Since the term proteomics was first coined in 1996 to describe the systematic study of the entire protein complement of a cell, tissue, or organism , advanceIn recent decades there has been a constant flow of technological advances that have enabled researchers to capture a deeper view of the proteome. With the development of 2D electrophoresis came the ability to map the complexity of a whole proteome , howeverAdvances in sample preparation have gone hand in hand with advances in instrumentation, with developments in protein extraction and derivatization methods and separation approaches such as electrophoresis and chromatography underpinning our ability to resolve complex protein mixtures into their individual components. This is particularly critical given the extreme diversity in structures afforded by post-translational modifications (PTMs) which have critical importance in modulating biological activity. At the other end of the experiment, obtaining biological insights requires interpretation of the often vast bodies of proteomics data. Hence improved methods of bioinformatics analysis are required to both better inform proteomic study design and evaluate and interpret the long list of protein identifications and abundances obtained from experiment to gain insight into biological function and regulation.In this research topic, we have collated a selection of original research and review articles detailing new developments in technology, critical to the advancement of protein identification and structural characterization. These articles also demonstrate the increasing importance of separation and data analysis pipelines in optimising the information content afforded by proteomics experiments.Kobayashi and Imai provide an overview of recent developments in fluorogenic derivatisation (FD) coupled with liquid chromatography (LC) for detection and quantification of proteins. They highlight its simplicity and reproducibility for protein separation and quantification prior to identification by traditional bottom-up MS approaches using a database search algorithm and demonstrate applications for comprehensive differential proteomics analysis. Finally, new developments for nano-LC-FD-LC systems and direct coupling to MS are described.To emphasize the impact of sample preparation and chemical labelling methods in proteomics analyses, Young et al., here in the context of investigating protein post-translational modifications. The significance of glycosylation in controlling protein function and dysfunction is widely acknowledged, as are the analytical challenges associated with MS structure determination of glycans, in part due to their isomeric structures and notable heterogeneity. This study establishes the utility of in-house packed porous graphitic carbon columns for the robust and reproducible LC-MS analysis of N-glycans, highlighting quantitative applications in biomarker discovery from ovarian cancer tissues.The importance of fractionation and separation methods has been further highlighted by Pang et al. build on the analysis of protein glycosylation in their study of monoclonal antibody biologics, where the glycosylation profile is key to optimal therapeutic outcomes and hence is a vital consideration in bio-manufacture quality control. They describe a robust, semi-automated workflow for glycoproteomic characterisation and quantitation from an LC-MS dataset of glycopeptides and peptides that is suitable to rapidly relay information back to the manufacturing line in biologic development scenarios, with limited user expertise and intervention. This work nicely highlights the importance of continued innovation in data analysis strategies to support integration of proteomics methodology into settings outside of the research environment.Medvecky and Mandalakis report a bioinformatic pipeline that overcomes this complexity for microbial systems by gathering homologous protein sets and extracting the most representative, process-specific and LC-MS/MS-amenable peptides for targeted analysis, to gain improved insights into microbial function.Moving from untargeted analyses to targeted proteomics strategies can be a powerful approach for sensitive detection and precise quantification of selected proteins underpinning key biological processes. However, this is complicated in samples from complex, multi-species systems, where biochemical processes of interest are often controlled by many homologous proteins with varied amino acid sequences which confounds LC-MS identification and quantification. Butler et al. demonstrate the power of proteomics approaches in the realm of structural biology. Ion mobility coupled with native MS can reveal insights into the tertiary and quaternary structures of proteins that may be intractable using traditional structural biology methods, particularly for heterogeneous systems. With a focus on Cu/Zn superoxide dismutase important in protection of cells from oxidative damage, they describe structural transitions along the unfolding pathway from the holo-dimer to the holo-monomer, single-metal monomer, and apo-monomer across multiple commercially available instrument platforms, and underscore the ability of this technology to provide valuable structural information for molecular modelling of protein structure and interactions.Finally,


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.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, which seriously affects crop stability and sustainability around the world  were first studied in depth. sRNAs refer to those that do not encode proteins in the organism and are mostly 18nt-40nt in length . The comIn addition, fungi as eukaryotes, their secreted proteins are transported across the membrane by endocytosis and exocytosis . SecreteM. oryzae infecting rice, this study proposed a new method to analyze the multi-omics data of M. oryzae infecting rice and constructed a multi-omics data integration-based M. oryzae-rice interaction network. It also wholly presented the interaction relationship between the markers of various omics in the process of M. oryzae infecting rice and revealed the key nodes that play a regulatory role in M. oryzae infection in rice. This paper found a possible solution for studying the mechanism of M. oryzae infecting rice and provided research ideas for preventing and controlling rice and other food crops.However, at present, the research on the mechanism of fungus-plant interaction is still in its infancy . In addiM. oryzae-rice sRNA interaction network and M. oryzae-rice protein interaction network. Then, the sRNA and protein interaction networks of M. oryzae and rice were analyzed. Finally, the PPI interaction networks and GO functional enrichment modules of M. oryzae and rice were excavated, respectively, and the key factors of multiple omics joint regulations and the biological processes involved were explored. The design roadmap for this work is shown in Firstly, the genomic, transcriptome, and proteome data were analyzed to establish the M. oryzae infection with rice at 72 h, sRNA data of M. oryzae cultured on a complete medium for 16 h, the mixed sRNA data of the rice infected by M. oryzae for 72 h  is based on the principle that homologous proteins have similar functional and structural characteristics . Here, tM. oryzae, and the threshold was set as E-value less than or equal to 1E-5 and similarity greater than or equal to 30. Then, the model pathogen and mode host protein pairs corresponding to M. oryzae and rice proteins were matched, and the protein pair files of M. oryzae and rice were obtained based on the interolog method.In this process, the interolog method was used to screen the protein interaction pairs between rice and M. oryzae protein B′ have the same interaction functional domains as protein A and protein B, then rice protein A′and M. oryzae protein B′ interact. The process is shown in The available domain-based protein interaction prediction method (domain-domain interaction method) is based on the principle that interacting protein pairs may have the exact functional domains . For exaM. oryzae through the Pfam database. E-value was selected as 1E-5 and the coincidence rate was selected as 90%. TSV files containing protein IDs, protein functional domains and E-values were obtained. Then, protein domain files were extracted and sorted to obtain protein interaction relationship pairs based on functional domains.In this process, functional domains were obtained from the protein amino acid sequences of mode hosts, mode pathogens, rice and M. oryzae proteins can be transported across the membrane, it is necessary to do the secreted protein identification of the above M. oryzae proteins and screen out the M. oryzae-rice protein interaction network that M. oryzae proteins were secreted proteins.By combining the interolog method and domain-domain method, 83664 pairs of protein interactions were obtained following the two methods. However, not all M. oryzae were predicted on TMHMM. The FASTA files of 323 M. oryzae protein amino acid sequences were obtained through the Uniport database and imported into the TMHMM website to obtain their secreted proteins’ predicted results. When the expected number of amino acids in the transmembrane helix of a protein is greater than or equal to 18, or when the transmembrane helix number of N-the best predicted is greater than or equal to 1, the protein can be considered a secreted protein. Therefore, protein IDs with parameters ExpAA greater than or equal to 18 or PredHel greater than or equal to 1 were extracted. The obtained M. oryzae secreted proteins were matched and screened with the previous 83664 M. oryzae-rice protein interaction pairs, and finally, 7352 M. oryzae-rice protein interaction pairs were obtained.In this paper, the secreted proteins of M. oryzae and rice interaction protein pairs and obtained protein interaction pairs through the sequence and functional structure prediction in experiments. The 7352 data of the effective interaction pairs of M. oryzae and rice obtained above were used as positive samples. The negative samples were randomly selected from other M. oryzae rice protein interaction pairs except the positive samples that the ratio of positive and negative samples was 1:1.This paper established a prediction model for M. oryzae and rice, the proteins’ amino acid sequences and functional domains were used as the feature data. In addition, functional domain texts were preprocessed before training, including unifying special symbols, spaces, upper and lower case letters of each functional domain and removing stop words to achieve standardized processing of data samples.For the protein features of Recurrent Neural Network (RNN) is mainly used in sequence prediction, character generation, emotion recognition, man-machine dialogue, etc. RNN is a kind of recursive neural network that takes sequence data as input, recurses in the sequence’s evolution direction, and connects all nodes in a chain. The sequence information determines the task of the event itself, which requires previous knowledge and current information to determine the output result jointly. As a result, textRNN can more effectively address the problem of contextual semantic relevance. Considering that the protein’s amino acid sequence and functional domains belonged to short texts, which have contextual semantic relevance characteristics, this paper used textRNN to construct the protein interaction pair binary classification model.M. oryzae and rice protein