Subpopulations of soluble, misfolded proteins can bypass chaperones within cells. The extent of this phenomenon and how it happens at the molecular level are unknown. Through a meta-analysis of the experimental literature we find that in all quantitative protein refolding studies there is always a subpopulation of soluble but misfolded protein that does not fold in the presence of one or more chaperones, and can take days or longer to do so. Thus, some misfolded subpopulations commonly bypass chaperones. Using multi-scale simulation models we observe that the misfolded structures that bypass various chaperones can do so because their structures are highly native like, leading to a situation where chaperones do not distinguish between the folded and near-native-misfolded states. More broadly, these results provide a mechanism by which long-time scale changes in protein structure and function can persist in cells because some misfolded states can bypass components of the proteostasis machinery. How soluble misfolded proteins can bypass chaperones is unknown. Utilizing a meta-analysis, multi-scale modelling, and new experimental data it is found that this phenomena is common and arises from misfolded states that are native-like and long-lived due to protein self-entanglements. Typically, in these assays the protein of interest is purified after it has been expressed either heterologously or constitutively from different synonymous messenger RNA (mRNA) variants. A synonymous mRNA variant is an mRNA molecule where one or more codons have been replaced by a synonymous codon, which does not alter the encoded protein’s primary structure but alters the mRNA’s nucleotide sequence.Some soluble, misfolded proteins can bypass the refolding action of chaperones in vivo according to folding and functional assays4. Since the specific activity is an ensemble average over the soluble fraction of proteins, it can be inferred that these synonymous mutations caused a portion of the soluble protein molecules to shift into a misfolded ensemble with decreased activity. Many other examples of this phenomenon exist. The ability of soluble FREQUENCY (FRQ) protein to bind to its partner protein ‘White Collar-2’ (WC-2) was decreased by half when a synonymous variant of FRQ was produced1. Since FRQ was expressed in vivo, this indicates that chaperones sometimes fail to catalyze the proper folding of soluble, misfolded FRQ protein molecules.For example, introducing synonymous mutations into the Chloramphenicol acetyltransferase (CATIII) enzyme decreased its specific activity by 20%In many of these studies, alternative explanations for the formation of soluble misfolded proteins have been ruled out. Most of these studies have characterized the properties only of soluble protein through the use of centrifugation, ruling out influences from insoluble aggregates. Many also controlled for changing expression levels, ruling out the possibility that it is changing in protein levels causing this phenomenon. Gel assays ruled out the presence of truncated protein forms in a number of studies. Finally, in some studies, native gels, analytical ultracentrifugation, or size-exclusion chromatography were run to rule out the presence of higher-order, non-native oligomers. Complete details of which controls were performed for each of a set of twenty experimental studies are provided in Supplementary Data Three fundamental questions arise from these observations: How common is it for soluble, misfolded proteins to bypass chaperones? How long does it take for these misfolded states to fold? And, finally, how do some misfolded proteins avoid the refolding action of chaperones at the molecular level? These are biologically important questions because the answer to the first two questions could impact our understanding of how protein homeostasis is maintained in cells. The answer to the final question would help to explain how synonymous mutations can have long term impacts on protein structure and function in vivo.To address these questions, we carried out a meta-analysis of the experimental literature focused specifically on in vitro studies where quantitative measurements can be carried out with appropriate controls Fig. . We findWe carried out a meta-analysis of the experimental literature reporting time-courses of protein refolding and acquisition of function Fig. . We focu24  we do not consider reverse transitions from the misfolded and folded states to the unfolded state and (ii) we use the same estimated rate parameters for all proteins based on global averages since protein-specific rate information is not available.Many chaperones, including GroEL/GroES, require ATP during their catalytic cycles. It is possible then that the 6–70% of molecules that we conclude are trapped in soluble misfolded states in our meta-analysis are the result of inactive chaperones due to ATP depletion during the experiments. To test this, we solved the time-dependent Master Equations for a simplified model of ATP-dependent protein refolding by GroEL (see Methods). This model considers GroEL-catalyzed folding under several assumptions including only the unfolded state of the client protein binds GroEL. Using this simplified model of GroEL-dependent folding, we re-fit the experimental data for the nine data sets involving GroEL . We conclude that ATP depletion leading to chaperone inactivity does not contribute to the lack of refolding observed in the original experimental data.Having verified the model, we next predicted the concentration of ATP as a function of time for each of the nine experiments given the reported initial concentrations  are experimentally known to bind GroEL, and (ii) populate long-lived misfolded conformations . Next, we sampled 20 different conformations across a range of D value for each of these conformations interacting with GroEL and plotted them as a function of We have only considered three conformational states of proteins. However, proteins populate an ensemble of conformations in solution. Therefore, we expect a range of KD values in Supplementary Fig. D with an increasing fraction of native contacts. Below D similar to that of the native state  around D values, some similar to the native state, but many not. The greatest predictor appears to be how native-like the non-native state is. Next, we selected nine different conformational states with We observe a range of KTo test if our conclusions are dependent on model resolution we back-mapped each of the ten coarse-grain folded, unfolded, and misfolded conformations of Isochorismate synthase bound to GroEL to an all-atom representation Fig. b and c aD with GroEL as this threshold is varied. We find that our conclusions are unchanged  cut sites in a protein that have changes in peptide abundance upon chemical refolding37  dataUsing a combination of published experimental data, kinetic modeling, and multi-scale simulations we have answered a number of basic molecular biology and biochemistry questions concerning protein structure and function in vivo. The observations that synonymous mutations can have long-term effects on protein structure and function in vivo strongly imply that soluble, misfolded subpopulations persist in cells and that chaperones do not catalyze their folding on biologically relevant time scales. This motivated us to re-analyze the last several decades of literature to examine if there was quantitative in vitro data to test this inference. We indeed find that in every single in vitro experiment in which there are fairly rigorous controls and normalization there is always a subpopulation of soluble, misfolded, less-functional proteins that do not fold in the presence of chaperones. These subpopulations can be as high as 70% of the total protein molecules in solution. Applying a kinetic model to the experimental time courses, we estimate these soluble misfolded states can take a minimum of days or longer to fold in the presence of chaperones. Thus, the in vivo and in vitro data indicate the same phenomenon: some soluble, misfolded proteins can bypass the chaperone machinery for long periods.These results do not mean that all misfolded and non-native conformations bypass chaperones. At equilibrium, proteins adopt an ensemble of distinct structures with different probabilities, existing on a continuum from more to less ordered and hence, for globular proteins, span from exposing less to more hydrophobic surface area. Thus, some protein conformations will be more or less likely to interact with chaperones, and hence different misfolded conformations will have different affinities for chaperones. Indeed, in our simulation results we observe that when the protein is less ordered and more unfolded the binding affinity for the chaperones increases , which can be a very slow process56. Indeed, applying a standard backtracking analysis method54, we observe that misfolding protein trajectories must partially unfold  have been shown to be more resistant to degradation63 so too it might be the case that already formed non-covalent lasso entanglements could be more resistant to degradation.It was recently shown that proteins that contain non-covalent lasso entanglements in their native state are more likely to get degraded as newly synthesized proteins, probably because they tend to be slow-folding proteins64. Additionally, synonymous mutations that alter the speed of translation but not the encoded protein sequence can impact a host of cellular processes68, including a protein’s structure and function in vivo. These observations suggest the molecular explanation from this study is likely to remain relevant in vivo, even if population levels of soluble misfolded states are different compared to in vitro.A critique of our meta-analysis is that we only analyzed in vitro data, and the lack of an in vivo environment, which includes vectorial synthesis by the ribosome and the presence of more types of chaperones, artificially increased the subpopulations of soluble misfolded protein. While it is possible that the fraction of soluble, misfolded protein may decrease in the cellular context they are not entirely eliminated. It has been observed, for example, that when a protein is synthesized by the ribosome it still populates states that remain soluble in non-functional form – thus, vectorial synthesis does not eliminate these subpopulations39. Age-related functional changes in aminoacyl-tRNA synthetases have also been suggested to arise from conformational changes in some organisms69. A challenge in such studies will be to control for side reactions, such as increased oxidative damage that can occur to non-native conformations compared to their native counterpart73. Thus, the phenomena we have identified may be of relevance to some of the molecular origins of aging.A promising connection will be to examine if the type of long-lived misfolded states we observe are relevant to organismal aging. In the case of enolase, there is evidence that it undergoes a thermodynamically reversible conformational change into a kinetically trapped state depending on the age of the organism in which it is expressed74 are providing a new perspective on protein structure and function in vivo suggesting proteins commonly exhibit subpopulations of structural ensembles that are soluble, misfolded, less functional, not rapidly degraded, not quick to aggregate, nor acted upon excessively by chaperones. The population of molecules with these characteristics can be influenced by both translation-elongation kinetics, as suggested by synonymous mutation studies, or through denaturation and renaturation, as