Interacting Protein Pairs Research Articles

Navigating the human-monkeypox virus interactome: HuPoxNET atlas reveals functional insights.

Monkeypox virus, a close relative of variola virus, has significantly increased the incidence of monkeypox disease in humans, with several clinical symptoms. The sporadic spread of the disease outbreaks has resulted in the need for a comprehensive understanding of the molecular mechanisms underlying disease infection and potential therapeutic targets. Protein-protein interactions play a crucial role in various cellular processes and regulate different immune signals during virus infection. Computational algorithms have gained high significance in the prediction of potential protein interaction pairs. Here, we developed a comprehensive database called HuPoxNET (https://kaabil.net/hupoxnet/) using the state-of-the-art MERN stack technology. The database leverages two sequence-based computational models to predict strain-specific protein-protein interactions between human and monkeypox virus proteins. Furthermore, various protein annotations of the human and viral proteins such as gene ontology, KEGG pathways, subcellular localization, protein domains, and novel drug targets identified from our study are also available on the database. HuPoxNET is a user-friendly platform for the scientific community to gain more insights into the monkeypox disease infection and aid in the development of therapeutic drugs against the disease.

A protein network refinement method based on module discovery and biological information

BackgroundThe identification of essential proteins can help in understanding the minimum requirements for cell survival and development to discover drug targets and prevent disease. Nowadays, node ranking methods are a common way to identify essential proteins, but the poor data quality of the underlying PIN has somewhat hindered the identification accuracy of essential proteins for these methods in the PIN. Therefore, researchers constructed refinement networks by considering certain biological properties of interacting protein pairs to improve the performance of node ranking methods in the PIN. Studies show that proteins in a complex are more likely to be essential than proteins not present in the complex. However, the modularity is usually ignored for the refinement methods of the PINs.MethodsBased on this, we proposed a network refinement method based on module discovery and biological information. The idea is, first, to extract the maximal connected subgraph in the PIN, and to divide it into different modules by using Fast-unfolding algorithm; then, to detect critical modules according to the orthologous information, subcellular localization information and topology information within each module; finally, to construct a more refined network (CM-PIN) by using the identified critical modules.ResultsTo evaluate the effectiveness of the proposed method, we used 12 typical node ranking methods (LAC, DC, DMNC, NC, TP, LID, CC, BC, PR, LR, PeC, WDC) to compare the overall performance of the CM-PIN with those on the S-PIN, D-PIN and RD-PIN. The experimental results showed that the CM-PIN was optimal in terms of the identification number of essential proteins, precision-recall curve, Jackknifing method and other criteria, and can help to identify essential proteins more accurately.

Abstract 4500: A high-throughput approach to discover and characterize glueable ligase-target pairs

Abstract Targeted protein degradation can be induced with monovalent small molecules, termed molecular glues, that modify the substrate-binding interface of E3 ligases and reroute them to novel targets. Despite there being over 600 E3 ligases encoded in the human genome, most glues have focused on only a few. Expanding to additional ligases and targets is challenging due to the low probability of successful 'glueing.' Many have suggested that a weak basal interaction between a ligase and neo-substrate is an important prerequisite for the function of a molecular glue, but such weak interactions are challenging to discover. To address this need, we have harnessed AlphaSeq, a synthetic biology platform that measures protein-protein interactions at scale and with high sensitivity, to identify and characterize novel E3-target pairs. Our approach is twofold. First, we test for novel weak interactions (~100nM to 10uM) between a panel of E3 ligases and a library of disease relevant target proteins. Second, starting from a weakly interacting protein pair, we build mutational libraries of both proteins and measure their binding strengths. Mutations that strengthen binding serve to validate the weak interaction, provide structural insights into the nature of binding, and help to inform subsequent small molecule discovery. Here, we present our approach to discover and evaluate glueable ligase-target pairs using AlphaSeq, validation through proof-of-concept experiments involving a known interaction agonized by small molecules, and examples of novel glueable ligase-target pairs that we have recently discovered. Citation Format: Arpita Sen, Phil Burke, Ruchi Bansal, Dasha Krayushkina, David Noble, Kerry McGowan, Natasha Seelam Murakowska, Paxton Reed, Juliana Barrett, Randolph Lopez. A high-throughput approach to discover and characterize glueable ligase-target pairs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4500.

