Food processing and consumption involves multiple contacts between biological fluids and solid materials of processing devices, of which steel is one of the most common. Due to the complexity of these interactions, it is difficult to identify the main control factors in the formation of undesirable deposits on the device surfaces that may affect safety and efficiency of the processes. Mechanistic understanding of biomolecule–metal interactions involving food proteins could improve management of these pertinent industrial processes and consumer safety in the food industry and beyond. In this work, we perform a multiscale study of the formation of protein corona on iron surfaces and nanoparticles in contact with cow milk proteins. By calculating the binding energies of proteins with the substrate, we quantify the adsorption strength and rank proteins by the adsorption affinity. We use a multiscale method involving all-atom and coarse-grained simulations based on generated ab initio three-dimensional structures of milk proteins for this purpose. Finally, using the adsorption energy results, we predict the composition of protein corona on iron curved and flat surfaces via a competitive adsorption model. The biological activity and biocompatibility of inorganic materials is controlled by interactions at the bio–nano interface—a nanoscale layer where the material comes in contact with biomolecules. Through biomolecule adsorption, change in protein conformation, and surface chemistry, the bio–nano interface plays a key role in medical applications and food processing. Understanding the underlying mechanism of these interactions may help to control biofilm growing, fouling, or contamination by metabolites arising from bacterial activity on the contact surfaces ,2,3,4. TAmong the materials used in industrial and medical applications, one of the most common ones is stainless steel, predominately consisting of metallic iron alloyed with chromium and carbon. These materials are widely used due to low cost, relative biological neutrality, and corrosion resistance at standard low-heat conditions. However, this might be not true at the higher temperatures, e.g., milk fouling and milk contamination is a critical problem in the heating process for the food industry . Daily, Besides the food industry, biomolecular interactions on metallic surfaces are essential for implants and other medical devices. When a tissue makes contact with such surfaces, unfavorable reactions such as infections, fibrosis, thrombosis, and inflammation may result . Recent Finally, the interest in bio–nano interactions is also driven by the concerns of the safety of nanoparticles (NPs) for human and animal health. The NP toxicity correlates to chemical aggressiveness of the material and scales with its physicochemical properties, such as surface area, charge, or reactivity . EstabliIn this work, we investigate interactions between milk proteins and three face-centered cubic (fcc) slabs of zero-valent iron, constituting a simplified model of stainless steel. To accomplish this, we selected six of the most abundant proteins from the major protein groups found in natural cow milk . Our priWe aim to calculate the content of milk protein layers on iron surfaces using first principles multiscale simulations. To do so, we use a CG kinetic Monte Carlo (KMC) method  to models (CdSe) ,35, the s (CdSe) ,29,31.Generally, the interaction between a protein and a solid surface, such as an inorganic engineered material, includes several contributions, both specific and nonspecific, and depends on the chemical composition, size, shape, surface roughness, surface charge, surface functionalization, and hydrophobicity of the material. All these aspects should be considered for building CG models of the bio–metal interfaces. Various examples of such models (including the UA model) have been previously used to study the competitive adsorption of proteins onto solid surfaces ,36.The UA model, previously developed in our lab ,26,31, iT is the temperature, I is the ionic strength i-th AA and the NP, includes short-range and long-range terms:The UA model divides the NP into core and surface segments according to the distances between them and the protein. Then, the interaction potential between each AA and the NP can be represented by a combination of a short-range surface van der Waals (vdW) potential  simulations by following the procedure described in the next section. The long-range term arising from the vdW forces acting through the water medium between the core of the NP and the We then calculate the interaction energy for all possible orientations of the protein, described by orientation angles Integration of the interaction potential over all possible orientations The set of rotational configurations along with the corresponding The interaction of atoms with a solid surface can be best performed using enhanced sampling methods ,38,39,402  and Ag [2 . The app2 ,45. A CH2 .z axis,The AA side chain analogues (SCAs are the compounds that result from cutting off the side chains of AAs at the protein backbone and substituting a hydrogen for the carbon) were placed in the center top of the slab. In this work, we calculated the energy for 22 SCAs, including two forms of Histidine, HID, and HIE, depending on the location of the protonation on the nitrogen atom and two forms of glutamic acid, GLU and GAN, negatively charged and neutral, respectively  software plugin in GROMACS ,45,49. Tz direction in vacuum, and was in periodic boundary conditions along the x and y coordinates. The final simulation box sizes were We simulated three fcc surfaces of iron slabs  using GROMACS with the CHARMM-GUI/Nanomaterial Modeler tool and force fields ,50,51. TIn order to investigate the interaction of the entire protein with the NP, the UA model reads the atomistic 3D structure of the protein. These structures might not be available from experiments such as X-ray crystallography, NMR spectroscopy, or cryo-electron microscopy. Thus, we predicted 3D structures of the proteins using the sequence data. Here, we used I-TASSER (Iterative Threading ASSEmbly Refinement) 5.1 software  to generTo model competitive protein adsorption from milk, we imitated the natural protein concentrations. The majority of the milk ingredients are water (86–88%), fat (3–6%), protein (3–4%), lactose (5%), and minerals (0.7%) . Milk prl casein . In decrproteins . In the proteins .With extracted individual protein UA adsorption heatmaps, we can predict the composition of the adsorbed protein layer (NP protein corona) on the metallic surface. This layer can be subdivided into hard and soft corona . The harThe short-range potentials  through both experimental and docking simulation methods. Analysis using fluorescence spectroscopy showed that the FeNPs formed a complex with HSA through hydrogen bonds and van der Waals interactions. Furthermore, circular dichroism spectroscopy showed that the secondary structure of HSA was not affected by the FeNP. An MD study indicated that the FeNPs interacted with polar residues on the surface of the HSA molecule. The docking study found that ASP, ARG, SER, LYS, and GLU residues are the most likely to be on the FeNP surface. The reported adsorption free energy In reality, the metallic iron surfaces are quickly oxidized and hydroxylized, which changes their adsorption affinity to water and reduces the binding strength for AAs and proteins. Another assumption that affects the range of the observed energies is that the orientation distribution for adsorbed proteins is equilibrium. This assumption is used implicitly in the ensemble averaging procedure represented by Equation . This avWe next determined the composition of the milk protein layer at the iron surfaces. As discussed above, in this method, we assumed that the surface is represented by a spherical NP with the protein layer adsorbed on the whole surface of the NP (protein corona), and the adsorption energy is determined by the UA method. The adsorption free energy controls the behavior between the NP and the protein by using the HS KMC model. The higher the adsorption energy, the more probable it is for the protein to be bound on the surface for long periods of time. Calculating the actual adsorption kinetics for different proteins is more challenging. After giving the system enough time, the proteins will compete and form a stable protein corona around the NP. The concentrations of milk proteins to determine the corona were chosen from the literature ,59. The We repeated the KMC simulations several times for each NP size and averaged the observables over at least three randomly selected runs. Results of these simulations are listed in The corona results obtained on NPs of us works , we demoIn this work, we presented the results of modeling the interaction between iron surfaces and the most abundant milk proteins in a multiscale scheme based on a combination of the CG UA model with the KMC model for predicting the protein corona composition in the deposited milk layer on iron surfaces. We considered a simplified model of milk formed from the solution of the six most abundant proteins occurring in natural cow milk. Our study ranked the proteins by adsorption strength as follows: Our multiscale model of protein corona formation on solid NPs and surfaces can be generalized to a large variety of systems. It essentially relies only on the existence of an atomistic force field for the solids. It can be used for a variety of applications, such as controlling fouling, biofilm growth in food processing and packaging, and medical devices. We also plan to extend the boundaries of the multiscale model to a wider range of temperatures and pH values employed in industrial settings.


