Protein networking is critical to understanding the biological functions of proteins and the underlying mechanisms of disease. However, identifying physical protein–protein interactions (PPIs) can be challenging. To gain insights into target proteins that interact with a particular disease, we need to profile all the proteins involved in the disease beforehand. Although the cross-linking mass spectrometry (XL-MS) method is a representative approach to identify physical interactions between proteins, calculating theoretical mass values for application to targeted mass spectrometry can be difficult. To address this challenge, our research team developed PPIAT, a web application that integrates information on reviewed human proteins, protein–protein interactions, cross-linkers, enzymes, and modifications. PPIAT leverages publicly accessible databases such as STRING to identify interactomes associated with target proteins. Moreover, it autonomously computes the theoretical mass value, accounting for all potential cross-linking scenarios pertinent to the application of XL-MS in SRM analysis. The outputs generated by PPIAT can be concisely represented in terms of protein interaction probabilities, complemented by findings from alternative analytical tools like Prego. These comprehensive summaries enable researchers to customize the results according to specific experimental conditions. All functions of PPIAT are available for free on the web application, making it a valuable tool for researchers studying protein–protein interactions. Abnormal PPIs involving endogenous proteins, proteins from pathogens, or both can lead to various human diseases, including cancer6. To understand a target protein’s interaction with a specific disease, it is necessary to profile all proteins involved in the disease beforehand. However, the profiling results may not match with an experimental target protein9. Therefore, it is essential to identify physical protein–protein interactions using various methods 11. Several web-based databases, such as STRING12, MINT13, BioGRID14, and IntAct15 provide theoretical protein–protein interaction search and contribute significantly to protein interactome profiling analysis.Protein–protein interactions (PPIs) play a crucial role in the formation of protein networks and complex structures, which are essential for understanding the biological functions of proteins16 footprinting17, hydrogen–deuterium exchange mass spectrometry (HDX-MS)18, cross-linking mass spectrometry (XL-MS or CX-MS or CL-MS)26, ion-mobility MS27, and native MS28. Among these methods, XL-MS has become a powerful approach for mapping protein–protein interactions over several decades23. However, its effectiveness has long been impeded by three primary obstacles: (1) complex tandem mass spectrometry (MS/MS or MS2) fragmentation of cross-linked peptides; (2) low abundance of cross-linked peptides in complex peptide mixtures; (3) heterogeneity of cross-linked products10. Furthermore, calculating all possible cases and their mass values by considering the site, charge, and modification where interactions between proteins occur can be challenging. The first hurdle makes accurate identification of cross-linked peptides and unambiguous assignment of cross-linked sites challenging, while the second and third hurdles hinder effective MS detection of cross-linked peptides4. The fourth hurdle becomes significantly hinders the efficiency of research. Selected reaction monitoring (SRM) method is used to identify PPIs perform based on MS/MS values obtained through profiling. However, MS/MS fragmentation of cross-linked peptides is typically convoluted and unpredictable. Therefore, to identify the site of the predicted theoretical actual interaction occurs and calculate their mass values, we need to compare experimental MS/MS spectra against a computed library of theoretical spectra. This process of calculating mass values must not only consider all cases of theoretical interaction but also the properties of cross-linker, charge of peptide/fragmented peptide ion, and modification. Finally, several tools can help analyze XL-MS, such as SRMcollider29, Prego30, and X-Link Transition Calculator31 in Skyline32. SRMcollider is software that predicts interference probability between target transitions29. Prego is software that predicts high-responding peptides for SRM experiments30. The X-Link Transition Calculator is software that calculates cross-linked mass values31. However, these tools are unable to search theoretical protein interactors for the target protein, and it must be known which sites are actual interact and modified. In this respect, the development of analysis tools for XL-MS is still needed.Various methodologies can identify these interaction sites, including covalent labelingWe proposed PPIAT, a targeted mass spectrometry-based protein–protein interaction analysis tool. PPIAT is a web-based analysis tool that can search interaction information about human proteins and calculate mass values given the properties of cross-linkers, enzymes, charges, and modifications. We expect PPIAT to improve experimental efficiency and become a significant contributor to theoretical information for XL-MS analysis.33. Second, it provides users with the ability to search for information on commercially available cross-linkers, including their name, binding site, cleavability25, spacer arm length, and mass values, which are differentiated between non-cleavage and cleavage mass values. Third, PPIAT can predict protein–protein interactions for the user’s target protein and calculate their mass values based on input parameters such as target protein, cross-linker, enzyme, peptide length range, peptide charge, ion charge, and modifications specified on the search page. Finally, the results obtained from PPIAT can be summarized and extracted based on the probability score of protein interaction in the derived results and other analysis tools such as Prego30. With these functions, PPIAT serves as a valuable for targeted mass spectrometry-based protein interactor analysis in XL-MS. , and CSS (Cascading Style Sheet). XAMPP is a software package that includes the Apache web server, PHP (Hypertext Preprocessor), and MySQL, which means that PPIAT runs on the Apache server and relies on the MySQL database. The software offers three main functions. First, it allows users to search for human proteins by utilizing information from UniProt’s reviewed human proteins databasesee Fig.  To aid u33, STRING12, and publicly published white papers. PPIAT employs the STRING database to identify interacting proteomes associated with target proteins. The databases for PPIs and reviewed human proteins, provided respectively by STRING and UniProt, are initially distinct in format. PPIAT enhances its predictive capability by integrating these two databases into a cohesive format for PPI search. The database forms the backbone of the PPIAT, enabling the efficient and accurate prediction of protein–protein interactions and the calculation of their respective mass values.PPIAT is built on a comprehensive database that integrates data from various sources, including reviewed human proteins, protein–protein interactions, cross-linkers, enzymes, and modifications. The database contains 20,386 reviewed human protein entries , approximately 20 billion interaction data, and 83 cross-linker entries. These data were collected from reputable sources such as UniProt26 for identifying protein–protein interactions, (4) the preferred peptide length range for MS/MS analysis, (5) the peptide and fragmented peptide ion charges, and (6) any anticipated modifications in the protein–protein interactions  the entry number of the desired target protein , 2) the enzyme used for protein digestion, (3) the cross-linker employed in XL-MS the enzy34. To address this issue, PPIAT divides the same entry name and protein name between canonical and isoform using a dash (“- “) in the entry number. However, while the values of the string column at UniProt33 and values of protein A and protein B at STRING12 are linked to each other , they are not completely matched, and the canonical and isoform use the same value. It should be noted that PPIAT does not differentiate between canonical forms and isoforms of a target protein based solely on the protein A and B values from STRING12. For distinguishing between these forms, PPIAT utilizes the entry number when searching for a target protein .Proteins are differentiated between canonical and isoform. Isoform refers to a member of a set of highly similar proteins that originate from a single gene or gene family and are the result of genetic differences. Although isoforms have the same or similar function, they do not have an identical sequence and therefore have different mass valuesUsers can select either trypsin or chymotrypsin as the enzyme for protein digestion, depending on the properties of the digestion site and exceptions. In addition, users can choose from a list of 83 commercialized cross-linkers to identify interactions between the target protein and other proteins based on the properties of the cross-linker binding site and cleavability. see Data  Finally,20. This procedure involves searching for the binding site of the cross-linker in each sequence of the precursor ion digested by the enzyme and the fragmented ion generated during SRM analysis. Considering the varying mass of the cross-linked interactor depending on the cross-linker's characteristics, PPIAT consults a database encompassing information about the cross-linker's properties, such as its cleavability. If there are amino acids that can bind to the cross-linker, PPIAT calculates the mass, accounting for the cross-linker's properties and all potential cross-linking types. Finally, the output of PPIAT can be summarized and extracted based on the probability score of protein interaction in the derived results and other analysis tools such as Prego30. This output can be used as input data for targeted mass spectrometry analysis. The combined score is divided into four groups based on the confidence level: highest confidence (> 0.9), high confidence (> 0.7), medium confidence (> 0.4), and low confidence (> 0.15). The analysis program Prego