Pattern recognition plays a critical role in integrative bioinformatics to determine the structural patterns of proteins of viruses such as SARS-CoV-2. This study identifies the pattern of SARS-CoV-2 proteins to depict the structure–function relationships of the protein alphabets of SARS-CoV-2 and COVID-19. The assembly enumeration algorithm, Anisotropic Network Model, Gaussian Network Model, Markovian Stochastic Model, and image comparison protein-like alphabets were used. The distance score was the lowest with 22 for “I” and highest with 40 for “9”. For post-processing and decision, two protein alphabets “C” (PDB ID: 6XC3) and “S” (PDB ID: 7OYG) were evaluated to understand the structural, functional, and evolutionary relationships, and we found uniqueness in the functionality of proteins. Here, models were constructed using “SARS-CoV-2 proteins” (12 numbers) and “non-SARS-CoV-2 proteins” (14 numbers) to create two words, “SARS-CoV-2” and “COVID-19”. Similarly, we developed two slogans: “Vaccinate the world against COVID-19” and “Say no to SARS-CoV-2”, which were made with the proteins structure. It might generate vaccine-related interest to broad reader categories. Finally, the evolutionary process appears to enhance the protein structure smoothly to provide suitable functionality shaped by natural selection. Nature has created an enormous diversity of patterns in diverse life forms. To understand the archetype, pattern recognition has been used by scientists to depict the structural and prototype similarities, and only then can the classification from noisy data to distinguishable data be smoothly completed by using structural designs, statistical inputs, big data analytics, and image inspections computational investigations ,2,3. ThePattern recognition is a predominant area in statistics, where scientists use data to develop theories, generate models, and apply methods for dimensional reduction, clustering, and classification through various approaches. Similarly, density estimation is one of the significant areas of statistical pattern recognition that uses normal-based models, normal mixture models, and Bayesian methods to estimate datasets for final application to recognize patterns ,5.In computer science, pattern recognition is essential, since scientists often apply big data and image analysis, plus computer graphics, to discover hidden messages ,7. HowevAlthough pattern recognition in bio-science is complicated, reports showed that bioinformatics could play a significant role in solving the existing difficulties ,17,18. AResearchers have been trying to solve the structure of SARS-CoV-2 proteins ever since the start of the pandemic ,22. As oUnderstanding the structural symmetry of elements is a significant process of pattern recognition ,32. ScieScientists extract data from different proteins 3D structures using algorithms, such as deep learning and machine learning ,42. TherA pattern was discovered in the 3D structures of SARS-CoV-2 proteins. The Protein Data Bank (PDB) was extensively used to retrieve alphabets, such as patterns from SARS-CoV-2 proteins, to design of various 3D structures of SARS-CoV-2 proteins .For developing two slogans for our paper, first, we tried to find similarities in the structural pattern of some SARS-CoV-2 proteins with the English alphabet. Some are not found in the SARS-CoV-2 proteins. In this case, some “non- SARS-CoV-2 proteins” were selected, similar to the English alphabet. We have added this part in the method section of the manuscript.Images of general alphabets were created and compared for similarities between the protein alphabets and English alphabets by using the image similarity API (application programming interface) . The imaAgain, we selected four protein alphabets with antibodies/immunological or vaccine-associated roles collected from protein alphabets pools. Distance score was also developed as protein alphabets to understand the similarity index/dissimilarity index.Structural pattern recognition of 3D structures of SARS-CoV-2 proteins was analyzed to understand the variations and patterns in symmetry. The pattern was evaluated using Mol* Viewer, a recent web app and modern software that provides structural symmetry of a protein. The Mol* Viewer was used to understand the structural pattern recognition .Computational interface was used to generate 2D graph of SARS-CoV-2 proteins 3D structural alphabets. We used an evolutionary protein interface classifier (EPPIC) to evaluate the assemblies inside the crystal structure coordinates. Using the assembly enumeration algorithm, the interface evaluated 3D structure (input PDB files) and generated a 3D lattice graph of the protein’s crystal structure. Then, it generated 2D graph of the protein assembly .The interface of 3D structures of a protein is essential for their function. The pattern recognition of protein structural assembly was studied using the protein–protein interface of 3D structures of SARS-CoV-2 proteins and non-SARS-CoV-2 proteins. PDBSum was used to study a protein–protein interface of the 3D structure of a protein ,48.First, two types of inter-residue contact models were created, which include the usage of atoms and the usage of chains. Then, DynOmics computational interface contact model was used to measure fluctuations of cross-correlations between residue and communication/signaling sites of protein . From thFinally, to evaluate the patterns generated from the collection of protein alphabets, especially using