A multi-source molecular network representation model for protein–protein interactions prediction

The prediction of potential protein–protein interactions (PPIs) is a critical step in decoding diseases and understanding cellular mechanisms. Traditional biological experiments have identified plenty of potential PPIs in recent years, but this problem is still far from being solved. Hence, there is urgent to develop computational models with good performance and high efficiency to predict potential PPIs. In this study, we propose a multi-source molecular network representation learning model (called MultiPPIs) to predict potential protein–protein interactions. Specifically, we first extract the protein sequence features according to the physicochemical properties of amino acids by utilizing the auto covariance method. Second, a multi-source association network is constructed by integrating the known associations among miRNAs, proteins, lncRNAs, drugs, and diseases. The graph representation learning method, DeepWalk, is adopted to extract the multisource association information of proteins with other biomolecules. In this way, the known protein–protein interaction pairs can be represented as a concatenation of the protein sequence and the multi-source association features of proteins. Finally, the Random Forest classifier and corresponding optimal parameters are used for training and prediction. In the results, MultiPPIs obtains an average 86.03% prediction accuracy with 82.69% sensitivity at the AUC of 93.03% under five-fold cross-validation. The experimental results indicate that MultiPPIs has a good prediction performance and provides valuable insights into the field of potential protein–protein interactions prediction. MultiPPIs is free available at https://github.com/jiboyalab/multiPPIs.

Identification and validation of NAD+ metabolism-related biomarkers in patients with diabetic peripheral neuropathy.

The mechanism of Nicotinamide Adenine Dinucleotide (NAD+) metabolism-related genes (NMRGs) in diabetic peripheral neuropathy (DPN) is unclear. This study aimed to find new NMRGs biomarkers in DPN. DPN related datasets GSE95849 and GSE185011 were acquired from the Gene Expression Omnibus (GEO) database. 51 NMRGs were collected from a previous article. To explore NMRGs expression in DPN and control samples, differential expression analysis was completed in GSE95849 to obtain differentially expressed genes (DEGs), and the intersection of DEGs and NMRGs was regarded as DE-NMRGs. Next, a protein-protein interaction (PPI) network based on DE-NMRGs was constructed and biomarkers were screened by eight algorithms. Additionally, Gene Set Enrichment Analysis (GSEA) enrichment analysis was completed, biomarker-based column line graphs were constructed, lncRNA-miRNA-mRNA and competing endogenouse (ce) RNA networks were constructed, and drug prediction was completed. Finally, biomarkers expression validation was completed in GSE95849 and GSE185011. 5217 DEGs were obtained from GSE95849 and 21 overlapping genes of DEGs and NMRGs were DE-NMRGs. Functional enrichment analysis revealed that DE-NMRGs were associated with glycosyl compound metabolic process. The PPI network contained 93 protein-interaction pairs and 21 nodes, with strong interactions between NMNAT1 and NAMPT, NADK and NMNAT3, ENPP3 and NUDT12 as biomarkers based on 8 algorithms. Expression validation suggested that ENPP3 and NUDT12 were upregulated in DPN samples (P < 0.05). Moreover, an alignment diagram with good diagnostic efficacy based on ENPP3 and NUDT12 were identified was constructed. GSEA suggested that ENPP3 was enriched in Toll like receptor (TLR) pathway, NUDT12 was enriched in maturity onset diabetes of the young and insulin pathway. Furthermore, 18 potential miRNAs and 36 Transcription factors (TFs) were predicted and the miRNA-mRNA-TF networks were constructed, suggesting that ENPP3 might regulate hsa-miR-34a-5p by affecting MYNN. The ceRNA network suggested that XLOC_013024 might regulate hsa-let-7b-5p by affecting NUDT12. 15 drugs were predicted, with 8 drugs affecting NUDT12 such as resveratrol, and 13 drugs affecting ENPP3 such as troglitazone. ENPP3 and NUDT12 might play key roles in DPN, which provides reference for further research on DPN.

