The featureless interface formed by protein–protein interactions (PPIs) is notorious for being considered a difficult and poorly druggable target. However, recent advances have shown PPIs to be druggable, with the discovery of potent inhibitors and stabilizers, some of which are currently being clinically tested and approved for medical use. In this study, we assess the druggability of 12 commonly targeted PPIs using the computational tool, SiteMap. After evaluating 320 crystal structures, we find that the PPI binding sites have a wide range of druggability scores. This can be attributed to the unique structural and physiochemical features that influence their ligand binding and concomitantly, their druggability predictions. We then use these features to propose a specific classification system suitable for assessing PPI targets based on their druggability scores and measured binding-affinity. Interestingly, this system was able to distinguish between different PPIs and correctly categorize them into four classes . We also studied the effects of protein flexibility on the computed druggability scores and found that protein conformational changes accompanying ligand binding in ligand-bound structures result in higher protein druggability scores due to more favorable structural features. Finally, the drug-likeness of many published PPI inhibitors was studied where it was found that the vast majority of the 221 ligands considered here, including orally tested/marketed drugs, violate the currently acceptable limits of compound size and hydrophobicity parameters. This outcome, combined with the lack of correlation observed between druggability and drug-likeness, reinforces the need to redefine drug-likeness for PPI drugs. This work proposes a PPI-specific classification scheme that will assist researchers in assessing the druggability and identifying inhibitors of the PPI interface. Protein interactions within cells modulate various physiological and pathological processes associated with health, constituting the human interactome network2. Moreover, anomalous protein–protein interactions (PPIs) and disordered proteins disrupt these intricate interactions, resulting in diseases like cancer and CNS, infectious or autoimmune disorders3. PPIs make up some of the most interesting yet challenging biological targets for drug discovery projects.Nearly every biological function within our body is mediated by proteins. Proteins do not function in isolation; they are powered by the interactions they form with other proteins and molecules4. In this context, druggability refers to the likelihood of a drug-like compound to modulate or inhibit an interaction between two proteins5. According to Cheng et al.6, an estimated 60% of drug discovery projects failed due to the undruggability of the target binding site and consequent inability to bind small drug-like molecules. PPIs are often considered as undruggable or difficult targets due to their large, shallow binding interface which lacks distinct, tractable concave pockets7. In addition, while traditional drug targets like enzymes, G protein-coupled receptors and ion channels, fortunately have endogenous ligands that act as a starting point for these drug discovery projects, this however is not the case for PPI targets8. With this in mind, designing inhibitors for PPI targets can carry a substantial risk of failure. The research program Illuminating the druggable genome9 (IDG) has aided the deciphering of the human genome, allowing for identification of some high-potential molecular targets for drug discovery. So far only 30% of screened PPIs have been found to have potentially druggable binding sites10. This, combined with the myriad of unsuccessful attempts at developing orally available inhibitors, has prompted an argument that these potentially high-value targets are difficult11.The core of any successful drug discovery project targeting a PPI lies within the nature of its interface and the druggability of associated binding pockets4 as shown in Fig. 12. This suggests that once notoriously undruggable PPI interfaces have revealed a certain ability to accommodate drug-like ligands. It was shown that hydrophobic grooves on the PPI interface, where the partner proteins bind and form stable interactions, can be utilized as hot spots in the design of small molecule PPI inhibitors13.Over the last decade, numerous small molecule ligands have been developed to bind directly onto the PPI interface, proving that certain PPIs can accommodate small molecule inhibitors14. These small molecules stabilize the protein–protein complex by targeting the pocket formed at the interface of two proteins15. In 2021, approximately 15 targeted protein degraders and molecular glues have entered the market including orally bioavailable protein degrader CFT745516. CFT7455 is a novel degrader of the IKZF1/3 complex used in the treatment of Multiple myeloma and non-Hodgkin's lymphoma17. So far, PPI stabilizers have shown to be successful in modulating PPIs with high selectivity, allowing us to target otherwise undruggable protein complexes18. Nonetheless, the development of novel PPI stabilizers is impeded by the lack of knowledge of the mechanisms and principles of these three-body systems18.Another approach for modulating PPI interactions is through PPI stabilizers5. Pock-etQuery19, SiteMap20, fPocket21, DoGSiteScorer22 and SiteFinder23 are some of the most popular druggability assessment servers. Despite the fact that these methods have shown an ability to successfully identify druggable pockets, the majority of them do not provide a ranking or classification system for those identified pockets. SiteMap20 stands out as one of the most reliable algorithms to assess the druggability of biological targets, having previously been used to evaluate the druggability of protein families such as NUDIX hydrolases, Human DNA Glycosylases and bromodomains26. Here, the druggability of the site is quantified by assigning a Druggability score (Dscore), hence evaluating its potential from a drug discovery perspective.In recent years, multiple in silico approaches have been developed to predict the druggability of PPI interfaces27 developed a classification system for SiteMap20, suggesting a cutoff point to distinguish druggable sites from difficult sites. Based on a validation set of 538 protein complexes, sites with a Dscore less than 0.8 were classified as difficult sites while protein sites with a Dscore greater than 1 were considered very druggable. However, the majority of these complexes were protein–ligand complexes, with MDM2/p53 being the only PPI included. Considering the need for a larger sample of PPIs and the numerous structural distinctions between PPI and protein–ligand interaction binding sites, it is unclear whether Halgren’s classification system27 can adequately describe the druggability of PPI interfaces. Modification of Dscore has been attempted previously, leading to Dscore+11, a score optimized based on a set of PPIs; however, this again only sets a cutoff for druggability scoring and does not offer a PPI-specific classification system. Moreover, Dscore+11 was not implemented within the SiteMap module. In this study, we rather focus on the direct application of Dscore to PPIs, proposing a PPI-focused classification system.To interpret Dscores, HalgrenWe use SiteMap to assess the protein binding interface of 320 crystal structures from 12 protein–protein interaction targets, and we introduce a new PPI-focused druggability ranking system based on Dscore. We also investigate the influence of Dscore parameters, drug-likeness, and protein flexibility on PPI druggability. The new classification system highlights the characteristic attributes of protein–protein interfaces and uses them to provide improved druggability predictions of novel PPI targets.20 and Fpocket21, rely on Cheng et al.’s data set6 for validation and assessment. Nonetheless, Cheng et al.’s data set included only one PPI , making it is rather unrepresentative of PPIs as a class of targets.The majority of druggability prediction tools, including popular servers SiteMap28 set has long been used to validate PPI-focused computational programs like PocketQuery19 and PPIMpred29. It does, however, have a relatively small number of targets. Recent discoveries have resulted in the identification of novel PPIs as well as orthosteric PPI inhibitors/stabilizers. Hence, new PPI databases have been curated to include additional PPI targets of high importance. The 2P2Idb30 database expands on the Wells set to include additional PPI targets with documented clinical implications.The WellsAs our goal was to define the parameters that guide the druggability assessment of binding sites on the PPI interface, we had to tailor the selection criteria for our data set to include high-resolution X-ray crystallographic structures of PPIs with established clinical implications. Given the unique nature of proteins, the availability of both (i) ligand-bound and (ii) protein/peptide-bound or apo-form crystal structures of a protein target is crucial for comprehensive and accurate assessment of druggability. Structures that exist as homodimers or bind covalent inhibitors were excluded from our analysis since they were beyond the scope of our research. Finally, each complex was visually inspected to ensure that the ligands bind directly to the PPI interface rather than distal locations on the protein.33.The final PPI dataset included 320 hand-curated protein crystal structures belonging to 12 PPI targets: DCN1, Bcl-xL, HDM2, XDM2, Bcl-2, MDMX, VHL, HPV E2, Menin, ZipA, IL-2, and XIAP , which correctly predicted the druggability of 86% of Cheng et al.’s data set6. This classification system is now widely used to analyze SiteMap results. Halgren’s classification was later adopted and modified to accommodate other protein families, such as Human DNA Glycosylases25 and bromodomains26. For instance, a new class was introduced to address the marginal targets that obtain a Dscore of just less than 0.8, in order to describe those targets with marginal druggability .To assess the druggability of PPIs, we required a tool that is readily available, reliable and allows for ligand-guided druggability estimation. More importantly, it must allow the classification and ranking of molecules included in the dataset with high accuracyTo sum up, Halgren’s classification system seems to be reasonable and robust for categorizing different targets based on their druggability; yet it needs to be refined further to address the characteristic nature of PPI interfaces, especially given that PPIs were not well-represented in the current system.After deciding on SiteMap for druggability assessment, 320 protein crystal structures, representing a dataset of 12 PPI targets, were acquired from the Protein Data Bank (PDB). The approach adopted in this work is a ligand-guided estimation approach. Accordingly, the “evaluate single binding region” option was used to define binding pockets and direct druggability assessment towards the PPI interface. Ligand-bound structures had co-crystalized ligands, whereas apo and protein/peptide bound structures were individually superimposed to a ligand-bound structure to include a drug-like molecule as a pocket identifier. The PDB with a co-crystalized ligand that best satisfies Lipinski's drug-like rules was chosen as the reference ligand-bound structure  were also considered in proposing the ranges for this PPI classification system. Out 139 