Alignment of protein structures is a major problem in structural biology. The first approach commonly used is to consider proteins as rigid bodies. However, alignment of protein structures can be very complex due to conformational variability, or complex evolutionary relationships between proteins such as insertions, circular permutations or repetitions. In such cases, introducing flexibility becomes useful for two reasons: (i) it can help compare two protein chains which adopted two different conformational states, such as due to proteins/ligands interaction or post-translational modifications, and (ii) it aids in the identification of conserved regions in proteins that may have distant evolutionary relationships.We propose ICARUS, a new approach for flexible structural alignment based on identification of Protein Units, evolutionarily preserved structural descriptors of intermediate size, between secondary structures and domains. ICARUS significantly outperforms reference methods on a dataset of very difficult structural alignments.https://github.com/DSIMB/ICARUS.Code is freely available online at  Protein structure is crucial for understanding evolution and function mechanisms at a molecular level. It is estimated that the number of protein folds is limited .Structure alignment method is determined by a scoring function used for protein structural similarity measurement and by an algorithm to perform the superposition. The main metrics used to quantify the difference between two protein structures are the Root Mean Square Deviation (RMSD) and the Template Modeling score (TM-score) . As compMost popular tools used for protein structure alignment such as TM-align , DALI H, and ComIn order to highlight protein structural similarity in case of structural flexibility and/or complex evolutionary relationship, a number of flexible structural alignment methods were developed such as DEDAL , KPAX R, and FATHowever, some protein cases remain difficult to align correctly. Indeed, even recent flexible methods are still unable to produce relevant structural alignment, especially in the cases of proteins with intrinsically disordered regions or with nonsequential relationships such as circular permutation. As a particularly interesting example, there are haloacid dehalogenase and chemotaxis protein CheY which share the same Mg2+-binding site and conserved residues involved in phosphate binding  which neIn this study, we present ICARUS  a flexible alignment method which uses the Protein Peeling algorithm  and the corrected AIC for small samples (AICc) values for each exploration level  in termsThe elbow curves  corroborFinally, ICARUS repeats the procedure after switching target and query proteins. This operation is necessary to perform better. On average, we obtain a difference of TM-score of 0.048, 0.067, 0.267, and 0.238 for levels 1, 2, 3, and 4, respectively when proteins to align are swapped . Yet, thOnce all the alignments for all PU exploration levels are calculated, the best alignment is chosen on the basis of the TM-score  optimizehttps://github.com/DSIMB/ICARUS. The PUs were determined using Protein Peeling version 3. The typical running time for a single comparison of a pair of protein structures ranges from a few seconds for exploration levels 1–3, to a few minutes for level 4, depending on the length of proteins and their topology  obtained for levels {1, …, n}. At level 1, ICARUS already outperformed all methods significantly (except KPAX) on the full RIPC dataset . Importantly, ICARUS is the only tool able to detect a structural relation at fold level for the particularly complex cases of the circular permutations associated with insertions  and 270 (d1nw5a_) residue long proteins. ICARUS was able to match 83.3% of reference alignment residues (10/12) while FATCAT, KPAX, and TMalign only 25% (3/12) and DEDAL only 58.3% (7/12). The sequence alignment produced by ICARUS can be found in Another even more complex example is presented in We also tested our method on the SISY dataset . This daAlignment of protein structures is very complex in case of their great conformational variability, or complex evolutionary events such as large insertions, circular permutations or repetitions. For these complex cases, classical methods are not efficient and correct structure superposition is only possible using flexible alignment approaches. The ICARUS method presented here is based on protein structure division into structurally and evolutionarily relevant Protein Units to determine the rigid regions reoriented during alignment procedure.Our method demonstrates excellent performances on the RIPC dataset with both significantly higher TM-scores obtained for 38/40 of resulting alignments as compared to the FATCAT reference method and 33/40 compared to KPAX, as well as higher percentage of correctly aligned positions according to reference alignments which highlights residues involved in functional and evolutionarily important positions , thus essential for protein structures annotation. ICARUS also shows great performances on full length proteins from the SISY dataset both in terms of TM-scores and percentage of agreement with the reference alignments.The ability of ICARUS to detect particularly complex relations between protein pairs comes from its nonsequential algorithm. Indeed, considering query proteins as consecutive protein fragments which can be swapped independently, ensures a more flexible representation of the structure. The only other method that uses a nonsequential algorithm is KPAX which demonstrates second best performances.The reason why ICARUS performs better on particularly complex cases of circular permutation, insertions, and conformational variability, comes from the use of Protein Units for identification of the rigid regions and hinge positions in the protein structure. PUs were previously shown to constitute rigid and stable structural units (btad459_Supplementary_DataClick here for additional data file.


Many essential cellular functions are carried out by multi-protein complexes that can be characterized by their protein–protein interactions. The interactions between protein subunits are critically dependent on the strengths of their interactions and their cellular abundances, both of which span orders of magnitude. Despite many efforts devoted to the global discovery of protein complexes by integrating large-scale protein abundance and interaction features, there is still room for improvement. Here, we integrated >7000 quantitative proteomic samples with three published affinity purification/co-fractionation mass spectrometry datasets into a deep learning framework to predict protein–protein interactions (PPIs), followed by the identification of protein complexes using a two-stage clustering strategy. Our deep-learning-technique-based classifier significantly outperformed recently published machine learning prediction models and in the process captured 5010 complexes containing over 9000 unique proteins. The vast majority of proteins in our predicted complexes exhibited low or no tissue specificity, which is an indication that the observed complexes tend to be ubiquitously expressed throughout all cell types and tissues. Interestingly, our combined approach increased the model sensitivity for low abundant proteins, which amongst other things allowed us to detect the interaction of MCM10, which connects to the replicative helicase complex via the MCM6 protein. The integration of protein abundances and their interaction features using a deep learning approach provided a comprehensive map of protein–protein interactions and a unique perspective on possible novel protein complexes. Protein complexes are multi-protein assemblies that play a crucial role in diverse biological processes, including the control of cellular homeostasis, growth, and proliferation . For exaTo identify protein complexes from protein–protein interactions, several experimental technologies are employed. For instance, yeast two-hybrid assays, which depend on bringing the DNA-binding domain (BD) and transcription activation domain (AD) of a eukaryotic transcription factor in close proximity by a bait-BD fusion protein and a pray-AD fusion protein, thereby enabling identification of protein interactions and protein complexes ,7. High-In the past few years, two large-scale studies . Subsequently, a deep learning (DL) model was built by using these features as input, and ultimately to infer an integrated protein interaction network. Next, a two-step unsupervised clustering procedure was performed to obtain a comprehensive map of human protein complexes. Our approach resulted in a comprehensive overview of protein complexes that also contain low-abundant and poorly characterized proteins, thereby providing a unique perspective on the human interactome.Although many efforts have been devoted to quantifying and classifying protein complexes, approaches by integrating large-scale protein abundance and interaction features need to improve. In this study, the integration of large-scale protein quantification data from multiple human cell samples was combined with AP–MS and CF–MS data to improve the construction of the human PPI network. We constructed a comprehensive map of human protein complexes via integrating protein interaction and protein abundance features. Briefly, the protein interaction features were obtained from three high-throughput AP–MS/CF–MS datasets ,8,12, coIn this study, we integrated two recently published AP–MS protein interaction datasets from BioPlex and Hein et al., and one CF–MS protein interaction dataset ,8,12. AsIn PPI studies, researchers expect to retrieve subunits of complexes in equimolar amounts after immunoprecipitation (IP) from biological experiments. However, in practice, the range of detected interacting protein abundances spans several orders of magnitude . This isHaving established the feature matrices, we next generated the training set and test set by labeling protein pairs based on a gold-standard literature-curated set of human protein complexes, CORUM . The posTo obtain an optimal classifier, we trained our DL models by varying the number of neurons in three densely connected layers and the probabilities in dropout layers  data. Subsequently, we randomly shuffled the protein members amongst the other complexes while maintaining the same complex size, and then calculated the Manhattan distance of proteins within these randomly generated complexes. This shuffling process was repeated 100 times. As indicated in Moreover, the functional annotation analyses of the protein complexes showed that a large proportion of predicted complexes could be enriched in functional terms E. For exOur final dataset achieved a high model performance based on the F1-measure 0.68) and k-clique evaluation (F-grand = 0.46). In addition, approximately 15% (737 of 5010) of our complexes exhibited a complete or partial overlap with 42% (1100 of 2597) of the gold-standard complexes from the CORUM database A [8]. Th and k-clCo-expression characteristics are of biological interest since co-expressed genes usually are controlled by the same transcriptional regulatory program, functionally related, or members of the same protein complex . ProteinMany vital cellular functions, including DNA replication, RNA transcription, and protein translation and regulation, require the coordination of proteins assembled into complexes. Thus, the analysis of protein complexes and PPI networks are of central importance in biological research. In the past decades, the combination of affinity purification/co-fractionation and mass spectrometry has advanced our understanding of protein complex composition. Increasing efforts have been devoted to generating larger-scale human protein interactions by integrating different AP–MS and CF–MS studies, and more comprehensive maps of protein complexes have been established . AlthougIn this work we developed a deep learning framework that incorporates multiple sources of data to establish a comprehensive human protein complex map. Our results show that a deep-learning-based approach, incorporating multiple sources of features (AP–MS/CF–MS interaction features and MS/MS protein abundance features), outperformed models using either interaction features or abundance features alone. In addition, these integrated deep learning models exhibit high robustness, not only on the F1-measure but also on the number of outperformed models . We alsoIn addition to model performance, the contribution of features is an important aspect of deep learning. Thus, we performed feature importance evaluation by the decrease of model performance by randomly shuffling the values of each feature (see Methods). As expected, the top-ranked features were the interaction features. For example, all of the top 15 features were interaction features, including “hein_neg_ln_pval”, “neg_ln_pval”, “hein_pair_count”, and “prey-bait correlation” , which aThe weak interactions have frequently been overlooked or remained undetected, and they have been thought to be less important in large-scale