Molecular interactions between respiratory syncytial virus (RSV) fusion protein (F protein) and the cellular receptor Toll-like receptor 4 (TLR4) and myeloid differentiation factor-2 (MD-2) protein complex are unknown. Thus, to reveal the detailed molecular interactions between them, in silico analyses were performed using various bioinformatics techniques. The present simulation data showed that the neutralizing antibody (NT-Ab) binding sites in both prefusion and postfusion proteins at sites II and IV were involved in the interactions between them and the TLR4 molecule. Moreover, the binding affinity between postfusion proteins and the TLR4/MD-2 complex was higher than that between prefusion proteins and the TLR4/MD-2 complex. This increased binding affinity due to conformational changes in the F protein may be able to form syncytium in RSV-infected cells. These results may contribute to better understand the infectivity and pathogenicity (syncytium formation) of RSV. Respiratory syncytial virus (RSV), which belongs to the genus Orthopneumovirus of Family Pneumoviridae, causes various respiratory illnesses, such as bronchitis, bronchiolitis, and pneumonia . MoreoveRSV consists of two major antigens: fusion protein (F protein) and attachment glycoprotein (G protein) . F proteThe F protein has antigenic domains defined by monoclonal antibody (mAb) competition and structural research of F protein-mAb complexes . MoreoveMore than 10 types of Toll-like receptors have been found to be closely associated with human innate immunity . Among tAnother molecule, myeloid differentiation factor-2 (MD-2), forms a complex with TLR4 (TLR4/MD-2 complex) ,20. MD-2Recent progress in bioinformatics technologies, i.e., the docking simulation method, allows us to analyze detailed protein–protein interactions. Thus, to better understand the molecular interactions between the RSV F protein and TLR4, detailed docking simulation analyses were performed.https://pdbj.org/, accessed on 16 September 2021). Subsequently, the 3D structures of TLR4/MD-2 and LPS were separated from the TLR4/MD-2/LPS complexes. Based on a previous report, six antigenic sites in the prefusion protein were identified: site Ø: Ser62–Leu96 and Leu195–Asn227; site I: Asn27–Leu45, Pro312–Thr318, and Glu378–Pro389; site II: Asn254–Asn277; site III: Ser46–Thr54, Val301–Thr311, Asn345–Phe352, and Cys367–Glu378; site IV: Cys422–Gly471; and site V: Ser55–Leu61, Ser146–Asp194, and Ser287–Val300 [Three-dimensional (3D) structures of RSV prefusion protein (PDBID: 4JHW), postfusion protein (PDBID: 3RKI), and the human TLR4/MD-2/LPS complex (PDBID: 3FXI) were obtained from Protein Data Bank Japan (PDBJ)  to perform molecular docking between the F protein and TLR4/MD-2. Six antigenic sites (Ø and I–V) in the prefusion protein were specified as the binding sites of the present docking simulation [http://cadd.zju.edu.cn/hawkdock/, accessed on 15 February 2022) and PPI-Affinity , were used to rescore the docking models generated by HDOCK [http://www.ebi.ac.uk/thornton-srv/databases/pdbsum/, accessed on 20 September 2022) [We employed the HDOCK web server  .https://alcazar.science.uu.nl/services/HADDOCK2.2/, accessed on 20 September 2022), which uses amino acid residue contact-based statistical functions to predict the binding affinity. This shows that the contributions to binding affinity are inter-residue contacts (ICs) and the noninteracting surface (NIS) [The binding affinity was calculated using the HADDOCK web server (ce (NIS) ,29. BaseThe %NIS represents the percentage of polar, apolar, and charged residues on NIS ,29. The To validate the reliability of the present docking simulation, the docking model and its native geometry were compared. However, as no structure of a complex of the F protein from any virus and TLR4 has been registered in the Protein Data Bank, we could not perform redocking between the F protein and TLR4. Thus, we used the TLR4/MD-2/LPS complex and RSV prefusion protein/antibody complex for this validation approach.First, a TLR4/MD-2/LPS complex model was constructed through docking simulations between TLR4/MD-2 and LPS using HDOCK. The HDOCK best-scored model was selected from the docking models. Subsequently, the molecular interactions and interacting sites between the docking model and the X-ray crystallography structure of the TLR4/MD-2/LPS complexes analyzed by PDBsum were compared.Next, the 3D structure of the RSV prefusion protein/antibody CR9501/motavizumab complex model (PDBID: 6OE5) was downloaded from the PDBJ. Then, the 3D structures of the prefusion protein, antibody CR9501, and motavizumab were separated from the complex model to perform a redocking simulation between the prefusion protein and antibody CR9501. The molecular interactions and interacting sites between the docking models generated by HDOCK and the Cryo-electron microscopy (cryo-EM) structure of the prefusion protein/antibody CR9501 complexes were compared, as described above.http://bioinfo3d.cs.tau.ac.il/ScanNet/, accessed on 11 October 2022), which predicts the protein-binding sites from a structure and shows its binding probability based on a deep learning approach [Furthermore, we evaluated the reliability of the interacting sites in the selected docking models using ScanNet  and TLR4/MD-2 complex in each suitably selected model. As shown in In the site II-associated prefusion protein/TLR4/MD-2 complex model, the interactions between the prefusion proteins and TLR4 were mediated by eight hydrogen bonds (two hydrogen bonds between Gln361 and Ser521), two salt bridges, and 262 non-bonded contacts, whereas the MD-2 molecules did not interact with the prefusion proteins a. Lys272Next, in the site IV-associated prefusion protein/TLR4/MD-2 complex model, the prefusion protein interacted with TLR4 through five hydrogen bonds, no salt bridge, and 197 non-bonded contacts and with MD-2 through 21 non-bonded interactions b. At sitThe molecular interactions between the postfusion proteins and TLR4/MD-2 were analyzed . In the Subsequently, in the site IV-associated postfusion protein/TLR4/MD-2 complex model, the postfusion proteins interacted with TLR4 via 12 hydrogen bonds (two hydrogen bonds between Lys75 and Asp238), 4 salt bridges, and 275 non-bonded contacts and with MD-2 through 23 non-bonded contacts and one hydrogen bond b. Lys465Molecular docking was performed between TLR4/MD-2 and LPS to compare it with an experimentally determined 3D structure . As showScanNet was used to predict the binding probability of all residues in the prefusion and postfusion proteins. Among the 449 residues in the prefusion protein, the sites with a binding probability > 0.5 were 64 residues (14.3%) . In addiThe detailed molecular interactions among RSV prefusion proteins, postfusion proteins, and the host cellular receptor TLR4/MD-2 complex were analyzed using authentic bioinformatics technologies. First, both sites II and IV of the prefusion and postfusion proteins interacted with the TLR4/MD-2 complex. The NT-Ab binding sites of sites II and IV of the proteins (prefusion and postfusion) were fully involved in the interactions between these proteins and the TLR4 molecule. Second, the binding affinity between postfusion proteins and the TLR4/MD-2 complexes was increased compared to that of prefusion proteins and the TLR4/MD-2 complexes. To the best of our knowledge, a previous report suggested that the host cellular receptors against the RSV fusion protein were TLR4 molecules using only cell biological and immunological methods . TherefoPrevious reports suggested that distinct antigenicity of RSV was found between prefusion and postfusion proteins . MoreoveIn this study, both models, in which sites II and IV were designated as the binding sites, showed an increasing binding affinity for the TLR4/MD-2 complex as prefusion changed to postfusion. Moreover, the selected docking models at sites II and IV differed from each other. This may indicate that binding to the TLR4/MD-2 complex occurs independently at sites II and IV of RSV. Furthermore, the increased binding affinity appears to be consistent with binding to TLR4, triggering the change from prefusion to postfusion in the F protein. These results suggest that conformational changes in the F protein promote viral adsorption to the host cell. However, the mechanism by which F proteins change from prefusion to postfusion after binding of TLR4 to F proteins may need to be elucidated for further studies.The binding sites of the TLR4/MD-2 complex for binding to the F protein differed from those for binding to LPS . TLR4 isIn general, cell membranes composed of phospholipid bilayers have strongly similar polarity . TherefoThe docking models generated by HDOCK were rescored to obtain suitable simulation data. The scoring function, which plunks near-native solutions from thousands of possible solutions created by docking algorithms, plays a pivotal role in the reliability of docking simulation . Hence, Next, HDOCK data were validated by redocking and binding site predictions. HDOCK employs knowledge-based scoring functions, which can provide discrepancies in the reliability of results depending on a given protein due to limited training sets of crystal structures ,36,39. TFinally, docking models in which TLR4/MD2 bound to sites II and IV of the F protein were selected for the present study. In contrast, based on the binding site probability prediction and the ranking of docking models by multiple scoring functions, it is possible that TLR4 binds more readily to site IV in the F protein. However, elucidation of this through in silico methodologies is limited; interdisciplinary approaches, including in vitro and in vivo, may be needed for future studies.We studied the detailed molecular interactions between RSV F proteins and TLR4/MD-2 complexes in silico. The NT-Ab binding sites in both prefusion and postfusion proteins at sites II and IV may be involved in counter ligands as the TLR4/MD-2 complexes. Furthermore, the present simulation models suggested that F proteins could strongly bind to the TLR4/MD-2 complexes due to conformational changes from prefusion to postfusion proteins, increasing the binding affinity. These new findings may contribute to better understand the infectivity and pathogenicity (syncytium formation) of RSV infection.


