involved in protein–protein interactions. However, with Sanger sequencing limited to 500–800 nucleotides, sequencing is usually carried from both ends for clones greater than this length. Analysis of such RY2H data thus requires assembly of sequencing products combined with trimming of vector sequences and of low-quality bases at the beginning and ends of sequencing products. Further, RY2H analysis requires collation of mutations that abrogate a DB/AD interaction. Here, we present 2HybridTools, a Java program with a user-friendly interface that allows addressing all these issues inherent to both Y2H and RY2H. Specifically, for Y2H, 2HybridTools enables automated identification of positive clones, while for RY2H, 2HybridTools provides detailed mutation reports as a basis for further investigation of given protein–protein interactions.

FKBP12: A partner of Snx10 required for vesicular trafficking in osteoclasts

Osteoclasts employ highly specialized intracellular trafficking controls for bone resorption and organelle homeostasis. The sorting nexin Snx10 is a (Phosphatidylinositol 3-phosphate) PI3P-binding protein, which localizes to osteoclast early endosomes. Osteoclasts from humans and mice lacking functional Snx10 are severely dysfunctional. They show marked impairments in endocytosis, extracellular acidification, ruffled border formation, and bone resorption, suggesting that Snx10 regulates membrane trafficking. To better understand how SNx10 regulates vesicular formation and trafficking in osteoclasts, we set out on a search for Snx10 partners. We performed a yeast two-hybrid screening and identified FKBP12. FKBP12 is expressed in receptor activator of nuclear factor kB ligand-stimulated RAW264.7 monocytes, coimmunoprecipitates with Snx10, and colocalizes with Snx10 in osteoclasts. We also found that FKBP12, Snx10, and early endosome antigen 1 (EEA1) are present in the same subcellular fractions obtained by centrifugation in sucrose gradients, which confirms localization of FKBP12 to early endosomes. Taken together, these results indicate that Snx10 and FKBP12 are partners and suggest that Snx10 and FKBP12 are involved in the regulation of endosome/lysosome homeostasis via the synthesis. These findings may suggest novel therapeutic approaches to control bone loss by targeting essential steps in osteoclast membrane trafficking.

Approches nouvelles pour l’étude des interactions protéine-protéine

Le protéome est un système dynamique où les interactions protéine-protéine occupent une place essentielle pour modeler ensemble le phénotype cellulaire. L’identification de ces interactions a toutefois longtemps représenté un obstacle important en protéomique tant les techniques disponibles ne permettaient pas de rendre compte de ces dynamiques d’interactions. Le développement récent du BioID et de l’APEX, deux technologies de marquage de proximité, ouvre aujourd’hui de nouvelles perspectives. Dans cette revue, nous décrivons les outils disponibles pour étudier les interactions protéine-protéine et discutons des progrès récents apportés par les marquages de proximité pour compléter notre vision du protéome et ainsi mieux comprendre les mécanismes cellulaires.

Aptamers: an emerging class of bioaffinity ligands in bioactive peptide applications

The food and health applications of bioactive peptides have grown remarkably in the past few decades. Current elucidations have shown that bioactive peptides have unique structural arrangement of amino acids, conferring distinct functionalities, and molecular affinity characteristics. However, whereas interest in the biological potency of bioactive peptides has grown, cost-effective techniques for monitoring the structural changes in these peptides and how these changes affect the biological properties have not grown at the same rate. Due to the high binding affinity of aptamers for other biomolecules, they have a huge potential for use in tracking the structural, conformational, and compositional changes in bioactive peptides. This review provides an overview of bioactive peptides and their essential structure-activity relationship. The review further highlights on the types and methods of synthesis of aptamers before the discussion of the prospects, merits, and challenges in the use of aptamers for bioaffinity interactions with bioactive peptides.

