Predicting the drug-target interaction is crucial for drug discovery as well as drug repurposing. Machine learning is commonly used in drug-target affinity (DTA) problem. However, the machine learning model faces the cold-start problem where the model performance drops when predicting the interaction of a novel drug or target. Previous works try to solve the cold start problem by learning the drug or target representation using unsupervised learning. While the drug or target representation can be learned in an unsupervised manner, it still lacks the interaction information, which is critical in drug-target interaction. To incorporate the interaction information into the drug and protein interaction, we proposed using transfer learning from chemical–chemical interaction (CCI) and protein–protein interaction (PPI) task to drug-target interaction task. The representation learned by CCI and PPI tasks can be transferred smoothly to the DTA task due to the similar nature of the tasks. The result on the DTA datasets shows that our proposed method has advantages compared to other pre-training methods in the DTA task. Predicting the drug-target interaction is an important task in drug discovery and drug repurposing . ExperimOver the years, many machine learning-based drug-target affinity (DTA) prediction methods , 30–32 hPre-training is an effective method to handle the cold-start problem. Pre-training helps the model to learn a robust and generalized representation by tapping into a huge amount of unlabeled and labeled data from other relevant tasks. Because both chemicals and proteins can be represented as sequences, language modeling is one of the common pre-training tasks. Thanks to the huge available unlabelled dataset, the model can learn the internal structure arrangement, or in short, the grammar of molecules and proteins by predicting the masked tokens in the sequences. Other pre-training methods such as pre-training graph neural networks, contrastive learning can be either share the same principle as the language model or use different schemes such as mutual information. All the unsupervised pre-training methods share the common strategy that exploits the relationship among components of the structure or between structure classes. These components can vary significantly across atoms, residues or functional groups. These relationships between components can help the model to learn the meaningful representation of each token as well as the whole sequence.Even though the unsupervised pre-training can model the intra-molecule interaction within the molecule or protein to provide the contextual information in the representation, it still lacks the inter-molecule interaction information. By saying inter-molecule interaction, we mean the interaction between the molecule or protein with other entities. Because the essence of the drug-target interaction is in the inter-molecule interaction, it raises the question of whether the intra-molecule interaction information learned by the language model is sufficient for the DTA task.Chemical-Chemical Protein-Protein Transferred DTA (C2P2). First, C2P2 transfers the inter-molecule interaction knowledge learned from chemical–chemical interaction (CCI) and protein–protein interaction (PPI). Then we combine the inter-molecule interaction with the intra-molecule interaction knowledge to learn the drug-target interaction space.To incorporate the inter-molecule interaction into the protein or molecule, we propose a transfer learning framework called PPI is the physical interaction between two or more protein macro-molecules. This interaction is the result of the electrostatics forces, hydrogen bonding or hydrophobic effect of the residues at the protein interface . The proCCI is the interaction between two chemical entities. The interaction can be derived from various ways such as pathway databases, text mining, structure or activities similarity . The DTAOur contribution is 2-fold. First, we propose enhancing the drug-target interaction prediction framework with not only inter-molecule interaction learned from language modeling but also intra-molecule interaction learned from related tasks such as PPI and CCI. We integrate the intra-molecule interaction information into unsupervised pre-training to enhance the representation in (DTA) task where understanding interaction is the key factor. Second, we provide different ways to integrate the learned intra-molecules information into sequence representation and graph representation.Recent developments , 29 in nIn the sequential representation, the structure information is lost. Another way to represent the protein is using the exact 3D structure information, meaning using the 3D coordinate to represent each residue. However, acquiring the protein-folding information through experimental methods such as X-ray can be time-consuming or expensive. Therefore, several computational methods are proposed , 24 to cTo balance between 3D structural information and simplicity, 2D representation via attributed graph can be used. Previous works , 31 haveThe molecules can be represented as SMILES sequence. Therefore, we can apply language modeling to learn the embedding of the molecules. Recent works , 44 usesGraph is the natural representation of the molecule in which the atoms are nodes and bonds are edges. The pre-training method on graph neural network allows the model to capture the robust representation at atom level and molecules level. On node level pre-training, Weihua et al.  propose DTA problem is predicting the binding affinity A between a drug compound D and a protein P. Mathematically, the DTA prediction problem can be formulated as a regression task, minimizing the loss function of the predicted affinity value The cold start in DTA prediction is inferring the binding affinity of drugs and proteins, which do not appear in the training set. Formally, we define the cold start problem for drugs (cold-drug) as follows. During the training time, we train the model with the set of proteins In this section, we present our framework to combine the intra-molecule interaction from language modeling with the inter-molecule interaction knowledge learned from PPI and CCI tasks. In Sec. The overall framework is presented in In this section, we propose the framework to learn the chemical inter-molecule interaction via the CCI prediction task. The overall framework consists of two main stsvg: learning molecule representation and interaction inference. Our CCI model takes two chemical SMILES sequences where The atom node feature where After Given the SMILES sequence sequence . (6)\doc RoBERTa  Transfor RoBERTa  is attenThe SMILES sequences from two chemical ork Sec.  or the pork Sec. . Then boamed ESM . (11)\domentclasspt{minimansformer . SimilarThe protein sequence representation Given the two protein sequence representations After being pre-trained with CCI Sec.  and PPI We use the STRING dataset  for the For the CCI task, we use the STITCH dataset . The datFor the DTA task, we conduct our experiments on two popular DTA datasets: Davis  and PDBBTable 2We use four benchmark methods to evaluate the performance of extra-interaction transfer learning on different representations. First, we compare our proposed method with the previous SOTA method GraphDTA . GraphDThttps://github.com/ngminhtri0394/C2P2. The hyper-parameters are tuned using the validation set. The hyper-parameters detail reported in Our methods are implemented using Pytorch. The source code and data is available at mentclasspt{minimaent} Eq.  \documenWe demonstrate the advantages of transferring the inter-molecule interaction learned from PPI and CCI tasks to the DTA tasks in cold-drug and cold-target settings across two benchmark datasets with balance distribution (PDBBind dataset) and long-tail distribution (Davis dataset).In the cold-target setting, we group the proposed methods by the drug encoder and compare the performance between models with and without PPI transfer learning. Overall, the models with PPI transfer learning show advantages compared with the models without transfer learning. With the graph-based drug encoder , PPI enhanced models have better overall performance compared to model using only ESM feature. Looking at the language model-based drug encoder, the combination of ChemBERTa as drug encoder and ESM–PPI as protein encoder consistently outperforms the model with only ESM as protein encoder. However, combining ChemBERTa-CCI with ESM feature outperforms ESM–PPI feature across two datasets. This suggests some degree of incompatibility between ChemBERTa-CCI and ESM–PPI in the cold-target setting. In the end, in general, cooperating the intra-molecule information learned from PPI task with a protein language model such as ESM benefits the DTA task performance.Similar to the cold-target setting, for the cold-drug setting, we group the proposed models by protein encoder and compare the performance of models with and without CCI transfer learning. Among graph-based drug encoders, pre-training graph neural network with CCI task outperforms Infograph pre-training and training from scratch across two datasets and two types of protein encoder. In case of language model-based drug encoder, while pairing with ESM protein encoder, models with CCI pre-training have better performance than models without pre-training. However, ChemBERTa-CCI and ESM–PPI show a certain degree of incompatibility shown in lower performance than ChemBERTa and ESM–PPI pair. Overall, integrating CCI information into DTA models enhances the DTA model performance, especially in graph representation.It is worth noting that the impact of pre-training encoder using auxiliary task CCI in cold-target is minimal and vice versa. In the cold-target scenario, the drugs in the test set are also in the training set. The encoder has already learned the representation as well as the interaction information of the test set’s drugs. As the result, the external information from auxiliary task CCI is redundant. This is also the case for PPI pre-training in the cold-drug setting. To further verify this point, we conduct our experiment in a warm setting in which drugs and proteins in the test set are also in the training set. The results from The performance of encoder architecture design is reported in Looking back to the complex of resistant strain of HIV-1 protease (v82a mutant) with Ritonavir in Sec. The CCI in STITCH dataset  consistsIn conclusion, migrating the cold-start problem in DTA prediction requires external knowledge from labeled and unlabeled data. Unsupervised learning such as language modeling learns the intra-molecule interaction and internal structure representation of the proteins and drugs from unlabeled data. The drugs and proteins representation are then imbued with inter-molecule interaction learned from similar tasks such as PPI and CCI. The PPI can provides knowledge regarding protein surface, activity, druggability. The CCI provides common pharmacological action, similarity in structure and targets. Combining both intra-molecule interaction and inter-molecule interaction information allows more robust drug and protein representation to deal with the cold-start problem. In addition, interactions curated from different resources such as text mining are also useful for learning interaction knowledge.PPI is a complex interaction. Our framework focus on protein sequence learned from the language model. Because the protein is represented as a sequence, the information on protein structure and the binding site is lost. Therefore, proteins with multiple interaction sites and binding configurations are not considered during the PPI pre-training. Modeling the exact interaction between two proteins requires surface and structure information reflected in the protein encoding architecture such as graph or cloud points. Learning PPI with more dedicated architecture could potentially benefit not only DTA task but other tasks such as druggability as well. In addition, the number of the high resolution of protein–protein 3D structures is limited. Using solely protein–protein 3D information for pre-training may lower the benefits of pre-training. However, with the advance in structure prediction, e.g. AlphaFold , more 3DWe have proposed a deep learning DTA framework that uses inter-molecule interaction information learned from unsupervised language model and intra-molecule interaction learned from auxiliary tasks to deal with cold start problem.The representation learned by unsupervised pre-training tasks can be further enhanced by auxiliary tasks to encourage the model to learn the key features relevant to the task of interest.Knowledge regarding protein surface, activity, druggability from protein–protein interaction and common pharmacological action, similarity in structure and targets from chemical–chemical interaction allows more robust drug and protein representation to deal with cold-start problem.Interactions curated from different resources such as text mining and experimental results are useful for learning interaction knowledge.