Protein-protein interactions play an important role in life activities. The study of protein-protein interactions helps to better understand the mechanism of protein complex interaction, which is crucial for drug design, protein function annotation and three-dimensional structure prediction of protein complexes. In this paper, we study the tetramer protein complex interaction. The research has two parts: The first part is to predict the interaction between chains of the tetramer protein complex. In this part, we proposed a feature map to represent a sample generated by two chains of the tetramer protein complex, and constructed a Convolutional Neural Network (CNN) model to predict the interaction between chains of the tetramer protein complex. The AUC value of testing set is 0.6263, which indicates that our model can be used to predict the interaction between chains of the tetramer protein complex. The second part is to predict the tetramer protein complex interface residue pairs. In this part, we proposed a Support Vector Machine (SVM) ensemble method based on under-sampling and ensemble method to predict the tetramer protein complex interface residue pairs. In the top 10 predictions, when at least one protein-protein interaction interface is correctly predicted, the accuracy of our method is 82.14%. The result shows that our method is effective for the prediction of the tetramer protein complex interface residue pairs. Protein-protein interactions are significant in various biological activities and processes, such as signal transmission, gene expression and transcriptional regulation . The intWith the development of technology, some experimental methods can be used to study the interactions of multibody protein complexes, such as X-ray crystallography, Cryogenic electron microssopy (Cryo-EM) and Nucleic Magnetic Resonance (NMR) . These eAt present, researchers have developed several calculation methods to predict protein complex interactions, such as Wang et al. proposed to use different machine learning methods to predict different types of protein-protein interaction interface residue pairs . OvchinnTo further improve above mentioned defections, we have done two parts of work on the study of tetramer protein complex interaction. The first part is to predict the interaction between chains of the tetramer protein complex. The second part is to predict the tetramer protein complex interface residue pairs, that is, assuming that the interaction between two chains of the tetramer protein complex is known, we predict the interface residue pairs formed by the interaction.In first part, according to the five geometric properties of residue, the protein sequence was mapped into five number sequences. Based on these number sequences, we defined the position change sequence and geometric feature change sequences of the same type of amino acids. Then combined with four mathematical statistics, we extracted a 20 × 24 feature map to represent a sample generated by two chains of the tetramer protein complex. Finally, we constructed a CNN model based on PyTorch framework to predict the interaction between chains of the tetramer protein complex.k-Average Cumulation Factor, and combined the Amino Acid k-Interval Product Factor to extract features based on protein sequence. We also defined the Residue k-Interval Product Factor, Residue k-Average Cumulation Factor and weight factor to extract features based on protein three-dimensional structure. Finally, we proposed a SVM ensemble method to predict the tetramer protein complex interface residue pairs.In second part, the influence of surrounding amino acids (residues) on the central amino acid  is fully considered in feature extraction. We defined the Amino Acid In this paper, we collect 111 tetramer protein complexes from the Protein Data Bank according to the following three requirements: the number of chains in the protein complex is 4, the number of amino acids in each chain is between 20 and 500, its crystal structure is obtained by X-ray experimental method. The PDB ID of these 111 protein tetramers is shown in L, see For protein sequence P with length 1, P2, P3, P4 and P5 to represent the 5 number sequences. These 5 number sequences are the ASA number sequence, RASA number sequence, ECA number sequence, ICA number sequence and EVA number sequence.1 in 2 in 1 in 2 in 1 in 2 in 1 in 2 in 1 in 2 in According to the 5 geometric properties of each amino acid, the protein sequence P is mapped into 5 number sequences, see n times in protein sequence P, the occurrence positions from left to right are For any amino acid We define the same type of amino acid position change sequence We define the same type of amino acid geometry feature change sequence The monomer protein can be represent by the same type of amino acid position change sequence Based on the same type of amino acid position change sequence x, denoted as (1). The frequency of amino acid x position change sequence (2). The arithmetic mean of the same type of amino acid x change sequence (3). The minimum of the same type of amino acid position x position change sequence (4). The maximum of the same type of amino acid Firstly, we extract four mathematical statistics from the same type of amino acid position change sequence x geometry feature change sequence (1). The arithmetic mean of the same type of amino acid x geometry feature change sequence (2). The minimum of the same type of amino acid x geometry features change sequence (3). The maximum of the same type of amino acid x geometry feature change sequence to the arithmetic mean of the same type of amino acid x position change sequence, denoted as (4). The ratio of the arithmetic mean of the same type of amino acid Secondly, we extract four mathematical statistics from the same type of amino acid geometry feature change sequence According to the above statistics, we obtain 4+4×5 = 24 features to characterize each type of amino acid. The monomer protein is composed of 20 types of amino acids. So we use a 20 × 24 dimension matrix The line represents the number of amino acid types, and the column shows 24 features of each type of amino acid.In order to better understand 24 features calculation process, we give an example as follows:For protein sequence P = ACAGAHHAALKAYAW, we calculate 24 features of the A amino acid. According to the definition of amino acid position change sequence, we can get Applying The calculation process of other four geometry feature change sequences statistics is the same as that of ASA feature change sequencesstatistics. So we can obtain 4+4×5 = 24 features to characterize A amino acid.A tetramer protein complex is composed of four chains, and any two chains can generate a sample, so a tetramer protein complex can generate six samples. We use a 20 × 24 dimension matrix S to represent a sample, where the matrix S is generated by the absolute value of the difference between the corresponding feature values of two matrices generated by two chains. For example 1REW_ABCD, 1REW is the tetramer protein complex PDB ID. A, B, C and D are the names of four chains. We use Sample 1 generated by A chain and B chain: Sample 2 generated by A chain and C chain: Sample 3 generated by A chain and D chain: Sample 4 generated by B chain and C chain: Sample 5 generated by B chain and D chain: Sample 6 generated by C chain and D chain: Where We use The matrix S of each sample is standardized by Convolutional Neural Network (CNN) is a kind of feedforward neural network with deep structure. The CNN model we created is based on PyTorch framework, which consists of 2 sets of convolution layer, a pooling layer and a full connected layer. In first convolution layer, we select 3 × 3 kernels slide over the input feature maps performing convolution operation (step size is 1), and process with the Rectified Linear Unit activation function. In second convolution layer, we use 2 × 2 kernels to perform convolution operation over feature maps (step size is 1), and also process with Rectified Linear Unit activation function. In the pooling layer, we collect the maximum values in every 2 × 2 patch of feature maps through a sliding window to form a more robust pooled feature maps. Then flatten it into a vector and output the results through a fully connected layer. The CNN model contains many hyper parameters that have different effects on its overall performance . In thisIn this paper, for a given amino acid , we consider the influence of surrounding amino acids (residues) on the central amino acid . Firstly, we consider the influence of each surrounding amino acid (residue) on the central amino acid (residue). Secondly, we take a certain amount of amino acids (residues) as a whole, and consider the influence of this whole on the central amino acid  and the influence of each residue in the whole on the central residue.L, see k-Interval Product Factor (AAIPF(k)i) to describe the influence of neighboring amino acids on the central amino acid (k Average Cumulative Factor (AAACF(k)i) to describe the overall effect.The physicochemical properties of different types of amino acids are different, and these physicochemical properties play important roles in protein-protein interactions. In this paper, we consider hydrophobicity, polarizability, polarity, secondary structure, and codon diversity of the amino acid, and values of these five physicochemical properties of each amino acid are shown in ino acid , see fork Average Cumulative Factor (AAACF(k)i) is defined as follows: for the central amino acid Pj, in the number sequence, divide the sum of the value of the central amino acid position and its forward k positions and backward k positions by 2k+1, as shown in The Amino Acid i, AAIPF(2)i, AAIPF(3)i, AAIPF(4)i, AAIPF(5)i, AAIPF(6)i, AAIPF(7)i, AAIPF(8)i, AAIPF(9)i and AAIPF(10)i to describe the effect of each amino acid on the central amino acid. We extract AAACF(1)i, AAACF(2)i, AAACF(3)i, AAACF(4)i, AAACF(5)i, AAACF(6)i, AAACF(7)i, AAACF(8)i, AAACF(9)i and AAACF(10)i to describe the effect of the whole formed by a certain number  of amino acids on the central amino acid. We also use the five physicochemical characteristics of the central amino acid as features to describe the amino acid. Thus we can use 5×(20+10)+5 = 155 features to describe each amino acid.In the previous study , the proIn several previous research studies , it has For a given central residue, we calculate the Euclidean distance between each residue and the given central residue according to the three-dimensional coordinates of the k-Interval Product Factor (RIPF(k)) to describe the effect. The RIPF(k) is defined as follows: on the monomer protein three-dimensional structure, for a given central residue Pj, multiply the geometric value of the kth residue closest to the central residue by the geometric value of the central residue, and divide the product by k (see k-Average Cumulative Factor (RACF(k)) and weight factor k-th residue closest to the central residue in the monomer protein three-dimensional structure.k+1) is the number of residues in the whole. In protein-protein interactions, the individual behavior of the central residue is affected by neighboring residues in the protein three-dimensional structure, we define Residue by k see . When weidue see -22.RIPFIn the protein three-dimensional structure, for the central residue, we consider the influence of 20 residues closest to the central residue. We extract U. Therefore, we use a 920 dimensional feature vector to represent a residue pair. Taking the residue pairs generated by residues on A chain and B chain of the 1DD3 tetramer protein complex as an example. We use j on A chain and use k on B chain. Then we use j on A chain and residue k on B chain. We use Y to represent the sample label .In summary, for each amino acid (residue), we can extract 155+305 = 460 features, and combine these 460 features to form a feature vector Support Vector Machine (SVM) has been widely used in the study of protein-protein interactions, and has achieved good results. In this paper, we also use SVM to predict the tetramer protein complex interface residue pairs. Compared with non-interface residue pairs, the number of interface residue pairs is very small in tetramer protein complex. Therefore, the positive and negative classes are very imbalanced in the date set . We take under-sampling method to deal with the class imbalance problem and use ensemble method to reduce the information loss caused by under-sampling.We propose SVM ensemble method to predict the tetramer protein complex interface residue pairs. Our method can be divided into two parts: Feature Extraction and Generate SVM Ensemble Classifier see . The feax represents a residue pair. x is an interface residue pair.The total number of positive samples in training set is 29,268. We randomly sample 10 times from all negative samples to generate 10 subsets. And we set the number of negative samples per random sampling to 29268. Then we combine each subset of negative samples with all positive samples to generate a balanced sample set. We obtain 10 balanced sample sets. By training the SVM model with each balanced sample set, 10 independent SVM models can be obtained. Finally, we use an integration strategy to fuse 10 independent SVM models to generate a SVM ensemble classifier ISVM, see TP indicates the number of positive samples predicted by the model to be positive class. FN indicates the number of positive samples predicted by the model to be negative class. FP indicates the number of negative samples predicted by the model to be positive class. TN indicates the number of negative samples predicted by the model to be negative class.We use 7 common evaluation indicators , accuracy and AUC) to evaluate the predictions. Their definitions as follows:PT||1 to evaluate our predictions. ||PT||1 represents the L1 norm of the vector PT, which means the sum of the number of interactions and non-interactions correctly predicted in a tetramer protein complex. PT=, where a represents the number of correctly predicted interactions in a tetramer protein complex, and b represents the number of correctly predicted non-interactions in a tetramer protein complex.We also define a new evaluation indicator ||We randomly divide 111 tetramer protein complexes into the training set, verification set and testing set, of which the number of tetramer protein complexes in training set is 63, the number of tetramer protein complexes in validation set is 20, and the number of tetramer protein complexes in testing set is 28 see . The numInput feature maps of training set and testing set into the CNN model to train the hyper parameters and verify the accuracy of the model. The hyper parameters finally selected are as follows: the learning rate is 0.000801, the number of convolution kernels is 2, and the number of epoch is 20. Under the above hyper parameters, the results of CNN model on validation set and testing set is shown in It can be seen from The specific prediction of each tetramer protein complex in testing set is shown in t predictions with the highest probability as the predicted t interface residue pairs.The output value of SVM ensemble method is between 0 and 1, which indicates the possibility that the residue pair is an interface residue pair. The predicted values are arranged in descending order. We take the t predictions of the Zth possible protein-protein interaction interface in the tetramer protein complex. Based on this six dimensional vector, we give definitions of three indicators, as followsIn addition to recall, specificity, precision, F1 score, MCC and AUC, these six commonly indicators. In this part, we also define three new indicators to evaluate the performance of SVM ensemble method. Before introducing these three new indexes, we define a six dimensional vector t predictions, we consider that the protein-protein interaction interface is correctly predicted.The first index is t predictions at a tetramer protein complex.The second index is 4t1 see , represet predictions, we consider that the tetramer protein complex is correctly predicted, when there are z protein–protein interaction interfaces that each interface has at least one positive interface residue pair. NTP represents the Number of Tetramer Protein complexes containing at least z native protein-protein interaction interfaces in the data set.The third index is We randomly divide 111 tetramer protein complexes into training set and testing set according to the ratio of about 3:1. Training set contains 83 tetramer protein complexes and testing set contains 28 tetramer protein complexes. The tetramer protein complexes PDB ID of each set is shown in Firstly, the feature vector of each residue pair is calculated and the specific process see In the top 10 predictions, the prediction of 2EPI tetramer protein complex is the best. Six protein-protein interaction interfaces are correctly predicted, and a total of 9 positive interface residue pairs are given. The prediction of 3VH5 tetramer protein complex follows closely. Five protein-protein interaction interfaces are correctly predicted, and a total of 13 positive interface residue pairs are given. On 1SWF tetramer protein complex, four protein-protein interaction interfaces are correctly predicted, and a total of 15 positive interface residue pairs are given. On 1NSW and 3STB tetramer protein complexes, four protein-protein interaction interfaces are correctly predicted, and a total of 13 positive interface residue pairs are given.We calculate the index When we give the top 50 predictions and all native protein-protein interaction interfaces on each tetramer protein complex are required to be correctly predicted, SVM ensemble method can correctly predict 9 tetramer protein complexes. The predictions of these 9 tetramer protein complexes are shown in In the top 200 predictions, the recall, precision, specificity, F1 and MCC of SVM ensemble method are 0.255, 0.052, 0.988, 0.081 and 0.102 respectively. In fact, if 200 residue pairs per protein-protein interaction interface are taken as interface residue pairs, a total of 29800 residue pairs are extracted as interface residue pairs in testing set. According to the proportion of interface residue pairs in the total residue pairs of testing set, there should be 45.98 interface residue pairs in the 29800 residue pairs, and the precision is 0.00154, the precision of SVM ensemble method is much higher than this value. Compared with non-interface residue pairs, interface residue pairs are too sparse, so the precision and F1 value of SVM ensemble method are not high.In reference , Sun et k-Average Cumulation Factor, together with Amino Acid k-Interval Product Factor to extract features based on protein sequence. We also defined the Residue k-Interval Product Factor, Residue k-Average Cumulation Factor and weight factor to extract features based on protein three-dimensional structure. Finally, we proposed a SVM ensemble method based on under-sampling and ensemble method to predict the tetramer protein complex interface residue pairs. The prediction shows that our method is feasible for the prediction of tetramer protein complex interface residue pairs. Compared with previous studies, which only studied tetramer protein complex interface residue pairs, we also studied the interaction between chains of the tetramer protein complex, which provides a new perspective for the future study of multibody protein interactions. However, there are also the following points that need to be further improved. The first point is the study of the interaction between chains of the tetramer protein complex, whose accuracy still needs to be further improved. The second point, when all native protein-protein interaction interfaces of each tetramer protein complex can be correctly predicted, our accuracy also needs to be further improved. In the future, we also hope that our predictions can be used in docking processes to predict the multibody protein complex three-dimensional structure.In this paper, we have done two parts of work to predict the tetramer protein complex interaction. In the first part, we defined the position change sequence and geometric feature change sequences of the same type of amino acid. Based on these sequences, we proposed a 20 × 24 feature map to represent a sample generated by two chains in a tetramer protein complex and constructed a CNN model to predict the interaction between chains of the tetramer protein complex. In the second part, we considered the influence of surrounding amino acids (residues) on the central amino acid  when extracting features. We defined Amino Acid


Metaproteomics based on high-throughput tandem mass spectrometry (MS/MS) plays a crucial role in characterizing microbiome functions. The acquired MS/MS data is searched against a protein sequence database to identify peptides, which are then used to infer a list of proteins present in a metaproteome sample. While the problem of protein inference has been well-studied for proteomics of single organisms, it remains a major challenge for metaproteomics of complex microbial communities because of the large number of degenerate peptides shared among homologous proteins in different organisms. This challenge calls for improved discrimination of true protein identifications from false protein identifications given a set of unique and degenerate peptides identified in metaproteomics. MetaLP was developed here for protein inference in metaproteomics using an integrative linear programming method. Taxonomic abundance information extracted from metagenomics shotgun sequencing or 16s rRNA gene amplicon sequencing, was incorporated as prior information in MetaLP. Benchmarking with mock, human gut, soil, and marine microbial communities demonstrated significantly higher numbers of protein identifications by MetaLP than ProteinLP, PeptideProphet, DeepPep, PIPQ, and Sipros Ensemble. In conclusion, MetaLP could substantially improve protein inference for complex metaproteomes by incorporating taxonomic abundance information in a linear programming model. Inferring a reliable list of proteins from identified peptides in metaproteomics is non-trivial because of the prevalence of degenerate peptides in many metaproteome databases. Degenerate peptides are shared among multiple proteins and, therefore, cannot be uniquely attributed to any protein. Here, we developed a protein inference algorithm, MetaLP, for shotgun proteomics analysis of microbial communities to better handle degenerate peptides. Two key innovations in MetaLP were the use of taxonomic abundances as prior information and the formulation of protein inference as a linear programming problem. These features enabled MetaLP to produce substantially more protein identifications in complex metaproteomic datasets than many existing protein inference algorithms. PLOS Computational Biology Methods paper.This is a Nearly all natural and human-associated ecosystems host microbial communities that are responsible for cycling necessary nutrients –4. Most Inferring reliable proteins from identified peptides is non-trivial because of degenerate peptides that are shared among multiple proteins and, therefore, cannot be uniquely attributed to any proteins. Many methods have been developed to assign unique and degenerate peptides to proteins and rank protein candidates based on their identification confidence. Generally, the protein inference tools are based on either statistical models or machine (deep) learning models. The methods using statistical models embody a set of statistical assumptions concerning the generation of proteins and peptides. For instance, ProteinProphet  employedProtein inference in proteomics has been assisted using other biological information, including transcriptomics , functioIn this study, we developed a protein inference algorithm, called MetaLP, for shotgun proteomics analysis of microbial communities. It was optimized for metaproteomics to improve the use of degenerate peptides for protein inference. MetaLP integrated taxonomic abundances as prior information and formulated protein inference as a linear programming problem. These features enabled MetaLP to produce substantially more protein identifications in complex metaproteomics datasets than the existing protein inference algorithms benchmarked here.Since a degenerate peptide can be mapped to multiple proteins and a protein can be generated by more than one organism, the input can be represented as a tripartite graph  is the probability that the ith identified peptide exists and is measured in the metaproteome samples. P(proj|MS) denotes the probability that the jth protein