provides a list of predicted high-responding peptides for SRM experiments30. PPIAT stands out for its advanced mass calculation capabilities, vital for integrating XL-MS studies with targeted mass spectrometry techniques such as SRM (Selected Reaction Monitoring) and PRM . It enables the generation of comprehensive mass spectral libraries from results obtained through DDA (Data-Dependent Acquisition) or DIA (Data-Independent Acquisition) analyses, effectively targeting specific proteins. It also facilitates conducting experiments with crosslinkers on peptide sequences predicted by Prego30.The XL-MS workflow can be divided into several steps. Firstly, a target protein complex is cross-linked in solution and digested with trypsin into peptides. Secondly, the peptides are analyzed by liquid chromatography coupled with mass spectrometry (LC–MS/MS) to obtain precursor masses and fragment masses for cross-linked peptides. Thirdly, the fragmentation spectra of all peptides are subjected to database searching to identify cross-linked peptidesm/z, protein A’ ion, protein A’ ion type, protein B’ ion, protein B’ ion type, product charge, and product m/z.The PPIAT software outputs results that can be utilized as input for SRM analysis. This software searches for PPIs and calculates their mass values. It generates separate tables for identified PPIs and predicts interaction information, indicating which proteins are interacting with the target protein based on a combined score. Additionally, another table provides printed information on the theoretical actual interaction, including combined score, the protein A’ peptide, cross linker, protein B’ peptide, precursor charge, precursor 12. In each column of the tables, “Protein A” refers to the target protein that was inputted, while “Protein B” refers to the proteins that interact with the target protein in both peptide and ion levels. The precursor m/z and product m/z columns are divided into the mass value of protein A linked with one side of the cross linker, the cross-linker mass after cleavage, and protein B linked with the other side of cross linker. If a modification is expected to occur statically, the fluctuating mass values are added. However, if the modification is expected to occur variably, the results are printed as two lines—one with the change in mass values due to the modification and one without. Figure After searching for the target protein using PPIAT, the software generates a list of theoretical PPIs for the target protein, along with the number of actual interactions that occur in the interactome and their corresponding mass values. The theoretical PPIs list predicts which proteins interact with the target protein and assigns scores based on the STRING database21 In addition, when conducting XL-MS experiments, it is possible to encounter situations where interactions occur on one side, and the other side of the cross-linker may interact with Protein A and Protein B, respectively. To obtain more accurate data of the protein–protein interactions, these possibilities must be considered.Cross-linking mass spectrometry (XL-MS) involves performing a mass spectrometry-based analysis on cross-linked complexes that have been digested by enzymes. When using PPIAT, the software calculates the mass values of the digested cross-linked protein complexes at the peptide and fragmented peptide ion levels. However, it does not consider the total mass of the cross-linked complex. To take into account all other products of the cross-linking reaction between Protein A and Protein B, it is necessary to consider four possibilities at the peptide and fragmented peptide ion levels: (1) cross-linked between Protein A and Protein B (cross-linking or interpeptide); (2) only cross-linked with Protein A, not cross-linked to Protein B (loop-link or intrapeptide); (3) only cross-linked with Protein B, not cross-linked to Protein A (loop-link or intrapeptide); and (4) non-cross-linked between Protein A and Protein B. (mono-link or dead-end)36. When using a cleavable cross-linker, it is important to consider its properties, such as the cleavage site and the type of cleavage that occurs  36. If a cleavable cross-linker is used and is cleaved symmetrically, the same mass values should be added to Protein A and Protein B, respectively. However, if the cross-linker is cleaved asymmetrically, different mass values must be added. It is important to consider these factors when using a cleavable cross-linker to obtain accurate results. All calculated mass values can be exported to CSV format and used as input data for MS/MS analysis, which can provide further insights into the protein–protein interactions.Cross-linkers are typically classified into two categories: cleavable and non-cleavable cross-linkers45. Our search was performed under the following conditions: APOE(UniProt ID: P02649), trypsin digestion, cross-linker DSSO, top 10 interactions, peptide length of 9, peptide charge of 2 and 3, ion charge of 1 and 2, and modifications of carbamidomethylation(static) and oxidation(variable). Ten proteins were identified by PPIAT, including LRP8, LRP1, A4, APOB, LDLR, APOA1, VLDLR, TAU, SORL, and TREM2  Table  and S3. 5) Table  and S3.TXL-MS is a widely used method for identifying protein interactions and elucidating protein structures in three dimensions. In recent years, significant technological advancements in XL-MS studies have propelled the field of proteomics forward. However, there are still several limitations that need to be overcome, such as the need for better strategies for cross-linking. Unfortunately, there is currently no available solution or analysis tool that can accurately calculate the mass values of theoretical interactions required for SRM analysis. To address this challenge, our research team has developed PPIAT, a tool that can search for protein–protein interactions with target proteins, identify theoretical interactions that occur, and calculate their mass values at the peptide and fragmented peptide ion levels. With PPIAT as input data, mass values can be accurately calculated. This new tool provides an important advancement in the field of proteomics, helping researchers to overcome the challenges of XL-MS and improve their ability to identify protein–protein interactions.29, Prego30, and X-Link Transition Calculator31 in Skyline. However, these tools are not integrated with the platform used to search for theoretical PPIs, and the protein modification, binding site, and mass value of the cross-linker must be calculated and entered directly for XL-MS.Overall, the development of PPIAT represents a significant advancement in XL-MS studies and has the potential to enhance our understanding of protein–protein interactions. Although strategies such as the development of various cross-linkers have been employed in XL-MS, a lack of solutions or analysis tools for accurately calculating the mass values of theoretical interactions required for MS analysis still exists. To address this issue, several software tools have been developed, including SRMcollider20, and the most commonly occurring modifications (oxidation and carbamidomethylation). While these updates are a step forward, we will continue to improve and update the database regularly to ensure that it remains a useful resource for the scientific community.While PPIAT is a valuable tool for XL-MS analysis, it has some limitations that need to be addressed. Firstly, it does not cover all the products of the cross-linker, nor does it provide information about the enzyme or modification. Additionally, the output generated by PPIAT is only theoretical and requires experimental verification to confirm the results. To address these limitations, our team has built a database that includes information on 83 different cross-linkers, two types of enzymes (trypsin and chymotrypsin)3646, which has been studied and commercialized. Additionally, researchers often synthesize their own cross-linkers with novel characteristics, such as BMSO, DBB, DHSO, and SDASO, for their experiments51. Although these synthetic cross-linkers are not currently included in the PPIAT database, an editing function will be added to the software to allow users to input information on these cross-linkers when searching for target proteins.The field of cross-linkers has seen a wide range of types with unique properties, including chemical cross-linkers and Photo/MS-cleavable cross-linkers12 only, our plan is to expand it by integrating with other databases such as MINT13, BioGRID14, and IntAct15 for PPI searching. The output generated by PPIAT can be downloaded in CSV format and used as input data for SRM analysis.The PPIAT database is continuously updated with reviewed information on human proteins and protein–protein interactions. While the current version of the database has been built using PPI information from STRINGThis analysis tool was developed on i9-10940X CPU, 128 GB RAM, Window server system. Researchers can identify PPIs up to the following conditions: (1) protein: AP02649 (APOE protein), (2) enzyme: trypsin, (3) cross-linker: DSSO, (4) peptide length range (min): 6, (5) peptide length range (max):10, number of interaction ranking: 10, (6) precursor charge: 2, 3, (7) product charge: 1, 2, (8) modifications: carbamidomethyl (C): static, oxidation (M): variable. The current analysis conditions are limited. We plan to make a server system that allows search conditions with at least 10 interacting proteins for the target protein and a peptide length ranging from 6 to 20.In conclusion, this paper introduces PPIAT, a web-based open platform designed for the analysis of XL-MS data. PPIAT provides a solution for the search of protein–protein interactions involving target proteins and calculation of the mass values of all theoretical interaction cases between proteins at both peptide and fragmented peptide ion levels, overcoming a significant obstacle in XL-MS analysis. We expect that this tool will be widely adopted by researchers in proteomics and bioinformatics, and we welcome contributions to its development. By continuously updating and refining the database, we aim to address the current limitations and improve the accuracy and efficiency of XL-MS analysis.Supplementary Information.