interaction research, even though they are crucial features of networks in general ,43. In aOverall, we observed a good performance of our model; however, there is still room for future improvements. Firstly, we included only the protein pairs with both interaction and abundance features to predict the PPI network. Ideally, the number of detected proteins accumulate and would ultimately reflect the total number of proteins in the human proteome when enough proteomic quantification features are collected. However, due to technical challenges, there is no such set of protein interaction studies that contains a comprehensive list of the whole proteomic-level baits. The lack of complete datasets limits the comprehensiveness of interaction features of protein pairs. The model based on these incomplete features therefore predicts an incomplete PPI network, which probably results in an incomplete protein complex map. Due to a lower performance of the deep learning model using only the protein abundance features, an expediency can be as follows: the core PPI network can be predicted using the integrated model, while the peripheral network is populated only using the protein abundance features model. Then the inference of the protein complex map based on this integrated core–peripheral network needs to be further explored. Secondly, protein structural information has been proved to be significant evidence for predicting PPI , which cIn conclusion, by incorporating interaction and large-scale protein abundance features, our deep learning framework serves as a pioneering protein complexes discovery analysis.A fundamental step in predicting protein complexes is the prediction of protein–protein interactions, which is considered a classification task in machine learning and requires a gold-standard reference set comprising a positive and a negative subset. The human protein complexes in the CORUM database  form a hWe retrieved 246 independent projects from the PRIDE repository   × 7330 protein abundance feature matrix for the training set and a 116,836 (protein pairs)  × 7330 protein abundance feature matrix for the test sets.Here, we assumed that the expression of proteins within a protein complex may exhibit concordance, which can be evaluated using the Manhattan distance of their expression abundances. Therefore, we calculated the predefined protein–protein interaction score for a protein pair using the following formula:For each protein pair in the training and test sets, the AP–MS/CF–MS features comprised 258 features  that werThe neural network model was implemented by using the R (version: 3.5.1) interface to Keras (version number: 2.2.5.0), which is a high-level neural network API . Our modtune.svm. To seek an optimal model, we performed a parameter tuning of the hyperparameters (C and gamma) for the SVM model training using 10-fold cross-validation by the tune.control function  , which iith feature in the test dataset were randomly shuffled, generating a sudo-test dataset (N (N = 50) times, and the mean value of the F1-measure for the ith feature , is a graph clustering algorithm , which si) to generate a set of intermediate clusters. Here the parameters for the ClusterOne algorithm “density” were tuned in the range of , and “overlap” was tuned in the range of . Since we allowed merging high-overlapping clusters in the ClusterOne process, this could have led to large clusters that were over-merged, i.e., biologically unrelated complexes merged into a single large cluster [We first sorted the edges in descending order by their weights, which were predicted by the deep learning model, resulting in a subnetwork with the top r percent of edges. Here, r (ranging from 1 to 20) is a tuning parameter that needed to be optimized to obtain the best set of complexes in the following steps. In the ClusterOne clustering step , a seed  cluster . Therefo cluster  was tunek . Secondly, we removed the predicted complexes in which all protein members were not in the gold-standard set. In other words, we only evaluated the complexes containing proteins that form known complexes to not penalize novel predicted complexes as false positives. Thirdly, for each clique size k, the true positive (TPk) was defined by the number of common complexes between the predicted complex set and gold-standard complex set; the false positive (FPk) was the number of complexes in the predicted complex set but not in the gold-standard complex set; and the false negative (FNk) was the number of complexes in the gold-standard complex set but not in the predicted complex set. Subsequently, the precision (Pk), recall (Rk), and F-measure (Fk) were defined as follows:To measure the accuracy of the reconstructed complexes, we used the k-clique algorithm for each of the two-stage-clustering results. As described above , this apk, iterating over clique sizes of k from 2 to K, where K is the largest cluster size of the predicted complexes set.Finally, a global F-measure ) was defined as the mean of FWe used the g:Profiler web tool  to perfo


The cell-to-cell communication primarily occurs through cell-surface and secreted proteins, which form a sophisticated network that coordinates systemic immune function. Uncovering these protein–protein interactions (PPIs) is indispensable for understanding the molecular mechanism and elucidating immune system aberrances under diseases. Traditional biological studies typically focus on a limited number of PPI pairs due to the relative low throughput of commonly used techniques. Encouragingly, classical methods have advanced, and many new systems tailored for large-scale protein–protein screening have been developed and successfully utilized. These high-throughput PPI investigation techniques have already made considerable achievements in mapping the immune cell interactome, enriching PPI databases and analysis tools, and discovering therapeutic targets for cancer and other diseases, which will definitely bring unprecedented insight into this field. Statement of Significance: Conclusion of various systems capable of high-throughput PPI investigation emerged in recent years. Preliminary analysis of the causes of deviations in different methods. The immune system is a complex and dynamic network of cells that communicate with each other through protein–protein interactions (PPIs) to fight against infection and disease. PPIs are also essential for immune cell differentiation and development, executing immune functions and maintaining tissue homeostasis throughout the body. Cell-to-cell communication in the immune system is primarily mediated by the receptor–ligand associations on the cell surface. These receptors and ligands are potential therapeutic targets for various diseases, as they play pivotal roles in regulating immune response. Extensive research has been conducted on immune checkpoint molecules, such as programmed cell death protein 1 (PD-1), cytotoxic T-lymphocyte associated protein 4 (CTLA-4) and programmed cell death ligand 1 (PD-L1). These proteins are validated drug targets for the treatment of cancer, as they can be used to normalize the immune surveillance functions. Monoclonal antibodies targeting B-cell activation receptors and ligands, such as clusters of differentiation 20 (CD20), have also been widely used as first-line drugs for multiple cancers and autoimmune diseases. Overall, understanding the mechanisms of cell-to-cell communication in the immune system and identifying potential targets for therapeutic intervention is a promising avenue for the treatment of a wide range of diseases.Traditionally, researchers have focused on a limited number of interacting pairs to study their mechanisms of action and functions. However, the advent of high-throughput, high-content and multi-omics technologies has enabled the study of numerous receptor and ligand interactions in batches. This capability has made it possible to analyze complex interactions and networks which were previously difficult or impractical to study. These advances have paved the way for the systematic mapping of the immune cell interactome and the identification of valuable therapeutic targets on a large scale.A variety of methods and platforms have been developed for large-scale exploration of PPIs, many of which are suitable for studying the receptor and ligand interactions of immune cells. Classical methods to investigate PPIs include yeast two-hybrid (Y2H), affinity purification-mass spectrometry (AP-MS), co-immunoprecipitation (Co-IP), mammalian protein–protein interaction trap (MAPPIT), bimolecular fluorescence complementation (BiFC), surface plasmon resonance (SPR), bioluminescence/Fmentclass2pt{minimResearchers have made significant discoveries by choosing the appropriate PPI methods that are tailored to their specific goals. However, the challenges posed by low binding affinity and high background signal have significantly limited the study of extracellular protein interactions at larger scales and higher resolutions. Fortunately, recent advancements have yielded innovative platforms with well-defined workflows that can be easily used by researchers to address these challenges. These platforms either build upon classical PPI methods or pioneer entirely novel approaches .et al. improved the AVEXIS method, successfully enabling it for larger-scale screenings, and called this improved system the scalable arrayed multi-valent extracellular interaction screen (SAVEXIS) [et al. screened 630 different surface proteins of immune cells, which include almost all types of extracellular proteins found on immune cells, and generated a high-confidence surface protein interactome. Nadia Martinez-Martin et al. used another modified version of AVEXIS method to screen PPIs involving pathogen–host interactions [et al. in 2020 combined the ELISA-based extracellular interactome assay (ECIA) with an automated pooled-prey strategy, known as apECIA. This platform has some distinct variations that allow large-scale screens [AVEXIS is a modified enzyme-linked immunosorbent assay (ELISA) method that is specifically designed to detect low affinity and transient protein–protein interactions. In this assay, both the “bait” and “prey” proteins are expressed and purified. The biotinylated “bait” proteins are immobilized on a streptavidin-coated plate, and the “prey” proteins are fused with the pentameric β-lactamase to allow avidity driven binding. The “prey” proteins are then added to the plate. If they bind to the “bait” proteins, the β-lactamase-catalyzed chemical reactions can be detected and quantified to measure the strength of the specific interactions. In 2022, Jarrod Shilts  screens . In the et al. developed a high-throughput functional screening system called T cell activity array (TCAA) in 2019 to screen cell surface proteins that could modulate T-cell activities [et al. constructed chimeric receptors by fusing the extracellular and transmembrane domains of human proteins with the intracellular signaling domain of clusters of differentiation 3 zeta chain (CD3ζ) [2+-NFAT signaling pathway activation when it was associated with activation ligands. With this system, the team screened a secretome protein library containing about 4000 human and mouse proteins, as well as ectodomains of some single-pass transmembrane proteins, to find the undetermined ligand for natural cytotoxicity triggering receptor 2 (NCR2). They finally discovered that platelet derived growth factor D was the ligand for NCR2.Another frequently used method for large-scale PPI screening involves cell signal transduction. In this approach, chimeric receptors are engineered by fusing the extracellular domain of query proteins with the intracellular fragments of signal transduction molecules. When ligands bind to the chimeric receptors, the receptors are activated, initiating signal transduction cascades that eventually lead to the activation of a reporter gene, such as the green fluorescent protein (GFP) . MAPPIT tivities . In the n (CD3ζ) . These cet al. creatively combined the nucleotide sequencing techniques with PPI investigation in their newly developed system known as protein–protein interaction sequencing (PROPER-seq) [et al. advanced Y2H technique and developed the massively parallel protein–protein interaction measurement by sequencing (MP3-seq) systems [Some other emerging large-scale PPI research methods have shown great promise. For example, Zhong PER-seq) . In the  systems . They co systems . In this systems . Another systems . Oslund  systems . By inteThe advancement of biotechnology and computer technologies has enabled the computational prediction of PPI. This has led to the development of a variety of in silico methods for large-scale PPI predictions, which leverage insights from both the experimental data and computational algorithms. There are several different types of techniques for PPI predictions . SequencSequence-based methods leverage the wealth information within protein sequences. These methods use the primary sequence and additional information such as physicochemical properties of amino acids and sequence similarity