PlantMWpIDB provides an easy-to-use and efficient interface for search options and generates a summary of basic protein parameters. The database represents a virtual 2D proteome map of plants, and the molecular weight and pI of a protein can be obtained by searching on the name of a protein, a keyword, or by a list of accession numbers. The PlantMWpIDB database also allows one to query protein sequences. The database can be found in the following link https://plantmwpidb.com/. The individual 2D virtual proteome map of the plant kingdom will enable us to understand the proteome diversity between different species. Further, the molecular weight and isoelectric point of individual proteins can enable us to understand their functional significance in different species.The molecular weight and isoelectric point of the proteins are very important parameters that control their subcellular localization and subsequent function. Although the genome sequence data of the plant kingdom improved enormously, the proteomic details have been poorly elaborated. Therefore, we have calculated the molecular weight and isoelectric point of the plant proteins and reported them in this database. A database, PlantMWpIDB, containing protein data from 342 plant proteomes was created to provide information on plant proteomes for hypothesis formulation in basic research and for biotechnological applications. The Molecular weight and isoelectric point ( Proteomes comprise all of the translated products of nucleotide sequences contained in messenger RNA (mRNA)5. The total mRNA of an organism encodes a wide array of proteins that vary in cellular function and homeostasis8. These proteins have diverse molecular weights and isoelectric points (pIs)2. Post-translational modifications that occur can alter the function of a protein and contribute to the ability to target the location of a protein to a specific subcellular compartment11. The shape, size, solubility, and pI of a protein determine its ability to move across different cellular compartments and also determine their function17. Plant cells contain a vast array of proteins with different molecular weight and pI1. The pI indicates the pH at which the net charge of a protein is zero1. The dissociation constant (pKa) of a polypeptide is determined by the presence of seven different charged amino acids; arginine, aspartate, cysteine, glutamate, histidine, tyrosine, and lysine20. The N-terminal NH2- and C-terminal COOH-group of a protein also influences the charge of a polypeptide25. Post-translational modifications, protein–protein interactions, dipole interactions, and other biochemical factors also influence the pI of a protein30. Molecular weight and pI are used to determine the position of a protein sequence in a proteome map and provide useful information to bioinformatics and genome scientists seeking to understand the molecular basis of subcellular localization and function32. Several attempts have been made to create a database of experimentally validated proteins38. It is difficult, however, to experimentally validate the pI and molecular weight of each and every individual protein in a proteome. Previous databases have contained experimentally validated pI data on a maximum of five thousand proteins, which is a relatively low number compared to the number of proteins present in the whole proteome of a species40. Therefore, we constructed a database containing the pI and molecular weight representative of the entire plant kingdom by including protein sequences from the whole proteome of 342 plant species. The PlantMWpIDB database comprises 6.115 million proteins sequences present in the plant kingdom. PlantMWpIDB has a search engine that allows one to explore a virtual 2D map of the global, plant proteome, and search by protein name, keyword, accession number, and protein sequence.Each and every molecule in a cell has its own special characteristics, including the individual proteins that comprise the proteome of an organism41 and the result obtained from the analysis was used to construct the database. PlantMWpIDB provides a user-friendly interface and display of information. It provides a user the ability to search for information about a specific protein of a species and provides a summary of information and statistics on plant proteomes.PlantMWpIDB is a novel database containing proteomic information on 342 plant species. The study contains a total of 13.82 million protein sequences. The molecular weight and isoelectric point of plant proteins were calculated using the Linux-based isoelectric point calculatorI) point of all the proteins. The latter was determined using a protein isoelectric point calculator (http://isoelectric.org/) (IPC Python) within a Linux-based platform41. The IPC program provided the molecular weight and isoelectric point of the individual protein sequences. The results were subsequently processed using Microsoft Excel 2016.Annotated protein sequences of plant species were downloaded from the National Center for Biotechnology Information (NCBI), Phytozome, and Uniprot in fasta/fna format. The downloaded protein files were used to calculate the predicted molecular weight and isoelectric .Text information about each species, including the accession number of each of the proteins, protein names, molecular weight (kDa), and isoelectric point  and CSS. Restful web API (JSON) and JQuery were used to provide efficient access to web pages. Java Script was used to avoid repeated reloading of pages when a user makes a query using text boxes, thus, improving the efficiency of the interface. Server-side programming language (Php) and query processing language (SQL) was used for developing access to the data within the database. Tables are used to display the data, and each table provides options to copy and save the retrieved information, with the ability to export the information in a variety of file types, including Excel, csv, pdf. Print options using JQuery are also available. The design structure of the PlantMWpIDB, web-based database is presented in Fig. search virtual 2D map of the plant proteome.search by protein name keyword(s).search by accession number(s).search by protein sequence.Summary statistics.PlantMWpIDB has five main modules:pI) protein data of each species. The summary statistics contain the proteomic details of proteomes downloaded from NCBI, Phytozome, and Uniprot.Using the module, “search virtual 2D map of the plant proteome”, allows one to find the virtual 2D map which is constructed using a scatter plot program and is based on Molecular mass (KDa) and Isoelectric point  to import the data into MySQL.Database Design: The database was designed in MySQL Server. The structure of the database tables is presented in Supplementary Table The construction of the two modules—“Search Proteins by Protein Name Keyword”, and by “Accession Number”, were completed in two steps:Preprocessing of the data: For the development of this module, Fasta files were converted to a comma-separated format (csv) using a python language script and then imported into the MySQL Server. The csv files were then compressed for efficient memory use.Database Design: Tables were created for the database for the protein sequences of each of the plant species. After creating the database tables for all species, they were imported using a command prompt. The database contains protein sequence tables for each of the 342 plant species.Proteome statistics are efficiently calculated using SQL queries. The summary statistics table of all the proteomes has a sorting option for each column that further facilitates data analysis.The construction of the “search protein sequence” was completed in two steps:Sequence count.Average molecular weight.pI.Average pI proteins.Number of acidic pI proteins.Percentage of acidic pI proteins.Number of basic pI proteins.Percentage of basic The module provides users an overview of the overall statistics of the database. The general statistics provided for the proteome of each species include:https://plantmwpidb.com/) are provided in Table The overall statistics of the PlantMWpIDB database (https://plantmwpidb.com/) is to facilitate the ability to obtain information on a specific protein in a specific species or groups of species. Different methods can be used to extract the proteomic data on a protein within an individual species.Searching a Virtual 2D Map of Plant ProteomespI and molecular weight of proteins downloaded from NCBI and phytozome and (2) pI and molecular weight proteins downloaded from Uniprot.This module provides the facility to search or browse a virtual 2D map of the proteome of a given plant species. The interface is shown in Fig. This interface has a list box with species names, along with a keyword search option, instructions for using the module, and a map window for displaying the virtual 2D map of the proteome of a selected species. By default, it will display the virtual 2D map of the proteome of the first species on the list.pI and molecular weight of individual species. Also, users can download the 2D proteome map of a selected species.Users can download the raw data of The main purpose of PlantMWpIDB (pI) within a species or by a keyword related to the protein name .pI) within a specific species by using a single accession number or a list of accession numbers. The user can also save the retrieved information for subsequent use. The interface has a list box of species names, along with a keyword search option. A text box for entering a single or list of an accession number(s), and instructions for using this module are also provided and there is a window that displays the search results within a selected species. By default, it will display two results within the first two accession numbers of the first species in the list. The module has options that allow the user to copy, print, or export the retrieved results as an Excel file, pdf, or csv file.This provides the ability to search the database using the accession number, protein name, molecular mass, or isoelectric point  if incomplete or incorrect data are entered into the search options as follows:pI and Proteome-pI 2.0 is a database having data of isoelectric point of several proteomes42. Recently, Kozlowski (2022) reported the Proteome pI-2.0: proteome isoelectric point database where the author reported the molecular weight, isoelectric point, and enzymatic digestion details of 61.329 million protein sequences from 20,115 proteomes42. They were from eukaryote, bacteria, archaea, and viruses42. From the mentioned eukaryotic species, Kozlowski (2022) reported the molecular weight and isoelectric point of more than 250 plant species. However, a lack of proper classification makes it difficult to find out the plants or animal species from a large number of species42. Therefore, we constructed PlantMWpIDB and reported here. Our study provided the proteomic data of 342 plant species from the proteomic sequences of NCBI, Phytozome, and Uniprot. The plant proteomic database (PPDB) provides proteomic