seen in our meta-analysis. It is natural to hypothesize other perturbations could influence their populations as well, such as changes in temperature75. Experimental efforts to structurally characterize these self-entangled states are likely to be a fruitful area of future research, as the implications of these states for protein structure, function, and homeostasis are broad and fundamental.These and other recent findingshttp://plotdigitizer.sourceforge.net/). These raw values, which represent the percent refolded as a function of time, were then converted to the percent non-native as a function of time by taking %non-nativeRaw data were extracted from the published experimental papers listed in Table 27. This kinetic scheme76  Transcription factor 1 (PDB ID: 1K7J), (2) purine nucleoside phosphorylase (PDB ID: 1A69), (3) S-adenosylmethionine synthetase (PDB ID: 1P7L), (4) enolase (PDB ID: 2FYM), (5) isochorismate synthase (PDB ID: 3HWO), and (6) galactitol-1-phosphate dehydrogenase (PDB ID: 4A2C). We selected these proteins because they are confirmed GroEL clients81 and each was previously observed to populate long-lived misfolded states in coarse-grained simulations of protein synthesis, ejection, and post-translational dynamics27.Monomers composing the molecular chaperone GroEL consist of the apical, equatorial, and interconnecting domains. Client proteins bind to a specific region within the apical domain. All structures of GroEL used in this study are based on PDB structure 1KP8, which has been used widely in GroEL simulation studies82 in the native-state reference structure are considered. For each of these proteins, we selected structures representing long-lived misfolded conformations from synthesis trajectories based on comparison of their tes Fig. . The fraUnfolded conformations for binding simulations were selected for each client protein as the first structure after ejection from the ribosome was complete (75 ps after ejection) in the same trajectories identified to be in long-lived misfolded states. Finally, representative native conformations were chosen for each protein as the final structure from a simulation initiated from the native state coordinates and run for 30 CPU days.83. Full-length coarse-grained models of DnaK and HtpG were constructed from PDB IDs 5NRO and 2IOQ, respectively. 2IOQ was used in several earlier HtpG simulation studies86 while the DnaK structure 5NRO was selected because it has been used in earlier studies88. Simulations of DnaK and HtpG were otherwise conducted in the same fashion as those described for GroEL and its client proteins.In addition to simulations with GroEL, we also examined the interactions of chaperones DnaK and HtpG with client-protein purine nucleoside phosphorylase (PNP)α coarse-grained representation for GroEL, DnaK, HtpG, and their client proteins89. The potential energy We use a Cα − Cα bonds, torsion angles, bond angles, electrostatic interactions, Lennard-Jones-like native interactions, and repulsive non-native interactions to the total potential energy , were placed on all GroEL interaction sites to maintain its conformation and position at the origin throughout the simulation. Root Mean Square Deviation (RMSD) restraints with a force constant of 0.1 kcal/ (mol  ×  Å2) were used to maintain the client proteins in their initial conformations. This system was then placed in a flat-bottom spherical restraining potential of radius 160 Å. The sphere center was placed such that the client proteins can interact with the surface, cavity, and sides of the GroEL heptamer but cannot access the back side of the GroEL heptamer that would typically be hidden by the other heptameric ring (GroEL is a double ring system). A 160 Å radius was found to easily accommodate each of the client proteins unfolded states. For each unfolded, folded, and near-native misfolded client protein conformation we ran simulations with ten different initial client protein orientations generated by randomly rotating the starting client protein conformation. Each initial conformation was then simulated for 2.4 µs in the presence of GroEL. Simulations of DnaK or HtpG and their client protein purine nucleoside phosphorylase were carried out in an analogous fashion. For DnaK and HtpG, the chaperone was placed at the origin, and a 200 Å radius sphere also centered on the origin was used with the client protein initially placed in a random orientation 50 Å away. All restraints, force constants, and other simulation parameters were otherwise the same as for the GroEL-client protein simulations. All simulations were performed using OpenMM96 with a Langevin thermostat at 310 K, a friction coefficient of 0.050 ps−1, a 15-fs integration time step, and all bonds constrained.Simulations were initialized with the center-of-mass of the GroEL coarse-grain model at the origin of the coordinate system. The client protein of interest was then placed in a random orientation such that the distance between its center-of-mass and the center of the top of GroEL ring was 70 Å, with no van der Waals contacts between them. Spherical harmonic restraints, with a force constant 0.1 kcal/ simulations followed by quenching to 310 K. We carried out 20 independent quenching simulations and then selected 20 different structures with distinct 28. Next, each of these all-atom composite structures of the GroEL heptamer and client protein was solvated in a box of SPC/E water97 with dimensions 16 × 16 × 16 nm3 and then neutralized by the addition of 128 sodium ions. This neutralized system was then energy minimized with the steepest descent algorithm. Spherical harmonic restraints with a force constant 1000 kJ/(mol x nm2) were applied to the GroEL heptamer heavy atoms. All-atom simulations were carried out with GROMACS 202098 using the AMBER03 force field99. Long-range electrostatic interactions were calculated with the Particle Mesh Ewald method100. Lennard-Jones interactions were calculated with a distance cut-off of 1.2 nm, and the temperature and pressure were maintained throughout the simulations at 310 K and 1 atm with a Nose-Hoover thermostat102 and Parrinello-Rahman barostat103, respectively. All bonds were constrained using the LINCS algorithm104 and an integration time step of 5 fs was used. We performed 1 ns of equilibration followed by a 1-ns production simulation with each of the 30 all-atom conformations before calculating the intermolecular interaction energies.We randomly chose one of the client proteins, isochorismate synthase, used in our coarse-grained simulations and simulated its interactions with GroEL at all-atom resolution. We chose ten representative structures from the coarse-grained ensembles of unfolded, folded, and near-native misfolded isochorismate synthase/GroEL systems and back-mapped these 30 coarse-grained structures to all-atom resolution using a previously reported procedureOdds ratios of the binding probabilities between chaperones and unfolded (U) or folded (F) conformations of client proteins with attractive van der Waals interactions on or off were calculated asIn Eq. In Eq. 27 when they misfold. These entanglements are local non-covalent lasso entanglements that are not present in the native state that are associated with long-lived misfolded states within the E. coli proteome. We calculated the entanglement  the 7 ATP molecules bind simultaneously and can be treated as a single molecule and (2) only the unfolded protein can bind GroEL. The differential equations that describe this reaction scheme are the following:112 and from Supplementary Data GroEL functions in a multi-step ATP-dependent cyclementclasspt{minima28 along two order parameters that capture the nativeness of the structures  using Topoly114. If a loss of self-entanglement was observed (i.e. the loop is threaded in the native state but not in the misfolded state) we use the native state structure to find the crossing residues, else if it was a gain of self-entanglement we use the representative structure. We can then describe each individual change in self-entanglement in a structure by a discrete vector of 6 clustering parameters: (1) Number of crossings in the N-terminal change in entanglement; (2) Number of crossings in the C-terminal change in entanglement; (3) Rounded partial linking number for the N-terminal change in entanglement; (4) Rounded partial linking number for the C-terminal change in entanglement; (5) Change type for the N-terminal change in entanglement; (6) Change type for the C-terminal change in entanglement. The change type of a change in self-entanglement are described in detail in our previous work27. To ensure we are not clustering we also ensure that any entanglements clustered together by the above 6 parameters also have crossing residues within ±5 residues of each other. After clustering across all the changes in self-entanglement observed across all the representative metastable conformations, we then generate a list of representative changes in self-entanglement by choosing the entanglement with the minimal loop from each cluster  by examining changes in their partial linking number with the N or C terminusTo answer the following questions: (1) how consistent are the changes in self-entanglement we observe with the experimentally observed changes in PK cut-site peptide abundance? (2) is this consistency more extreme than what you would expect with a random set of cut sites? We first quantify the consistency between our model and the experimental evidence by examining the existence of primary sequence overlap of the entangled residues with the PK cut-site residues and the consistency in the direction of change in solvent exposure of the PK cut-sites in the back-mapped representative MSM structures and LiP-MS data. We, therefore, define two test statistics for each time point If all the PK cut sites have overlap with representative changes in self-entanglement in a structure and the direction of change in the solvent exposure relative to the native state is the same that would indicate a complete consistency and both test statistics would be at their maximum. On the other hand, if none of the PK cut sites have overlap with representative changes in self-entanglement in a structure that would indicate a completely non-consistent result and both test statistics would be 0.We employ the Permutation Test to determine the probability of observing the consistency between our model and the experimental data by random chance. For each experimental time point, we draw a random set of new PK cut-sites from a theoretical distribution of all potential half-tryptic peptides (those peptides cut by PK on one side and trypsin on the other). This is done in such a way that we maintain the same number of unique PK cut sites observed across all timepoints in the original experiment. We then calculate the new test statistics As the LiP-MS data was analyzed across a proteome-wide analysis the data for each individual protein may suffer from a lack of coverage sufficient for random sampling. Therefore, we must generate a theoretical distribution of half-tryptic peptides. First calculating the intrinsic probability of PK cutting at a specific site across the proteome-wide set of dataFurther information on research design is available in the Supplementary InformationDescription to Additional Supplementary InformationDataset 1Dataset 2Supplementary Movie 1Reporting Summary