exists and is measured in the metaproteome samples. P(otuk|MS) denotes the probability that the kth operational taxonomic unit cluster exists and is measured in the metaproteome samples, which serves as priors to adjust P(pepi|MS).Let jth protein and the kth operational taxonomic unit cluster do not contain the ith peptide, the corresponding probability has the value of zero. Here, the joint probability, P, denotes the probability that the ith peptide and the jth protein are present and measured, given that the kth operational taxonomic unit cluster. This joint probability relates the peptide probability to the protein probability given the presence of operational taxonomic units.A peptide is present if at least one of its parent proteins and one of its operational taxonomic units are present, which can be described as in ϵ denotes the difference between the observed and theoretical peptide probabilities. This parameter reflects how confident the peptide identification tool is. For example, ϵ = 0 means that the input peptide probability is perfectly accurate. In our experiments, we used 0 as the default setting. The constraint 5 is used to find the minimum value in P(proj|MS). Since only a subset of candidate proteins are truly present and measured in the samples, some protein probability values should be zero. Thus, we minimize P, based on constraint 5 to set some protein probabilities to zero. The MetaLP model can be quickly solved with standard LP solvers. In this study, we used Gurobi Optimizer v9.1.2 [We formulate the protein inference problem as an optimization problem using the linear programming model. The objective is to minimize r v9.1.2 .OTU is considered as the operational unit to classify groups of closely related organisms. In this work, we assume that a microorganism with a larger population may generate more proteins. Thus, the OTU population serves as the prior probability that a protein originates from the OTU. We defined the OTU clusters based on either the metagenomic binning or the 16S rRNA sequence clustering. Specifically, the OTU clusters were constructed in one of the following three ways depending on the input data. When the reference genomes were available for the microbial community, the DNA sequencing reads were mapped to the reference genomes using BBSplit in BBTools package version 38.94 . When thP(otuk) = P(otuk|MS). We also used the sequencing depth to estimate the OTU probability. The probability of the OTU clusters was calculated as in Eq. A in Once the OTU clusters were constructed, the protein sequences can be predicted from reference genomes or assembled genomes and assigned to the corresponding clusters using the mapping between genomes and clusters. Here, we do not need the OTU clusters at high granularity levels. It would be impractical to have each cluster contain the sequences only from one species since existing reference genomes may not match the organisms in the experimental samples. Also, the results in Section 3 demonstrate that the OTU clusters generated by MetaBAT2 were accurate enough to significantly improve the number of identified proteins. The probability of the OTU clusters was calculated as in The schematic overview of our MetaLP is shown in P(pep|MS).In the metaproteomics pipeline, the mass spectra data were extracted from raw data and reformatted by MSConverter . MetaLP In the metagenomics pipeline, we implemented two workflows to obtain OTU clusters, one based on the whole-genome DNA sequencing (WGS) (Part B) and the other based on 16s rRNA gene sequencing data (Part C). Details about these two workflows are described in Section 2.3. The resultant probabilities of OTU clusters were used in our MetaLP model.P(otu|MS) were set to 1). Note that Sipros-Ensemble is a complete framework that contains database searching, filtering, and protein inference, which are denoted as SE-S, SE-F, and SE-PI, respectively, in the following sections. For the PIPQ algorithm, we employed all the options provided, which include equal division, multiple counting, and linear programming, and the results from the variants of PIPQ are labelled as PIPQ-e, PIPQ-m, and PIPQ-lp. We used two combinations of database search engines and filters, one with Comet [MetaLP was compared with five other popular protein inference algorithms, including ProteinLP , Proteinth Comet  as a seath Comet  as the fth Comet  for bothThe performance of MetaLP was evaluated on four microbial communities, including three metaproteome datasets from mock communities , three mFor the mock community, the taxonomy of the bacteria was known, so the genomes of corresponding bacteria were used as OTU clusters. For the marine and soil metaproteome samples, the organism compositions were unknown and the metagenomic sequencing data was used to construct OTU clusters. For the human gut metaproteome, we used 16s rRNA gene sequencing data to obtain OTU clusters. Benchmarking datasets were searched using Comet 2018.01 rev. 2 and Sipros-Ensemble Version 1.2. The precursor mass tolerance was set to 0.09 Da, fragment mass tolerance was set to 0.01 Da, peptide mass range was set from 700 Da to 7000 Da, Trypsin/P was used for digest enzyme, and the allowed number of missing cleavage was set to three. All other parameters were set to default. Comet and Sipros-Ensemble were executed on a workstation with two 2.4GHz Intel Xeon E5–2680 v4 CPUs and 64 GB memory. The PSM identification results were filtered by PeptideProphet from TPP v5.2.0 with default configuration settings and executed on a workstation with one 2.6 GHz Intel(R) Xeon(R) Silver 4112 CPU and 32 GB memory. The protein inference tools were executed on a workstation with a 2.6 GHz Intel(R) Xeon(R) Silver 4112 CPU and 32 GB memory. The metagenomic sequencing data were assembled and binned by metaSPAdes and MetaBAT2, respectively, using the default parameters on a workstation with an Intel Xeon E5–4640 CPU and 512 GB memory. The 16s rRNA gene sequencing data were clustered and annotated by vsearch v11 on a workstation with a 2.3 GHz Intel(R) Xeon(R) Gold 5118 CPU and 32 GB memory.The time and memory consumption of MetaLP is shown in Table D in For all the benchmark methods and datasets, we applied the target-decoy strategy  to contrIn addition to the commonly used targetdecoy strategy in The MetaLP was compared with eight different combinations of existing database searching and filtering algorithms on the three mock community samples . Table 2The MetaLP and benchmarked methods were also compared on a human gut microbial sample with matched MS/MS proteomics data and 16s rRNA gene sequencing data. For MetaLP, the OTU clusters were generated and annotated as in Section 2.3. The protein database was constructed by extracting translated proteins from the NCBI database  using thWe also compared the performance of MetaLP and other tools on marine and soil microbial communities samples. All samples had matched metagenome and metaproteome datasets. The OTU clusters were processed as in Section 2.3. The microbial compositions were highly complex for the marine and soil microbial communities, given the large numbers of OTU clusters and the high percentages of cross-cluster peptides as shown in All the experiments above used reverse sequences as decoys to estimate FDRs. To make the benchmarking a better simulation of real-world analysis, we combined the marine database containing 2,876,135 protein sequences and the human gut database containing 106,140 protein sequences and searched the human gut MS/MS dataset against this synthetic database with reverse decoys. The FDR was estimated as before, but we regarded protein identifications from the marine database as false identifications and protein identifications from the human gut database as true identifications after the FDR was controlled at 1%. Note that protein groups that contained at least one human gut protein were classified as true. The probabilities of the OTUs to which marine proteins belonged were set to zero since we knew no marine proteins were present in the human gut samples. This is one of the key contributions of MetaLP that it can utilize the genomic information, which informed us that no marine microbes were in the proteome samples. The identification results from all benchmarked tools are shown in In order to assess the impact of different ways of estimating OTU probabilities, we did a comparison for mock communities and the human gut metaproteome because they have similar microbial complexities (there were 22 species in mock communities and 13 species in the human gut metaproteome). The OTU probability estimations for the mock communities and the human gut metaproteome used the whole genome sequencing (WGS) data and the 16s rRNA sequencing data, respectively. For the mock communities, MetaLP identified 2.6% and 3.9% more proteins on average compared to the second best for the Comet & PeptideProphet pipeline and Sipros-Ensemble framework, respectively. For the human gut dataset, MetaLP identified 2.2% and 3.4% more proteins compared to the second best for the Comet & PeptideProphet pipeline and Sipros-Ensemble framework, respectively. The comparison revealed that the performance improvement using the 16s rRNA data was slightly lower than using the WGS data but not significant. It may be because of the better accuracy of the OTU clustering using the WGS data than the 16s rRNA sequencing data. In general, MetaLP is not sensitive to the OTU estimation accuracy.To investigate the performance of different methods in tackling the peptide degeneracy issue, we present the identification results of five methods when inferring proteins containing degenerate peptides  inferred by MetaLP from the taxonomic and functional aspects, we analyzed the proteins using BLASTP  and annoTo show the impact of MetaLP on the taxonomy analysis, we mapped the identified proteins at 1% FDR to the corresponding species for mock and soil datasets. For mock communities, we found that MetaLP identified significantly more proteins for the low-abundant species. Fig I in For the soil metaproteomes, we analyzed the taxonomic profile by searching inferred proteins against the non-redundant protein database of NCBI. The phylogenetic tree for the taxa that were only detected from the inferred protein list by MetaLP is shown in From the functional aspect, we annotated the molecular functions for the proteins of the least abundant species . We used proteins inferred only by MetaLP from the human gut metaproteome and the functional annotation is shown in Supplementary biological information could be incorporated to protein inference. For example, Gerster et al. proposed a protein inference method, called MIPGEM . It usedThe benchmarked protein inference algorithms based on the bipartite graph usually assume that the proteins and their peptides are equally likely to be present and measured. However, the presence of a protein is highly related to the distribution of their parent species in the metaproteome samples. MetaLP incorporates the species abundance as the prior knowledge. Thus, the probability of a protein being present and measured is adjusted with regarding to the probabilities of its peptides and the abundances of its parent species. When there are two proteins with the same support at the peptide level, MetaLP tends to pick the one with evidence from the metagenomic sequencing data, whereas other tools may randomly pick a protein. This could be the main reason that some proteins were missed by MetaLP but reported by other benchmarked tools.In this study, an integrative linear programming model, called MetaLP, was designed to generate a reliable list of proteins from identified peptides. MetaLP incorporates the taxonomic abundances as prior information and formulates the protein inference as a linear programming problem. We extracted the taxonomic abundances from the metagenomic sequencing data or the 16s rRNA gene amplicon sequencing data. The experiments on both mock and real-world microbial communities demonstrated that MetaLP obtained the highest number of protein identifications compared to five existing protein inference methods. The improvement of the metaproteomics results of microbial communities using their taxonomic information shows the value of integrated meta-omics studies.S1 Text(PDF)Click here for additional data file.