Identifying function modules from protein–protein interaction networks based on Szemerédi’s Regularity Lemma

Szemerédi’s Regularity Lemma (SRL) is a crucial tool in the analysis of large graphs, having made significant contributions in the proof of some sensational results in mathematics. Traditional methods for studying proteins in Protein–Protein Interaction (PPI) networks typically only extract the first-order or second-order neighbor information of proteins, ignoring the potential third-order or higher-order neighbor information between proteins, which may hide certain relationships between proteins. To explore more in-depth insights for PPI networks, we take into account the fourth-order neighbor information of proteins and reconstruct the network in this paper, naming it the weighted dense PPI network. We then partition it using SRL, which primarily utilizes the structural information and corresponds to a unique partition of the original network. Bioinformatics analyses such as those for pathway enrichment analysis and multiple sequence alignment show that our method can classify interacting protein pairs, grouping proteins with functional association, disease association, and sequence similarity together. Overall, this paper has three essential contributions: (1) we present a new model to overcome the astronomically large demand of vertices in applying SRL, and achieve protein classification; (2) we reconstruct a weighted dense PPI network which can make SRL work and mine potential interactions more efficiently; and (3) proteins in the same class partitioned by our method not only have sequence similarity, but also have functional associations.

Interactome‐guided machine learning modeling reveals information embedded in blood plasma protein‐protein interactions in Alzheimer’s Disease patients treated by the complex plasma fraction GRF6019

AbstractBackgroundThe relevance of plasma fraction treatment to reverse age‐related conditions was underlined by our previous pre‐clinical results. In two Phase 2 clinical trials (GRF6019‐201 and GRF6019‐202), patients with Alzheimer’s Disease (AD) were treated with the complex plasma fraction GRF6019, which was determined to be safe and well‐tolerated. Interestingly, no cognitive decline and minimal functional decline were observed. While GRF6019 treatment was associated with a quick and widespread proteomics response, lasting molecular changes were more subtle. Here, we developed new machine learning models guided by protein‐protein interaction (PPI) information to deeper understand the molecular effects of GRF6019 plasma fraction treatment.MethodMore than 7k proteins were measured in 120 plasma samples from treated subjects enrolled in the GRF6019‐201 and GRF6019‐202 trials (n = 21 and 18, respectively) using the Somascan assay. Interacting protein pairs were curated from STRING and OmniPath PPI databases. For each protein‐protein interaction, linear Support Vector Machine (SVM) models were trained to separate before and after treatment samples based on measured protein levels and compared to single‐protein models. Proteins were ranked based on their individual performance and their gain of performance when combined with interaction partnersResultProteasome‐associated elements were overrepresented among proteins with top individual performance. Interaction models provided more details about related processes involving ubiquitination. Furthermore, various other immune, hormonal, and neuronal terms found to be enriched among the top interactors.ConclusionComplex biological functions can be mapped into pathways which are composed of and regulated by PPIs. Biological functionality can be encoded in concentrations of individual proteins, also in relative concentrations of interacting proteins. Our results indicate that the latter aspect may contain a significant amount of complementary information, not accessible with traditional approaches analyzing proteins as independent entities. Consequently, the development and the application of novel system biology tools on blood plasma proteomics will help to better understand patients’ responses to treatment in clinical trials.

Investigating Protein-Protein Interactions Between Grapevine Leafroll-Associated Virus 3 and Vitis vinifera.

Grapevine leafroll disease (GLD) is a globally important disease that affects the metabolic composition and biomass of grapes, leading to a reduction in grape yield and quality of wine produced. Grapevine leafroll-associated virus 3 (GLRaV-3) is the main causal agent for GLD. This study aimed to identify protein-protein interactions between GLRaV-3 and its host. A yeast two-hybrid (Y2H) library was constructed from Vitis vinifera mRNA and screened against GLRaV-3 open reading frames encoding structural proteins and those potentially involved in systemic spread and silencing of host defense mechanisms. Five interacting protein pairs were identified, three of which were demonstrated in planta. The minor coat protein of GLRaV-3 was shown to interact with 3-deoxy-D-arabino-heptulosonate 7-phosphate synthase 02, a protein involved in primary carbohydrate metabolism and the biosynthesis of aromatic amino acids. Interactions were also identified between GLRaV-3 p20A and an 18.1-kDa class I small heat shock protein, as well as MAP3K epsilon protein kinase 1. Both proteins are involved in the response of plants to various stressors, including pathogen infections. Two additional proteins, chlorophyll a-b binding protein CP26 and a SMAX1-LIKE 6 protein, were identified as interacting with p20A in yeast but these interactions could not be demonstrated in planta. The findings of this study advance our understanding of the functions of GLRaV-3-encoded proteins and how the interaction between these proteins and those of V. vinifera could lead to GLD.

DensePPI: A Novel Image-Based Deep Learning Method for Prediction of Protein-Protein Interactions.

Protein-protein interactions (PPI) are crucial for understanding the behaviour of living organisms and identifying disease associations. This paper proposes DensePPI, a novel deep convolution strategy applied to the 2D image map generated from the interacting protein pairs for PPI prediction. A colour encoding scheme has been introduced to embed the bigram interaction possibilities of Amino Acids into RGB colour space to enhance the learning and prediction task. The DensePPI model is trained on 5.5 million sub-images of size 128×128 generated from nearly 36,000 interacting and 36,000 non-interacting benchmark protein pairs. The performance is evaluated on independent datasets from five different organisms; Caenorhabditis elegans, Escherichia coli, Helicobacter Pylori, Homo sapiens and Mus Musculus. The proposed model achieves an average prediction accuracy score of 99.95% on these datasets, considering inter-species and intra-species interactions. The performance of DensePPI is compared with the state-of-the-art methods and outperforms those approaches in different evaluation metrics. Improved performance of DensePPI indicates the efficiency of the image-based encoding strategy of sequence information with the deep learning architecture in PPI prediction. The enhanced performance on diverse test sets shows that the DensePPI is significant for intra-species interaction prediction and cross-species interactions. The dataset, supplementary file, and the developed models are available at https://github.com/Aanzil/DensePPI for academic use only.

Potential biomarkers of aortic dissection based on expression network analysis

BackgroundAortic dissection (AD) is a rare disease with severe morbidity and high mortality. Presently, the pathogenesis of aortic dissection is still not completely clear, and studying its pathogenesis will have important clinical significance.MethodsWe downloaded 28 samples from the Gene Expression Omnibus (GEO) database (Accession numbers: GSE147026 and GSE190635), including 14 aortic dissection samples and 14 healthy controls (HC) samples. The Limma package was used to screen differentially expressed genes. The StarBasev2.0 tool was used to predict the upstream molecular circRNA of the selected miRNAs, and Cytoscape software was used to process the obtained data. STRING database was used to analyze the interacting protein pairs of differentially expressed genes under medium filtration conditions. The R package "org.hs.eg.db" was used for functional enrichment analysis.ResultsTwo hundred genes associated with aortic dissection were screened. Functional enrichment analysis was performed based on these 200 genes. At the same time, 2720 paired miRNAs were predicted based on these 200 genes, among which hsa-miR-650, hsa-miR-625-5p, hsa-miR-491-5p and hsa-miR-760 paired mRNAs were the most. Based on these four miRNAs, 7106 pairs of circRNAs were predicted to be paired with them. The genes most related to these four miRNAs were screened from 200 differentially expressed genes (CDH2, AKT1, WNT5A, ADRB2, GNAI1, GNAI2, HGF, MCAM, DKK2, ISL1).ConclusionsThe study demonstrates that miRNA-associated circRNA-mRNA networks are altered in AD, implying that miRNA may play a crucial role in regulating the onset and progression of AD. It may become a potential biomarker for the diagnosis and treatment of AD.

Computational Prediction of Interactions Between SARS-CoV-2 and Human Protein Pairs by PSSM-Based Images

Identifying protein-protein interactions is essential to predict the behavior of the virus and to design antiviral drugs against an infection. Like other viruses, SARS-CoV-2 virus must interact with a host cell in order to survive. Such interaction results in an infection in the host organism. Knowing which human protein interacts with the SARS-CoV-2 protein is an essential step in preventing viral infection. In silico approaches provide a reference for in vitro validation to protein-protein interaction studies by finding interacting protein pair candidates. The representation of proteins is one of the key steps for protein interaction network prediction. In this study, we proposed an image representation of proteins based on position-specific scoring matrices (PSSM). PSSMs are matrices that are obtained from multiple sequence alignments. In each of its cells, there is information about the probability of the occurrence of amino acids or nucleotides. PSSM matrices were handled as gray-scale images and called PSSM images. The main motivation of the study is to investigate whether these PSSM images are a suitable protein representation method. To determine adequate image size, conversion to grayscale images was performed at different sizes. SARS-CoV-2-human protein interaction network prediction based on image classification with siamese neural network and Resnet50 was performed on PSSM image datasets of different sizes. The accuracy results obtained with 200x200 size images and siamese neural network as 0.915, and with 400x400 size images and Resnet50 as 0.922 showed that PSSM images can be used for protein representation.

Data-driven design of orthogonal protein-protein interactions

Engineering protein-protein interactions to generate new functions presents a challenge with great potential for many applications, ranging from therapeutics to synthetic biology. To avoid unwanted cross-talk with preexisting protein interaction networks in a cell, the specificity and selectivity of newly engineered proteins must be controlled. Here, we developed a computational strategy that mimics gene duplication and the divergence of preexisting interacting protein pairs to design new interactions. We used the bacterial PhoQ-PhoP two-component system as a model system to demonstrate the feasibility of this strategy and validated the approach with known experimental results. The designed protein pairs are predicted to exclusively interact with each other and to be insulated from potential cross-talk with their native partners. Thus, our approach enables exploration of uncharted regions of the protein sequence space and the design of new interacting protein pairs.

DockNet: High Throughput Protein-Protein Interface Contact Prediction.

Over 300,000 protein-protein interaction pairs have been identified in the human proteome and targeting these is fast becoming the next frontier in drug design. Predicting protein-protein interaction sites, however, is a challenging task that traditionally requires computationally expensive and time-consuming docking simulations. A major weakness of modern protein docking algorithms is the inability to account for protein flexibility, which ultimately leads to relatively poor results. Here we propose DockNet, an efficient Siamese graph-based neural network method which predicts contact residues between two interacting proteins. Unlike other methods that only utilize a protein's surface or treat the protein structure as a rigid body, DockNet incorporates the entire protein structure and places no limits on protein flexibility during an interaction. Predictions are modelled at the residue level, based on a diverse set of input node features including residue type, surface accessibility, residue depth, secondary structure, pharmacophore and torsional angles. DockNet is comparable to current state-of-the-art methods, achieving AUC value of up to 0.84 on an independent test set (DB5), can be applied to a variety of different protein structures and can be utilized in situations where accurate unbound protein structures cannot be obtained. DockNet is available at https://github.com/npwilliams09/docknet and an easy-to-use webserver at https://biosig.lab.uq.edu.au/docknet. Supplementary data are available at Bioinformatics online.

3D genome assisted protein–protein interaction prediction

Protein–protein interactions (PPIs) play key roles in the biological system, making revealing protein interactome critical. A large portion of the protein interactome however, are yet to be revealed. Dozens of computational methods have been proposed to predict PPIs, but none of the existing method considers the DNA origin the interacting proteins in the perspective of 3D genome. In this work, we project the interacting proteins into the corresponding 3D genome loci and discovered that for the interacting protein pairs, their corresponding gene pairs are more likely to be proximate in chromatin 3D conformation. By incorporating 3D genome information, as well as adopting heterogeneous protein sequence encoding schemes, existing PPI prediction methods can be significantly improved in terms of accuracy and AUC. Combining our previous discoveries, we conjecture the existence of 3D genome driven cellular compartmentalization, which leads to an important principle that co-localized DNA elements can lead to increased probability of the co-localization of their downstream RNA and protein elements, thus facilitating the efficiency of the biological system.

Efficacy and specificity of inhibitors of BCL-2 family protein interactions assessed by affinity measurements in live cells.

Cytoplasmic and membrane-bound BCL-2 family proteins regulate apoptosis, a form of programmed cell death, via dozens of binary protein interactions confounding measurement of the effects of inhibitors in live cells. In cancer, apoptosis is frequently dysregulated, and cell survival depends on antiapoptotic proteins binding to and inhibiting proapoptotic BH3 proteins. The clinical success of BH3 mimetic inhibitors of antiapoptotic proteins has spawned major efforts by the pharmaceutical industry to develop molecules with different specificities and higher affinities. Here, quantitative fast fluorescence lifetime imaging microscopy enabled comparison of BH3 mimetic drugs in trials and preclinical development by measuring drug effects on binding affinities of interacting protein pairs in live cells. Both selectivity and efficacy were assessed for 15 inhibitors of four antiapoptotic proteins for each of six BH3 protein ligands. While many drugs target the designed interaction, most also have unexpected selectivity and poor efficacy in cells.

Prediksi interaksi protein-protein berbasis sekuens protein menggunakan fitur autocorrelation dan machine learning

Protein-protein interaction (PPI) can define a protein's function by knowing the protein's position in a complex network of protein interactions. The number of PPIs that have been identified is relatively small. Therefore, several studies were conducted to predict PPI using protein sequence information. This research compares the performance of three autocorrelation methods: Moran, Geary, and Moreau-Broto, in extracting protein sequence features to predict PPI. The results of the three extractions are then applied to three machine learning algorithms, namely k-Nearest Neighbor (KNN), Random Forest, and Support Vector Machine (SVM). The prediction models with the three autocorrelation methods can produce predictions with high average accuracy, which is 95.34% for Geary in KNN, 97.43% for Geary in RF, and 97.11% for Geary and Moran in SVM. In addition, the interacting protein pairs tend to have similar autocorrelation characteristics. Thus, the autocorrelation method can be used to predict PPI well.

Large-scale protein-protein post-translational modification extraction with distant supervision and confidence calibrated BioBERT

MotivationProtein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. A range of protein functions are mediated and regulated by protein interactions through post-translational modifications (PTM). However, only 4% of PPIs are annotated with PTMs in biological knowledge databases such as IntAct, mainly performed through manual curation, which is neither time- nor cost-effective. Here we aim to facilitate annotation by extracting PPIs along with their pairwise PTM from the literature by using distantly supervised training data using deep learning to aid human curation.MethodWe use the IntAct PPI database to create a distant supervised dataset annotated with interacting protein pairs, their corresponding PTM type, and associated abstracts from the PubMed database. We train an ensemble of BioBERT models—dubbed PPI-BioBERT-x10—to improve confidence calibration. We extend the use of ensemble average confidence approach with confidence variation to counteract the effects of class imbalance to extract high confidence predictions.Results and conclusionThe PPI-BioBERT-x10 model evaluated on the test set resulted in a modest F1-micro 41.3 (P =5 8.1, R = 32.1). However, by combining high confidence and low variation to identify high quality predictions, tuning the predictions for precision, we retained 19% of the test predictions with 100% precision. We evaluated PPI-BioBERT-x10 on 18 million PubMed abstracts and extracted 1.6 million (546507 unique PTM-PPI triplets) PTM-PPI predictions, and filter approx 5700 (4584 unique) high confidence predictions. Of the 5700, human evaluation on a small randomly sampled subset shows that the precision drops to 33.7% despite confidence calibration and highlights the challenges of generalisability beyond the test set even with confidence calibration. We circumvent the problem by only including predictions associated with multiple papers, improving the precision to 58.8%. In this work, we highlight the benefits and challenges of deep learning-based text mining in practice, and the need for increased emphasis on confidence calibration to facilitate human curation efforts.

Predicting Primary Sequence-Based Protein-Protein Interactions Using a Mercer Series Representation of Nonlinear Support Vector Machine

The prediction of protein-protein interactions (PPIs) is essential to understand the cellular processes from a medical perspective. Among the various machine learning techniques, kernel-based Support Vector Machine (SVM) has been commonly employed to discriminate between interacting and non-interacting protein pairs. The main drawback of employing the kernel-based SVM to datasets with many features, such as the primary sequence-based protein-protein dataset, is the significant increase in computational time of training stage. This increase in computational time is mainly due to the presence of the kernel in solving the quadratic optimisation problem (QOP) involved in nonlinear SVM. In order to fix this issue, we propose a novel and efficient computational algorithm by approximating the kernel-based SVM using a low-rank truncated Mercer series as well as desired. As a result, the QOP for the approximated kernel-based SVM will be very tractable in the sense that there is a significant reduction in computational time of training and validating stages. We illustrate the novelty of the proposed method by predicting the PPIs of “S. Cerevisiae” where the protein features extracted using the multiscale local descriptor (MLD), and then we compare the predictive performance of the proposed low-rank approximation with the existing methods. Finally, the new method results in significant reduction in computational time for predicting PPIs with almost as accuracy as kernel-based SVM.

Using Surface Hydrophobicity Together with Empirical Potentials to Identify Protein-Protein Binding Sites: Application to the Interactions of E-cadherins.

Studying the interactions within protein structures can inform about the details of how proteins of various types interact and aggregate. Empirical contact potentials have proven to be extremely important in the evaluation of individual modeled protein structures, but have found few applications to protein-protein interactions. In part, this is caused by a lack of properly formulated potentials with a proper reference state. Since the comparisons are made between different bound structures, the proper reference state should take into account other contacts. Therefore, a preferred reference state should be defined with respect to a given residue type interacting with an average residue instead of interacting with solvent as typically is used in derivation of statistical contact potentials. Here, a two-stage procedure for generating and evaluating interacting protein pairs is described, and an example of E-cadherin interactions is shown.

Construction of sRNA Regulatory Network for Magnaporthe oryzae Infecting Rice Based on Multi-Omics Data.

Studies have shown that fungi cause plant diseases through cross-species RNA interference mechanism (RNAi) and secreted protein infection mechanism. The small RNAs (sRNAs) of Magnaporthe oryzae use the RNAi mechanism of rice to realize the infection process, and different effector proteins can increase the autotoxicity by inhibiting pathogen-associated molecular patterns triggered immunity (PTI) to achieve the purpose of infection. However, the coordination of sRNAs and proteins in the process of M. oryzae infecting rice is still poorly understood. Therefore, the combination of transcriptomics and proteomics to study the mechanism of M. oryzae infecting rice has important theoretical significance and practical value for controlling rice diseases and improving rice yields. In this paper, we used the high-throughput data of various omics before and after the M. oryzae infecting rice to screen differentially expressed genes and sRNAs and predict protein interaction pairs based on the interolog and the domain-domain methods. We were then used to construct a prediction model of the M. oryzae-rice interaction proteins according to the obtained proteins in the proteomic network. Finally, for the differentially expressed genes, differentially expressed sRNAs, the corresponding mRNAs of rice and M. oryzae, and the interacting protein molecules, the M. oryzae-rice sRNA regulatory network was built and analyzed, the core nodes were selected. The functional enrichment analysis was conducted to explore the potential effect pathways and the critical infection factors of M. oryzae sRNAs and proteins were mined and analyzed. The results showed that 22 sRNAs of M. oryzae, 77 secretory proteins of M. oryzae were used as effect factors to participate in the infection process of M. oryzae. And many significantly enriched GO modules were discovered, which were related to the infection mechanism of M. oryzae.