SARS-CoV-2 proteins, structural symmetry, classification of evolutionary protein interface of protein alphabets, and the dynamics of the structural proteome of protein alphabets were processed. The final correlation of structure–function relationship of SARS-CoV-2 proteins was created at last.Extraction of protein alphabets from SARS-CoV-2 proteins was performed to develop two words, i.e., “SARS-CoV-2” A and “COTwo slogans using the diversified 3D structures of proteins as alphabets were included. The PDB was searched extensively to derive different protein alphabets from SARS-CoV-2 and non-SARS-CoV-2. Finally, two slogans were generated using the protein alphabets: the first was titled, “VACCINATE THE WHOLE WORLD WITH COVID-19 VACCINE” C, and alThis study fetched the 12-number SARS-CoV-2 proteins and the 14-number non-SARS-CoV-2 proteins to design the words and slogans. Again, the 12-number SARS-CoV-2 proteins alphabet was compared with the English alphabets, and a distance score was generated after image comparison. The lists between the protein alphabets and English alphabets for “SARS-CoV-2” and “COVID-19” are recorded in The study also fetched four SARS-CoV-2 protein alphabets with antibodies/immunological or vaccine-associated roles from our previous protein alphabets pools. A detailed description of these alphabets with the PDB id is recorded in The concept of structural pattern recognition to understand the structural symmetry pattern is shown in A structural symmetry pattern of protein alphabets “SARS-CoV-2” and “COVID-19”, was developed with the letters C, O, I, Hyphen(-), 1(One), S, and A. At the same time, non-symmetric proteins were also found from “SARS-CoV-2” and “COVID-19”, which were V, D, 9, R, and 2. From the generated two slogans, symmetric proteins for the rest of the words other than COVID-19 and SARS-CoV-2 were V, A, C, I, E, W, H, and O. Similarly, non-symmetric proteins from the words other than COVID-19 and SARS-CoV-2 were N, L, S, Y, and R.At the same time, the symmetrical structure of the non-SARS-CoV-2 proteins was also illustrated in The classification of evolutionary protein interface is shown in The classification of the evolutionary protein interface of protein alphabets of “SARS-CoV-2” and their lattice graph in the 2D representation of the protein assembly are shown in The interface of the assemblies is essential for understanding the clues of the pattern of the 3D protein structure; therefore, this study focused on the interface of protein chain assemblies. The protein chain assemblies provide the proper shape and surface area of the protein to give appropriate functionality, as shown in the schematic diagram depicted in Similarly, our analyses evaluated the pattern of the protein–protein interface of 3D structures of non-SARS-CoV-2 proteins alphabets, which are displayed in To understand the dynamics of the structural proteome, we created an inter-residue contact model representing through the nodes in a 3D protein, which provides the landscape of a spring connection or interaction between the pair of interest residues or chains A. DifferFurthermore, to understand the additional information about the dynamics of the structural proteome, we developed a cross-correlation (CC) map. The CC map provides extra information about the residue interaction pattern and residue fluctuations of a protein. However, the concept of the generation of the CC map is visualized in Simultaneously, the study developed the CC map of 3D structures of non-SARS-CoV-2 proteins alphabets, as shown in The cross-correlation (CC) map shows the calculated residue interface visualized  alongside the axes in the map.Using the hitting and signal communication times, researchers can generate protein residues from communication/signaling sites related to the residue’s functionality. The functional tendency of residues can be reflected in the map. It represents sending signal tendency, or to receive the trend of signals. The higher direction for communication can be indicated by the smaller hitting time A. The maFinally, the hitting and signal communication times/site of protein alphabets of two SARS-CoV-2 protein alphabets with antibodies/immunological or vaccine-associated roles (“D” and “Y”) were generated and represented in The 2D maps were generated for communication/signaling sites and hitting/signal communication times using 3D structures of non-SARS-CoV-2 proteins, which were used to develop the two slogans .A color gradient 2D map was generated from the signaling rate, signaling receiving time, and signaling communication time from protein residues A. It alsSimilarly, the 2D maps were generated from the signaling rate, signaling receiving time, and signaling communication time of residue of non-SARS-CoV-2 proteins, which were used to develop the two slogans .The structure–function relationship of SARS-CoV-2 proteins was developed and fine-tuned to their functionality. The protein alphabet ‘C’ (PDB ID: 6XC3) is a complex SARS-CoV-2 S-glycoprotein in the RBD receptor binding domain. The structural conformation of S-glycoprotein provides different functions, such as: (i) It provides more surface area of RBD for interaction with ACE2 receptor. (ii) The structural conformation of spike protein offers a better cleavage pattern and, thus, increases the host infectivity. (iii) The spike protein shape provides proper functional interface for S1 and S2 subunits. (iv) The structural interface provides a more binding interface and provides