Computational function prediction is one of the most important problems in bioinformatics as elucidating the function of genes is a central task in molecular biology and genomics. Most of the existing function prediction methods use protein sequences as the primary source of input information because the sequence is the most available information for query proteins. There are attempts to consider other attributes of query proteins. Among these attributes, the three-dimensional (3D) structure of proteins is known to be very useful in identifying the evolutionary relationship of proteins, from which functional similarity can be inferred. Here, we report a novel protein function prediction method, ContactPFP, which uses predicted residue-residue contact maps as input structural features of query proteins. Although 3D structure information is known to be useful, it has not been routinely used in function prediction because the 3D structure is not experimentally determined for many proteins. In ContactPFP, we overcome this limitation by using residue-residue contact prediction, which has become increasingly accurate due to rapid development in the protein structure prediction field. ContactPFP takes a query protein sequence as input and uses predicted residue-residue contact as a proxy for the 3D protein structure. To characterize how predicted contacts contribute to function prediction accuracy, we compared the performance of ContactPFP with several well-established sequence-based function prediction methods. The comparative study revealed the advantages and weaknesses of ContactPFP compared to contemporary sequence-based methods. There were many cases where it showed higher prediction accuracy. We examined factors that affected the accuracy of ContactPFP using several illustrative cases that highlight the strength of our method. Proteins are working molecules in a cell. Virtually all cellular functions are carried out mainly by proteins. Therefore, elucidating the biological function of proteins is a central problem in molecular biology, biochemistry, genetics, and genomics. Ultimately, the function of proteins needs to be determined by experiments. However, in the process of experimental elucidation of protein function, computational function prediction is very useful for guiding experiments by, for example, helping biologists construct hypotheses in designing experiments.As sequencing the whole genome has become a standard experimental protocol for studying an organism, many protein sequences are now available in various databases , and manIn this work, we used protein 3D structure information for inferring the function of proteins. It has been long known that the 3D structures are better conserved than protein sequences during evolution , and thuHere, we explore how protein structure information, particularly amino acid contact information, can contribute to the accuracy of function prediction. To do so, we developed a new protein function prediction method, ContactPFP. In ContactPFP, instead of performing sequence-based database search, a query protein is compared with proteins in a database in terms of predicted contact maps. Since an amino acid contact map is, in principle, sufficient to build a 3D structure of the protein, using contact maps is conceptually equivalent to considering 3D structure similarity. We benchmarked ContactPFP on a dataset of 9,642 proteins and compared its performance with sequence-based function prediction methods that performed among the top in CAFA. The benchmark revealed the strengths and weaknesses of ContactPFP. We report the performance of ContactPFP relative to several key parameters. Also, to characterize ContactPFP’s performance, we discuss examples where ContactPFP showed its strengths and cases where ContactPFP did not perform as well as the sequence-based methods that were compared against.i is computed as follows:i in its annotation. Finally, predicted GO terms for a query is normalized by the highest score among them, so that the most confident GO term has a score of 1.0.The reference database was constructed from Swiss-Prot . SequencGO terms for proteins in the reference database were compiled from 12 data sources. The primary database used was the UniProtKB/Swiss-Prot. GO terms with IEA (Inferred from Electronic Annotation) evidence code  were alsWe started from representative sequences in UniRef50  22 Octo with a 2To characterize the performance of ContactPFP, we compared it with four sequence-based methods, PSI-BLAST , PFP Ha, ESG Ch, and PhyThese sequence-based methods identify the query itself as the top hit in a database search. To avoid taking GO terms from the query itself for PFP, ESG, and Phylo-PFP, we removed the query and all hits that had an E-value of 0.0 in the last round of PSI-BLAST before extracting GO terms. These excluded proteins were also removed from the hit list of ContactPFP. For PSI-BLAST, we extracted GO terms from the top 10 hits  in the third iteration of a PSI-BLAST run and assigned a score of 1.0 to all the predicted terms.To start with, we examined how two important hyperparameters in ContactPFP, the definition of residue-residue contacts and choices of top hits from a database search, affect the function prediction accuracy . When con = 2) with a distance cutoff of 12 Å. In the last panel, To examine the effect of the parameters, we performed a four-fold cross validation. In combination with the distance cutoffs, we examined three different ways to select top hits from a search . The FmaOverall, the combination of 12 Å for the distance cutoff and using the top 2 hits showed the best performance among the conditions tested. Therefore, we report the results with this condition in the subsequent sections. Regarding the contact distance cutoff, 12 Å showed the best performance in all three panels, which is consistent with what the GR-Align paper reported .We now report the overall performance of ContactPFP in On the other hand, when predictions given to individual target proteins were compared between two methods . The scoIn We examined how contact prediction accuracy affects to the GO prediction accuracy in ContactPFP. For this analysis, we used 1,029 targets in the benchmark dataset, which have an experimentally determined protein structure that covers more than 80% of the residues in the target protein. If we consider precision of all predicted contacts , all theIn We were also curious how ContactPFP performs for proteins that have intrinsic disordered regions (IDRs) because an IDR does not usually form residue contacts. In In this section, we discuss cases that illustrate ContactPFP’s performance relative to sequence-based methods.E. coli, for which GO terms such as “cell outer membrane” (GO: 0009279) are annotated in the CC category. This protein is transmembrane and transports sugars from outside to inside the cell. This corresponds to “maltose transporting porin activity” (GO: 0015481). For this protein, ContactPFP showed a high prediction accuracy, a Fmax score of 0.754, while it was 0.083, 0.140, and 0.085 for PFP, ESG, and Phylo-PFP, respectively. The first example shows a successful prediction by ContactPFP for outer membrane porin G (UniProt ID: P76045). This protein is present in the outer membrane of E. coli O6:H1, that also have a β barrel fold . The function of this protein includes “actin binding” (GO: 0003779), “alpha-actinin binding” (GO: 0051393), “cardiac muscle cell development” (GO: 0055013), and the protein is localized in the “nucleus” (GO: 0005634), “cytoskeleton” (GO: 0005856), “mitochondrion” (GO: 0005739), “sarcomere” (GO: 0030017), and “myofibril” (GO: 0030016). As shown in –40 or smaller, but only a few of them have correct GO terms. As a result, incorrect GO terms (shown in symbols) that frequently appear in the top 50 hits accumulated higher scores. Thus, in this case, the structure information was able to select the most functionally relevant proteins among proteins that are similar in sequence but not in function.The correct GO term predictions by ContactPFP were transferred from the two most structurally similar proteins shown in The last one is the opposite case where ContactPFP did not perform as well as the other sequence-based methods. The query is Cyclin-dependent kinase inhibitor 4 in Arabidopsis . This protein has GO annotations of “cyclin-dependent protein serine/threonine kinase inhibitor activity” (GO: 0004861), “negative regulation of cell cycle” (GO: 0045786), “negative regulation of cyclin-dependent protein serine/threonine kinase activity” (GO: 0045736), “nucleoplasm” (GO: 0005654), “nucleus” (GO: 0005634), and “cytoplasm” (GO: 0005737) . As showAccording to UniProt, more than half of residues are annotated as disordered. Therefore, it is highly likely that predicted contacts  and struContactPFP’s characteristic prediction performance discussed in We constructed ensemble methods of all possible combinations of the methods starting from single methods to a combination of all five methods ; Table 6One thing which drew our attention is that the top combination included PSI-BLAST, which performed the worst among the five non-ensembled methods. To examine why adding PSI-BLAST improved the performance, we compared the ensemble of ContactPFP with Phylo-PFP with the ensemble of ContactPFP with PSI-BLAST . We compIn ContactPFP developed in this work identifies proteins with similar contact maps and transfers their functions to the query. Despite the knowledge that protein structure and function are closely related, protein structure information has not been effectively used for automatic protein function prediction mainly because of the low coverage of experimentally determined structure information for proteins. It is now possible to employ structure prediction methods to cover structure information of remaining proteins that have no experimentally determined structures. ContactPFP showed a slightly lower average Fmax score than one of the best sequence-based methods, Phylo-PFP, but had more wins over Phylo-PFP when predictions for individual proteins were counted. Thus, overall, we could say ContactPFP performed on par with Phylo-PFP. Combining ContactPFP in ensemble methods with sequence-based methods successfully achieved higher accuracy than the individual methods. In the current work we used simple averaging to ensemble scores from different methods. It would be worthwhile to explore other approaches beyond averaging, such as learning-to-rank, or even other signals that might indicate that a given query protein will benefit more from sequence similarity instead of structural similarity.Since ContactPFP is based on database search, it can predict any GO terms, including very rare ones, as long as proteins found by a search have such GO annotations. This is very important for practical use of a function prediction method and is different from recent machine learning-based methods , which nBesides proving practical usefulness, we have a couple of important findings. Through comparison with sequence-based methods, we observed the strengths and the weakness of ContactPFP, which would apply to any function prediction methods that use predicted protein structures. The notable strength is that, as illustrated in the first case study, ContactPFP can often identify distantly related proteins by considering structural similarity, which leads to more accurate function prediction than sequence-based methods. ContactPFP was also able to select the most relevant proteins among proteins that are similar in the sequence (the second case study). On the other hand, weaknesses originate from the accuracy and current challenges of protein structure prediction. Structure-based retrieval does not work well when predicted structures (predicted contact maps) are not accurate. Also, handling intrinsically disordered proteins is a challenge because all disordered proteins look alike, and it is hard to distinguish functionally similar ones by their structures. An interesting finding is that the structure-based approach showed complementary strengths from sequence-based methods , and thuWe compared simplified protein structure representations, residue contact maps as graphs, instead of directly using the three-dimensional structures. The comparison of contact maps made it possible to scan the reference database within a realistic amount of time, although it still took about 20 min for prediction on one query protein. A further speed up will be possible by using a different, efficient structure representation, such as the 3D Zernike descriptor  as it waThe development of various bioinformatics tools using predicted protein structures will progress further in the future as a more recent method, Alphafold2  made sig


We then used the PIC scores to determine the probability that two proteins interact. Using those probabilities, we paired 37 Alzheimer’s disease-associated proteins with 8608 other proteins and determined the likelihood that each pair interacts, which we report through a web interface. The PIC had significantly higher sensitivity and residue-specific resolution not available in other algorithms. Therefore, we propose that the PIC can be used to prioritize potential protein interactions, which can lead to a better understanding of biological processes and additional therapeutic targets belonging to protein interaction groups.Protein–protein functional interactions arise from either transitory or permanent biomolecular associations and often lead to the coevolution of the interacting residues. Although mutual information has traditionally been used to identify coevolving residues within the same protein, its application between coevolving proteins remains largely uncharacterized. Therefore, we developed the Protein Interactions Calculator (PIC) to efficiently identify coevolving residues between two protein sequences using mutual information. We verified the algorithm using 2102 known human protein interactions and 233 known bacterial protein interactions, with a respective 1975 and 252 non-interacting protein controls. The average PIC score for known human protein interactions was 4.5 times higher than non-interacting proteins ( Protein–protein interaction networks can help researchers understand cellular function, predict genetic variant effects, and map basic biological processes ,2,3. TheEscherichia coli [Arabidopsis thaliana [Direct coupling analysis models built using the maximum entropy principle have recently identified a three-dimensional structure and conformational effects caused by mutations in protein sequences ,26,27,28hia coli  and pre-thaliana . Until rthaliana ,42,43. Tthaliana ,32,33,34https://pic.byu.edu). The website also allows users to upload two multiple sequence alignments and calculate the probability that the residues within those sequences will interact. A command line interface (https://github.com/kauwelab/pic) is also available for users to locally perform multiple queries in tandem. We anticipate that the PIC will be used to screen for protein interactions to identify potential secondary effects of genetic variants implicated in disease studies.Here, we present the Protein Interaction Calculator (PIC), which is optimized for detecting residue interactions between protein sequences. This calculator is not meant for the prediction of protein folding but for the prediction of the interactions between distinct proteins. The results from the PIC are normalized to allow direct comparisons between different analyses, even when the underlying protein sequences are vastly different. Additionally, the PIC provides a probability that the two proteins interact based on extensive validation using known interactions in both humans and bacteria. We performed pairwise comparisons of all Alzheimer’s disease-associated proteins against all human proteins to determine the extent to which coevolution might confound Alzheimer’s disease research. We identified 1933 protein interactions that are very likely to occur (>95% probability) and have displayed those interactions on our web-based application  to ensure sufficient sampling of the protein and its residues. All of the species not found in both files will be dropped from the analyses. Because orthologs in multiple-sequence alignments can contain insertions and deletions, a user-defined reference species must be used to report the residue positions in the final output file. The missing positions in the multiple sequence alignment  in the reference species are dropped from the analyses. A mutual information score is calculated for each residue–residue pairing, and the highest residue–residue score is used to determine whether a possible relationship exists between the two proteins.Equation (1) shows the base equation for calculating mutual information  a summary file in tab-separated value (TSV) format, (2) a detailed file with position-wise scores in comma-separated value (CSV) format, and (3) a heatmap image. The summary file is better suited for studying multiple protein interactions because the output simply appends a single line to the file containing the highest score, positions of interaction for the highest score, the likelihood of interaction based on the highest score, and the names of the two proteins. The same output file name may be used for different analyses because the program appends and does not overwrite, resulting in a neat file summarizing several results of the algorithm. Since the detailed file provides each position-wise score, it is better suited to single protein–protein analyses. Any combination of the outputs may be generated when run on the command-line. On the website, the detailed (CSV) file and heatmap are made available for the user to download, while an output, such as the summary (TSV) file  is reported on the screen. The likelihood is determined based on the distributions of several comparisons of known and unknown protein interactions discussed below.We first needed to generate multiple sequence alignments of orthologs spanning at least 100 species to validate our algorithm. All sequence data were downloaded from RefSeq ,45,46 inNext, we generated two separate lists of protein pairs that (1) were known to interact and (2) had no known interactions. In December 2020, we downloaded protein interaction networks from GPS-Prot , which iWe chose a random representative sample of 2102 known protein interactions and 1975 protein pairs that do not interact in GPS-Prot. GPS-Prot requires a primary protein, which then returns an interaction network based on previously reported interactions. Our inclusion criteria required orthologs for each protein to be annotated in at least 100 species, and non-interacting pairs were checked for both primary and secondary interactions . https://github.com/kauwelab/pic/tree/master/supporting_work/exampleData.We used all available data in the STRING database ,14,15,16t-test, Cohen’s D, and area under the curve statistic for a ROC curve using various combinations of values. The https://github.com/kauwelab/pic/tree/master/supporting_work/verification_I.Hyperparameters for the minimum The PIC also aims to provide the likelihood that a predicted interaction is a true interaction. After fine-tuning the hyperparameters, we established 20 bins of scores for both the known and unknown interactions ranging from zero to the maximum reported score for each of the bacteria and vertebrate sets. We then calculated the probability that each bin identified known interacting pairs. Bins where the probability of identifying known interactions was ≤50%  were merged since our validation method is more likely to identify false negatives  than false positives. These probabilities are reported on the website as well as the command-line interface to help users contextualize their scores against our training data.We conducted pairwise proteome-wide mutual information calculations to identify proteins that were most likely to interact with 62 proteins previously prioritized in Alzheimer’s disease genome-wide association studies (GWAS) ,48,49. TWe calculated the probability that each calculated pairwise protein interaction score was a true interaction based on the probabilities from our vertebrate test set. We also used fine-tuned filters from our previous verification tests to evaluate the Alzheimer’s disease proteins. Finally, we report the identified proteins that were previously implicated in Alzheimer’s disease, the proteins that were previously reported in GPS-Prot , and theBIS2Analyzer  is a powMISTIC2  identifiSome more recent protein functional calculators, such as AlphaFold ,56,57, fSince available mutual information calculators are not able to quickly process bulk protein interactions, we compared the runtimes of the PIC, MISTIC2, and BIS2Analyzer using five randomly chosen proteins listed in Because the PIC is used to identify potential new interactions and not simply predict known interactions, we compared how often our calculator identified the same interactions as BIS2Analyzer as well as how often those predictions are correct. We did not compare the PIC to MISTIC2 because the MISTIC2 compute time makes it computationally intractable to perform these types of bulk analyses.BIS2Analyzer was meant to be run on single protein sequences, so rather than reporting the likelihood of protein interactions, it instead reports which amino acid residues interact with one another in different residue clusters. To calculate mutual information between two proteins using BIS2Analyzer, the BIS2Analyzer website suggests concatenating protein sequences together. Then, if clusters are found to have positions from both proteins, those two proteins are classified as interacting. In contrast, the PIC results were assumed to interact if the chance of interaction was above 50%.Batch computing is not possible on BIS2Analyzer, so we ran the 135 known interactions and a randomly selected sample of 100 of the 57,426 unknown protein interactions to compare BIS2Analyzer to the PIC. We do not report 20 known interactions and nine unknown interactions in our final BIS2Analyzer results because they did not produce any output when run on the BIS2Analyzer.We identified the optimal filters for min call see A,B. The ates see D,E and weria see . The AUCeria see D.−5). For the bacteria, p ranged from 5.77 × 10−38 to 5.15 × 10−7, and for the vertebrates, p ranged from 1.68 × 10−114 to 7.32 × 10−68. At the chosen filter settings, the two-sample t-tests yielded p = 1.03 × 10−108 for the vertebrates and p = 3.29 × 10−20 for the bacteria.We also performed a two-sample t-test on the results using each filter combination to assess the differences between the interacting and unproven-to-interact groups. At each filter combination, the results were significant at the α = 0.05 level and were even significant using the Bonferroni correction for multiple testing . Of the 135 known interactions in the Alzheimer’s disease dataset, BIS2Analyzer identified 88 of them as interacting and failed to run to completion on 20 pairs. The PIC identified 64 as interacting. Of the 91 pairs not known to interact, BIS2Analyzer identified 70 as interacting, whereas the PIC identified only 24 , and the online algorithm will calculate the probability that the two proteins interact. The website will then automatically produce a heatmap of all of the residue interactions and display the highest residue–residue interaction . A command-line version of the algorithm is also available at https://github.com/kauwelab/pic to facilitate high-throughput programmatic analyses.We allow users to query all Alzheimer’s disease interactions at The PIC is significantly more computationally efficient for calculating pairwise protein–protein interactions than other available software packages. For instance, MISTIC2 and BIS2Analyzer calculate mutual information from a single protein sequence but do not allow users to calculate mutual information between protein sequences. or doing so requires several extra data preparation and interpretation steps. Past analyses combined the protein sequences by adding a string of 20 amino acids at the end of the first alignment and then concatenating the second multiple sequence alignment to that of the first alignment. While that method allows mutual information to be calculated between proteins, it requires extra programmatic steps, and the algorithm is very inefficient comparing amino acids that do not need to be compared.Further, when compared to other developed tools, even when ignoring the barriers to comparing two different proteins, the PIC runs significantly faster. As MISTIC2 is unable to perform bulk analysis, we report the runtimes of five randomly selected intra-protein interactions to assess the differences in the runtime See .The PIC is a fast mutual information calculator