Discovery of New Cyclopentaquinoline Analogues as Multifunctional Agents for the Treatment of Alzheimer's Disease

Here we report the two-step synthesis of 8 new cyclopentaquinoline derivatives as modifications of the tetrahydroacridine structure. Next, the biological assessment of each of them was performed. Based on the obtained results we identified 6-chloro-N-[2-(2,3-dihydro-1H-cyclopenta[b]quinolin-9-ylamino)-hexyl]]-nicotinamide hydrochloride (3e) as the most promising compound with inhibitory potencies against EeAChE and EqBuChE in the low nanomolar level 67 and 153 nM, respectively. Moreover, 3e compound is non-hepatotoxic, able to inhibit amyloid beta aggregation, and shows a mix-type of cholinesterase's inhibition. The mixed type of inhibition of the compound was confirmed by molecular modeling. Then, yeast three-hybrid (Y3H) technology was used to confirm the known ligand-receptor interactions. New derivatives do not show antioxidant activity (confirmed by the use of two different tests). A pKa assay method was developed to identify the basic physicochemical properties of 3e compound. A LogP assay confirmed that 3e compound fulfills Lipinsky's rule of five.

The Methods Employed in Mass Spectrometric Analysis of Posttranslational Modifications (PTMs) and Protein-Protein Interactions (PPIs)

Mass Spectrometry (MS) has revolutionized the way we study biomolecules, especially proteins, their interactions and posttranslational modifications (PTM). As such MS has established itself as the leading tool for the analysis of PTMs mainly because this approach is highly sensitive, amenable to high throughput and is capable of assigning PTMs to specific sites in the amino acid sequence of proteins and peptides. Along with the advances in MS methodology there have been improvements in biochemical, genetic and cell biological approaches to mapping the interactome which are discussed with consideration for both the practical and technical considerations of these techniques. The interactome of a species is generally understood to represent the sum of all potential protein-protein interactions. There are still a number of barriers to the elucidation of the human interactome or any other species as physical contact between protein pairs that occur by selective molecular docking in a particular spatiotemporal biological context are not easily captured and measured.PTMs massively increase the complexity of organismal proteomes and play a role in almost all aspects of cell biology, allowing for fine-tuning of protein structure, function and localization. There are an estimated 300 PTMS with a predicted 5% of the eukaryotic genome coding for enzymes involved in protein modification, however we have not yet been able to reliably map PTM proteomes due to limitations in sample preparation, analytical techniques, data analysis, and the substoichiometric and transient nature of some PTMs. Improvements in proteomic and mass spectrometry methods, as well as sample preparation, have been exploited in a large number of proteome-wide surveys of PTMs in many different organisms. Here we focus on previously published global PTM proteome studies in the Apicomplexan parasites T. gondii and P. falciparum which offer numerous insights into the abundance and function of each of the studied PTM in the Apicomplexa. Integration of these datasets provide a more complete picture of the relative importance of PTM and crosstalk between them and how together PTM globally change the cellular biology of the Apicomplexan protozoa. A multitude of techniques used to investigate PTMs, mostly techniques in MS-based proteomics, are discussed for their ability to uncover relevant biological function.

Using Yeast Hybrid System to Identify Proteins Binding to Small Molecules

Protein-small molecule interaction studies provide useful insights into biological processes taking place within the living cell. A special yeast hybrid system, the yeast three-hybrid method, has been developed and used to explore proteins that bind to small molecules, by which means it may be possible to unravel biological processes and dissect function of biological systems. Here we present a protocol employing this method for identifying such binding proteins.

Cell-Based Assay Design for High-Content Screening of Drug Candidates

To reduce attrition in drug development, it is crucial to consider the development and implementation of translational phenotypic assays as well as decipher diverse molecular mechanisms of action for new molecular entities. High-throughput fluorescence and confocal microscopes with advanced analysis software have simplified the simultaneous identification and quantification of various cellular processes through what is now referred to as highcontent screening (HCS). HCS permits automated identification of modifiers of accessible and biologically relevant targets and can thus be used to detect gene interactions or identify toxic pathways of drug candidates to improve drug discovery and development processes. In this review, we summarize several HCS-compatible, biochemical, and molecular biology-driven assays, including immunohistochemistry, RNAi, reporter gene assay, CRISPR-Cas9 system, and protein-protein interactions to assess a variety of cellular processes, including proliferation, morphological changes, protein expression, localization, post-translational modifications, and protein-protein interactions. These cell-based assay methods can be applied to not only 2D cell culture but also 3D cell culture systems in a high-throughput manner.