more binding affinity with the ACE2 receptor A. The sePattern recognition engages the collection of information based on observations of particular objects consistently . It alsoIn a previous study, Howarth searched the PDB and developed the proteins alphabets using 3D structure . To creaIn recent work, Cicaloni et al. have demonstrated research on cross-reactive T cell recognition between circulating common cold coronaviruses and SARS-CoV-2, including the most recent variants, Delta and Omicron. Further, a deep learning approach based on Siamese networks was used to suggest accurately and efficiently calculate a BLAST-like similarity score between protein sequences. Researchers also tested a neural network model for aligning protein structures. This Siamese long short-term memory model was trained to score the alignments based on BLAST supervision and tested on the set of COVID-19 proteins previously analyzed . HoweverThe question however is: does nature favours a biased form of a particular shape of a protein? The protein molecules are often fine-tuned through the evolutionary process, and the particular shape of a protein is often generated through natural selection to provide its proper functionality via folding process ,60. The The considerable challenge of pattern recognition remains in algorithm selection in bioinformatics. The algorithm should help to identify the primary structural pattern linked with the function at features, such as active sites, functional domains, etc. At the same time, proper model building and analysis of protein pattern recognition are essential. Although our study used several models or algorithms, such as deep AI model assembly enumeration, GNM (Gaussian network model), ANM (anisotropic network model), and Markovian stochastic model, for understanding the structural and functional similarity of the protein, detailed analysis of future work is necessary with next-generation algorithms.Structural protein pattern alphabets have three important implications. First, the structures draw the concentration of new learners in structural biology studying 3D design of protein and PDB. Secondly, they are excellent examples for the natural creation of protein patterns. Finally, the result of the protein patterns implies the natural selection of these proteins, due to their functional importance. We conclude that, due to the unique function of these proteins, specific structural patterns were developed as a result of natural selection. However, pattern recognition remains a critical area of integrative bioinformatics that can be used to determine structural patterns of SARS-CoV-2 proteins and non-SARS-CoV-2 proteins. It will be a next-generation toolkit for the determination of the structure–function paradigm. This computational approach may assist in solving patterns related to the structural aspect of protein and help to decipher the riddles and puzzles of the complex structure–function relationships of protein and be an important area of modern biology. This area might promise to capture the evolutionary information of proteins and the potential for success in future work.(i)Importance of the field. Pattern recognition is a rapidly developing field with enormous applicability in biological sciences. This study tried to understand the pattern identification of SARS-CoV-2 proteins. Finally, the study presents new information on the pattern identification of SARS-CoV-2 proteins.(ii)A summary of the current thinking. We have searched for protein-like alphabets involving 3D structure of SARS-CoV-2 from PDB and created two words, “SARS- CoV-2” and “COVID-19”. We have also developed two slogans using non-SARS-CoV-2 proteins, and the slogans are “Vaccinate the world against COVID-19” and “Say no to SARS-CoV-2”. We have used 12 SARS-CoV-2 proteins and 14 non-SARS-CoV-2 proteins to design those words and slogans. We have performed image comparison with protein-like alphabets with English alphabets using the deep AI model. The structural symmetry analysis indicates alphabet-shaped symmetric proteins, such as C, O, I, Hyphen (-), 1(One), S, and A. To determine the dynamics of the structural proteome, we evaluated the inter-residue contact by developing inter-residue contact models with both residue and chain and illustrated the cross-correlations between residues through a cross-correlation (CC) map. In order to understand the residue functionality of proteins, we analyzed the communication/signaling sites of protein residue and signal communication/signal receiving rate of protein alphabets. The assembly enumeration algorithm, anisotropic network model, Gaussian network model, Markovian stochastic model, and other integrative bioinformatics approaches, and tools were used to depict the structural and functional relationships of the protein alphabets of SARS-CoV-2 and COVID-19. After image comparison of protein-like alphabets, the distance score of “I” was the lowest with 22, and “9” was the highest with 40. For post-processing and decision, two protein alphabets were evaluated, protein alphabet “C” (PDB ID: 6XC3) and alphabet “S” (PDB ID: 7OYG), and we understood the structural, functional, and evolutionary relationships using modeling approaches.(iii)Future directions. This study sheds further light on the uniqueness in the functionality of SARS-CoV-2 proteins. The evolutionary process appears to enhance the protein structure smoothly to provide suitable functionality shaped by natural selection. The computational approach may assist in solving patterns related to the structural aspects of other proteins and help to decipher the riddles and puzzles involving the complex structure–function relationships of proteins, which is an important area of modern biology. It has a great promise for capturing the evolutionary information of proteins and the potential for success in future work. It might help to understand the therapeutic target protein pattern, which will be beneficial as a potential therapeutic target discovery.