X7 = 26733 PDB structures. We visualize the generated structures and show that AF2 pLDDT values are correlated with state-of-the-art disorder approximations, implying some internal protein dynamics are also captured by the model. Joint increasing mutational coverage of both structural and phenotype data coupled with advances in machine learning can be leveraged to accelerate virology research, specifically future variant prediction. We hope this data release can offer assistance into further understanding of the local and global mutational landscape of SARS-CoV-2 as well as provide insight into the biological understanding that 3D structure acts as a bridge between protein genotype and phenotype.Leveraging recent advances in computational modeling of proteins with AlphaFold2 (AF2) we provide a complete curated data set of all single mutations from each of the 7 main SARS-CoV-2 lineages spike protein receptor binding domain (RBD) resulting in 3819 The receptor binding domain (RBD) of the SARS-CoV-2 spike protein, in its active conformation, is the domain that binds directly the to ACE2 receptor which itself is a protein on the surface of many cell types that acts as a “cellular doorway” for the SARS-CoV-2 virus. Understanding the competitive binding between RBD, ACE2, mono, and polyclonal antibodies is core to assessing the potential evolutionary fitness of a given variant.1 make gathering biophysical (phenotype) values such as protein expression as well as RBD-ACE2 binding affinity for close mutants in parallel possible, further enabling mapping of the local evolutionary landscape of any antigen. However, the variant combinatorial space hugely expands with the number of mutations of a protein; x is the mutational distance (x = 15 for Omicron BA12) and n is the sequence length (201 for the RBD). So there are approximately 4.23*1030 “Omicron-distant” variants from the original Wuhan sequence. DMS experiments produce data orders of magnitude too small to fully cover this number of possible mutants. Omicron for example, with its 15 mutations from the original Wuhan variant is a prime example of a more distant variant that has been shown to escape many antibodies evoked by early vaccines. It would have been hugely valuable to know this ahead of time which motivates the development of in-silico machine learning techniques to predict biophysical values with regard to the stability of proteins, expression extent, and most importantly protein-protein interaction Gibbs-free energies: ΔG.Deep mutational scanning (DMS) experiments4. However, the recent advanced in Supervised machine learning (ML) has led to models that often outperform classical models in presence of enough training data. This is due to their ability to learn task-specific features. Supervised machine learning as a tool fits the problem description where we have some distribution we want to learn and we require generalization to new unseen data which in our case would be unseen combinatorially distant variants. In Fig. X, Y) for model training where X is the 3D protein or protein-protein complex structure and Y is the corresponding biophysical measurement. Such a predictive model could be summarised as f where Y = f(X) and the loss ℓ is minimized with some minimization procedure. Such a model would allow vaccine developers to investigate how new antibodies/mixtures could be potent against future variants as well as allow the scientific and political community to get ahead of the virus evolution and be in a proactive position, not a reactive one that we are in at the time of writing. Bloom et al. have provided a preliminary tool5 based on a simple linear model to predict antibody escape , however, this may deteriorate for more distant mutants. Predictive profiling of SARS-CoV-2 variants by deep mutational learning6 aims to produce a similar result but is limited by its 3D model-free nature. 3D model representations are only worth leveraging for predictive models if they contain additional signal that truly is physically relevant on top of the amino acid sequence loaded with chemical information. This motivated our study.Classical predictive models with hand-crafted features exist for the genotype to phenotype prediction problem7 (AF2) to produce accurate predictions for single mutants is under debate. Variable methodologies and datasets12 are used to make claims that predicted structures correctly resemble their measured counterpart. This is potentially difficult to assess as the effect of a mutation may be small compared to the inherent conformational dynamics and disorder of a given protein. In ref. 9 the authors chose specific illustrative examples of selected proteins for which experimental and structural data for both wild-type (WT) and structure-disrupting mutations are available. The authors compare the root mean squared distance (RMSD.) between structure disrupting mutations to the WT both for measured structures and AF2 predicted mutants. AF2 was unable to predict when a point mutation causes defective protein folding as both RMSD and pLDDT values were not concordant. This study however is impossible to perform for large sets of variants where there are no PDB ground truth reference structures so its conclusions are limited in ability to generalize to other proteins. In ref. 10 the authors find no correlation between ΔpLDDT values and biophysical measurement values of GFP fluorescence in 976 mutations of 90 proteins from the Thermo-Mut database13. The authors argue that some correlation between phenotypic values that relate to ΔΔG and ΔpLDDT should be observed to indicate that generated AF2 single mutants possess a physically meaningful structure.The ability of AlphaFold211 leverage large-scale DMS data of 33 proteins with 117,135 mutations by investigating the correlations between the DMS predicted protein function values with both AF2 and experimentally derived structures. These protein phenotype quantities were calculated with structure-based protein function predictors; FoldX14, Rosetta15 and DynaMut216. Strong correlations were seen for both experimentally derived structures and AF2-generated structures. It is, however, not possible to know if structure-based protein function predictors actually leverage 3D signal or simply use distilled sequence information to make their predictions. Additionally, in ref. 12 the authors show local structural change 8 metric) is correlated in experimental (PDB) and AF2-predicted pairs. Linking structure to phenotype as a data validation approach they find significant correlations between local structural changes in AF2-predicted structures and three categories of phenotype; fluorescence, folding, and catalysis across multiple experimental data sets.On the other side of the debate, ref. 17. Because we validate a diverse set of generated mutants, this validation should generalize to other RBD variants, not in our data release that researchers may want to generate themselves with AF2. In this work, we present a curated and validated dataset “SARS-CoV-2 RBD deep mutational AlphaFold2 structures”. Namely, 26733 aligned PDB structures to accelerate SARS-CoV-2-related research.This debate is still open and general validation of all structures generated by AF2 is out of the scope of this paper. Generalization of the model to new regions of the structural space may be very difficult to globally validate. At the time of writing, the utility of predicted 3D models needs to be evaluated on a case-by-case basis. In this spirit, we aim to validate SARS-CoV-2 RBD deep mutational AF2 structures which exist on a small manifold of this space. Despite this debate, we believe our validation methodology is well supported by the literature. The data set we release should help speed up research in the field of structural virology. This is because accessibility to our curated dataset alleviates resource and time burden for data generation and organization. This data can be used for downstream tasks for example predicting biophysical qualities or antibody escape18) sequence and choosing the RBD section amino acids 331–531. From this sequence, we created the 6 other main mutants using the given positions and amino acid changes in Stanford University Coronavirus Antiviral Resistance Database2. This data is openly available. This was cross-validated against the main variant sequences19 and the GitHub repository from the Bloom lab where the complementary phenotypic data is hosted and experimental details of data generation is described1. The WT + 6 variant sequences were then iteratively mutated to create every possible single mutant. The RBD consists of 201 amino acid residues so the number of single mutants per variant is 201 × 19 = 3819. We will refer to this as a mutant cluster. This represents the positional range 331–531 of the full spike protein. The 7 mutants clusters produced were wuhan, alpha, beta, delta, eta, omicronBA1, and omicronBA2 amounting to 7 × 3819 = 26733 RBD protein FASTA sequence20 and structure files. These outputs contain all metadata files that were generated during AlphaFold2 runs. The running time for one RBD model was between 15 and 20 minutes. 