scores, as input to generate predictive models. Classical models such as support vector machines and random forests are often used to assess the likelihood of PPIs and classify them into interactive or non-interactive pairs. Sequence-based methods are generally less accurate than other methods, and can be prone to false positives, overfitting and underfitting. To improve the performance of sequence-based PPI prediction methods, it is often necessary to incorporate additional information, such as functional or evolutionary data. Furthermore, it can be challenging to decipher the interaction mechanism after prediction using these methods. Despite their limitations, sequence-based methods remain valuable in PPI prediction. This is because amino acid sequence information is available for all proteins, including those that lack structural information. Sequence-based methods are also more time-efficient and easier to implement than other methods. Therefore, they are a viable option for guiding experimental design or cases where high accuracy is not a strict requirement.Structure-based methods that use the 3D structures of proteins to predict PPIs are more informative and consistent with biological processes than sequence-based methods. These methods model PPI at the atomic level and provide insights into the underlying interaction mechanism. Structure-based methods are diverse in the underlying theoretical principles, but some common approaches include template-based prediction techniques, which align the structural surfaces of the target PPI with known PPI interfaces; and domain-based techniques, which treat domains as the basic units of PPI and aim to predict the most likely interacting domains. Machine learning models are commonly used in structure-based PPI prediction methods because they can incorporate protein structural information to various extents, including local docking methods or global docking methods. Although structure-based methods tend to be more accurate than sequence-based methods, their applicability is constrained by the availability of protein structural data. Data for structure-based models are often limited, and the models are difficult to apply to proteins lacking 3D structural information. Besides, determining the 3D structure of a protein demands a specialized knowledge and skill-sets, and structure-based methods are generally time consuming and requires expensive computational resources.In recent years, the rapid development of deep learning methodologies has introduced transformative tools for PPI prediction, such as SAE , DPPI 2, DNN-PPIIn addition to these methods, a variety of other methods have been developed for PPI prediction, including ortholog-based techniques, gene fusion-based approaches and network topology-based techniques. Ortholog-based techniques leverage the knowledge of conserved interactions in other species to predict the interacting networks of target proteins . Gene fuPPIs are essential for many biological processes, including immunity. The successful applications of large-scale PPI screenings have benefited a wide range of research, and they can be extended to countless other studies requiring PPI investigation in any field. Large-scale screening is the most effective and comprehensive way to discover the binding partners for functionally important orphan receptors, and it is also the most efficient approach to systematically map all types of interactomes. With the PPI data, it is possible to measure interactions between different cell types, understand the mechanisms of numerous biological processes and discover novel therapeutic targets with a reduced risk of side effects. This approach is remarkable for its ability to achieve comprehensive results on a broad scale within a short time frame.Large-scale screening is particularly valuable in the field of tumor immunity. The tumor immune microenvironment (TIME) is a specialized immune niche where infiltrating and resident immune cells, stromal cells, blood vessels and extracellular matrix surround tumor cells to form a complex and continuously evolving entity. Immune cells in the TIME are diverse in types and states, working in coordination to execute their unique functions or counteracting each other to inhibit and exhaust their capabilities. Cell-to-cell communication within the TIME is exceptionally active and dynamic, forming a complex network of PPIs on a large scale. These PPIs are the subject of intensive investigation, as they play a pivotal role in regulating tumor immunity. High-throughput techniques are crucial for revealing novel functional targets within this intricate network that could be used for therapeutic purposes .The most direct use of large-scale PPI research is to systematically map various interactomes, providing tremendous PPI information for further investigation. The complete immune physical wiring diagram has been accomplished, which systematically maps the network of immune cells formed by the interactions of leukocyte cell-surface proteins. In recent years, interactome maps for specific protein types, such as IgSF family proteins, single-pass transmembrane proteins and secreted proteins, have also been completed , 4, 27. A good example of how large-scale PPI screening has promoted the identification of novel therapeutic targets is the discovery of fibrinogen-like protein 1 (FGL1) as a ligand of LAG-3. FGL1 is a promising target for new generation immunotherapy research . LAG-3 wOther renowned and promising immune checkpoint targets, such as T-cell immunoreceptor with immunoglobulin and ITIM domain (TIGIT) and T-cell immunoglobulin and mucin-domain containing-3 (TIM-3), have also benefited from large-scale PPI screening. These studies have enabled a much more comprehensive understanding of their interaction networks. Large-scale PPI screening systems have identified new interaction partners for several hotspot molecules, including carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1) for PD-1, ephrin type-A receptor 3 (EPHA3), natural killer protein 30 (Nkp30) for PD-L1, reversion-inducing cysteine-rich protein with Kazal motifs (RECK), FcγRIIa for TIGIT, family with sequence similarity 200 member A (FAM200A), WW domain-binding protein 1 (WBP1) and protocadherin gamma subfamily B, 4 (PCDHGB4) for CTLA4. The findings have provided a comprehensive understanding of the interaction modes for these promising therapeutic targets, which has boosted the development of numerous drugs that have entered into clinical trials. The newly detected interaction between PD-L1 and EPHA3 is particularly interesting, as it may have implications for cancer therapy. An EPHA3 monoclonal antibody, KB004, had already entered clinical trials for patients with glioblastoma and advanced hematologic malignancies before this interaction was discovered , 33. ThiThe availability of interactomes has enabled the use of in silico methods to estimate the interplay among various cell types. This can shed light on many immunology mechanisms, such as the reciprocal influence between immune cells and cancer cells in the tumor microenvironment. Several major PPI interaction databases have been established to facilitate this endeavor , includiet al. used the two-way median polish normalization method, while Verschueren et al. used the random forest machine learning model to predict the probability of interactions. It is important to use a tailored pipeline for specific systems and optimize the utilization of the statistics data, as data processing can also affect the detection performance.We have observed that the interaction landscape varies across different large-scale PPI screening systems. This is because different systems use different methods and criteria to detect interactions, and they may also be subject to different biases. As a result, the same interacting pairs may be detected in one system but not in another. The reasons for these discrepancies are complex and multifaceted, but they also indicate a lack of integration and standardization in this field. One factor that can contribute to variation in PPI screening is the nature of the protein itself. For instance, the interaction properties of membrane proteins can be influenced by the specific lipid environment in which they reside. Membrane proteins are typically found in the cell membrane, where they interact with other proteins and the surrounding lipids. When these proteins are isolated from the membrane, they may adopt a different conformation and lose their abilities to interact with other proteins. Also on the cell surface, they have binding partners. One example is CD47, which is a component of a big complex, which is missed in its purified form. Cell-based systems are more similar to real biological context than that of other methods to detect PPIs. However, they rely on signal transduction and reporting, which can result in detection sensitivity variations depending on the signal pathway used. The protein preparation protocol also matters since proteins are usually expressed and purified from cell lines. Different expression cell lines may cause certain modification variances such as glycosylation. This concern can be addressed in part through rigorous quality control using both biophysical methods and functional assays. Besides, for some detection systems that only express extracellular domains or specific molecular fragments for detection, the missing domains may also affect the interaction properties, especially for the multiple-pass transmembrane proteins. In addition to the experimental part of large-scale PPI screening, the analysis pipeline also introduces some deviations that need to be standardized in the future. Almost all analyses require the selection of a threshold for the detection of true signal, which is typically selected to maximize the system’s precision or sensitivity. However, the precision and sensitivity of a system need to be evaluated using previously identified positive and negative binding pairs. Previously identified interaction pairs are not always rigorously validated, and defining negative pairs, especially within a limited set of screened proteins, poses a challenge. As a result, the manually selected reference pairs used in analysis can introduce bias and inconsistency. In addition, the measured values are processed in different ways. For example, Shilts Kd) and association constant (Ka). However, SPR lacks the cellular context and the results obtained may vary depending on the protein purification methods used. These variations may not reflect the true biological conditions. Therefore, the validation approaches should be tailored to specific cases.In addition, the different lower-throughput approaches used for further validation may also lead to different conclusions. For instance, FRET/BRET approaches are sensitive to short-lived transient interactions and can detect PPIs in live cells with location information. However, the results are influenced by the compatibility of fusion proteins with appropriate fluorophores or bioluminescence. SPR is considered by many to be the gold standard in PPI detection because it is highly sensitive and does not require labeling. SPR can monitor interaction dynamics and acquire critical PPI parameters, such as dissociation constant  and deep learning in the biomedical field, we anticipate that novel PPI prediction methods using AI and deep learning will deliver highly accurate and efficient results. More standard and lower-throughput detection approaches should be used to validate target PPIs, which can then provide feedback to in silico prediction methods to improve their performance. Computational methods, including deep learning, should also do a better job of integrating PPI networks and resolving PPIs in a comprehensive view, which would be beneficial for further functional analysis. Overall, experimental and computational methods form a mutually beneficial feedback loop, and should be used together to advance the interactome mapping.With the continual refinement and establishment of diverse PPI screening systems, it will be possible to map a comprehensive interactome of the immune system. This will allow researchers from diverse fields to conveniently explore various potential interactions that are relevant to their unique research objective. The systematically integrated PPI databases will greatly promote the identification of effective immunotherapy targets and provide a comprehensive and deep understanding of their mechanisms of action. The insights gained from PPI studies will reshape the landscape of cancer treatment in the near future.This work was supported by National Key Research and Development Program of China (2022YFA1304500 to M.D.), National Natural Science Fund  and Peking University .Antibody Therapeutics and is blinded from reviewing or making decisions for the manuscript. R.P. declares no conflicts of interest.M.D. holds the position of Assistant Editor for R.P. wrote and M.D. reviewed and edited the manuscript.The authors confirm that the data supporting the findings of this study are available within the article.