used to identify the residue-specific coevolution between two proteins. We envision that the PIC will help researchers prioritize potential protein interactions for additional biological validation. While we recognize that low mutual information alone is not enough to exclude a potential interaction, a high mutual information score indicates that residues have coevolved through functional interactions.The PIC improves on previous calculators by providing both online and command-line interfaces with standardized probabilities for users to interpret scores. Additionally, the PIC was developed for large-scale analyses, which enables proteome-wide comparisons that are not currently possible using available calculators, such as BIS2Analyzer and MISTIC. While BIS2Analyzer identified more known interactions than the PIC, it had a much higher false positive rate, which can be misleading. The PIC, on the other hand, was fine-tuned for increased precision, which gives users higher confidence in the reported values. The optional parameters and comprehensive comparisons of different hyperparameters allow users to adapt the PIC to their own research in ways that are not possible with the other available calculators.Additionally, we present the PIC as an open-source software package to facilitate the use of mutual information in future protein interaction calculators and models. Since the PIC identifies residue-specific interactions, it has the potential to improve the resolution of machine learning algorithms that often analyze broader features. Since the PIC was specifically developed to analyze inter-protein interactions, it is much more user-friendly for these types of analyses than available software that analyze intra-protein interactions.We validated the PIC with proteins that were previously implicated in Alzheimer’s disease because the etiology of Alzheimer’s disease remains unclear, and the heterogeneity contributes to the current dearth of viable treatments. We hypothesized that secondary interactions between the primary disease-associated loci and coevolving residues on other proteins might mask the effects of interacting proteins on Alzheimer’s disease. Therefore, our proteome-wide analysis of coevolving residues prioritizes potential protein interactions for follow-up studies. Since the PIC ranks potential protein interactions by their probability of being true interactions, we propose that the identified interactions with the highest scores should be prioritized for additional validation.The PIC is currently the best mutual information calculator to identify the residue-specific coevolution between two different proteins. The PIC was designed to reduce the computational complexity of these types of analyses and provide a user-friendly online interface to facilitate high-throughput protein interaction calculations across various domains. We specifically report proteins that the PIC prioritized as most likely to interact with Alzheimer’s disease-associated proteins. Those predictions provide users with a residue-specific resolution that indicates specific amino acids that are most likely to interact with each other. Additional scoring indicates the probability that the reported interactions are true interactions based on our validation set. The biological validation of the highest scoring interactions is warranted and may lead to a better understanding of the etiology of Alzheimer’s disease. We anticipate that the PIC will be widely used as an additional prioritization filter to increase the protein interaction resolution and identify functional interactions in disease-associated proteins, which may lead to additional therapeutic targets.


As practitioners of machine learning in the area of bioinformatics we know that the quality of the results crucially depends on the quality of our labeled data. While there is a tendency to focus on the quality of positive examples, the negative examples are equally as important. In this opinion paper we revisit the problem of choosing negative examples for the task of predicting protein-protein interactions, either among proteins of a given species or for host-pathogen interactions and describe important issues that are prevalent in the current literature. The challenge in creating datasets for this task is the noisy nature of the experimentally derived interactions and the lack of information on non-interacting proteins. A standard approach is to choose random pairs of non-interacting proteins as negative examples. Since the interactomes of all species are only partially known, this leads to a very small percentage of false negatives. This is especially true for host-pathogen interactions. To address this perceived issue, some researchers have chosen to select negative examples as pairs of proteins whose sequence similarity to the positive examples is sufficiently low. This clearly reduces the chance for false negatives, but also makes the problem much easier than it really is, leading to over-optimistic accuracy estimates. We demonstrate the effect of this form of bias using a selection of recent protein interaction prediction methods of varying complexity, and urge researchers to pay attention to the details of generating their datasets for potential biases like this. Most ofttention . As longWhile databases of PPIs and HPIs are abundant and provide curated information on protein interactions, finding reliable examples of non-interacting proteins is more of a challenge. The Negatome database is one such resource ; howeverAlthough some researchers have rightfully shunned the technique of similarity-constrained negative example selection , this prTo demonstrate the effect of using similarity-based sampling on HPI prediction accuracy we implemented the strategy proposed by It is worth noting that PIPR, which is the most sophisticated deep learning method among those tested is able to maintain a reasonable level of accuracy even for random pairing, and is the most responsive to even low deviations from random sampling. All the other methods required more help in terms of the separation between train and test sets in order to achieve high accuracy.In this paper, we discussed pitfalls in the selection of negative examples for host-pathogen and protein-protein interactions. There are other issues that come into play when designing machine learning experiments in this domain. While our focus was on negative example selection, there are multiple issues that are relevant for the choice of positive examples as well: data from experimental methods such as yeast-two-hybrid are known to have a sizable fraction of false positives, and it is common practice to select positive examples by choosing interactions that have been assigned a high confidence score Denovo paper  based on the latest Host-Pathogen Interaction Database (HPIDB) In our experiments we used datasets parameterized by the maximum allowed sequence similarity between host proteins in the train and test sets with thresholds ranging from 10%  to 100% (no constraint on similarity between the host proteins in the train and test sets). The original


Proteins are the central machines of cells, and they perform their actions by interacting with each other as well as with other molecules. Today, large-scale efforts in genomics, proteomics, lipidomics and metabolomics are producing complete lists of the molecules in a cell as well as in different subcellular compartments, including the membrane. Further, detailed knowledge of composition  and expression levels in different cells are becoming available.However, proteins do not act on their own but by interacting with other proteins. The interactions are of many different types, from very stable interactions in large protein complexes to transient interactions by disordered regions containing linear motifs . These pHowever, experimental techniques are not applicable for all type of protein interactions, as many biological interactions are transient and contain weakly interacting proteins. These complexes are virtually impossible to purify or crystallize and therefore for many biological complexes no or only low-resolution structural information is available. Here, only computational methods will be able to provide detailed structural information, but also for the stable complexes computational methods will be of great importance.Computational methods to predict the structure of individual proteins or protein complexes have, until recently, almost exclusively been based on homology transfer. Here, structural information is transferred from one protein to another, assuming that the structure if homologous proteins (or complexes) is conserved. However, today by using co-evolution and advances in deep-learning, it is now possible to predict the structure of many individual proteins and complexes directly using no other information than the sequences and their evolutionary history. Below we will briefly describe, the past, present and future of these type of methods.The basis for the progress in protein structure prediction is the development of contact predictions methods using direct coupling information. The predicted contacts can then be used to predict the structure of individual protein  as well 1.Structure prediction of individual proteins.2.Identification of what molecular components interact.3.Predicting the structure of interacting molecules.In short, a complete 3D-proteome would require that the following subproblems are solved.The progress of the prediction of individual proteins has been tremendous in the last decade. The idea to use co-evolution to predict contacts has been around since the mid-1990s, but until ten years ago, the success was minimal. Then the direct coupling analysis (DCA) methods were introduced, and everything changed. In the first DCA studies, only protein families with more than a thousand sequences could be predicted accurately. Now often accurate predictions can be obtained for much smaller families, and besides, the families have grown.The last CASP meetings have reported a leap in performance for difficult protein structure prediction targets showing that ab-initio structure prediction is accurate for most proteins . InitialHowever, large parts of the human proteome lack domains this sometimes referred to as the dark proteome . We haveInteraction between proteins can be of many different types. Some proteins are tightly bound together in a molecular machinery, while others interact only transiently. By a combination of large-scale studies using methods, the general properties of interaction networks of proteins are rather well understood. For instance, the analysis of these networks has shown that proteins with many interactions often contain long disordered regions or domain repeats . HoweverLarge scale information on how proteins interact is obtained from various experimental methods including, yeast two-hybrid, tap-tag, and co-expression. However, other information such as gene-fusion, and genome localization, can also contribute to the identification of interacting protein pairs. Therefore, by combining experimental and, bioinformatical data insights can be gained . UnfortuFor proteins to interact, they do need to exist in the vicinity of each other, i.e., be located in the same subcellular compartment. Although subcellular localization of proteins has been studied for decades, it is still not at all clear in what compartment many proteins exist. For instance, recent studies indicate that many proteins are found in the nucleus than previously observed . TherefoCurrently, the only docking method that has been shown to provide any useful information if two proteins interact or not is template-based docking. In 2012 Honig and co-workers used PrePPI to estimate if protein pairs interacted or not showing that a purely computational method could be as efficient as the experimental methods . This meMany proteins, in particular in eukaryotes, have closely related paralogs. These often do not have the same interaction partners. To distinguish which paralog interacts with another protein is often not possible using template-based docking, as the paralogs all are quite similar. Here, co-evolution can aid. Bitbol showed that it is possible to identify the correct interacting pairs within the bacterial two-component system using co-evolution and a maximum entropy approach .In addition to detecting if two proteins