5 structures were produced from each variant FASTA file and the model with the highest overall confidence was chosen for our library resulting in 7.2 GB for the entire library. We optimized the distribution of AlphaFold2 running 40 parallel jobs simultaneously. The selected structures were then aligned to the RBD part of the 6M0J RBD-ACE2 protein structure (PDB ID: 6M0J_2|Chain B[auth E] (15:208)22, UniProt ID: P0DTC2 (334:526)18) from the protein data bank with the Schrödinger Maestro built-in “structalign”23 post-processing method to both 6M0J and each cluster to its aligned parent variant as described in the Data Records section. All atoms are used for this alignment, this software uses an optimization method to minimize the RMSD between the two protein structures.We used the Ampere01 machine at the Wigner Research Centre for Physics . A 5 TB SSD was used for the AlphaFold2 database access time acceleration. The input FASTA sequences were generated by first downloading the original spike protein  license.We provide a In Figs. pLDDT scores generated for the variants30. Recent literature suggests pLDDT scores predicted by the model are correlated with disorder metrics34. Using the state-of-the-art disorder calculator IUPred235 we find that 100-pLDDT∝1/IU pred2 for each cluster of single mutants  representations of each variant A  can be expected to be more “difficult” to align. We found that these same proteins have a larger structalign RMSD. This validates the structalign alignment as, if the alignment was not good this observed correlation would be washed out. For example, highly distorted structures may not necessarily have the largest structalign RMSD and vice versa. There is a very strong correlation (R = 0.962) between variant RMSD from WT and Structure-based RMSD post-alignment. This should give users of the released data confidence in PDB alignments for further 3D modeling tasks.All structures we release are aligned to the RBD part of the 6M0J RBD-ACE2 protein structureBloom lab. Full experimental details are also published38. All mutants can be matched by filename to the CSV file entry. These measurements include ACE2 binding and protein expression relative to their given parent WT. Some mutants have nan values in places where no reliable experimental results were possible to publish and may need to be removed or ignored for downstream tasks.The structures can be compared with phenotypic values measured by the PQR-APBS.ipynb which can be used to transform all the structures to PQR39 and then APBS40 electrostatics form. The combined charge and structure features may offer complementary signal for downstream learning tasks.We have prepared a script


Identifying pathogenetic variants and inferring their impact on protein–protein interactions sheds light on their functional consequences on diseases. Limited by the availability of experimental data on the consequences of protein interaction, most existing methods focus on building models to predict changes in protein binding affinity. Here, we introduced MIPPI, an end-to-end, interpretable transformer-based deep learning model that learns features directly from sequences by leveraging the interaction data from IMEx. MIPPI was specifically trained to determine the types of variant impact  on protein–protein interactions. We demonstrate the accuracy of MIPPI and provide interpretation through the analysis of learned attention weights, which exhibit correlations with the amino acids interacting with the variant. Moreover, we showed the practicality of MIPPI in prioritizing de novo mutations associated with complex neurodevelopmental disorders and the potential to determine the pathogenic and driving mutations. Finally, we experimentally validated the functional impact of several variants identified in patients with such disorders. Overall, MIPPI emerges as a versatile, robust, and interpretable model, capable of effectively predicting mutation impacts on protein–protein interactions and facilitating the discovery of clinically actionable variants. G, and categorizing the effect of mutations on PPIs by category would provide a more straightforward and simplified approach to clinical assistance such as drug guidance. Furthermore, most of these techniques require protein structure information to make predictions, but only a limited number of protein complexes have experimentally validated structure data, and predicted structures may not be accurate. Some sequence-based methods do not need structural information . W. W61]. WThere is basic and nonbias information in the primary amino acid sequence to describe a protein. As mentioned above, the 51-length fixed window was used for the affected protein. For involved protein sequences with fewer than 25 residues at both sides of the mutated position, 0 was used as padding at the end of the sequences. For partner proteins shorter than 1,024 AA, 0 was used as padding after the sequence. Each amino acid in the protein sequence was represented as a token for model input. Each sequence token ranges from 0 to 20 to represent zero padding or one of the 20 standard amino acids.e value for pairwise alignment was set to 1 × 10−3, using Uniref90 [Sufficient protein conservation information has been widely used in protein structure prediction, which is highly related to PPI and other biological processes. Here, we used PSI-BLAST  tool to Uniref90  as a seaAfter the feature engineering described above, we obtained 2 types of features for all 3 parts of the model input. For the original protein and corresponding mutant protein, a 51 × 1 sequence token vector and 51 × 20 PSSM matrix were generated, while a 1,024 × 1 sequence token vector and 1,024 × 20 PSSM matrix were generated for the partner protein. Each position in the reference sequences, the mutated sequences, and the partner sequences was encoded as a 64D sequential feature (SF) spliced by a 20D PSSM profile and a 44D embedding vector. The 44D embedding vector was obtained by utilizing the “Embedding Layer” of TensorFlow based on the OneHot encoding of input sequences. All these features formed the whole multifeature input for our model.We designed a deep neural network architecture (MIPPI) for mutation impact classification, using the basic architecture of a modified transformer encoder and residual block  Fig. 1DD1D. Tranpos is the position of an amino acid in the input sequence, i is the embedding index, and dmodel is the dimension of the output embedding space.We changed the ordinary transformer sequence embedding part to adapt to our multifeature input rather than only sentence tokens in NLP tasks Fig. . ProteinTo avoid the adverse effect of the zero-padding part in the sequence on the subsequent structure of the model, we applied zero masking to minimize the weights of the zero-padding sequence when processing the data.The transformer encoder block mainly consists of 2 substructures: multihead attention and a feedforward network. Multihead attention in a transformer is a type of self-attention. Query (Q), key (K), and value (V) are each calculated from the input embedding, and the result from various heads will combine to give the final output. The feedforward network in MIPPI is a double-dense layer with layer normalization and dropout operation.Each transformer block comprised a 4-head self-attention layer and a feedforward subnetwork, which is a classical structure of the transformer. The self-attention layer checks attention with all residues in the same sequence at once, which gives the interpretability to MIPPI accompanied by visualization. Concurrently, multiple heads would look into various regions simultaneously and independently, thereby enabling a comprehensive assessment of the contributions of each residue of proteins. The feedforward subnetwork in MIPPI contains 2 fully connected layers with layer normalization and dropout operation, designed to generate an element-wise nonlinear transformation of incoming vectors. Each residual block contains 2 two-layer convolutional neural network (CNN) subblocks, with a double-layer skip that has nonlinearities (ReLU), and a max pooling layer at the end of the skip in each subblock to make the effective network deeper while reducing the impact of vanishing gradients.−4, and the exponential decay rate as default to 0.9 and 0.999, batch size 64. To make MIPPI pay more attention to those hard samples, categorical weighted focal loss [tp denotes prediction probability, ty denotes ground truth label, γ denotes modulating factor, and ta denotes a balanced variant.When comparing the performance of MIPPI with other machine learning methods, validating the effectiveness of model components through ablation experiments, and assessing the efficacy of data augmentation, we used a 5-fold cross-validation to demonstrate the stability and accuracy of the methodology. After finalizing the architecture of MIPPI and establishing the data augmentation strategy, considering the simplicity in subsequent analysis and visualization, we divided the whole dataset into training, validation, and test sets, with multilabel stratified sampling in the ratio of 8:1:1, and retrained the model. In the training procedure, we applied the Adam optimizer  with thecal loss  was usedFurthermore, different class weights  were set to further ease the class imbalance problem. As mentioned above, only training data used hypothetical reverse mutation and no-mutation measure as a special data augmentation in our method. MIPPI model was implemented, trained, and tested using TensorFlow2.2 . All theAs for the model evaluation metric, we chose 5 typical functions, including accuracy, precision, recall, F1 score, and MCC :accuracyTP/TN/FP/FN is the number of true positive/true negative/false positive/false negative for each class. F1 score and MCC are comprehensive metric functions for a multiclass classification task.Here, In a class imbalanced dataset, accuracy can be misleading as it only provides the general proportion of correct predictions but ignores the performance in each class. The other 4 metrics made up for this shortcoming, giving performance scores in each class to make a more detailed evaluation.First, we