co-crystallized protein structures, 109 ligands bind in the nanomolar levels, while another 11 ligands bind in the subnanomolar level . The second class ‘druggable’ represents targets with a Dscore range of 0.75 and less than 1.0. Interestingly, all of these PPIs showed to have inhibitors in the medium to high nanomolar inhibition range, namely Menin, MDMX, and HPVE2. The third class ‘moderately druggable’ describes targets with Dscore values ranging from 0.5 to less than 0.75 along with inhibitors in the micromolar range . XIAP is an exception here as it has got a mean IC50 value in the nanomolar range, however, it has the least druggable pocket at all which makes it classified as moderately druggable and as challenging as the other two PPIs in this category. Finally, the ‘difficult’ category includes targets with a Dscore of less than 0.5 along with inhibitors in the millimolar (mM) range .As shown in Fig. 3. In fact, nearly all reported inhibitors for moderately druggable targets are in the high nanomolar to micromolar ranges. No well-defined pocket was detected by SiteMap for protein ZipA, which is known for having a distinctive flat interface. As a result, ZipA was classified as a “difficult” target. This protein has a few inhibitors known in the literature; however, these do not bind to a cavity in the PPI interface, but rather to high energy hotspots on the protein11, which explains why efforts over the last 20 years have failed to generate a ZipA inhibitor that can reach clinical trials13.Figure Having proposed this classification system, it is still important to exercise caution when designing small molecule inhibitors for PPI targets. We believe that the druggability of a PPI target cannot be confined to an assessment of the interface alone. When thoroughly evaluating a protein binding site, several factors must be considered, including the effect of ligand binding; the flexibility of the protein; and the drug discovery history of the PPI of interest.2 = 0.74 and 0.58, respectively). In different protein binding sites, a directly proportional relationship between pocket size and Dscore is observed in Fig. SiteMap computes descriptors other than Dscore to provide further insight into the physiochemical properties of a binding site. Given that PPI targets have distinct structural features, it would be interesting to see which descriptors have the strongest correlation with the Dscore values of the PPI studied . This subtle effect becomes more evident as the protein shifts from one class to another; thus, as the hydrophilicity of the identified pocket increases, the Dscore decreases.The degree of hydrophilicity is another parameter used in the Dscore druggability equation. It differentiates between druggable and difficult pockets by penalizing highly polar sites that are less likely to bind drug-like ligands. Figure Overall, the observed correlation suggests that larger, well-defined hydrophobic pockets are more likely to bind drug-like ligands, which is reasonable , which are unlikely to accommodate drug-like small molecules. IL-2 exhibited a pocket structure similar to its peptide/protein bound structure, but yet again a 33% smaller Dscore than the ligand-bound form because of the large variation in the pocket size. Lastly, apo HDM2, Menin and HPV E2 structures deviated from the overall median by less than 10%, indicating that they have comparable Dscores and pocket size values. To sum up, crystal structures should be analyzed carefully especially if they have no ligand bound to the PPI interface, as they tend to show poorly druggable pockets and hence can give false conclusions about the druggability of the tested target.40, which might explain the aforementioned data regarding the druggability variation found for different types within a PPI structure. Notably, several studies have analyzed the chain flexibility in the Bcl-xL family of proteins and its effect on predicted druggability42. When the druggability score of each conformational sample is compared, significant differences can be observed between the apo , peptide-bound  and ligand-bound structures . Therefore, the analysis of the Bcl-xL apo form does not predict grooves and cavities in the protein interface which are able to bind small molecules, whereas the assessment of peptide- and ligand-bound Bcl-xL structures are successful in predicting cavities of varying features  over 100 ns MD simulations  are the only clinically available oral PPI drugs. Drug candidates in the preclinical stages were omitted from the analysis, as the goal is to assess the progress and current status of PPI medication development beyond the initial drug discovery phase.Up to our knowledge, these ten compounds (2 of 0.11). This is consistent with the findings in Table 53. With QED scores of less than 0.5 and multiple violations of Lipinski’s rules, nearly all these reported PPI drugs were classified as non-drug-like. The only exception here is the XIAP inhibitor ASTX660, an oral anticancer agent that is currently in phase I/II clinical trials54. Despite violating Lipinski’s size requirements, ASTX660 has a QED score of 0.55, indicating that it has more favorable drug-like properties than any currently approved or tested oral PPI drugs.As shown in Fig. 55. To do so, we need to examine published PPI drugs in terms of drug-likeness and see where they deviate from conventional drugs.Recent breakthroughs in PPI Inhibition have featured a series of small molecules that modulate protein function and act as new therapeutics. However, as the mode of action and common features of these inhibitors remain unclear, it is crucial to understand the distinctive features of PPI drugs52 is the most well-known. Moreover, the Quantitative Estimate of Drug-likeness (QED)53 has recently become a more extensively utilized technique for characterizing drug-likeness, particularly because it allows us to quantitatively assess and rank various compounds based on their score. Therefore, employing Lipinski’s Ro552 and QED53 would provide insight into the structural features of published orthosteric PPI inhibitors, and can also describe any correlation with the druggability of their PPI targets. These two were applied together to our data set of 221 different small molecules that inhibited 12 PPI complexes from different druggability classes, all of which directly interact with the binding interface of one protein partner.Amongst all drug-like rules, Lipinski’s rule of 5 (Ro5)52  demonstrated drug-like properties, with a QED score of 0.5 or higher. This is to be expected because QED is a more rigorous assessment tool that extends the number of considered parameters to eight53. Inhibitors with QED scores of less than 0.5 demonstrated unfavorable chemical properties, thereby reducing their drug-likeness. Regardless of the tool used, violations predict potential bioavailability issues; thus, as the number of violations increases, the compound is more likely to have low cell permeability and poor overall oral activity.Unsurprisingly, only 30% of these inhibitors (66 out of 220) seem to follow the conventional Lipinski’s Ro552 Table , the rem57. Morelli et al.55 have studied many of them and they accordingly proposed the Rule-of-Four which expands the limits of the Ro5 to consider a higher molecular weight (Mwt > 400 Da), hydrophobicity (ALogP > 4), unsaturation index (HBA > 4) and ring complexity (Rings > 4) observed in PPI drugs compared to non-PPI drugs. For instance, HDM2 inhibitor ABT-737 exhibited a very high oral bioavailability in phase II trials despite being large in size (Mwt = 813.43 Da)58. This is because PPI inhibitors may bind to multiple high energy hots spots rather than binding to a well-defined pocket, which requires them to have special characteristics in order to do so59.According to the Ro5 , 142 of published PPI inhibitors are drug-like, which is more than double the number of inhibitors suggested by standard drug-like rules (Ro5 and QED). This would greatly expand the chemical space for PPI targets, allowing for more successful hits with favourable ADMET properties.The rule of four serves as a descriptive tool that describes the overall physicochemical properties of currently known PPI inhibitors55.Applying the Ro5-1 to a set of ten clinically tested oral drugs/candidates, shown in Fig. To summarize, it seems that the current drug-like rules do not necessarily apply on the PPI inhibitors as no correlation was found between the ligand drug-likeness and the pocket druggability parameters. This informs researchers about the importance of proposing a PPI-specific drug-likeness rules similar to what has been suggested in this study for PPI interface druggability assessment and classification.In this study, we assessed the druggability of 12 commonly targeted PPIs using SiteMap, revealing a range of druggability scores to their respective binding sites. We attribute these differences to their unique structural and physiochemical features. Interestingly, these features were used to propose a new druggability classification system geared towards PPI targets. The newly suggested system classifies PPIs into four categories based on their druggability score (Dscore) as well as the inhibition activity of their respective inhibitors. The analysis of the different Dscore parameters shows that the pocket size and hydrophilicity have the strongest correlation with Dscore values of the studied PPIs. This is readily detected in the moderately druggable proteins, for which binding pockets are small in size and hydrophilic in nature, such as IL-2 and XIAP. We also studied the importance of protein flexibility on calculated druggability score, and found that protein conformational changes accompanying ligand binding in proteins such as Bcl-xL and IL-2 resulted in significantly higher druggability scores and more favorable structural features than observed for the apo form or, to a lesser extent, the protein/peptide bound structures MD simulations were in favor of this too as it showed significant variations in the pocket size and Dscore values throughout the course of the simulations. Finally, published PPI inhibitors were studied to determine their “drug-likeness” using Lipinski’s rule of 5 (Ro5) and QED score. Our findings revealed that the vast majority of PPI drugs, including those that have been orally tested/marketed, exceeded the typically acceptable size and hydrophobicity parameters. This made us propose relaxing the drug-like rules and allow at least on violation (Ro5-1) which had a considerable impact on preventing the exclusion of many important PPI drug candidates that have already shown clinical value and could have been eliminated by the conventional drug-like rules. This work proposes a PPI-specific classification scheme that will assist researchers in assessing druggability and identifying PPI inhibitors with a potential oral activity.28 set and the 2P2I database61 are commonly used data sets for the validation of in silico PPI assessment tools. Accordingly, 12 protein–protein interaction targets have been derived from both lists and included in our PPI dataset: these proteins are Defective in cullin neddylation protein 1 (DNC1), Menin, Human double minute 2 (HDM2), Xenopus double minute 2 (XDM2), Protein MDM4 (MDMX), Interleukin-2 (IL-2), Regulatory protein E2 (HPV E2), Bcl2-associated agonist of cell death (Bcl-2), Apoptosis regulator Bc-X (Bcl-xL), Von Hippel Lindau protein (VHL), E3 ubiquitin-protein ligase XIAP (XIAP) and Cell division protein ZipA (ZipA). A search