Proteolysis-targeting chimeras (PROTACs) are hetero-bifunctional molecules that induce the degradation of target proteins by recruiting an E3 ligase. PROTACs have the potential to inactivate disease-related genes that are considered undruggable by small molecules, making them a promising therapy for the treatment of incurable diseases. However, only a few hundred proteins have been experimentally tested for their amenability to PROTACs, and it remains unclear which other proteins in the entire human genome can be targeted by PROTACs. In this study, we have developed PrePROTAC, an interpretable machine learning model based on a transformer-based protein sequence descriptor and random forest classification. PrePROTAC predicts genome-wide targets that can be degraded by CRBN, one of the E3 ligases. In the benchmark studies, PrePROTAC achieved a ROC-AUC of 0.81, an average precision of 0.84, and over 40% sensitivity at a false positive rate of 0.05. When evaluated by an external test set which comprised proteins from different structural folds than those in the training set, the performance of PrePROTAC did not drop significantly, indicating its generalizability. Furthermore, we developed an embedding SHapley Additive exPlanations (eSHAP) method, which extends conventional SHAP analysis for original features to an embedding space through in silico mutagenesis. This method allowed us to identify key residues in the protein structure that play critical roles in PROTAC activity. The identified key residues were consistent with existing knowledge. Using PrePROTAC, we identified over 600 novel understudied proteins that are potentially degradable by CRBN and proposed PROTAC compounds for three novel drug targets associated with Alzheimer’s disease. Many human diseases remain incurable because disease-causing genes cannot by selectively and effectively targeted by small molecules. Proteolysis-targeting chimera (PROTAC), an organic compound that binds to both a target and a degradation-mediating E3 ligase, has emerged as a promising approach to selectively target disease-driving genes that are not druggable by small molecules. However, not all of proteins can be accommodated by E3 ligases, and be effectively degraded. Knowledge about the degradability of a protein will be crucial for PROTAC design. However, only hundreds of proteins have been experimentally tested if they are amenable to the PROTACs. This leaves us uncertain about which other proteins in the entire human genome can be targeted by PROTACs. In this paper, we propose an intepretable machine learning model, PrePROTAC, which takes advantage of powerful protein language modeling. PrePROTAC achieves high accuracy when evaluated with an external dataset which comes from different gene families from the proteins in the training data, suggesting the generalizability of this model. We apply PrePROTAC to the human genome, and identify more than 600 understudied proteins that are potentially responsive to PROTACs. Furthermore, we design PROTAC compounds for three novel drug targets associated with Alzheimer’s disease. Despite rapid progress in the development of small-molecule drugs, traditional drug discovery is limited by the requirement for efficient inhibitions of functional binding sites in protein targets. However, many of disease-associated proteins neither have suitable binding pockets for small molecule drugs nor bind inhibitors with considerable affinity . It is eDifferent from small molecule and biological drugs, PROTAC molecules are hetero-bifunctional molecules that consist of a small molecule binding moiety for a target protein  and an E3 ubiquitin ligase-recruiting moiety coupled by a chemical linker. With the two binding moieties, PROTACs recruit an E3 ubiquitin ligase to a targeted protein and induce the degradation of the entire targeted proteins in the ubiquitin–proteasome system –6. TherePROTACs can enhance both selectivity and efficacy of targeted therapies. In the PROTAC-induced degradation, a ternary complex structure is formed by a targeted protein, a PROTAC, and an E3 ligase. The selectivity of PROTACs is determined not only by the warhead but also by the non-native protein-protein interactions (PPIs) between the E3 ligase and the targeted protein, as well as the stability of the ternary complex structure , 12, 13.Moreover, for multi-functional proteins, small molecule or biological drugs can only inhibit the occupied functional sites, whereas PROTACs can target all functions by eliminating the entire proteins. Thus, PROTACs can enhance pharmaceutical efficiencies. For instance, a PROTAC designed for receptor tyrosine kinases has been shown to inhibit both cell proliferation and downstream signaling ; and a fAnother advantage of PROTAC induced degradation over traditional inhibition is the ability to address acquired drug resistances , 29–31. These intrinsic advantages of PROTACs’ mechanism of action make PROTAC-induced degradation a highly promising therapeutic modality. It has the potential to reduce drug exposure requirements, enhance drug selectivity, circumvent drug resistance mechanism, and target the undruggable disease genes. A growing body of research is focused on this field, encompassing both experimental and computational approaches. However, computer-based drug design for PROTACs is still limited to a small number of published ternary complex structures, and mainly involves the modeling of ternary complex structures with a pre-designed PROTAC through protein docking –34, combHowever, no study has been conducted to explore understudied proteins in the entire human genome that could be amenable to PROTAC-induced degradation. In this work, we have developed an interpretable machine learning model called PrePROTAC. It utilizes embedding features extracted from protein sequences to predict degradable proteins targeted by PROTACs. PrePROTAC is the first model of this kind, enabling us to identify understudied proteins whose degradability can be induced by PROTACs on a genome scale. We have identified approximately 615 new proteins responsible for multiple diseases that can be targeted by the PROTACs. Furthermore, we screened the PROTACs for three novel drug targets related to Alzheimer’s disease.The workflow of our PrePROTAC model was shown in The trained PrePROTAC model was applied to the whole human proteome, using a threshold of 0.9 to select proteins with the potential to be degraded by CRBN when induced by PROTACs. As shown in Since there were only less than 500 proteins with annotated degradability information, but protein sequences were usually represented by high dimensional vectors, we selected Random Forest (RF) and Gradient Boost Tree (GBT) to train the prediction model. This choice was made because these algorithms are less prone to overfitting when working with high-dimensional data and a small number of samples. In order to achieve optimal performances, we tuned the hyper-parameters of the RF and GBT models with different features using 5-fold grid search cross-validation. The optimized hyper-parameters for different models and features were listed in Table A and B in With the tuned hyper-parameters, RF and GBT classification models corresponding to 21 Ifeature descriptors , D-ScripFor the 21 Ifeature descriptors, the one with the best ROC-AUC score in each group was selected and compared with the D-script and ESM features. Models based on TPC, GTPC, Geary, CTDD, CTriad and QSOrder had the best performances compared with the other descriptors in the same groups. As shown in The D-script contact feature, which is trained to predict native PPIs, may not be suitable for representing the formation of E3 ligase-PROTAC-target complex structures. Unlike native PPIs, E3 ligand and target proteins may not form a stable PPI complex without using RPOTAC as a molecule glue between them. Simply using native PPI predictions could not identify the CRBN-target pairs. When D-script was applied to predict interactions of 265 degradable proteins with CRBN in both training set and test set, none of them could be identified to interact with CRBN. Another PPI prediction method, PrePPI  46], wa, wa46], In order to investigate whether the performance could be improved by adding other features in combination with the ESM feature, TPC, GTPC, Geary, CTDD, CTriad, QSOrder and Dscript features were combined with the ESM feature. Hyper-parameters for the RF models with these combined features were trained and 5-fold cross validations of these models were performed on the training set. The ROC-AUC scores and AP scores were shown in Fig H in We also investigated whether the performance could be improved when combining the RF and GBT models. Two different ensemble methods were used: the soft voting method and the consensus method. In the soft voting method, the average probability scores of the RF and GBT models were used to predict the degradation of proteins. For the consensus method, a positive result was only obtained when both models agreed, otherwise the result was negative. The performances of the soft voting model and consensus model were evaluated using the five-fold cross-validation method on the training set. The ROC-AUC, false positive rate-threshold, precision-recall curves for them were shown in Fig I in We further rigorously evaluated the performance of models using an external test data set, where none of the proteins contain structural domains belonging to the SCOP protein kinase-like fold that was used in the training data  were obtained from 497 human protein kinase domains . The ideβ-strand 3, TRP496, MET499 and LEU501 on the strand 5, GLU506, ARG508 and SER509 on the D helix, ASP546 and ASN551 on the catalytic loop, LEU553, LEU562 and GLY563 on the N terminal of the activation loop (ALN), GLY566 on the DFG motif (part of ALN), ASP573 and Met589 on the C terminal of the activation loop (ALC), TRP613 and MET617 on the F helix, and ILE635 and GLU636 on the G helix. All of these segments are of functional importance for the protein kinase.In these protein kinases, FAK has a ternary complex structure with CRBN and PROTAC. The key positions identified for FAK were mapped on the complex structure (PDB ID: 7PI4). As shown in β-strand 3 is the catalytic residue that typically interacts with the α- and β- phosphates of ATP. MET499 is the gatekeeper residue that influences the accessibility to a buried region at the end of the ATP binding pocket and controls inhibitor sensitivity of protein kinases. Other residues on the β-strand 3 and 5 contribute to the formation of the buried region in the ATP binding pocket. ASP546 and ASN551 are located on the catalytic loop and are in close proximity to the gamma-phosphate group of ATP, making them directly involved in the catalytic functions of FAK. LEU553, LEU562 and GLY563 are located on the ALN, while GLY566 is one of the DFG residues. ALC contains the APE motif, which can stabilize the activation loop by docking to the F helix. MET589 is adjacent to the APE motif. GLY566 and ASP573 on the activation loop could bind to the substrates and promote catalysis. The F helix serves as a stable anchor for several other motifs in the C-lobe, including the catalytic loop and the activation loop, through hydrophobic contacts. The G helix is a solvent exposed helix and a part of the GHI-domain, which can interact with various substrate proteins and regulatory proteins, acting as allosteric sites. The key positions identified for other protein kinases also correspond to these functionally important segments. Representatives of the positive and negative samples for each protein kinase subgroup were selected based on the key-position only MSA. The structural mapping of their key positions and the key-position only MSA were shown in Figs M-X in LYS454 on the Besides FAK, the BD1 domain of BRD4 (PDB ID: 6BOY) also form a ternary complex structures with CRBN and its PROTAC. The key positions for this protein were also identified and mapped on their complex structures to elucidate the functional roles of these key positions. BRD4 is a member of the BET family, which regulates the expression of numerous immunity-associated genes and pathways. It is also a well-studied target protein for PROTAC-induced degradation , 34, 55.To apply PrePROTAC to unseen proteins on a genome scale, we further developed a soft voting classification model based on the ten models with equal weight , death domain-associated protein 6 (UniProt ID: Q9UER7) and activating signal cointegrator 1 complex subunit 2 (UniProt ID: Q9H1I8). These proteins are highly expressed in the brain of Alzheimer’s disease patients and are associated with the risk of AD –59.Protein-protein docking methods Zdock and Rosetta were utilized