Chemical Dimerizers in Three-Hybrid Systems for Small Molecule-Target Protein Profiling

The identification of the molecular targets and mechanisms underpinning the beneficial or detrimental effects of small-molecule leads and drugs constitutes a crucial aspect of current drug discovery. Over the last two decades, three-hybrid (3H) systems have progressively taken an important position in the armamentarium of small molecule-target protein profiling technologies. Yet, a prerequisite for successful 3H analysis is the availability of appropriate chemical inducers of dimerization. Herein, we present a comprehensive and critical overview of the chemical dimerizers specifically applied in both yeast and mammalian three-hybrid systems for small molecule-target protein profiling within the broader scope of target deconvolution and drug discovery. Furthermore, examples and alternative suggestions for typical components of chemical dimerizers for 3H systems are discussed. As illustrated, more tools have become available that increase the sensitivity and efficiency of 3H-based screening platforms. Hence, it is anticipated that the great potential of 3H systems will further materialize in important contributions to drug discovery.

Reconstruction and Application of Protein-Protein Interaction Network

The protein-protein interaction network (PIN) is a useful tool for systematic investigation of the complex biological activities in the cell. With the increasing interests on the proteome-wide interaction networks, PINs have been reconstructed for many species, including virus, bacteria, plants, animals, and humans. With the development of biological techniques, the reconstruction methods of PIN are further improved. PIN has gradually penetrated many fields in biological research. In this work we systematically reviewed the development of PIN in the past fifteen years, with respect to its reconstruction and application of function annotation, subsystem investigation, evolution analysis, hub protein analysis, and regulation mechanism analysis. Due to the significant role of PIN in the in-depth exploration of biological process mechanisms, PIN will be preferred by more and more researchers for the systematic study of the protein systems in various kinds of organisms.

Current Experimental Methods for Characterizing Protein-Protein Interactions

Protein molecules often interact with other partner protein molecules in order to execute their vital functions in living organisms. Characterization of protein-protein interactions thus plays a central role in understanding the molecular mechanism of relevant protein molecules, elucidating the cellular processes and pathways relevant to health or disease for drug discovery, and charting large-scale interaction networks in systems biology research. A whole spectrum of methods, based on biophysical, biochemical, or genetic principles, have been developed to detect the time, space, and functional relevance of protein-protein interactions at various degrees of affinity and specificity. This article presents an overview of these experimental methods, outlining the principles, strengths and limitations, and recent developments of each type of method.

Current Experimental Methods for Characterizing Protein-Protein Interactions

Protein molecules often interact with other partner protein molecules in order to execute their vital functions in living organisms. Characterization of protein-protein interactions thus plays a central role in understanding the molecular mechanism of relevant protein molecules, elucidating the cellular processes and pathways relevant to health or disease for drug discovery, and charting large-scale interaction networks in systems biology research. A whole spectrum of methods, based on biophysical, biochemical, or genetic principles, have been developed to detect the time, space, and functional relevance of protein-protein interactions at various degrees of affinity and specificity. This article presents an overview of these experimental methods, outlining the principles, strengths and limitations, and recent developments of each type of method.

Proteomics-Based Analysis of Protein Complexes in Pluripotent Stem Cells and Cancer Biology

A protein complex consists of two or more proteins that are linked together through protein-protein interactions. The proteins show stable/transient and direct/indirect interactions within the protein complex or between the protein complexes. Protein complexes are involved in regulation of most of the cellular processes and molecular functions. The delineation of protein complexes is important to expand our knowledge on proteins functional roles in physiological and pathological conditions. The genetic yeast-2-hybrid method has been extensively used to characterize protein-protein interactions. Alternatively, a biochemical-based affinity purification coupled with mass spectrometry (AP-MS) approach has been widely used to characterize the protein complexes. In the AP-MS method, a protein complex of a target protein of interest is purified using a specific antibody or an affinity tag (e.g., DYKDDDDK peptide (FLAG) and polyhistidine (His)) and is subsequently analyzed by means of MS. Tandem affinity purification, a two-step purification system, coupled with MS has been widely used mainly to reduce the contaminants. We review here a general principle for AP-MS-based characterization of protein complexes and we explore several protein complexes identified in pluripotent stem cell biology and cancer biology as examples.