was then conducted on the protein data bank (PDB)62 to obtain all ligand-bound, protein/peptide bound and apo structures for each PPI from the aforementioned set. PPI structures containing a covalent inhibitor or an inhibitor not bound to the PPI interface, or with mutated residues, were excluded from the PPI dataset. Overall, a total of 320 crystal structures were included in the study.The Wells23. If multiple chains were present for the same protein, they were manually removed so that only the chain bound to the inhibitor remained. After that, all structures were corrected to add missing atoms, residues, chains or loops. Protonation states were assigned to each atom using Protonate3D in MOE. These protein crystal structures were then imported into Maestro63 to ensure the structural correctness of prepared structures, proteins were refined using the Protein Preparation wizard64 module where hydrogens were added through hydrogen bond optimization and subsequently underwent restrained minimized to a lower energy state with a maximum RMSD of 0.30 Å.Each crystal structure had solvent atoms and co-crystalized heteroatoms removed using Molecular Operating Environment (MOE)23. Only the inhibitor-bound protein was then removed, keeping its bound inhibitor in the PPI pocket of the apo structure or the protein/peptide bound complexes. Consequently, the aligned inhibitor was used to identify the respective pocket in the PPI interface.For protein/peptide bound complexes, secondary structure assignments were manually removed from each complex. Given both apo and protein/peptide complexes contained one chain of the desired protein, they underwent sequence alignment and structural superimposition with an inhibitor-bound protein from the same family, using the align/superimpose feature in MOE20 module with all settings kept to default. To define protein binding pockets, the “Evaluate single binding region” option was selected. SiteMap generates various physiochemical descriptors including size, volume, and degree of enclosure, hydrophobicity and hydrophilicity. Most importantly, it scores a protein binding pocket by calculating its Dscore:Proteins were then processed through the SiteMap65 was used to run MD simulations of the apo form of HPV protein (PDB code 1R6K50). Partial charges and other parameters were assigned to the protein structure using the ff19SB force field. The protein system was built using the xleap module of AmberTools, where it was neutralized by the addition of Na + counter ions and solvated by a truncated octahedral box of TIP3P water. The energy of the system was subsequently minimized in two steps using the pmemd program in the AMBER 18 package65 first, all solute atoms were constrained with a force constant of 500 kcal mol−1 (− 2) during minimization, and then the entire system was subjected to minimization without any constraints. The system was then gradually heated from 0 to 300 K in the NVT ensemble. Using the Langevin thermostat, the SHAKE algorithm was applied to all bonds containing hydrogen atoms with a collision frequency of ps-166. Finally, the protein was subjected to a 100 ns MD simulation in the NPT ensemble, with the system temperature and pressure set to 300 K and 1.01 × 105 Pa, respectively.Molecular dynamics (MD) simulation studies were conducted to investigate the dynamic nature of protein by generating numerous conformations of the protein for druggability assessment. The AMBER18 package67 via the cpptraj module of AmberTools68. Every tenth frame (10 ns) was used in clustering. Ions and solvent molecules were removed from each system. The distance cutoff between points for forming a cluster, ε, was set to 3.0 . The trajectory files were evaluated by extracting the graph of root-mean-square deviation (RMSD) using centering utilities. The size of the binding site on the protein interface was computed for each structure using the Sitemap20 module of the Schrodinger’s Maestro63.After completion of MD runs, trajectories were analyzed using DBSCAN51. Their structural features were then studied using Lipinski’s rule of 5 (Ro5)52 and the Quantitative Estimate of Drug-likeness (QED)53. Individual physiochemical parameters were calculated for each inhibitor, including the number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA), molecular weight (Mwt) and LogP to estimate their drug-likeness in accordance with the Ro552. Ligands were then reprocessed using QED53 which classifies and ranks ligands according to their QED score. The QED score takes into account 8 parameters: molecular weight (Mwt), number of rotatable bonds (nRotB), number hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA), octanol–water partition coefficient (ALogP), number of aromatic rings (Arom), number of structural alerts (Alerts) and molecular polar surface area (PSA). The QED score ranges from 0 to 1; a QED score of 0 indicates unfavorable non-drug-like properties, whereas a score of 1 indicates favorable drug-like properties.To evaluate the drug-likeness of published PPI inhibitors, these ligands were isolated from their protein complexes and compiled into databases. Recent review articles were used to obtain orally tested/approved PPI drugs and their respective structures23 descriptors Lip_drug-like and Lip_violations were calculated for each inhibitor to estimate their drug-likeness using the relaxed drug-like rules (Ro5-1). A drug-like inhibitor is expected to have a Lip_drug-like score of 1 if it has no more than one violation. Inhibitors with more than one violation will have a Lip_drug-like score of 0 and therefore are classified as non-drug-like.MOESupplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.


The stability of protein sequence structure plays an important role in the prevention and treatment of diseases.In this paper, particle swarm optimization and tabu search are combined to propose a new method for protein structure prediction. The experimental results show that: for four groups of artificial protein sequences with different lengths, this method obtains the lowest potential energy value and stable structure prediction results, and the effect is obviously better than the other two comparison methods. Taking the first group of protein sequences as an example, our method improves the prediction of minimum potential energy by 127% and 7% respectively.Therefore, the method proposed in this paper is more suitable for the prediction of protein structural stability. Bioinformatics uses computer software and hardware technology to calculate, analyze and mine biological data, which greatly improves the efficiency of biological data information solution, and is of great significance to more accurately grasp the operation law of biological structure , 2.Protein molecules constitute the tissues and organs of the human body. The spatial structure of white matter determines the life function of protein, and then affects and even determines the life activities of the human body. Many diseases occur because the structure of protein molecules is destroyed or mutated, which makes the protein lose its stable state , 4.According to the principle of thermodynamics, when the potential energy value of protein reaches the lowest, the protein structure is in the most stable state. Therefore, using computer algorithm to predict protein stable structure has become an important subject in the field of bioinformatics. The analysis methods of protein structure prediction can be divided into three categories: comparative modeling method, reverse folding method and ab initio prediction method –7. The cFor protein structure modeling, HP lattice model and ab non lattice model are the most commonly used. For HP model, Zhou uses Monte Carlo algorithm and genetic algorithm to solve it. Monte Carlo algorithm uses random number to generate a molecular structure, and then calculates the energy value of the molecular structure. Through continuous iteration, a molecular structure with the lowest energy value is obtained, which is regarded as the real structure of the protein sequence to be tested . During To sum up, there are many methods in the field of protein structure prediction, but the accurate prediction of the lowest potential energy point of protein stable structure is still a difficult problem. In addition, the amount of data corresponding to protein is huge, which requires high convergence speed and accuracy of the algorithm. Therefore, this paper improves the particle swarm optimization algorithm and integrates tabu search strategy to improve its performance in protein structure prediction.In order to verify the effectiveness of the protein structure prediction method proposed in this paper, the next experimental study is carried out. The computer CPU used in the experiment is Intel dual core, single core dominant frequency is 3.60ghz, memory size is 16 GB. The operating system of the computer is windows 10.In the experiment, the parameters of this algorithm are: the total number of particles is 260, and the upper limit of iterations is 800, two learning factors In the experiment, in order to compare with the algorithm in this paper, particle swarm optimization algorithm and tabu search method are also selected as the control method in the experiment.In the experiment, the experimental subjects selected the artificial protein sequences commonly used in the experimental standards, and were divided into four groups. The first group of artificial protein sequence is 13, which contains 5 hydrophilic amino acids (a) and 8 hydrophobic amino acids (b); The second group of artificial proteins is 21 in length, including 8 hydrophilic amino acids (a) and 13 hydrophobic amino acids (b); The length of the third group of artificial proteins is 34, including 13 hydrophilic amino acids (a) and 21 hydrophobic amino acids (b); The length of the fourth group of artificial proteins is 55, including 21 hydrophilic amino acids (a) and 34 hydrophobic amino acids (b). The amino acid configuration of the four artificial protein sequences is as Fig. In simulation experiments, the stability of protein structure is generally judged by potential energy value. The lower the potential energy value, the more stable the protein structure is. Therefore, the comparison of potential energy becomes the key criterion for the stability of protein structure.According to the above four groups of artificial protein sequences, the mathematical models were constructed according to tabu search method, particle swarm optimization algorithm and the algorithm in this paper, and the lowest potential energy value was predicted. The results are shown in Table It can be seen from the experimental results in Table Furthermore, the stable structures of the above four artificial protein sequences were predicted, and the results are shown in Fig. The research on the stability of protein sequence structure is an important content in the field of bioinformatics. In order to solve this problem, a new protein structure prediction method is proposed by combining tabu search and particle swarm optimization. This method fully considers the dependence of tabu search method on the initial solution. Firstly, particle swarm optimization algorithm is used to obtain the initial solution, and then tabu search method is used to construct neighborhood function, candidate solution set, tabu list and tabu criterion. The fusion of the two algorithms is used to complete the prediction of protein structure. In the experiment, four groups of artificial protein sequences were