generated a “reversed entry” by swapping the labels between the mutant and its unmutated protein from “decreasing,” “no effect,” and “increasing” entries in the initial training set so that the mutant protein would be labeled as an unmutated protein and the reference  would be labeled as a mutant to form a new pseudo entry. We then labeled these new pseudo entries as “increasing,” “no effect,” or “decreasing” correspondingly and added 10,999 new data entries to the initial training set . Next, we generated a “nonmutated entry” by using the unmutated sequence for the reference protein as well as the mutant sequence, pretending that mutations did not occur for all available entries in the initial MIPPI training data. We labeled these new pseudo entries as “no effect” and generated 16,451 new entries (Table S8). As a result, we managed to increase the MIPPI training set from an initial version of 16,451 entries to a modified version training set of 43,901 entries by using our modified data augmentation strategy and making the dataset more balanced (Table S8).G prediction. Although the reference protein will not cause PPI changes, various machine learning models have not considered this type and might output unexpected results if no corresponding sample data were included during training. In MIPPI, we added no-mutation samples with “no effect” labels for every original entry in the training set to learn a more robust distribution [Considering the biological significance of the “disrupting” category, generating a hypothetical reverse category of “causing” or “increasing” from it might not be appropriate and biologically meaningful. Therefore, we excluded the “disrupting” category from the hypothetical reverse mutation strategy. To ensure data authenticity and prediction validity, we only applied hypothetical reverse mutation in the training set, not in the test set. Another easy-to-ignore issue is that proteins without mutations would cause “no effect” on the original PPI. Most studies did not consider this condition and excluded no-mutation sequences as input in similar tasks, such as ΔΔribution . We geneLR, KNN , NB, RF First, we retrained the MIPPI by replacing the He uniform initialization layer with the Glorot uniform initialization layer, a commonly used default weight initialization method, at the model’s input and tested the model performance. For testing the GAP layer, we replaced it with a fully connected layer at the end of the model and retrained MIPPI. Next, we removed the PSSM profiles from the input of MIPPI and retrained the model to test its contribution to the prediction.The partner protein branch was removed from the original MIPPI model structure to test the contribution of partner protein in model prediction. MIPPI-no partner was trained using the same protocol as MIPPI. The other 3 MIPPI variant models were implemented by modifying the partner branch input in the test set prediction. MIPPI-random partner input random amino acids to form an artificial partner protein sequence and input random PSSM data conforming to Gaussian distribution with mean and SD of all dataset PSSM matrices. MIPPI-random real partner input random paired partner protein sequence and PSSM in the training set. All the experiments were conducted on 5-fold cross-validation.When evaluating the effectiveness of the transformer blocks, while preserving the other components, such as input branches and residual modules, we replaced the 3 transformer layers with 3 fully connected layers, 3 CNN layers, or one LSTM layers, respectively. The architectures and parameters of the alternative models used for comparison were empirically fine-tuned to maximize performance, and their performance was evaluated through a 5-fold cross-validation.The SETD2-HA expression plasmid was received from the Lili laboratory. All mutated plasmids  were introduced using the Q5 Site-Directed Mutagenesis Kit (New England BioLabs), and mutations were confirmed via Sanger sequencing. The following primers were used:SETD2-S1624C F: GGAAATTGCTgTCGTTTCATGR: TTTTTGAGTGGCATCTATTATCSETD2-Y1666C F: ACGTTTGACTgTCAGTTCCAGR: TAACTCTGAGCCTGAAGGSETD2-L1815W F: AAAAATATGTgGGAGGAAAGCAAAGR: AGTAGGAATGGGCAAGTG2. For transient transfection experiments, HEK293T cells were seeded in 6-well plates. The following day, cells were respectively transfected with plasmid WT, S1624C, Y1666C, and L1815W using Lipo2000 reagent . Then, 48 h after transfection, cells were subjected to the corresponding analysis.HEK293T cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM), supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin at 37°C and 5% COHEK293T cells were lysed in 1× lysis buffer on ice for 30 min and centrifuged at 12,000 rpm for 15 min. The whole-cell lysates were incubated with primary antibody anti-HA at 4°C overnight. Then, the immunoprecipitated multiprotein complexes were pulled down with protein G beads  at 4°C for 2 h. The immunoprecipitated products were separated by SDS-PAGE and then detected by immunoblotting with indicated antibodies. Antibodies used in the study were anti-HA , anti-SMAD3 , and anti-TP53 .Kd) of the wild-type, and affinities of the mutant complexes. PPI binding affinity changes (ΔΔG) upon mutation were calculated as:R = 1.987 × 10−3kcal · K−1mol−1 is the gas constant, T is the temperature (K), and Kdmut and Kdwt are the equilibrium dissociation constant of mutant and wild-type protein–protein complexes, respectively.SKEMPI v2  is an inhttp://psymukb.net/). We chose the missense mutations with ANNOVAR annotation. As the input branch of the mutated protein of MIPPI only takes 50 amino acids close to mutation, we did not consider every transcript at the variant location, but the first transcript in the annotation. Corresponding protein sequences were retrieved by the biomaRt R package using RefSeq mRNA ID. PPIs for every mutation were acquired from the BioGRID database. Only physical PPIs with ≥2 reported pieces of evidence were chosen to guarantee PPI credibility. Protein sequences of BioGRID partner protein were acquired by a UniProt Retrieve/ID mapping search (https://www.uniprot.org/uploadlists/) using SwissProt ID. The Gene Ontology (GO) biological process enrichment of the DD-correlated genes was implemented using Metascape [The de novo mutations of DD and the control were downloaded from PsyMuKB (etascape .http://interactomeinsider.yulab.org/) as the interface benchmark and mapped the output weights of the last self-attention layer of partner proteins to these PPI interfaces. The output weights of padding positions were discarded, and only the top 5 highest attention weights of each partner protein were selected. For each attention head of the self-attention layer, we performed a Mann–Whitney U test [We downloaded the “Highest Confidence Interfaces” files from Interactome INSIDER  , a complex formed by binding between protein RAC1 and protein NCF2 and as part of a multiprotein enzyme complex that produces superoxide ions in response to microbial infection, from PDB. We then visualized the structure of Rac-p67phox using PYMOL [For the case study, we first extracted the crystal structure of Rac-p67ng PYMOL , with rehttps://www.psymukb.net/MIPPI.The tools and packages used in this study include Python version 3.7, NumPy version 1.19, pandas version 1.0, TensorFlow version 2.2, Matplotlib version 3.1, seaborn version 0.11, and SciPy version 1.3. MIPPI is hosted on our web server


K-embedding strategies to encode biomolecular interactions without relying on 3D structure-based docking. We demonstrate that SVS outperforms state-of-the-art performance for four regression datasets involving protein-ligand binding, protein-protein, protein-nucleic acid binding, and ligand inhibition of protein-protein interactions and five classification datasets for protein-protein interactions in five biological species. SVS has the potential to transform current practices in drug discovery and protein engineering.Virtual screening (VS) is a critical technique in understanding biomolecular interactions, particularly in drug design and discovery. However, the accuracy of current VS models heavily relies on three-dimensional (3D) structures obtained through molecular docking, which is often unreliable due to the low accuracy. To address this issue, we introduce a sequence-based virtual screening (SVS) as another generation of VS models that utilize advanced natural language processing (NLP) algorithms and optimized deep  K-embedding strategies to encode biomolecular interactions without relying on 3D structure-based docking.A sequence-based virtual screening method uses natural language processing algorithms and optimized deep  These interactions highly depend on the three-dimensional (3D) structures and the dynamics of molecules, as well as biomolecular conformational changes, due to their flexibility and allostery. The understanding of biomolecular interactions is the holy grail of biological science.Biomolecules are the building blocks of life and can be classified into various categories including carbohydrates, lipids, nucleic acids, and proteins based on their sizes, structures, physicochemical properties, and/or biological functions. Additionally, the realization of biomolecular functions is often accompanied by direct physical/chemical interactions with other biological molecules, small ligands, ions, and/or cofactors4. The ability to understand biomolecular interactions via advanced AI approaches has a far-reaching significance to a wide range of research fields, including drug discovery3, virus prevention5, directed evolution4, etc. However, the accurate and reliable prediction of biomolecular