Streptococcus pneumoniae (S. pneumoniae), the major etiological agent of community-acquired pneumonia (CAP) contributes significantly to the global burden of infectious diseases which is getting resistant day by day. Nearly 30% of the S. pneumoniae genomes encode hypothetical proteins (HPs), and better understandings of these HPs in virulence and pathogenicity plausibly decipher new treatments. Some of the HPs are present across many Streptococcus species, systematic assessment of these unexplored HPs will disclose prospective drug targets. In this study, through a stringent bioinformatics analysis of the core genome and proteome of S. pneumoniae PCS8235, we identified and analyzed 28 HPs that are common in many Streptococcus species and might have a potential role in the virulence or pathogenesis of the bacteria. Functional annotations of the proteins were conducted based on the physicochemical properties, subcellular localization, virulence prediction, protein-protein interactions, and identification of essential genes, to find potentially druggable proteins among 28 HPs. The majority of the HPs are involved in bacterial transcription and translation. Besides, some of them were homologs of enzymes, binding proteins, transporters, and regulators. Protein-protein interactions revealed HP PCS8235_RS05845 made the highest interactions with other HPs and also has TRP structural motif along with virulent and pathogenic properties indicating it has critical cellular functions and might go under unconventional protein secretions. The second highest interacting protein HP PCS8235_RS02595 interacts with the Regulator of chromosomal segregation (RocS) which participates in chromosome segregation and nucleoid protection in S. pneumoniae. In this interacting network, 54% of protein members have virulent properties and 40% contain pathogenic properties. Among them, most of these proteins circulate in the cytoplasmic area and have hydrophilic properties. Finally, molecular docking and dynamics simulation demonstrated that the antimalarial drug Artenimol can act as a drug repurposing candidate against HP PCS8235_RS 04650 of S. pneumoniae. Hence, the present study could aid in drugs against S. pneumoniae. It is atric CAP .S. pneumoniae also known as pneumococcus is identified as the most recognizable causative agent of CAP in individuals with a compromised immune system [S. pneumoniae engendered 1, 517, 388 deaths globally [S. pneumoniae [S. pneumoniae also causes bacterial meningitis, bacteremia, sinusitis, otitis media, septic arthritis, aortitis, gingival lesions, phlegmonous gastritis, inguinal adenitis, testicular and tubo-ovarian abscesses, and necrotizing fasciitis [e system . In 2016eumoniae . Apart fasciitis .S. pneumoniae serotypes. Currently, 98 serotypes with different polysaccharide capsules have been acknowledged [Currently, the therapeutics offered for patients with pneumonia are antibiotic therapies and vaccines. The application of these treatments varies from place to place and also on the severity of the disease. Usually, if the disease is not treated at the right time or is caused by virulent/ resistant strains; it leads to septic shock, empyema, parapneumonic effusion, lung abscess, necrotizing pneumonia, and often death –13. Therowledged . NotablyS. pneumoniae [Streptococcus mitis and Streptococcus oralis [S. pneumoniae directly bringing a halt to its protein synthesis. However, repeated use of this antimicrobial agent resulted in a spontaneous number of mutations in the chromosomal genes that encode this enzyme [S. pneumoniae to commonly used antibiotics and non-vaccine serotypes underlines the urgent need for a new therapeutic target.Over the last few decades, a significant rise in antimicrobial resistance was observed in eumoniae , 20. Thieumoniae . Pneumocs oralis . This ins oralis , 24. Thes oralis . Macrolis oralis . Generals enzyme . As a reStreptococcus strains are aligned together, several core genes can be observed. Around 33% of the S. pneumoniae genome constitutes uncharacterized proteins documented as Hypothetical proteins (HPs) [Streptococcus mutans’ HPs are critical for antibiotic resistance and biofilm formation [S. pneumoniae using a computational approach to uncover novel targets for drug development. Since the HPs are mutual in many Streptococcus species they might be interesting targets.Examination of the whole genome of the organism is an important approach in combating the regulation of antibiotic resistance, non-vaccine serotypes, and developments of therapeutics. When varieties of genus ns (HPs) . These pns (HPs) . Bioinfons (HPs) , 30. Preormation , 31. Addormation . TherefoStreptococcus strains from different Streptococci species were extracted using the Efficient Database framework for comparative Genome Analyses using BLAST score Ratios (EDGAR 3.0) and visualized by BioCircos [S. pneumoniae PCS8235 (NCBI Reference Sequence: NZ_CM001835.1) was taken as the reference strain. HPs were mined manually from the proteome datasets of the strains.The core genome and proteome of ioCircos . EDGAR 3ioCircos . S. pneuhttps://web.expasy.org/protparam/). The parameters of the proteins included the molecular weight, theoretical pI point, amino acid composition, extinction coefficient, instability index, aliphatic index, and grand average of hydropathicity (GRAVY) of the protein [The functions of the HPs were unveiled using the Gene Ontology Functional Enrichment Annotation Tool (GO FEAT) webserver which works through sequence homology search. The proteins were classified based on the conservation of domains, motifs, families, and superfamilies and categorized via the InterPro, UniProt, European Molecular Biology Laboratory (EMBL), Kyoto Encyclopedia of Genes and Genomes (KEGG), and the National Center for Biotechnology Information (NCBI) databases respectively . The phy protein .http://cello.life.nctu.edu.tw/) and PSORTb 3.0 (https://www.psort.org/psortb/) were utilized to predict the subcellular localization of the HPs [http://www.outcyte.com/) and SecretomeP 2.0 (http://www.cbs.dtu.dk/services/SecretomeP/) were also used to reveal the unconventional protein secretion as well as the HPs taking non-classical secretory pathways respectively [CELLO  database (http://origin.tubic.org/deg/public/index.php) in search of homologous genes within the inquired sequence [All the HPs of sequence . Only thhttp://bioinfo.icgeb.res.in/virulent/) server was used to evaluate the virulence activity of the HPs. This online tool is an SVM-based method that calculates a virulence potential score for a given protein [VirulentPred ( protein . Simulta protein . This sohttps://string-db.org/) network between the functionally annotated HPs was visualized using the STRING database [S. pneumoniae D39 in the STRING database was selected as the reference genome and an interconnected PPI network was constructed. The basis for the network lay in high-throughput lab experiments, gene expression data, and computational data. The network was built with the default confidence parameters.The protein-protein interaction (PPI) (database . In thishttps://go.drugbank.com/) is a comprehensive bioinformatics and cheminformatics resource containing relevant information about drugs and their corresponding targets.The druggability of the annotated proteins was assessed through Drugbank BlastP . Drugbanhttps://robetta.bakerlab.org/). Robetta analyzes the putative domains of the submitted sequences and generates 3-dimensional structural models [https://swissmodel.expasy.org/). SWISS Structure assessment includes Ramachandran Plots and MolProbity scores [Models for the selected proteins were obtained from Robetta (l models . The mody scores . Ramachay scores .https://cactus.nci.nih.gov/translate/). The druggable proteins were then docked using AutoDock within PyRx software, which is a combination of several tools necessary for Molecular Docking [https://discover.3ds.com/).The Canonical Simplified molecular-input line-entry system (SMILES) of the interacted drugs were collected from DrugBank BLASTp. These canonical smiles were converted into PDB files via the CACTUS online smile translator ( Docking . The prohttps://ggplot2.tidyverse.org/).To evaluate the stability of the complexes under physiological conditions, 100 ns Molecular Dynamics (MD) simulation was carried out using GROningen MAchine for Chemical Simulations (GROMACS version 5.1.1). The GROMOS96 43a1 force-field was applied to the protein-ligand complexes. The physiological condition of the system was defined as . The structures were solvated in a dodecahedral box of the SPC (simple point charge) water model with its edges at a 1nm distance from the protein surface. The overall charge of the system was neutralized through the addition of 2 sodium ions using the genion module. Energy minimization of the neutralized system was carried out using the steepest descent minimization algorithm with a maximum number of minimization steps to perform was set at 50000. The ligand was restrained before carrying out the isothermal-isochoric (NVT) equilibration of the system for 100 ps with a short-range electrostatic cutoff value of 1.2 nm. Isobaric (NPT) equilibration of the system was carried out for 100 ps following the NVT with a short-range van der Waals cutoff fixed at 1.2 nm. Later, a 10 ns molecular dynamic simulation was run using periodic boundary conditions and a time integration step of 2 fs. The energy of the system was saved every 100 ps. For calculating the long-range electrostatic potential, the Particle Mesh Ewald (PME) method was applied. The short-range van der Waals cutoff was kept at 1.2 A modified Berendsen thermostat was used to control simulation temperature while the pressure was kept constant using the Parrinello-Rahman algorithm. The simulation time step was selected as 2.0 fs. The snapshot interval was set to 100 ps for analyzing the trajectory data. Finally, all of the trajectories were concatenated to calculate and plot root mean square deviation (RMSD), root mean square fluctuation (RMSF), the radius of gyration (Rg), and solvent accessible surface area (SASA) data. Root Mean Square Deviation (RMSD) calculation was performed to evaluate when a system attains equilibrium. The “rms” module built into the GROMACS software was utilized to extract RMSD information throughout the simulation. The results were plotted graphically using the ggplot2 package of R (Room Mean Square Fluctuation (RMSF) is used to determine the flexibility of a certain region of the protein. The radius of gyration of our proteins was measured to determine their degree of compactness. A relatively steady value of the radius of gyration means stable folding of a protein. Fluctuation of the radius of gyration implies the unfolding of the protein. The “gyrate” module was used to generate the radius of gyration graphs for our proteins.Hydrophobic interactions composed of non-polar amino acids are crucial for maintaining the stability of the hydrophobic core of proteins. They do so by covering the non-polar amino acids within the hydrophobic cores and keeping them at a distance from the solvent. Solvent Accessible Surface Area (SASA) is used in MD simulations to predict the hydrophobic core stability of proteins.S. pneumoniae that encodes hypothetical proteins (HPs) was computationally annotated and analyzed to identify potential drug targets. The workflow has been illustrated in In the study, the core genome of S. pneumoniae encoded 28 HPs  analysis of the 28 HPs revealed that 21 annotated proteins had been involved in biological processes (BPs), molecular functions (MFs) and cellular components (CCs) and the remaining belonged to uncharacterized domains/ protein families . The HPsin vivo experimentation. The extinction coefficient measures the amount of light that a protein