selected. Firstly, the AB non lattice model was used to build the model, and then three methods were used to calculate the lowest potential energy. Experimental results show that, compared with tabu search and particle swarm optimization, this algorithm can get lower potential energy value of protein sequence and predict more reasonable protein stable structure. Taking the first group of protein sequences as an example, the lowest potential energy calculated by this method is further reduced by 127% compared with tabu search method and 7% compared with particle swarm optimization algorithm.Protein structure prediction is actually a complex polynomial non deterministic problem, namely NP complete problem. In this paper, ab initio prediction is selected as a method to solve the problem of protein structure prediction. Therefore, it is necessary to determine the simplified model of protein structure. By constructing a simplified model, the relationship between protein structure and energy is simulated as a potential energy function. Then, according to the thermodynamic hypothesis, when the potential energy function of protein is the lowest, it is the most stable structure of protein.At present, there are two commonly used simplified protein structure models, one is HP lattice model, the other is AB non lattice model.In the two-dimensional plane, the two-dimensional HP lattice model and the two-dimensional AB non lattice model are used to simulate the protein folding structure. The structure is as Fig. Figure Figure The energy function of two-dimensional AB non lattice model is expressed as follows:By comparing the advantages and disadvantages of HP lattice model and ab non lattice model, AB non lattice model is used to express protein structure.The stable structure of protein corresponds to the lowest energy value of protein potential energy function, which makes the prediction of protein stable structure into the optimization of energy function. In this field, tabu search and particle swarm optimization are common methods.Because of its good memory function, tabu search algorithm has strong local search ability. By using tabu list and recording the searched local optimal solution, tabu search algorithm can avoid circuitous search and jump out of local optimal solution. At the same time, the tabu search algorithm can avoid missing good individuals by spurning some better solutions.However, tabu search algorithm has a certain dependence on the initial solution. The better initial solution can make the tabu search algorithm find the better value, and the worse initial solution will reduce the convergence speed of tabu search algorithm.Generation of initial valueParticle swarm optimization algorithm can get a better solution. Therefore, in this paper, the better solution obtained by particle swarm optimization algorithm is taken as the initial solution of tabu search algorithm, and then an optimization algorithm with both global search ability and local search ability is formed by using tabu strategy and contempt criterion of tabu search algorithm.First, the particle swarm optimization algorithm is used to search globally. After converging to a certain extent, the better solution is used as the initial value of tabu search algorithm, so as to improve the search accuracy and efficiency of the algorithm.v is velocity; x is position; (2)Neighborhood functionThe velocity and position of each particle are updated as shown in formula  and form(3)Candidate solution setThe neighborhood solution of tabu search algorithm is realized by single point mutation. In order to ensure the diversity of neighborhood solutions, this paper uses the silk line of disturbance variation to generate neighborhood solutions. Here, the neighborhood function is designed as follows:L neighborhood solutions, the first (4)Taboo listThe candidate solution set is a subset of neighborhood solutions. In the algorithm, by calculating the fitness value of each of (5)Taboo criterionTabu list is used to store the recently searched solutions, and its length is Let the fitness of the candidate solution Then, the candidate solution z satisfies the tabu criterion, and the candidate solution is tabu.Here, set


Post-translational modification (PTM) is an important biochemical process. which includes six most well-studied types: phosphorylation, acetylation, methylation, sumoylation, ubiquitylation and glycosylation. PTM is involved in various cell signaling pathways and biological processes. Abnormal PTM status is closely associated with severe diseases (such as cancer and neurologic diseases) by regulating protein functions, such as protein–protein interactions (PPIs). A set of databases was constructed separately for PTM sites and PPI; however, the resource of regulation for PTM on PPI is still unsolved.https://ptmint.sjtu.edu.cn/) that contains manually curated complete experimental evidence of the PTM regulation on PPIs in multiple organisms, including Homo sapiens, Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Saccharomyces cerevisiae and Schizosaccharomyces pombe. Currently, the first version of PTMint encompassed 2477 non-redundant PTM sites in 1169 proteins affecting 2371 protein–protein pairs involving 357 diseases. Various annotations were systematically integrated, such as protein sequence, structure properties and protein complex analysis. PTMint database can help to insight into disease mechanism, disease diagnosis and drug discovery associated with PTM and PPI.Here, we firstly constructed a public accessible database of PTMint (PTMs that are associated with PPIs) (https://ptmint.sjtu.edu.cn/.PTMint is freely available at: Bioinformatics online. Post-translational modification (PTM) is an important biochemical process among several organisms. There are over 400 known PTM types, of which six types are well studied, including phosphorylation (Phos), acetylation (Ac), methylation (Me), sumoylation (Sumo), ubiquitylation (Ub) and glycosylation (Glyco). Most biological process and signaling pathway occur by interaction of two or more proteins , which aThousands of PTM sites of several organisms have been identified owing to the advancements in mass spectrometry (MS) . HoweverUniprot database offers the ‘PTM/Processing’ section to record the PTM sites and/or processing events. However, its relatively hard for inexperienced users to search and browse, and it also lacks the information of PTM onto the 3D structures. Another database, PhosphoSitePlus  The PTM sites existed on the structures. (ii) The protein name of matched sequence was same as query protein. (iii) The matched complexes with two chains were preferred. Due to the limited crystal structures, a large scale of protein–protein dockings was performed by molecular docking softwares. ZDOCK is a fast Fourier transform-based docking procedure for rigid proteins that searches for all possible binding modes in the translational and rotational space between two proteins and evaluates each pose using an energy-based scoring function  (function . For homhttps://pymolwiki.org/) for complex interfaceResidues analysis. Briefly, this Python script splits the complex into two pieces for two interacting chain and then calculates the difference between the complex-based accessible surface areas and the chain-only-based accessible surface areas. If the value is greater than cutoff (the default is 1.0 Å2), the residues is marked as interfacial residue. The same process was handled for PTM sites to label interfacial PTM sites.Interaction assignment was handled with in-house software , Threonine (T), Tyrosine (Y), Lysine (K) and Arginine (R). Serine and Threonine can be modified by Phos and Glyco. And Lysine can be modified by multiple modifications, such as Ac, Me, Sumo and Ub. To assess the important regulatory role of PTM in PPI networks This importance score takes into account of the number of PTM types and interacting proteins and calculated with In which N is the number of protein which specific PTM site regulates.And the Score can be normalized with The normalized_score reflects the relative importance of specific regulatory PTM sites, which will increase as the number of PTM types or interaction proteins increase.The web interfaces were implemented in Hyper Text Markup Language (HTML), JavaScript (JS) and Cascading Style Sheets. And the web frame was supported by Bootstrap v4 framework. Furthermore, 3Dmol.js plugin was employed to visualize protein 3D structures . And theThe current version of PTMint contains 2477 non-redundant PTM sites in 1169 proteins affecting 2371 protein–protein pairs involving 357 diseases. Uniport database provides the 425 records of PTM effect on PPI and 322 functional PTM sites. PTMD database provides the 45 records of PTM effect on PPI and 34 functional PTM sites. And PhosphositePlus guides us to search some reference literature based on text mining. In the two regulatory roles (Enhance and Inhibit), ‘Enhance’ has a bigger proportion, suggesting that PTM might tend to increase PPIs . In our As shown in To further understand the intrinsic characteristics of PTM-modified proteins and interactor proteins, these all proteins were grouped into multiple classifications according to the biological function , mainly The top 10 diseases affected by PTM were counted and shown in Quick search and advanced search were implemented on the homepage and ‘Search’ page, respectively. On the homepage, the user can directly search the database by inputting keyword  . Single The typical result page consists of six main sections , includiThe PTM types and genes were sorted and organized in alphabetical order, which allow the user to quickly obtain interested results .All data in the PTMint database can be downloaded in the ‘Download’ page, including PTM experimental evidence and protein structure information. And detailed instructions were available in the ‘Help’ page.To our knowledge, PTMint database is the first comprehensive database of experimental evidence of the PTM effects on PPIs, which not only includes complete experimental records, such as PTM types and sites, interacting proteins, detection methods, associated diseases and co-localization, but also integrates the according sequence and structure annotation , systematically.PTM level in proteins is controlled precisely based on a temporal and spatial context . And theAccording to previous reports . For exaDue to the limited crystal structures, several software and webserver offer the solution by molecular dock, such as ZDOCK . TherefoPTMint database can be further improved in the following aspects. First, the current version of the database contains six main model organisms and PTM types. More organisms and PTM types will be added. Second, additional curations, such as PTM-targeted drugs, PTM expression analysis, PTM-associated survival analysis and association between PTM and mutation will be integrated. Third, we will replace the original predicted complex structures when newly high-resolution complex structures are released in the PDB database. In the future, we will continually maintain and update the PTMint database, when newly regulatory PTM sites are reported in the literature.In conclusion, we developed PTMint, a comprehensive database of experimentally verified PTM effects on PPIs. We believed that this database should be a useful platform for biologists and bioinformaticians to explore PTM roles on disease development, diagnosis and drug discovery.btac823_Supplementary_DataClick here for additional data file.


Physically interacting proteins form macromolecule complexes that drive diverse cellular processes. Advances in experimental techniques that capture interactions between proteins provide us with protein–protein interaction (PPI) networks from several model organisms. These datasets have enabled the prediction and other computational analyses of protein complexes. Here we provide a systematic review of the state-of-the-art algorithms for protein complex prediction from PPI networks proposed in the past two decades. The existing approaches that solve this problem are categorized into three groups, including: cluster-quality-based, node affinity-based, and network embedding-based approaches, and we compare and contrast the advantages and disadvantages. We further include a comparative analysis by computing the performance of eighteen methods based on twelve well-established performance measures on four widely used benchmark protein–protein interaction networks. Finally, the limitations and drawbacks of both, current data and approaches, along with the potential solutions in this field are discussed, with emphasis on the points that pave the way for future research efforts in this field. The three-dimensional shape of a protein is described by its tertiary structure. The protein ternary structure enables specific chemical groups to be placed at exact positions in a three-dimensional space, leading to particular enzymatic functions and other important structural, transport, and regulatory functions in an organism Protein-protein interactions (PPI) have different structural characteristics which are related to their physiological function and evolution Furthermore, protein complexes can be grouped into transient and permanent based on their lifespan. The PPIs of transient complexes are established and resolved transiently, whereas the PPIs of permanent complexes are stable. Interestingly, most obligate protein complexes are also permanent; however, the non-obligate complexes can be permanent or transient Proteins are often involved in more than one complex in different subcellular compartments and biological processes. Therefore, it is important to accurately identified protein complexes to understand not only protein complex formations but also the higher-level cellular organization Although experimental data contain biological and technical noise, that may lead to false-positive and false-negative interactions, several computational approaches have been proposed to moderate these limitations by efficiently analyzing a large amount of data to predict protein complexes. Consequently, several surveys In this study, before going through the current approaches, we first introduce important terminologies in this field. We then provide a comprehensive and updated review of various state-of-the-art computational methods in the field of protein complex identification. The computational methods are organized into three categories, namely: (i) cluster-quality-based methods, (ii) node-affinity-based methods, and (iii) network embedding methods see . Further2Let 3There are already several databases of PPIs across different model organisms. However, it is worth noting that regardless of which technique is used, the molecular interaction data may contain artifacts due to their design The Database of Interacting Proteins (DIP) E. coli PPI networks. The former is obtained experimentally from affinity purification mass spectrometry (AP-MS), while the latter contains interactions that are predicted by utilizing evolutionary signatures in protein sequence and structure. Finally, PIPs There exist other datasets that are species-specific and for simplicity, these PPI networks are just named after the corresponding first author. Gavin Saccharomyces Genome Database (SGD) Besides PPI networks, different sets of protein complexes are available as gold standards. CYC2008 4Several computational approaches have been developed to identify the underlying protein complexes and functional modules in PPI networks. They arise as a complementary tool next to experimental techniques to enhance the existing recourses and knowledge by finding novel protein interactions and complexes. However, due to the highlighted limitations of experimental screens, the performance of computational approaches is restricted. These limitations convey three main challenges of computational methods for protein complex predicton: The computational approaches can be categorized in several ways. Some methods rely solely on PPI networks, whereas others depend on additional biological information. The methods in the latter category, such as Dense neighborhood Extraction using Connectivity conFidence Features (DECAFF) 5Node affinity-based approaches consider the inherent relationship among nodes during the process of identifying clusters. The clusters are generated from seeds and expanded by nodes in their neighbor with a high affinity score.The Markov CLustering algorithm (MCL) is one of the most widely used graph clustering algorithms The Molecular COmplex DEtection (MCODE) method is a heuristic approach and works based on local neighbor densities The CFinder algorithm is based on the clique percolation method The Affinity Propagation algorithm (AP) finds clusters based on a random walk and passing messages between nodes The Detect Module from Seed Protein (DMSP) integrates PPI networks and microarray data to predict protein complexes The Clustering based on Maximal Clique (CMC) is another clique percolation-based algorithm The PEWCC consists of two steps: pre-processing and finding protein complexes based on a local clustering coefficient The ProRank+ is based on a ranking algorithm and has several steps, including pruning, filtering, ranking, finding protein complexes, and post-processing The Discovering Protein Complexes based on Neighbor Affinity and Dynamic Protein Interaction Network (DPC-NADPIN) is a neighbor affinity-based algorithm The identify Protein Complexes from weighted PPI networks using Mutual Important Interacting Partner relation (idenPC-MIIP) 5.1The cluster quality-based approaches define a quality function and detect the clusters such that the maximum quality is obtained. The clusters are formed from different seeds via iteratively adding or removing nodes to gain their optimal quality.The Restricted Neighborhood Search Clustering (RNSC) algorithm identifies protein complexes based on two cost functions and the algorithm has two main steps, (i) clustering and (ii) filtering clusters based on their functional similarity The Module Inference by Parametric Local Modularity (miPALM) algorithm The Clustering with Overlapping Neighborhood Expansion (ClusterOne) algorithm aims to detect clusters with high cohesiveness The Core&Peel method The Inter-Module Hub Removal Clustering (IMHRC) algorithm identifies clusters based on the cohesiveness cluster quality measure in four steps The last three approaches in this category, namely: Protein Complexes from Coherent Partition (PC2P) Given a graph GCC-v Unlike the two previous approaches based on local graph properties, CUBCO 5.2Network embedding transforms nodes of a given graph The GANE algorithm predicts protein complexes based on Gene Ontology (GO) attributed network embeddings th protein plays role The Complex Prediction algorithm based on Network Motif (CPNM) Detecting Protein Complexes from protein-protein interaction networks via Multi-level Network Embedding (DPCMNE) 6+, recall+, and F-measure+There exist twelve well-established metrics to evaluate the performance of protein complex prediction approaches by comparing the reference protein complexes from gold standards with predicted complexes, that is sensitivity, positive predictive value, accuracy, separation Let The overlap score between the pair of protein sets If The fraction match (FRM) calculates the ratio of matched predicted protein complexes to the number of reference complexes. The maximum matching ratio (MMR) is based on a bipartite graph, in which the vertices in each partition correspond to reference and predicted protein complexes, individually, and the edges are weighted by the overlap score between the two partitions. Then, MMR is given by the ratio of the sum of the weight of the maximal matching edges to the number of reference complexes.The precision, recall, and F-measure are based on the matched predicted protein complexes and defined as:+ and recall+ are given by The precision+ is calculated the same way as the original F-measure but with precision+ and recall+.The + across different threshold values To summarize the twelve performance measures and to enable its visualizations, two composite scores are defined. The first composite score is given by the sum over MMR, FRM, SEP, ACC, F-measure To further evaluate the predicted protein complexes concerning biological relevance, two different types of analyses can be performed, that is: Over Representation Analysis (ORA) ORA is one of the commonly used approaches to determine whether a set of genes, i.e. proteins in a predicted protein complex, is overrepresented by known biological functions or processes more than what we expected by chance. To this end, the Where The GO semantic similarity The ORA and GO semantic analyses have their advantages and disadvantages. Both analyses evaluate the predicted protein complexes biologically and give an overview of how each computational approach performs regarding biological significance. However, the required information to calculate them is limited. The number of predicted protein complexes and the number of proteins in each of them can affect the two measurements. In 6.1E. coli, S. cerevisiae, and H. sapiens, by calculating the median GO semantic similarity of their reference complexes. It is expected that the reference complexes would achieve high semantic similarity values; however, this is not the case. In the case of E. coli, the number of proteins, protein complexes, and protein complexes with more than three proteins is lower in the gold standard of metabolic protein complexes than Ecocyc, by ∼ 57%, ∼45%, ∼53%, respectively. As a result, Ecocyc contains larger protein complexes and covers more proteins than Metabolic protein complexes.Here, we analyzed the biological significance of complexes in the gold standards of three species, S. cerevisiae, the CYC2008 and SGD gold standards contain almost the same number of protein complexes with more than three proteins, SGD includes two complexes fewer than CYC2008. The number of proteins and protein complexes is smaller in SGD compared with CYC2008 by ∼ 27%, and ∼ 26%, respectively.In H. sapiens is less than the other two species, on average by ∼ 23% and ∼ 36%, respectively, and on average in GO:CC, H. Sapiens shows semantic similarity value close to that in the other two species. To compare between categories of GO, it appears that data on GO:CC is more incomplete than the other two categories in E. Coli and S. cerevisiae. While GO:BP obtains better values in E. Coli and S. cerevisiae and is comparable with GO:CC in H. sapiens  the public