An overview of bioinformatics methods for modeling biological pathways in yeast

The advent of high-throughput genomics techniques, along with the completion of genome sequencing projects, identification of protein-protein interactions and reconstruction of genome-scale pathways, has accelerated the development of systems biology research in the yeast organism Saccharomyces cerevisiae In particular, discovery of biological pathways in yeast has become an important forefront in systems biology, which aims to understand the interactions among molecules within a cell leading to certain cellular processes in response to a specific environment. While the existing theoretical and experimental approaches enable the investigation of well-known pathways involved in metabolism, gene regulation and signal transduction, bioinformatics methods offer new insights into computational modeling of biological pathways. A wide range of computational approaches has been proposed in the past for reconstructing biological pathways from high-throughput datasets. Here we review selected bioinformatics approaches for modeling biological pathways inS. cerevisiae, including metabolic pathways, gene-regulatory pathways and signaling pathways. We start with reviewing the research on biological pathways followed by discussing key biological databases. In addition, several representative computational approaches for modeling biological pathways in yeast are discussed.

Fundamentals of protein interaction network mapping

Studying protein interaction networks of all proteins in an organism (“interactomes”) remains one of the major challenges in modern biomedicine. Such information is crucial to understanding cellular pathways and developing effective therapies for the treatment of human diseases. Over the past two decades, diverse biochemical, genetic, and cell biological methods have been developed to map interactomes. In this review, we highlight basic principles of interactome mapping. Specifically, we discuss the strengths and weaknesses of individual assays, how to select a method appropriate for the problem being studied, and provide general guidelines for carrying out the necessary follow‐up analyses. In addition, we discuss computational methods to predict, map, and visualize interactomes, and provide a summary of some of the most important interactome resources. We hope that this review serves as both a useful overview of the field and a guide to help more scientists actively employ these powerful approaches in their research.

Next-Generation Sequencing for Binary Protein-Protein Interactions

The yeast two-hybrid (Y2H) system exploits host cell genetics in order to display binary protein-protein interactions (PPIs) via defined and selectable phenotypes. Numerous improvements have been made to this method, adapting the screening principle for diverse applications, including drug discovery and the scale-up for proteome wide interaction screens in human and other organisms. Here we discuss a systematic workflow and analysis scheme for screening data generated by Y2H and related assays that includes high-throughput selection procedures, readout of comprehensive results via next-generation sequencing (NGS), and the interpretation of interaction data via quantitative statistics. The novel assays and tools will serve the broader scientific community to harness the power of NGS technology to address PPI networks in health and disease. We discuss examples of how this next-generation platform can be applied to address specific questions in diverse fields of biology and medicine.

The power of the yeast two-hybrid system in the identification of novel drug targets: building and modulating PPP1 interactomes

Since the description of the yeast two-hybrid (Y2H) method, it has become more and more evident that it is the most commonly used method to identify protein-protein interactions (PPIs). The improvements in the original Y2H methodology in parallel with the idea that PPIs are promising drug targets, offer an excellent opportunity to apply the principles of this molecular biology technique to the pharmaceutical field. Additionally, the theoretical developments in the networks field make PPI networks very useful frameworks that facilitate many discoveries in biomedicine. This review highlights the relevance of Y2H in the determination of PPIs, specifically phosphoprotein phosphatase 1 interactions, and its possible outcomes in pharmaceutical research.

Multi-scale perturbations of protein interactomes reveal their mechanisms of regulation, robustness and insights into genotype-phenotype maps

Cellular architectures and signaling machineries are organized through protein-protein interactions (PPIs). High-throughput methods to study PPIs in yeast have opened a new perspective on the organization of the cell by allowing the study of whole protein interactomes. Recent investigations have moved from the description of this organization to the analysis of its dynamics by experimenting how protein interaction networks (PINs) are rewired in response to perturbations. Here we review studies that have used the budding yeast as an experimental system to explore these altered networks. Given the large space of possible PPIs and the diversity of potential genetic and environmental perturbations, high-throughput methods are an essential requirement to survey PIN perturbations on a large scale. Network perturbations are typically conceptualized as the removal of entire proteins (nodes), the modification of single PPIs (edges) or changes in growth conditions. These studies have revealed mechanisms of PPI regulation, PIN architectural organization, robustness and sensitivity to perturbations. Despite these major advances, there are still inherent limits to current technologies that lead to a trade-off between the number of perturbations and the number of PPIs that can be considered simultaneously. Nevertheless, as we exemplify here, targeted approaches combined with the existing resources remain extremely powerful to explore the inner organization of cells and their responses to perturbations.