They combined whole‐cell crosslinking, co‐fractionation mass spectrometry, and open‐source data mining with artificial intelligence (AI)‐based structure prediction of PPIs in the well‐studied organism Bacillus subtilis. This innovative approach reveals architectural knowledge for in‐cell PPIs that are often lost upon cell lysis, making it applicable to genetically intractable organisms such as pathogenic bacteria.Integration of experimental and computational methods is crucial to better understanding protein–protein interactions (PPIs), ideally in their cellular context. In their recent work, Rappsilber and colleagues  identify bacterial PPIs in their cellular context, by combining whole‐cell crosslinking, co‐fractionation mass spectrometry and AI‐based PPI structure prediction.Integrative approaches are crucial for identifying protein‐protein interactions (PPIs). In their recent work, Rappsilber and colleagues (O'Reilly  After removing structurally known or homologous interactions, they ended up with 1,218 previously known PPIs with no high‐quality structural information. Many of these come from PPI mapping techniques such as affinity pulldowns so it is unknown which ones involve direct interactions. The authors also performed their own PPI mapping and identified 909 PPIs by whole‐cell crosslinking mass spectrometry and co‐fractionation analysis  which Alphafold used for training  may approximate in‐cellular conformers as crosslinks are not violated.As a next step, the authors predicted the structures of these 2032 PPIs using AlphaFold‐Multimer to reveal direct interactions and suggest functions Fig . An inteEscherichia coli. The predicted structures also suggested a potential protein regulator (Pdhl) of the activity of the pyruvate dehydrogenase subunit E1p. To check its function, O'Reilly et al generated two strains, one that overexpressed PdhI and one where PdhI was knocked out. They observed that the strains did not show growth defects compared to the wild type when grown on glucose as main carbon source. However, cells overexpressing PdhI showed growth defects when grown on pyruvate as the sole carbon source, indicating that PdhI acts as an inhibitor of pyruvate dehydrogenase. Point mutations suggested by the Alphafold models were able to rescue this phenotype, highlighting the usefulness of using these predicted structures to understand function.The authors then selected interactions from the pool of highest‐quality predictions to validate Alphafold models via functional experiments Fig . The preet al have significant implications for systems biology as identifying PPIs in their native context provides valuable insights into the functions of these interactions. The combination of data mining, crosslinking mass spectrometry, and co‐fractionation mass spectrometry, followed by computational modeling using AlphaFold‐Multimer and functional validation, offers a powerful approach for characterizing PPIs, particularly for genetically intractable organisms. The next big challenge in structural systems biology lies in improving the depths of such analyses, predicting and validating stoichiometries on large scale, and adding strong visualization, such as data from cryo‐electron microscopy, super‐resolution microscopy, and other multi‐scale approaches to further systems‐based understanding of biomolecular interactions, a key to understand life itself.Overall, the findings of O'Reilly


Model quality assessment is a crucial part of protein structure prediction and a gateway to proper usage of models in biomedical applications. Many methods have been proposed for assessing the quality of structural models of protein monomers, but few methods for evaluating protein complex models. As protein complex structure prediction becomes a new challenge, there is an urgent need for model quality assessment methods that can accurately assess the accuracy of interface residues of complex structures.Here, we present DeepUMQA3, a web server for evaluating the accuracy of interface residues of protein complex structures using deep neural networks. For an input complex structure, features are extracted from three levels of overall complex, intra-monomer, and inter-monomer, and an improved deep residual neural network is used to predict per-residue lDDT and interface residue accuracy. DeepUMQA3 ranks first in the blind test of interface residue accuracy estimation in CASP15, with Pearson, Spearman, and AUC of 0.564, 0.535, and 0.755 under the lDDT measurement, which are 17.6%, 23.6%, and 10.9% higher than the second best method, respectively. DeepUMQA3 can also assess the accuracy of all residues in the entire complex and distinguish high- and low-precision residues.http://zhanglab-bioinf.com/DeepUMQA_server/.The web sever of DeepUMQA3 are freely available at  Predicting the three-dimensional structure of proteins is one of the major basic research issues in the field of bioinformatics, and it is important for understanding protein functions, innovative drug development, and disease treatment  participated in the blind test of the EMA of CASP15 and ranked first in the accuracy estimation of interface residues see . The perDeepUMQA3’s Pearson correlation coefficient, Spearman correlation coefficient, and the area under the receiver operating characteristic curve (AUC) on lDDT . For theThe submission page of the web server is shown in btad591_Supplementary_DataClick here for additional data file.


Membrane proteins are encoded by approximately a quarter of human genes. Inter-chain residue-residue contact information is important for structure prediction of membrane protein complexes and valuable for understanding their molecular mechanism. Although many deep learning methods have been proposed to predict the intra-protein contacts or helix-helix interactions in membrane proteins, it is still challenging to accurately predict their inter-chain contacts due to the limited number of transmembrane proteins. Addressing the challenge, here we develop a deep transfer learning method for predicting inter-chain contacts of transmembrane protein complexes, named DeepTMP, by taking advantage of the knowledge pre-trained from a large data set of non-transmembrane proteins. DeepTMP utilizes a geometric triangle-aware module to capture the correct inter-chain interaction from the coevolution information generated by protein language models. DeepTMP is extensively evaluated on a test set of 52 self-associated transmembrane protein complexes, and compared with state-of-the-art methods including DeepHomo2.0, CDPred, GLINTER, DeepHomo, and DNCON2_Inter. It is shown that DeepTMP considerably improves the precision of inter-chain contact prediction and outperforms the existing approaches in both accuracy and robustness. Membrane proteins are encoded by approximately a quarter of human genes. Here, the authors propose a deep transfer learning method for predicting inter-chain residue-residue contacts of transmembrane protein complexes. Frequently, transmembrane proteins (TMPs) assemble to form symmetric homo-oligomers to perform their specific biological functions by interacting with themselves under the drive of hydrophobic interaction and hydrogen bond networks5. For example, most of the single-spanning homo-oligomeric transmembrane complexes are stabilized by the hydrogen bonds6, and the transmembrane region of polytopic membrane proteins would consist of hydrophobic segments with regular secondary structures7. However, the experimental determination of transmembrane protein complex structures is challenging, primarily due to the influence of the complicated membrane environment and the large size of these proteins. Therefore, it is highly desirable to develop computational methods for predicting the homo-oligomeric structures of TMPs and providing molecular interaction insights.Membrane proteins (MPs) play diverse roles and essential functions in living cells including molecular transporters, ion channels, signal receptors, immune response, and enzymes. It is estimated that up to approximately a quarter of the human genome encodes membrane proteins, which constitute about half of current drug targets17. Although those methods can accurately predict the intra-protein contacts of soluble proteins, the corresponding precision for TMPs is still not satisfactory due to the different structural characteristics and physicochemical environments between soluble proteins and TMPs. Hence, some methods have been proposed to address the specific structures of TMPs20. Since most regions of TMPs are composed of α-helical structures which are mainly driven by hydrophobic interactions, several approaches use machine learning-based methods to predict the helix-helix contacts24. In addition, utilizing co-evolutionary information, Wang et al. also proposed a deep learning-based method to predict the reside-residue contacts for guiding ab initio folding of membrane proteins25. Although these methods improve the prediction of membrane protein monomer structures, it is needed to develop computational methods for addressing the oligomeric structures of TMPs and investigating their molecular interactions.Many statistical and deep learning-based methods have been developed to predict protein structures or intra-protein contacts28, various advanced deep learning methods have been developed to predict the inter-chain contacts for protein complexes40. Our previous work, DeepHomo29, utilizes sequence and structure features to predict inter-chain contacts with ResNet2 architectures41. With the progress of protein language models, DeepHomo2.030, GLINTER37, and CDPred40 apply the embedding vector and multi-head attention features from the ESM-MSA-1b model42 to capture the interfacial interaction. However, these deep learning-based methods are only trained on the data set of mainly soluble proteins, which are different from the TMPs. Therefore, it is urgent to specially develop a deep learning model to predict the inter-chain contacts for the homo-oligomers of TMPs.Motivated by the success of intra-protein contact predictions in monomer structure prediction43, which poses a major obstacle to direct training on transmembrane proteins. Addressing the challenge, we here develop a deep transfer learning method to predict the inter-chain contacts of transmembrane proteins, named DeepTMP, which first trains an initial model on a large number of soluble proteins and then transfers it to transmembrane proteins by utilizing the features of protein language model and the geometric triangle-aware module. Compared with the common ResNet and attention mechanism, the geometric triangle module can efficiently consider the many-body effect and reduce the geometric inconsistency, which helps the pre-trained model to more effectively capture the interfacial interaction from the evolution information generated by the ESM-MSA-1b model and better predict the inter-chain contacts of TMPs.However, compared with thousands of soluble protein complexes, the number of transmembrane protein complexes is rather limited. For example, there are only <350 non-redundant homo-oligomeric transmembrane protein complexes in the PDBTM databaseFigure DeepTMP was extensively evaluated on a diverse test set of 52 TMP homo-oligomers from the PDBTM database with the experimental and the AlphaFold2-predicted monomer structures . Table Figure To verify whether IT_Model and DT_Model learn the characteristics of soluble and transmembrane proteins, respectively, we compare the precisions of IT_Model and DT_Model for the top L predicted contacts, as shown in Fig. We compared DeepTMP with other inter-chain contact predictors including CDPred, DeepHomo2.0, GLINTER, DeepHomo, and DNCON2_Inter on the test set of 52 targets with experimental and AlphaFold2-predicted monomer structures, respectively. Specifically, we downloaded the programs