interaction pair segmentation were used as the input variable of the RNN model. The first hidden layer activated this input. Then the successive activations were performed layer by layer to get the output. Each hidden layer had its own weight and bias. Parameters such as the classification results, accuracy and loss function of the output protein interaction pairs were output by the output layer. The optimal RNN protein interaction model was obtained by adjusting learning_rate, dropout_keep_prob and total iteration cycles according to the learning curve and confusion matrix. Finally, different evaluation indexes were applied to evaluate and verify the model. The accuracy of protein interaction pairs predicted by the interolog method and domain-domain method in this paper was proved.A multi-layer RNN network needs to be established in the construction of RNN model. The dropout layer was added after each RNN kernel function, and the amino acid sequences after the M. oryzae-rice, and the network diagram of M. oryzae-rice protein interaction was too significant. Therefore, the PPI networks of M. oryzae and rice jointly regulated by various omics were explored, respectively. First, the PPI network of M. oryzae was mined based on the proteins regulated by M. oryzae differentially expressed sRNAs and M. oryzae proteins in the M. oryzae-rice protein interaction network. And the PPI network of rice was mined based on the proteins regulated by rice differentially expressed genes and rice proteins in the M. oryzae-rice protein interaction network. Then the PPI networks of M. oryzae and rice were analyzed for GO pathway enrichment, and the modules were separated. Finally, by analyzing the isolated M. oryzae and rice protein networks, the main modules’ biological functions and KEGG enrichment pathways were described. The key nodes of M. oryzae-rice and their interaction networks were mined by using multi-omics network data to explore the molecular mechanism of M. oryzae and rice interaction.In order to analyze the obtained sRNA and protein interaction network of The learning curves of the textRNN model on the functional domain and amino acid sequence are shown in The model was evaluated according to the precision, recall and F1 indexes, and the accuracy indexes of the TEXTRNN model in the functional domain and amino acid sequence are shown in When textRNN model with the functional domain as feature data was tested, the testAcc of textRNN model was 98.81%, testLoss was 0.029, and the confusion matrix was: When textRNN model with protein amino acid sequence as feature data was tested, the testAcc of textRNN model was 97.49%, testLoss was 0.086, and the confusion matrix was: M. oryzae-rice protein interaction pairs, and the prediction model performed well in this paper. Furthermore, the prediction of protein interaction pairs in plants infected by other fungi can also refer to this model.Therefore, the textRNN model can be used to predict the M. oryzae sRNAs, 1,857 M. oryzae-rice sRNA interaction pairs and 664 M. oryzae internal sRNA interaction pairs were obtained. By digging positive and negative regulatory factors, 1,166 M. oryzae genes, 1,121 rice genes, 1,173 M. oryzae proteins and 1,677 rice proteins were found to be involved in the biological process of M. oryzae infection to rice. In addition, the transcriptome network of M. oryzae and rice was visualized by the Cytoscape tool. There were 20 sRNA-mRNA interaction clusters with two or more sRNAs involved in regulation. The network diagram was shown in After target prediction of the 623 kinds of M. oryzae and rice protein interaction pairs obtained previously, the M. oryzae-rice protein interaction network diagram was drawn with a total of 11 rice protein interaction clusters. The network diagram was shown in Based on the 7,352 M. oryzae sRNAs and 581 kinds of M. oryzae secreted proteins directly involved in the two interaction mechanisms were excavated through the M. oryzae-rice sRNA interaction network and protein interaction network, and they were put into the STRING database for GO pathway enrichment analysis and KEGG enrichment analysis. First, the p-value was set as 1E-16, and the GO enrichment results and KEGG enrichment results were derived. The PPI network diagram of M. oryzae was too large to be shown in this paper and was shown in M. oryzae. It can be seen that most of these enrichment pathways were involved in the biological processes of sRNA synthesis, protein synthesis and transport in M. oryzae. To some extent, the above conclusions proved that M. oryzae could complete the infection process of rice through sRNAs and secreted proteins.A total of 593 kinds of M. oryzae and rice were analyzed by GO enrichment and KEGG enrichment. However, there were too many rice-related protein nodes. Firstly, the protein nodes obtained by three ways were mined through the STRING database for their PPI. Then the rice protein interaction pairs obtained were imported into Cytoscape to obtain the rice protein interaction network. Finally, the rice protein interaction network was divided into modules and the largest five rice modules were screened out. The GO enrichment pathways  represents the shortest path from s to t and through n.Cytoscape calculated the network topology attributes, and its plug-in NetworkAnalyzer was used to calculate the degree and betweenness of nodes in each subnet. Betweenness is a measure of the centrality of a node in the network. In some sense, it measures the influence of a node on information spread through the network. The following formula calculates betweenness:The nodes of each subnet were sorted according to betweenness, and the top 6 nodes with the highest betweenness in each subnet were obtained, which were regarded as the central nodes of the subnet and marked in the subnet interaction diagram.M. oryzae, five largest significant functional enrichment subnets were selected, which were the M. oryzae helicase activity and protein synthesis module (Cluster 1), M. oryzae DNA repair-related module (Cluster 2), M. oryzae RNA transport and molecular transport-related module (Cluster 3), M. oryzae gene expression and mRNA processing-related module (Cluster 4) and M. oryzae biosynthetic pathway-related subnet (Cluster 5). Cluster 1 was closely related to a series of protein synthesis processes and helicase activity , rice GTP and nucleoside triphosphatase-related module (Cluster 7), rice gene expression, transport and metabolism-related module (Cluster 8), rice protein synthesis module (Cluster 9) and rice gene expression and defense response regulation module in rice (Cluster 10). Cluster 6 was significantly enriched in the unfolded protein binding function module . Cluster of rice . Cluster of rice . The go  in rice .M. oryzae infecting rice process to predict the target genes of rice mRNAs, 1,857 rice mRNAs were obtained, pointing to 1,121 rice genes. After the 257 sRNAs were down-regulated during the M. oryzae infecting rice process to predict the target genes of M. oryzae mRNAs, 664 M. oryzae mRNAs were obtained, and 264 M. oryzae genes were involved in regulation. The 664 kinds of M. oryzae mRNAs were input into the Uniport database to obtain 2,644 protein IDs corresponding to these mRNAs. According to GO, the obtained protein IDs were matched with their interacting protein IDs to expand the proteins involved in regulation by M. oryzae. These expanded proteins also used TMHMM to predict secreted proteins, and 337 M. oryzae proteins were obtained. Then 601 protein IDs, which were involved in the transboundary regulation of the secreted proteins of M. oryzae, were matched with the M. oryzae-rice protein interaction pair network to obtain the M. oryzae and rice sRNA-protein interaction network (After the 366 sRNAs up-regulated during the  network .M. oryzae infecting rice interaction network diagram and the rice response of M. oryzae infection network diagram obtained above were combined to find the biomolecules that play a role in them. However, the large number of these biomolecules was not conducive to our further analysis of M. oryzae and rice interaction mechanism, so core node mining was needed. In this study, the biomolecules involved in the infection of rice by M. oryzae were extracted by multi-omics joint analysis, including 8 rice differentially expressed genes, 31 rice mRNAs, 77 rice proteins, 22 M. oryzae sRNAs, 1 M. oryzae mRNA, and 38 M. oryzae proteins (The proteins .M. oryzae, 8 newly increased sRNA data of M. oryzae, and 2 down-regulated sRNA data of M. oryzae. 20 up-regulated and newly added sRNA data were used to infect rice by targeting rice mRNAs for rice RNA silencing. And 2 down-regulated sRNAs of M. oryzae may increase some proteins in M. oryzae to achieve the purpose of invading rice by secreted proteins.22 differentially expressed sRNAs were found, including 12 up-regulated sRNA data of 77 rice core proteins were imported into the STRING database, 32 influential rice gene nodes were obtained, and GO function enrichment analysis and KEGG pathway enrichment analysis was conducted. There were 19 interacting gene nodes. The PPI interaction network diagram of rice core nodes is shown in The enrichment analysis of the GO pathway of rice core protein nodes found that the significantly enriched GO functions in rice were distributed in three aspects. One was gene expression-related modules, including negative regulation of gene expression, gene expression regulation, epigenetic regulation, gene silencing, and gene expression. The second was protein molecular synthesis and transport-related modules, including protein complex, protein heterodimerization activity, nucleic acid binding, protein binding, organic circular compound binding, heterocyclic compound binding, DNA binding, intracellular protein transport. The third was metabolism-related modules, including protein metabolism process, macromolecular metabolism process, proteolysis, nitrogen compound metabolism process, cellular macromolecular decomposition process, regulation of nitrogen compound metabolism process, regulation of primary metabolic process, primary metabolic process, etc. According to the KEGG pathways enrichment analysis of rice core nodes, the significantly enriched KEGG pathways were protein processing and endocytosis in the endoplasmic reticulum. These GO functional modules with significant enrichment of rice key proteins were basically consistent with the GO functions of the main modules of the rice regulatory network, which verified the accuracy of the rice core proteins mined through multi-omics joint analysis.M. oryzae infecting rice mechanism and rice core nodes for the combined analysis found that the GOs were significantly enriched in the gene expression regulation module, protein synthesis and transport module, and metabolism module. The significant enrichment of gene expression modules indicated that M. oryzae silenced rice genes through RNA silencing mechanism to achieve the purpose of infecting rice. In addition, the protein synthesis and transport module showed that M. oryzae infected rice by secreted proteins. The module included protein synthesis, nucleic acid binding, protein binding, organic cyclic compound binding, heterocyclic compound binding and transport. These results indicated that M. oryzae invaded rice by secreted proteins which combined with some proteins or biomolecules in rice to affect the defense mechanism of rice, thus realizing the infection process. Based on the analysis of KEGG metabolic pathway in rice, it was found that these key proteins in rice affected the metabolic mechanism of rice. After M. oryzae infected rice by sRNAs and secreted proteins, the rice metabolism was affected, including nitrogen compound metabolism, protein metabolism, other biological macromolecules metabolism, etc. The table of these GO enrichment modules was shown in GO functional modules of the M. oryzae and rice were used to excavate the interaction network between M. oryzae and rice to explore the mechanism of M. oryzae infection on rice to mine the key nodes involved in the interaction process. The data of each omics used in this paper included sRNA data before and after M. oryzae infecting rice, M. oryzae mRNA data, M. oryzae protein data, M. oryzae gene expression data before and after M. oryzae infecting rice, rice mRNA data, rice protein data, and protein data of mode host-mode fungus. First, each omics data was screened separately to mine differentially expressed rice gene data, M. oryzae-rice sRNA interaction pairs, and M. oryzae-rice protein interaction pairs. Then, the interaction network of each omics was analyzed longitudinally to construct the regulatory network of M. oryzae-rice multi-omics interaction and explore its biological process.In this study, a variety of omics data of M. oryzae. In transcriptomics, this study analyzed the sRNA data of M. oryzae before and after infection with rice and obtained 366 kinds of up-regulated and newly added sRNAs of M. oryzae, which all had the possibility of interacting with host rice, that is, to infect rice by RNA silencing mechanism. In addition, for the 257 species of M. oryzae sRNAs whose expression levels were reduced during the infection process, it may be through the regulation of the protein expression in M. oryzae, through the secreted protein into the rice to achieve the purpose of infection. Therefore, according to the two infection mechanisms of M. oryzae, the 623 kinds of M. oryzae sRNAs screened were analyzed. Furthermore, through the method of target gene prediction, 1,857 sRNA interaction pairs of M. oryzae-rice and 664 sRNA interaction pairs of M. oryzae were found.In genomics, a total of 1,368 1.5-fold differentially expressed rice genes were extracted by screening the gene expression data of rice before and after the infection of M. oryzae to affect the defense and growth of rice in the existing studies. In this paper, the protein interaction pairs between mode pathogens and mode hosts that experiments have verified were collected and used as the prediction template. Firstly, the interolog method based on homology was used to predict the protein interaction pairs between M. oryzae and rice. Next, the domain-domain method was used to make the second prediction of the protein interaction pairs predicted by the interolog method. Then TMHMM secreted protein prediction tool was used to screen the secreted proteins of M. oryzae. In the screening of the final protein interaction pairs, the three prediction methods should be met simultaneously, and 7,352 protein interaction pairs of M. oryzae-rice were obtained.In proteomics, some studies have proved that the secreted proteins of the pathogen can enter the host body and interact with the host proteins to interfere with the protein expression of the host. However, it is not clear which protein molecules are involved in the infection process of M. oryzae sRNAs, 1 M. oryzae mRNA and 38 M. oryzae proteins were identified as the core nodes of the M. oryzae and rice multi-omics interaction network by high-throughput data analysis, combined with joint analysis of M. oryzae and rice multi-omics data, which involved significantly enriched GO modules. Most of them were related to gene expression, molecular protein synthesis, molecular transport and metabolism, that is, the infection mechanism of M. oryzae. However, all the experiments in this paper were based on the premise that sRNA and protein interaction mechanisms exist between M. oryzae and rice. The accuracy of this experiment still needs to be further verified. In addition, due to the mutual regulation between plants and pathogens, some host sRNAs and secreted proteins can enter the fungi during the infection process to resist infection. However, this paper only studied the infection mechanism of M. oryzae and neglected the analysis of the defense mechanism of rice. Moreover, significant enrichment of biomolecular transport modules was found in the GO function enrichment analysis of key factors of M. oryzae in this study, but it is not clear which rice biomolecules are involved in the defense mechanism. And although there are some insufficient, this paper for the M. oryzae infecting rice joint analysis of multi-omics data, which provided a specific data basis for further study of the mechanism of M. oryzae-rice interaction, made some specific contributions to the prevention of diseases and insect pests in rice and provided a new train of thought and theoretical basis for the fungus-plant interactions mechanism research.In this study, a total of 8 rice differentially expressed genes, 31 rice mRNAs, 77 rice proteins, 22


Fusarium oxysporum f. sp. cubense race 4(Foc4) infects almost all banana species, and it is the most destructive. The molecular mechanism of the interactions between Fusarium oxysporum and banana still needs to be further investigated.The pathogen of banana We use both the interolog and domain-domain method to predict the protein–protein interactions (PPIs) between banana and Foc4. The predicted protein interaction sequences are encoded by the conjoint triad and autocovariance method respectively to obtain continuous and discontinuous information of protein sequences. This information is used as the input data of the neural network model. The Long Short-Term Memory (LSTM) neural network five-fold cross-validation and independent test methods are used to verify the predicted protein interaction sequences. To further confirm the PPIs between banana and Foc4, the GO (Gene Ontology) and KEGG (Kyoto Encylopedia of Genes and Genomics) functional annotation and interaction network analysis are carried out.The experimental results show that the PPIs for banana and foc4 predicted by our proposed method may interact with each other in terms of sequence structure, GO and KEGG functional annotation, and Foc4 protein plays a more active role in the process of Foc4 infecting banana.This study obtained the PPIs between banana and Foc4 by using computing means for the first time, which will provide data support for molecular biology experiments.The online version contains supplementary material available at 10.1186/s12953-022-00186-2. Fusarium oxysporum f. sp. cubense race 4(Foc4), also known as yellow leaf disease and Panama disease, is a typical fungal soil-borne disease caused by Fusarium oxysporum f.sp.cubense infection, which destroys banana vascular bundles and causes plant death , so as to remove the influence of protein length on frequency counting. Let  follows :1\documeThe interactions between amino acids are reflected by seven physical and chemical characteristics of amino acids. The seven physical and chemical properties are hydrophobicity, hydrophilicity, net charge index, polarity, polarizability, solvent accessible surface area, and side chain volume, respectively. Each protein sequence is transformed into a 7-dimensional vector, and each amino acid is represented by a normalized value of seven descriptors. The initial values of seven physical and chemical properties of 20 amino acids can be found in . The var follows :2\documelag represents the distance between the two amino acid residues, n is the length of protein sequence X, Xi,j represents the j-th descriptor in the i-th position of a protein sequence. In this paper, seven physical and chemical properties are used and the optimal value of lag is set to 30 [where et to 30 . After AWe used the interolog method and domain-domain method to deal with the proteins of banana and Foc4 to obtain their PPIs, and treated these PPIs as the positive samples with size 739. We verified the predicted results by the five-fold cross-validation method and independent test method, respectively. The Long Short-Term Memory(LSTM) neural network  was usedhf and hb. The corrected linear unit(relu) was used as the activation function in the hidden layer, and the softmax function was used in the output layer. According to the results of the CT and AC coding schemes, the input sequence was m, the size of negative samples is 10 × m. We selected the samples with size of 2 × m/3 in the positive samples and the samples with size of 2 × m/3 in the negative samples to form the training set, and selected the remaining positive samples with size of m/3 and the remaining negative samples with size of 10 × m-2 × m/3 = 28 × m/3 to form the test set.By using the characteristic coding of the PPIs between banana and Foc4, the original protein sequence was converted into a fixed-length numerical vector which was used as the input of the LSTM neural network. The input layer of LSTM neural network was a feature vector composed of the forward and backward hidden layer output vectors ACC, sensitivity Sn, specificity Sp, receiver operating characteristic curve ROC, and area under curve AUC to evaluate the prediction effect [In this paper, we used the accuracy n effect :3\documeTN is the number of true counterexamples, TP represents the number of true examples, FN denotes the number of false counterexamples, and FP is the number of false-positive examples.where i of node i in the network is calculated as follows:Each protein is used as a node and the interaction between each pair of proteins is represented as an edge, a PPIs network is created by all the nodes and edges. We used the software Cytoscape3.7  to visuas to node t, and s to node t via node i in the network.where p < 0.05 and used Bonferroni correction [p-value < 0.05) of PPIs was performed by using KOBAS2.We applied the software TBTools  to carryrrection . KEGG (KThe computer used was with Intel (R) Xeon (R) W-2133 CPU @ 3.6 GHz processor and memory capacity 8 GB running operating system Windows10. The prediction algorithm was implemented by Python3 programming.We first predicted 26,910 PPIs and 376,755 PPIs between banana and Foc4 by using the interolog method and domain-domain method, respectively. Table It can be seen from the results in Table Sn, specificity Sp, accuracy ACC, and receiver operating characteristic curve ROC of the predicted banana-Foc4 PPIs.We extracted the feature vector of proteins in banana-Foc4 PPIs, and analyzed the reliability of banana-Foc4 PPIs predicted by the LSTM neural network-based five-fold cross-validation method and independent test method. Table We can see from Table The following is to analyze the network structure characteristics of the PPIs between banana and Foc4 predicted by the experiment. By using Cytoscape, each protein in the interactions between banana and Foc4 was treated as a node, and each interaction between banana and Foc4 was treated as an edge. The result of the PPIs network between banana and Foc4 is shown in Fig. In the PPI network, the connectivity of a protein is defined as the number of all other proteins linking to this protein. The connectivity is an index of evaluating the importance of a protein in the network. From Fig. To annotate the GO function of PPIs for banana and Foc4, we first aligned the banana protein with SwissProt protein by the software BLAST. Then, we compared the obtained Foc4 