Quantitative Yeast-Yeast Two Hybrid for the Discovery and Binding Affinity Estimation of Protein-Protein Interactions

PPI-CoAttNet: A Web Server for Protein-Protein Interaction Tasks Using a Coattention Model

Predicting protein-protein interactions (PPIs) is crucial for advancing drug discovery. Despite the proposal of numerous advanced computational methods, these approaches often suffer from poor usability for biologists and lack generalization. In this study, we designed a deep learning model based on a coattention mechanism that was capable of both PPI and site prediction and used this model as the foundation for PPI-CoAttNet, a user-friendly, multifunctional web server for PPI prediction. This platform provides comprehensive services for online PPI model training, PPI and site prediction, and prediction of interactions with proteins associated with highly prevalent cancers. In our Homo sapiens test set for PPI prediction, PPI-CoAttNet achieved an AUC of 0.9841 and an F1 score of 0.9440, outperforming most state-of-the-art models. Additionally, these results are generated in real time, delivering outcomes within minutes. We also evaluated PPI-CoAttNet for downstream tasks, including novel E3 ligase scoring, demonstrating outstanding accuracy. We believe that this tool will empower researchers, especially those without computational expertise, to leverage AI for accelerating drug development.

Intelligence model on sequence-based prediction of PPI using AISSO deep concept with hyperparameter tuning process

Protein-protein interaction (PPI) prediction is vital for interpreting biological activities. Even though many diverse sorts of data and machine learning approaches have been employed in PPI prediction, performance still has to be enhanced. As a result, we adopted an Aquilla Influenced Shark Smell (AISSO)-based hybrid prediction technique to construct a sequence-dependent PPI prediction model. This model has two stages of operation: feature extraction and prediction. Along with sequence-based and Gene Ontology features, unique features were produced in the feature extraction stage utilizing the improved semantic similarity technique, which may deliver reliable findings. These collected characteristics were then sent to the prediction step, and hybrid neural networks, such as the Improved Recurrent Neural Network and Deep Belief Networks, were used to predict the PPI using modified score level fusion. These neural networks' weight variables were adjusted utilizing a unique optimal methodology called Aquila Influenced Shark Smell (AISSO), and the outcomes showed that the developed model had attained an accuracy of around 88%, which is much better than the traditional methods; this model AISSO-based PPI prediction can provide precise and effective predictions.

N-terminal truncated phospholipase A1 accessory protein PlaS from Serratia marcescens alleviates inhibitory on host cell growth and enhances PlaA1 enzymatic activity

Phospholipase A1 (PLA1) is a kind of specific phospholipid hydrolase widely used in food, medical, textile. However, limitations in its expression and enzymatic activity have prompted the investigation of the phospholipase-assisting protein PlaS. In this study, we elucidate the role of PlaS in enhancing the expression and activity of PlaA1 through N-terminal truncation. Our research demonstrates that truncating the N-terminal region of PlaS effectively overcomes its inhibitory effect on host cells, resulting in improved cell growth and increased protein solubility of the protein. The yeast two-hybrid assay confirms the interaction between PlaA1 and N-terminal truncated PlaS (∆N27 PlaS), highlighting their binding capabilities. Furthermore, in vitro studies using Biacore analysis reveal a concentration-dependent and specific binding between PlaA1 and ∆N27 PlaS, exhibiting high affinity. Molecular docking analysis provides insights into the hydrogen bond interactions between ∆N27 PlaS and PlaA1, identifying key amino acid residues crucial for their binding. Finally, the enzyme activity of PLA1 was boost to 8.4 U/mL by orthogonal test. Study significantly contributes to the understanding of the interaction mechanism between PlaS and PlaA1, offering potential strategies for enhancing PlaA1 activity through protein engineering approaches.

Uncovering domain motif interactions using high-throughput protein-protein interaction detection methods

Protein-protein interactions (PPIs) are often mediated by short linear motifs (SLiMs) in one protein and domain in another, known as domain-motif interactions (DMIs). During the past decade, SLiMs have been studied to find their role in cellular functions such as post-translational modifications, regulatory processes, protein scaffolding, cell cycle progression, cell adhesion, cell signalling and substrate selection for proteasomal degradation. This review provides a comprehensive overview of the current PPI detection techniques and resources, focusing on their relevance to capturing interactions mediated by SLiMs. We also address the challenges associated with capturing DMIs. Moreover, a case study analysing the BioGrid database as a source of DMI prediction revealed significant known DMI enrichment in different PPI detection methods. Overall, it can be said that current high-throughput PPI detection methods can be a reliable source for predicting DMIs.

Strategies for enriching and characterizing proteins with inhibitory properties on the yeast surface

Display technologies are powerful tools for discovering binding proteins against a broad range of biological targets. However, it remains challenging to adapt display technologies for the discovery of proteins that inhibit the enzymatic activities of targets. Here, we investigate approaches for discovering and characterizing inhibitory antibodies in yeast display format using a well-defined series of constructs and the target matrix metalloproteinase-9. Three previously reported antibodies were used to create model libraries consisting of inhibitory, non-inhibitory, and non-binding constructs. Conditions that preferentially enrich for inhibitory clones were identified for both magnetic bead-based enrichments and fluorescence-activated cell sorting. Half maximal inhibitory concentration (IC50) was obtained through yeast titration assays. The IC50 of the inhibitory antibody obtained in yeast display format falls within the confidence interval of the IC50 value determined in soluble form. Overall, this study identifies strategies for the discovery and characterization of inhibitory clones directly in yeast display format.

MARPPI: boosting prediction of protein-protein interactions with multi-scale architecture residual network

Protein-protein interactions (PPIs) are a major component of the cellular biochemical reaction network. Rich sequence information and machine learning techniques reduce the dependence of exploring PPIs on wet experiments, which are costly and time-consuming. This paper proposes a PPI prediction model, multi-scale architecture residual network for PPIs (MARPPI), based on dual-channel and multi-feature. Multi-feature leverages Res2vec to obtain the association information between residues, and utilizes pseudo amino acid composition, autocorrelation descriptors and multivariate mutual information to achieve the amino acid composition and order information, physicochemical properties and information entropy, respectively. Dual channel utilizes multi-scale architecture improved ResNet network which extracts protein sequence features to reduce protein feature loss. Compared with other advanced methods, MARPPI achieves 96.03%, 99.01% and 91.80% accuracy in the intraspecific datasets of Saccharomyces cerevisiae, Human and Helicobacter pylori, respectively. The accuracy on the two interspecific datasets of Human-Bacillus anthracis and Human-Yersinia pestis is 97.29%, and 95.30%, respectively. In addition, results on specific datasets of disease (neurodegenerative and metabolic disorders) demonstrate the ability to detect hidden interactions. To better illustrate the performance of MARPPI, evaluations on independent datasets and PPIs network suggest that MARPPI can be used to predict cross-species interactions. The above shows that MARPPI can be regarded as a concise, efficient and accurate tool for PPI datasets.

Deciphering Spatial Protein-Protein Interactions in Brain Using Proximity Labeling

Cellular biomolecular complexes including protein-protein, protein-RNA, and protein-DNA interactions regulate and execute most biological functions. In particular in brain, protein-protein interactions (PPIs) mediate or regulate virtually all nerve cell functions, such as neurotransmission, cell-cell communication, neurogenesis, synaptogenesis, and synaptic plasticity. Perturbations of PPIs in specific subsets of neurons and glia are thought to underly a majority of neurobiological disorders. Therefore, understanding biological functions at a cellular level requires a reasonably complete catalog of all physical interactions between proteins. An enzyme-catalyzed method to biotinylate proximal interacting proteins within 10 to 300 nm of each other is being increasingly used to characterize the spatiotemporal features of complex PPIs in brain. Thus, proximity labeling has emerged recently as a powerful tool to identify proteomes in distinct cell types in brain as well as proteomes and PPIs in structures difficult to isolate, such as the synaptic cleft, axonal projections, or astrocyte-neuron junctions. In this review, we summarize recent advances in proximity labeling methods and their application to neurobiology.