to generate complex structures between CRBN and these target proteins. The starting structure for CRBN was obtained from the ternary complex structure of CRBN-PROTAC-BRD4 (PDB id: 6BOY) and the ligand mimic E3 ligase moiety was sourced from the CRBN-ligand complex structure ). Using the selected complex structure, docking was performed to screen three sets of compound libraries, including 2,074 CRBN related PROTACs in PROTAC-DB, 1,920 fragment-based compounds from high fidelity fragment library of Enamine  and 2,01Compound PROTAC3269 was selected for P12757 and its E3 ligase moiety precisely occupied the binding pocket of Lenalidomide in CRBN. All interactions between Lenalidomide and CRBN aere retained in the PROTAC3296, including interactions with ASN351, PRO352, HIS353, HIS378, SER379, TRP380 and TRP386. Similarly, for the other two target proteins, several such interactions were observed between CRBN and the CRBN ligand moiety on the predicted PROTACs. For instance, interactions between residues ASN351 and HIS353 and PROTAC3055 were maintained for Q9UER7, and the interaction between PRO352 and PROTAC2145 remained for Q9H1I8, resembling the interactions between CRBN and the co-crystallized ligand LVY. Other binding site residues on these target protein-CRBN complex structures were different from those in the original CRBN-ligand complex structure, but they still formed similar hydrogen bonds with oxygen atoms on the ring of the CRBN ligand moiety. On the warhead side, numerous residues on the target proteins also formed favorable interactions with the warheads on these PROTACs. These interactions between CRBN and E3 ligase ligand moieties, as well as between the target proteins and warheads, indicate the predicted PROTACs could form dual bindings with CRBN and target proteins and have the potential to recruit the target proteins to CRBN and induce their degradation.After Autodock screening on the compounds from the Enamine and Medchem Express, ZINC000725236954, ZINC000103760984 and ZINC000029047404 were selected as the warheads for P12757, Q9UER7 and Q9H1I8, respectively, based on their docking scores. CRBN related moieties and linkers from PROTAC-DB were chosen to connect with the predicted warheads and formed new PROTACs which can dock into both binding pockets of CRBN and the target proteins in the complex structures. Interactions between these new PROTACs and the CRBN-target protein complex structures were shown in Key positions were also identified for these three proteins through eSHAP analysis. In order to compare them with the docking results, the top ranked positions were mapped onto the modeled complex structures for P12757, Q9UER7 and Q9H1I8, as shown in Protein degradation is an essential process involved in protein turnover within the cell. It provides a mechanism of quality control during protein folding, enables rapid response to cellular signals, and allows modulation of the pool of available amino acids. The majority of proteins undergo degradation through the ubiquitin–proteasome system, which involves a diverse group of proteins. The ubiquitin E3 ligase family is the largest family in ubiquitin signaling, comprising approximately 700 members with identified or predicted ligase activities, and plays a crucial role in ubiquitin signaling . This faNow, the majority of PROTACs are designed from known potent ligands that bind to well-studied proteins. However, many understudied proteins that could be potential drug targets have not been investigated using PROTACs. One of the most promising advantages of PROTACs lies in their ability to induce the degradation for these proteins. Our results regarding understudied human proteins can serve as a starting point for the development of PROTACs targeting these proteins. Based on our prediction results, further investigations such as sequence and structure comparisons, complex structure modeling, and linker modeling can be conducted and help to design PROTACs for these proteins.Our method is the first machine learning method that utilizes protein sequence features to predict PROTAC-induced degradation for target proteins. In this study, we employed 23 different feature descriptors, three classification methods, and various combinations thereof to construct our machine learning models. These models were trained and validated by protein kinase degradation data and tested on other degradable, non-protein kinase proteins from PROTAC-DB. Extensive benchmark studies have shown that a RF model incorporating the ESM feature exhibits the best performance, while avoiding significant overfitting. The fully trained best model can be employed for PROTAC screening and predict possible target proteins which can be degraded by CRBN. Compared to other computational methods, his approach enables the screening of a large number of proteins in a short period. As more degradation data becomes available in the future, the accuracy of such models is expected to be greatly improved.The limited availability of high-quality annotated data on PROTAC-induced degradation poses a significant challenge in the development of PROTAC machine learning models. In this study, experimentally determined protein kinase degradation data from Fischer’s group and additional data for proteins from other gene families in PROTAC-DB were used to train and evaluate our model. Despite the predominant presence of protein kinase degradation data in the available dataset, our model exhibits potential generalizability to other proteins due to its utilization of a pre-trained protein language model. This notion is further supported by external benchmark studies, where the testing proteins belonged to gene families distinct from those present in the training data. Moreover, as more PROTAC degradation data becomes accessible for various protein types in the future, the performance of our model can be enhanced even further.In our current work, we focused on sequence information to construct and train our models. Incorporating structural features of the binding pockets for both E3 ligases and the target proteins could provide additional information and enhance the accuracy of PrePROTAC prediction. Current protein structure prediction methods, such as AlphaFold , are capThe random forest (RF) and gradient boosting tree (GBT) classification methods, implemented in scikit-learn , were evOne of the difficulties on PROTAC-induced protein degradation is few data available for systematic modeling studies. Comparing with traditional drug discovery, only a small number of PROTACs and targeted proteins are reported. Recently, Fischer’s group developed a large library of kinase-targeting degraders and identified degradable protein-kinases among human protein kinases and kinase-like proteins. . Based oIn order to evaluate the machine learning classification model trained using the CRBN-protein kinase pairs, additional PROTAC-induced degradation data was collected from PROTAC-DB  to buildThere are few known negative PROTAC data. To incorporate negative controls into the test set, two groups of negative samples were generated based on the structural classifications from SCOP , 48. TheThe goal of this study is to identify the proteins that can be induced by PROTAC molecules and subsequently degraded by CRBN. Two different classification methods were investigated for this purpose, including RF and GBT classification methods. RF is a widely used classification algorithm that comprises multiple decision trees. Its underlying principle, known as “the wisdom of crowds,” makes it a simple yet powerful approach. On the other hand, GBT optimizes a cost function across the function space and combines weak learners into a single strong learner through iterative processes.Here, these two different classification methods were trained to learn from the features describing proteins and distinguish between degradable and undegradable proteins in the training set. Limited by the size of the training data, four hyper-parameters were selected and tuned to reduce the over-fitting, including max_depth, min_samples_split, min_samples_leaf and n_estimators. The others were set to their default values in Scikit-learn. Max_depth defines the longest path between the root node and the leaf node. As the value increases, the accuracy may initially increase to a certain limit and then decrease due to overfitting. Setting this value appropriately is important to avoid overfitting. Min_samples_split decides the minimum number of samples for an internal node required to hold before being split into further nodes. required for an internal node before it can be split further. Increasing this value could limit the number of splits and help reduce overfitting, although setting it too large may lead to underfitting. Min_samples_leaf specifies the minimum number of samples that a node should have after getting split. It also helps to reduce overfitting. N_estimators determines the number of trees in the forest of the model. This parameter is closely related to the size of the data. These four Hyper-parameters were tuned using 5-fold grid-search cross validation method, and the ones with the best ROC-AUC scores were chosen for the next step of modeling.Since there are only a few hundred data points available for PROTAC induced degradation, conducting large-scale training is not practical. Therefore, in this study, pre-trained features from other large-scale protein language modeling approaches were employed as initial inputs for the RF and GBT classification methods. These features include sequence features from iFeature , pre-traWe first tested the features based on structural and physio-chemical descriptors extracted from protein sequence data. iFeature  is a pytIn PROTAC induced degradation, targeted proteins and E3 ligase will form complex structures. But such complex structures are different with the naturally formed protein-protein complex since PROTAC binds to the interface between them and hold them to stay together. That is why current PPI prediction methods cannot directly predict such pairs. However, the information hidden in the PPI is still useful for the prediction of PROTAC induced degradation because the formation of E3 ligase-target protein complex structures still follows basic physical-chemical principles. Thus, the pre-trained features used in the PPI prediction method can be a start point for PROTAC induced degradation.n-by-m contact prediction matrix which indicates the predicted probability that two residues (one in CRBN and the other one in target protein) are in contact. Here, n represents the sequence length of the CRBN and m represents the length of the target protein. This matrix was used as the initial features in our model. In order to get a fix-length array for each pair, a max pooling operation was used to change the matrix to n-dimensional array. This array represents n-dimensional contact features for each sample in the RF and GBT classification models.In this work, the contact feature in D-SCRIPT , a deep Recently, Facebook AI research group developed a deep contextual language model with unsupervised learning to train on 86 billion amino acids across 250 million protein sequences . The resPerformance of RF and GBT models with different features were evaluated by the repeated Stratified 5-Fold cross validation method. Area under the curve of Receiver operating characteristic curve (ROC-AUC) and precision score were calculated and compared between these models. The average precision (AP) score was calculated as the following AP score function :AP=∑n(RSHAP (SHapley Additive exPlanations)  81] [82 [8281] [The importance of all the amino acid positions were measured by the difference scores. This embedding SHAP (eSHAP) analysis could help identify the residues which contribute most on the selected features. The bigger the difference score is, the higher impact of this position has on these features. The positions with the top-ranked difference scores were selected as the key positions for this protein.It is well known that only a subset of the human genome that is considered druggable in terms of drug-like small molecules. There is only limited information for the understudied disease proteins . Hence, In order to design potential PROTACs for the target proteins, protein-protein docking and protein-ligand docking methods were applied on target proteins to build complex structures between E3-ligases and target proteins, and to predict PROTACs which were able to interact with these target proteins. Zdock  was firsS1 TextSupplemental materials for PrePROTAC model training, testing and prediction. Additional results for eSHAP analysis on the human protein kinases.(PDF)Click here for additional data file.


Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score > 0.7) 72% of the complexes among the Top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding PDB entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold’s high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins. These assemblies perform important functions, such as energy transduction4, transport5, and signal transduction6. The determination of the 3D structures of these assemblies is critical for understanding their function and evolution, interpreting the effects of mutations, and potential applications in drug discovery. The large size of some assemblies and conformational heterogeneity poses challenges for traditional structural characterization techniques, such as X-ray crystallography and NMR spectroscopy. While progress has been made using cryo-Electron Microscopy (cryo-EM), high-throughput structure determination of large assemblies is still challenging.Most proteins function as multimolecular assemblies in the cells. There are on average a few dozen interactions per protein7 and RosettaFold8. While AlphaFold2 was designed to predict single-chain proteins, it can also apply to predict protein complexes using the same architecture. Soon after its release, several techniques were developed to use AlphaFold2 to predict multi-chain protein complexes - first by using a linker9 and later by offsetting the residue index10. Similar techniques were used for the training of AlphaFold-Multimer (AFM)11 which is able to predict multimeric complexes with high accuracy using paired and padded multiple sequence alignment (MSA). On several pairwise protein-protein docking benchmarks AFM achieves a success rate of 40–70% for complexes consisting of two to nine chains up to 1,536 in total length13.Recently deep learning techniques significantly advanced our ability to predict high-accuracy protein structures. One of the most significant advancements was the release of AlphaFold213. The first difficulty is the requirement for significant resources, such as a graphical processing unit (GPU) with a large memory size. Currently, common GPUs have no more than 20 gigabytes of memory, enabling the prediction of complexes up to 1,800 and 3,000 amino acids for AFMv2 and AFMv3, respectively. Also, as AFM memory usage increases roughly quadratically with the number of amino acids7, any potential hardware advancements in the future are unlikely to have a significant impact. This limits the practical capability of many researchers to predict structures of large size, leaving many macromolecular complexes without a structure prediction. The second difficulty is sampling with a large number of restraints: as the number of chains and amino acids increases, the number of residue-residue contacts and distance restraints to optimize increases as well, making it harder for the model to converge to accurate structures. Large, multimolecular complex prediction is an out-of-domain inference setup for AFM since it was trained only on cropped regions and thus is not expected to perform well. The third difficulty is that AFM converges to a single (sometimes incorrect) structure (for each of the five available trained models) and it is highly challenging to obtain a diverse set of predictions for the same target14.However, AFM application for predicting structures of large assemblies is still challenging16, and the second is docking-based methods that rely on pairwise protein-protein docking19. Integrative modeling methods rely on information from multiple sources, such as crosslinking mass spectrometry, Förster resonance energy transfer, co-evolution, cryo-EM, and small angle x-ray scattering to compute models. This information is converted into spatial restraints and combined into an integrative modeling approach21, using specialized software packages23 to generate a set of structural models that are consistent with it. The generation of candidate models is often performed by a Monte Carlo search of the conformational space. Docking-based methods significantly rely on pairwise protein-protein docking for the prediction of complexes26 and do not require additional input information. In pairwise docking, the two input proteins are docked to one another using geometric shape and physicochemical complementarity. The main problem is that they sample thousands of docked configurations. While the correct ones are usually sampled, it is difficult to rank them as top-scoring. Typically, pairwise docking methods succeed in ranking a correct model among the Top-10 best scoring in 25–40% of the cases28. This low accuracy further complicates the multi-protein assembly stage, where methods have to consider a large number of pairwise protein-protein docking models. For example, Multi-LZerD18 builds the multimolecular assembly by applying a stochastic search driven by a genetic algorithm. Kuzu et al.19 construct the multimolecular complex iteratively, where a single subunit is added to the subassembly in each iteration. The CombDock method is hierarchical and combinatorial29. The complexes are constructed hierarchically by generating subassemblies of two or more subunits. At each stage, subassemblies are connected using pairwise docking configurations between subunits. Due to multiple possible hierarchical assembly pathways, the algorithm combinatorially enumerates assembly trees. Since the algorithms used for docking and scoring pairwise interactions have low accuracy, it is difficult to reach high accuracy in multi-subunit docking.Prior to the deep learning revolution, methods developed for the assembly of multi-protein complexes could be divided into two main categories. The first category is integrative modeling methods that mainly rely on experimental data30. However, the approach is applicable mainly to homomeric complexes with a success rate of ~30%. Inspired by this work, here we combine AlphaFold2 with a deterministic combinatorial assembly algorithm29. Our new method, CombFold, uses a small number of pairwise subunit interactions generated by AlphaFold2 for assembly instead of thousands generated by docking. The hierarchical and combinatorial assembly stage exhaustively enumerates possible assembly trees, maximizing the probability of correctly assembling the complex based on pairwise AlphaFold2 interactions. We validate our approach on two benchmarks of large heteromeric assemblies  and obtain a Top-1 success rate of 62% and Top-10 success rate of 72% (TM-score > 0.7). Moreover, CombFold is able to increase the structural coverage by 20% relative to experimental structures in our benchmarks. We also test the method on the benchmark of homomeric complexes used for MoLPC validation and obtain a Top-1 success rate of 57%. CombFold successfully assembles six out of seven CASP15 targets with over 3,000 amino acids. We apply the method on a set of complexes with known stoichiometry and without known structure from Complex Portal31 and obtain confident predictions.The recently developed MoLPC method relies on AlphaFold2 to produce configurations for pairs and triplets of chains and assemble them using Monte Carlo Tree SearchThe input to CombFold is the subunit sequences and optionally distance restraints, the output is a set of assembled structures. A subunit can be a single chain or a domain. The approach is based on combinatorial and hierarchical assembly via pairwise interactions. In principle, there is no limitation on complex size, as the complex can be divided into subunits suited for the GPU memory limit, and our current implementation supports up to 64 subunits. CombFold works in three major stages: (i) generation of pairwise subunit interactions by AFM, (ii) creation of a unified representation of subunits and interactions, and (iii) combinatorial assembly of subunits .In the first stage, we apply AFM to all possible subunit pairings. Following this, we create three additional AFM models for each subunit, ranging in size from three to five subunits, that include subunits with which the given subunit had the highest confidence-scored predicted pairwise interactions (Methods). The underlying concept is that some groups of more than two subunits form intertwined structures and therefore all of them should be predicted as a single model by AFM (Methods).In the second stage, to prepare input for the third assembly stage, a single representative structure for each subunit is selected and the transformations between representative subunits are calculated. This is required since there are multiple AFM structures for each subunit from pairwise AFM runs and their enumeration during the assembly stage is intractable. The representative subunit structures are extracted from the predicted modeled subcomplexes according to the maximal average plDDT score for this subunit. Next, we use all interacting subunit pairs (Cα-Cα distance < 8Å) from AFM models to extract pairwise transformations (rotation and translation in 3D) between their representative structures in the global reference frame. The representation of the input by representative subunit structures and transformations between them enables us to apply the combinatorial assembly algorithm with AFM interactions instead of docking-based ones. Each transformation is coupled with a score based on AFM’s PAE score (Methods).35. If a protein chain is divided into subunits (eg. domains), distance constraints are added to enforce sequence connectivity. This combinatorial assembly stage consists of i-th iteration we construct 3 and In the third stage, we use The model confidence score produced by our method is based on the AFM PAE score. Each pairwise interaction (represented by a transformation) has a PAE-based score (Methods). The confidence of an assembled structure is a weighted score of the transformations that were used for assembly, where the weight is proportional to the sizes of the subunit subsets that were merged by each transformation.30. It contains 153 complexes ranging between 500 and 10,000 amino acids with 10 to 30 chains per complex. This dataset contains mainly symmetric homomers (98 complexes consisting of one unique chain and 27 consisting of two unique chains). Finally, Benchmark 4 dataset contains seven CASP15 targets with more than 3,000 amino acids.We tested the method on four benchmark datasets . We gene36 which assesses the global accuracy of the complex, similar to CASP and MoLPC30. Similarly to CAPRI assessment37, a model is considered Acceptable-quality if the TM-score is above 0.7 and High-quality if the TM-score is above 0.8. The success rate is measured as a fraction of the benchmark complexes with Acceptable- or High-quality models among the Top-N best-scoring predictions.To evaluate the accuracy of the modeled structures we rely on the TM-scoreWe obtain a Top-1 success rate of 60% for CombFold on this benchmark, accurately modeling 21 out of 35 complexes  with TM-We compare CombFold to an end-to-end AFM on all the Benchmark 1 complexes using the A100 GPU card with 40-gigabyte memory. AFM succeeded in producing at least one result for 17 out of 35 complexes with up to 3,700 amino acids. Of these, 10 complexes were modeled with Acceptable- or High-quality, resulting in success rates of 26% and 29% for Top-1 and Top-5 results respectively , d.The largest complex assembled by CombFold was eIF2B:eIF2 . The performance on this dataset is comparable to Benchmark 1 , with To41. The information is used for scoring and sampling models to produce structures that are consistent with the available data. Here, we add to CombFold support for integrating information about known interactions between subunits and distance restraints that originate from crosslinking mass spectrometry. This type of information can be obtained for individual complexes in vitro or for multiple assemblies identified from in situ experiments45. AFM does not currently support the integration of this type of data. Recently, AlphaLink46 was developed to add distance restraints support to AlphaFold2/OpenFold as a bias to residue-residue contacts, similar to template support in AlphaFold2. This method requires subsampling of MSA to give more weight to distance restraints and is currently not applicable for complex structure prediction. The advantage of CombFold is that it can integrate additional information during the assembly stage (Methods).Integrative structure modeling is often used to determine the structures of large macromolecular assemblies using information from a variety of sources, such as crosslinking mass spectrometry, cryo-Electron Microscopy, or bioinformatics analysis47 to compile a set of 12 distance restraints. We also divided the chains into two groups for assembly (Methods), based on a known structure of a subcomplex of TIM9 and TIM10 (PDB 2BSK). The resulting model is of High-quality with a TM-score of 0.85 , a Benchmark 1 case, for which both CombFold and AFM failed to produce an accurate prediction . We used crosslinking mass spectrometry experiment for this complex of 0.85 .We obtain a Top-1 success rate of 57% on this benchmark, accurately modeling 87 out of 153 complexes , Table 113. In contrast, in the assembly of heteromeric structures, multiple pairwise interactions are considered, and pairwise interaction can form indirectly even if it is not predicted correctly by AFM. Therefore, the success rate of CombFold on heteromeric complexes is higher , a partial subcomplex of two HucS and two HucL subunits could be assembled . The oth31. We queried the database for all complexes with over 5,000 amino acids, known stoichiometry, and without homology to any experimentally determined structure (Methods) to obtain 28 complexes from three organisms . High-confidence structures were found for seven complexes  that is mainly applicable to homomeric complexes, our combinatorial algorithm doubles the success rate from ~30% to ~60% . Further, we only kept the entries where the numbering and residue type of ATOM records was consistent with the SEQRES records to enable simple and accurate model validation. This filtering protocol resulted in 35 structures that were used as the test dataset for the pipeline  and retained only those that contain at least 5 unique (distinct) chains, resulting in 4,997 entries. Next, we removed entries containing DNA or RNA molecules because they can cause conformational changes upon binding to the protein complex, as well as, entries with short (<50 amino acids) or long chains , resulting in 1,383 entries. We also filtered entries that contained multiple source organisms, such as host-pathogen interactions, as AFM performance for complexes often depends on the availability of paired alignment, leaving 682 entries. To remove redundancy, the complexes were clustered based on sequence identity. An entry was removed if it had a matching entry with an earlier release date and at least 80% of the chains matching with 70% sequence identity, leaving 213 entries. We also removed all entries released before May 2018 because they were part of the AFM training datasetThis benchmark was prepared following a protocol used for Benchmark 1 with several changes. First, the PDB entries used are all entries up to March 2023. Second, instead of filtering entries released before May 2018 (AFMv2 cutoff date), we used September 2021 (AFMv3 cutoff date). Also, we did not filter complexes with chains over 1,500 amino acids. This resulted in 25 complexes .30 that contains 175 complexes. 17 complexes were removed due to incorrectly annotated biological units in the PDB . In addition, 5 structures of amyloid fibrils were also removed  due to the expected compositional and conformational heterogeneity.We relied on a dataset by Bryant et al.31 is a database that curates information from literature on stable, macromolecular complexes. We queried all complexes listed with over 5,000 amino acids with known stoichiometry. Entries with a homologous PDB structure were removed using a stricter threshold for homology to focus on entries that are more likely to be novel. Therefore, we defined a PDB entry as homologous to the Complex Portal entry if at least 66% of the chains were matching with at least 30% sequence identity.The Complex Portal48 which were used to identify linkers between domains. The linkers connecting the domains were not included in the prediction. For Complex Portal predictions, the UniProt sequences were divided until each pair of sequences was under 1,800 amino acids.A subunit is a sequence that can either be an independent chain of the complex or a part of a chain (for example a certain domain). Sometimes it is necessary to divide a chain into a number of subunits - either because the chain is too long to be predicted by AFM or because domains are connected by a long linker and are not in spatial proximity. For Benchmarks 1 and 3 each subunit was a full single chain as defined in the SEQRES segment of the PDB entry. In two cases , the longest chain was too long to be predicted as part of a pair  so it was divided into two subunits, one containing the first 1,000 amino acids and the other with the rest. For Benchmark 2, long chains were divided into subunits evenly until every pair could be predicted by AFMv3. This affected five complexes . In the CASP15 benchmark, the chains were divided into subunits according to IUPred3 domains68. To account for this, AFM prediction is applied for larger subsets of three to five subunits as follows. For each subunit, we select the most likely interacting subunits based on the pairwise PAE interaction score and use them to build larger subsets (Methods). Here, we limit our calculations to the total length of input sequences of 1,800 which can be run on standard GPUs.In the first stage, we run AFM on each possible pairing of the subunits. Proteins, both homomers and heteromers, have the ability to create intertwined structures where the interacting chains exchange small segments or compact protein substructures. These interactions can result in a wide range of quaternary arrangements, including dimers, or higher-order oligomers69 with default parameters (without templates), producing five structures per run. Subunits were inputted as separate chains. For Benchmarks 1 and 3, we used AFMv2 and AlphaFold-ptm to obtain 10 structural models. For comparison to CombFold on Benchmark 1, only end-to-end AFMv2 was used. For Benchmark 2, CASP15, and Complex Portal predictions, we used AFMv3 only, as it was not trained on these targets. CombFold predictions on Benchmark 2 were compared to end-to-end AFMv3.AlphaFold2 runs were performed using ColabFoldEach subunit structure from AFM predictions is ranked based on the mean plDDT score using all predicted structures from AFM runs for pairs and larger subsets. The structure with the maximal score is selected as the “representative subunit structure” for the assembly stage. Additional criteria were examined as possible ranking scores including, the average PAE score for the structure, the maximal plDDT, or the interaction score with other subunits in AFM prediction. There were no significant differences between the described possibilities, the mean plDDT which is easy to calculate and more widely used was chosen.71. Similarly, the transformation The method computes for each pair of subunits a list of possible transformations between them based on their interaction models from AlphaFold2. All pairs of subunits are extracted from multi-subunit predictions. For each pair, if it is interacting (Cα-Cα distance < 8Å), the transformation between the subunits is calculated. We can mark the predicted interacting structure for two subunits Each transformation is scored using the PAE score of the two subunits. PAE score is computed by AFM for any two amino acids in the structure, predicting their alignment error relating to each other. The PAE score values are between 0 to 30 with lower values corresponding to a lower predicted error. The transformation score is calculated and normalized to be between 1 to 100 by the equation: 72 was also a good indicator for RMSD from correct transformation however not as good as the average PAE . In the case of ambiguity of crosslinked residues due to multiple copies of the same subunit, we require that one of the possible distances restrained by the crosslink is below the distance threshold.CombFold runtime is dominated by the AFM prediction runs for subunit pairs and larger subsets. On Benchmark 1, the average GPU time for AFM predictions was 548 and 1,164 seconds for subunit pairs and larger subsets, respectively, running on NVIDIA A30 with 24GB of memory. However, since our method requires 73 score relative to the transformation in the target complex is at an acceptable level (DockQ > 0.23). We calculate the connected components of this graph. The pairwise connectivity ratio is defined as the ratio between the number of amino acids in the largest connected component and the total number of amino acids in the complex. A single connected component in the graph (pairwise connectivity = 1.0) indicates that there are pairwise transformations that can lead to the assembly of the complex. In contrast, multiple connected components indicate that accurate assembly is not possible with available transformations.Given a set of pairwise transformations and a target complex structure, this metric measures how many of the pairwise transformations between subunits from the target complex are present in the set. A graph is built, where each node is a subunit in the target complex and an edge is present if there exists a transformation in the set between those subunits for which the DockQ74. Graphs were created using Matplotlib75.Protein complexes were visualized using ChimeraXSupplement 1