absorbs at a certain wavelength. In silico identification of the extinction coefficient is necessary to evaluate the study of protein-protein interaction in solution [The physicochemical properties of the 28 HPs are provided in solution . The extsolution . With vasolution . Here, tIdentification of essential genes contributes a principal role in the development of new drugs or vaccines that inhibit the dissemination of resistant strains . ThroughVirulence factors are the basis of bacterial infections. The virulent and pathogenic proteins help microbes invade the host and manipulate the host immune system for its survival . The comS. pneumoniae and other proteins is visualized in Protein-Protein interactions are crucial for all cellular functions. The association between the hypothetical proteins of During drug discovery, knowledge of subcellular localization can be leveraged to significantly improve the identification of druggable targets . ProteinThe druggability prediction of HPs classified three proteins with homologous genes  available in the Drugbank database . DrugBanRobetta predicted 5 models for each druggable protein. Robetta modeling program was implemented to predict the best model among all. The Ramachandran plot percentage and the Molprobity score of the proteins are tabulated below . MolProbBlind docking was performed to evaluate the binding energy between each ligand and receptors. Two of the expected potential HPs, PCS8235_RS02820 and PCS8235_RS04650 were docked against the interacted drugs from DrugBank . The binThe RMSD graph of PCS8235_RS02820 and PCS8235_RS02820-drug complex demonstrated that the backbone of the proteins started to deviate around 12 ns and overlapped around 75 ns . The RMSS. pneumoniae has been the prime cause of death via community-acquired pneumonia (CAP) worldwide [S. pneumoniae encodes HPs. Some homologs of HPs are conserved within various streptococci species [Streptococcus species, we have found 498 core genes among 22 Streptococci species where 28 belonged to HPs (around 5.6%). Since they are widely distributed in the Streptococcus genus through evolution, they might be critical for the survival of the bacteria. Proper characterization of these HPs could give plausible targets against S. pneumoniae pathogen. Previous studies demonstrated that HPs play a vital role in biofilm formation, pathogenesis, and virulence of tooth decaying S. mutans [orldwide . Combatiorldwide . Followiorldwide , 59. Yet species . In the . mutans  whereas,S. pneumoniae. Later, the virulence and pathogenicity of the essential HPs were noted. Out of the 19 proteins analyzed for virulence and pathogenic characteristics, 21% were found to be both virulent and pathogenic.When the core HPs went under gene ontology-based characterization, 75% of the proteins revealed their characteristic. Most of them were enzymes and binding proteins. Enzymes are involved in several biochemical reactions and self-defense mechanisms playing a key role in bacterial survival inside the host. Here, protein PCS8235_RS3915 and PCS8235_RS4650 were involved in hydrolase activity. Protein PCS8235_RS3915 is a DNA repair RadC homolog while PCS8235_RS4650 is a haloacid dehalogenase homolog. Haloacid dehalogenase homologs are a superfamily of enzymes involved in cellular functions starting from amino acid biosynthesis to detoxification , 63. In S. pneumoniae [Moreover, the integrated roles of the bacterial proteins were elucidated via protein-protein interactions. Protein-protein interaction is required to understand the specific functions of bacterial protein which might be influenced by the neighboring proteins. This helps in finding its significance in bacterial physiology. In the provided network, the protein PCS8235_RS05845 formed the highest nodes (8 interactions) with other HPs that might have critical cellular functions and the potential to go under unconventional protein secretions. Most of the HPs play a physiological role in the cytoplasmic region and have hydrophilic properties. Therefore, they can be considered as drug targets rather than vaccines , 67. Thieumoniae .S. pneumoniae were explored; however, it has only screened the potential drug targets for the pathogen [The druggability of the essential HPs was checked to ensure the proteins’ susceptibility toward the active site of the small inhibitor molecules. In other studies, the drug targets in pathogen . Here, oS. pneumoniae and have a major role in the survival, virulence, and pathogenesis of the pathogen. The majority of the observed proteins are mainly involved in bacterial transcription and translation while a high proportion of the proteins were recognized as essential proteins. Hence they make up good targets for broad spectrum anti-streptococcal drug development.This study focused on the HPs that are found in the core genome of S1 Data(XLSX)Click here for additional data file.S2 Data(XLSX)Click here for additional data file.S3 Data(XLSX)Click here for additional data file.S4 Data(XLSX)Click here for additional data file.S5 Data(DOCX)Click here for additional data file.S6 Data(XLSX)Click here for additional data file.


Inflammasomes are cytoplasmic intracellular multiprotein complexes that control the innate immune system's activation of inflammation in response to derived chemicals. Recent advancements increased our molecular knowledge of activation of NLRP3 inflammasomes. Although several studies have been done to investigate the role of inflammasomes in innate immunity and other diseases, structural, functional, and evolutionary investigations are needed to further understand the clinical consequences of NLRP3 gene. The purpose of this study is to investigate the structural and functional impact of the NLRP3 protein by using a computational analysis to uncover putative protein sites involved in the stabilization of the protein-ligand complexes with inhibitors. This will allow for a deeper understanding of the molecular mechanism underlying these interactions. It was found that human NLRP3 gene coexpresses with PYCARD, NLRC4, CASP1, MAVS, and CTSB based on observed coexpression of homologs in other species. The NACHT, LRR, and PYD domain-containing protein 3 is a key player in innate immunity and inflammation as the sensor subunit of the NLRP3 inflammasome. The inflammasome polymeric complex, consisting of NLRP3, PYCARD, and CASP1, is formed in response to pathogens and other damage-associated signals (and possibly CASP4 and CASP5). Comprehensive structural and functional analyses of NLRP3 inflammasome components offer a fresh approach to the development of new treatments for a wide variety of human disorders. Inflammasomes are multiprotein complexes that have the inherent capacity to trigger an innate immune reaction upon the detection of a molecular pattern  or a damage-associated molecular pattern  . Patternhttps://www.ncbi.nlm.nih.gov) [http://www.rcsb.org [We searched for the human NLRP3 nucleotide and amino acid sequences in the online databases of Ensembl (ENSG00000162711)  and the nih.gov) . Ensemblrcsb.org .http://blast.ncbi.nlm.nih.gov/Blast.cgi. The retrieved protein sequences were aligned using the multiple sequence alignment technique Clustal Omega (version 1.2.4) [http://blast.ncbi.nlm.nih.gov/Blast.cgi) was queried with the same sequence as a query against the PDB to identify structural homologs [http://www.ibi.vu.nl/programs/pralinewww/) [z score values. ProtParam [The human NLRP3 protein sequence was retrieved in FASTA format from UniProt (accession no. Q96P20) and used as a query for BLAST against a nonredundant protein database at n 1.2.4) . The PSIhomologs . The aliinewww/) . The UCSinewww/) . The ProrotParam  was usedUnderstanding the functional component of a protein's structural characteristic is important for proteomics; as a result, multiple bioinformatics methods were used to predict the NLRP3 protein's ligand, ligand-binding sites, and domains. In addition, the protein ligands were grouped according to how similarly they served a purpose. RaptorX, a template-based, reliable 3D modelling online tool, predicted the protein structures' ligand-binding residues . RaptorXhttp://crdd.osdd.net/raghava/lpicom performed ligand module clustering to better comprehend the interaction between the ligands and amino acids of the target protein. Additionally, utilising the LPIcom web server, residues, preferred interactions, and binding motifs for a specific ligand were predicted. All target proteins' domains were looked up in NLRP3 using InterProScan [The LPIcom web server  accessibrProScan  and the rProScan . The phorProScan .https://string-db.org) was used [Evaluation of the functional connections that take place within the cell must first and foremost focus on the interactions that take place between different proteins. In order to identify NLRP3 protein interactions with other proteins, the Search Tool for Retrieving Interacting Genes/Proteins (STRING) database , an investigation on the degree to which the individual amino acids that make up the NLRP3 protein are evolutionary conserved was carried out [Utilising the ConSurf web server  archive  was utilised . This arWe identified probable split genes inside the multiple alignment by looking for occasions where suspected split gene sequences did not overlap. A representative protein was utilised for each protein-coding gene in Ensembl. Scores obtained from BLAST are input into hcluster so that it may organise the sequences into gene families. Following the alignment of the proteins using either MCoffee or MAFFT, a phylogenetic tree was constructed using TreeBeST. In conclusion, orthologues and paralogues are deduced from the tree using the information provided. In order to construct the phylogenetic tree, alignments from both Infernal and PRANK were utilised, and then, the tree was combined with TreeBeST to form the final model. At last, orthologues and paralogues were deduced from the tree using the information provided. Every protein that can be found in Ensembl and every metazoan protein that can be found in UniProt are utilised. Scores from BLAST are input into MCL, which then organises the sequences according to their degree of similarity. The MAFFT algorithm was used to align the proteins. Gene trees help with the evolutionary history of gene families, evolving from a common ancestor. For inferring orthologues and paralogues, duplication and speciation events are distinguished, reconciling the gene tree against the species trees. As a simple case of unique orthologous genes concern, reciprocal best approaches show a clear concordance. Still, concerning more complex one-to-many and many-to-many relations, the gene tree pipeline is considered the best. This approach can significantly raise the orthologues number of hominines to mammal and has even more extreme effects on the orthologous gene in mammal predictions. Furthermore, it aids in exploring the time duplication events that result in paralogues by ascertaining the most recent common ancestor  for a given internal node of the tree .The final tree, also known as the consensus tree, is built by merging the five individual trees using an algorithm designed specifically for tree merging. These allow TreeBeST to leverage DNA-based trees and protein-based trees for closely connected sections of trees and distant connections, respectively, and that a set of algorithms may overtake others in particular cases. In addition, this enables TreeBeST to utilise the fact that protein-based trees are more accurate than DNA-based trees. An individual consensus tree is produced as a result of the simultaneous consolidation of five input trees by the method. The consensus topology has clades included from all of the input trees, which helps to limit the amount of duplicates and losses that are inferred and achieves the maximum bootstrap percentage. The HKY model is used in phyml to make estimates of the branch lengths that are based on the DNA alignment of the final consensus tree.The programme InterPro was used to figure out the structural components of the NLRP3 protein as well as the locations of variations. Proteins are categorized into their respective protein families and then subjected to a functional analysis by InterPro. It provides an additional estimation of the look of active websites and domains. Therefore, the NLRP3 protein is a member of the NLRP family that identifies the most diverse set of PAMPs and DAMPs. These acronyms stand for pathogen-associated molecular patterns and danger-associated molecular patterns, respectively (DAMPs). The NACHT domain and the LRR domain were discovered to be present . In addiThe protein-ligand-binding site residues predicted by PDB sum and visualised by both LIGPLOT and PLIP are provided for NLRP3 protein . For eacSeveral fundamental biological processes rely on the positional behavior of amino acids, whether located in catalytic sites or elsewhere, as is required for protein interactions. As a consequence of this, certain amino acids end up being more recognizable and stable than the many residues that make up a specific protein sequence. Amino acid variations that are positioned at locations that have been evolutionarily conserved are expected to give rise to more damaging mutations than amino acid variations found at places with less conversed positions. Applying the ConSurf algorithm to the amino acids of the human NLRP3 protein and looking at it from an evolutionary perspective allowed for the determination of the rate of conservation, which was relevant to the more sophisticated study of the likely impacts of the high-risk SNPs.https://npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_server.html). We found that NLRP3 protein comprised of 22 amino acids, the most abundant residue is alanine, which is 11.05% and 11.76% of total protein, and the other large residue is glutamic acid, which is 10.06% and 9.01% of the protein  browser is a fundamental component of the GTEx database. This database stores and makes accessible the results of a national study that sought to establish a connection between genetic variants and high-throughput molecular-level expression phenotypes. A significant number of genes have been shown to be associated with a number of tissues. The NLRP3 inflammasome has been implicated in a wide variety of physiological and pathological processes, including inflammation, apoptosis, and necrosis. The expression of the NLRP3 inflammasome was much higher in the cell bodies and axons of the prefrontal cortex, hippocampus, and peripheral blood monocytes (PBMCs) . The invhttp://ftsite.bu.edu/ to predict binding sites in the NLRP3 protein. Using an energy-based procedure, the FT site server can correctly predict ligand-binding sites 94% of the time in experiments. Three ligand-binding sites were found for our target protein by using the FT site server, and the ligand-binding sites were identified using the native NLRP3 protein . The process of recruiting proCASP1 to the inflammasome speeds up its activation, which in turn speeds up the maturation and secretion of IL1B and IL18 in the extracellular milieu, which are both catalyzed by CASP1. It is also necessary for the activation of the NLRP3 inflammasome in order for HMGB1 to be secreted .The NLRP3 inflammasome has been activated by a variety of stimuli, but the precise mechanism is still unknown and is the subject of ongoing research. Additionally, research has shown that posttranslational changes play a crucial role in the activation of NLRP3 , 50. HowUsing homology-modelling methods, we began to use in silico research by performing a 3D structural modelling for human NLRP3 . This moIn this study, the ProtParam server was used to assess the primary structure of NLRP3 and other physicochemical parameters inferred from protein sequences in order to predict gene structure and function. Theoretical pI (isoelectric point), molecular weight, atomic composition, amino acid composition, predicted half-life, extinction coefficient, instability index, high hydropathicity, and aliphatic index are some of the properties that were measured. The NACHT domain and the LRR domain were discovered to be present . In addiDespite the pathway's intricacy, there has been some advancement in the creation of treatments that focus on the NLRP3 inflammasome and the pathways it is connected to. However, additional knowledge is required to completely define the pathways that will expand our understanding of how to discover exact medication targets and which ones may result in more effective treatments. In this context, the investigation of cutting-edge technologies such as artificial intelligence and machine learning is building the groundwork for the creation of medicines that are more effective. Because of this, conducting an in silico investigation of the physicochemical properties of a protein is extremely crucial if one want to obtain a theoretical overview of an enzyme. This paper gives the very first structural analysis of human NLRP3 that has been disclosed. Nevertheless, additional clarity is required regarding the regulatory systems and the role that they play in the progression of the disease. The recent finding of the NLRP3 inflammasome has presented researchers with a fresh window of opportunity to investigate the pathophysiology of disorders that are associated with inflammation. In-depth study of the NLRP3 inflammasome, which controls IL-18 and IL-1b, will offer a fresh approach to the management and prevention of inflammatory illnesses. By comparing the results of genomes and transcriptional investigations, we were able to obtain a greater understanding of the NLRP3 gene.


LYZ C) gene using different computational methods. Lyz C is an important antimicrobial peptide capable of damaging the peptidoglycan layer of bacteria leading to osmotic shock and cell death. The nsSNPs were first analyzed by SIFT and PolyPhen v2 tools. The nsSNPs predicted as deleterious were then assessed by other in silico tools — SNAP, PROVEAN, PhD-SNP, and SNPs & GO. These SNPs were further examined by I-Mutant 3.0 and ConSurf. GeneMANIA and STRING tools were used to study the interaction network of the LYZ C gene. NetSurfP 2.0 was used to predict the secondary structure of Lyz C protein. The impact of variations on the structural characteristics of the protein was studied by HOPE analysis. The structures of wild type and variants were predicted by SWISS-MODEL web server, and energy minimization was carried out using XenoPlot software. TM-align tool was used to predict root-mean-square deviation (RMSD) and template modeling (TM) scores.Single-nucleotide polymorphisms (SNPs) have a crucial function in affecting the susceptibility of individuals to diseases and also determine how an individual responds to different treatment options. The present study aimed to predict and characterize deleterious missense nonsynonymous SNPs (nsSNPs) of lysozyme C  were found to be potentially deleterious. I-Mutant 3.0 determined that the variants decreased the stability of the protein. ConSurf predicted rs121913547, rs121913549, and rs387906536 nsSNPs to be conserved. Interaction network tools showed that LYZ C protein interacted with lactoferrin (LTF). HOPE tool analyzed differences in physicochemical properties between wild type and variants. TM-align tool predicted the alignment score, and the protein folding was found to be identical. PyMOL was used to visualize the superimposition of variants over wild type.LYZ C gene and could be used in further experimental analysis. These high-risk nsSNPs could be used as molecular targets for diagnostic and therapeutic interventions.This study ascertained the deleterious missense nsSNPs of the  Single-nucleotide polymorphism (SNP) is a variation at a single position in the genetic sequence and one of the common sources of sequence alterations in humans which are present in greater than 1% of the population [Missense nonsynonymous SNPs (nsSNPs) can produce a variation in the amino acid sequence and have the ability to alter the structure and function of a protein, thereby affecting disease pathogenesis and progression in individuals. SNPs can also affect gene expression by influencing promoter activity, conformation and stability of messenger RNA (mRNA), and translational efficiency regulating the susceptibility of individuals to diseases, drug metabolism, and genomic evolution .LTF) [MTUS1) [GCH1) [GJA3) [DEFB1) [BCL11A) [APAF1) [Deleterious SNPs of various genes have been identified using in silico methods, and some of them include lactoferrin (LTF) , mitocho [MTUS1) , guanosi) [GCH1) , gap jun) [GJA3) , beta-de [DEFB1) , B-cell [BCL11A) , and apo [APAF1) . Observi [APAF1) .LYZ C gene can reduce its antimicrobial potential and increase the susceptibility to infections.Antimicrobial peptides (AMPs) are principal constituents of the innate immune system and have suppressive effects on bacteria, fungi, viruses, and parasites . LysozymLYZ C gene and the effect of variants on the protein’s three-dimensional structure and function.Not all SNPs identified are deleterious, and it is important to distinguish deleterious SNPs from neutral SNPs. The high number of SNPs makes it difficult to carry out experiments in the laboratory to find out the importance and biological contribution of each SNP. However, computational tools can be used to initially filter potentially damaging SNPs that might affect susceptibility to diseases and drug metabolism before further laboratory investigations. This study analyzed missense nsSNPs of https://www.ncbi.nlm.nih.gov/snp/) was used to retrieve the SNPs of the LYZ C gene (accessed on 3 September 2021). Only the missense nsSNPs of the LYZ gene were retrieved from the database as the nucleotide change results in an altered codon that codes for a different amino acid and potentially impacts the structural and functional features of the protein. The FASTA sequence of LYZ C protein was obtained from the UniProt web server .The National Center for Biotechnology Information (NCBI)-SNP database (http://sift.bii.a-star.edu.sg/) and PolyPhen v2  tools. SIFT uses a query sequence and builds a multiple sequence alignment and based on position-specific information predicts tolerated and deleterious substitutions [Several web servers were used to distinguish deleterious nsSNPs from neutral ones. First, the missense nsSNPs obtained from the NCBI-SNP database were submitted to SIFT  provides a sequence-based prediction and incorporates evolutionarily conserved information, that is how a residue is conserved within the sequence families and also uses other predicted information such as secondary structure and solvent accessibility and analyzes whether a SNP has any effect on function  or no effect  [http://provean.jcvi.org)) uses a region-based alignment score that measures the effect of amino acid variation not only at the position of interest but also takes into account the alignment of neighborhood flanking sequences for determining the consequence of the variant on the functional aspect of the protein [http://snps.biofold.org/phd-snp/phd-snp.html) tool is based on support vector machines (SVMs) that use protein sequence and predicts whether a nsSNP is associated with a genetic disease in humans [http://snps-and-go.biocomp.unibo.it/snps-and-go/) uses evolutionary information and function as encoded in the GO sequence-associated terms and predicts whether the variation has any effect on the gene functionality [To increase the accuracy of prediction, the nsSNPs that were found to be deleterious by both SIFT and PolyPhen were subjected to the following tools. SNAP  . PROVEAN protein . PROVEANn humans . SNPs & ionality .http://gpcr.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi) was employed to predict how a single-point variation affects the thermodynamic stability of the protein. It is focused on the difference in free energy changes (Delta Delta G (DDG)) between the wild-type and variant proteins [I-Mutant 3.0 tool (proteins . The outhttp://consurf.tau.ac.il/) was used to study the evolutionary conservation of amino acid position in LYZ C protein. ConSurf tool first develops a multiple sequence alignment of the given sequence, constructs a phylogenetic tree, and gives a position-specific conservation score [ConSurf web server (on score . The scohttp://www.genemania.org) predicted the gene interaction network of the LYZ C gene.Studying the gene interaction network is of prime importance to understand the disease phenomenon. As the genes are interlinked, a mutation in a gene can affect its interacting partners in the network, and therefore, it is important to analyze disease-related genes . GeneMANhttps://string-db.org/) [Proteins are part of complex molecular mechanisms, and it is important to identify protein-protein interactions to elucidate the function of proteins and their specific roles in the disease process. The protein-protein interaction of LYZ C protein was studied by the STRING  tool (db.org/) .https://services.healthtech.dtu.dk/service.php?NetSurfP-2.0) uses a primary sequence and detects the surface accessibility and secondary structure of a protein [NetSurfP 2.0 ( protein . The secHOPE (Have (y) Our Protein Explained) is an web-based application that analyzes the impacts of point mutations on the structure and function carried out by a protein . It builhttps://swissmodel.expasy.org/). The quality of the models was examined and analyzed by online servers such as PROCHECK, ERRAT, VERIFY3D, and PROVE (https://saves.mbi.ucla.edu/). Energy minimization was carried out using XenoPlot software with the steepest descent and 1000 steps per structure with a resolution of 10 Å. Amber_94 force field was utilized to minimize the energy of the molecule to a more stable position. The 3D models built for wild type and variants were uploaded in TM-align tool  to get root-mean-square deviation (RMSD), align, and template modeling (TM) scores [Protein homology modeling was carried out for both wild type and variants using the SWISS-MODEL web server  scores . The tooLYZ C gene were extracted from the NCBI-SNP database. There were a total of 2855 SNPs out of which 44 were synonymous, 105 were missense, 1351 introns, and others. Missense nsSNPs were selected for further analysis as a change in the coding sequence could result in altered protein sequence and hence could affect the protein structure rendering protein nonfunctional and increasing the susceptibility to different diseases. But not all missense substitutions affect protein’s structure and function; some remain neutral without causing significant changes. Hence, it is essential to distinguish deleterious SNPs from neutral ones.The nsSNPs of the First, 105 missense nsSNPs were subjected to SIFT tool which showed that 12 nsSNPs were deleterious with a SIFT score less than ≤ 0.05. The nsSNPs were then subjected to the PolyPhen v2 tool. To increase the accuracy of prediction, both SIFT and PolyPhen v2 tool results were taken into consideration. The nsSNPs having SIFT score ≤ 0.05 and PolyPhen v2 score > 0.90 were considered for further investigation. SIFT and PolyPhen v2 tools predicted 8 SNPs to be deleterious and probably damaging, respectively. These 8 SNPs were further submitted to other online tools — SNAP, PROVEAN, PhD-SNP, and SNPs & GO. The results of the in silico tools are presented in Table Advances in different genotyping methods have led to the identification of a significant number of missense variations. The consequence of amino acid substitutions on protein stability will help to predict variations that lead to disease phenotypes . I-MutanSlowly evolving sites on the protein molecule are critical for its function, and the ConSurf tool predicts evolutionarily conserved regions in the protein query macromolecule . ConSurfLYZ C and lactoferrin (LTF) interact with each other. Both are components of innate immunity and form a part of the first line of defense against microbes. LYZ C gene has physical interactions with the following genes — amyloid P component, serum (APCS), heparin sulfate proteoglycan 2 (HSPG2), interferon-induced protein 44 like (IFI44L), and poly (ADP-ribose) polymerase family member 11 (PARP11). The interaction network of the LYZ C gene as predicted by GeneMANIA is shown in Fig. GeneMANIA tool predicted that Proteins function in a coordinated fashion and interact with other proteins to carry out cell signaling and other functions of a cell. Therefore, amino acid variations in a protein can affect other proteins in a network. The functional partners of LYZ as predicted by the STRING tool are LTF, lysozyme-like protein 1(LYZL1), chitotriosidase-1 (CHIT1), myeloblastin serine protease (PRTN3), fibrinogen alpha chain (FGA), lipocalin-1 (LCN1), alpha-crystallin B chain (CRYAB), transmembrane immune signaling adaptor (TYROBP), serum albumin (ALB), and macrophage-expressed gene 1 protein (MPEG1). LTF was found to be the main interacting partner of LYZ with a maximum score of 0.874. Both gene and protein interaction networks predicted that LYZ C interacts with LTF and possibly acts in a synergistic manner against the invading microbes. The protein-protein interaction network of LYZ C protein is given in Fig. Helix and strand formed by the amino acids of LYZ C protein are mapped to their primary sequence and given in Fig. HOPE tool analyzed the consequences of variation on LYZ protein’s 3D structure and function by comparing the physicochemical properties between variant and wild-type amino acids. The effect of variation is shown in Table Homology modeling scores of the structures are given in Table Identifying biologically relevant SNPs can help in developing SNP-based genetic profile that can be used as genetic screening markers in identifying the risk of individuals to different diseases and help in studying inheritance patterns . PolymorLYZ C gene on the structural and functional aspects using various in silico tools. First, 105 missense nsSNPs obtained from the NCBI-SNP database were analyzed by SIFT and PolyPhen tools which predicted 8 nsSNPS to be deleterious. These SNPs were then studied by other tools such as SNAP, PROVEAN, Phd-SNP, and SNPs & GO. Then, the effect of these deleterious nsSNPs on protein stability was studied by I-Mutant 3.0 tool which compared the free energy change between wild type and variants. Change of amino acids located in the conserved region produces deleterious effects, and the ConSurf tool was employed to study the phylogenetic conservation of amino acids. ConSurf predicted polymorphisms I74T, F75I, and W82R of LYZ C to be conserved. Gene-gene and protein-protein interaction tools suggested that LTF and LYZ interact and possibly produce a synergistic effect. AMPs such as LTF synergize with LYZ, whereby LTF permeabilizes the outer membrane of gram-negative bacteria and enhances the access of LYZ to the peptidoglycan layer for the effective killing of gram-negative bacteria [The current research work identified the impact of nsSNPs of the bacteria . LTF alsPolymorphisms in host genes can increase or decrease susceptibility to diseases by altering the host’s ability to fight against infections. Mutations can also occur in microbial genomes causing an increase in virulence leading to higher infectivity and transmission rates. The variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), D614G, was the predominant form which increased the affinity of the virus to bind to human receptor angiotensin-converting enzyme 2 (ACE2) . There wLYZ C gene, and these nsSNPs need to be further validated by experimental procedures. Once experimentally demonstrated and validated, these deleterious SNPs could be useful in developing a panel of biomarkers to predict the susceptibility of individuals to different diseases.Lysozyme can inhibit virus entry by preventing its binding with cell receptors and thereby virus-mediated cell fusion. As lysozyme is an important AMP, deleterious polymorphisms can affect the structure and hence the function of LYZ C resulting in decreased immunity against infections including viral diseases. In silico tools have been used to predict deleterious missense nsSNPs of the LYZ C gene, 8 nsSNPS were predicted to be deleterious by various bioinformatics tools. The effect of these missense nsSNPs of the LYZ C gene on protein structure and function needs to be confirmed by experimental investigations. The use of multiple in silico methods provides cost-effective and rapid screening which could guide further laboratory analyses.In this study, out of 105 missense nsSNPs of the


We undertake our task by exploring “the state of the art” in AI and machine learning (ML) applications to protein science in the scientific literature to address some critical research questions in this domain, including What kind of tasks are already explored by ML approaches to protein sciences? What are the most common ML algorithms and databases used? What is the situational diagnostic of the AI–PS inter-field? What do ML processing steps have in common? We also formulate novel questions such as Is it possible to discover what the rules of protein evolution are with the binomial AI–PS? How do protein folding pathways evolve? What are the rules that dictate the folds? What are the minimal nuclear protein structures? How do protein aggregates form and why do they exhibit different toxicities? What are the structural properties of amyloid proteins? How can we design an effective proteostasis network to deal with misfolded proteins? We are a cross-functional group of scientists from several academic disciplines, and we have conducted the systematic review using a variant of the PICO and PRISMA approaches. The search was carried out in four databases , resulting in 144 research articles. After three rounds of quality screening, 93 articles were finally selected for further analysis. A summary of our findings is as follows: regarding AI applications, there are mainly four types: 1) genomics, 2) protein structure and function, 3) protein design and evolution, and 4) drug design. In terms of the ML algorithms and databases used, supervised learning was the most common approach (85%). As for the databases used for the ML models, PDB and UniprotKB/Swissprot were the most common ones . Moreover, we identified that approximately 63% of the articles organized their results into three steps, which we labeled pre-process, process, and post-process. A few studies combined data from several databases or created their own databases after the pre-process. Our main finding is that, as of today, there are no research road maps serving as guides to address gaps in our knowledge of the AI–PS binomial. All research efforts to collect, integrate multidimensional data features, and then analyze and validate them are, so far, uncoordinated and scattered throughout the scientific literature without a clear epistemic goal or connection between the studies. Therefore, our main contribution to the scientific literature is to offer a road map to help solve problems in drug design, protein structures, design, and function prediction while also presenting the “state of the art” on research in the AI–PS binomial until February 2021. Thus, we pave the way toward future advances in the synthetic redesign of novel proteins and protein networks and artificial metabolic pathways, learning lessons from nature for the welfare of humankind. Many of the novel proteins and metabolic pathways are currently non-existent in nature, nor are they used in the chemical industry or biomedical field.Proteins are some of the most fascinating and challenging molecules in the universe, and they pose a big challenge for artificial intelligence. The implementation of machine learning/AI in protein science gives rise to a world of knowledge adventures in the workhorse of the cell and proteome homeostasis, which are essential for making life possible. This opens up epistemic horizons thanks to a coupling of human tacit–explicit knowledge with machine learning power, the benefits of which are already tangible, such as important advances in protein structure prediction. Moreover, the driving force behind the protein processes of self-organization, adjustment, and fitness requires a space corresponding to gigabytes of life data in its order of magnitude. There are many tasks such as novel protein design, protein folding pathways, and synthetic metabolic routes, as well as protein-aggregation mechanisms, pathogenesis of protein misfolding and disease, and proteostasis networks that are currently unexplored or unrevealed. In this systematic review and biochemical meta-analysis, we aim to contribute to bridging the gap between what we call  Nowadays, it is possible to classify, share, and use a significant number of structural biology databases helping researchers throughout the world. Once the mechanism of DNA for protein synthesis is deduced, it will then be possible to replicate it via computational strategies through artificial intelligence (AI) and machine learning (ML) algorithms that can provide important information such as pattern recognition, nearest neighbors, vector profiles, back propagation, among others. AI has been used to exploit this novel knowledge to predict, design, classify, and evolve known proteins with improved and enhanced properties and applications in protein science  by the ML techniques, they use and narrowing down the subareas, 2) by the classes, methods, algorithms, prediction type and programming language, 3) by some protein science queries, 4) by protein science applications, and 5) by protein science problems. Moreover, we present the main contributions of AI in several tasks, as well as a general outline of the processes that are carried out throughout the construction of the models and their applications. We outline a discussion on the best practices of validation, cross-validation, and individual control of testing ML models in order to assess the role that they play in the progress of ML techniques, integrating several data types and developing novel interpretations of computational methodology, thus enabling a wider range of protein’s-universe impacts. Finally, we provide future direction for machine learning approaches in the design of novel proteins, metabolic pathways, and synthetic redesign of protein networks.This study puts forward the use of ML classes and methods to address complex problems in protein science. Our point of departure is the state of the art of the AI–PS binomial; by binomial, we mean a biological name consisting of two terms that are partners in computational science as well as in biomedical or biotechnological science as a “two-feet principle” in order to understand, enhance, and control protein science development from an artificial intelligence perspective. Our cross-functional team aims at accelerating the steps of translating the basic scientific knowledge from protein science laboratories into AI applications. Here, we report a comprehensive, balanced systematic review for the literature in the inter-field and a biochemical meta-analysis, which includes a classification of screened articles: A systematic review of the scientific literature found in the period (until February 2021) was carried out for this study –3 followOne of the main objectives is to discuss new information in the latest findings about the functions of AI in protein design. Furthermore, this review and meta-analysis intend to include a wide scope of the status of artificial intelligence in protein science. The PIO  strategy was used to systematically search all databases and was the methodology to address the following research questions: What is the state of art in the use of artificial intelligence in the protein science field? What is the use of neural networks in the rational design of proteins? Which neural networks are used in the rational design of proteins? Protein design is currently considered a challenge. As artificial intelligence makes progress, this is presented as a solution to various issues toward addressing how this new branch can be used for the creation of high precision models in protein design. Following the PIO strategy, the next terms were used for the research.Participants: articles about proteins and their MeSH terms in general were considered for inclusion; we gave special consideration to protein design and their related terms such as scaffold (as a main structure or template), rational design, and biocatalysts :• protein• protein design• scaffold• rational protein design• biocatalystsIntervention: studies with any types of algorithms, software, programming language, platform, or paradigm using alone or in combination were selected.• neural networks• recurrent neural networks• network LSTM/GRU• convolutional neural network• deep belief networks• deep stacking networks C5.0• genetic algorithms• artificial intelligence• decision trees• classification• prediction C&ATypes of algorithms:• Weka• RapidMiner• IBM ModelerSoftware:• Python• Java• OpenGL• C++• ShellProgramming languages:• Caffe• DeepLearning4j• TensorFlow• IBM distributed deep learning (DDL)Development platform:• supervised learning• unsupervised learning• reinforced learningParadigm:• novel proteins• protein structure prediction• novel biocatalysts• new fold• evolved protein• new functionOutcomes:Mendeley, eliminating duplicates for the final result.The electronic databases used were PubMed, Bireme, EBSCO, and OVID. The concepts with similarity were searched with “OR,” and within the groups of each element of the PIO research, they were searched with the word “AND.” Next, a diagram was constructed in order to show the history of searches and concepts used (figure tree diagram). This figure describes in full detail the searching strategy in the PubMed database as well as all keywords used. Moreover, it includes the number of resulting articles. Subsequently, the results obtained from these searches were recorded. The references themselves were then downloaded into the Mendeley database. All references were taken, organized, and saved in etc.), and building up tables and figures for the interpretation of evidence and presenting the results.The biochemical meta-analysis included formulating the research question, searching and classifying protein tasks in the 144 selected studies, gathering AI–PS information from the 144 studies, evaluating the quality of the studies , analyzing and classifying the intervention and outcome of studies : identification of filtered data, curated data, and features implemented; machine learning input relationship with protein science servers.DATA PROCESSING (training data and feature extraction): observation of input data and data encoding format. Record of machine learning algorithms and methods. Recognition of key information for processing data within databases.DATA POST-PROCESSING: observation of post-processing treatment, rule quality processing, filtering, combination, or unification of information.MEASURE: explanation of the process, the values of different metrics for the quantification of magnitudes, and the contribution for the completion within the process of information.ANALYZE: identify the application of machine learning algorithm in which the input of the dataset to process data format, training set, and 3D structures.IMPROVE: determine the set to whom these new forms will be applied in models of the researched data and contribute to future implementations in protein science.Concerning the computational aspects as to how articles were classified, three initial divisions were made and are displayed in pre-processdatabase, pretreatment, and inputprocessmachine learning paradigm and input, algorithm and development software, three aspects of the neural network used  and output.post-processinput and web server when applied.Most of the research reported in these articles performs a pretreatment over the protein database used, that is, processes of randomization and training, in order to leave the data prepared for the computational process itself, for when the algorithm is to be executed on a software platform and within a particular machine learning paradigm  was performed using the following set of quality criteria:A total of 93 articles were included for further analysis, and 51 studies were removed based on quality criteria.etc.) because of their multiple structure-folding-function within protein science (PS), phenomena that can be challenged through the use of artificial intelligence (AI).There are several questions within this interdisciplinary approach such as How do proteins evolve? How do proteins fold and get their tridimensional structure? What are their networks within proteins? Given the astronomical numbers of possibilities for protein structures, configurations, and functions that require the use of AI as a tool to fully understand protein behavior.Proteins are influenced by epigenetic phenomena  b) Novel biocatalyst design.c) Novel function and ligand interaction.d) Evolution of non-existent proteins in nature.e) Chemical structure and properties.f) Drug–drug interaction.g) Drug–receptor interaction.h) Drug effects.II. Protein function, function prediction, and novel function a) Protein–ligand interactions.b) Hydroxylation site prediction.c) Prediction of the local properties in proteins.d) Enzymatic function prediction.e) Predicting protein–protein interactions.f) Function prediction.g) Molecular property prediction.III. Fold ID, physicochemical properties, and protein classification a) Fold Id.b) Glycation site predictor.c) Phosphorylation site predictor.d) Protein–protein interaction.e) Intrinsically disordered protein prediction.IV. Protein structure prediction a) Protein structure prediction: primary, secondary, and 3D-structures; domains, active sites, allosteric sites, and structural feature prediction.etc.b) Protein structure classification: folds, structural families, intrinsically disorder proteins, c) Protein–protein interactions and protein networks.etc.d) Protein–ligand interactions: substrates, inhibitors, activators, ions, V. Protein contact map prediction, protein-binding prediction, protein site prediction, and genomics 1) Contact map prediction.2) Protein sub-mitochondrial site prediction.3) Genomics.A total of 144 articles were assessed for quality  resultinE. coli, Drosophila, Caenorhabditis elegans, Homo sapiens, S. cerevisiae yeast, Mus musculus (mouse), Geobacillus, and Coronavirus.1) PDB (30/144) 21%.2) Author’s dataset construction (21/144)15%.3) UniProt either UniProtKB or UniProtKB/SwissProt (12/144)8%.4) CASP  database (5/144)3%.5) SCOP  (4/144)3%.6) N/A, GenBank (4/144) 3%.7) Protherm (3/144) 2%.8) BioLip  (2/144) 1%.9) PLMD (protein lysine modifications database) (2/144) 1%.10) And each of the next databases ChEMBL, eSol, GEO, DSSP, Drugbank, BioCreative, Transfac, STRING, BRENDA, SPINE, PISCES, NCBI, D3R Grand challenge 3, and KEGG with a (1/144)1%.The 40% (57/144) of the protein studies by AI applications were the following ones: myoglobin, silk protein, amyloid proteins, Rab family, cathepsin S family, kinases family, K proteinase, barnase, apolipoprotein family, protein DND_4HB, and antimicrobial peptides. Studies in enzymes should be pointed out, oxidoreductases, transferases, hydrolases, lyases, isomerases, ligases, NOS (nitric oxide synthase), lysozyme, which are included in the columns of the initial scaffold . These pFrom the studies reviewed, (23/144), 16% use two databases. Of these, the latter (11/23) 48% uses a combination of the PDB and HSPP, PISCES, ProTherm, MOAD, SPx dataset, ChEMBL, DisProt, and UniProt/SwissProt; (4/23)17% use a combination of the GO database with UniProt or STRING; (4/23)17% uses a combination of the UniProt/SwissProt database with ENZYME, DIP, TrEMBL, and CAFA database; and a (2/23)9% combination among DIP, HPRD, SKEMPI database, and SPx dataset. The rest (24/144)17% belongs to a combination of three or more databases with PDB, UniProt, among others.Moreover, several authors  focused The following tables show the principal protein categories that were found in this study. etc.) and regression algorithms  that are used for a variety of tasks: detection of functional sites, hydroxylation sites, amino acid composition, DNA expression sequences, protein interaction, biomarker finding, protein design, drug design, 3D structure prediction, and protein folding 85%, which focuses on classification algorithms  for various purposes, such as protein solubility prediction, protein prediction of new functions, discovery of DNA motifs, detection of protein structures, and prediction of the nuclear Overhauser effect at low energies. Of the eight articles using this approach, two of them report an improvement in performance as an advantage, one of them in time reduction 12% focusing on unsupervised learning, using clustering algorithms 53% of the deep learning articles combine two clustering algorithms: CNN (47/77)61% and LSTM (16/77)21%. Of course, some articles put forward optimization procedures in an algorithmic genetic fashion –7.7/14453% Regarding hybrid algorithms using neural networks, we found that all 11 articles explicitly stating their use of hybrid algorithms belong to the deep learning paradigm, combining CNN and LSTM or RNN and CNN. One of them  goes eveIt is interesting to note as well that nine articles are used for prediction , which are mutually exclusive. The first part of the database is used to feed the input vectors of the methods and train the machine or deep learning algorithms, while the rest is used to evaluate and validate the results obtained with their proposed algorithms. In contrast, with this type of validation technique, hold-out takes a long time for computational processing, especially for large datasets, in particular case, the large protein databases. As a result of our meta-analysis, we found the use of the hold-out methodology to train and validate their AI proposals, as CNN, RNN, LSTM, and FFNN  obtain the ability of a model to fit an unknown dataset given a collected dataset. In this context, the k-fold cross-validation is an iterative process that consists of dividing the dataset randomly into k groups of approximately the same size. In this sense, although not all possible combinations of sets are examined, an estimate of the average accuracy more than acceptable can be obtained by training the model only k-fold. The first set is used to train the AI models and the other is used to test and validate them, doing this process k times using a different group for validation in the iteration. Although cross-validation is computationally an intensive method of training and validation, its advantages are the reduction of computational time because the process is repeated k times, where all the data are tested once and used for training, maintaining a reduced variance and bias. Of the total 93 articles in this review, 41 of them (47%) used the following cross-validation schemes: leave-one-out, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 10-fold, and 20-fold cross-validations. For most of them, the use of 5-fold and 10-fold cross-validations to analyze the performance of their AI proposals predominated, with 16 and 17 articles, respectively. This method was preferred for the evaluation to the performance of CNN and SVM algorithms, with databases such as PBD, ProTherm, UniProt, GO, and ChEMBL. Additionally, in seven articles (17%), they carried out various types of cross-validations to obtain more information on the performance of their proposals. Another variant to evaluate performance was observed in three articles (7%), which combined the use of both hold-out and cross-validation methodologies in their proposals, which provide them more effective comparison of results in terms of validation schemes.In contrast, in 22 articles of this review, 25% did not mention neither their training methods nor the validation performed to evaluate the performance of their algorithms used. Likewise, 7% of the articles evaluated their methods using various types of cross-validations at the same time to obtain more information on the performance of their proposals, e.g., 4-fold, 6-fold, 8-fold, and 1-fold, or 3-fold, 5-fold, 7-fold, and 1-fold, or 10- and 20-fold, for databases of PDB, UniProt, GO, ChEMBL, ProTherm, PISCES, GenBank, STRING, and new databases as NOS, SPx, D-B, and Ext D-B.R2, F1-score, area under the curve (AUC), receiver operating characteristic (ROC) curve, and Matthew’s correlation coefficient (MCC) , nt (MCC) .Of the 87 articles selected as finalists, we have the following: 32 use one single algorithm and 55 use a combination of two or three algorithms sequentially. In machine learning, we found 30; in deep learning, we found 20 applying machine learning (SVM); 11 deep learning (RNN); and 6 using optimization through genetic algorithms.seeRegarding the programming language in which each study was developed, we found 47 articles do not specify what language they are based on, 75 articles are based on the Python language, of which 57 are based entirely on Python and 18 are in combination with other software; Twelve articles are based on the C++ language of which only three are based exclusively on that language and nine in combination with Python, with C, R, and CUDA and C++ language in the Linux environment.Other nine articles are based on MATLAB of which only four are based exclusively on that language and five in combination in conjunction with Python and Bioinformatics and with Python and C++.Six articles are based on the C language of which three are based exclusively on that language and three in combination in conjunction with C++, R, and CUDA, with Java and Python and one with Linux and Windows environment.Finally, seven articles are based on the Java language of which two are written exclusively in this language and five in combination with TensorFlow and with C and Python.Regarding software licenses, 90 articles were found to be Open Source. An article is licensed by Neural Power version 2.5. One article specifies an open license type belonging to IBM and GNU, respectively. Unfortunately, 45 items did not specify the type of license they own.AlphaFold algorithm using a deep neural network. They generated an outstanding accuracy of the 3D structure of a protein with an unknown fold in CASP14  the procedure to obtain raw data and which type of processing should be followed for the model to be adequate, 2) the type of algorithm that may be used depending on the complexity of the problem, and finally, 3) the interpretation of results.An interesting finding is the implementation of free code and software, as shown in the AI Training, Validation, and Performance section. Our results exhibit a tendency to create models with transparency, which means that every study implemented in a public server has access to all new models created. Another crucial result is the one depicted in the Road Map of Artificial Intelligence in Protein Science section, which is an abstraction that reduces the design of an artificial intelligence model to be used in the resolution of a specific problem in protein science. The whole process follows three steps directed to build a competent model; these steps are Overall, AI displays a window of opportunities to solve complex problems in PS because of its potential in finding patterns and correlating information that requires the integration of protein data exceeding many petabytes. However, we are still far away from solving all the protein tasks computationally. As a result of our biochemical meta-analysis, we showed that AI applications are strongly directed to function identification and protein classification , for macThe evaluation accuracy horizon is an open epistemic horizon, as shown in coding architecture, which is based on creating a specific-weight matrix or a bit vector that represents the sample. This practice was observed in some articles , together with those in computer science  promote the innovation process in tecno-sciences by combining tacit and explicit knowledge, sharing skills, methodologies, tools, ideas, concepts, experiences, and challenges to fully explore the binomial AI–PS promising area of research . A very lphafold , one whilphafold . At the One challenge for the binomial AI–PS research area is to tackle the representation of tacit knowledge and include it in the ML algorithms. The relevance of tacit knowledge in the building up of protein science knowledge has come a long way since Polanyi first noted it, extending to different fields in the search for an improvement of their practical skills. In AI, the predominant way of knowledge acquisition and performance is a formal one in which the machine learns and expresses explicitly through guidelines and that works in a focalized mean; the new task alludes to a tacit dimension , which rTo sum up, the systematic review and the biochemical meta-analysis offered in this article focused on the enormous innovation that has been made in the binomial AI–PS research, both in its applications and its road maps to solve protein structures and function prediction, protein and drug design, among other tasks. The contribution of this study is 3-fold: firstly, the setup of a cross-functional group in which computer scientists, professionals in biomedicine, and a philosopher constructed a common language and together identified relevant literature in the inter-field of AI–PS and constructed a bridge between the two fields, which can serve as a framework for further research in either area.in silico simulations, ML, direct evolution approach, etc.) that allowed us to classify, stratify, and contribute to the evolving protein science field. Thirdly, we showed that the binomial AI–PS, a progressive research program, as Lakatos would say and has still several challenges to tackle, such as the development of a comprehensive machine learning benchmarking enterprise, the experimental confirmation of the structure of the 3D modeling in laboratories, the classification, etc., controls the vulnerability of the neural networks, the development of a tool-kit to design novel biocatalysts not found in nature using reverse engineering, human-made metabolic routes, the design of new antibody molecular factory, novel proteostasis systems, the understanding of protein folding and protein-aggregation mechanisms, etc. Finally, we suggested that there may be a paradigm shift in the AI–PS research as a result of the recent great outcome of Alphafold, encouraging its use to the new generation of scientists.Secondly, we stressed the importance of a finer-grain understanding of training and validation methods of ML models and their outcomes, combining databases from several areas of knowledge (life-science experiments, In any case, what is clear is that a cross-functional group of scientists from several knowledge domains is required to work in coordination for sharing ideas, methodologies, and challenges toward the development of road maps and computational tools, paradigms, tacit, and explicit knowledge to fully explore and close the gap of the binomial AI–PS, a promising research area.


Comparing chemical structures to infer protein targets and functions is a common approach, but basing comparisons on chemical similarity alone can be misleading. Here we present a methodology for predicting target protein clusters using deep neural networks. The model is trained on clusters of compounds based on similarities calculated from combined compound-protein and protein-protein interaction data using a network topology approach. We compare several deep learning architectures including both convolutional and recurrent neural networks. The best performing method, the recurrent neural network architecture MolPMoFiT, achieved an F1 score approaching 0.9 on a held-out test set of 8907 compounds. In addition, in-depth analysis on a set of eleven well-studied chemical compounds with known functions showed that predictions were justifiable for all but one of the chemicals. Four of the compounds, similar in their molecular structure but with dissimilarities in their function, revealed advantages of our method compared to using chemical similarity.The online version contains supplementary material available at 10.1186/s13321-022-00622-7. In the early days of drug discovery and development, researchers believed that chemically similar molecules would have similar biological functions. Although this stands true for many molecules, there are numerous examples where this is not the case. Considerable differences in biological function can even be seen in chemicals with only bioisosteric replacements of atoms/groups and optical enantiomers . When chInferring functions by predicting one or more protein targets for a compound using machine learning algorithms is a widely used methodology. Examples include TargetNet  and the Another means of comparing chemical structures is to define a measure of biological similarity , for exaThe previously mentioned methodologies for predicting targets and functions based on chemical structure used various machine learning methodologies such as random forests and support vector machines , 7. ReceIn this manuscript, we introduce a new method for predicting protein clusters, constructed on the basis of a similarity measure from network topology analysis of compound-protein and protein-protein interaction data. Our method expands the work of Edberg et al.  and SchaInteraction data was extracted from the STITCH and STRING databases for the species homo sapiens, where STITCH contains chemical-protein interactions and STRING protein-protein interactions. We used the same versions of the databases as used by Schaal et al.  to maintFor all molecules in all resulting clusters, SMILES strings were generated from their 3D representations using RDKIT . A SMILEIn our work we used different deep learning approaches to predict functional clusters based on chemical structures. These approaches require different forms of transformations of the molecular data (the SMILES strings).For the Deep Neural Networks (DNNs) the SMILES strings were converted to bit vectors of Morgan fingerprints . These fThe data was prepared for Convolutional Neural Networks (CNNs) by converting the SMILES strings to feature matrices using the method proposed by Hirohara et al.  Fig 2A)A2A). DatFor Recurrent Neural Networks (RNNs) the input SMILES strings were converted to tokens using the methods of Li & Fourches . Both atData augmentation is a popular technique used in deep learning to compensate for lack of variation in the training data and often improves the model’s ability to generalize to unseen data . The traAll models were trained with categorical cross-entropy loss and Adam optimizers . The hypA fully connected Deep Neural Network (DNN) architecture consisting of 3 hidden layers with 4096, 4096, and 1024 neurons, respectively, was used. Each layer was followed by a ReLU activation function and dropout with a rate of 0.4. Alternative choices for the number of hidden layers and neurons per layer was investigated (results not shown), but this 3 layer neural network performed the best.Convolutional Neural Networks (CNNs) are the network architecture of choice for many computer vision applications . They caSeq2seq: The Seq2Seq model, adapted from Xu et al. , consistMolPMoFiT: The MolPMoFiT architecture was adapted from Li and Fourches . This arRecurrent Neural Network (RNN) architectures are widely used for applications involving temporal or sequential data and can accommodate input data of variable lengths . For QSAThe workflow used in this study is given in Fig. When training on all clusters the data was split into 80/10/10 for training, validation, and testing, respectively. Performance on the test set was then evaluated based on the F1 statistic.To assess the real-world applicability of the model, the cluster assignments for a set of well-known chemicals were investigated using the final model. These chemicals were not included in the data used for training or validation. The known functions of these chemicals were compared to the functions represented in their assigned clusters. To assign properties to a particular cluster, the set of proteins interacting with the chemicals in the clusters were obtained from the STITCH database. The functions for the obtained set of proteins were assigned as the functions of the cluster. Two sets of functional data were generated for the clusters, one using the higher quality cutoff parameter (Qcutoff) and the other one using a lower cutoff parameter (Lcutoff), which includes an increased set of proteins and their functions, but with lower confidence.Cross-validation was carried out for the obtained subsets of data from each distance threshold derived dataset. From the F1 statistics and their standard deviations Fig. , and ranFor assessment of the best performing model, MolPMoFiT, the clustering obtained for the entire dataset, using an intermediate distance threshold of 0.005, was used. This resulted in 241 clusters of 89216 chemicals  without augmentation (for comparison), (2) with one class-wise augmentation, and (3) with five class-wise augmentations and obtained test set F1 scores of 0.831, 0.873 and 0.887, respectively. Based on the F1 scores, there is a clear performance improvement when using data augmentation. Therefore the MolPMoFiT model utilizing SmilesPE tokenization with five class-wise augmentations was selected for further evaluation.The functions for a set of chosen chemicals excluded from the model training  were predicted using the final model and the distances between these chemicals  were determined using cluster distances obtained initially from hierarchical clustering Fig. . The preFor the chemical nalorphine, there is currently no DrugBank annotation, but it is known that it acts as an antagonist while morphine acts as an agonist . Since bmentclass2pt{minimConcerning the chemicals with similar structures and different functions, our model predicted that chlorpromazine act on dopamine receptors, promethazine on cholinesterases and ion channels, while imipramine and desipramine act on neurotransmitters. This corresponds well with their evidence from article  and DrugHowever, for the chemical estrogen, the predicted functions were not in line with those of DrugBank. Nevertheless, if we look at the second and third best clusters for this chemical, based on the sorted softmax probabilities prior to the prediction made for this model, we see functions for this chemical that are more in line with those of DrugBank , is due to the two major features present in the MolPMoFiT model, dropconnect and ASGD (Averaged Stochastic Gradient Descent), along with regularization techniques introduced in the model. Furthermore, AWD-LSTM (the underlying model of MOLPMOFIT) has shown to outperform the other RNN based models for language processing .The predictions of this model were explored for a set of well-studied chemicals, that were not used during the model development. The predicted biological functions were compared to those in DrugBank and elsewhere in the research literature for both high and low confidence data. The functional predictions were reasonable for all but one of the chemicals, estrogen. This could be due to an incorrect classification made by the model or due to unknown interactions of estrogen. As estrogen is such a well-known chemical the latter seems unlikely. It is also possible that the ideal cluster for estrogen had been removed from the dataset. Several small clusters (those with less than 100 compounds) had been removed prior to model training. This was a necessary step as the models required sufficient information for each cluster to make predictions.Even though the model gave reasonable predictions for most of the molecules, estrogen appeared to be an outlier. This can be explained in two ways: (i) there was not enough evidence in the literature to prove the interaction of estrogen with the receptors in the cluster (ii) the model made an incorrect classification. In a general sense, cases of the former type could aid in future research for identify novel interactions by chemicals, or to expose overlooked interactions (helpful for understanding the side effects of molecules). Cases of the later type could be addressed by incorporating confidence into the model output, for example Conformal prediction . It is aWe present a new method for predicting protein targets, and implicitly biological functions, based on chemical structure using a deep learning model trained on data from network topology analysis. The method has high accuracy for predicting the correct protein target cluster, and produces lists of potential functions that were in accordance with a set of well-known compounds. Our results demonstrates the usefulness of complementing target predictions based on chemical similarity with ’biological’ distances from network topology analysis of compound-protein and protein-protein interaction networks.Additional file 1: Fig. S1 SMILES augmentation for the molecule aspirin. Fig. S2 Randomization test results for all the architectures used for different set of data. Fig. S3 Structures of the chemicals used for evaluating the model. Table S1 Functions predicted by the model for estrogen predicted for the second and third ranked clusters (based on the output softmax probabilities).


The BioPlex project has created two proteome scale, cell-line-specific protein–protein interaction (PPI) networks: the first in 293T cells, including 120k interactions among 15k proteins; and the second in HCT116 cells, including 70k interactions between 10k proteins. Here, we describe programmatic access to the BioPlex PPI networks and integration with related resources from within R and Python. Besides PPI networks for 293T and HCT116 cells, this includes access to CORUM protein complex data, PFAM protein domain data, PDB protein structures, and transcriptome and proteome data for the two cell lines. The implemented functionality serves as a basis for integrative downstream analysis of BioPlex PPI data with domain-specific R and Python packages, including efficient execution of maximum scoring sub-network analysis, protein domain–domain association analysis, mapping of PPIs onto 3D protein structures and analysis of BioPlex PPIs at the interface of transcriptomic and proteomic data.bioconductor.org/packages/BioPlex), and the BioPlex Python package is available from PyPI (pypi.org/project/bioplexpy). Applications and downstream analyses are available from GitHub .The BioPlex R package is available from Bioconductor ( R and Python that provide analysis capabilities for a series of downstream applications and that connect the networks with domain-specific functionality of the two major data science ecosystems.Protein–protein interactions (PPI) are physical contacts between proteins that are essential for the function, organization and regulation of molecular processes. PPIs can be experimentally detected based on yeast two-hybrid screening (Y2H) or affinity-purification mass spectrometry (AP-MS), and computationally inferred using a variety of strategies. Although comprehensive databases of validated and predicted PPIs exist, available PPI networks remain fragmentary and provide limited information on context dependency of the contained interactions. Using a large-scale AP-MS platform for two human cell lines, the BioPlex project has recently started to explore context dependency of human PPIs at proteome scale, which revealed shared and cell-specific modules between cell lines .Conflict of Interest: none declared.