availability of executable code and implementations and (ii) that the method does not rely on any additional biological knowledge or data. Therefore, MCL S. cerevisiae , fraction match (FRM), separation (SEP), sensitivity (SN), Positive predictive value (PPV), accuracy (ACC), precision, recall, F-measure, precisionOverall, based on comparative analysis, the approaches belonging to cluster quality-based achieved better composite score than the approaches in the other two categories across all PPI networks. While MCL from node affinity-based methods ranked first in its category and showed results on par with the cluster quality-based approaches for Collins and KroganExt PPI networks. Moreover, the methods based on biclique spanned partitioning of the network, PC2P, CC, WCC, OCC, OWCC, and CUBCO indicate similar and consistent results across all PPI networks and exhibited the highest performance. More precisely, PC2P obtained the highest accuracy for all PPI networks and outperformed the other approaches regarding separation, F-measure, and F-measure + for three out of four PPI networks, Collins, KroganCore, KroganExt. Likewise, WCC achieved the highest positive predictive value across all PPI networks. Therefore, not surprisingly, WCC ranked first in Collins and Gavin PPI networks, while PC2P and CC outperformed the other contenders in KroganExt and KroganCore, respectively concerning the composite score  networks. In the past decades, several computational approaches have been proposed to address the problem of predicting protein complexes given a PPI network as input. The computational approaches show increasing improvement of performance over time which has led to the detection of more accurate protein complexes. However, there is still room for improvement in this field, as pointed out in the following.The increasing availability of high-throughput data facilitates the 8.1H. Sapiens in its recent version (4.4.206), and we compared BioGRID with PPIs in Negatome. First, we converted the protein identifiers in Negatome, from UniProt to gene name, and with this, we could only retrieve the gene name for 5808 PPIs in Negatome, of which 965 PPIs are also presented in the BioGRID PPI network. On the other hand, due to the limitations of high-throughput approaches, different types of false-negative interactions are also present in PPI networks, such as week transient interactions Since PPI networks are the input of protein complex prediction algorithms they play an important role in their performance. However, these networks are still incomplete and noisy Another issue with current PPI networks is that most of the existing data are largely static, providing only limited to no insights into the dynamics of cellular activity E. Coli  by casting the problem of protein complex prediction into biclique spanned partitioning of the network. However, in the recent category of protein complex prediction algorithms, network embedding-based approaches, after integrating multiple data into the network by constructing a weighted adjacency matrix of the original network, employ core attachment methods to detect final protein complexes. Therefore, the approaches in this category still suffer from predicting large as well as dense protein complexes.Another issue is that different approaches heavily depend on multiple parameters, which render it difficult to interpret the predicted protein complexes. Depending on which PPI network, protein complex gold standard, and performance measure are used, the algorithms predict different sets of protein complexes. This problem is even worse in the case of network embedding-based approaches since they must not only set the parameters but also the hyperparameters to find the optimal set of protein complexes.Moreover, most algorithms utilize different metrics to score the protein interactions and remove those with a score below a given threshold. However, they did not consider a way to bring the false-negative interactions into the PPI networks except CUBCO Finally, as mentioned earlier, PPIs play a significant role in molecular functions and biological processes, and they can contribute to our understanding of cellular activities. Due to the labor-extensive and time-consuming experimental approaches, several computational methods have been developed to facilitate the prediction of PPIs . It is possible to improve the performance of link prediction algorithms by utilizing protein complex prediction algorithms in such a way that, first, the protein complex prediction algorithms cluster proteins with similar structures or attributes into the same group, and then several similarity measures can be used to compute the probability of interactions between proteins in the same group. These ideas provide directions that can be explored in future studies.Sara Omranian: Conceptualization, Writing – review & editing. Zoran Nikoloski: Conceptualization, Writing – review & editing. Dominik G. Grimm: Conceptualization, Writing – review & editing, Supervision, Funding acquisition.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Target identification and validation are crucial for the discovery of protein and peptide drugs, and bioinformatics prediction of targets based on the characteristics of known target proteins will help improve the efficiency and success rate of target selection. However, owing to the developmental history in the pharmaceutical industry, previous systematic exploration of the target spaces has mainly focused on traditional small-molecule drugs, while studies related to protein and peptide drugs are lacking. Here, we systematically explore the target spaces in the human genome specifically for protein and peptide drugs. Compared with other proteins, both successful protein and peptide drug targets have many special characteristics, and are also significantly different from those of small-molecule drugs in many aspects. Based on these features, we develop separate effective genome-wide target prediction models for protein and peptide drugs. Finally, a user-friendly web server, Predictor Of Protein and PeptIde drugs’ therapeutic Targets (POPPIT) (http://poppit.ncpsb.org.cn/), is established, which provides not only target prediction specifically for protein and peptide drugs but also abundant annotations for predicted targets.After decades of development, protein and  Outlook by EvaluatePharma forecasts that the market share of biologics (the vast majority of which are protein and peptide drugs) was expected to increase from 25% in 2016 to 30% in 2022, and in 2022, 52% of the top 100 product sales would come from biologics After decades of development, protein and peptide drugs, whose emergence has revolutionized the pharmaceutical industry, have become a major drug class in the marketplace. Since the approval of the first recombinant protein drug in 1982 , the number of approved protein and peptide biopharmaceuticals has increased rapidly, from less than 10 in the 1980s to an approval rate of 10–12 per year (from 1995 to 2014), based on United States and European Union markets Protein and peptide drugs currently include hormones, growth factors, blood factors, thrombolytics and anticoagulants, interferons and interleukins, and monoclonal antibodies, and have covered a wide range of therapeutic areas including cancer, various inflammation-related conditions, metabolic disorders, autoimmune diseases, and cardiovascular diseases i.e., prediction of whether a protein is suitable as a drug target) based on the characteristics of known protein/peptide drug targets would be helpful for this process. A previous analysis showed that ∼ 70% of first-in-class drugs  were identified through a target-based drug discovery strategy Target identification is crucial for protein and peptide drug discovery, and bioinformatics prediction of candidate targets , using the signal peptide and transmembrane domain features for the former and the domain family distribution features for the latter . roughly distinguished putative cancer targets for protein, peptide, and small-molecule drugs individually using extracellular domain inclusion, peptide-binding domain inclusion, and the existence of corresponding small-molecule inhibitors . found that the targets of biologics have higher degrees and smaller cluster coefficients than those of small-molecule drugs It is necessary to systematically explore the target spaces in the human genome specifically for protein and peptide drugs because of the potential differences that may exist between the targets of different types of drugs. Realizing this potential difference, Sanseau et alConsidering these issues, in this paper we have systematically explored target spaces in the human genome specifically for protein and peptide drugs . First, Here, we only collected therapeutic targets of approved drugs. Therapeutic targets are defined as those proteins (or other biomolecules) that a drug targets to exert its therapeutic effects, excluding side-effect targets and other binding proteins without pharmacological efficacy To establish target prediction models, first, the gold standard positive (GSP) and gold standard negative (GSN) datasets were constructed for the protein and peptide drugs, respectively. The GSP set for protein drugs was the non-redundant target set of protein drugs established above. For the GSN set for protein drugs, considering the difficulty in developing an experimentally negative dataset, we adopted the following construction scheme. First, we removed as many known protein drug targets as possible from the whole human genome. To ensure that the GSN set was as “pure” as possible, here, the removed known targets not only included the targets collected above but also other possible targets. These other possible targets included targets of trial protein drugs from the study by Rask-Andersen et al. To assess the performance of the prediction models, in addition to cross-validation, three independent test sets were also established.The first independent positive test set was composed of the newly added therapeutic target set of the approved protein/peptide drugs from the latest version of DrugBank . After removing the overlap with the GSP and GSN sets, 45/29 targets remained to constitute this independent positive test set for protein/peptide drugs.The second independent positive test set was composed of 141/60 targets of clinical trial protein/peptide drugs from the study by Rask-Andersen and colleagues https://www.accessdata.fda.gov/scripts/cder/daf/).In the third independent positive test set, we divided the GSP set of the protein/peptide drug targets into two parts. One part was composed of drug targets approved before 2010 . This part together with the GSN set built above was used to train the prediction model. The other part involved new therapeutic targets introduced by drugs approved in or after 2010, and was used as the independent positive test set. The approval date information of drugs was obtained from related annotations of corresponding data sources or identified by Drugs@FDA .The independent negative test set for each of these three groups of positive test sets was established in a similar way to the construction of the GSN set. Specifically, before randomly choosing a similar number of proteins to that of the corresponding independent positive test set, in addition to removing the known protein/peptide drug targets, the corresponding GSN set and the independent positive test set were also excluded. Considering the randomness of the construction of the independent negative test set, we also repeated the construction process 100 times. Finally, the assessment results of the prediction models on independent test sets were calculated as the mean ± SD of 10,000 results (https://www.perl.org/cpan.html). The AA sequences of human proteins were obtained from Swiss-Prot  AAs were classified into 9 groups: tiny (A+C+G+S+T), small (A+B+C+D+G+N+P+S+T+V), aliphatic (A+I+L+V), aromatic (F+H+W+Y), nonpolar (A+C+F+G+I+L+M+P+V+W+Y), polar (D+E+H+K+N+Q+R+S+T+Z), charged (B+D+E+H+K+R+Z), basic (H+K+R), and acidic (B+D+E+Z). Here, “B” is the abbreviation of “Aspartate or Asparagine” and “Z” is the abbreviation of “Glutamate or Glutamine” Pfam domain if is the gene expression level of the gene in tissue i divided by the sum of expression levels of the gene across all tissues, and p = 1/n (n is the number of tissues). The larger the TSPS value is, the more tissue-specific the expression of the gene is. Here we used RNA-sequencing data from 32 tissues provided by Uhlén et al.The tissue specificity score (TSPS) was adopted to measure the degree of tissue specific expression of a gene Homo sapiens” class of age 1 (whose proteins are only found in humans). We used ratioC to check the gene polymorphism, which was computed as:nsN and sN are the number of nonsynonymous and synonymous single nucleotide polymorphisms (SNPs) in the gene, respectively, and ε was set to 0.01 as per Yao et al.The data on evolutionary rates and original ages of human proteins were obtained from our previous work http://www.uniprot.org/docs/7tmrlist and http://www.uniprot.org/docs/pkinfam)  The list of 1639 human transcription factors (TFs) was from the study by Lambert and colleagues stσ represents the number of the shortest paths between nodes s and t, stσ(n) is the number of those paths that go through node n, and N is the total number of nodes in the network. Degree reflects a node’s local importance, while betweenness centrality captures the degree to which the node influences the communication between other nodes in the network Human gene–reaction associations were from Recon 2  I (File S1).We used the minimum redundancy maximum relevance (mRMR) method for feature selection. mRMR ranks features based on both their relevance to the classification variable and the redundancy between each other S and T are separately the already-selected and to-be-selected feature sets and |S| and |T| are the number of features in the corresponding sets. mRMR first moves the feature most relevant to the classification variable into S and then moves the remaining features from T into S one by one, requiring that each time the selected feature jf optimizes:jf to the classification variable c and S.Suppose that http://home.penglab.com/proj/mRMR/.The GSN set was repeatedly constructed 100 times as stated above, and thus, the mRMR feature ranking was implemented based on the mean MI of the 100 times. The mRMR program was obtained from postO that a protein is a target can be computed as the product of its prior odds pirorO and the likelihood ratio (LR), represented by Equations .According to the Bayes rule, the posterior odds quations , where Pf is referred to as the ratio of the probability of feature f being observed in the GSP set to that in the GSN set:T and F are the number of true and false drug targets in the gold standard dataset, and fTP and fFP are the number of those satisfying evidence f. The LR can reflect the prediction ability of biological evidence f. When the LR is larger than 1, postO is larger than pirorO. This means that the feature has some ability to discriminate true drug targets from false ones. When the integrated n features are independent, the naïve Bayes rule can be adopted, where the combined_LR can be calculated simply as the product of the LRs of individual features:The LR of biological evidence pirorO is actually constant for different proteins, and thus postO is proportional to the LR. In practice, we directly used the LR as the prediction score to reflect the probability that a protein is a drug target The prior odds The naïve Bayes classifier was implemented by Perl scripts written in-house.We used the receiver operating characteristic (ROC) curve to measure the performance of the prediction model based on 10-fold cross-validation and the independent test sets (File S1). Here, the ROC curves were plotted using SPSS software The foundation of the POPPIT web server was a MongoDB database. Above this database, the analysis application was implemented in Perl and Python, and the web presentation application was written in JavaScript and CSS.To systematically explore the target spaces of protein and peptide drugs, we first comprehensively collected the therapeutic targets of approved drugs. In total, we obtained 132, 55, and 634 target proteins for approved protein, peptide, and small-molecule drugs, respectively (see Method) .First, we noticed that although there was overlap, most targets were specific to a certain drug type  A, suggesP = 0.0010) (P = 0.0010)  of the targets of drugs newly approved every five years were “new targets”, while this average proportion (32.29%) was much smaller for small-molecule drugs  B. “New t 0.0010) C, where  0.0010) B or “jum 0.0010) C was larP = 2.20E–16) . We found that this proportion for protein drugs was significantly larger than that for small-molecule drugs  D. This i.20E–16) D.In this section, we intend to answer two questions. One is “what characteristics do protein and peptide drug targets have with respect to other proteins, respectively”. The other is “are they different from conventional small-molecule drug targets, and if so, how”.For the first question, in terms of sequence and physicochemical property, we found that compared with other, nontarget proteins, successful protein drug targets tend to contain significantly higher proportions of tiny, small, and aromatic AAs, lower proportions of basic and charged AAs, and they are significantly more hydrophobic (measured by GRAVY), and their charges tend to be negative and therefore have a significantly lower theoretical pI .Table 1QIn terms of structure, compared with other proteins, protein drug targets contain significantly more domains , indicatratioC) at the human population level  , which mon level .Furthermore in terms of functional aspects, 91% of the collected protein drug targets participate in signal transduction, several times higher than the proportion of other proteins . HousekeFinally in terms of pathway and network analyses, compared with other proteins, we first observed that protein drug targets are involved in significantly fewer metabolic reactions . This inFor the second question, we observed that successful protein drug targets are significantly different from those of conventional small-molecule drugs in many aspects, including AA composition and physicochemical properties, protein structure, evolution, network topology, and especially function . For exaFor peptide drug targets, compared with both other nontarget proteins and small-molecule drug targets, we found that they also have distinctive characteristics . In addiIn summary, both successful protein and peptide drug targets have their own specific characteristics , and areIn this section, we constructed separate target prediction models for protein and peptide drugs.To build the prediction models, efficient prediction features were first collected. Theoretically, all statistically significant features of the protein/peptide drug targets revealed above can be efficiently used for prediction, which was confirmed by the LR results    or thoseomatic”) , was ranomatic”) , which wFurther, we adopted the naïve Bayes rule to integrate these features to construct the target prediction model. For the model construction for protein drugs, starting from the most powerful feature, “Betweenness centrality_signal”, according to the feature order ranked by mRMR , we addelate the real development process of drug targets, to approximately estimate the performance of our prediction system on future truly successful targets. The results showed that when “Model_6_protein” and “Model_6_peptide” were trained using this scheme, they still yielded satisfactory performance against the corresponding independent test sets  as the independent positive test set (see Method). Against this independent test set, “Model_6_protein” achieved an AUC of 0.9678 ± 0.0102 and “Model_6_peptide” achieved an AUC of 0.9428 ± 0.0194, validating the high discriminative efficacy of these two models A and B. est sets E and F, de novo targets” , we adopted three additional types of independent test sets as follows. For the first type, we removed homologous redundancy from the original independent test positive and negative sets (as described above), respectively. Against such three non-redundant independent test sets, both “Model_6_protein” and “Model_6_peptide” still obtained good performance . For thetargets” . For thetargets” , indicatFor ease of use of our target prediction methods, we developed a user-friendly web server named POPPIT. The purpose of this web server was to perform genome-wide target prediction for protein and peptide drugs, and to also provide abundant annotations for the predicted targets together with their relevance to various diseases. POPPIT aims to prioritize candidate targets and increase the efficiency and success rate of target selection during drug discovery for protein and peptide drugs A.Figure In POPPIT, for a protein of interest, a target prediction score for the protein/peptide drug is given by “Model_6_protein”/“Model_6_peptide” B. Each cIL5RA, combined_LR = 7049, rank = 3) has been a successful therapeutic target of a protein drug. It is specifically bound by benralizumab, which is a humanized recombinant monoclonal antibody and was recently approved by the FDA, as a maintenance treatment for patients older than 12 years of age with severe eosinophilic asthma (https://www.accessdata.fda.gov/drugsatfda_docs/label/2017/761070s000lbl.pdf). Meanwhile, according to the “Disease relevance analysis” function of POPPIT, we found that the alpha subunit of interleukin-5 receptor is related to asthma (distance = 1) by its direct interaction with interleukin-5 (gene name: IL5), which is known to play a role in the etiology of asthma CD22), interleukin-4 receptor subunit alpha (gene name: IL4R), leptin receptor (gene name: LEPR), and melanocortin receptor 4 (gene name: MC4R), whose corresponding drugs have also been approved by the FDA recently , but after controlling the “transmembrane region” factor, protein drug targets inversely tend to be hydrophilic compared with other proteins . The “transmembrane region” factor control was implemented by constructing a negative control set with a (approximately) same distribution as the positive target set on “transmembrane region” property. This result suggests that the characteristic of higher hydrophobicity of protein drug targets may be related to their higher probability of containing transmembrane regions, and indeed the “transmembrane region” property has a positive correlation with the “hydrophobicity” property  between different features and performed mRMR feature selection (see Method).Here we found that protein/peptide drug targets have many distinctive characteristics. However, we should be cautious that all the properties have certain correlations between each other . For exa. simply considered the extracellular membrane proteins among them as candidate targets for protein drugs in vitro and in vivo experiments should be designed to further validate this potential target. Then, for a validated candidate target, candidate drugs can be screened or designed The aim of the constructed protein/peptide drug target prediction model and the corresponding web server POPPIT is to predict proper protein/peptide drug targets from disease-related proteins, improving the efficiency and success rate of the “target identification” step in a “target-based drug discovery” strategy Generally, the positive and negative sets of drug targets should be very imbalanced. However, it is difficult to estimate the real ratio between the positive and negative set sizes. Fortunately, we find that the impact of the ratio is very small on the prediction ability of the constructed prediction model. ROC AUCs of “Model_6_protein” almost remained unchanged across different ratios against both 10-fold cross-validation and three independent test sets .Naïve Bayes was adopted to establish the prediction models mainly based on the following considerations. On the one hand, for this work, the performance of naïve Bayes was better than other shallow machine learning methods, including the support vector machine (SVM), neural network (NN), decision tree (DT), random forest (RF), logistic regression (LR), and K-nearest neighbor (KNN) . On the In summary, this study performed a systematic exploration of target spaces in the human genome for protein and peptide drugs. It not only contributes to the understanding of the distinctive cellular roles of the targets and the MoA of protein/peptide drugs from an overall perspective, but also, more importantly, provides algorithms and a practical tool for performing quantitative target prediction, together with lengthy annotations. This work will help improve the efficiency and success rate of target selection, and ultimately contribute to novel target discovery for protein and peptide drugs.http://poppit.ncpsb.org.cn/.POPPIT is available at Zhongyang Liu: Conceptualization, Methodology, Software, Validation, Formal analysis, Writing - original draft, Writing - review & editing, Visualization, Funding acquisition. Honglei Li: Software. Zhaoyu Jin: Writing - review & editing. Yang Li: Investigation. Feifei Guo: Investigation. Yangzhige He: Investigation. Xinyue Liu: Writing - review & editing. Yaning Qi: Writing - review & editing. Liying Yuan: Investigation. Fuchu He: Supervision, Funding acquisition. Dong Li: Supervision, Funding acquisition, Writing - review & editing. All authors have read and approved the final manuscript.The authors have declared no competing interests.


Deep eutectic solvents (DESs) are a mixture of hydrogen bond donor (HBD) and hydrogen bond acceptor (HBA) molecules that can consist, respectively, of natural plant metabolites such as sugars, carboxylic acids, amino acids, and ionic molecules, which are for the vast majority ammonium salts. Media such as DESs are modular tools of sustainability that can be pointed toward the extraction of bioactive molecules due to their excellent physicochemical properties, their relatively low price, and accessibility. The present review focuses on the application of DESs for protein extraction and purification. The in-depth effects and principles that apply to DES-mediated extraction using various renewable biomasses will be discussed as well. One of the most important observations being made is that DESs have a clear ability to maintain the biological and/or functional activity of the extracted proteins, as well as increase their stability compared to traditional solvents. They demonstrate true potential for a reproducible but more importantly, scalable protein extraction and purification compared to traditional methods while enabling waste valorization in some particular cases. In order to maintain the environmental balance, several sectors of industry, most notably the food industry, have started to use greener solvents as alternatives in their manufacturing processes rather than synthetic, petroleum-derived organic solvents . SynthetDESs are classified into four categories according to the general formula described in The most commonly used DES involved in protein studies belongs to type III and consists mostly of choline chloride (ChCl) as HBA and amines, amide, carboxylic acids, sugars, and polyols as HBD . The molType III is one of the simplest types of DES in its preparation, costing significantly less than other DESs and are highly biocompatible as they commonly use ChCl as HBA . ChCl isNatural deep eutectic solvents (NADESs) are a concept first introduced by Proteins are the fundamental unit of life that can be extracted from various sources like plants, animals, and microorganisms . InvestiA remarkable example of this concept has been made in the work of Convergently, Oppositely, Finally, Throughout these various studies, it was shown that DES-based extraction could be coupled with other techniques such as ultrasonication to improve the extraction efficiency of proteins and opens a promising horizon for development at the industrial scale, more notably for waste valorization in biorefineries. Nowadays, PEG-based DES extraction is getting more commonly used as it stabilizes the extracted proteins and is, in addition, approved by the FDA (Food and Drug Administration) . This opApart from solid–liquid extraction of proteins, liquid–liquid extraction of proteins has also been extensively studied and often comes along aqueous two-phase systems (ATPS) as it ensures, in some cases, an efficient recovery of the targeted protein . This system is formed by mixing a water-soluble polymer with another polymer or inorganic salt above the critical concentration , for exaThere are numerous chemicals that can fulfill the roles of HBD and HBA inside a DES, and thus, many combinations allowing a great number of DESs are possible. However, not every DES is suitable for protein extraction, and a universal method for their selection based on the desired application is lacking. Therefore, one needs to screen for suitable DESs that facilitate protein extraction of a given biomass, thus demanding extensive time and resources . InherenDespite the reportedly high efficiencies of extraction that were previously compiled, the limitation to this system concerns the recovery and isolation of proteins extracted in the DES phase. This step is known as the back extraction and is crucial for both DES recycling and protein separation. Protein recovery from DES is very slow due to high interfacial mass transfer resistance . Xu et aOenothera biennis oils that both reduce blood lipid and possess the anti-arteriosclerosis activity. However, the production of these oils generates a large amount of deoiled cake waste. Thus, extracting compounds from this process waste is economically viable for the industry due to its valorization. O. biennis cakes with ChCl: glycerol (1:2) and obtained a protein-rich precipitate by adding water as an anti-solvent to the protein extraction solution.A wide range of raw biomasses have been identified in the previous literature as containing proteins and were compatible for a DES-mediated extraction . Many ofMore than simply extracting plant proteins, DESs are also involved in the extraction of protein from animal hair, that is, rabbit hair and wool fiber. As considerable amounts of wool waste are produced every year globally by the textile industry, a tremendous environmental challenge arises. Herein, the concerned protein is keratin, a reactive, biocompatible, and biodegradable material that can be recovered when recycling wool. Preparation of pure protein is of prime importance for research, the pharmaceutical industry, and other sectors of industry in order to commercialize safe products for use or actual consumption. Conventional ways of protein purification include alkali, ammonium sulfate or acetone precipitation; salting-out, ion exchange , electroIn this aim, Similar studies have shown the effects of DES on the purification of proteins by ATPS. Indeed, 2HPO4 aqueous two-phase system to purify R-phycoerythrin with a recovery efficiency reaching 92.60%  based on magnetic adsorbents can be seen as a complementary approach to ATPS in order to recover purely isolated proteins . In MSPE3O4@TiO2@[ChCl][Xylitol] gave the best results among the four DES-modified particles, when using 10% sodium dodecyl sulfate-acetic acid as the eluent to reach an extraction efficiency of 85.9%. In addition, Fe3O4@TiO2@[ChCl][xylitol] particles maintained excellent extraction efficiency toward chymotrypsin after six cycles of use (s of use . MSPE ins of use .DES are a class of greener alternative solvents that have attracted considerable attention in biorefinery research due to their biodegradability, sustainability, low toxicity, and facile preparation. In addition to their numerous applications in biocatalysis, they are currently used as greener solvents but nonetheless efficient extraction and separation media for the recovery of animals and plants as protein-rich renewable biomass and thus provides new perspectives to biorefineries. Despite technical limitations intrinsic to DESs, such as viscosity, they have been shown to be a promising alternative solvent for extraction of a wide variety of proteins and can be compatible with various raw biomasses. It is to foresee that their implementation in industrial refineries could solve technical and methodological issues that are currently encountered in the food industry such as the valorization of wastes or the volatility of currently used organic solvents. Selection of isomeric analogs of HDB, the presence of water during DES formation, and the structure–function relationship existing between extracted proteins and DESs need to be kept in mind. Therefore, optimization of DES preparation in order to be simple and environmentally friendly needs to be a priority when designing protein extraction processes when starting from either animal or plant sources. Further research should facilitate the use of DESs in the sustainable development of protein purification in the food industry by notably demonstrating proof of their scalability.