interactions is still a severe challenge.The last decade has witnessed the rapid advance in computational biology fueled by the achievement of artificial intelligence (AI) and increased computer power. With advanced techniques in data collecting, processing, analyzing, and representing, modern computational biology can study biological processes at extraordinary scales and multiple dimensions. It has achieved great success for various biological tasks11. As a result, current analysis and prediction of biomolecular interactions rely heavily on the high-quality 3D structures of interactive biomolecular complexes. Unfortunately, experimental determination of 3D structures is both time-consuming and expensive, leading to the scarcity of experimental structures, particularly, the structures of interactive biomolecular complexes. To overcome this difficulty, molecular docking based on searching and scoring algorithms is designed to generate 3D structures of the interactive complexes, such as antibody-antigen complexes and protein–ligand complexes. Molecular docking is widely incorporated in the virtual screening (VS) of biomolecular interactions, offering an alternative means to construct the 3D structures of interactive biomolecular complexes and is a crucial step in computer-aided drug discovery (CADD). However, current molecular docking is prone to mistakes, rendering inaccurate 3D structures and leading to unreliable virtual screening12. Despite the breakthrough in (non-interactive single) protein folding prediction by Alphafold22, the structure prediction of interactive biomolecular complexes remains a severe challenge. There is a pressing need to develop innovative strategies for the virtual screening of biomolecular interactions.Due to the inherently high correlation between structure information and molecular functions, the structure-based approaches achieved high accuracy and reliability in modeling and learning biomolecular interactions13, nucleic acid composition (NAC)14, and pseudo AAC (PseAAC)15; (2) autocorrelation-based methods such as auto-covariance16; and (3) evolution-based methods such as position-specific frequency matrix (PSFM) and position-specific score matrices (PSSM)15. Meanwhile, the use of NLP models to analyze the hidden information in molecular sequences, including protein models, has been successful in recent decades19.Alternatively, sequence-based approaches may provide efficient, robust, and easily accessible deep embeddings of biomolecular interactions without invoking 3D structure docking. Sequenced-based approaches are much more widely applicable than structure-based ones because the Genebank has over 240,000,000 sequences, compared to only 200,000 3D protein structures in the Protein Data Bank (PDB), endowing sequence-based approaches much boarder applicability. There are three major types of sequence-based approaches: (1) composition-based methods such as amino acid composition (AAC)20. An in-house autoencoder was trained with 104 million sequences21. Evolutionary scale modeling (ESM) is a large-scale Transformer trained on 250 million protein sequences, which achieved state-of-art performance in many tasks, including structure predictions22. For DNA in the genome, pre-trained bidirectional encoder representation model DNABERT has achieved success in non-coding DNA tasks, such as the prediction of promoters, splices, and transcription factor binding sites23. Furthermore, an in-house small molecular Transformer was trained with over 700 million sequence data24. However, none of these methods was designed for biomolecular interactions.Composition-based methods construct embeddings based on the distribution of single residues or substrings. Autocorrelation-based methods are based on statistical measurement of physicochemical properties of each residue, such as hydrophobicity, hydrophilicity, side-chain mass, polarity, solvent-accessible surface area, etc. Evolution-based methods extract the evolutionary information from large databases by evaluating the occurrence of each residue or the score of that residue being mutated to another type. These methods usually outperform composition-based and autocorrelation-based methods due to their efficient use of a large number of molecular sequences selected by billions of years of natural evolution. Natural language processing (NLP) based methods have been widely used to embed molecules. Among them, autoencoders (AE), long short-term memory (LSTM), and Transformer are most popular. A LSTM model, UniRep, provides enables sequence-based rational protein engineeringK-embedding strategy to study the interactions between multiple (bio)molecules with negligible computational cost. The intramolecular patterns and intermolecular mechanisms can be efficiently captured by our SVS without performing the expensive and time consuming 3D structure-based docking. We showed the cutting-edge performance of SVS on nine prediction tasks, including four binding affinity scoring functions  and five classification datasets for protein–protein interactions (PPIs). Extensive validations indicate that SVS is a general, accurate, robust, and efficient method for the virtual screening of biomolecular interactions.In this work, we proposed a sequence-based visual screening (SVS) of biomolecular interactions that can predict a wide variety of biological interactions at structure-level accuracy without invoking 3D structures. The biological language processing module in SVS consists of multiple NLP models, extracts evolutionary, and contextual information from different biomolecules simultaneously to reconstruct sequence representations for interactive molecules, such as proteins, nucleic acids, and/or small molecules. SVS has a strong generalizability to various types of tasks for biomolecular properties and interactions. In particular, SVS provides the optimal K-embedding module further engineers interactive K-embeddings from individual NLP embeddings to infer their interactive information. Last, the downstream machine learning algorithm module offers the state-of-the-art regression and classification predictions of various biomolecular interactions.Our SVS is a sequence-based framework offering deep learning predictions of biomolecular interactions Fig. . First, 20, protein Transformer (ESM)22, DNA Transformer (DNABERT)23, small molecular Transformer24, and small molecular autoencoder21. We particularly focus on Transformer models due to their state-of-art performance with the consideration of sequence dependencies via an attention mechanism27. Enrich information, such as evolutionary information, 3D structure, and biochemical properties24 can be inferred by Transformers.In the biological language processing module, NLP embeddings are generated for proteins, nucleic acids, and small molecules using their sequence data Fig. b. We empK-embedding module (K-embedding strategies) takes multiple embeddings from interactive molecular components as inputs and integrates them into an optimal deep K-embedding model to decipher biomolecular properties and intermolecular interactions  and gradient boost decision tree (GBDT) for predictive tasks. The hyperparameters of both models are systematically optimized via Bayesian optimization or grid search to accommodate for different sizes of datasets and deep K-embeddings, and different tasks . For each task, the optimal K-embedding strategy is chosen with the above optimization hyperparameters which achieve the best predictive score in accuracy for classification or in the Pearson correlation coefficient for regression.The machine learning module takes the Kd), inhibitor constants (Ki), half maximal inhibitory concentration (IC50), or corresponding Gibbs free energy30. Accurate predictions of molecular binding affinities are not only an important step in modeling biological systems but also a fundamental issue for several practical usages including drug discovery31, molecular engineering, and mutagenesis analysis4.Quantitatively, binding affinity, defined as the strength of molecular interactions, is reflected in the physicochemical terms of dissociation constant  fingerprintRp = 0.832 and RMSE 1.696 kcal mol−1 , NNScore+RDKit38 (Rp: 0.826)  Fig. . This ac22 and UniRep LSTM20 model for protein embedding, and a Transformer24 and an autoencoder21 model for ligand embedding. Our analysis indicates that the small molecular Transformer outperforms the autoencoder. Additionally, Transformer achieves better performance than LSTM model for protein embedding. Further feature analysis is provided from the feature importance analysis from GBDT , which are vital for many biological processes.40.Understanding protein–protein binding affinity is important for many applications, including drug discovery, antibody design, protein engineering, and molecular biology. For example, knowing how antibody-antigen binding affinity is affected by the shape of the antibody, the charge and hydration of the antibody, and the presence of specific binding sites or residues on the antibody, one can engineer antibodies with specific binding properties to neutralize viruses41. This dataset is employed to show the versatile nature of SVS. Sequences of these PPI complexes are extracted and embedded using the Transformer. The PPIs are represented by the stack of their Transformer embeddings in our study. Our SVS model reached the Rp of 0.743 and the RMSE of 1.219 kcal mol−1 via 10-fold cross-validation, and the comparison of predicted value versus the ground truth is shown in Fig. The protein–protein binding affinity can be quantified by Gibbs free energies. The surface plasmon resonance (SPR), isothermal titration