protein with SwissProt protein. Finally, we used the TBTools to annotate the GO function PPIs for banana and Foc4. The top 20 annotated results of proteins for Foc4 are shown in Table It can be seen from Table Table It can be seen from Table The GO annotation results of predicted PPIs between banana and Foc4 show that Foc4 protein were annotated the functions related to cell membrane such as vesicle fusion, transmembrane export, transmembrane transport and membrane tissue, and banana protein were annotated the functions related to external stress response such as transport, tropism, cell automatic regulation and cell signal transduction. The KEGG annotation results show that the Foc4 protein annotates membrane transport, ABC transporters, interactions in vesicular transport and transporters. The banana protein were annotated the functions related to the environmental information processing pathway. This illustrates that the PPIs between banana and Foc4 predicted by our method are reliable from the perspective of GO and KEGG functional annotation.One of the characteristics of this study is that the intra-species and inter-species PPIs downloaded from the database were used as interaction templates, the PPIs between banana and Foc4 were predicted by the interolog method and the domain-domain method respectively, and the intersection of PPIs predicted by these two methods was taken as the final predicted result which was more accurate. In addition, the problem studied here is inter-species protein interaction, which uses not only intra-species protein interaction of model species as prediction template but also uses inter-species protein interaction of multiple species as prediction template. The template of interspecific interaction prediction comes from the database HPIDB, which contains PPIs of 66 species of animals, plants, and many pathogens, including interspecific protein interactions between animals and microorganisms and the ones between plants and microorganisms.In this paper, we coded the sequence of PPIs by the combined use of CT method and AC method. The CT method regards every three consecutive amino acids as a basic unit and counts the class frequency of all basic units in protein, while the AC method mainly pays close attention to the proximity effect. In this way, the continuous and discontinuous sequence information of proteins can be used at the same time, which makes the prediction result more accurate. We verified the PPIs dataset between banana and Foc4 by LSTM neural network-based five-fold cross-validation method and independent test method.By observing the results of GO and KEGG function annotation and PPIs network analysis, we found that there were many Foc4 interacting with host protein in PPIs between banana and Foc4. In addition, we also discovered that many Foc4 protein GO annotations were related to vesicle fusion, export across membrane, transmembrane transport, and membrane organization. The Foc4 protein KEGG annotations were related transporters, environmental information processing, ABC transporters, and membrane transport pathway. This indicates that Foc4 protein needs to be secreted outside the cell and must cross the cell membrane in order to infect bananas. At the same time, we can see that in the predicted PPIs between banana and Foc4, the functions of proteins related to external stress, cellular homeostasis, and cell–cell signaling are enriched, and the pathogenic molecules in vitro are recognized by proteins in banana and a series of immune responses downstream are stimulated. Therefore, these enriched proteins may be involved in the identification of pathogenic proteins of Foc4. This illustrates that the PPIs between banana and Foc4 proteins predicted by our method are reliable from the perspective of GO and KEGG functional annotation.The innovation and characteristic of this paper is that both the interolog method and domain-domain method were applied to predict the PPIs between banana and Foc4, and the dataset of PPIs between banana and Foc4 was obtained by computing means for the first time. The combination of the CT and AC methods was used to encode protein characteristics to obtain the continuous and discontinuous sequence information of proteins. The predicted banana-Foc4 PPIs dataset was verified by LSTM neural network-based five-fold cross-validation method and independent test method. The GO, KEGG annotation, and interaction network analysis of banana and Foc4 protein interactions shows that there were indeed PPIs between banana and Foc4, and several Foc4 proteins interact with host protein together. The dataset of PPIs between banana and Foc4 predicted by computing method will provide a basis for the study of banana Fusarium wilt, and also offer a new means for analyzing the molecular mechanism of interactions between banana and Foc4. In the future, we will investigate the biological experiment method to verify whether there may be some false positives in the protein mutual network between banana and Foc4 constructed by the computation method.Additional file 1:Supplementary table 1. Predicted protein-protein interaction pairs between banana and Foc4.Additional file 2:Supplementary table 2. The degrees of nodes.


The protein-protein interaction (PPI) networks can be regarded as powerful platforms to elucidate the principle and mechanism of cellular organization. Uncovering protein complexes from PPI networks will lead to a better understanding of the science of biological function in cellular systems. In recent decades, numerous computational algorithms have been developed to identify protein complexes. However, the majority of them primarily concern the topological structure of PPI networks and lack of the consideration for the native organized structure among protein complexes. The PPI networks generated by high-throughput technology include a fraction of false protein interactions which make it difficult to identify protein complexes efficiently. To tackle these challenges, we propose a novel semi-supervised protein complex detection model based on non-negative matrix tri-factorization, which not only considers topological structure of a PPI network but also makes full use of available high quality known protein pairs with must-link constraints. We propose non-overlapping (NSSNMTF) and overlapping (OSSNMTF) protein complex detection algorithms to identify the significant protein complexes with clear module structures from PPI networks. In addition, the proposed two protein complex detection algorithms outperform a diverse range of state-of-the-art protein complex identification algorithms on both synthetic networks and human related PPI networks. Specific biological functions are usually carried out by a group of interacted proteins rather than by a single protein in human cells. In the past few decades, a large amount of protein-protein interactions (PPI) have been exploited with the development of a broad range of high-throughput experimental technologies, such as two-hybrid systems . Many unsupervised methods have been proposed to detect non-overlapping protein complexes from PPI networks, such as the betweeness-based algorithm proposed by Holme et al. , shortesFortunately, substantial high-quality hand-curated protein complex data are available . This combines network topological structure and prior information indicating that some proteins belong to the same protein complex, thus enabling efficient discovery of protein complexes from PPI networks. Moreover, the physical meanings of the two factorized matrices in SSNMTF are clear, such that one matrix depicts protein community membership and the other matrix indicates module structure. Additionally, the prior information is modeled as a pairwise constraint to guide the learning process regarding each protein's module membership and clarify the module structures of detected protein complexes.We propose a novel semi-supervised module detection model (SSNMTF) that uses protein pairwise constraint as prior information to identify non-overlapping or overlapping protein complex from weighted or un-weighted PPI networks.We use prior information to simultaneously guide the learning processes of a protein membership indicator matrix and a module relationship matrix in SSNMTF.We develop a novel parameter-free method to automatically identify overlapping protein complexes using the protein membership indicator matrix.We provide a corresponding initialization strategy for each respective variable to ensure that the proposed model SSNMTF has a stable solution.In this work, our main contributions are as follows:In this section, we provide a detailed demonstration of the proposed novel semi-supervised protein complex detection model based on non-negative matrix tri-factorization. The process of protein complex identification is likely to benefit from incorporating prior information, which is available from various sources. For example, when the gene expression level or semantic similarity and biological meaning are comparable between two proteins, this valuable prior information can support the protein complex detection algorithm by allowing clustering into a single protein complex, although these proteins may not directly interact in a PPI network. Furthermore, we show that the proposed model SSNMTF is more efficient than traditional NMF-based methods for detecting protein complexes.P = , where n proteins and E is a set of m edges, such that each edge links a pair of proteins in V. Generally, a symmetric adjacency matrix P and the element aij indicates the weight of a link between the ith and jth proteins. If a PPI network is un-weighted, by convention, we set aij = 1 if and only if the ith protein and jth proteins interact with each other; otherwise, we set aij = 0. Because of the limited small amount of protein-protein interaction data, the adjacency matrix A is sufficiently sparse to elicit poor performance from protein complex detection algorithms.In this manuscript, we denote a PPI network by an undirected graph NMF is a method of decomposing a given original non-negative matrix into low rank non-negative matrices; multiplication of these matrices can approximate the original matrix. The NMF exhibits great power with respect to better parts-of-whole interpretability of the factorized matrices, and it has been successfully applied to a broad range of real-world context   is the number of modules. However, the intuitional physical meanings of the two factorized matrices W and H are ambiguous  are indexed by the same objects   is the number of modules, G can be regarded as the structure of detected modules because gij describes the relationship between modules i and j. Additionally, the product of FGFT can identify relationships between any two proteins. Therefore, NMTF is used in this work to detect protein complexes from PPI networks.To overcome the drawback of NMF and SNMF, a bounded nonnegative matrix tri-factorization . If we know that the prior information which formulated as must-link constraint in advance, then a must-link constraint matrix i and j) with must-link constraint has the same module label, the two corresponding module indicator vectors (fi and fj) should be similar to each other. Thus, the distance between fi and fj should be as small as possible. Euclidean distance is utilized as the distance metric between two vectors in this work which can be denoted as F can be guided by the must-link constraint matrix M with the following term:M  denotes the trace of matrix. In each