interact it is important to model how they interact. Protein-protein docking has, for a long time, been a challenge for computational biology . AlthougIf two proteins have perfectly complementary surfaces, protein-protein docking is trivial. However, this is rarely the case as structural plasticity of the involved proteins that change the interaction surfaces. Further, the use of modeled structures change the interaction surfaces even more, making the practical use for protein-protein docking methodologies to be of limited practical use. Therefore, often it has been necessary to use low-resolution experimental information to obtain reliable results as we did when predicting the structure of the Tom-complex in mitochondria .Recently an alternative method, template-based docking, has gained popularity . Here, tA and B interact in one organism and that both proteins have homologs The use of the same direct-information contact prediction methods as described above can be applied for protein-protein docking . In theoA comparison between the three methods can be found in It is clear from this brief analysis that 1) for the majority (165/218) of protein no method provides an accurate model 2) still traditional docking methods provide the highest number of acceptable models  3) the mCells consists of complex networks of interacting biomolecules. In addition to the physical-stable interactions between proteins that I have discussed above other regulatory interactions are important. These includes miRNA bases regulations, proteins binding to promoter regions, and many other type of interactions. Many if these are studied and predicted in databases such as string .Although all interactions are of great interest, it is unlikely that the revolution seen by deep learning and DCA will equally affect our ability to predict all types of interactions. The first requirement is that we have sequence data for both molecules interacting and that there is an evolutionary pressure for these to co-evolve. This excludes all interactions including anything but RNA, DNA and proteins. However, it has been shown that co-evolutionary signals also can be used to detect epistatic interactions in bacteria  and viruComplete structural knowledge of all proteins and their interactions in a cell will change our understanding of cell biology in the same way as the human genome project changed our understanding of genetics. It is not only a question about knowing what is there but also what is missing, i.e. what interactions do not exist. Given the rapid progress in both experimental and computational methods, this is no any longer an unrealistic scenario. During the revision of this paper Deepmind presented their impressive results at the CASP14 conference, strengthening the assumption that the prediction of the structure of stable protein domains is basically a solved problem and that one of the focus should be on how proteins interact in the cell.Clearly the interaction data provided from computational methods needs to be complemented with experimental data. Here, one has to distinguish between transient and permanent interactions. Cryo-EM tomography will provide very valuable low-resolution data for permanent interactions, but not for transient interactions.Transient interactions are fundamental for regulation in a cell. These interactions can be of different natures, from the folding-upon-binding type of disordered regions to rigid body interactions to phase transitions in cellular bodies. The evolutionary pressure on these interactions is also different making it difficult to use co-evolutionary methods for some type of interactions. For instance, the binding of disordered motifs often shows no co-evolutionary signal, as the disordered motif is extremely variable, while the interaction surface is very conserved.Anyhow, we believe that results from the rapid advancement in structure prediction show the power of machine learning methods. Therefore, this author is convinced that it is just a question of time until these methods (based on co-evolution) will be applied to predict interactions on a large scale. Further, I believe that the main problem in the short time will be to construct the optimal multiple sequence alignments for detecting the interactions.


The COVID-19 pandemic has been a major public health event since 2020. Multiple variant strains of SARS-CoV-2, the causative agent of COVID-19, were detected based on the mutation sites in their sequences. These sequence mutations may lead to changes in the protein structures and affect the binding states of SARS-CoV-2 and human proteins. Experimental research on SARS-CoV-2 has accumulated a large amount of structural data and protein-protein interactions (PPIs), but the studies on the SARS-CoV-2–human PPI networks lack integration of physical associations with possible protein docking information. In addition, the docking structures of variant viral proteins with human receptor proteins are still insufficient. This study constructed SARS-CoV-2–human protein–protein interaction network with data integration methods. Crystal structures were collected to map the interaction pairs. The pairs of direct interactions and physical associations were selected and analyzed for variant docking calculations. The study examined the structures of spike (S) glycoprotein of variants Delta B.1.617.2, Omicron BA.1, and Omicron BA.2. The calculated docking structures of S proteins and potential human receptors were obtained. The study integrated binary protein interactions with 3D docking structures to fulfill an extended view of SARS-CoV-2 proteins from a macro- to micro-scale. COVID-19 has sparked a global pandemic since the year 2020. A coronavirus was identified as the causative agent of COVID-19 and named SARS-CoV-2 . When SAThe sequence of SARS-CoV-2 has undergone frequent mutations, and multiple mutant strains have been distinguished based on a large number of mutational characteristics . The mutThe protein–protein interactions (PPIs) between SARS-CoV-2 and human receptor candidates have been detected by experimental methods. Affinity purification-mass spectrometry (AP-MS)  has beenThe docking structures analyzed in previous studies are based on the sequence of the wildtype (WT) strain of SARS-CoV-2. Wierbowski et al.  construc2. Calculations of affinities for these structures revealed differences from the WT strain proteins. In this study, an interaction network with 2385 pairs of interactions between 29 viral proteins and 1144 human proteins was constructed. Among the 196 interaction pairs of the S protein, the crystal structures and protein complexes were collected. On the other hand, for the direct interactions and physical associations that may have structural binding in the integrated network, the structures of the two interacting proteins were collected respectively for docking. In comparison with the crystal structures and protein complexes of the SARS-CoV-2 wildtype (WT) strain, this study obtained some reliable computational docking structures of the S protein of the variant strain and the human receptor protein. The proposed integrated framework of the PPI network (macro view) and the virus–host protein binding (micro view) was named CoVMThe SARS-CoV-2 and human interaction pairs were obtained from Liu’s work , IntAct All protein sequences of SARS-CoV-2 and human are retrieved from UniProt . The canOther than the main viral proteins with 3D structures that have been solved experimentally, some of the variant strains’ structures have not been solved at the time this study was completed. Modeling of the structurally unknown viral proteins was performed with SWISS-MODEL . HomologProcheck in the Structural Analysis and Verification Server (SAVES)  was applThe docking of the SARS-CoV-2–human protein pairs was performed by the HDOCK standalone package . For eacThe integrated SARS-CoV-2–human protein interaction network was constructed and visualized A. PhysicS protein and 196 interacting human proteins were extracted from the whole PPI network B. The is2) view  view , and the2) view . The seqThe variant S proteins’ structures were used as ligands to perform the docking processes with the human receptors. Thirty-six docking structures of nine human proteins with S proteins of four SARS-CoV-2 variants were modeled. Each pair of SARS-CoV-2–human docking structures generated 100 models via the HDOCK program. Among the models, the model with the lowest score was selected. The structure of the Omicron BA.2 variant S protein binding with ACE2 had an RMSD value of 0.43 with the experimental structure. The free energy values of the selected docking structures were calculated by PRODIGY . For sim2) view is a PPI network integration with possible docking structures of SARS-CoV-2–human PPI pairs. Experimental and computational docking structures have been mapped to corresponding interactions. The alterations of the SARS-CoV-2 variant S protein interaction subnetwork under the differences in their binding states were achieved. OverCOVID [2 network and structural datasets are available to access on OverCOVID’s new page , which could be a great expansion of this field of research. At the top of the website is the visualization of the integrated SARS-CoV-2–human PPI network. By clicking the panel, users can view the interactive functions such as the protein detail tables. Users can scroll down the page to find the download link of the data sets including network, structural details of virus–human protein binding, and python scripts.The coronavirus macro-to-micro (CoVMverCOVID  is a webMost proteomics and structural studies on SARS-CoV-2–host interactions have focused on the spike–ACE2 interaction. More data have accumulated around S protein interactions based on its important mechanism. However, with the discovery of other viral proteins’ mechanisms, more interactions with host cell proteins can be investigated. On the enriched cluster, which potentially plays a critical role in viral invasion, the large-scale analysis of variant protein structure docking has not been conducted yet.In this study, the binding states of SARS-CoV-2 S protein with more known or potential human receptor proteins were achieved. The influence of variant sequences in the docking structures was also considered. The sequence of the Omicron BA.2 variant as a new ligand was implemented in the SARS-CoV-2–human protein docking. A change in the viral proteins’ binding affinity with human proteins was observed.The SARS-CoV-2–human PPI network is a macroscopic map of the node–node binary associations. It requires combination with the details of protein docking to assess the actual situation of a particular variant protein on viral invasion from a microscopic perspective . The impOnce the structures of the proteins of emerging SARS-CoV-2 variants are experimentally solved, the association of these proteins with host proteins will be better understood. The topology of the interaction network of each variant strain and the characteristics of the virus-host protein docking interface will provide more help for the prevention and treatment of SARS-CoV-2.Gene ontology enrichment analysis can also yield enrichment for subcellular locations of human proteins that also need to be analyzed in more detail. Studies of the SARS-CoV-2 interactome  have revIn this study, functional analysis and structural docking calculations were performed on the interaction between the viral protein and the host. It was found that the mutations in sequences may influence the structural affinity. The Omicron BA.2 variant has a stronger spike–ACE2 binding affinity as compared to other variants. In binding with AXL and CD209, the BA.1 spike had stronger affinities than the others as well. Compared with BA.2, the S protein of the Delta variant showed weaker affinity with receptors. However, when the Delta variant was compared to BA.1, the opposite relationship was observed in the different protein dockings. Wu et al.  providedThese results suggested the existence of potential receptors of viral proteins and their binding state alteration with different variants. However, the affinity of the docking structure could also be affected physically or biologically. Combining some specific experimental conditions and more datasets could improve the analysis and is helpful for SARS-CoV-2 protein receptor identification, drug design, and repurposing.