data on Arabidopsis and maize4. A database on moonlighting plant proteins (PlantMP) contains protein functions searchable by UniProt IDs and names, canonical and moonlighting functions, or gene ontology numbers43. The Arabidopsis Nucleolar Protein Database (AtNoPdb) provides information on 217 proteins found in Arabidopsis44. In general, these databases provide information on a limited number of plant species and/or a limited number of proteins in a sporadic manner.Several databases are available on the web for use by genomic researchers that provide different types of information on either a small or large scale. Proteome-42. For this, a user need to download whole data file to find the molecular weight and isoelectric point of a single protein. Also, Proteome-pI 2.0 has provided the isoelectric point of protein sequences using 21 different parameters including Bjellqvist, DTASelect, Dawson,EMBOSS, Grimsley, IPC2_peptide, IPC2_protein, IPC_peptide, IPC_protein, Lehninger, Nozaki, Patrickios, ProMoST, Rodwell, Sillero, Solomon, Thurlkill, Toseland, Wikipedia, IPC2.protein.svr19, and IPC2.peptide.svr1942. It will be difficult for the user to decide which isoelectric point is correct for a particular protein sequence. However, our study revealed isoelectric point obtained using IPC_protein best suit with the native isoelectric point of the protein. Therefore, we have constructed our database using the feature of IPC_protein.Although, Proteome-pI 2.0 provided the proteomic data of more than 250 plant species, a lack of specific classification making it difficult to identify which of the data is plant, animal, or fungi species. Therefore, we have constructed PlantMWpIDB based on the 342 species of the plant kingdom to provide information on the molecular weight and isoelectric point of 13.82 million protein sequences. In addition, the Proteome-pI 2.0 do not have any option to search for the molecular weight and isoelectric point of any particular protein using the “keyword”, “accession number”, or protein sequencepH at which the net charge of the protein is zero, and represents an important analytical and molecular parameter1. The pI of a protein is often used in biochemistry to determine differential expression of a protein based on 2D-PAGE gel electrophoresis, x-ray protein crystallography, and capillary isoelectric focusing47. The global virtual 2D map of the plant kingdom exhibited a trimodal distribution within the global proteome, with acidic pI proteins dominating over the basic pI proteins1. This suggests that the pI of the majority of plant proteins are close to the physiological pH of a cell. In our previous study1, we reported that the molecular weight in the global plant proteome ranged from 0.54 to 2236.8 kDa and the isoelectric point ranged from 1.99 to 13.961. The acidic pI proteins of monocot plants were closely correlated with the acidic pI proteins of bryophytes, while they were distantly related to algae and eudicot plants1. The amino acid composition of the global plant proteome was observed to be lineage specific1. The amino acid composition of algae, monocot, and eudicot proteome form independent clusters. Leu, Ser, Ile, Lys, and Gln are amino acids that are highly abundant in the plant proteome, while Tyr, Trp, Cys, His, and Met are low abundant amino acids1.The isoelectric point of a protein represents the The PlantMWpIDB database contains the molecular  weight and isoelectric point of the plant proteomes of 342 species, including algae, bryophytes, pteridophytes, gymnosperms, monocots, and eudicots. Future versions of the PlantMWpIDB database will include protein molecular modeling module to decipher the 3-D structure of each protein. Future versions will also include target site prediction for metacaspases, palmitoylation, myristiylation, and methylation for each protein. Collectively, this additional information will provide important information to researchers investigating protein modification, function, structure, and evolution.PlantMWpIDB provides researchers with the ability to retrieve information on the molecular mass and pI of proteins within the proteome of 342 plant species, ranging from algae to eudicots. PlantMWpIDB is the most comprehensive database available on plant proteomes and contains several modules for searching, retrieving, and saving data. Future versions of PlantMWpIDB will make the database even more powerful for obtaining information on the proteome of the entire plant kingdom.Supplementary Information.


Homolog detection, finding similar proteins to an unknown protein, is usually the first step in understanding the role and function of that protein. However, if the identity of protein sequences between query and target proteins is low (< 30%), traditional tools struggle to distinguish a correct match from a random one, failing to identify important similarities. We have used protein representations from deep learning language models to solve this problem. Reducing the size of these representations significantly improved homolog detection capabilities. Our tool can find putative homologs for more than 93% of human proteins that were not able to assign a function as of March 2022. https://mesihk.github.io/prost.There are several hundred million protein sequences, but the relationships among them are not fully available from existing homolog detection methods. There is an essential need for an improved method to push homolog detection to lower levels of sequence identity. The method used here relies on a language model to represent proteins numerically in a matrix (an embedding) and uses discrete cosine transforms to compress the data to extract the most essential part, significantly reducing the data size. This PRotein Ortholog Search Tool (PROST) is significantly faster with linear runtimes, and most importantly, computes the distances between pairs of protein sequences to yield homologs at significantly lower levels of sequence identity than previously. The extent of allosteric effects in proteins points out the importance of global aspects of structure and sequence. PROST excels at global homology detection but not at detecting local homologs. Results are validated by strong similarities between the corresponding pairs of structures. The number of remote homologs detected increased significantly and pushes the effective sequence matches more deeply into the twilight zone. Human protein sequences presently having no assigned function now find significant numbers of putative homologs for 93% of cases and structurally verified assigned functions for 76.4% of these cases. The data compression enables massive searches for homologs with short search times while yielding significant gains in the numbers of remote homologs detected. The method is sufficiently efficient to permit whole-genome/proteome comparisons. The PROST web server is accessible at  The revolution in genome sequencing has rapidly increased the volume of gene/protein sequences. However, the relationships among sequences are incomplete but critical to understanding function, evolution, and synthetic biology. Extracting meaningful relationships from the vast amount of sequence data is an important challenge. Here, significant progress is made with a method that relates protein sequences to one another for large numbers of cases previously unknown. Finding similar sequences is usually needed to understand any new sequence, and such homolog detection is one of the most important and most frequently used kinds of computations throughout biology, as evidenced by the heavy use of BLAST and its large number of citations.O(n2) time complexity. To speed up the search times, successful heuristic methods were developed such as BLAST  of the input amino acid sequences. These representations retain biochemical, biophysical, and evolutionary information about the input sequences. Embeddings encode remote homology, protein family information, secondary structures, tertiary contacts, and mutational effects. When compared with current predictive bioinformatics tools, neural network-generated embeddings usually can either outperform state-of-the-art methods or reach a similar level of success .The present work is based on the powerful neural network embeddings of protein sequences from Rives et al.  and RaimThe first step is obtaining the protein representation with the ESM1b protein language model, which is a transformer-based neural network model inspired by the recent successes of transformers in the natural language processing field. The ESM1b model is trained by masking some of the residues from the input sequence and training the model to predict the masked residues. Hence, the model must learn the relationships among the residues. It is trained on the UniParc database from UniProt  with 250SI Appendix, Fig. S2). The algorithm is highly parallelizable and uses vector operations. A single protein is represented by just 475 bytes. The detailed architecture of PROST is shown in SI Appendix for some further details, particularly for the optimization of the compression parameters). We note that the compression parameters of PROST are chosen to minimize the memory requirements and to perform better than state-of-the-art methods. However, with a larger more complete representation, it is possible to achieve even better results. (SI Appendix shows those results as PROST-L). PROST uses a fixed-length (variable-ratio quantization) representation of proteins. Combined with simple distance calculations, this allows the fastest possible searches. However, this feature undermines the ability to detect local homology. Local fragments are not usually detected well by PROST.Direct cosine transform (DCT) is widely used for lossy compression resembling those used in the JPEG format. It has been used in protein–protein interaction prediction , secondaIn a traditional bioinformatics workflow, a query protein is searched against a large protein database. For this query protein, PROST will calculate a distance for each target protein in the database, resulting in a distribution of distances. This distribution is usually similar to a normal distribution. Putative homologs to the target protein will have better scores and be outliers in such a distribution. An example distribution is shown in In the case of a search with a member of a big protein family, putative homologs may not be outliers. They might be the majority of proteins in the database. In such a case, probable homologs may be first filtered out before e-value calculation. This can be done using the PROST distance threshold that is found by maximizing the F1 score at the max50 dataset. Distance values smaller than this threshold will not be used in the calculation of mean and SD. This way, the normality of distance distribution of nonhomologs will be preserved even with the high number of putative homologs. Lastly, e-values can be calculated for the whole database using the mean and SD found for nonhomologs.C. elegans. NCBI-BLAST and PHMMER do not find any human homologous sequence for HPO30 in the SwissProt database. However, it is known that this protein belongs to the Pfam claudin-like domain (PF07062). When searched against SwissProt, PROST quickly identifies hundreds of putative homologs to HPO30 that have around 20% sequence identity. The PROST distance distribution of SwissProt for this protein is shown in HPO30 is used as an example to demonstrate the PROST pipeline. It is an uncharacterized protein of In the evaluation of our method, we use a benchmark set  which coSI Appendix). PROST performs significantly better than other tools. The largest extent of improvement is a significant 9.8% gain, e.g., NCBI-BLAST compared with PROST for the SUPERFAMILY dataset.The evaluation of the PROST tool has been carried out against the most commonly used alignment-based homolog detection tools. The Pfam, Gene3D, and SUPERFAMILY datasets are used for testing. The AUC1000 score indicating the quality of the ranking is used to judge performance. We have compared against CSBLAST , PHMMER SI Appendix, Table S2 shows the AUC and AUC1000 metrics for all selected commonly used tools. SI Appendix, Fig. S3 shows the distribution of the homology predictions based on protein pair size differences. Overall, PROST has 1,239 incorrect predictions out of 31,896 pairs, which is a remarkably high level for correct predictions. The number of false negatives is 207, while the number of false positives is 1,032. Having lower false negatives and higher false positives may be desirable to a certain extent since a second step could reevaluate the false positives as follows: For example, a search with the SwissProt database requires over half a million comparisons. PROST can be used first to reduce the search space from millions to hundreds, and then, computationally intensive structure predictions and structural comparisons could be used to reduce some of the false positives if their structures do not match well.SI Appendix, Table S3 shows the results for the nomax50 benchmarking dataset. SI Appendix, Fig. S4 shows the ROC curves for the methods we tested in the nomax50 dataset. We found that PROST performs mediocre for this benchmark, which signifies the global alignment nature of PROST. However, PROST-L, the version with less reduction in embedding information, has a good performance comparable to state of the art methods. PHHMER performs best on the nomax50 benchmark. It is followed by CSBLAST and PROST-L. PROST is similar to BLAST in performance, and the method that uses the mean of only the last layer (ESM1bL34M) performs the worst. To exemplify the global alignment nature of PROST, we have searched human zinc finger protein 268 (Q14587) with PROST and BLAST on the SwissProt database. BLAST found 1605 putative homologs, while PROST found only 560 putative homologs. This signifies the global alignment nature of PROST. The results are presented in SI Appendix, Dataset 1.Every PROST search begins with the embedding process and quantizing the input sequences, i.e., the preprocessing. PROST can create a database of 100 sequences randomly sampled from SwissProt within 13.15 s ± 60 ms and 1,000 sequences within 71.89 s ± 347.8 ms using one GPU. PROST needs to create a database only once, and then, the database can be used over and over. Searching a database of 100 sequences against itself takes 519 ms ± 6.3 ms, and 1,000 sequences against itself takes 976 ms ± 10.6 ms. The nonlinear time difference is caused by differing startup overheads and disk accession delays. Embedding and searching a sequence in the preprocessed SwissProt PROST database takes only 1.02 s ± 7.3 ms. These tests have been carried out ten times to obtain the error estimates. These were a Nvidia A100 40GB GPU and a single-core AMD EPYC 7543 processor. Database creation runs faster on GPUs. However, it can also run on a CPUS. Search operations use only one core CPU.The method of homolog detection in PROST is extremely different from traditional sequence or PSSM-based profile alignment tools. We have investigated the effects of this difference for its ability to identify homologs as well as on the performance by using different metrics. In a recently published paper , it was S. cerevisiae genes, where there are a total of 325 lineage-specific genes, 155 of them with at least three homologs in the lineage so that the evolutionary rate can be calculated . For this protein, BLAST and PHMMER fail to identify homologous sequences other than the mouse C20orf204 variant, while PROST identifies 731 such sequences. It was recently shown that C20orf204 is associated with hepatocellular carcinoma (HCC) . This prOf note, 51% of the whole human proteome is considered to be intrinsically disordered proteins or proteins with disordered regions . AccordiP-value of 2.91e-03 but have a TM-Scoreof 0.47.PROST can identify hits with very low sequence identity, and this represents major progress in putative homolog detection. An example case with only 16.4% sequence identity is shown in Here, we have presented PROST, an exact search-based, extremely fast and accurate homolog detection tool that utilizes a protein language model. It is a significantly different method from other search methods in its alignment-free and sequence similarity-free approach that relies upon reduced protein representations, enabling it to perform huge numbers of comparisons within a short time. PROST outperforms all commonly used traditional alignment-based tools on benchmarks taken from three databases—Pfam, Gene3d, and SUPERFAMILY. PROST efficiently uses memory and can keep hundreds of millions of protein representations in the memory of a server-grade computer. PROST is not limited to the usual limits of sequence similarity-based tools. It can find putative remote homologs for proteins that have a high evolution rate that makes them completely indistinguishable from noise with traditional alignment-based tools, and these results push sequence alignment well below the usual twilight zone. This is exemplified by human proteins that presently do not have any annotations. PROST finds hits with highly similar structures at lower levels of sequence identity, which act to validate the sequence homologies. The recent advances in structure predictions , 43, 44 Appendix 01 (PDF)Click here for additional data file.Dataset 01 (XLSX)Click here for additional data file.


Protein–protein interaction (PPI) is vital for life processes, disease treatment, and drug discovery. The computational prediction of PPI is relatively inexpensive and efficient when compared to traditional wet-lab experiments. Given a new protein, one may wish to find whether the protein has any PPI relationship with other existing proteins. Current computational PPI prediction methods usually compare the new protein to existing proteins one by one in a pairwise manner. This is time consuming.M proteins can be transformed into a much more simpler problem: to find a number inside a sorted array of length M. This pre-screening process narrows down the search to a much smaller set of candidate proteins for further confirmation. As a final step, DHL-PPI uses the Hamming distance to verify the final PPI relationship.In this work, we propose a more efficient model, called deep hash learning protein-and-protein interaction (DHL-PPI), to predict all-against-all PPI relationships in a database of proteins. First, DHL-PPI encodes a protein sequence into a binary hash code based on deep features extracted from the protein sequences using deep learning techniques. This encoding scheme enables us to turn the PPI discrimination problem into a much simpler searching problem. The binary hash code for a protein sequence can be regarded as a number. Thus, in the pre-screening stage of DHL-PPI, the string matching problem of comparing a protein sequence against a database with M proteins. With the proposed approach, a protein database can be preprocessed and stored for later search using the proposed encoding scheme. This can provide a more efficient way to cope with the rapidly increasing volume of protein datasets.The experimental results confirmed that DHL-PPI is feasible and effective. Using a dataset with strictly negative PPI examples of four species, DHL-PPI is shown to be superior or competitive when compared to the other state-of-the-art methods in terms of precision, recall or F1 score. Furthermore, in the prediction stage, the proposed DHL-PPI reduced the time complexity from  Protein–Protein Interaction (PPI) in biological cells is vital for molecular processes and biochemical reactions, such as intracellular communications, signal transduction and gene regulation. Hence,the identification of PPI is important in life process research, disease diagnosis and treatment, and in drug development –3.To identify PPI using wet-lab experiments is costly and time consuming. Though current high throughput methods have significantly improved the efficiency and cost, for instance using yeast 2-hybrid (Y2H) , mass spThe protein sequence is a key element in algorithmic approaches to PPI prediction. The existing sequence-based PPI recognition methods can be divided into three basic groups: co-occurrence based, pattern matching based, and machine learning based methods.The methods based on co-occurrence  judge thPattern matching based methods search for potential PPI by establishing certain pattern rules. Fundal  proposedKernel based methods are often used to reduce the problems of predefining pattern rules. Haussler et al.  presentek-nearest neighbors (KNN) . It combines with the hash constraint loss to minimize the quantization loss . It combThe DHL-PPI model has three types of losses, namely, discrimination error loss  is close to 0 or 1, its derivative tends to 0. The design objective in DHL-PPI is to minimize the total error during training. Ideally, the output of f(z) should be close to 0 or 1. This would lead to the disappearance of the gradient. Therefore, in this study, we set the weights as follows: N is the dimension of the hash code, set at The sigmoid function is given as Here, we describe our approach to PPI prediction . The state-of-the-art deep learning methods usually contain two parts, one is encoding and the other is discriminating. The proposed DHL-PPI only utilizes the encoding part and does not use the traditional discrimination stage in the deep learning model. That is, the deep learning techniques in DHL-PPI are only used to generate the required binary hash codes (query code and ontic code) for the given protein sequence. In the prediction phase, DHL-PPI first conducts a pre-screening process to narrow down the whole database. And then it compares a query code to an ontic code by calculating the Hamming distance between them.M number of protein sequences) in a database is turned into a problem of finding a binary number (an integer) inside an array of size M. This pre-screening process significantly narrows down the subset of the database that need to be further analyzed to verify the hypothesised PPIs.In the pre-screening stage, the proposed DHL-PPI searches a binarized hash query code against all binarized hash ontic codes in a database. Because they are all binarized codes, these digital bits can be regarded as a binary number. The problem of searching a protein sequence against all proteins  with any other protein inside the database D. The intuitive way is to compare a given protein sequence P with all proteins inside the database D one by one which requires M number of comparisons (the number of proteins inside D). If one wishes to determine whether there is any interaction between every pair of proteins in the database D, , we need to perform M proteins in the database D will need to be compared against the other Given a protein sequence P is used to search on the binary ontic codes by calculating the Hamming distance. Using the proposed DHL-PPI prediction method, the comparison time is reduced significantly. In the proposed DHL-PPI prediction method, this can be done in In the pre-screening stage of PPI prediction, we convert the sequence comparison problem into a much simpler problem of finding a number in an array. Next, a binary query code of a protein d, which determines whether there is a relationship between two proteins. The second one is the ontic codes of a protein database, D, which are generated in the previous DHL model. The third is the query code of a protein database, Q, which is also generated in the previous DHL model. The output parameter PPIset contains the predicted PPI relationships.Here, we introduce the PPI prediction algorithms. Algorithm  1 is the overall process of PPI prediction. There are three input parameters in the algorithm. One is the Hamming distance threshold, PPIset is initialized to empty. And M is assigned to the number of proteins in the query codes of database Q. In Line 2, the indexes of ontic codes of database D are built. There are total of d. Please see more detailed information in the following indexing step. The for loop from Line 3 to Line 7 checks each protein’s query code in Q. In Line 4, it takes a query code Q. In Line 5, it calls the binary code searching algorithm (BCSA) to obtain the predicted results T. In Line 6, the relationship of query q and the predicted results T is added into the output set PPIset. After all members inside database Q are checked throughout, the final result is returned in Line 8.Let’s examine the algorithm line by line. In Line 1, set D. The other is the binary code searching algorithm (BCSA) called in Line 5. Both of these are discussed in the following subsection. Before that, we first introduce the Pigeonhole principle, a key concept used in this work.Two key steps in Algorithm  1 require further explanation. One is in Line 2, the process to build indexes for an ontic codes of database N, if the Hamming distance between them is less than or equal to d, after dividing these two sequences into 2d fragments evenly, inside these 2d fragments , there are at most d number of fragments which are different, that is, the remaining d fragments would have the same exact sequences.Given two sequences each with length of d fragments, if one finds that d fragments are the same, then we can claim that the distance between these compared sequences is less than or equal to d. To choose d parts from 2d, there are d from 2d fragments will be: The Pigeohole principle is utilized in the pre-screening step. According to the Pigeonhole principle, in 2d. For example, in this study, if the distance between two sequences is less than or equal to 4, then at least four out of the eight parts have the same exact bit patterns, that is, at least half of the 64 bits (32 bits) in the hash codes are the same. These 32 bits can be regarded as numbers. In this way, we can turn the sequence comparison problem to a much simpler problem of searching a number inside an array. This operation greatly improves the pre-screening process and narrows down the candidate set.In the pre-screening step, the algorithm searches all these The goal of indexing process is to build indexes which are binary numbers, and then turn sequence comparison problem into a much simpler problem of finding a number inside an array.d fragments. And then, d fragments are randomly selected from these 2d fragments. The exact same d fragments are chosen from all ontic codes in a database. Finally, these d fragments are connected together serving as an index code for a protein.The indexing process are conducted as follows. First, the protein binarized hash codes are divided into 2d fragments from 2d fragments together is still a binary number. There are a total of Remembering that, the ontic binary hash codes and query binary hash codes are binary numbers, an index by concatenating random M proteins, it takes M in order. Totally, the time requirement is For a database containing d, where D here; (3) the corresponding index of ontic hash codes, called idx, which is constructed during the indexing process; (4) a query hash code of the queried protein, called q.The binary hash code searching algorithm (BCSA) is a nearest-neighbor filtering algorithm based on the binarized bash code. This is shown in Algorithm 2. There are four input parameters in the algorithm: (1) the distance threshold, S is set to empty, the output PPI relationship set T is also set to empty. In Line 2, the query hash codes q are divided into 2d fragments evenly. From Line 3 to Line 8, there is a for loop which goes through each possible combinations .In Algorithm 2, in Line 1, the candidate set S contains all proteins who at least have one of their Di equal to qi. Note that the number of proteins in Set S is much less than the number of proteins in the original dataset D. From Line 9 to Line 13, the algorithm further uses Hamming distance to compare the query code q to each ontic code whose protein ID is in the Candidate Set S. The protein who has the distance which is less than and equal to the threshold d  constant time. In Line 2, dividing a query code q into 2d fragments evenly requires time d parts out of 2d fragments, there are d parts out of 2d parts from ontic codes D and the query code q respectively. The time complexity for Line 4 and Line 5 are O(1). In Line 6, it uses the binary hash code for qi (a number) to search against a sorted array M is the number of protein sequences inside database D. Thus, using binary search, all the sequences in the database D whose S (Line 7). Let the number of search results be denoted S is verified by using Hamming distance. Pairs of sequences with distances less than or equal to the threshold d are then added to the final output set T. The time complexity for Line 9 to Line 13 is Let’s first check Algorithm 2. In Line 1, the Candidate Set Together, the time complexity for Algorithm 2 is M proteins in the database D, M]. The ontic code and the query code are binary sequences each of length 64. Then, in each index search, there are D with M proteins, since the hash codes are generally balanced, the probability of an exact match is : Let’s consider the average situation for D is Q which has M protein sequences. Both Line 4 and Line 6 run in O(1). Line 5 calls Algorithm 2 which runs in d is set to 4 and M is the number of proteins inside a database. This is a big improvement over the state-of-the-art Now, let’s consider the time complexity of Algorithm 1. Line 1 runs in O(1). In Line 2, the time complexity of constructing indexes for database


Natural products from plants exert a promising potential to act as antioxidants, antimicrobials, anti-inflammatory, and anticarcinogenic agents. Xanthohumol, a natural compound from hops, is indeed known for its anticarcinogenic properties. Xanthohumol is converted into three metabolites: isoxanthohumol  as well as 8- and 6-prenylnaringenin . An inverse molecular docking approach was applied to xanthohumol and its three metabolites to discern their potential protein targets. The aim of our study was to disclose the potential protein targets of xanthohumol and its metabolites in order to expound on the potential anticarcinogenic mechanisms of xanthohumol based on the found target proteins. The investigated compounds were docked into the predicted binding sites of all human protein structures from the Protein Data Bank, and the best docking poses were examined. Top scoring human protein targets with successfully docked compounds were identified, and their experimental connection with the anticarcinogenic function or cancer was investigated. The obtained results were carefully checked against the existing experimental findings from the scientific literature as well as further validated using retrospective metrics. More than half of the human protein targets of xanthohumol with the highest docking scores have already been connected with the anticarcinogenic function, and four of them  also have a known experimental correlation with xanthohumol. Another important protein target is acyl-protein thioesterase 2, to which xanthohumol, isoxanthohumol, and 6-prenylnaringenin were successfully docked with the lowest docking scores. Moreover, the results for the metabolites show that their most promising protein targets are connected with the anticarcinogenic function as well. We firmly believe that our study can help to elucidate the anticarcinogenic mechanisms of xanthohumol and its metabolites as after consumption, all four compounds can be simultaneously present in the organism. Humulus lupulus L.). As hops are most commonly used in brewing industries , beer represents the main dietary source of xanthohumol [Natural compounds are known to possess antioxidative, antimicrobial, anti-inflammatory, and anticarcinogenic properties and are generally considered to be pharmacologically safe . An everthohumol ,5. Xanththohumol . Likewisthohumol ,7.It has already been reported that xanthohumol can inhibit the growth of breast, colon, hepatocellular, ovarian, pancreatic, and prostate cancer cells as well as leukemia cells ,3. XanthCoronaviridae family [Inverse molecular docking represents a novel, state-of-the-art approach used in drug discovery  and druge family . In thisAn inverse molecular docking approach  was applThe CANDOCK algorithm  applies The inverse molecular docking of xanthohumol and its metabolites into the prepared human protein database was performed according to the following steps:Step 1. Xanthohumol and its three metabolites—isoxanthohumol, 8-prenylnaringenin, and 6-prenylnaringenin—were used as ligands. The 3D structure of xanthohumol was obtained from the ZINC15 database , and theStep 2. The ProBiS-Dock database was prepared, as described by Konc et al. .Step 3. The prepared molecules and database were used as the inputs for the CANDOCK algorithm. The calculations were caried out on an in-house server.Step 4. We took into consideration all the ligand poses for each target protein, and the one with the lowest docking score was considered the best. The protein–ligand complexes were obtained and ranked from these conformations, and a ranked list of protein targets was prepared. Step 5. The best-ranking proteins from the list were selected based on the assumption of normal distribution and the 99.7% confidence criteria . ProteinStep 6. For these protein targets, the known connection with cancer was investigated. Numerous novel potential protein targets, into which the four investigated molecules were successfully docked, were identified. y-axis) and the false  positive fraction is shown. In our case, the TPF represents the experimentally confirmed protein targets of xanthohumol from the ChEMBL database [y-axis) versus the entire data set on a logarithmic scale (x-axis). By applying the PC, a score threshold for potential protein targets to be tested experimentally can then be determined [The validation was caried out using receiver operating characteristics (ROC) , enrichmdatabase  with thedatabase . Enrichmdatabase  and PC [database  allow thtermined . The eartermined , the Boltermined , and thetermined . TherefoThe standardized total gain (TG) is also calculated using the PC and represents the discrimination of experimentally confirmed proteins imputable to the variation of the docking scores over the entire data set. ROC AUC values over 0.5 combined with TG over 0.25 signify that docking score variations are relevant in the discrimination of experimentally confirmed protein targets . To coveThe calculated conformations of xanthohumol, docked in human protein binding sites, were arranged according to their docking scores. The docking scores were assumed to be normally distributed, and by using the 99.7% confidence interval, we were able to identify 26 top-scoring xanthohumol protein targets, with a docking score cut-off of −55.56 arbitrary units. The predicted docking scores, protein functions, and reported experimental correlation of identified proteins with anticarcinogenic functions are collected in Out of 26 identified potential protein targets of xanthohumol (by using a threshold of 99.7%), 16 are known to have a connection with cancer. From these, four proteins also have an experimental correlation with xanthohumol. Based on these results, the identified proteins provide a promising opportunity to create a novel design process for chemopreventive compounds. In the following subsections, we will describe in detail the protein targets of xanthohumol that are, to the best of our knowledge, connected to cancer.We identified the N-lysine methyltransferase SMYD2, whose function is to suppress cell proliferation and directly regulate p53 function . The tumAcyl-protein thioesterases (APT) are members of a protein group involved in depalmitoylation processes. The palmitoylation/depalmitoylation cycle takes place by moving proteins between the plasma membrane and the Golgi apparatus. This dynamic cycle is tightly regulated by palmitoyl transferases  and acyl protein thioesterases 1 and 2  . Human APT-1 and APT-2 represent major components in the control of the palmitoylation dynamic cycle of Ras oncogenes . For theAnother protein identified was glutamate carboxypeptidase III (GCPIII), a metalloenzyme that belongs to the transferrin receptor⁄glutamate carboxypeptidase II (GCPII) superfamily . GCPIII Protein arginine methyltransferase 6 (PRMT6) is a nuclear enzyme whose function is to methylate arginine residues on histones and transcription factors. Similar to other PRMTs, PRMT6 is overexpressed in several cancer types ,38,39. DThe main function of identified matrix metalloproteinase-9 (MMP-9) is its proteolytic activity in the extracellular environment. MMP-9 plays a role in basement membrane degradation since basement membrane contains collagens. During tumor development, basement membrane destruction is usually an essential step that supports tumor invasion and metastases ,41. CarcThe mitogen-activated protein kinase (MAPK) pathway controls the growth and survival of a broad spectrum of human tumors . As a coPoly (ADP-ribose) polymerase (PARP) represents a family of proteins involved in several cellular processes such as DNA repair, genomic stability, and programmed cell death. PARP is an enzyme included in the post-translational modifications of proteins as a response to numerous endogenous and environmental genotoxic agents . The dirThe family of RAB6 proteins consists of three different isoforms: RAB6A, RAB6A, and RAB6B. RAB6B is predominantly expressed in the brain. Wanschers et al.  concludeThe SET and MYND domain containing protein 3 (SMYD3) represents a novel histone lysine methyltransferase. The main function of SMYD3 is the regulation of chromatin during the development of myocardial and skeletal muscles. Lysine methylation plays a vital role in histone modification. The consequences of the downregulation of lysine methyltransferases have been observed in multiple human cancer types . SMYD3 also plays a crucial role in the transcriptional regulation of carcinogenesis and the development of human cancers. Moreover, the SMYD3 has been suggested as a potential prognostic marker .Matrix metalloproteinase (MMP)-14 represents a membrane-bound MMP that plays a critical role in conferring cells with the ability to remodel and penetrate the extracellular matrix . High MMRibosomal protein S6 kinase alpha 5 (RPS6KA5) possesses several biochemical functions, for example, ATP binding, histone kinase activity (H3-S10 specific), and magnesium ion binding. RPS6KA5 siRNAs were co-delivered with anti-apoptotic Mcl-1 siRNAs, and RPS6KA5 was found to be one of the appropriate candidates for simultaneous silencing with Mcl-1. In both wild type and resistant xenografts in nude mice, the double silencing of Mcl-1/RPS6KA5 also led to an improved inhibition of tumor growth by exhaustion from chemotherapy .Aldo-keto reductase family 1, member C1 (AKR1C1) is involved in maintaining steroid hormone homeostasis, prostaglandin metabolism, and the metabolic activation of polycyclic aromatic hydrocarbons . MicroarIt is probable that dimethyladenosine transferase (DIMT) is involved in the pre-rRNA procedure, which leads to the production of the small subunit rRNA. The direct connection of DIMT with cancer diseases has not been reported yet, but a recent article about DIMT1 shows that it is closely associated with the proliferation, apoptosis, invasion, and migration to tumor cells. Liu et al.  revealedGelatinase A (together with gelatinase B) is a member of the gelatin-binding structure group and is a part of the matrix metalloproteinases (MMPs). Gelatinase A is therefore also known as MMP-2 and gelatinase B as MMP-9. These two gelatinases play an important role in the development and progression of colorectal cancer . GelatinDihydrofolate reductase (DHFR) converts dihydrofolate into tetrahydrofolate and plays a crucial role in cell metabolism and cellular growth . Kalogri2. Barak et al. [Peroxisome proliferator-activated receptor delta (PPARδ) is involved in differentiation, lipid accumulation, directional sensing, polarization, and migration in keratinocytes. Gupta et al.  concludek et al.  also revA detailed analysis of the interactions of the docked xanthohumol within the binding pocket of matrix metalloproteinase 9 (PDB ID 4jij) was also performed  to reveaThe calculated conformations of isoxanthohumol, 8-prenylnaringenin, and 6-prenylnaringenin, docked into human protein binding sites, were ordered according to their docking scores. The docking scores were assumed to be normally distributed , with thA total of 22 novel protein targets of isoxanthohumol, 8-prenylnaringenin, and 6-prenylnaringenin have known connections to cancer. From these, we would like to stress those proteins that appear to be some of the best targets for two or more of the investigated xanthohumol metabolites. Xanthohumol, isoxanthohumol, and 6-prenylnaringenin were successfully docked to acyl-protein thioesterase 2. Isoxanthohumol and 6-prenylnaringenin were successfully docked to folate receptor beta. Isoxanthohumol and 8-prenylnaringenin were successfully docked to dihydrofolate reductase. Xanthohumol was also successfully docked to the dihydrofolate reductase (PDB ID: 3gz9A), with the proteins with the PDB codes 3eigA and 3gz9A having a sequence similarity of 98.39%. The isoxanthohumol and 6-prenylnaringenin were successfully docked to 72 kDa type IV collagenase. Finally, xanthohumol and isoxanthohumol were successfully docked to Ras-related protein Rab-7. Based on these results, the identified proteins, where more than a single xanthohumol was docked, provide the most promising opportunity for the development of novel chemopreventive compounds.A detailed analysis of the interactions of xanthohumol and two of its metabolites (isoxanthohumol and 6-prenylnaringenin) within the same binding pocket of acyl-protein thioesterase 2 was also performed . Their b50 values for the xanthohumol binding were <10 µM. Our protocol successfully identified 10 of these confirmed target proteins. The ability of the CANDOCK algorithm to distinguish the confirmed protein targets of xanthohumol was assessed using the established retrospective metrics, receiver operating characteristics (ROC), predictiveness, and enrichment curves, shown in Inverse molecular docking was performed on the set of 15,451 human proteins from the Protein Data Bank (PDB), from which 13 were experimentally confirmed targets of xanthohumol, and whose measured ICThe validation using ROC, predictiveness, and enrichment curves shows that our protocol was indeed successful in discriminating between true protein targets of xanthohumol, with the area under the ROC (ROC AUC) at 0.684. Moreover, the early detections of targets, with the Boltzmann-enhanced discrimination of ROC (BEDROC) at 0.088, the robust initial enhancement (RIE) at 1.738, and the enrichment factor of 1% of screened compounds (EF1%) at 11.885, were satisfactory. The detection of true target proteins (TG of 0.210) in combination with the ROC AUC above 0.6 shows that the applied protocol is in good agreement with the experiments .In this study, xanthohumol and its metabolites—isoxanthohumol, 8-prenylnaringenin, and 6-prenylnaringenin—were docked into human protein structures collected from the Protein Data Bank (PDB) . The CANThe most influential results of our study are the proteins, to which more than one investigated polyphenol was successfully docked with the lowest docking score value. Based on these, the acyl-protein thioesterase 2 should be exposed. Xanthohumol, isoxanthohumol, and 6-prenylnaringenin were successfully docked to acyl-protein thioesterase 2. Furthermore, the experimental correlation with the anticarcinogenic function of this protein was also found. Regarding the human protein targets to which xanthohumol was successfully docked in our study, more than half of them also have known experimental correlations with xanthohumol and anticarcinogenic function. Based on these results, the mechanisms of action could be substantiated, through which xanthohumol or its metabolites perform anticarcinogenic/chemoprotective activities.On the other hand, the results obtained in this study  can be a promising source in the process of developing new chemopreventive compounds.


Classification of disease severity is crucial for the management of COVID-19. Several studies have shown that individual proteins can be used to classify the severity of COVID-19. Here, we aimed to investigate whether integrating four types of protein context data, namely, protein complexes, stoichiometric ratios, pathways and network degrees will improve the severity classification of COVID-19.We performed machine learning based on three previously published datasets. The first was a SWATH  MS (mass spectrometry) based proteomic dataset. The second was a TMTpro 16plex labeled shotgun proteomics dataset. The third was a SWATH dataset of an independent patient cohort.Besides twelve proteins, machine learning also prioritized two complexes, one stoichiometric ratio, five pathways, and five network degrees, resulting a 25-feature panel. As a result, a model based on the 25 features led to effective classification of severe cases with an AUC of 0.965, outperforming the models with proteins only. Complement component C9, transthyretin (TTR) and TTR-RBP (transthyretin-retinol binding protein) complex, the stoichiometric ratio of SAA2 (serum amyloid A proteins 2)/YLPM1 (YLP Motif Containing 1), and the network degree of SIRT7 (Sirtuin 7) and A2M  were highlighted as potential markers by this classifier. This classifier was further validated with a TMT-based proteomic data set from the same cohort (test dataset 1) and an independent SWATH-based proteomic data set from Germany (test dataset 2), reaching an AUC of 0.900 and 0.908, respectively. Machine learning models integrating protein context information achieved higher AUCs than models with only one feature type.Our results show that the integration of protein context including protein complexes, stoichiometric ratios, pathways, network degrees, and proteins improves phenotype prediction.The online version contains supplementary material available at 10.1186/s12014-022-09370-0. COVID-19 caused by SARS-CoV-2 remains an ongoing pandemic . Based oHowever, all the above-mentioned protein classifiers for COVID-19 are based on individual biomolecules, mainly proteins, ignoring the fact that no protein functions in an isolated manner. As the ultimate effectors of diseases, protein complexes regulate many core biological processes. For example, mitochondrial complexes, such as the mitochondrial ribosomal small subunit, respiratory chain complex I and the mitochondrial pyruvate dehydrogenase complex, are involved in energy production and found to be highly conserved but dysregulated in diseases such as cancers . SeveralAs discussed above, although some studies have stratified COVID-19 severity based on protein levels, no protein functions in an isolated manner, and the information obtained from protein quantification alone may not be comprehensive. Besides the abundance, multidimensional information of proteins, including but not limited to protein complexes, protein topology, post translational modifications, etc., are essential to understand the disease biology. In this study, we tried to investigate the aspect of protein complex using several COVID-19 datasets. We performed machine learning based on three previously published datasets , 4, 10. The training set for this study consisted of 54 sera samples from 40 Chinese patients with COVID-19 , which were quantified using SWATH MS based proteomics . Its perRegarding to the training set, the procedures for serum sample collection, peptides preparation and SWATH acquisition for the training set have been described in our previous study . BrieflyRegarding to the test sets, the first test set used 35 min TMTpro 16plex proteomics. For each patient, the serum sample was obtained within 48 h after hospital admission [The training set consisted of 54 sera samples from 40 Chinese COVID-19 patients, and resulted in a MS data matrix generated using 20 min SWATH, containing 331 proteins and 3474 peptides . The tesThe proteins in the training set of this study were from the dataset of previously published literature . SubsequThe network degrees, which shows the degree to which one protein is related to another, were also generated based on proteins from dataset published before . The valThe differential expression of proteins and other four features between severe cases and non-severe cases was determined using R package Limma (version 3.44) by fitting a linear model. Features with a p value < 0.05 were considered as differential features. It should be noted that although the adjustment of p values based on multiple hypothesis correction can avoid type-1 errors, the risk of introducing type-2 errors is also increased, eliminating some potentially differential proteins. Especially, when the sample size and number of proteins are not too large, there were few differential proteins left after p value adjustment, which was not enough for subsequent random forest machine learning model. Therefore, based on actual situation, multiple hypothesis correction was not performed in this study.We used five categories of variables as the input for the machine learning model, including proteins, complexes, pathways, stoichiometric ratios in a complex, and network degrees. The values of all differential features were normalized by Z-score for machine learning. The output predictor of the machine learning model was the non-severe or severe disease type, and we built a random forest machine learning model for the binary classification task. The random forest model was based on an R package random forest (version 4.6-14) with 5000 trees and 5 nodes as the minimum size of terminal nodes, while the type of prediction was chosen to be probabilistic. The best features were selected by 100 times random forest machine learning. Ten-fold cross validation was performed for each training process of the machine learning model. Receiver Operator Curve (ROC) was estimated by predicting results of the cross-validation using R package pROC (version 1.15.3).PCAs were plotted using R package PCA. Heatmaps were plotted by R package heatmap (version 1.0.12). Density plots were performed by Kernel Density Estimation, a base function of R (version 4.0.0) (a Gaussian kernel with default bandwidth was used).p < 0.01) and a higher incidence of hypertension and diabetes than the non-severe cases  between severe and non-severe patients by G:profiler [We established a few protein context features including protein complexes, protein stoichiometric ratios in a protein complex, pathways, proteins and network degrees. A total of 868 features were obtained based on the quantification of 331 proteins   Fig. A. The beUC) Fig. : Fig. S1UC) Fig. C.Fig. 2TAs shown in Fig. To validate whether the selected features were optimal for our classifier, we built 200 models with random features and validated them with the TMT data set and the SWATH-based German cohort dataset. The median AUC of these models is 0.756, which is significantly lower than the AUC of the model with top 25 features (0.900), indicating the superiority of our selected features.To explore whether the machine learning models with five different types of features are superior to the models with only one type of feature, we trained models with only one type of feature and tested them with the TMT-based proteomics data set (test set 1) and the SWATH-based German cohort data set (test set 2). As shown in Fig. The AUC for the model with only the protein network degree as features reached 0.932, 0.667, and 0.467 in the training set and the two test sets, respectively, indicating that it performs well in the training set, but not in the two test sets  enhances the activity of C5a in the complement system , which mNotably, some proteins associated with the complement system were also altered in severe cases. Abnormal response of complement system can trigger cytokine storm, which can further develop into severe cases , 41. CarIn addition to proteins, other types of protein context feature further shed light on the mechanism of severe COVID-19 cases. Cytolysis pathway is induced after viral infection and serves as a clearance mechanism for infected cells . The altSome limitations of this study should be noted. There were missing features in the two test sets. Seven features were not included in the TMT data, and nine features were excluded in the German cohort data. Median value of all the valued features were used to impute these missing features. The sample size of the training set is limited. Nevertheless, the model achieved satisfactory AUCs in these independent tests. Neither these limitations compromise the major conclusion of this study that integrating protein context information improves COVID-19 severity classification. Moreover, the protein complex information was obtained from cellular complexes, meaning that not all the complexes are necessarily formed in the serum, which needs to be verified by future research. Finally, building ratios may create an overfitting danger, but this can be avoided by building models with other types of features together.Protein complexes, stoichiometric ratios, pathways and network degrees could be used as biomarkers to identify severe cases. Our present study confirms some of the previously reported molecular changes and identifies some new features that may contribute to understand the pathogenesis of COVID-19.Additional file 1: Fig. S1. Expression of the 25 features in COVID-19 sera. (A) log2-scaled protein intensity; (B) Protein complex features indicated by z score and stoichiometric ratio of SAA2/YLPM1; (C) z scores of pathways; (D) z scores of network degree features.Additional file 2: Table S1. Information of 40 patients in the training set and all total patients.Additional file 3: Table S2. The total 868 features based on 331 proteins.Additional file 4: Table S3. The complexs, differential proteins and enriched pathways used as features.Additional file 5: Table S4. The total 192 differential features between severe and non-severe patients.


One of the bottlenecks in the application of basic research findings to patients is the enormous cost, time, and effort required for high-throughput screening of potential drugs for given therapeutic targets. Here we have developed LIGHTHOUSE, a graph-based deep learning approach for discovery of the hidden principles underlying the association of small-molecule compounds with target proteins. Without any 3D structural information for proteins or chemicals, LIGHTHOUSE estimates protein-compound scores that incorporate known evolutionary relations and available experimental data. It identified therapeutics for cancer, lifestyle related disease, and bacterial infection. Moreover, LIGHTHOUSE predicted ethoxzolamide as a therapeutic for coronavirus disease 2019 (COVID-19), and this agent was indeed effective against alpha, beta, gamma, and delta variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that are rampant worldwide. We envision that LIGHTHOUSE will help accelerate drug discovery and fill the gap between bench side and bedside.  •LIGHTHOUSE discovers therapeutics solely on the basis of the primary sequence•The predictions of LIGHTHOUSE against multiple diseases were experimentally correct•LIGHTHOUSE facilitates optimization of lead compounds as well Drugs; Systems biology DSP-118Lead Identification with a GrapH-ensemble network for arbitrary Targets by Harnessing Only Underlying primary SEquence), an ensemble, end-to-end, graph-based deep learning tool that can predict chemicals able to interact with any protein of interest without 3D structural information. We have applied LIGHTHOUSE to malignant, infectious, and metabolic diseases. In addition, we show that LIGHTHOUSE successfully discovered a drug effective against wild-type and variant forms of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with this drug already having been approved for other purposes. We therefore believe that LIGHTHOUSE will promote drug discovery by identifying, from the vast chemical space, candidate compounds for a given protein with a reduced cost, time, and effort and with a wide range of potential biomedical applications.With this background, we have developed a new AI-based drug discovery platform, designated LIGHTHOUSE (50) values.We developed an end-to-end framework that relies on a message passing neural network (MPNN) for compound embedding  to calcuTo train the platform to estimate confidence, we used ∼1.3 million compound–(human) protein interactions (CPIs) stratify-sampled from STITCH , which iWe fed the network with protein primary structures and chemicals and trained it to output the scores from the STITCH training dataset B. When w50 values. For this purpose, we used data from BindingDB , and a “Negative” dataset in which the amino acid sequences of the “Positive” dataset were inverted so that they would no longer be expected to interact with the corresponding chemicals. Calculation by LIGHTHOUSE of the confidence scores for both datasets revealed that those for the “Positive” dataset were heavily skewed toward 1 A. Receiv−14, Fisher exact test). Furthermore, 8 of the 10 known sorafenib targets were among the top 25 candidate proteins (confidence score of >0.75 and interaction score of >7.5)  B. In addWe next validated the effectiveness of LIGHTHOUSE for well-studied compound-protein pairs. LIGHTHOUSE yielded high confidence scores for adrenergic receptors  and epinephrine C. HistamLIGHTHOUSE also proved informative both for macrocyclic chemicals such as rapamycin, yielding a high confidence score for this drug and mechanistic target of rapamycin (MTOR) E, as welWe also applied LIGHTHOUSE to five drugs that were approved by the US Food and Drug Administration (FDA) in 2020 but which had not yet been registered in the STITCH database. LIGHTHOUSE successfully predicted the association between these new drugs and their target proteins G, indica50 data from BindingDB, which are derived from actual bioassays. We found that the predicted value and observed value correlate well for the BindingDB test dataset , a rate-limiting enzyme in the r growth . Althougdatabase  , with a predicted IC 1–10 μM D. We perf 7.5 μM E. This cBacterial infections pose a clinical problem worldwide, especially in developing countries, and the emergence of drug-resistant bacterial strains as a result of the overuse of antibiotics has exacerbated this problem. β-Lactamase enzymes produced by antibiotic-resistant bacteria  target tmrdA), PBP3 (ftsI), and PBP5 (dacA), all of which are essential for cell wall synthesis in Escherichia coli  similar to that of ethoxzolamide, revealed that acetazolamide did not inhibit SARS-CoV-2 infection, despite its LIGHTHOUSE confidence score for ACE2 being very similar to that of ethoxzolamide . We therefore hypothesized that ethoxzolamide might preferentially target a SARS-CoV-2 protein rather than host ACE2. To test this hypothesis, we computed the scores of ethoxzolamide and acetazolamide for each of the SARS-CoV-2 protein sequences  . The virWe tested another 10 drugs and found another 2 compounds that also inhibited SARS-CoV-2 infection . This hiAlthough recent advances in biological and medical research have uncovered various proteins as promising therapeutic targets in a variety of diseases, the clinical application of these research findings has been limited because of the difficulty in identifying therapeutic chemicals for these targets in a cost-effective and high-throughput manner. Acquisition of 3D structural data for target proteins has been labor-intensive, and processing of such data requires a huge amount of computer capacity and time, resulting in a delay in the translation of research findings from the laboratory to the clinic. We have now shown that LIGHTHOUSE facilitates the identification, from a vast chemical space, of candidate compounds for given target proteins solely on the basis of the primary structure of these proteins. Furthermore, the AUROC for LIGHTHOUSE is equivalent to or better than that for state-of-the-art 3D docking simulation methods as well as for other AI methodologies.Existing in silico drug discovery methods can be broadly classified into two categories: SBDD (structure-based drug discovery) and LBDD (ligand-based drug discovery). SBDD requires the 3D structure of the target protein and manual configuration of the binding box. LBDD requires a list of existing compounds with activity data for the target protein. These substantial requirements limit the applicability of AI-powered drug discovery. LIGHTHOUSE is a computational method that eliminates the drawbacks of these two approaches: It does not require the 3D structure of the target protein or the setting of the binding box, and it can be applied to “undruggable” targets for which no inhibitors are currently available. Conventional methods, in particular SBDD, require desktop computers or high-performance cloud computing (such as Amazon EC2) and can calculate only up to 10 CPIs per minute. In contrast, LIGHTHOUSE requires only a simple laptop computer and is able to calculate >1000 CPIs per minute. The time and cost savings of LIGHTHOUSE are therefore several orders of magnitude relative to conventional docking simulation techniques.10 transformed). The important point with regard to our manuscript is that the virus-challenged cells could be rescued by applying ethoxzolamide, thereby providing experiential validation of the prediction of LIGHTHOUSE.We have applied LIGHTHOUSE to attractive targets for various diseases, including cancer, bacterial infection, metabolic diseases, and COVID-19. We have presented three examples that show the effectiveness of LIGHTHOUSE with experimental validation. The actual targets of identified compounds require further biological investigation, especially in the case of pyridoxal 5′-phosphate and PBPs. As for ethoxzolamide, we attempted to obtain clinical evidence in support of its effectiveness against COVID-19. However, ethoxzolamide is an old drug and is essentially no longer prescribed as a result of the development of more potent agents such as acetazolamide. We were therefore not able to find data to address whether COVID-19 patients taking ethoxzolamide have a better clinical outcome. We were also not able to decipher the mechanism of action of ethoxzolamide, but our results suggest that the drug might block the interaction between ACE2 and the viral S protein . The betAs for COVID-19, there are many published AI systems to help clinicians. For example, various imaging data , blood tests, age, gender, region, etc., are integrated to predict the severity of COVID-19 and the occurrence of various symptoms, which definitely help physicians to choose the appropriate approved drugs for that patient . LIGHTHOThe hit compounds themselves identified in the present study are not sufficient for immediate clinical use. An additional potential application of LIGHTHOUSE is drug optimization. One promising method to support such optimization is to apply LIGHTHOUSE and either reinforcement learning  Figure  or Metrode novo drug discovery and drug optimization as well as to drug repurposing.LIGHTHOUSE is not limited to compounds for drug repurposing; it can be applied to any compound for which an SMILES representation is available. LIGHTHOUSE facilitates the discovery of compounds that have the potential to interact with targets for which no binding agents have previously been identified. For instance, PPAT has had no known inhibitors and has therefore been categorized as undruggable. In addition, the 3D structure of PPAT has not been reported, with the result that conventional docking simulation methods cannot be applied to discover potential inhibitors. With the use of the amino acid sequence of PPAT alone, LIGHTHOUSE was able to predict potential inhibitors, one of which, compound ZINC8551105, was indeed shown to inhibit PPAT activity. PPAT is therefore no longer an undruggable target. Furthermore, with the use of a hit compound as a starting point, LIGHTHOUSE can be applied to drug optimization. We have calculated the LIGHTHOUSE scores for thousands of analogs of hit compounds that interact with PPAT, and have thereby discovered several PPAT inhibitors that are more potent than the original compounds (manuscript in preparation). LIGHTHOUSE can thus be applied to Open Targets is a tool that allows data mining on the basis of the function of a gene of interest and a summary of genome-wide association study (GWAS) data available for the relation of the gene to disease . PandaOmde novo drug design, it will be necessary to develop an approach to efficiently explore the huge chemical space (1060 compounds), and the development of algorithms using Bayesian optimization with appropriate acquisition function as well as chemical generators  data, with the recent introduction of academic journals that specialize in big data reflecting the growth of this field. Moreover, development of improved approaches to the handling of imbalanced data  is anothnerators  is desirIn summary, we have developed LIGHTHOUSE as a means to discover promising lead compounds for any target protein irrespective of its 3D structural information. Furthermore, we have demonstrated the power of LIGHTHOUSE by identifying and validating therapeutics for various global health concerns including COVID-19. LIGHTHOUSE will serve as a guide for researchers in all areas of biomedicine, paving the way for a wide range of future applications.50 data from BindingDB, which is derived from actual bioassays. We found that the predicted value and observed value correlate well in the BindingDB test dataset . It may also be important to perform a counter–virtual screening to determine whether an identified small molecule reacts specifically with the target protein or whether it scores highly with many proteins. Such an approach has the potential to reduce the number of false positives and provide more accurate guidance. Furthermore, we also modeled IC dataset . By combAnother potential limitation is that the performance for STITCH data might be overestimated because we downsampled the original imbalanced data . There aDespite these limitations, LIGHTHOUSE proved to be effective for the identification of lead compounds for all conditions tested. It can theoretically be applied to any protein of any organism, and even to proteins that do not exist naturally. This is an advantage over 3D docking simulation methods, which require prior 3D structural knowledge of the protein of interest. LIGHTHOUSE computes and embeds structural information in numerical vectors, which are then readily retrieved by the subsequent decoding module. Given the accelerating development of protein embedding technologies  and grapnakayak1@bioreg.kyushu-u.ac.jp).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Keiichi I. Nakayama (This study did not generate new unique reagents.50 (median tissue culture infectious dose) were also added to the plates, which were then incubated at 37°C for 3 days. The viability of the cells was then determined with the MTT assay, and the culture supernatants were harvested for determination of the TCID50 value as a measure of viral load. For indirect immunofluorescence analysis, cells infected with wild-type SARS-CoV-2 at a MOI of 0.0001 were cultured in the presence of various concentrations of ethoxzolamide for 64 h, fixed with 3.7% buffered formaldehyde, permeabilized with 0.05% Triton X-100, and incubated with antibodies to SARS-CoV-2 N protein . Immune complexes were detected with Alexa Fluor Plus 488–conjugated goat antibodies to rabbit immunoglobulin G . Nuclei were stained with Hoechst 33342 (Invitrogen). Fluorescence images were captured with an IX73 fluorescence microscope (Olympus).Vero-TMPRSS2 cells  were maig for 6 min at 4°C, and the resulting supernatant (100 ng/mL) was incubated for 4 h at 37°C together with 5 mM glutamine , 1 mM phosphoribosyl pyrophosphate , 10 mM MgCl2, 50 mM Tris-HCl (pH 7.4), and various concentrations of riboflavin 5′-monophosphate . Enzyme activity was assessed on the basis of glutamate production as measured with a glutamate assay kit . The IC50 value was estimated from biological quadruplicates with a four-parameter logistic model  supplemented with 10 μM ferric ammonium citrate. They were transfected with a bacmid encoding human PPAT for 64 h, harvested, washed three times with phosphate-buffered saline, and lysed in a solution containing 150 mM NaCl, 25 mM Tris-HCl (pH 7.4), 0.5% Triton X-100, and 5 mM EDTA. The lysate was centrifuged at 10,000 × ic model  and withE. coli strain JM109 (1 × 1010 colony-forming units (CFU)/mL) were cultured for various times in 2 mL of 2xYT liquid medium  containing various concentrations of pyridoxal 5′-phosphate (pH adjusted to 7.0), after which OD600 was measured with a GENESYS 30 visible spectrophotometer . In addition, the JM109 strain was transformed with 1 μg of the pBlueScript II SK + plasmid (Invitrogen), which harbors an ampicillin resistance gene as a selection marker, and was then spread on LB agar plates containing ampicillin (100 μg/mL)  with or without pyridoxal 5′-phosphate (3 mg/mL) and incubated overnight.Portions (20 μL) of Homo sapiens (Taxonomy ID 9606) from the STITCH database (version 5.0). Given that the STITCH score is heavily biased toward 0, we separated the data into nine bins on the basis of the score and stratify-extracted the same number of CPIs , yielding 1,260,000 CPIs , validation (10%), and test (10%) datasets . The protein sequences of the dataset were extracted from the UniProt protein database on the basis of gene names/RefSeq accession numbers or the UniProt IDs. We downloaded the protein-chemical link dataset of 000 CPIs . We then000 CPIs A. With rdatasets A. Given ormation  and usedt time steps and is defined in terms of message functions Mt and vertex update functions Ut. During this phase, hidden states hvt (128 dimensions in our model) at each node in the chemical graph are updated with the incoming messages mvt+1 according to the following equations  denotes the neighbor nodes of vertex v in graph G, and message functions Mt and update functions Ut are learned differentiable functions. After T (= 3) cycles of message passing and subsequent update, a readout function (average) is used to extract the embedding vectors at the graph level.The proposed overall model comprises two encoder networks  and one decoder network. MPNN is a message passing graph neural network that operates on compound molecular graphs . In briequations  and 3:(EO(n2) in computation time and memory, where n is the input size. This bottleneck prevented us from considering each amino acid as a token. We therefore used partition fingerprints to decompose amino acid sequence into protein substructures of moderate size and then fed each of the partitions into the model as a token  = max, as the activation function in the decoder network.As for the decoder, we exploited a previously described architecture . In briePi is the LIGHTHOUSE-predicted score for the ith compound-protein pair and Yi is the true label in the corresponding training data, with a batch size of 128. We trained three architectures  separately for the STITCH and BindingDB training data with the Adam optimizer and a learning rate of 0.001. For evaluation metrics, we used MSE, concordance index, and Pearson correlation as well as AUROC. For every 10 epochs, we compared the current loss  with that of 10 epochs ago; if the loss was not decreasing, we terminated the training for that model. As a result of this early termination, we trained MPNN_CNN for 40 epochs, MPNN_AAC for 70 epochs, and MPNN_Transformer for 100 epochs with regard to the confidence score , where alpha represents the relative weight of the interaction score. We initially set alpha in order to scale the maximum values of the confidence (max 1) and interaction (max 10) scores. We then conducted a grid search with regard to alpha, which ranges from 0.05 to 0.2, and identified the optimal alpha to distinguish between positive and negative data. Finally, the combination score was defined as confidence score plus 0.075∗interaction score.We prepared nearly 1 billion purchasable substances, which were downloaded from the ZINC database  as of 30Three representative statins were fixed as chemical inputs, and all human protein-coding genes in the UniProt database were iteratively changed. The harmonic mean of the three confidence scores was calculated as an affinity score for statins, and the human protein-coding genes were sorted on the basis of this score. The resulting top 500 potential targets were then subjected to enrichment analyses with the use of the Metascape web server .The crystal structure of the receptor binding domain of the spike protein of SARS-CoV-2 in complex with ACE2  was down50 calculation presented in A p value of <0.05 was regarded as statistically significant. Statistical tests are indicated in the