row of matrix F, the index of the largest element is the protein complex label for the corresponding protein.To ensure that a protein pair th element fic denotes the propensity that the ith protein belongs to the cth module. Considering the relationships between the cth module and all detected modules, we reconstruct a new propensity that the ith protein belongs to the cth module by p and module 1 can be acquired by p belongs to module 1, the propensities between protein p and all detected modules are considered, as are the relationships between module 1 and all modules.It is difficult to use prior information to guide the learning process regarding the relationship of the detected protein complexes to directly obtain a clear module structure. Moreover, we noticed that matrix G, we projected the protein membership indicator matrix F into the protein-to-module relationship matrix R; each row of R denotes the reconstructed protein module indicator vector for each protein. The two corresponding rows (ri and rj) of a protein pair (i and j) with the must-link constraint should also be similar to each other to enable clustering into a single protein complex. Hence, to achieve a clear module structure of all detected protein complexes, we proposed a novel penalty term for the must-link constraint. The Euclidean distance is used to measure the difference between ri and rj which denotes the memberships for the ith and jth proteins, respectively. Thus, ri and rj should be as close to each other as possible if proteins i and j with the must-link constraint. This yield D, M, and L are the same as in Equation (4). Moreover, this term can be utilized to concurrently guide the learning process of matrices F and G.The reconstructed matrix is defined as F and module structure matrix G. Therefore, the objective function of the proposed model SSNMTF has been formulated as follows:G, thus ensuring that the module structure of the detected protein complexes is clear. The corresponding protein complexes can more easily be separated from each other. The third term is used to learn the protein membership indicator matrix, which can ensure that two proteins with the must-link constraint are clustered into a single protein complex. According to the trace knowledge of matrices such as Tr(A) = Tr(AT), Tr(AB) = Tr(BA), Equation (6) can be rewritten as follows:F = [fij] ≥ 0 and G = [gij] ≥ 0, two Lagrange multipliers F and G as concurrent variables, but it is convex when one matrix (only F or only G) is constant. To obtain optimal F and G, thus achieving a local minimum for (Equation 6), we iteratively update one matrix while keeping the other fixed. The partial derivatives of Equation  conditions ψikfik = 0 and ϕjkgjk = 0, then the updating rules of matrices F and G can be obtained as follows:I is the identity matrix. The two matrices F and G will be updated iteratively until the value of Equation (6) does not change or the maximum number of iterations set in advance has been reached.The main idea in this work is not to simply incorporate the prior information into the original PPI network;instead, it is intended to merge prior information into a unitary model to concurrently guide the learning process of protein membership indicator matrix F and G, we provide an initialization strategy for each matrix, thus achieving a stable solution for SSNMTF. A community detection algorithm named K-rank-D  of corresponding protein complexes, accordingly, the diagonal elements indicate the size of corresponding protein complexes .F represents a protein complex and each entry of one column indicates the strength of a protein's contribution to this protein complex. Therefore, all entries are ranked in descending order according to their values in each column. For any protein complex c, the cth column of ordered F is indicated by c which is the cth item in a vector composed of the diagonal elements of matrix FTF. For example, if cth protein complex. The detected protein complexes will overlap with each other if they are essentially overlapped. The predicted protein complexes containing fewer than three member proteins will be filtered out. We also assign k a comparatively large value, which allows it to adaptively determine the number of modules. In this manuscript, the proposed overlapping protein complex detection method is denoted as OSSNMTF.We propose a novel parameter-free method to detect overlapping protein complexes, using matrix In this manuscript, five binary PPI networks of humans extracted from HuRI   and the golden standard complex sets (G). To measure the matching rate between a predicted protein complex and a benchmark one, we follow the neighborhood affinity (NA) score which is defined as follows:p and a golden standard reference complex g, and p, and g are regarded to match each other when NA ≥ 0.25. Then the precision, recall, F1 sensitivity (Sn), positive predictive value (PPV), and accuracy (Acc) are introduced to assess the performance of the protein complex detected algorithms. After all detected protein complexes get their best matched real protein complexes in terms of NA values, then precision, recall, and F1 are defined asm = |P|, n = |G|, tij denote the number of common proteins that exist in both the ith predicted protein complex and jth reference golden standard complex, and Nj denote the number of proteins in the jth complex and then Sn, PPV and Acc are defined as follows:p-value from the hypergeometric distribution  term gt and q indicates the number of proteins annotated by gt in any one detected protein complex. The smaller the p-value, the more biologically significant the corresponding detected protein complex , cluster-wise positive predictive value (PPV), Accuracy (Acc), precision, recall, F1 , and two unsupervised module identification algorithms  with different α by taking CORUM as the golden standard in N (number of nodes), ad (average degree of nodes), dmax (maximum degree of nodes), mmin (minimum module size), mmax (maximum module size), μ (mixing parameter), on (number of overlapping nodes), and om (number of modules for overlapping nodes). The mixing parameter μ determines the clarity of the topological structure, and the greater value of μ means the more blurred module structure of generated networks. In this manuscript, we set N = 1, 000, ad = 15, dmax = 50, mmin = 20, mmax = 50 and μ varying from 0.6 to 0.7 for non-overlapping and overlapping module benchmark networks. In addition, for overlapping module benchmark networks, we set on = 200 and om = 2. The normalized mutual information (NMI) /2 must-link constraints will be extracted from a specific complex, which contains Nc proteins. In this work, we randomly chose 10% must-link constraints, based on Np, as prior information. The information regarding extracted must-link constraints from CORUM and PCDq for all three human PPI networks is listed in The prior information  used in SSNMTF is extracted from CORUM and PCDq using the approach suggested in Yang et al. . BecauseWe first used precision, recall, and F1 as the evaluated metrics to assess the performance of our proposed models NSSNMTF, OSSNMTF and other compared algorithms. The comprehensive comparison results using CORUM and PCDq as gold standard datasets of the five unweighted and one weighted PPI networks are showed in After that, we then employ Sn, PPV, ACC, and MMR as evaluate metrics to compare detected protein complexes with two golden standard datasets. The results of all compared algorithms are listed in p-values with false discovery rates. These results imply the enrichment of a specific GO term in a single protein complex. Thus, the detected protein complexes were presumed to have greater biological importance when they exhibited smaller p-values. The proportions of protein complexes with p-values below a given threshold can represent the performance of protein complex detection algorithms. In this paper, we varied the threshold from 10−10 to 0.01, then measured the proportions of functionally enriched protein complexes in each threshold interval for each algorithm. A higher proportion indicated better performance of the corresponding algorithm in the detection of protein complexes from PPI networks. p-value proportion distributions for all compared algorithms with respect to BP. The proposed method NSSNMTF outperformed other non-overlapping protein complex detection methods , while OSSNMTF outperformed other overlapping protein complex detection methods . Overall, the protein complexes detected by the proposed models NSSNMTF and OSSNMTF had greater probabilities of predicting real protein complexes.To further probe the functional homogeneities of detected protein complexes, gene ontology (GO)  in the fifth protein complex (ID=5) are annotated by the COVID-19 pathway from Furthermore, to better demonstrate the biological meaning of our detected protein complex, we do KEGG pathway analysis for all six of the protein complexes. The results are showed in To demonstrate the superiority of our proposed model, two well-studied protein complexes  and an overlapping protein complex detection algorithm (OSSNMTF). We have conducted experiments on both synthetic networks and human PPI networks to evaluate the performances of our proposed algorithms, and the experimental results showed that SSNMTF is superior to all compared algorithms. These findings indicate that the use of high-quality prior information can help to efficiently discover important biological protein complexes with clear module structures. In our future work, we will simultaneously consider protein complexes and GO information when mining protein complexes from human PPI networks.https://dip.doe-mbi.ucla.edu/dip/Main.cgi, https://string-db.org/.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: GL, XZ, and JY conceived the study, carried out its design and all experiments, and drafted the manuscript. BL, AL, and XW helped draft the manuscript and participated in its design and coordination. All the authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Now, as never before in the history of humankind, people are constantly communicating and aware of their surroundings. It is astonishing to see how similar our society is to a basic cellular organism, a society of cells. In order to function and survive, cells need to communicate with each other and gather information about the external environment. They do not use mobiles or social media, but they are able to codify and transfer information using signaling pathways.AR and mouse apoferritin, were solved at atomic resolution using cryo-EM, 1.7 and 1.2 Å, respectively , Intermolecular Nuclear Overhauser Effect (NOE) and Residual Dipolar Coupling (RDC) for the study of protein complexes, and Chemical Shift Perturbation (CSP) for the determination of binding modes and KD values for protein-ligand with weak interactions (KD in the μM-mM range) in fast exchange regime . The authors also discuss several approaches to study medium size protein (~100 kDa) and overcome the molecular weight limit of solution NMR, focusing mostly on solid-state NMR sequences, like REDOR and PAINCP.Signaling pathways are defined by an intricate network of protein-protein and protein-nucleic acid interactions that governs intra- and inter-cellular activities. Nearly all cellular processes are controlled by such interactions, including motility, growth, proliferation, gene expression, survival, and apoptosis. Structural biology has offered unprecedented insight into the function and regulation of protein-interaction networks. Advances at third generation synchrotron sources and the use of XFELs is having a massive impact on elucidating the structural rationale and dynamics of macromolecules , highlighting the diverse mechanisms adopted by M. tuberculosis to downregulate cytokine expression and promote entry into macrophages.  Marshall and Bavro used a combination of structure-guided mutational studies to characterize another system linked to bacterial survival, the Escherichia coli tripartite efflux-pump AcrAB-TolC, assembly of which relies on the recognition and interaction of the outer-membrane factor TolC with the periplasmic adapter protein AcrA. Their results support the presence of a deep interpenetration of the AcrA-TolC interface and indicate that TolC contributes to the substrate selection during efflux events, uncovering an overlooked feature of the outer-membrane factor protein family. Khan and Goult reviewed the diverse range of interactions triggered by integrin binding to talin and kindlin in the cytoplasm, and the extracellular matrix, highlighting the key role microscopy played in dissecting the organization of integrin adhesion complexes. The authors provide an elegant mechanosensitivity regulation model, describing extensively the link between auto-inhibition and the formation of inactive pre-complexes, further discussing the role autoinhibition plays in driving the correct protein interaction in response to a specific signal. Similarly, Guarino et al. provided a comprehensive review of the structural features that guide the clustering of acetylcholine receptors (AChRs), essential for the formation and maintenance of Neuromuscular junctions (NMJ) and synaptic connections. The authors critically discussed the well-characterized “canonical” (or agrin-induced) AChRs clustering model that involves interaction between the AChRs and the LRP4-MuSK complex bound to agrin, as well as the less well-characterized “non-canonical” model, exploring the possibility that the AChRs clustering is mediated by interactions with the Wnt-LRP4-MuSK complex. Chataigner et al. embarked on a challenging task to review the molecular mechanisms used by type-I transmembrane proteins to control adhesion and activation of signaling, focusing mostly on the extracellular portion of the proteins. Using different examples of prototypical transmembrane proteins, the authors focused on the roles X-Ray crystallography, NMR and cryo-EM played in unraveling proteins interactions, protein complexes structures and conformational changes underlying activation of signaling processes.Transmembrane proteins involved in cellular interactions or extracellular environment sensing are fascinating examples of highly spatial and temporal regulated signaling complexes. Zhang et al. present a quintessential application of computational study to explore the diverse functions of SMYD3 by analyzing its interactome. Using a pathway enrichment analysis coupled with structural analysis and sequence motif scanning, the authors identified two major classes of proteins forming transient interactions with SMYD3: epigenetic transcriptional regulators, like histone H3-4, and proteins related to calcium dependent signaling, such as PLCB3, supporting the hypothesis that SMYD3 is not merely a methyltransferase involved in histones modification, but could be an integral part of calcium signaling pathways.Despite the astonishing technical advances of the last 10–15 years in structural biology, experimental approaches are intrinsically time and resource demanding and computational studies, spanning from classic molecular dynamics simulations to more recent machine learning algorithms and network topology and proximity measures, have the potential to bypass some of these shortcomings. The studies collected in this Research Topic represent just the tip of the iceberg of the possibilities offered by structural biology applications in understanding and characterizing signaling pathways, helping define future direction of research in biology and related fields. As the structural biology technologies and resources continue to improve, it will be possible to address additional biological challenges. We envision that in the next 10 years, a thorough structural understanding of larger and more complex systems will be possible, offering possibilities to map the dynamic processes in signaling networks in order to better understand the transmission of signals and the roles that different structural components have in relaying those signals within an organism.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The complex was designed using computational docking approaches to predict compatible interfaces between the two proteins. Models of the complexes where then used to engineer residue coupling sites in each protein to link them together. Genetic code expansion was used to incorporate azide chemistry in cytochrome b562 and alkyne chemistry in GFP so that a permanent triazole covalent linkage can be made between the two proteins. Two linkage sites with respect to GFP were sampled. Spectral analysis of the new heterodimer revealed that haem binding and fluorescent protein chromophore properties were retained. Functional coupling was confirmed through changes in GFP absorbance and fluorescence, with linkage site determining the extent of communication between the two proteins. We have thus shown here that is possible to design and build heterodimeric proteins that couple structurally and functionally disparate proteins to form a new complex with new functional properties.The formation of protein complexes is central to biology, with oligomeric proteins more prevalent than monomers. The coupling of functionally and even structurally distinct protein units can lead to new functional properties not accessible by monomeric proteins alone. While such complexes are driven by evolutionally needs in biology, the ability to link normally functionally and structurally disparate proteins can lead to new emergent properties for use in synthetic biology and the nanosciences. Here we demonstrate how two disparate proteins, the haem binding helical bundle protein cytochrome  Protein oligomerisation, commonly referred to as protein quaternary structure, is the association of specific individual polypeptide chains through defined intermolecular interactions to form a single multimeric complex . So prevGiven the benefits of protein oligomerisation, protein designers and engineers have sort to address this area by constructing bespoke, artificial protein oligomeric systems  ranging The next challenge arguably involves the generation of heterodimers from structurally and functionally diverse proteins. Heterodimers are potentially rich in new functional features as they have the potential to combine drastically different and disparate functions leading to new emergent properties. Here we aim to take the next step in dimer construction and demonstrate that it is feasible to design and build intimately linked heterodimers comprised of structurally and functionally disparate proteins by linking a helical bundle protein to a β-barrel protein using a combination of computational protein design and bio-orthogonal chemistry. While bioorthogonal chemistry has been used previously to link different proteins together , little b562 (cyt b562) as the helical bundle protein and the superfolder version of green fluorescent protein (sfGFP) as the β-barrel protein . Structural analysis revealed that the two original proteins are close in space within the single polypeptide unit but do not form many distinct inter-unit interactions as would be expected of a true oligomeric system. There was also little change in the inherent function of each domain compared to the starting parent protein suggesting limited synergy between the two.To test our approach, we will use cytochrome  protein . Cyt b5valently  while sforia GFP . We haveapproach . The domb562. We then used 1-to-1 strain promoted alkyne-azide cycloaddition to covalently link the two proteins through a triazole link by genetically encoded incorporation of the azide group  into cyt b562 and a strained cyclooctyne group  in sfGFP. The successfully constructed heterodimers lead to enhanced sfGFP molar absorbance coefficients and were capable of energy transfer from sfGFP to cyt b562, with linkage site determining transfer efficiency.Here, we used computational approaches to predict compatible interfaces between sfGFP and cyt https://cluspro.bu.edu) “Dock” function was utilised to predict potential interface sites between proteins. ClusPro simulation was run with cyt b562  as the PDB 1qpu ) as the PDB 1qpu . The Rosb562 was present in the pBAD plasmid. The cyt b56250AzF variant was generated and produced as described previously (b56271AzF variant was generated by introducing a TAG codon in place of the Q71 encoding codon by whole plasmid PCR (Forward primer 5′-C GGT TAG ATT GAC GAC G-3′ and reverse primer 5′-AC CAG AAT GTC GAA ACC G-3). Incorporation of AzF  at 37°C overnight. After incubation, the formation of oligomeric protein was determined by SDS-PAGE. Purification of oligomeric protein was achieved by size exclusion chromatography using Hiload™ 26/600 Superdex™ S200 gel filtration column (Section 2.4.2) and the purity of the resultant protein oligomer was assessed by SDS PAGE. Yield of dimer was estimated by ImageJ  with cyt y ImageJ  analysisb56271AzF was determined using the DC-protein assay (BioRad) using the wild-type cyt b562 as the standard. The subsequent molar absorbance co-efficient was calculated using the Beer-Lambert law with the absorbance of a known concentration of cyt b56271AzF. Fluorescence emission spectra were recorded on a Cary Eclipse Fluorimeter, using 5 mm × 5 mm QS quartz cuvette. Samples were excited at the λmax and emission was recorded at every 1 nm from the point of excitation to 700 nm. A scan rate of 120 nm/min was used for all spectra recorded with a 5 nm slit width and voltage set to medium. To measure the emission in reducing conditions protein was first incubated with a 10-fold molar excess of DTT.UV-visible absorption spectra were recorded with Cary 60 spectrophotometer using 1 cm pathlength quartz cuvettes. Absorbance was recorded between 200–800 nm at a scan rate of 300 nm/min. The molar absorbance co-efficient (ε) for the sfGFP variants have been determined previously . The conb562 places the non-canonical amino acid (ncAA) within a dynamic extended surface loop  to cyt 62 (1qpu ). Haem aettaDock  through ettaDock . Thus, Rb562 helices three and four comprised the main docking interface; residue Q71 in helix three was chosen as it was close to both residues 148 and 204 in sfGFP (148SCO) and 204 (sfGFP204SCO) and characterised (b562 at residue 71 (cyt b56271azF) in response to a TAG codon has been demonstrated previously as part of a separate directed evolution codon exchange study . The cyt b562 unit in GFPb204-71 is still capable of redox state switching as shown by the switch in the 418 nm absorbance peak for the oxidised form to 426 nm characteristic of reduced cyt b562 with the typical higher molar absorbance  on addition of reducing agent  could bind and respond to haem. Linking haem binding to cyt o-factor . The apong agent . These rb562 . The rationale for using SPAAC to covalently stabilise the heterodimer structure is that classical approaches such as disulphide bridges cannot discriminate between homo-dimers and heterodimers leading to a mixed population; the bioorthogonal nature of SPAAC means only heterodimer will form. Furthermore, the ncAAs used here have longer side chains than the short -CH2-SH group of cysteine meaning steric clashes between monomers is less likely to inhibit covalent bond formation but still allow an intimate interaction between the individual monomer units. The triazole link is also more stable than a disulphide bridge. While our aim was not to generate a newly designed dimer interface mimicking natural protein-protein interactions, we did need to identify compatible interfaces that will at least persist for a length of time to allow covalent functionalisation. Without identifying compatible interfaces, the chances of stabilising the interface through SPAAC is minimal. Residue 50 in cyt b562 is largely surface exposed in a flexible extended loop and would normally be considered an ideal residue to target for covalent coupling with another protein  than for structurally homologues proteins tested previously suggesting a weaker interaction  and a helical protein (cyt  protein  but SPAA protein . Previou protein . Lack of protein ). Our co protein  and else protein  in identferences ) but stiferences . The modve sites . Thus, gb562 interface in a position to H-bond to the chromophore.Two heterodimers were designed based on the top ranked model . In termb204-71, we see enhancement of the sfGFP molar absorbance  between EGFP and cyt b562 is 46 Å (r) and energy transfer (E) through equation E = 1/[1+(r/R0)6], we can estimate the distance between the chromophores. Previously, we showed that there was a good correlation between r from energy transfer (99%) and structure in our domain insert EGFP-cyt b562 construct  so the of sfGFP . Even unidue 204 .in silico docking helped identify suitability compatible protein-protein interfaces that where then stabilised by a triazole link formed by SPAAC. In both heterodimer configurations tested, the ability of phenol anion chromophore form of the sfGFP to interact with light was enhanced, with energy transfer to the haem centre of cyt b562 demonstrating functional linkage.To conclude, here we have shown that it is feasible to design and construct artificial heterodimers between structurally and functionally disparate proteins linked by a genetically encoded bio-orthogonal link. The


Long noncoding RNA (lncRNA) plays a crucial role in many critical biological processes and participates in complex human diseases through interaction with proteins. Considering that identifying lncRNA–protein interactions through experimental methods is expensive and time-consuming, we propose a novel method based on deep learning that combines raw sequence composition features, hand-designed features and structure features, called LGFC-CNN, to predict lncRNA–protein interactions. The two sequence preprocessing methods and CNN modules (GloCNN and LocCNN) are utilized to extract the raw sequence global and local features. Meanwhile, we select hand-designed features by comparing the predictive effect of different lncRNA and protein features combinations. Furthermore, we obtain the structure features and unifying the dimensions through Fourier transform. In the end, the four types of features are integrated to comprehensively predict the lncRNA–protein interactions. Compared with other state-of-the-art methods on three lncRNA–protein interaction datasets, LGFC-CNN achieves the best performance with an accuracy of 94.14%, on RPI21850; an accuracy of 92.94%, on RPI7317; and an accuracy of 98.19% on RPI1847. The results show that our LGFC-CNN can effectively predict the lncRNA–protein interactions by combining raw sequence composition features, hand-designed features and structure features. Long noncoding RNA (lncRNA) is a type of noncoding RNA with at least 200 nucleotides that plays vital roles in many critical biological processes , such asExisting computational methods can be categorized into network-based methods and machine learning-based methods. Network-based methods construct a lncRNA/protein similarity matrix and then use network algorithms to calculate correlation scores to make predictions. For example, Zhang et al. proposed a model called LPLNP, in 2017, which calculated the linear neighborhood similarity between lncRNAs and proteins and the regularized linear neighborhood similarity and predicted the observed lncRNA–protein interactions by a label-propagation process . Zhao etThe machine learning-based methods extract manual features from lncRNA and protein sequences to represent lncRNA–protein pairs and then input them into machine learning classifiers to predict lncRNA–protein interaction pairs. For example, Ge et al. proposed a model named RPISeq, in 2015, that input the 4-mer frequency characteristics of RNA sequences and the 3-mer frequency characteristics of proteins into a random forest classifier and support-vector-machine classifiers to identify RNA-protein interactions . In 2019In this paper, we propose a new deep-learning model (LGFC-CNN) that combines raw sequence-composition features, hand-designed features, and structure features to comprehensively predict lncRNA–protein interactions. First, we improve the sequences’ preprocessing, originally used to predict RNA-protein binding sites, and apply it to transform the sequences into fixed-length sequences . After tAn illustration of LGFC-CNN for predicting lncRNA–protein interactions is shown in To evaluate the performance of LGFC-CNN, we test it on the lncRNA–protein interaction datasets of database ,26. To fDue to the lack of negative samples in the NPInter4.0 database and the assumption that obtaining the negative dataset by randomly pairing lncRNA and protein is not entirely reasonable, we adopted the following criteria from FIRE  to buildsS between all proteins from the positive dataset. Then, we sorted the global sequence similarity scores For a lncRNA–protein interaction of protein eracts r . Therefoeracts r  to calcuTo reduce the repeated lncRNA–protein interactions and consider that lncRNA has a certain probability of having a relationship with those proteins with higher scores, instead of selecting the lncRNA–protein pairs with the lowest interaction scores, we divided all lncRNAs into two equal parts. In the first part, the lncRNA–protein pairs were selected for which their lncRNA matched with a random To further assess the reliability and robustness of LGFC-CNN, RPI7317 and RPI1847 in LPI-BLS  were conAs the CNN model requires fixed-length sequence inputs, whereas different lncRNA(or protein) sequences vary significantly in their lengths, we improved the sequences preprocessing in iDeepE  to transragments —which caFor the GloCNN module, if the lncRNA sequence length was greater than C, according to the sequence length. If the number of channels for one sequence was greater than Ns to C. For the LocCNN module, a lncRNA sequence was divided into subsequences of M. For the padded nucleotide at the start and end of sequences, we assumed four nucleotides were equally distributed. Thus,  was used in the padded nucleotides and For a given protein sequence, we adopted the same preprocessing to transform it into the fixed-length sequence with encoding ,23. Giveessed as :(1)Mi,j=A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y). When using one-hot encoding to encode the protein sequence, the encoding matrix can be vast and sparse. Thus, we compressed the 20 amino acid alphabets into seven groups, based on their dipole moments and side chains [R, converted by one-hot encoding, can be expressed as:For a given protein sequence e chains : R1={A,GIn this work, six hand-designed features of lncRNA are combined with ten hand-designed features of the protein. Each feature combination is ranked according to their average performance in the random forest classifier. Then the top three features with superior predictive effect were selected to represent the lncRNA and protein, respectively. For the lncRNA, the top three features were RNA-coding potential characteristics, CPPred is a tool developed by Xiaoxue Tong et al. to predict coding potential based on the global description of an RNA sequence . It is bLDNC can reflect the chemical properties of the accumulated energy of di-nucleotide and reflect the evolutionary information of lncRNA sequences. It can be computed as follows:r and l vector ,35. LDNCsequence , which hsequence . It can PACC provides information regarding the percentage of each residue present in the protein [PACC can measure the correlation of two properties or the same properties  along the protein sequence and convert the matrix to a fixed-length vector [t, The protein sequence is composed of 20 kinds of amino acids.  protein . PACC cah vector ,39. It cR2 = {I, L, F, P}, For  protein : R1={A,GMolecular features that rely on lncRNA and protein structure information play a significant role in their interactions. Therefore, we used the secondary structure, hydrogen bonding propensities, and van der Waals interactions to represent the lncRNA’s and protein’s structure information.For the lncRNA, its secondary structure was obtained through RNAfold  based onFor the protein, its secondary structure was obtained through Predator , based oHowever, each lncRNA and protein feature vector is of different dimension, which depends on the length of the corresponding RNA or protein sequence, and the CNN model requires fixed matrix inputs. We adopted the Fourier transform to unify the dimension. The formula of the Fourier series can be expressed as:Here, those criteria in lncPro were adopted that use the first ten terms of the Fourier series as the new numerical feature vector. In this way, we obtain the lncRNA structure feature vector Convolutional neural network (CNN) is an effective tool in the field of predictive lncRNA–protein interaction ,51. TherFor the GloCNN module, the lncRNA and protein encoding matrices were fed into two-layer 1-channel CNNs  to extract raw sequence global features, where all the sequences were transformed into fixed-length equivalents. By experimentation, the prediction accuracy did not grow significantly when the layer number was larger than 2, and a larger hidden layer number brought more computation. In addition, a dropout layer was used to accelerate the training process and avoid overfitting. Finally, the lncRNA and protein feature maps were concatenated and their dimensionality reduced to 64 through a fully connected layer.The LocCNN module used two-layer multi-channel CNNs to extract raw sequence local features, wherein all the sequences had multiple subsequences. The number of channels was determined by the local sequence encoding. After a dropout layer and fully connected layer, the lncRNA and protein feature maps were concatenated and their dimensionality reduced to 32.A2, were fed into a two-layer fully connected layer to extract high-level features, followed concatenating the two feature maps, from which we extracted useful information through a fully connected layer.For the FC module, the lncRNA and protein hand-designed feature vectors, The SS module used a two-layer fully connected layer to analyze the lncRNA and protein structure feature vectors, Finally, the feature maps of four basic module outputs were concatenated and further fed into the two fully connected layers to predict the probabilities of lncRNA–protein interactions.TP, TN, In this study, we used the metrics of accuracy (ACC), Matthew’s correlation coefficient (MCC), F1_score (F1), sensitivity (SN), specificity (SP), and positive predictive value (PPV) to measure the performance of LGFC-CNN. The formulas of the six measurements are as follows:In this section, we first downloaded and ran the algorithms RPISeq-RF , RPISeq-To assess the performance of our LGFC-CNN, we first compared LGFC-CNN with RPISeq-RF, RPISeq-SVM, LPI-BLS, and IPMiner on the benchmark dataset RPI21850. The results of LGFC-CNN and the other four methods are shown in The ROC curves and the PRC curves of LGFC-CNN and the other four methods on RPI21850 are shown in To further test the reliability and robustness of LGRF-CNN, we also comparde LGRF-CNN with four comparison methods on the RPI7317 and RPI1847 datasets. Before feeding the hand-designed feature vectors into the FC module, we needed to select the top three features of superior predictive effect to represent the lncRNA and protein. Six features of the lncRNA were combined with ten features of the protein and each feature combination was ranked according to their individual average performances in the random forest classifier. For ranking the feature combinations, we compared the ACC of each combination to predict the lncRNA–protein interactions in RPI21850, and the results obtained through experiments are shown in Lmer3, PseKRAACP, Ldnc, and Pmer4 were selected to represent the protein. These results indicate that the features related to coding potentials and physicochemical properties appear to be more suitable for predicting lncRNA–protein interactions.As shown in our tables and figures, the average prediction accuracies of the combinations of Our LGFC-CNN model contains four fundamental modules: a LocCNN module, a GloCNN module, an FC module, and an SS module. To investigate the superiority of LGFC-CNN, we compared LGFC-CNN with four basic modules on datasets RPI21850, RPI7317, and RPI1847. The performance of LGFC-CNN and four different basic modules are shown in a histogram The compared results show that, on the benchmark dataset RPI21850, the prediction accuracy of LocCNN module was 93.98%, which was higher than the 93.52% of the GloCNN module, the 93.54% of the FC module, and the 93.12% of the SS module. On PRI7317, the prediction accuracy of the FC module was 92.53%, which was higher than the 92.07% of the GloCNN module, the 91.9% of the LocCNN module, and the 92.16% of the SS module. On the dataset PRI1847, the prediction accuracy of LocCNN module was 98.04%, which was higher than the 96.91% of GloCNN module, the 97.15% of the FC module, and the 97.11% of the SS module. It can be seen from the results that the prediction accuracy of any single module could always exceed that of the other modules, and none of them was as good as the overall LGFC-CNN. It shows that the LGFC-CNN model we have proposed has the advantages of the four basic prediction modules. The combination of global sequence features, local sequence features, hand-designed features, and structural features can provide more comprehensive lncRNA–protein prediction results. To show the effectiveness of our strategy for selecting negative samples, we first constructed three new datasets, ranRPI21850, ranRPI7317, and ranRPI1847, by randomly pairing the lncRNA and protein and removing the duplicate interaction pairs. ranRPI21850 contained 21,850 lncRNA–protein interaction pairs and 21,850 pairs of lncRNA–protein non-interaction pairs; ranRPI7317 contained 7317 lncRNA–protein interaction pairs and 7317 pairs of lncRNA–protein non-interaction pairs; and ranRPI1847 contained 1847 lncRNA–protein pairs. Then we predicted the lncRNA–protein interactions in RPI21850, RPI7317, RPI1847, ranRPI21850, ranRPI7317, and ranRPI1847 under the same conditions. It can be seen from Our LGFC-CNN model consists of four basic modules for analyzing global sequence features, local sequence features, hand-designed features, and structure features. To inves-tigate how the hyper-parameters of the convolutional layer kernel number and the fully connected layer neuron number affect the performance of LGFC-CNN, we change one pa-rameter value at a time by fixing other parameters on the validation set of RPI21850 to im-plement our LGFC-CNN. For the GloCNN module, Kernel-G of the convolutional layer was set as n × 10, n × 20, n × 30, n × 40, n × 50 . For the LocCNN module, Kernel-L of the convolutional layer was set as n × 10, n × 20, n × 30, n × 40, n × 50 . The neuron number of the four fully connected layers Dense-G, Dense-L, Dense-FC, and Dense-SS was set to 16, 32, 48, and 64, respec-tively. In this study, we proposed a novel method based on deep learning and using multiple types of features to predict lncRNA–protein interactions. On the benchmark dataset we constructed, LGFC-CNN achieved an accuracy of 94.14%, an MCC of 0.8853, an F1-score of 0.9435, an SN of 97.9%, an SP of 90.39%, and a PPV of 91.06%. The experimental results on RPI7317 and RPI1847 also showed the effectiveness of LGFC-CNN.LGFC-CNN had superior performance in predicting lncRNA–protein interactions; we believe that there are several reasons why. Firstly, for the structure of the negative sample strategy, our method considers that if protein eracts r , and we Although LGFC-CNN achieves better performance in predicting lncRNA–protein interactions, there are still limitations. On the one hand, since most of the high-quality experimentally verified human lncRNA–protein pairs are mainly derived from the NPInter dataset , our metTo understand the various regulatory mechanisms and pathogenic mechanisms involved in lncRNA through lncRNA–protein interactions, some computed methods were developed for predicting lncRNA–protein interactions ,12,13. H


The identification of disease-related protein-protein interactions (PPIs) creates objective conditions for their pharmacological modulation. The contact area (interfaces) of the vast majority of PPIs has some features, such as geometrical and biochemical complementarities, “hot spots”, as well as an extremely low mutation rate that give us key knowledge to influence these PPIs. Exogenous regulation of PPIs is aimed at both inhibiting the assembly and/or destabilization of protein complexes. Often, the design of such modulators is associated with some specific problems in targeted delivery, cell penetration and proteolytic stability, as well as selective binding to cellular targets. Recent progress in interfacial peptide design has been achieved in solving all these difficulties and has provided a good efficiency in preclinical models (in vitro and in vivo). The most promising peptide-containing therapeutic formulations are under investigation in clinical trials. In this review, we update the current state-of-the-art in the field of interfacial peptides as potent modulators of a number of disease-related PPIs. Over the past years, the scientific interest has been focused on following clinically significant heterodimeric PPIs MDM2/p53, PD-1/PD-L1, HIF/HIF, NRF2/KEAP1, RbAp48/MTA1, HSP90/CDC37, BIRC5/CRM1, BIRC5/XIAP, YAP/TAZ–TEAD, TWEAK/FN14, Bcl-2/Bax, YY1/AKT, CD40/CD40L and MINT2/APP. Stable and dynamic protein-protein interactions (PPIs) play extremely important roles in metabolic, regulatory and signaling processes in living systems ,2,3. TheThree contrasting groups of PPI modulators can be distinguished, such as therapeutic proteins (Mr > 10 kDa), peptides (Mr = 1–5 kDa) and low molecular weight compounds of a non-peptide origin (Mr < 1 kDa) ,13. Acco2. The stability of the PPI is provided through a set of non-covalent interactions in hot spots, that is, interactions of key amino acid residues (a.a.r) of both proteins, which account for the bulk of the total binding energy n were capable of self-assembly in situ on the surface of gold nanoparticles by means of one-pot synthesis [The apoptosis regulator Bcl-2 (B-cell lymphoma 2) suppresses the pro-apoptotic functions of Bax (Bcl-2-like protein 4). The BH3-domain containing proteins activates Bax, competing with Bcl-2, thereby opening up the possibility of creating peptidomimetics based on BH3 motifs. The limitations of the pilot variants of peptidomimetics were their low level of cell penetration and proteolytic degradation, which were further overcome by conjugation with spheroidal gold nanoclusters. At the same time, polymer elements (Au1ynthesis .The oncoprotein binding domain of YY1 (Yin Yang 1) located between Gly201 and Ser226 is involved in the regulation of cell proliferation through PPIs with MDM2, EZH2 E1A, and AKT . A peptidomimetic corresponding to Gly206-Ser226 motif inhibited the YY1/AKT complex, proliferation and migration of tumor cells. In addition, the peptide caused the arrest of the cell cycle in the G1 phase and a decrease in the level of phosphorylation of Ser473 of AKT .The KGYY-15 peptide was derived from the amino acid motif CD40L, which is responsible for the interaction with CD40 (tumor necrosis factor receptor superfamily member 5). KGYY-15 showed a weak potential for inhibiting the CD40/CD40L interaction . Verification of the peptide in the cell tests revealed only insignificant inhibitory activity (~33%) at a concentration of 100 μM. It was also known that low molecular weight compound DRI-C21095 disrupted the interaction of CD40 with other protein CD154 (IC50 = 9 μM) ,129.The abnormal accumulation of beta-amyloid (Aβ) accompanies Alzheimer’s disease. Aβ is formed during the proteolytic cleavage of APP (amyloid precursor protein). The hypothesis that pharmacological targeting of PPI Mint2/APP can influence the processing and formation of pathogenic Aβ was verified in . It was D values in the low micromolar range [Retrospectively, the efficacy of interfacial peptides has been extensively studied in relation to inhibiting HIV1 protease homodimerization and reverse transcriptase heterodimerization in the late 1990s and early 2000s ,133. Curar range . Oncoproar range . HoweverFor a panoramic view of the relationships between the cellular and pharmacological context, we performed a functional enrichment of a set of target proteins with the categories “gene ontology”, “human phenotype”, “pathway”, “drug” and “disease” using the ToppFun tool of the ToppGene Suite annotation platform . Table 3As for the current section of recruited and launched clinical trials (CTs) of therapeutic peptides, their number is still quite modest. It is in marked contrast with the total number of clinically significant PPIs whose interface can be modulated by drugs. In the analytical review by Cabri and co-authors , there wTargeting the interfaces of disease-associated PPIs with peptide modulators is a successful strategy for correcting some pathological processes. The optimization of design approaches, pharmacological and binding kinetics profiles of peptide inhibitors will contribute to the development of highly selective and affine drug prototypes. In turn, the progress in interfacial peptides is organically linked with the identification of novel clinically significant PPIs, advances in deciphering 3D crystallographic models of protein complexes and mapping hot amino acid residues in the contact area of proteins. Along with peptide modulators of PPIs, there is much literature data on pharmacologically active low molecular weight organic compounds and therapeutic protein. All the above-mentioned facts directly testify to the growing interest in this field from the scientific audience.Recently, the AlphaFold2 approach has emerged as a powerful in silico instrument for the prediction of structure, topology, conformation and variants interpretation of most soluble and membrane proteins ,145. In