Homo sapiens and 85.34% for Mus musculus, and it performed well on three types of non-interaction networks. For protein-protein interactions (PPIs), we obtained accuracies of 99.20, 94.94, 98.56, 95.41, and 94.83% for Saccharomyces cerevisiae, Drosophila melanogaster, Helicobacter pylori, Homo sapiens, and Mus musculus, respectively. Furthermore, NVDT outperformed established sequence-based methods and demonstrated high prediction results for cross-species interactions. NVDT is expected to be an effective approach for predicting PPIs and PPNIs.Predicting protein–protein interaction and non-interaction are two important different aspects of multi-body structure predictions, which provide vital information about protein function. Some computational methods have recently been developed to complement experimental methods, but still cannot effectively detect real non-interacting protein pairs. We proposed a gene sequence-based method, named NVDT , for the prediction of interaction and non-interaction. For protein–protein non-interactions (PPNIs), the proposed method obtained accuracies of 86.23% for  Protein-protein non-interactions and interactions are distinguished and predicted by gene sequence using single nucleotide and contiguous nucleotides combined with machine learning models. Proteins with similar functions are more likely to interact with each other to form protein complexes that participate in complex and diverse biochemical activities. Detecting and characterizing PPIs provides insight into the functions of unannotated proteins and the mechanisms underlying cellular biochemical processes and complex diseases, and can provide a basis for protein engineering and drug design2. The vast majority of proteins do not form interactions in physiological conditions. The number of non-interacting protein pairs vastly exceeds that of interacting pairs. Although some protein pairs have great structural similarities with available experimental complexes, interactions between them can be avoided if they are potentially hazardous to organism3. Identifying PPNIs is critical for understanding biological processes and reducing noise in datasets aimed at determining representative features of protein classification. Mastering the biological characteristics of non-interacting protein pairs helps to better study the three-dimensional structure and function of multi-body.Most vital life activities are related to protein interactions, including physiological and pathological processes5. Therefore, many computational methods have emerged as an alternative for the experimental prediction of PPIs based on different data types, such as genomic information6, evolutionary information8, structural information10, network information11, and sequence information13. Since protein sequences are easily obtained and sequence-based schemes do not need prior knowledge, various sequence-based computational models are favored by researchers. For example, Bock and Gough utilized amino acid physicochemical properties to predict interactions based solely on primary sequences14. Shen et al. characterized protein sequence by conjoint triad (CT), where CT considers the properties of adjacent amino acids15. Guo et al. expressed protein sequence by auto covariance (AC), which considered interactions between residues separated by a certain distance16. Yang et al. employed local descriptors  to evaluate the effect of discontinuous amino acids, but the extracted features ignore global information17. Yin et al. numerically represented protein sequences using biochemical properties of amino acids and conducted coevolution analysis based on Fourier transform to detect interacting protein pairs18. Due to the difficulty in accurately characterizing protein sequence information by these single feature extraction methods, researchers have proposed many computational methods integrating multiple features for prediction20. For instance, Zhang et al. used AC, LD, and multi-scale continuous and discontinuous (MCD) local descriptor to extract features information of protein sequence21. Chen et al. exploited pseudo amino acid composition, Moreau-Broto, Moran and Geary autocorrelation descriptor, position-specific scoring matrix, and LD to encode biologically relevant features22. These feature vectors generally summarize physicochemical properties and position distribution of amino acids, but their large dimensions greatly increase the computational complexity.Rapid developments of high-throughput experimental technologies have enabled large-scale discovery and identification of PPIs, but they remain some disadvantages of time-consuming, labor-intensive, and high levels of false-positive and false-negative predictions23, drugs’ efficacy24, drug–target binding recognition25, medical diagnosis26, protein complex structure prediction27, and protein–protein/peptide/ligand predictions30. Traditional machine learning algorithms are often combined with feature engineering to detect PPIs, such as amino acid physicochemical properties, co-occurrence frequency13, CT15, AC31, LD17, signature product32, sequence order, and dipeptide information33 were performed with support vector machine (SVM), Gabor feature34, chaos game representation and wavelet transform35 were performed with random forest (RF). In addition, deep learning algorithms are also widely used in PPIs detection, such as Siamese-like convolutional neural network (CNN) in DPPI36 and deep residual recurrent CNN in PIPR37. However, deep learning algorithms remain challenging as follows: (i) Lack of interpretability. The deep network is similar to a “black box” in which the physical meaning of features cannot be explained. But traditional machine learning algorithms involve feature engineering, which makes the model easy to interpret and understand. Previous successful methods show that the model generated by the combination of clear sequence features and traditional machine learning performs well. (ii) Complexity and time-consuming. The “inside” of deep learning is difficult to fully understand, which makes hyper-parametric and network design still a considerable challenge. But now that we have a more comprehensive understanding of traditional machine learning underlying algorithms, it is easier to adjust parameters and change model designs. Our lab had effectively captured amino acid sequence information using traditional machine learning algorithms to model and validate PPIs, and had many applications in other biological and medical problems. Yu et al. used SVM with heterogeneous kernels from a specific kernel set, including the Hadamard, RBF, and linear kernels, to perform the breast cancer outcome evaluation and the results proved to be effective38. Lyu et al. developed a two-layer SVM ensemble-classifier to predict interface residue pairs of protein trimers and showed its effectiveness and reliability39. Wang et al. employed linear SVM, RF, logistic regression with lasso penalty, and logistic regression with hierarchy interaction to predict interface residue pairs respectively, and showed that diverse machine learning methods tend to predict different protein–protein interface patterns40. As a result, we select the appropriate feature engineering combined with traditional machine learning algorithms to predict protein–protein interaction and non-interaction.The recent advancements in machine learning techniques have been greatly applied in the bioinformatics field, including RNA-binding site identification41. Negative samples in the computational methods are almost constructed by pairing proteins located in different subcellular positions, but these samples restrict the distribution of non-interacting protein pairs and lead to biased estimates of prediction accuracy42. Srivastava et al. introduced a triple-layer validation method to collect reliable non-interacting protein pairs and then characterized the most relevant protein correlation features to train the PPIs identification model, which showed excellent predictive capabilities43. The training and performance evaluation of models are affected by strong bias in negative samples45, so high-quality and practical negative samples are needed to train less biased models. Smialowski et al. developed the Negatome database, a set of protein pairs unlikely to show direct physical interactions, in 2009 by manually collating the literatures and analyzing three-dimensional protein complex structures46. Blohm et al. proposed the second version of this database using a new advanced text-mining process to guide the manual annotation process47. High-quality non-interacting samples are important to capture the interaction and non-interaction information from sequences, so Negatome samples are currently used in many studies as an alternative to pairing proteins located in different subcellular. Bryant et al. used negative samples collected in the Negatome database, combined with AlphaFold2, and optimized multi-sequence alignment to predict heterodimeric protein complexes48. Das et al. used a negative dataset from the Negatome database when utilizing interface properties and SVM to classify and differentiate native and non-native complexes49. Consequently, the non-interacting protein pairs in the Negatome database can facilitate the training of highly generalizing models to predict PPIs and PPNIs.Plenty of computational models are proposed under the paradigm of supervised learning, so the quality of training data is a key issue to determine the prediction performance. For the training of learning algorithms, interacting protein pairs  and non-interacting protein pairs  are equally important to computational biologists. However, biologists generally focus on interacting protein pairs, extensively collecting experimentally or computationally validated interacting protein pairs into public databases, while ignoring or discarding non-interacting protein pairs52. Moratorio et al.53 and Carrau et al.54 found that codon usage may be selected to maintain mutational robustness. Some triple nucleotides often encode the same amino acid, which is called synonymous codons. Codon usage bias, where certain codons are used more frequently than their synonymous codons, is influenced by mutation, selection, and genetic drift. Zhuo et al. found that some interface codons had the obvious propensity to interface residues and the genetic codon affected the interaction interface between proteins55. Because codons carry very important information about proteins, some methods consider extracting features from gene sequences. Zhou et al. used the codon pair frequency difference to predict protein interactions with comparable performance to those of other sequence-based methods56. Najafabadi et al. utilized the relative codon frequency differences combined with a naive Bayesian classifier to validate PPIs, the approach showed good performance on Saccharomyces cerevisiae (S. cerevisiae), Escherichia coli, and Plasmodium falciparum datasets57. Consequently, extracting additional biological information from gene sequences, like codon frequencies, may further improve the prediction ability. Deng et al. proposed natural vector based on the distributions of nucleotides in each DNA sequence to analyze the virus genome58. Later, many methods improved on the natural vector. For example, Dong et al. proposed an improved natural vector method called Accumulated Natural Vector to analyze sequences, genomes, and their phylogenetic relationships59. Zhao et al. added the covariances of amino acids to natural vector and used it to classify proteins and detect the evolutionary relationships among species60. In addition, certain dinucleotides have connections with the regulation of metabolism, aging, and neurodegeneration61. Atkinson et al. provided that dinucleotide bias was related to evading cell defense mechanisms62. Takata et al. developed that codon and dinucleotide usage biases may be associated with the need to maintain the RNA secondary structure involved in splicing and gene expression63. Kokate et al. studied codon and dinucleotide preferences of 29 Drosophila species and observed their association with speciation64. Simón et al. analyzed dinucleotide and codon usage in all available non-redundant viral sequences and discovered that four dinucleotides  were self-complementary and codon usage bias was mainly determined by the genomic composition65. Therefore, we hypothesized that the distribution of contextual nucleotides in a gene sequence could be helpful for sequence classification prediction, so dinucleotide and triplet nucleotide information was added to the natural vector.Feature selection is a major determinant of the generalizability of predictive models. Common sequence-based computational methods extract features from the amino acid sequence, but nucleotide-related information is missing in the amino acid sequence since multiple codons encode the same amino acid. Triplet nucleotides  are associated with various diseases, and their repeats may lead to toxic proteins, alter RNA function, and control transcription and translationHomo sapiens (H. sapiens), Mus musculus (M. musculus), S. cerevisiae, Drosophila melanogaster (D. melanogaster), and Helicobacter pylori (H. pylori) datasets, and obtained high prediction results. We harnessed NVDT to produce network visualizations for three types of PPNI networks, including the one-core, multiple-core and crossing network, to further evaluate the capabilities of this approach. In addition, model evaluations indicated that NVDT improved PPIs prediction accuracies over state-of-the-art methods. Our PPNIs prediction results showed that our method was robust and would help to improve multi-body complex structure predictions.In this study, we developed a sequence-based approach for PPIs and PPNIs predictions called NVDT. NVDT firstly employed the distribution of nucleotides, dinucleotides, and triplet nucleotides to extract protein information from gene sequences, where the correspondence between a protein gene sequence and its feature vector was one-to-one. Second, we combined all pairs of corresponding natural vectors into a single feature vector describing a protein pair and then normalized feature vectors using the Z-score method. Finally, these features were fed into the classifier to obtain the final prediction results. NVDT not only combined the advantages of local and global protein information but also had high computational speed with low dimensions, making it a robust and efficient prediction method. We applied our approach to H. sapiens increased from 80.92 to 86.23%, and that for M. musculus increased from 80.17 to 85.34%. But the differences in accuracy of the constructed dataset were relatively small.To ensure the reliability of our approach, five-fold cross-validation was first used to evaluate model performance and select the optimal parameters (Supplementary Tables We extended our proposed method to predict PPNI networks consisting of non-interaction pairs (NIPs). The knowledge of PPNI networks is helpful to overcome the noise in the datasets and find the relevant features that can best represent proteins, so as to better classify and design the three-dimensional structure of proteins. It can also be used to identify the proteins with the least interaction in a pathway. As a potential indispensable factor in the pathological process, these proteins are likely to be effective targets for drug design. At the same time, systematic analysis of the non-interaction relationship between a large number of proteins in biological systems is very helpful for multi-body complex structure predictions.We predicted three types of PPNI networks using our approach. First, a one-core network is the simplest network in which only one core protein radially does not interact with other proteins. We found a guanine nucleotide-binding protein, P62879, which may be a modulator or transducer in various transmembrane signaling systems. Among 26 NIPs, 23 pairs were correctly predicted by our method Fig. , supportSecond, a multiple-core network is essentially composed of several one-core networks, satisfying the corresponding non-interaction relationships among these nuclear proteins at the same time. We found a PPNI network with the pathway Q8TBX8-O75175-P31150-Q16828-Q8TAU0-Q9H6S3, involving 26 proteins. Of the 82 NIPs in this network, our method correctly predicted 66 pairs , NVD (using nucleotide and dinucleotide information), NVT (using nucleotide and triplet nucleotide information), and NVDT . As shown in Fig. i and protein j, the 92-dimensional feature vectors of two proteins can be obtained by the NVDT method. Suppose that the feature vector of protein i is A = , and the feature vector of protein j is B = . Now we encode the protein pairs in five different means, which are defined as follows.H. sapiens and M. musculus datasets were shown in Table Similarly, distinct feature combination methods based on the same classifier will produce different prediction results. For one protein pair composed of protein S. cerevisiae, 94.94% for D. melanogaster, and 98.56% for H. pylori, which were better than the two other methods, indicating that NVDT was more suitable for predicting PPIs than other gene sequence-based methods.The prediction results for alternative methods based on gene sequence data using three datasets were shown in Table 56 on the S. cerevisiae dataset. We used the same ten-fold cross-validation as the CCPPI method and obtained an average accuracy of 98.05%, precision of 98.14%, sensitivity of 97.95%, and MCC of 96.10% (Supplementary Table We further compared our method with the codon pair-based method(CCPPI) proposed by Zhou et al.D. melanogaster dataset, we further compared the predictive performance of our method with other state-of-the-art sequence-based approaches using S. cerevisiae and H. pylori datasets. The comparison results on the S. cerevisiae dataset were presented in Table H. pylori dataset were presented in Table In order to make an effective comparison of our proposed SVM-NVDT model, and considering the limited literature based on the S. cerevisiae samples and used the other five species in the DIP database as independent test datasets. For these five test datasets, all samples were interacting protein pairs. As summarized in Table When a large number of interacting proteins in an organism show correlated evolution, orthologs in other taxa will also interact. Therefore, we trained the model with SVM using all H. sapiens and M. musculus addressed the limitations of established computational methods for PPIs prediction, including low accuracies and inefficient prediction of real non-interactions. At the same time, the validity of the model was verified on S. cerevisiae, D. melanogaster, H. pylori, H. sapiens, and M. musculus constructed datasets based on the commonly used protein pairs using different subcellular locations to construct negative samples. Several aspects of the proposed approach were worth highlighting. (1) We utilized information from gene sequence data, enabling the extraction of more extensive information not accessible in protein sequence data. The dinucleotides and triplet nucleotides are related to many diseases, metabolism, and mutations, and their information can be well obtained by NVDT. (2) NVDT had better performance than those of other gene sequence-based methods, with higher accuracy and better stability and this can be explained by the integration of polynucleotide information, which dramatically reduces the repeatability of different protein feature vectors. (3) NVDT juxtaposed the two feature vectors of a protein pair to form a new vector with a single dimension, retaining more information. Especially when combined with SVM, the prediction classifications of the two feature vectors  and  corresponding to each sample (assuming the protein pair composed of protein A and protein B) are always consistent. The reason may be that each dimension in SVM is independent, and the classification fundamentally depends on calculating the Euclidean distance of any two samples, so the order of features does not affect the classification results. (4) Ensemble classifiers typically show higher accuracy and robust performance than those of single classifiers. However, our model using a single classifier SVM showed better performance than those methods using ensemble classifiers on the H. pylori dataset. (5) The constructed negative datasets were restricted to protein pairs located in different cellular compartments, potentially leading to a functional bias in downstream analyses and predictions. The high accuracy of these datasets hardly reflected the true prediction effect, but the good performance on the real dataset can well reflect the feasibility of our model. Among them, results showed that the prediction accuracy of the real dataset was not sufficiently high, which may be explained by the limitation of real non-interacting protein pairs.Our method calibrated on known PPNIs in The proposed method utilized detailed gene sequence information, including the distribution information of three kinds of nucleotides, and showed better predictive performance than those of established protein sequence-based methods. Moreover, by comparing combined and single features, we found that various features may be complementary. And our model performed well on three types of networks. These results proved that gene sequence information can be used to distinguish interacting and non-interacting protein pairs and ultimately to establish a complete PPI and PPNI map to better understand biochemical and biological processes.In this section, we elaborated on the proposed NVDT approach for predicting PPIs and PPNIs based on gene sequence. NVDT consisted of the following three steps: (1) Encode the protein pairs’ interaction and non-interaction information into natural vectors via the distribution of nucleotides, dinucleotides, and triplet nucleotides. (2) Generate and normalize feature vectors of protein pairs. 3) Train the model via different classifiers (SVM and RF) and test on an independent test set. Finally, we briefly described the common model performance evaluation metrics used in the study. The flowchart of NVDT was shown in Fig.  Train thhttps://dip.doe-mbi.ucla.edu/dip/)66. To reduce fragments and sequence similarity, samples with fewer than 50 amino acids and >40% pairwise sequence identity to one another were excluded.Seven datasets were obtained. The positive datasets consisted of interacting protein pairs collected from the public Database of Interacting Proteins (DIPs: http://mips.helmholtz-muenchen.de/proj/ppi/negatome)47, which currently contained experimentally supported non-interacting protein pairs. In the Negatome database, the subcellular structure information for some proteins was unclear or even absent, and some proteins belonged to at least two or more subcellular localizations simultaneously, which was consistent with the actual phenomenon. After a selection of protein pairs from multiple species in the Negatome database, the majority belonged to H. sapiens (1217 pairs), followed by M. musculus (347 pairs) and Rattus norvegicus (33 pairs). Accordingly, we collected 2434 protein pairs for H. sapiens and 694 protein pairs for M. musculus, with interacting protein pairs and non-interacting protein pairs each accounting for half. The datasets obtained in this way were called “real dataset”.The negative datasets were composed of non-interacting protein pairs obtained in two ways. First, negative samples were derived from the Negatome Database 2.0 . To verify the performance of the proposed method, the positive samples of H. sapiens and M. musculus datasets in the real dataset and constructed dataset were consistent, only the negative samples were obtained differently  of a gene sequence S was given as follows:kth protein pair corresponds to protein i and protein j, we then juxtaposed the two natural vectors. The features of the protein pair can be described as two 184-dimensional feature vectors:Ni and Nj were feature vectors for proteins i and j.The first normalized central moments 69, with the mean value of 0 and standard deviation of 1:lth feature of the kth protein pair, and n was the number of protein pairs. μ(l) and σ(l) were the mean value and standard deviation of all proteins for the lth feature, respectively:However, protein feature vectors correlated to the length of protein (the number of nucleotides) complicate the comparison between two protein pairs. The feature vectors were standardized by the Z-score method70 is a supervised learning method that has been popularly utilized for classification and regression in computational biology. It constructs a hyperplane to maximize the margin. Specifically, SVM finds several samples as support vectors that minimize the distance of samples between different classes, and the minimum distance is called the margin. The optimal hyperplane is located in the center of the margin, where the larger the margin is, the smaller the generalization error will be. Therefore, this hyperplane can better divide the samples in the training set according to the labels given. In this study, the Gaussian radial basis function was chosen as the kernel function, and the regularization parameter c and the Gauss kernel function parameter γ were optimized by a grid search approach. Here, the parameters were the default of LIBSVM, and cross-validation was used to avoid over-fitting , the number of randomly selected features at each decision split (denoted as n_feature), and the minimum node size of a terminal node (denoted as min_leaf). In our study, n_Tree values under 200 were evaluated to find the optimal value concerning computational time, cost, and overfitting (Supplementary Table Random forest (RF)TP represents the number of positive samples that are correctly predicted; FN represents the number of positive samples that are incorrectly predicted; TN represents the number of negative samples that are correctly predicted; FP represents the number of negative samples that are incorrectly predicted. The receiver operating characteristic (ROC) curve was generated by plotting the TP rate against the FP rate at various thresholds; the abscissa of the ROC curve was 1-specificity and the longitudinal coordinate is sensitivity.Common performance evaluation metrics were used, including accuracy (Acc.), precision (Pre.), sensitivity (Sen.), specificity (Spe.), Matthews correlation coefficient (MCC), F1-score, and the area under a ROC curve (AUC) to evaluate the predictive performance of the proposed method. These metrics were defined as follows:All quantitative results of K-fold cross-validation were shown using mean ± standard deviation. Graphing of three types of PPNI networks was performed with Pajek. The computational experiments were repeated at least two additional times with a similar outcome.Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary