Protein-protein Interaction Sites Research Articles

Protein-Protein Interaction Site Prediction Based on Attention Mechanism and Convolutional Neural Networks.

Proteins usually perform their cellular functions by interacting with other proteins. Accurate identification of protein-protein interaction sites (PPIs) from sequence is import for designing new drugs and developing novel therapeutics. A lot of computational models for PPIs prediction have been developed because experimental methods are slow and expensive. Most models employ a sliding window approach in which local neighbors are concatenated to present a target residue. However, those neighbors are not distinguished by pairwise information between a neighbor and the target. In this study, we propose a novel PPIs prediction model AttCNNPPISP, which combines attention mechanism and convolutional neural networks (CNNs). The attention mechanism dynamically captures the pairwise correlation of each neighbor-target pair within a sliding window, and therefore makes a better understanding of the local environment of target residue. And then, CNNs take the local representation as input to make prediction. Experiments are employed on several public benchmark datasets. Compared with the state-of-the-art models, AttCNNPPISP improves the prediction performance. Also, the experimental results demonstrate that the attention mechanism is effective in terms of constructing comprehensive context information of target residue.

SaLT&PepPr is an interface-predicting language model for designing peptide-guided protein degraders

Protein-protein interactions (PPIs) are critical for biological processes and predicting the sites of these interactions is useful for both computational and experimental applications. We present a Structure-agnostic Language Transformer and Peptide Prioritization (SaLT&PepPr) pipeline to predict interaction interfaces from a protein sequence alone for the subsequent generation of peptidic binding motifs. Our model fine-tunes the ESM-2 protein language model (pLM) with a per-position prediction task to identify PPI sites using data from the PDB, and prioritizes motifs which are most likely to be involved within inter-chain binding. By only using amino acid sequence as input, our model is competitive with structural homology-based methods, but exhibits reduced performance compared with deep learning models that input both structural and sequence features. Inspired by our previous results using co-crystals to engineer target-binding “guide” peptides, we curate PPI databases to identify partners for subsequent peptide derivation. Fusing guide peptides to an E3 ubiquitin ligase domain, we demonstrate degradation of endogenous β-catenin, 4E-BP2, and TRIM8, and highlight the nanomolar binding affinity, low off-targeting propensity, and function-altering capability of our best-performing degraders in cancer cells. In total, our study suggests that prioritizing binders from natural interactions via pLMs can enable programmable protein targeting and modulation.

B-291 Proximity Labeling at Sites of Protein-Protein Interactions Using a Switchable DNA Catalyst

Abstract Background Extracellular protein-protein interactions (PPIs) play a crucial role in cell-cell adhesion, communication, and signaling. The dysregulation of PPIs in cancer caused by differential protein expression can drive oncogenic factors. Traditional methods to study PPIs disfavor weak or transient interacting protein partners, leaving them undiscovered. Proximity labeling (PL) is an emerging tool in which a catalyst is attached to a protein of interest (POI) and creates reactive species that tag nearby endogenous proteins. Tagged proteins are subsequently isolated and identified as potential interacting partners with the POI. If PL could be activated at a more specific subcellular location, such as the site of a PPI, more detailed information could be unveiled concerning the interactome of cancer-relevant PPIs. Recent advances in PL involve the use of a synthetic catalyst that can be localized to a POI via an antibody or ligand and avoids the need for PL enzyme-POI fusion proteins to be constructed, allowing for analysis of primary tissue samples. Methods To create a PL catalyst with pseudo-allosteric regulation, we created a switchable DNA catalyst. The DNA catalyst is composed of synthetic photocatalyst and quencher moieties covalently linked to DNA oligomers such that when hybridized together the photocatalyst is quenched and inactive. The DNA scaffold affords conformational control that is utilized to change the distance between photocatalyst and quencher, thus regulating photocatalyst activity. Split triggers composed of DNA oligos that bind to catalyst-DNA and simultaneously disrupt quencher-DNA hybridization were used to create an AND logic gate where both split triggers need to be in proximity to activate the PL catalyst. The split triggers were localized to POIs via connection to aptamers, short pieces of synthetic DNA that bind targets. Using the c-Met homodimer as a model system, we first labeled c-Met expressing HEK293T cells with aptamer-split trigger DNA oligomers as well as the quenched photocatalyst. After the catalyst was activated at the site of the PPI, we performed proximity labeling using a biotin-phenol substrate in the presence of an oxidizing agent and 450 nm light. Fluorescent streptavidin was used to visualize sites of biotinylation and detected using fluorescence microscopy. Results We observed extracellular biotin signal only of c-Met transfected cells as evidenced by the expression of a co-transfection plasmid encoding a fluorescent nuclear protein. When one of the split triggers had the aptamer motif removed, meaning it couldn’t be localized to the cell surface, no labeling was observed which shows that both split probes must be localized to the cell surface to activate labeling. Additionally, controls including omission of light, photocatalyst, or biotin phenol showed no labeling. Using a free-floating version of the photocatalyst to label cells, we confirmed that lysates contained biotinylated proteins via western blot. Conclusion We localized two split triggers to interacting proteins to activate a PL catalyst and performed proximity labeling at the site of the PPI. This technique demonstrates non-genetic split proximity labeling to enable downstream analysis of cancer cell proteomes in an ultra-specific manner and expands the toolkit for spatiotemporally controlled proximity labeling.

Spatom: a graph neural network for structure-based protein-protein interaction site prediction.

Accurate identification of protein-protein interaction (PPI) sites remains a computational challenge. We propose Spatom, a novel framework for PPI site prediction. This framework first defines a weighted digraph for a protein structure to precisely characterize the spatial contacts of residues, then performs a weighted digraph convolution to aggregate both spatial local and global information and finally adds an improved graph attention layer to drive the predicted sites to form more continuous region(s). Spatom was tested on a diverse set of challenging protein-protein complexes and demonstrated the best performance among all the compared methods. Furthermore, when tested on multiple popular proteins in a case study, Spatom clearly identifies the interaction interfaces and captures the majority of hotspots. Spatom is expected to contribute to the understanding of protein interactions and drug designs targeting protein binding.

Protein-protein interaction and site prediction using transfer learning.

The advanced language models have enabled us to recognize protein-protein interactions (PPIs) and interaction sites using protein sequences or structures. Here, we trained the MindSpore ProteinBERT (MP-BERT) model, a Bidirectional Encoder Representation from Transformers, using protein pairs as inputs, making it suitable for identifying PPIs and their respective interaction sites. The pretrained model (MP-BERT) was fine-tuned as MPB-PPI (MP-BERT on PPI) and demonstrated its superiority over the state-of-the-art models on diverse benchmark datasets for predicting PPIs. Moreover, the model's capability to recognize PPIs among various organisms was evaluated on multiple organisms. An amalgamated organism model was designed, exhibiting a high level of generalization across the majority of organisms and attaining an accuracy of 92.65%. The model was also customized to predict interaction site propensity by fine-tuning it with PPI site data as MPB-PPISP. Our method facilitates the prediction of both PPIs and their interaction sites, thereby illustrating the potency of transfer learning in dealing with the protein pair task.

DeepSG2PPI: A Protein-Protein Interaction Prediction Method Based on Deep Learning.

Protein-protein interaction (PPI) plays an important role in almost all life activities. Many protein interaction sites have been confirmed by biological experiments, but these PPI site identification methods are time-consuming and expensive. In this study, a deep learning-based PPI prediction method, named DeepSG2PPI, is developed. Firstly, the protein sequence information is retrieved and the local context information of each amino acid residue is calculated. A two-dimensional convolutional neural network (2D-CNN) model is employed to extract features from a two-channel coding structure, in which an attention mechanism is embedded to assign higher weights to key features. Secondly, the global statistical information of each amino acid residue and the relationship graph between the protein and GO (Gene Ontology) function annotation are built, and the graph embedding vector is constructed to represent the biological features of the protein. Finally, a 2D-CNN model and two 1D-CNN models are combined for PPI prediction. The comparison analysis with existing algorithms shows that the DeepSG2PPI method has better performance. It provides more accurate and effective PPI site prediction, which will be helpful in reducing the cost and failure rate of biological experiments.

Glaucoma-Associated CDR1 Peptide Promotes RGC Survival in Retinal Explants through Molecular Interaction with Acidic Leucine Rich Nuclear Phosphoprotein 32A (ANP32A).

Glaucoma is a complex, multifactorial optic neuropathy mainly characterized by the progressive loss of retinal ganglion cells (RGCs) and their axons, resulting in a decline of visual function. The pathogenic molecular mechanism of glaucoma is still not well understood, and therapeutic strategies specifically addressing the neurodegenerative component of this ocular disease are urgently needed. Novel immunotherapeutics might overcome this problem by targeting specific molecular structures in the retina and providing direct neuroprotection via different modes of action. Within the scope of this research, the present study showed for the first time beneficial effects of the synthetic CDR1 peptide SCTGTSSDVGGYNYVSWYQ on the viability of RGCs ex vivo in a concentration-dependent manner compared to untreated control explants (CTRL, 50 µg/mL: p < 0.05 and 100 µg/mL: p < 0.001). Thereby, this specific peptide was identified first as a potential biomarker candidate in the serum of glaucoma patients and was significantly lower expressed in systemic IgG molecules compared to healthy control subjects. Furthermore, MS-based co-immunoprecipitation experiments confirmed the specific interaction of synthetic CDR1 with retinal acidic leucine-rich nuclear phosphoprotein 32A (ANP32A; p < 0.001 and log2 fold change > 3), which is a highly expressed protein in neurological tissues with multifactorial biological functions. In silico binding prediction analysis revealed the N-terminal leucine-rich repeat (LRR) domain of ANP32A as a significant binding site for synthetic CDR1, which was previously reported as an important docking site for protein-protein interactions (PPI). In accordance with these findings, quantitative proteomic analysis of the retinae ± CDR1 treatment resulted in the identification of 25 protein markers, which were significantly differentially distributed between both experimental groups (CTRL and CDR1, p < 0.05). Particularly, acetyl-CoA biosynthesis I-related enzymes (e.g., DLAT and PDHA1), as well as cytoskeleton-regulating proteins (e.g., MSN), were highly expressed by synthetic CDR1 treatment in the retina; on the contrary, direct ANP32A-interacting proteins (e.g., NME1 and PPP2R4), as well as neurodegenerative-related markers (e.g., CEND1), were identified with significant lower abundancy in the CDR1-treated retinae compared to CTRL. Furthermore, retinal protein phosphorylation and histone acetylation were also affected by synthetic CDR1, which are both partially controlled by ANP32A. In conclusion, the synthetic CDR1 peptide provides a great translational potential for the treatment of glaucoma in the future by eliciting its neuroprotective mechanism via specific interaction with ANP32A's N terminal LRR domain.

Discovery of AI-2 Quorum Sensing Inhibitors Targeting the LsrK/HPr Protein-Protein Interaction Site by Molecular Dynamics Simulation, Virtual Screening, and Bioassay Evaluation.

Quorum sensing (QS) is a cell-to-cell communication mechanism that regulates bacterial pathogenicity, biofilm formation, and antibiotic sensitivity. Among the identified quorum sensing, AI-2 QS exists in both Gram-negative and Gram-positive bacteria and is responsible for interspecies communication. Recent studies have highlighted the connection between the phosphotransferase system (PTS) and AI-2 QS, with this link being associated with protein-protein interaction (PPI) between HPr and LsrK. Here, we first discovered several AI-2 QSIs targeting the LsrK/HPr PPI site through molecular dynamics (MD) simulation, virtual screening, and bioassay evaluation. Of the 62 compounds purchased, eight compounds demonstrated significant inhibition in LsrK-based assays and AI-2 QS interference assays. Surface plasmon resonance (SPR) analysis confirmed that the hit compound 4171-0375 specifically bound to the LsrK-N protein (HPr binding domain, KD = 2.51 × 10-5 M), and therefore the LsrK/HPr PPI site. The structure-activity relationships (SARs) emphasized the importance of hydrophobic interactions with the hydrophobic pocket and hydrogen bonds or salt bridges with key residues of LsrK for LsrK/HPr PPI inhibitors. These new AI-2 QSIs, especially 4171-0375, exhibited novel structures, significant LsrK inhibition, and were suitable for structural modification to search for more effective AI-2 QSIs.

Protein-Protein Interaction Sites Prediction Using Batch Normalization Based CNNs and Oversampling Method Borderline-SMOTE.

The recognition of protein-protein interaction sites (PPIs) is beneficial for the interpretation of protein functions and the development of new drugs. Traditional biological experiments to identify PPI sites are expensive and inefficient, leading to the generation of various computational methods to predict PPIs. However, the accurate prediction of PPI sites remains a big challenge due to the existence of the sample imbalance issue. In this work, we design a novel model that combines convolutional neural networks (CNNs) with Batch Normalization to predict PPI sites, and employ an oversampling technique Borderline-SMOTE to address the sample imbalance issue. In particular, to better characterize the amino acid residues on the protein chains, we employ a sliding window approach for feature extraction of target residues and their contextual residues. We verify the effectiveness of our method by comparing our method with the existing state-of-the-art schemes. The performance validations of our method on three public datasets achieve accuracies of 88.6%, 89.9%, and 86.7%, respectively, all showing improved accuracies compared with the existing schemes. Moreover, the ablation experiment results suggest that Batch Normalization can greatly improve the generalization and the prediction stability of our model.

Abstract 459: Targeting the non-junctional tumorigenic function of connexin43 in cancer cell plasticity

Abstract Tumors are highly heterogeneous and dynamic, with both cancer cells and cancer stem cells (CSCs) displaying a high degree of plasticity contributing to metastasis and tumor recurrence following treatment. Epithelial-mesenchymal transition (EMT) enables cancer cells to acquire migratory and invasive characteristics, access the circulatory system, and metastasize. Moreover, EMT is associated with therapeutic resistance and CSC generation. Our previous research demonstrated a redistribution of the gap junction protein connexin43 (Cx43) from the plasma membrane to the cytosol during EMT. Using super-resolution STochastic Optical Reconstruction Microscopy (STORM), we have recently identified increased interaction of such intracellular Cx43 with microtubules during EMT and in primary glioblastoma (GBM) CSCs (GSCs). We further report a tumorigenic function for cytosolic Cx43/microtubule complexing in cancer cell and CSC proliferation, migration, and survival. Cx43 localization and activity is regulated by multiple sites for protein-protein interaction within the Cx43 carboxy-terminus (CT), including a tubulin binding domain. We employed a Cx43 mimetic peptide named JM2 (juxtamembrane 2) encompassing the microtubule binding sequence of the Cx43 CT. Following administration, JM2 efficiently and specifically disrupts the interaction of Cx43 with microtubules, inhibits GSC-derived neurosphere formation in vitro, and patient GBM-derived organoid growth ex vivo. Using an orthotopic GBM patient derived xenograft mouse model, we demonstrate in vivo that JM2 significantly decreases the number of GSCs within brain tumors, inhibits the formation of highly invasive GBM tumors, and increases mouse survival in combination with the chemotherapy alkylating agent temozolomide. Further examining the role of Cx43/microtubule complexing in the context of metastatic disease, we observed JM2 also decreases mesenchymal marker expression and inhibits cell migration and invasion in EMT models. Using an in vivo orthotopic breast cancer mouse model we find that intratumoral administration of JM2 prevents metastasis, significantly decreasing cancer cell lung colonization. Our findings validate targeting Cx43 complexing with microtubules, with JM2, as a novel therapeutic approach for cancer treatment through inhibition of EMT-associated increases in invasion and metastasis, and elimination of CSCs. Modulation of non-junctional Cx43 functions therefore represents a viable therapeutic approach to target cellular plasticity with broad therapeutic implications for many cancers. Citation Format: Samy Lamouille, Christina E. Wheeler, Stacie E. Deaver, Michael J. Zeitz, Zhi Sheng, Robert G. Gourdie, James W. Smyth. Targeting the non-junctional tumorigenic function of connexin43 in cancer cell plasticity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 459.

AGAT-PPIS: a novel protein-protein interaction site predictor based on augmented graph attention network with initial residual and identity mapping.

Identifying protein-protein interaction (PPI) site is an important step in understanding biological activity, apprehending pathological mechanism and designing novel drugs. Developing reliable computational methods for predicting PPI site as screening tools contributes to reduce lots of time and expensive costs for conventional experiments, but how to improve the accuracy is still challenging. We propose a PPI site predictor, called Augmented Graph Attention Network Protein-Protein Interacting Site (AGAT-PPIS), based on AGAT with initial residual and identity mapping, in which eight AGAT layers are connected to mine node embedding representation deeply. AGAT is our augmented version of graph attention network, with added edge features. Besides, extra node features and edge features are introduced to provide more structural information and increase the translation and rotation invariance of the model. On the benchmark test set, AGAT-PPIS significantly surpasses the state-of-the-art method by 8% in Accuracy, 17.1% in Precision, 11.8% in F1-score, 15.1% in Matthews Correlation Coefficient (MCC), 8.1% in Area Under the Receiver Operating Characteristic curve (AUROC), 14.5% in Area Under the Precision-Recall curve (AUPRC), respectively.

Improving protein-protein interaction site prediction using deep residual neural network

Accurate identification of protein-protein interaction (PPI) sites is significantly important for understanding the mechanism of life and developing new drugs. However, it is expensive and time-consuming to identify PPI sites using wet-lab experiments. Developing computational methods is a new road to identify PPI sites, which can accelerate the procedure of PPI-related research. In this study, we propose a novel deep learning-based method (called D-PPIsite) to improve the accuracy of sequence-based PPI site prediction. In D-PPIsite, four discriminative sequence-driven features, i.e., position specific scoring matrix, relative solvent accessibility, position information and physical properties, are employed to feed into a well-designed deep learning module, consisting of convolutional, squeeze and excitation, and fully connected layers, to learn a prediction model. To reduce the risk of a single prediction model getting stuck in local optima, multiple prediction models with different initialization parameters are selected and integrated into one final model using the mean ensemble strategy. Experimental results on five independent testing data sets demonstrate that the proposed D-PPIsite can achieve an average accuracy of 80.2% and precision of 36.9%, covering 53.5% of all PPI sites while achieving the average Matthews correlation coefficient value (0.330) that is significantly higher than most of existing state-of-the-art prediction methods. We implement a new standalone-version predictor for predicting PPI sites, which is freely available at https://github.com/MingDongup/D-PPIsite for academic use.

Design of a Hierarchical Assembly at a Solid-Liquid Interface Using an Asymmetric Protein Needle.

Design and control of processes for a hierarchical assembly of proteins remain challenging because it requires consideration of design principles with atomic-level accuracy. Previous studies have adopted symmetry-based strategies to minimize the complexity of protein-protein interactions and this has placed constraints on the structures of the resulting protein assemblies. In the present work, we used an anisotropic-shaped protein needle, gene product 5 (gp5) from bacteriophage T4 with a C-terminal hexahistidine-tag (His-tag) (gp5_CHis), to construct a hierarchical assembly with two distinct protein-protein interaction sites. High-speed atomic force microscopy (HS-AFM) measurements reveal that it forms unique tetrameric clusters through its N-terminal head on a mica surface. The clusters further self-assemble into a monolayer through the C-terminal His-tag. The HS-AFM images and displacement analyses show that the monolayer is a network-like structure rather than a crystalline lattice. Our results expand the toolbox for constructing hierarchical protein assemblies based on structural anisotropy.

Structural basis of signal transduction within PAS regulated Ser/Thr kinase.

To control integral cellular processes, nature has developed molecular switches where the sensory domain senses external cues and controls the function of relevant effector domains. Here, we focus on the superfamily of PAS (Per-ARNT-Sim) sensory domains and their roles in regulating Ser/Thr Kinases (STKs). PAS domains achieve regulatory control by internally binding ligands/cofactors (e.g., O2 binding to heme in FixL; flavins within the Light-Oxygen-Voltage (LOV)-type PAS domains). While aspects of PAS sensing are understood, the signaling mechanism from sensory to effector domain and Protein-Protein Interactions (PPIs) within full-length proteins or complexes remain elusive. To address these, we investigated plant phototropin, Avena sativa Phototropin1 (AsPhot1). AsPhot1 has two LOV domains followed by the STK domain, where the LOV2 regulates the kinase function to maintain essential plant physiology via blue light sensing. We used biochemical assays in combination with HDX-MS to study the signal transduction from LOV2 to the STK domain. Our data shows substantial conformation changes in the dark vs. light state upon photoactivation, highlighting key regions might be necessary for LOV-regulated kinase functions. We also observed the activation of kinase involves destabilizing the linker region between LOV2-STK, which interacts with the n-lobe of the kinase near the ATP binding site. We speculated that upon illumination, rearrangement of these linkers might hold the key to the STK substrate association and downstream functions. Additionally, using a low-resolution cryo-EM map, combined with local HDX-MS data and computational resources (e.g., AlphaFold), we built a model for the LOV2-STK complex. Our model highlights critical PPI sites within AsPhot1 and provides additional insights into understanding the generality of how protein kinases interact with their associated domain via conserved residues and subdomains. These findings may help develop novel optogenetic tools and therapeutics for mammalian PAS regulated kinase.

An inclusive study of deleterious missense PAX9 variants using user-friendly tools reveals structural, functional alterations, as well as potential therapeutic targets.

Mutations in the PAX9 are responsible for non-syndromic tooth agenesis in humans, although their structural and functional consequences on protein phenotype, stability, and posttranslational modifications (PTMs) have not yet been adequately investigated. This in silico study focuses on retrieving the six most deleterious mutations (L21P, R26W, R28P, G51S, I87F, and K91E) of PAX9 that has been linked to severe oligodontia. Several computational algorithm methods were used to determine the deleterious effects of PAX9 mutations. Analysis of gene ontology, protein interactions, and PTMs indicated significant functional changes caused by PAX9 mutations. The structural superimposition of the wild-type and mutant PAX9 variants revealed structural changes in locations that were present in the structures of all six variations. The conserved domain analysis revealed that the areas shared by all six variations contained unique sections that lacked DNA binding or protein-protein interaction sites, suggesting prospective drug target sites for functional restoration. The protein-protein interaction network showed KDM5B as PAX9's strongest interacting partner similar to MSX1. The PAX9 protein's structural conformations, compactness, stiffness, and function may all be impacted by changes, according to MD simulations. In addition, research on cell lines and animal models may be valuable in establishing their specific roles in functional annotations.

Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning

Protein-protein interactions (PPIs) govern cellular pathways and processes, by significantly influencing the functional expression of proteins. Therefore, accurate identification of protein-protein interaction binding sites has become a key step in the functional analysis of proteins. However, since most computational methods are designed based on biological features, there are no available protein language models to directly encode amino acid sequences into distributed vector representations to model their characteristics for protein-protein binding events. Moreover, the number of experimentally detected protein interaction sites is much smaller than that of protein-protein interactions or protein sites in protein complexes, resulting in unbalanced data sets that leave room for improvement in their performance. To address these problems, we develop an ensemble deep learning model (EDLM)-based protein-protein interaction (PPI) site identification method (EDLMPPI). Evaluation results show that EDLMPPI outperforms state-of-the-art techniques including several PPI site prediction models on three widely-used benchmark datasets including Dset_448, Dset_72, and Dset_164, which demonstrated that EDLMPPI is superior to those PPI site prediction models by nearly 10% in terms of average precision. In addition, the biological and interpretable analyses provide new insights into protein binding site identification and characterization mechanisms from different perspectives. The EDLMPPI webserver is available at http://www.edlmppi.top:5002/.

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.

CtP2ISP: Protein-Protein Interaction Sites Prediction Using Convolution and Transformer With Data Augmentation.

Protein-protein interactions are the basis of many cellular biological processes, such as cellular organization, signal transduction, and immune response. Identifying protein-protein interaction sites is essential for understanding the mechanisms of various biological processes, disease development, and drug design. However, it remains a challenging task to make accurate predictions, as the small amount of training data and severe imbalanced classification reduce the performance of computational methods. We design a deep learning method named ctP2ISP to improve the prediction of protein-protein interaction sites. ctP2ISP employs Convolution and Transformer to extract information and enhance information perception so that semantic features can be mined to identify protein-protein interaction sites. A weighting loss function with different sample weights is designed to suppress the preference of the model toward multi-category prediction. To efficiently reuse the information in the training set, a preprocessing of data augmentation with an improved sample-oriented sampling strategy is applied. The trained ctP2ISP was evaluated against current state-of-the-art methods on six public datasets. The results show that ctP2ISP outperforms all other competing methods on the balance metrics: F1, MCC, and AUPRC. In particular, our prediction on open tests related to viruses may also be consistent with biological insights. The source code and data can be obtained from https://github.com/lennylv/ctP2ISP.

In Silico Prediction of MYO1C-Rhodopsin Interactions and Its Significance in Protein Localization and Visual Function.

Rods and cones are photoreceptor neurons in the retina that are required for visual sensation in vertebrates, where proper protein localization and compartmentalization are critical for phototransduction and visual function. In human retinal diseases, improper protein transport to the outer segment (OS) or mislocalization of proteins to the inner segment (IS) could lead to impaired visual responses and photoreceptor cell degeneration, causing a loss of visual function. We showed involvement of an unconventional motor protein, MYO1C, in the proper localization of rhodopsin to the OS, where loss of MYO1C in a mammalian model caused mislocalization of rhodopsin to IS and cell bodies, leading to progressively severe retinal phenotypes. In this study, using modeling and docking analysis, we aimed to identify the protein-protein interaction sites between MYO1C and Rhodopsin to establish a hypothesis that a physical interaction between these proteins is necessary for the proper trafficking of rhodopsin and visual function.

Using PlaPPISite to Predict and Analyze Plant Protein-Protein Interaction Sites.

Proteome-wide characterization of protein-protein interactions (PPIs) is crucial to understand the functional roles of protein machinery within cells systematically. With the accumulation of PPI data in different plants, the interaction details of binary PPIs, such as the three-dimensional (3D) structural contexts of interaction sites/interfaces, are urgently demanded. To meet this requirement, we have developed a comprehensive and easy-to-use database called PlaPPISite ( http://zzdlab.com/plappisite/index.php ) to present interaction details for 13 plant interactomes. Here, we provide a clear guide on how to search and view protein interaction details through the PlaPPISite database. Firstly, the running environment of our database is introduced. Secondly, the input file format is briefly introduced. Moreover, we discussed which information related to interaction sites can be achieved through several examples. In addition, some notes about PlaPPISite are also provided. More importantly, we would like to emphasize the importance of interaction site information in plant systems biology through this user guide of PlaPPISite. In particular, the easily accessible 3D structures of PPIs in the coming post-AlphaFold2 era will definitely boost the application of plant interactome to decipher the molecular mechanisms of many fundamental biological issues.