of the methods other than DeepHomo2.0 and DeepHomo from their official web sites, and then ran the programs locally using their default parameters. It is noted that GLINTER only gave the results for 45 targets for the TMP test set because it can only handle the targets with a sequence length of <500. Table It can be seen from the table that DeepTMP significantly outperforms the other five methods for different numbers of predicted contacts. Specifically, DeepTMP achieves a precision of 82.3% for the top 10 predicted inter-chain contacts with the input of experimental monomer structures, which is much >48.7% for DeepHomo2.0, 48.5% for CDPred, 38.0% for GLINTER, 27.7% for DeepHomo, and 13.3% for DNCON2_Inter. Similar trend can also be observed for the input of AlphaFold2-predicted monomer structures. Specifically, DeepTMP gives the precisions of 76.5% and 62.3% for top 10 and L predicted contacts, which are considerably >49.6% and 35.3% for CDPred, 46.7% and 27.2% for DeepHomo2.0, 36.4% and 27.4% for GLINTER, 23.3% and 12.1% for DeepHomo, and 13.1% and 8.3% for DNCON2_Inter. Given the difference in structure characteristics between soluble and transmembrane protein complexes, it is expected that DeepTMP performs better than DeepHomo2.0, CDPred, DeepHomo, and DNCON2_Inter which are mainly trained on soluble protein complexes. This can also be understood because DeepTMP extracts the high-order representations of transmembrane protein complexes through transfer learning and learns the interaction knowledge of both two soluble and transmembrane protein systems.Figure 5TIN) with the predicted inter-chain contacts by four methods. Although the helix-helix interactions in the transmembrane region belong to the hydrophobic interactions which may be similar to those at the interface of soluble protein complexes, CDPred, DeepHomo2.0, and GLINTER still cannot well predict the inter-chain contacts. In contrast, DeepTMP obtains high precisions of 100% and 87% for top 10 and top L predictions, respectively. This demonstrates that DeepTMP can correctly capture the hydrophobic interaction knowledge at the interface of transmembrane protein complexes which is affected by the lipid environment.Figure To further examine the robustness of DeepTMP, we also evaluated the performance of DeepTMP on the 296 soluble dimeric proteins from the test set of DeepHomo2.0, and compared it with CDPred, DeepHomo2.0, GLINTER and DeepHomo. Supplementary Table L is the sequence length of monomer structure for transmembrane protein complexes. Figure The number of interfacial contacts in a protein complex is an important feature of its inter-chain interaction. Here, the contact density is used to characterize the interface. Specifically, the number of inter-chain contacts is first obtained from the maximum interface in a target. Then, the contact density is calculated as follows,eff is used to quantify the depth of MSA. Specifically, we used a sequence identity of 70% to calculate the Meff on the TMP test set and set seven reasonable intervals. Figure eff) < 4.0, DeepTMP is capable of achieving a top L precision of over 55%, which is comparable to the best performance of the other methods, surpassing most of the other methods by a significant margin.We further investigated the influence of the depth of MSA on the performances of DeepTMP and other five methods. The effective sequence number Meff) from 6.0 to 7.0 have a relatively lower precision for all methods  in this MSA range have low contact density of 0.343 and 0.324  from 7.0 to 7.25. If we excluded these three targets from the test set, the precisions will always increase with the increase of ln(Meff). Another reason for such an abnormal phenomenon is that we used hhfilter to screen the sequences of the original MSA for extracting the ESM-MSA-1b features, which often leads to a drop in the number of effective sequences for ESM-MSA-1b. Therefore, we have instead adopted the number of effective sequences for ESM-MSA-1b to measure the depth of MSA. With this correction, the precision indeed increases with the number of effective homologous sequences  instead of the original UniRef_2020_03 database. Figure Compared with other methods like DeepHomo2.0, DeepTMP has two additional types of important features. One is the use of Resnet-Inception and geometric triangle-aware modules in the network architecture, and the other is the inclusion of transmembrane protein complexes in the training set. To investigate their roles in DeepTMP, we have retrained a model using the same network as DeepHomo2.0 and the same training procedure as DeepTMP, named DeepHomo2_TMP. Figure Table There are 75/12/13, 25/7/3, and 38/3/11 cases for alpha/beta/alpha+beta topologies in the training, validation, and test sets, respectively. Although alpha-helix and beta-barrel transmembrane proteins span and interact with lipid bilayer through hydrophobic interactions, their topologies, function, and interaction mechanism are different. Most transmembrane protein complexes belong to the alpha-helix category, where the alpha-helical domains are mostly hydrophobic and non-polar. Therefore, the intra-protein and inter-chain interactions are similar and hydrophobic for the alpha-helix transmembrane proteins. However, the outside surface of beta-barrel is non-polar, and the inside channel is hydrophilic, which is different from those of alpha-helix transmembrane proteins. Therefore, it is valuable to investigate the impact of different topologies of transmembrane protein complexes on inter-chain contact predictions.Figure 48. If the MSA of beta-barrel transmembrane protein complexes displays a greater degree of conservation, it is expected to have an enhanced prediction of inter-chain contacts on such kind of topology. Hence, we simply employ an entropy formula as a straightforward measurement of the MSA conservation. Specifically, the average entropy for each target is calculated as follows,N is the number of amino acids in the sequence of a target, naa is the 20 standard amino acids, and j at the i-th position.This topology-dependent phenomenon may be understood as follows. Generally, beta-barrel transmembrane proteins play a crucial role in transporting cargo and signaling across biological membranes. The efficient execution of these functions necessitates the formation of multi-chain structures with a barrel-like architecture, which may lead to a large interacting interface. Thus, we calculated the average contact density of the two kinds of topologies. It is found that the targets with alpha and beta topologies have 1.33 and 2.33 contact densities, respectively. Therefore, it is expected to predict inter-chain contacts on the beta-topology targets more easily. In addition, beta-barrels often exist in the outer membranes of Gram-negative bacteria and chloroplast and mitochondriaFigure 43. The corresponding results are shown in Fig. Transmembrane proteins consist of three different regions, including extracellular (Extra), transmembrane (TM), and cytoplasmic (Cyto) segments. Among these three categories, the transmembrane segment is embedded in the lipid bilayer, while extracellular and cytoplasmic segments are located in water and belong to soluble parts. Given the different physiochemical environments of extracellular, transmembrane, and cytoplasmic regions, we have calculated the precisions of DeepTMP for the three topologies of transmembrane proteins on the 52 targets (Supplementary Data 43 according to the following criteria: (i) The protein only has one chain; (ii) The length of sequence is <800. Then, we used the MMseqs2 with an E-value of 0.1 to remove the overlapped monomeric transmembrane proteins with our TMP test set. Finally, we used a sequence identity cutoff of 40% to cluster the remained monomeric transmembrane proteins and randomly selected 50 monomer targets.DeepTMP is developed to predict the inter-chain contacts of transmembrane protein complexes. Another critical challenge in the field is to predict the oligomeric state of a protein. To examine whether DeepTMP can be used for such purpose, we have investigated the maximum predicted contact probabilities of 50 monomers and 52 oligomers from our test set. Here, the 50 monomeric transmembrane proteins are obtained from the PDBTM databaseFigure Figure 6UWM, 1UUN, and 5SVK. For comparison, the corresponding contact maps for AlphaFold-Multimer (AFM) and native complexes are also given in the figure. Here, the contact probability map for AFM is converted from the predicted inter-chain distances in the AFM model with the best “iptm+ptm” score. It can be seen from the figure that DeepTMP obtains a better prediction with the precisions of 62.7%, 68.5%, and 93.1% for the top L contacts on these three targets, respectively, compared with 36.6%, 52.2%, and 89.0% for AFM. In addition, compared with the ground-truth contact map, DeepTMP also shows a good complementarity to AFM in terms of predicted inter-chain contacts. In other words, DeepTMP is able to predict correct contacts that are failed by AFM. For example, within the top 100 predicted contacts, DeepTMP alone gives the correct prediction for 42 and 49 contacts, AFM alone gives the correct prediction for 24 and 12 contacts, and both models give the correct prediction for 34 and 26 contacts on targets 1UUN and 6UWM, respectively.Figure 49. Specifically, the putative binding models are first sampled and then evaluated by our iterative knowledge-based scoring function ITScorePP50. The predicted inter-chain contacts by DeepTMP are integrated into the scoring process as follows30:rij is the distance between two contacting residues. The E0 represents an empirical constraint energy between two residues for a predicted contact and is set to −100.0 kcal/mol30. Similar to DeepHomo2.0, we only considered the top 10 predicted contacts with a contact probability threshold of >0.65 to minimize the number of false positive contacts. To mimic real experiments, we used the AlphaFold2-predicted monomer structure as input for docking. Figure 4K7R and 2JAF. It can be seen from the figure that with inclusion of predicted inter-chain contacts, HSYMDOCK is able to construct the correct complex structures with high TM-scores of 0.89 and 0.96 for targets 4K7R and 2JAF, respectively. By contrast, without considering the inter-chain contacts, HSYMDOCK leads to wrongly predicted complex structures with low TM-scores of 0.46 and 0.47 for the two targets, respectively. These results suggest the value of DeepTMP in building complex structures.With the predicted inter-chain contacts, we also constructed the complex structure of transmembrane proteins by using our protein-protein docking program HSYMDOCKIn this study, we have developed a transfer learning-based method for predicting the inter-chain contacts of transmembrane protein complexes by transferring the knowledge gained from the initial training model which is trained on a large data set of soluble protein complexes. DeepTMP is extensively evaluated on the TMP test sets of 52 transmembrane protein complexes, and compared with the other five methods including DeepHomo2.0, CDPred, GLINTER, DeepHomo, and DNCON2_Inter. It is shown that DeepTMP achieves the best performance among the compared methods and predicts the inter-chain contacts with the precisions of 82.2% and 68.4% for the top 10 and L predicted contacts, respectively, compared with 48.7% and 31.8% for DeepHomo2.0, 48.5% and 33.8% for CDPred, 38.0% and 27.7% for GLINTER, 27.7% and 15.7% for DeepHomo, and 13.3% and 7.9% for DNCON2_Inter on the test set. These results demonstrate the accuracy and robustness of DeepTMP. The better performance of DeepTMP than the other methods is attributed to not only the use of Resnet-Inception and geometric triangle-aware modules in the network architecture but also the inclusion of transmembrane protein complexes in the training set.In addition, we also compare DeepTMP with the initial training model (IT_Model) to demonstrate the importance of transfer learning. Through the comparison of the different impacting factors including the proportion of transmembrane regions and the order of symmetry, it is found that DeepTMP can retain the similar physical interaction learned from the pre-trained model such as interfacial hydrophobic interactions, and capture the characteristics of transmembrane protein complexes. Furthermore, we directly train a deep learning-based model without transfer learning on the TMP training set and compare it with DeepTMP to illustrate the importance of the pre-trained model. We also investigate the impact of different features to verify the robustness of DeepTMP. Moreover, we study the performance of DeepTMP on different topologies which have different mechanisms of interactions and structural characteristics, compared with the other methods. It is shown that DeepTMP can effectively predict the correct inter-chain contacts regardless of topologies. Finally, it is revealed that DeepTMP is to some extent able to distinguish between monomers and oligomers as well as between dimers and higher-order complexes according to the maximum predicted contact probability. It is anticipated that DeepTMP will serve as an indispensable tool for the inter-chain contact prediction of homo-oligomeric transmembrane proteins.Figure 51, and the sequence representation and multi-head attention matrix generated by protein language model ESM-MSA-1b. The structure feature only includes the intra-protein distance map calculated from the monomer structure of the homo-oligomeric transmembrane proteins.The input of DeepTMP includes sequence and structure features. For the sequence features which are part of the features used by the DeepHomo2.0, it contains the position-specific scoring matrix (PSSM) feature, two types of direct co-evolution features calculated by CCMpred52, we set the maximum pairwise sequence identity to 99% for the UniRef30_2020_03 database53 and applied an E-value of 0.001. Then, the PSSM matrix was calculated by the LoadHMM script in the RaptorX-Contact package13, with a dimension of L×20, where L is the length of monomer sequence. Afterwards, the raw scores and average product correction of direct coupling analysis (DCA-DI and DCA-APC) were calculated by CCMpred, with a dimension of L×L×1 for each of them. Finally, we used the hhfilter54 to select the most diverse sequence with the diff 512 option for the MSA. The filtered 512 sequences in the MSA are fed to the pre-trained ESM-MSA-1b model to generate the ESM-MSA-1b vector embedding and ESM-MSA-1b row attentions, with the dimensions of L×768 and L×L×144, respectively. It is noted that the above features are generated from the MSA of monomer sequence. Since it is trivial for homo-oligomers to generate the paired MSA, we used the monomer MSA to substitute the paired MSA of complexes and generated the same features.The above sequence features are all produced from the multiple sequence alignment (MSA). Specifically, we first generate the corresponding MSA by searching the sequence database. Since we focus on the inter-chain interaction on homo-oligomeric transmembrane proteins composed of identical sequences in this study, we only need to run the HHblits software to search the homologous sequences of the monomer sequence. For the hyperparameter of HHblits55. The formulation is described as follows:d is the intra-protein distance. dk is a hyperparameter, which ranges from 2 to 22 Å and is divided into 64 bins with a variance σ = 0.3125 Å.For the structure feature of monomer structures, we applied the radial basis function to convert the intra-protein distance of monomer as input featuresFor the ground truth of inter-chain contact map, we defined a pair of residues from different chains as being contacted if any two heavy atoms of the two residues are within 8 Å. Since the indistinguishability of the chains in the homo-oligomeric transmembrane proteins, we have implemented symmetrical operations during the evaluation of inter-chain contact predictions.57. As such, before the triangle-aware module, we use four ResNet-Inception modules to preliminarily capture the intra-protein and inter-chain interaction.Previous methods use ResNet module to avoid the explosion of gradient and a large kernel size to capture the long-range interaction between two residues. Recently, several methods combine ResNet and Inception to increase the effective receptive field of the network with fewer parameters58 proposes the evoformer module including the intra-molecular triangular multiplication update and triangular self-attention, which can reduce the unsatisfied region by the geometric triangular inequality. Therefore, we have also applied the triangle-aware module after the ResNet-Inception module to reduce the geometric inconsistency and more effectively capture the correct inter-chain interactions. The triangle-aware module includes a triangle update, two triangle self-attention, and a transition layer  The maximum area of the interface between any pair of chains in the complex is <500 Å2; (ii) The number of contacts in the interface is <10. After being filtered by those criteria, there were 52 transmembrane protein complexes in the final test set (Supplementary Data The data sets for transfer learning were obtained by downloading the structures from the Protein Data Bank of Transmembrane Proteins (PDBTM) on January 2021, which contains 5692 transmembrane protein complexes. Since we focus on the homo-oligomers of TMPs, the heterologous proteins in the PDBTM were excluded, resulting in 2226 structures. In addition, it is noted that some structures from the PDBTM only have one chain even though they are annotated with high-order symmetries. Therefore, we also removed those target structures and obtained 2020 complexes. Furthermore, we filtered their biological assembles with the following criteria: (i) The chain number of complexes is equal to the order of symmetry; (ii) Any pair of chains in the assembly share >99% sequence identity. Finally, the remained 1907 complexes were clustered by MMseqs2 with a sequence identity cutoff of 30%E-value of 0.1 to remove the redundant sequences from the training and valid sets of homodimers for the initial model, which results in 3079 and 271 targets in the two sets, respectively  and python (v3.7.0) on A100 GPU. For other hyper-parameters, we used a mini-batch size of 1, a learning rate of 0.001, and a dropout rate of 0.1. Since the triangle self-attention layer would occupy a large GPU memory, we set a maximum length of 256 amino acids in the training process due to the GPU memory limitation. Specifically, for the target proteins with a sequence length of >256 residues, we used a window of 256 and a stride of 1 to scan the sequence and collect the fragments which have the maximum inter-chain contacts. After that, we randomly chose one cropped sequence to represent the protein sequence. For the transfer learning stage, we loaded the initial training model and re-trained the network with the transmembrane protein complexes.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-11Reporting SummarySource Data


In typical protein–nanoparticle surface interactions, the biomolecule surface binding and consequent conformational changes are intermingled with each other and are pivotal to the multiple functional properties of the resulting hybrid bioengineered nanomaterial. In this review, we focus on the peculiar properties of the layer formed when biomolecules, especially proteins and peptides, face two-dimensional (2D) nanomaterials, to provide an overview of the state-of-the-art knowledge and the current challenges concerning the biomolecule coronas and, in general, the 2D nano-biointerface established when peptides and proteins interact with the nanosheet surface. Specifically, this review includes both experimental and simulation studies, including some recent machine learning results of a wide range of nanomaterial and peptide/protein systems. Nanomaterials offer unique chemical, physical, and mechanical properties due to their small size and large surface area. Hybrid biointerface-engineered nanoplatforms with proteins and peptides can pave the way for huge progress in several applications, such as nanomedicine, including, e.g., dual-targeted tumor hypoxia relief and enhanced photodynamic therapy ,2, sensi2), and (M)Xenes.Graphene, since its discovery in 2004, has attracted great attention from the nanoscience field due to its remarkable optoelectronic, photothermal, physical, and chemical properties; it is the most exploited among the two-dimensional (2D) nanomaterials. Other 2D nanomaterials beyond graphene that have drawn much interest for bionanotechnology applications include graphene oxide (GO), reduced GO (rGO), and many other non-carbon-based nanosheets such as hexagonal boron nitride (h-BN), molybdenum disulfide  and a biological medium, the biomolecules that are present rapidly compete to bind to the NP surface, thus leading to the formation of a complex dress of biological molecules, referred to as the ‘corona’. The corona surrounding the NP surface largely defines the biological identity of the colloidal particle and is made up of the adlayer(s) of proteins, lipids, sugars, nucleic acids, and metabolites biomolecules, spontaneously adsorbed and bound either strongly or weakly onto nanomaterial surfaces and/or among each other . To noteIn the case of 2D nanomaterials, owing to the large specific surface area/mass ratio, referred to as specific surface area (SSA), and the high surface free energy, the adsorption of proteins and other biomacromolecules to form this corona is especially effective, affecting the interaction between the nanosheets and the biointerface when exposed to biological microenvironments.Over the last five decades, the research on enhanced knowledge on the understanding of mechanisms of interaction between the biomolecule corona and the NP surface has been especially focused on the protein corona (PC), where the NP physicochemical properties, such as size and surface chemistry and the protein identity, have been identified as the main factors to drive the equilibrium binding between proteins and nanomaterial .The biotransformation of the pristine nanomaterial surface on the spontaneous formation of PCs has a relevant impact on its pharmacological and toxicological profile, most often in an unpredictable manner. The PC covering nanomaterials in a physiological environment is the molecular interface that determines the response of the nanomaterial, mediating the interaction between the ‘bare’ NP surface and the biological system, thus influencing the mechanisms of action of the nanomaterial up to the sub-cellular level . PCs affIn the context of the bioinspired and biomimetic approach, the harnessing of PCs around a 2D nanomaterial diminishes interaction with the plasma membrane by providing the nanomaterials with stealth properties . This isThe 2D nanomaterials, i.e., those materials where the third dimension is almost negligible (approximately 1 nm per layer) than the other two dimensions, have a greater SSA compared to 3D nanomaterials. While the NP surface charge and chemistry unquestionably influence the PC formation and its spatial–temporal evolution, the size-dependence effect of curvature for 3D nanomaterials can be surely neglected for 2D nanomaterials, where the SSA is mass dependent but not size dependent  Scheme . As an eThe physicochemical features are scrutinized for shedding light on the protein coronas onto the NP surface, and the most common experimental techniques used for this purpose include (a) the surface charge ; (b) the hydrodynamic size ; (c) the colloidal morphology and concentration ; and (d) the PC composition , and enzyme-linked immunosorbent assays, ELISAs) .To assess the biological functions of the PCs, one can genrally measure (i) the bound protein structure and conformation ; (ii) the protein–protein interactions and conformational changes ; (iii) the protein stability and conformational changes ; and (iv) the protein accessibility and function in the corona (by immunoblotting) .Various computational methods, such as molecular dynamics (MDs), density functional theory (DFT), and coarse-grained simulations, have been employed to study protein/peptide–nanomaterial interactions. Recently, various effective in silico methods to predict PC composition on 2D nanomaterials by the use of machine learning (ML) algorithms have been developed .The size and surface properties of nanoparticles determine the protein corona with potential effects for biological influences . In geneAn elegant approach in recent developments has exploited and rationally designed the protein corona to achieve superior nanomaterial functionality, with a focus on preventing cytotoxicity and, in general, uncontrolled changes in the colloidal stability of the NP .Graphene and its derivatives can interact strongly with different serum proteins . In partThe higher adsorption of serum proteins on GO than rGO are in the following order: bovine fibrinogen (Fg) > immunoglobulin (Ig) > transferrin (Tf) > BSA, which was attributed to differences in surface chemistry . In partThe effect of the defect-induced hydrophilicity of MWNTs on the PC formation, as can be revealed using micro-Raman, photoluminescence, infrared spectroscopy, electrochemistry, and MD simulations, points out a strong influence of charge-transfer processes between the 2D nanomaterial and proteins , which in turn induces protein unfolding and enhances conformational entropy, thus resulting in higher protein adsorption .2 hybrid orbital; therefore, for both borophene and graphene, stable low-dimensional nanosheets can be formed without being easily oxidized. On the other hand, phosphorene’s unique honeycombed fold structure allows for one phosphorus atom, the covalent bond, with three other monolayer P atoms, thereby exposing a lone pair of electrons, which are easy to react with oxygen to form oxides that are soluble in water.A study by proteomics analysis on the adsorption of plasma proteins onto Xenes, i.e., 2D monoelemental nanomaterials, including graphene, phosphorene, and borophene, showed that plasma proteins changed the surface identities of borophene nanosheets, with 46.5% of the 94 proteins bound being immune-relevant proteins . In compn+1XnTx (n = 1–3), where an early transition metal  is interleaved with n layers of carbon and/or nitrogen (X) with various surface functional groups  [A new family of 2D materials is MXenes, which are based on the carbide and nitride of transition metals with a general formula of M– or –F) . While t– or –F) .3C2Tx nanosheets, prepared by different etching methods to grow diverse surface functional groups, were exposed to human plasma to scrutinize the protein corona by LC-MS/MS-based label-free proteomic analysis. The results revealed a significant difference in relative protein abundance for the three MXene nanosheets, with the main driving forces for the adsorption of plasma proteins on Ti3C2Tx being identified as hydrogen bonding, steric hindrance, and hydrophobic interaction [Three types of MXene Tieraction .2 to reproduce the nanosheet experimentally measured water contact angle and predict the slip-length of water [2 consists of hydrophobic nanosheets and low-friction surfaces, despite the significant charges of surface atoms and relatively strong strength of van der Waals potentials [Using MD simulations, force field parameters were developed for MoSof water . The restentials .Proteins and peptides at the interfaces of nanostructured materials can be a result of their use in the synthesis and functionalization of nanostructures. Indeed, the chemical and structural properties of peptides and proteins allow their use as reducing, stabilizing, and functionalization agents .2 nanosheets, while peptide bonds have much higher binding affinity to graphene compared to other groups [Proteins and enzymes can be used as exfoliants by shear force to produce biofunctionalized nanosheet suspensions. In a study concerning the protein-induced layer-by-layer exfoliation process of various 2D nanomaterials, it was revealed by control experiments  and DFT simulations that benzene rings and disulfide groups of the BSA have much higher binding affinity to MoSr groups .2 carbon hybridization, introduce defective sp3 carbon moieties, distort the hexagonal benzene ring structure of graphene, and affect protein binding due to steric hindrance and potential wrinkling of the graphene layer [During the functionalization and modification of graphene, chemical defects are often created, which typically alter the spne layer .Several simulation studies on the role of graphene wrinkles and roughness revealed that rough graphene nanosheets trigger the adsorption of proteins, such as collagen fibers and peptides (RGD cell adhesive sequence), more than smooth surfaces due to the higher number of ‘contact points’ and reactive sites at the 2D nano-biointerface ,41.A protein-bonding layer functioned to dynamically tune the silver conductance in response to external pressures and strains and was prepared by the merging of silver nanoparticles under ambient conditions in an aqueous solution, with the formation of a freestanding large-area 2D silver film bound by ultrathin amyloid-like β-sheet stacking of lysozyme .Not only the chemical composition but also the shape of the NP is important in determining its interaction with proteins and peptides; indeed, the contacting surface curvature can lead to different adsorption capabilities. If the biomolecule’s size is much smaller than the substrate size, the surface curvature effects can be safely neglected. For NPs, whose size is comparable to proteins, the particle curvature must be accounted for by investigating the 2D nano-biointerface. Although the effect of the negative curvature of graphene surfaces is not fully understood, the protein adsorption onto the surface of carbon-based nanomaterials is known to be more prominent as the local curvature decreases. In fact, parallel fluorescence experiments and MD simulations for BSA adsorption onto carbon nanotubes of increasing radius  and graphene (flat) nanosheets demonstrated an inverse relationship between protein adsorption capacity and surface curvature . In a reA simulation study to account for nanostructured surfaces using the Random Sequential Adsorption model modified from protein adsorption on flat surfaces pointed out that convex geometries can lead to lower steric hindrance between particles and higher degrees of surface coverage per unit area . A more A study on GO nanosheets and the PCs formed by adsorption from fetal bovine serum (FBS), a commonly employed component in cell culture media, evidenced an FBS-mitigated GO cytotoxicity, arising from the hampering of direct interactions between the cell membrane and GO nanosheets that result in physical damage to the cell membrane . AnotherRecent research findings evidenced that the adsorption of enzymes onto nanosheet surfaces of transition metal dichalcogenides, graphene, α-zirconium phosphate, and graphene oxide led to a decrease in the entropy of the enzyme’s denatured state and enhanced the stability of the bioengineered 2D nanomaterials . FurtherStudies on cytotoxicity, reactive oxygen species (ROS) production, and mitochondrial function disruption induced by rGO nanosheets coated with different concentrations of surfactants and ‘stealth’ functionalized with the protein corona formed in 10% FBS pointed out that the PC-coated rGO was more damaging than the bare nanosheets, inducing the most ROS and cellular death. The analysis of the nanomaterial and the PC correlated the more exposed rGO surface to higher lipid peroxidation, higher oxidative stress, and more cell death .The adsorption of proteins on GO can also modify the nanosheets’ toxicity. GO functionalized with the angiogenin (ANG) protein represents a novel nanomedicine for modulating angiogenesis ,52. ANG 2 nanocomposites were successfully used as adsorbents for the selective adsorption/isolation of proteins of interest, namely hemoglobin (Hb), from complex sample matrices, such as the human whole blood [The 2D nano-biointerface is largely dependent on the biological environment, including biomolecular components, solution conditions, and surrounding temporal dynamics and hydrodynamics . Bindingle blood .Protein folding is a self-organized process that spontaneously occurs in the aqueous solution with high precision in a time length as short as milliseconds or fractions of seconds . To noteIt is not easy to understand this process considering the huge number of allowed conformations in the protein unfolded state, but there is agreement in identifying in the primary sequence the code of the folding . HoweverGO and graphene, with their atomic flat surface, the abundance of surface functional groups, and the ultra-large aromatic structure, are ideal platforms for elucidating, theoretically and experimentally, the immobilization mechanisms of biomolecules on them. It has been proved that enzymes, proteins, and short-chain peptides can be immobilized through either covalent or non-covalent interactions onto low-dimensional nanosheets ,64,65.The interaction of a protein or peptide with a surface can lead to modification of their native conformation and activity. On the other hand, more complicated supramolecular architectures can be assembled, paving the way for new applications. Peptides can form self-organized formations, such as fibers, rods, tubes, and ribbons, by tuning experimental conditions .Greater changes in the protein/peptide conformation are observed in adlayers onto hydrophobic NP surfaces relative to their hydrophilic counterparts, with the shape of the protein impacting its stability on NPs with high surface curvatures. As the biological activity of a protein is strictly connected to its conformation and folded structure, even slight differences in the surrounding environment, such as pH and ionic strength, as well as the NP surface, may cause proteins either to lose or gain a function .2 nanosheet-protein complexes was studied using the four most abundant proteins in human blood, i.e., HSA, Tf, Fg, and immunoglobulin G IgG [2 internalized by macrophages by producing and releasing more cytokines, although all of the MoS2-protein complexes induced inflammation [2 nanosheets, triggering a stronger pro-inflammatory response in macrophages after the NP uptake due to the specific receptors recognition and the biochemical pathways activation [Significant structural rearrangement can be measured by CD spectroscopy analyses in BSA and Tf, from α-helical to enhanced β-sheet conformation, upon adsorption on GO nanosheets, while BSA unfolds on graphite surfaces, as expected on aromatic carbon surfaces . The nanin G IgG . The quaammation . The IgGtivation .2 nanosheet interface, as a consequence of ground-state complex formation by electron transfer, van der Waals, and hydrophobic forces, an altered conformation of HSA, as monitored by the quenching of the protein intrinsic fluorescence and the disruption of its binding domain with bilirubin, were found [In the case of the HSA-MoSre found . In addiThe interaction of the peptide with the interface and its orientation can induce a change in the conformational features of the free biomolecule. For example, the GAMHLPWHMGTL peptide sequence adsorbed onto graphene nanosheets by forming a complex reticular structure in a helical conformation that was different than the native α-helix structure .2+, is able to form metal complexes with peptides and tune the formation of FF nanostructures, driving the aggregation on the GO sheets plane [Aromatic amino acids, such as phenylalanine (F) and tyrosine (Y), are known to assemble into nanotube or nanofiber structures via a process mostly driven by π–π stacking, charge transfer, and H-bonding ,70,71. Fts plane .Dye-labeled peptide nanostructures assembled onto 2D nanomaterials have been used for the development of 2D bionanosensing devices. The dye-labeled peptide sequence HSSKLQ, sensitive to the prostate-specific antigen (PSA), can self-assemble onto GO via electrostatic and π–π interactions . When thNoteworthy, the substrate-driven one-step aqueous self-assembly of lysozyme has been shown to be a cost-effective method to form a protein-based bilayer membrane that can efficiently recover precious metals from resources, including ores, waste electrical components, or wastewater . To thisIn the complex interactions between 2D nanomaterials and proteins/peptides, the surface chemical structure, i.e., the atomic composition and arrangement of the outermost nanomaterial layer plays an important role in adsorption interactions, which may modify the secondary and tertiary structure of protein upon binding to the material surface . In the The π–π interactions predominantly occur between aromatic amino acids such as Phe, His, Tyr, Trp, and nanomaterials. These interactions arise from the delocalization of pi-electrons on the nanomaterial’s surface due to the presence of aromatic groups. The strength of these interactions is directly linked to the polarizability of the aromatic ring. Nanosheets such as GO feature a hydrophobic basal plane along with a hydrophilic edge and additionally exhibit a robust capacity for π–π interactions with aromatic amino acids, which significantly enhance protein binding ,81,82.In general, small peptides assemble preferentially to the edge or planar surface of GO or graphene via electrostatic or π–π interactions . Based oStudies carried out by paralleling SFG vibrational spectroscopy and MD simulations showed that the presence in a peptide sequence of planar sidechains containing charged hydrophilic (Lys), aromatic , amide (Asn and Gln), and guanidine (Arg) determines a preferential orientation of the peptide molecules adsorbed onto the graphene surface. The peptide interaction with graphene was demonstrated to be influenced by the competition between the peptide planar and hydrophilic residues .A strong π–π interaction between GO’s basal plane and glucose oxidase has been demonstrated, along with the aromatic rings of amino acids aligning parallel to the substrate plane . New quaSchultz et al. have emphasized the significance of π–π stacking interactions between GO and biomolecules apigenin and orientin, as revealed through DFT calculations. Their investigation highlights that more stable arrangements arise when the structures are oriented parallel to each other. Electronic charge transfer confirmed that these weak interactions take place via a physisorption process .In a recent study, Khedri et al. employed MD simulations to demonstrate the inhibitory impact of 2D nanomaterials against SARS-CoV-2. In all investigated scenarios, van der Waals interactions played a crucial role in the overall energy landscape. Notably, the spike protein was distorted by all categories of simulated 2D nanomaterials, leading to the reduced affinity for interacting with ACE2. Among these, functionalized (-) p-doped graphene exhibited the most effective performance, resulting in the inhibition of SARS-CoV-2 replication at the infection site .Through molecular docking analysis, Unal et al. showed that GO sheets possess the capability to engage with surface components of SARS-CoV-2, thereby mitigating infectivity, even in the presence of any mutations on the viral spike. This study delved into the roles played by hydrogen bonds and hydrophobic interactions . Using fRecent in silico investigations have been carried out to examine the nanotoxicity of 2D nanomaterials, like graphene and h-BN, toward the Hen Egg White Lysozyme (HEWL) protein. In this study, classical molecular dynamics simulations were employed. The results pointed to a notably stronger adsorption affinity of the protein onto h-BN in comparison to graphene. In this scenario, the protein’s native structure is significantly disrupted, whereas insignificant alterations are observed in the case of graphene. A strong perturbation of the secondary structure induced by h-BN can be attributed to van der Waals interactions and the electrostatic effect between the materials and aromatic amino acids. As a result of these interactions, the beta structure experiences complete diminishment. The study’s prediction indicates that h-BN might have noteworthy nanotoxicity toward proteins with a higher prevalence of aromatic amino acid residues. On the other hand, pristine graphene can be deemed a safer material when it comes to interactions with proteins ; see FigSimilarly, mutation studies by a combination of experimental methods and modeling and the bio-conjugation of BP7, a peptide sequence known with an affinity for h-BN, identified the Tyr residue as the key anchoring species, pointing to the importance of this residue in peptide/h-BN interactions. A further bio-conjugation of BP7 to a fatty acid allowed for the down-modulation of the Tyr contact to h-BN, resulting in the presentation of the extended peptide to the solution .An MD study on the non-covalent interaction between the G protein and the hydrophobic surfaces of SWNT and graphene revealed that the G protein interacts with both surfaces, achieving a favorable dispersion in the media. Nonetheless, SWNT can influence the biological activity of the protein by reducing the alpha-helical structure, inducing an incorrect protein orientation and a diminished affinity toward its target. In contrast, graphene can appropriately orient the protein G toward its target, causing minimal structure alterations in the protein .MD investigations have also demonstrated the graphene’s capability to elicit toxicity by interfering with protein–protein interactions (PPI). Simulations of PPI in the presence and absence of graphene showed that graphene inserted into PP led to the destabilization of hydrophobic interactions, disrupting the protein complex ; see FigMolecular dynamics simulations were carried out to study the interface between neurotrophin-mimicking peptides ,101,102 Notably, the glutamic acid residue assumes a crucial role since this moiety appears highly distanced from the surface in the case of BDNF (1–12), which coincides with the peptide displaying the lowest interaction energy; see 2 has demonstrated remarkable achievements in the realm of biosensor research.Being a trailblazing non-graphene substance, the single-layer 2D MoS2 or Au-modified MoS2 surfaces. In the case of pristine MoS2, the calculations unveiled adsorption via parallel interactions between the aromatic rings of the biomolecules and the surface sulfur atoms. For the Au-modified MoS2, a combination of chemi- and physisorption was found, with covalent attachment or non-covalent interactions toward Au atoms or the MoS2 sheet surface, respectively. In both cases, the adsorption energy followed the order Trp > Tyr > Phe [2 surface and a wide range of chemical entities encompassing amino acid groups. Data obtained using this force field are in excellent agreement with the experimental structural data of peptides adsorbed at the aqueous MoS2 surface, reproducing the helical secondary structure in the surface-absorbed state [Song et al. employed first-principle calculations to scrutinize the adsorption mechanisms of aromatic amino acids  onto monolayers of pristine MoSyr > Phe . A new fed state ; see Fig2 nanosheet and a model protein used in protein folding studies, the Villin Headpiece (HP35), pointed out that for the protein anchoring onto the MoS2 surface, aromatic and basic residues play a pivotal role, which, in turn, prompt the following protein unfolding [2 nanosheet exhibits a denaturing capability toward HP35 (within hundreds of nanosecond simulations), and the main driving force behind the adsorption was identified in the dispersion interaction between the protein and the nanosheet. Furthermore, a synergic effect from H2O molecules at the interface between some key hydrophobic residues  and MoS2 surface triggered the secondary structure disruption by providing a strong hydrophobic force driven by nanoscale drying.Furthermore, all atom MD simulations of the interaction between a MoSnfolding . The MoS2 surface [2. The facet edges of MoS2 were utilized as a landmark to identify the crystallographic orientation of the self-assembled structures of l/d-type peptides on molybdenum disulfide [2 with mirror symmetry, according to the facet edges of MoS2.Differently from graphene and GO surfaces, the aromatic amino acids do not have a substantial effect on peptides interacting with the MoS surface . The sysisulfide . Both peA selection of the recent literature on 2D peptide nanomaterial assemblies for fabricating functional nanoplatforms for various applications is provided in The ultrathin structure of nanosheets, the high aspect ratio, surface area, and surface free energy, enable them to more easily penetrate biological membranes and promote the adsorption of drugs and proteins, which originates the peculiar response at the biointerface and many potentialities for the application of 2D nanomaterials in the biomedical field. However, even though in vitro studies are very promising, one of the main reasons for the wide gap between benchtop discoveries and clinical practice, other than toxicity and scale-up issues, is certainly our limited knowledge about the physicochemical transformation of the 2D nanomaterial in vivo. The investigation and thorough understanding of the structure−performance relationship of the hybrid 2D nano-biointerface can pave the way for novel material design aiming at special functions and applications, not only in nanomedicine but also in environmental management, such as eco-friendly systems to recover precious metals from resources and pollutant removal from aqueous environments.This report covers the basic information and the most emerging applications of the nano-biointerface established between 2D nanomaterials and proteins/peptides. In particular, regarding both experimental and computational points of view, we focused on the structure–property correlation of the bioengineered nanocomposite material formed when biomolecules are adsorbed/grafted onto the nanoparticle surface. Recommendations for future studies include the emerging areas of bioinspired chemistry on multifunctional bioengineered 2D nanoplatforms for multimodal theranostics and smart, stimuli-responsive systems for sustainable, eco-friendly approaches to environmental applications regarding contaminant removal and recyclability.


The inverse protein folding problem, also known as protein sequence design, seeks to predict an amino acid sequence that folds into a specific structure and performs a specific function. Recent advancements in machine learning techniques have been successful in generating functional sequences, outperforming previous energy function-based methods. However, these machine learning methods are limited in their interoperability and robustness, especially when designing proteins that must function under non-ambient conditions, such as high temperature, extreme pH, or in various ionic solvents. To address this issue, we propose a new Physics-Informed Neural Networks (PINNs)-based protein sequence design approach. Our approach combines all-atom molecular dynamics simulations, a PINNs MD surrogate model, and a relaxation of binary programming to solve the protein design task while optimizing both energy and the structural stability of proteins. We demonstrate the effectiveness of our design framework in designing proteins that can function under non-ambient conditions. Protein sequence design is the process of generating a sequence of amino acids that folds into a desired shape to perform a certain function. To achieve this, several approaches have been developed which can be divided into traditional-physics-based e.g., ,2, and Mde novo design difficult.Traditional physics-based methods led to the initial steps in protein design and have contributed significantly to our understanding of protein structure and function. Yet, these methods suffer from several limitations, including model complexity, sensitivity to model accuracy, computational cost, and oftentimes a lack of flexibility. Traditional algorithms often presume a fixed or almost fixed backbone. Such an assumption consequently means that the protein structure is weakly sequence dependent; thus, changing the sequence does not significantly change the structure ,6,7,8. TMachine Learning methods have proven to be effective tools for predicting protein structure and function, as well as designing new proteins ,9,10,11,Physics-based and deep learning-based methods rely on empirical energy functions and machine learning models, respectively. Both are parameterized by the known protein structures, regardless of the optimal conditions for their folding and operations. For most of them, however, these conditions are ambient. Thus, interpolating these functions and models to specific domains of non-ambient conditions  is speculative, and likely wrong.In this work, we tackle the problem of protein design from a different perspective. Rather than using the commonly used energy functions on a static structure, we use molecular dynamics (MD) simulations to compute the protein energy landscape. In this way, we both avoid the fixed backbone assumption and infer an energy landscape that is MD-derived for a target under specific environmental conditions. Thus, this method should allow us to design proteins that approximately fold into a target structure, optimize their properties even at non-ambient conditions, and account for their flexibility, under the assumption that molecular dynamics simulations faithfully describe protein interactions . TheThere are a number of challenges when considering the binding energy and structural stability of a complex. First, there is no unique value to the binding energy. Proteins are flexible and may even change conformation. Second, taking into account the energy alone may not be sufficient. A low energy protein may be structurally unstable/disordered if its energy landscape has several local minima or is very flat, allowing for a high level of coordinate perturbation. We thus turn to a different approach that takes these limitations into consideration. Let us assume that we have a sequence Note that these quantities depend only on Let us now consider the design of a new protein. In this case, we want to design a protein with a particular structure of interest The optimization problem  and (2b)A major challenge in such an optimization task is the discrete nature of the sequence problems ,28,29. ASince solving the optimization problem requires the MD simulation of every given sequence-structure set, and since each MD simulation is computationally intensive, we opt for a so-called surrogate deep learning model to replace the MD simulation. The goal of the surrogate is to predict the binding energy  problem  and (2b)Protein folding is influenced by both local and global interactions between residues, making it a system that can be described well using a graph. We experimented with graph neural networks (GNNs)  and seleThe input to the network is the sequence (one hot encoded) and the coordinates of the alpha carbon , n is the length of the protein, Here, The output of the network, Thus, if the network is trained successfully, it can approximate both the binding energy and the structural stability of a given sequence; it can therefore be used for protein design without the computational cost of MD simulations.The core of the protein design problem requires solving an optimization problem based on a specific energy. The sequence searches ,28,29. Hsearches ,33,34 anTo this end, rather than solving for a binary matrix n non-zero entries, group sparsity yields a structure of The first two properties simply imply that each element in the matrix is between 0 and 1 and that the sum of each column of the matrix is also 1. The last term in the set is the so called The set ned  is a protein found in heart muscles . Upon daIn cardiac cells, cTpnI forms a complex with the following two regulatory proteins: troponin C (TpnC) and troponin T (TpnT) . The cryThis study is based on the experimentally-resolved structure of the cTpnI-TpnC-TpnT complex, PDB ID: 1J1D . Our firT is the temperature, To prepare the complex for MD simulations, we first protonated the 1J1D structure using PDB2PQR ,44 with BE is a sum of BE was decomposed to each residue of TpnT and it was decided that residues 104–130 would constitute the starting template for our peptide design, herein referred to as the TpnT-template.While it is possible to randomly sample peptide structures and compute their trajectories, it is better to sample structures that are considered feasible, that is, those structures that do not diverge when running molecular dynamics. As explained in the introduction, there are many software packages that can provide an initial design using either empirical energy or machine learning. Here, we use the popular Rosetta package and its protein complex generation process .Beta_nov16 scoring function [Beta_nov16 scoring function was up-weighted by a factor of 1.5. This modification to the scoring function biases mutagenesis towards residues that would form more hydrogen bonds, as this would increase the designed peptide specificity. The mutant fragment was then redocked to the target complex and minimized before the final complex structure was output. A total of 15,000 design attempts were made using Rosetta. Only about 2600 designs passed the following initial screening criteria: general design score of −190 Rosetta Energy Units (REUs), a binding energy of −50 REU, a shape complementarity score of 0.75 and no more than 3 buried unsatisfied hydrogen bonds. These 2600 structures were further filtered using MD simulations. Each designed peptide complexed with cTpnI-TpnC was prepared for MD simulations, as described in The proteins cTpnI and TpnC complexed with residues 104–130 of TpnT were first repacked and minimized based on the function . This waThree pieces of data were used to train our algorithm.Complex coordinates were extracted from the 5 ns simulations at regular intervals for a total of 50 coordinate sets per designed peptide. These were used to compute the center of mass coordinates, The average MMGBSA binding energies of the designed peptides to the target complexes, representing MPBSA.py .The contribution of each individual residue to the average MMGBSA binding energy for each complex was also calculated.As part of trajectory analysis, we also computed the MMGBSA binding energies for the 2600 complexes over the 5 ns simulation time. While more accurate methods for calculating binding energies exist, such as thermodynamic integration ,51 and fWe also calculated the RMSD of the design atomic coordinates over the simulation time. In An ideal design would have a low binding energy and would not considerably deviate from the original design coordinates. We plot the MMGBSA binding energy of 950 structures verses their RMSD in Given the training data, we trained the graph neural network described in Given the initial 950 sequences, we chose the best 50 sequences and used the optimization Algorithm 1. We used a total of 5 mutations in each protein before the optimization algorithm returned. A graph of the energy as a function of iterations as well as the number of total mutations  is presented in The initial 50 sequences and the designs obtained by using the algorithm are plotted in The newly designed proteins are then used as validation data. The energy of the designed proteins as well as their structural stability is plotted in In this work, we proposed a new framework that combines MD, deep learning, and binary programming optimization that leverage the strengths of each to solve the protein design task. Our goal is to use the most accurate energy model possible in order to achieve a stable design. To this end, we use MD to account for the protein environment and flexibility, since proteins are in constant motion. The MD trajectories are used for the calculation of an average binding energy. To assess the structural stability of the protein, we also record the deviation of the designed protein from the desired design. The average binding energy and RMSD of each sequence are then approximated using a graph neural network. The goal of the network is to recover the average binding energyand the average deviation of the design, which is more achievable than recovering the whole simulation. This approach is commonly referred to as PINNS and has gained popularity in many science and engineering problems. We train a graph neural network on data that are generated using MD. The resulting network is then used for solving the protein sequence design problem. For optimization, we use a convex relaxation of the problem with a sparsity-promoting norm. This yields sparse designs that are used to probe the energy surface. The code was tested on a true design of a binder to troponin. We have shown that we are able to find designs that indeed have a low binding energy (as computed by MD) given the trained data. Our designs reduce the energy by an average of Our method heavily relies on MD, which accounts for protein flexibility. It is worth noting that simulation conditions can be set to facilitate the evaluation of designed proteins at different temperatures. Furthermore, different pH values may be considered by adjusting residue protonation. We assumed in this procedure that if the structure for a given sequence stably binds to the target protein with high affinity then it will manifest this prediction in the lab. On the other hand, if the simulations were not reliable our design would lead to unreliable binder sequence predictions. Clearly, any final design needs to be validated in the laboratory. It is important to note that 5 ns simulations are too short to assess the structural stability of peptide–protein complexes. Longer simulation times could be employed to address this issue although this would be computationally expensive. Alternatively, longer simulations could be obtained for filtered designs that have an initially low RMSD. Validating MD results against laboratory experiments is beyond the scope of this paper. However, recent computational techniques are proposed to obtain an even higher accuracy for MD when solving the full Schrödinger system. Our approach, which can be used with any energy function, is designed with the aim of reducing the need for laboratory experiments by improving the accuracy of protein design simulations.