calorimetry (ITC), enzyme-linked immunosorbent assay (ELISA), and Western blotting are used to determine protein–protein binding affinities. In our work, we build a SVS model to predict protein–protein binding affinities from protein sequences. We collect and curate a set of 1795 PPI complexes (Datasets) in the PDBbind databaseAnother class of biomolecular interactions is protein–nucleic acid binding which plays important roles in the structure and function of cells, including catalyzing chemical reactions, transporting molecules, signal transduction, transcription, and translation. It is also involved in the regulation of gene expression and in the maintenance of chromosome structure and function. Dysregulation of protein–nucleic acid binding can lead to various diseases and disorders, such as cancer, genetic disorders, and autoimmune diseases. The understanding of the factors, such as hydrogen bonding, dipole, electrostatics, Van der Waals interaction, hydrophobicity, etc. that influence protein–nucleic acid binding affinities can be utilized to design new therapeutic molecules.41. A total of 186 protein–nucleic acid complexes was collected (Datasets). This dataset is chosen to demonstrate that the SVS works well for predicting nucleic acid-involved biomolecular interactions. For this problem, our SVS utilizes a Transformer (ESM) for embedding protein sequences and another Transformer (DNABERT) for embedding nucleic acid sequences. Our model shows good performance with an average Rp/RMSE of 0.669/1.45 kcal mol−1 in a 10-fold cross-validation. Our results are depicted in Fig. In this work, we apply SVS to analyze and predict protein–nucleic acid binding affinity. Due to the lack of existing benchmark datasets, we extract a dataset from the PDBbind databaseHaving demonstrated SVS for protein–ligand, protein–protein, protein–nucleic acid binding predictions, we further consider a problem involving multiple molecular components. The small molecule inhibition of protein–protein interaction prediction (iPPI) involves at least three molecules.42. Therefore, the inhibition of PPIs (iPPIs) is of great interest in drug design and discovery. Recent studies have demonstrated substantial biomedical potential for iPPIs with ligands43.Protein–protein interactions are essential for living organisms. Dysfunction of PPIs can lead to various diseases, including immunodeficiency, autoimmune disorder, allergy, drug addiction, and cancer44. Traditional computational methods for iPPI predictions have various limitations. For example, structure-based approaches have to overcome the complexity of ligand docking caused by the large and dynamic interfaces of PPIs even with stable and reliable experimental complex structures45. Recently, Rodrigues et al.42 have developed an interaction-specific model, called pdCSM-PPI, which utilizes graph-based representations of ligand structures in the framework of ligand-based virtual screening. An important characteristic of their approach is that their models are ligand-based and target-specific: the input of each model is a set of ligands that target one particular PPI. Instead of exploring the hidden mechanism of iPPI, their models rely on a comparison of ligands by assuming that ligands with similar structures exhibit similar behavior, i.e., the similar property principle. Their approach avoids the difficulties of lacking iPPI structures and molecular mechanisms by using target-specific predictions, in which one machine-learning model is built for ligands targeting the same PPI system. Therefore, it cannot be used for the screening of new targets. By contrast, SVS can avoid this difficulty by sequence embedding of PPI targets. As a result, SVS can be directly applied to explore the inhibition of new PPIs without matching targets in existing iPPI datasets.However, iPPI with ligands is challenging in a vast range of investigation phases including target validation, ligand screening, and lead optimizationK-embedding strategies, to predict the half-maximal inhibitor concentration (IC50) of the ligand inhibition of PPI. For each iPPI complex, a small molecular Transformer and a protein Transformer are used to embed one ligand sequence and two protein sequences in our SVS. We tested our model on the dataset considered by Rodrigues et al.42. Our model shows an Rp of 0.766 and RMSE of 0.761 mol/L in the 10-fold cross-validation, while the Rp and RMSE of the earlier pdCSM-PPI model are 0.74 and 0.95 mol/L, respectively. SVS shows a better performance in both Rp and RMSE, illustrating the superiority of the SVS method. The comparison of predictive results versus the ground-truth value of our model can be found in Fig. In this work, we analyzed PPIs and ligands by using various K-embedding strategies via various NLP deep embeddings. We examine three integrating functions in this study, i.e., Stack, Prod, and Diff, to generate K-embedding strategies with the higher-order embedding built from lower-order embeddings. Stack concatenates two biomolecular language processing embeddings from two proteins in a PPI complex into a single embedding vector. This method preserves the complete information provided by the biomolecular language processing module, but the downside is its high dimensionality. Since two proteins in a PPI complex are encoded by two vectors of identical length, 2-embedding can be done via the component-wise operations between these two vectors. We also tested the component-wise product (Prod) and the absolute value of the difference (Diff). These component-wise 2-embedding approaches result in lower-dimensional 2-embeddings for the downstream machine-learning module. The specific formulas corresponding to these three strategies are described in Eqs. K-embeddiHere, we choose 14 kinds of higher-order deep embeddings that take the full consideration of the homogeneity or heterogeneity of NLP models, which are shown in Fig. We further analyze the feature importance of our best schemes from GBDT for features encoding ligands and proteins. Interestingly, features for ligands are substantially more important than that for proteins Fig. . Specifi46. However, the selectivity and strength of PPIs depend on species and the cellular environment. Identifying and studying PPIs can help researchers understand the molecular mechanism of protein functions and how proteins interact with one another within a cell or organism.Protein–protein interactions (PPIs) regulate many biological processes, including signal transduction, immune response, and cellular organization47. Positive samples were defined as interacting protein pairs that are in direct physical contact through intermolecular forces, while negative samples were generated by randomly selecting protein pairs in distinct sub-cellular compartments47. Five PPI datasets with different species including Homo sapiens (HS), Mus musculus (MM), Saccharomyces cerevisiae (SC), Drosophila melanogaster (DM), and Helicobacter pylori (HP) are employed for the benchmark. Here, we explore three K-embedding strategies: Stack, Prod, and Diff.We utilized the SVS method to identify PPIs, where our model classified protein pairs in a given dataset following standard training and test splitting protocols in the literatureR-S plot to visualize feature residue score (R) versus similarity score (S)48. The R-score and S-score of a given sample are calculated by considering the distances of its features with that of inter-class samples and intra-class samples, formulated as Eqs. , though they overlap extensively over all datasets. In addition, from the perspective of the R-S index, Stack and Diff have advantages in two datasets, and Prod has advantages in one dataset.Since the performance of regression models is complicated, we first analyze the performance of interactive features without downstream regression models. In particular, we employed the  as Eqs.  and 11)R-S plot res Fig. . Notablydex (Eq. ). The R-K-embedding strategies by coupling with the identical regression models using fivefold cross-validation  analysis using R-S plot and R-S index48 for classification tasks  or the activity cliff (regression) between the nearest-neighbors of samples . Our analysis shows that there is no substantial correlation between the magnitude index and with modelability index or the predictive index, with the only exception being the PN dataset. This dataset, compared to other datasets of the same task , has the same level of modelability index, but with lower levels of the predictive index. We believe that this is because the magnitude index is too small, and this dataset is tested by cross-validation. Therefore, the randomly selected data leads to a void in the feature space, making it difficult for our model to fit this dataset. In conclusion, SVS can be broadly applied for biomolecular predictions and is robust against data size variation. Moreover, SVS has a strong adaptability to molecules with different sequence compositions. Since proteins were involved in each of our previous numerical experiments, we show the length distribution of protein sequences in each dataset , Drosophila melanogaster (DM), Helicobacter pylori (HP), Homo sapiens (HS), and Mus musculus (MM). This is because samples in the previous datasets are also provided with experimentally determined structures. The availability and reliability of large-size protein structures are subjected to experimental techniques as well as practical considerations, which leads to inevitable systematical bias for structure-based approaches. On the other hand, our SVS models show excellent performances for tasks involving various sequence length distributions. Furthermore, the