Quantifying Biomolecular Interactions Using Slow Mixing Mode (SLOMO) Nanoflow ESI-MS

Electrospray ionization mass spectrometry (ESI-MS) is a powerful label-free assay for detecting noncovalent biomolecular complexes in vitro and is increasingly used to quantify binding thermochemistry. A common assumption made in ESI-MS affinity measurements is that the relative ion signals of free and bound species quantitatively reflect their relative concentrations in solution. However, this is valid only when the interacting species and their complexes have similar ESI-MS response factors (RFs). For many biomolecular complexes, such as protein-protein interactions, this condition is not satisfied. Existing strategies to correct for nonuniform RFs are generally incompatible with static nanoflow ESI (nanoESI) sources, which are typically used for biomolecular interaction studies, thereby significantly limiting the utility of ESI-MS. Here, we introduce slow mixing mode (SLOMO) nanoESI-MS, a direct technique that allows both the RF and affinity (K _d) for a biomolecular interaction to be determined from a single measurement using static nanoESI. The approach relies on the continuous monitoring of interacting species and their complexes under nonhomogeneous solution conditions. Changes in ion signals of free and bound species as the system approaches or moves away from a steady-state condition allow the relative RFs of the free and bound species to be determined. Combining the relative RF and the relative abundances measured under equilibrium conditions enables the K _d to be calculated. The reliability of SLOMO and its ease of use is demonstrated through affinity measurements performed on peptide-antibiotic, protease-protein inhibitor, and protein oligomerization systems. Finally, affinities measured for the binding of human and bacterial lectins to a nanobody, a viral glycoprotein, and glycolipids displayed within a model membrane highlight the tremendous power and versatility of SLOMO for accurately quantifying a wide range of biomolecular interactions important to human health and disease.

Experimental and Analytical Framework for "Mix-and-Read" Assays Based on Split Luciferase

The use of immunodetection assays including the widely used enzyme-linked immunosorbent assay (ELISA) in applications such as point-of-care detection is often limited by the need for protein immobilization and multiple binding and washing steps. Here, we describe an experimental and analytical framework for the development of simple and modular "mix-and-read" enzymatic complementation assays based on split luciferase that enable sensitive detection and quantification of analytes in solution. In this assay, two engineered protein binders targeting nonoverlapping epitopes on the target analyte were each fused to nonactive fragments of luciferase to create biosensor probes. Binding proteins to two model targets, lysozyme and Sso6904, were isolated from a combinatorial library of Sso7d mutants using yeast surface display. In the presence of the analyte, probes were brought into close proximity, reconstituting enzymatic activity of luciferase and enabling detection of low picomolar concentrations of the analyte by chemiluminescence. Subsequently, we constructed an equilibrium binding model that relates binding affinities of the binding proteins for the target, assay parameters such as the concentrations of probes used, and assay performance (limit of detection and concentration range over which the target can be quantified). Overall, our experimental and analytical framework provides the foundation for the development of split luciferase assays for detection and quantification of various targets.

SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction

BACKGROUND

Protein-protein interactions (PPIs) dominate intracellular molecules to perform a series of tasks such as transcriptional regulation, information transduction, and drug signalling. The traditional wet experiment method to obtain PPIs information is costly and time-consuming.

RESULT

In this paper, SDNN-PPI, a PPI prediction method based on self-attention and deep learning is proposed. The method adopts amino acid composition (AAC), conjoint triad (CT), and auto covariance (AC) to extract global and local features of protein sequences, and leverages self-attention to enhance DNN feature extraction to more effectively accomplish the prediction of PPIs. In order to verify the generalization ability of SDNN-PPI, a 5-fold cross-validation on the intraspecific interactions dataset of Saccharomyces cerevisiae (core subset) and human is used to measure our model in which the accuracy reaches 95.48% and 98.94% respectively. The accuracy of 93.15% and 88.33% are obtained in the interspecific interactions dataset of human-Bacillus Anthracis and Human-Yersinia pestis, respectively. In the independent data set Caenorhabditis elegans, Escherichia coli, Homo sapiens, and Mus musculus, all prediction accuracy is 100%, which is higher than the previous PPIs prediction methods. To further evaluate the advantages and disadvantages of the model, the one-core and crossover network are conducted to predict PPIs, and the data show that the model correctly predicts the interaction pairs in the network.

CONCLUSION

In this paper, AAC, CT and AC methods are used to encode the sequence, and SDNN-PPI method is proposed to predict PPIs based on self-attention deep learning neural network. Satisfactory results are obtained on interspecific and intraspecific data sets, and good performance is also achieved in cross-species prediction. It can also correctly predict the protein interaction of cell and tumor information contained in one-core network and crossover network.The SDNN-PPI proposed in this paper not only explores the mechanism of protein-protein interaction, but also provides new ideas for drug design and disease prevention.

Protein-protein interaction networks as miners of biological discovery

Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein-complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid, mass spectrometry, co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks. This article is protected by copyright. All rights reserved.

Engineering Proteins Containing Noncanonical Amino Acids on the Yeast Surface

Yeast display has been used to advance many critical research areas, including the discovery of unique protein binders and biological therapeutics. In parallel, noncanonical amino acids (ncAAs) have been used to tailor antibody-drug conjugates and enable discovery of therapeutic leads. Together, these two technologies have allowed for generation of synthetic antibody libraries, where the introduction of ncAAs in yeast-displayed proteins allows for library screening for therapeutically relevant targets. The combination of yeast display with genetically encoded ncAAs increases the available chemistry in proteins and advances applications that require high-throughput strategies. In this chapter, we discuss methods for displaying proteins containing ncAAs on the yeast surface, generating and screening libraries of proteins containing ncAAs, preparing bioconjugates on the yeast surface in large scale, generating and screening libraries of aminoacyl-tRNA synthetases (aaRSs) for encoding ncAAs by using reporter constructs, and characterizing ncAA-containing proteins secreted from yeast. The experimental designs laid out in this chapter are generalizable for discovery of protein binders to a variety of targets and aaRS evolution to continue expanding the genetic code beyond what is currently available in yeast.

Modified Histone Peptides Linked to Magnetic Beads Reduce Binding Specificity

Histone post-translational modifications are small chemical changes to the histone protein structure that have cascading effects on diverse cellular functions. Detecting histone modifications and characterizing their binding partners are critical steps in understanding chromatin biochemistry and have been accessed using common reagents such as antibodies, recombinant assays, and FRET-based systems. High-throughput platforms could accelerate work in this field, and also could be used to engineer de novo histone affinity reagents; yet, published studies on their use with histones have been noticeably sparse. Here, we describe specific experimental conditions that affect binding specificities of post-translationally modified histones in classic protein engineering platforms and likely explain the relative difficulty with histone targets in these platforms. We also show that manipulating avidity of binding interactions may improve specificity of binding.

NanoLuc luciferase as a quantitative yeast two-hybrid reporter

The yeast two-hybrid (Y2H) assay is a powerful technique to identify protein-protein interactions. However, the auxotrophic markers that are the most common Y2H reporters take several days to yield data and require subjective assessment of semiquantitative data to identify interactions. Several reporters have been developed to overcome these disadvantages, but there is still a need for a Y2H reporter that is objective, fast and able to be performed with common laboratory equipment. In this report, we replaced the ADE2 reporter in BK100 with NanoLuc luciferase to yield BK100Nano. We developed an optimized assay to measure NanoLuc activity in 96-well plates and analyzed a set of 74 pairs identified in Y2H library screens, which revealed 44 positive interactions using an unbiased cutoff based on the mean luminescence of negative control samples. The same set was also tested for growth on Y2H selection medium via expression of the HIS3 reporter. We found 91% agreement between the two assays, with discrepancies attributed to weak interactions that displayed variable growth on Y2H medium. Overall, the new BK100Nano strain establishes a quantitative and convenient method to identify Y2H interactions and has potential to be applied to a high throughput manner.