diversity of the amino acid appearance rate distribution supports the adaptability of our model for tackling different biological tasks, regardless of whether the sequence composition involved has some specificity. In conclusion, our SVS models are robust against sequence length variation and adaptive to biomolecular variability, which reveals the potential of our SVS method as a universal approach for studying biological interactions.We further analyze SVS behaviors on different datasets in terms of magnitudes and modelability Fig.  where thes (Eqs.  and 16)Rp for reset Fig.  as well set Fig. . On averThe success of the SVS is due to the use of powerful NLP models, such as LSTM, autoencoder, and particularly Transformers trained with hundreds of millions of molecules. These models extract the constitutional rules of molecules and biomolecules without resorting to molecular property labels. The proposed SVS will become more powerful as more advanced NLP models become available.To showcase the proposed SVS method, we choose nine representative biomolecular interaction datasets involving four regression datasets for protein–ligand binding, protein–protein binding, nucleic acid binding, and ligand inhibition of protein–protein interactions and five classification datasets for the protein–protein interactions in five biological species. SVS can be applied to the large-scale virtual screening of multiple targets and multiple molecular components without any structural information.52. This issue undermines the ability of the model to learn the underlying pattern of interactions among biomolecules. However, our approach, SVS, avoids data leakage by utilizing NLP-based K-embeddings. By extracting a wide range of hidden information from sequences, including structure, contextual, biochemical, and evolutionary information, our SVS model is less dependent on sequence similarity. Recent studies also demonstrate the effectiveness of NLP-based methods in predicting single or multiple mutations of protein interactions that may completely alter or abandon molecular interactions53, further confirming the low dependence of SVS on sequence similarity.Recently, there has been a growing concern about possible data leakage in machine learning models, where the model may rely too heavily on sequence similarity to make predictions41 for predicting the protein–ligand binding affinity. The dataset used in protein–protein binding affinity was constructed from PDBbind database41. The original PDBbind version 2020 contains binding affinity data of 2852 protein–protein complexes. We selected 1795 samples with only two different sub-chain sequences as shown in Supplementary Table k-mers  before feeding into the Transformer model we used. Thus, one unconventional letter  in a sequence will result in k unknown k-mers. In addition, nucleic acids binding to proteins are generally short in length. Therefore, thus unconventional letters in their sequences may completely destroy the context of k-mer representations. For example, a nucleic acid sequence “ACXTG” will be converted into three 3-mers: “ACX”, “CXT”, and “XTG”. Note that these three 3-mers all contain an “X”, so the biomolecular language processing model will treat them as unknown tokens, and will not be able to read any useful sequence information. In order to guarantee the effectiveness of sequence information, we apply a stricter excluded criterion: 1) exclude those protein–nucleic acid complexes that their sequence numbers do not equal two; 2) exclude those protein–nucleic acid complexes that have unclear labels; 3) exclude those protein–nucleic acid complexes that have abnormal letters  in its nucleic-acid sequences; 4) exclude those protein–nucleic complexes that whose nucleic acid sequence length is fewer than 6. The resulting dataset contains 186 protein–nucleic acid complexes as shown in Supplementary Table Kd), inhibitor constant (Ki), and half maximal inhibitory concentration (IC50) to Gibbs free energy based on the Supplementary Eq. In this study, we used PDBbind-2016 datasetsThe original dataset iPPI dataset focuses on ligands thus the availability of PPI targets is obscure and only 31 targets are provided at the family level while 1694 ligands are available. For each protein family, we selected one protein to represent the whole family . More specific correspondences can be found in Supplementary Table Homo sapiens, 694 protein pairs from Mus musculus, 11,188 protein pairs from Saccharomyces cerevisiae, 2140 protein pairs from Drosophila melanogaster, and 2916 protein pairs from Helicobacter pylori14. Each dataset consists of an equal quantity of interacting pairs and non-interacting pairs. The interacting protein pairs, serving as positive samples, were collected from the public Database of Interacting Proteins (DIPs)54. Samples with fewer than 50 amino acids and more than 40% pairwise sequence identity to one another were excluded to reduce fragments and sequence similarity. Negative samples of each dataset were generated by randomly selecting protein pairs in distinct sub-cellular compartments. Proteins from different sub-cellular compartments usually do not interact with each other, and indeed, this construction assures high confidence in identifying negative samples14.The protein–protein interaction identification involves five benchmark datasets, namely, 2434 proteins pairs from All additional information of datasets used in this study can be found at the Supplementary Note m molecules, denote Sm = {s1, s2, … , sm}(m ≥ 2) the set of the corresponding sequences. The set of associated NLP 1-embeddings is ui) is the embedding dimension, e.g., 512 for the latent space dimension of small molecular Transformer24. Our goal is to construct an optimal m-embedding model , we can define the Prod and Diff as follows:μ and σ are the mean value and standard deviation, andFurthermore, if the lower form strategies are homogenous , i.e., Stack, Prod, and Diff.In this work, the optimization is made over individual NLP embedding , one of the most popular ensemble methods. GBDT has the advantages of robustness against overfitting, insensitiveness to hyperparameters, effectiveness in the performance, possession of interpretability. GBDT was mainly used to implement regression tasks. The hyperparameters including “n_estimators, max_depth, min_sample_split, subsample, max_features” are chosen based on the data size and embedding dimensions of each task. The Supplementary Note We use two set of machine learning algorithms. The first set is the artificial neural networks (ANN), a deep learning algorithm that inspired from the complicated functionality of human brain. For each task, we use Bayesian optimizationf(x) in a space x* is the set of optimal hyperparameters, and f(x) is an evaluating metric for machine learning performance.Bayesian optimization is a popular approach to sequentially optimize hyperparameters of machine learning algorithms. The Bayesian optimization is to maximize a black-box function t data points Xt =  and their values of evaluating matrics Yt = , Gaussian process can model the landscape of f on the entire space Xt, Yt)56. At any novel point x, f(x) is modeled by a Gaussian posterior distribution: μt(x) is mean and σ is the standard deviation of f(x) predicted by Gaussian process regression:k is the kernel function, K is a row vector of kernel evaluations between x and the elements of Xt with K is the kernel matrix with ϵn is the noise term, which is learned from the regression.Given f(x) for a greedy search, or pick the point with the largest standard deviation to gain new knowledge and improve the Gaussian process accuracy on f(x) landscape. The greedy search may largely maximize f(x) in a few iterations and the exploration of uncertain points can benefit for long-term iterations. To balance such a exploitation-exploration trade-off, an acquisition function, α(x), needs to be picked. The decision for the next evaluating point xn is picked such that it maximizes the acquisition function57 for the black-box optimization.In Bayesian optimization, both predicted mean and standard deviation are used for the decision making for the next evaluating data point. One can either pick the point maximize the mean values of R-S scores, R-S index, and modelability index are described below.In addition to the evaluation metrics introduced in the Supplementary Note R-S) plot is a new kind of visualization and analysis method that can be applied to an arbitrary number of classes proposed by Hozumi et al.48. An R-S plot evaluates each sample of given data by two components, the residue and similarity scores. For given dataset xm, ym) are defined as follows:l = ym, Cl = {xm∣ym = l}, and Rm ≤ 1 and 0 ≤ Sm ≤ 1. If a sample is far from other classes, it will have a larger residue score; if a sample is well-clustered, it will have a larger similarity score.Residue-similarity (l-th class can be defined as 48 can be defined:R-score and S-score.The Class residue index (CRI) and class similarity index (CSI) for the cl and MODIreg, respectively, defined as follows51:L represents the number of classes, Ni is the count of samples in the i-th class whose first nearest neighbor is also in the i-th class, Mi is the number of samples in the i-th class, M is the total number of samples, i-th sample, Ki is the count of samples in i-th sample, and yi represents the i-th samples’ normalized label.The modelability index is defined independently for classification tasks and regression tasks, namely MODIWe marked the standard deviation of all our cross-validation results on the Supplementary Table Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary