Encoding the space of protein-protein binding interfaces by artificial intelligence.

The physical interactions between proteins are largely determined by the structural properties at their binding interfaces. It was found that the binding interfaces in distinctive protein complexes are highly similar. The structural properties underlying different binding interfaces could be further captured by artificial intelligence. In order to test this hypothesis, we broke protein-protein binding interfaces into pairs of interacting fragments. We employed a generative model to encode these interface fragment pairs in a low-dimensional latent space. After training, new conformations of interface fragment pairs were generated. We found that, by only using a small number of interface fragment pairs that were generated by artificial intelligence, we were able to guide the assembly of protein complexes into their native conformations. These results demonstrate that the conformational space of fragment pairs at protein-protein binding interfaces is highly degenerate. Features in this degenerate space can be well characterized by artificial intelligence. In summary, our machine learning method will be potentially useful to search for and predict the conformations of unknown protein-protein interactions.

Removal of Pseudomonas type IV pili by a small RNA virus.

The retractile type IV pilus (T4P) is important for virulence of the opportunistic human pathogen Pseudomonas aeruginosa. The single-stranded RNA (ssRNA) phage PP7 binds to T4P and is brought to the cell surface through pilus retraction. Using fluorescence microscopy, we discovered that PP7 detaches T4P, which impairs cell motility and restricts the pathogen's virulence. Using cryo-electron microscopy, mutagenesis, optical trapping, and Langevin dynamics simulation, we resolved the structure of PP7, T4P, and the PP7/T4P complex and showed that T4P detachment is driven by the affinity between the phage maturation protein and its bound pilin, plus the pilus retraction force and speed, and pilus bending. Pilus detachment may be widespread among other ssRNA phages and their retractile pilus systems and offers new prospects for antibacterial prophylaxis and therapeutics.

2D Nanomaterials Reinforced Organic Coatings for Marine Corrosion Protection: State of the Art, Challenges, and Future Prospectives.

2D nanomaterials, with extraordinary physical and chemical characteristics, have long been regarded as promising nanofillers in organic coatings for marine corrosion protection. The past decade has witnessed the high-speed progress of 2D nanomaterial-reinforced organic composite coatings, and plenty of breakthroughs have been achieved as yet. This review covers an in-depth and all-around outline of the up-to-date advances in 2D nanomaterial-modified organic coatings employed for the marine corrosion protection realm. Starting from a brief introduction to 2D nanomaterials, the preparation strategies and properties are illustrated. Subsequently, diverse protection models based on composite coatings for marine corrosion protection are also introduced, including physical barrier, self-healing, as well as cathodic protection, respectively. Furthermore, computational simulations and critical factors on the corrosion protection properties of composite coatings are clarified in detail. Finally, the remaining challenges and prospects for marine corrosion protection based on 2D nanomaterials reinforced organic coatings are highlighted.

ADSC-Exos enhance functional recovery after spinal cord injury by inhibiting ferroptosis and promoting the survival and function of endothelial cells through the NRF2/SLC7A11/GPX4 pathway.

BACKGROUND: Spinal cord injury (SCI) is a devastating disease that causes major motor, sensory and autonomic dysfunctions. Currently, there is a lack of effective treatment. In this study, we aimed to investigate the potential mechanisms of Exosomes from adipose-derived stem cells (ADSC-Exos) in reducing ferroptosis and promoting angiogenesis after spinal cord injury. METHODS: We isolated ADSC-Exos, the characteristics of which were confirmed. In vitro, we tested the potential of ADSC-Exos to promote the survival and function of human brain microvascular endothelial cells (HBMECs) and analyzed the ferroptosis of HBMECs. In vivo, we established rat models of SCI and locally injected ADSC-Exos to verify their efficacy. RESULTS: ADSC-Exos can reduce reactive oxygen species (ROS) accumulation and cell damage induced by an excessive inflammatory response in HBMECs. ADSC-Exos inhibit ferroptosis induced by excessive inflammation and upregulate the expression of glutathione peroxidase 4(GPX4) in HBMECs. It can also effectively promote proliferation, migration, and vessel-like structure formation. In vitro, ADSC-Exos improved behavioral function after SCI and increased the number and density of blood vessels around the damaged spinal cord. Moreover, we found that ADSC-Exos could increase nuclear factor erythroid-2-related factor 2(NRF2) expression and nuclear translocation, thereby affecting the expression of solute carrier family 7 member 11(SLC7A11) and GPX4, and the NRF2 inhibitor ML385 could reverse the above changes. CONCLUSION: Our results suggest that ADSC-Exos may inhibit ferroptosis and promote the recovery of vascular and neural functions after SCI through the NRF2/SLC7A11/GPX4 pathway. This may be a potential therapeutic mechanism for spinal cord injury.

The preventive effects of Lactobacillus casei 03 on Escherichia coli-induced mastitis in vitro and in vivo.

BACKGROUND: Lactobacillus casei possesses many kinds of bioactivities, such as anti-inflammation and anti-oxidant, and has been applied to treating multiple inflammatory diseases. However, its role in mastitis prevention has remained ambiguous. METHODS: This study aimed to examine the mechanisms underlying the preventive effects of L. casei 03 against E. coli- mastitis utilizing bovine mammary epithelial cells (BMECs) and a mouse model. RESULTS: In vitro assays revealed pretreatment with L. casei 03 reduced the apoptotic ratio and the mRNA expression levels of IL1ß, IL6 and TNFα and suppressed phosphorylation of p65, IκBα, p38, JNK and ERK in the NF-κB signaling pathway and MAPK signaling pathway. Furthermore, in vivo tests indicated that intramammary infusion of L. casei 03 relieved pathological changes, reduced the secretion of IL1ß, IL6 and TNFα and MPO activity in the mouse mastitis model. CONCLUSIONS: These data suggest that L. casei 03 exerts protective effects against E. coli-induced mastitis in vitro and in vivo and may hold promise as a novel agent for the prevention and treatment of mastitis.

Computational and experimental approaches to quantify protein binding interactions under confinement.

Crowded environments and confinement alter the interactions of adhesion proteins confined to membranes or narrow, crowded gaps at adhesive contacts. Experimental approaches and theoretical frameworks were developed to quantify protein binding constants in these environments. However, recent predictions and the complexity of some protein interactions proved challenging to address with prior experimental or theoretical approaches. This perspective highlights new methods developed by these authors that address these challenges. Specifically, single-molecule fluorescence resonance energy transfer and single-molecule tracking measurements were developed to directly image the binding/unbinding rates of membrane-tethered cadherins. Results identified predicted cis (lateral) interactions, which control cadherin clustering on membranes but were not detected in solution. Kinetic Monte Carlo simulations, based on a realistic model of cis cadherin interactions, were developed to extract binding/unbinding rate constants from heterogeneous single-molecule data. The extension of single-molecule fluorescence measurements to cis and trans (adhesive) cadherin interactions at membrane junctions identified unexpected cooperativity between cis and trans binding that appears to enhance intercellular binding kinetics. Comparisons of intercellular binding kinetics, kinetic Monte Carlo simulations, and single-molecule fluorescence data suggest a strategy to bridge protein binding kinetics across length scales. Although cadherin is the focus of these studies, the approaches can be extended to other intercellular adhesion proteins.

Computational simulations of bispecific T cell engagers by a multiscale model.

The use of bispecific antibodies as T cell engagers can bypass the normal T cell receptor-major histocompatibility class interaction, redirect the cytotoxic activity of T cells, and lead to highly efficient tumor cell killing. However, this immunotherapy also causes significant on-target off-tumor toxicologic effects, especially when it is used to treat solid tumors. To avoid these adverse events, it is necessary to understand the fundamental mechanisms involved in the physical process of T cell engagement. We developed a multiscale computational framework to reach this goal. The framework combines simulations on the intercellular and multicellular levels. On the intercellular level, we simulated the spatial-temporal dynamics of three-body interactions among bispecific antibodies, CD3 and tumor-associated antigens (TAAs). The derived number of intercellular bonds formed between CD3 and TAAs was further transferred to the multicellular simulations as the input parameter of adhesive density between cells. Through the simulations under various molecular and cellular conditions, we were able to gain new insights into how to adopt the most appropriate strategy to maximize the drug efficacy and avoid the off-target effect. For instance, we discovered that the low antibody-binding affinity resulted in the formation of large clusters at the cell-cell interface, which could be important to control the downstream signaling pathways. We also tested different molecular architectures of the bispecific antibody and suggested the existence of an optimal length in regulating the T cell engagement. Overall, the current multiscale simulations serve as a proof-of-concept study to help in the future design of new biological therapeutics.

Prediction of interactions between cell surface proteins by machine learning.

Cells detect changes in their external environments or communicate with each other through proteins on their surfaces. These cell surface proteins form a complicated network of interactions in order to fulfill their functions. The interactions between cell surface proteins are highly dynamic and, thus, challenging to detect using traditional experimental techniques. Here, we tackle this challenge using a computational framework. The primary focus of the framework is to develop new tools to identify interactions between domains in the immunoglobulin (Ig) fold, which is the most abundant domain family in cell surface proteins. These interactions could be formed between ligands and receptors from different cells or between proteins on the same cell surface. In practice, we collected all structural data on Ig domain interactions and transformed them into an interface fragment pair library. A high-dimensional profile can then be constructed from the library for a given pair of query protein sequences. Multiple machine learning models were used to read this profile so that the probability of interaction between the query proteins could be predicted. We tested our models on an experimentally derived dataset that contains 564 cell surface proteins in humans. The cross-validation results show that we can achieve higher than 70% accuracy in identifying the PPIs within this dataset. We then applied this method to a group of 46 cell surface proteins in Caenorhabditis elegans. We screened every possible interaction between these proteins. Many interactions recognized by our machine learning classifiers have been experimentally confirmed in the literature. In conclusion, our computational platform serves as a useful tool to help identify potential new interactions between cell surface proteins in addition to current state-of-the-art experimental techniques. The tool is freely accessible for use by the scientific community. Moreover, the general framework of the machine learning classification can also be extended to study the interactions of proteins in other domain superfamilies.

Machine-learning-based Structural Analysis of Interactions between Antibodies and Antigens.

Computational analysis of paratope-epitope interactions between antibodies and their corresponding antigens can facilitate our understanding of the molecular mechanism underlying humoral immunity and boost the design of new therapeutics for many diseases. The recent breakthrough in artificial intelligence has made it possible to predict protein-protein interactions and model their structures. Unfortunately, detecting antigen-binding sites associated with a specific antibody is still a challenging problem. To tackle this challenge, we implemented a deep learning model to characterize interaction patterns between antibodies and their corresponding antigens. With high accuracy, our model can distinguish between antibody-antigen complexes and other types of protein-protein complexes. More intriguingly, we can identify antigens from other common protein binding regions with an accuracy of higher than 70% even if we only have the epitope information. This indicates that antigens have distinct features on their surface that antibodies can recognize. Additionally, our model was unable to predict the partnerships between antibodies and their particular antigens. This result suggests that one antigen may be targeted by more than one antibody and that antibodies may bind to previously unidentified proteins. Taken together, our results support the precision of antibody-antigen interactions while also suggesting positive future progress in the prediction of specific pairing.

Protein-lipid interactions drive presynaptic assembly upstream of cell adhesion molecules.

Textbook models of synaptogenesis position cell adhesion molecules such as neurexin as initiators of synapse assembly. Here we discover a mechanism for presynaptic assembly that occurs prior to neurexin recruitment, while supporting a role for neurexin in synapse maintenance. We find that the cytosolic active zone scaffold SYD-1 interacts with membrane phospholipids to promote active zone protein clustering at the plasma membrane, and subsequently recruits neurexin to stabilize those clusters. Employing molecular dynamics simulations to model intrinsic interactions between SYD-1 and lipid bilayers followed by in vivo tests of these predictions, we find that PIP2-interacting residues in SYD-1's C2 and PDZ domains are redundantly necessary for proper active zone assembly. Finally, we propose that the uncharacterized yet evolutionarily conserved short γ isoform of neurexin represents a minimal neurexin sequence that can stabilize previously assembled presynaptic clusters, potentially a core function of this critical protein.

Prediction of Interactions between Cell Surface Proteins by Machine Learning.

Cells detect changes of external environments or communicate with each other through proteins on their surfaces. These cell surface proteins form a complicated network of interactions in order to fulfill their functions. The interactions between cell surface proteins are highly dynamic and thus challenging to detect using traditional experimental techniques. Here we tackle this challenge by a computational framework. The primary focus of the framework is to develop new tools to identify interactions between domains in immunoglobulin (Ig) fold, which is the most abundant domain family in cell surface proteins. These interactions could be formed between ligands and receptors from different cells, or between proteins on the same cell surface. In practice, we collected all structural data of Ig domain interactions and transformed them into an interface fragment pair library. A high dimensional profile can be then constructed from the library for a given pair of query protein sequences. Multiple machine learning models were used to read this profile, so that the probability of interaction between the query proteins can be predicted. We tested our models to an experimentally derived dataset which contains 564 cell surface proteins in human. The cross-validation results show that we can achieve higher than 70% accuracy in identifying the PPIs within this dataset. We then applied this method to a group of 46 cell surface proteins in C elegans. We screened every possible interaction between these proteins. Many interactions recognized by our machine learning classifiers have been experimentally confirmed in the literatures. In conclusion, our computational platform serves a useful tool to help identifying potential new interactions between cell surface proteins in addition to current state-of-the-art experimental techniques. The tool is freely accessible for use by the scientific community. Moreover, the general framework of the machine learning classification can also be extended to study interactions of proteins in other domain superfamilies.

A computational study for understanding the impact of p120-catenin on the cis-dimerization of cadherin.

A prototype of cross-membrane signal transduction is that extracellular binding of cell surface receptors to their ligands induces intracellular signaling cascades. However, much less is known about the process in the opposite direction, called inside-out signaling. Recent studies show that it plays a more important role in regulating the functions of many cell surface receptors than we used to think. In particular, in cadherin-mediated cell adhesion, recent experiments indicate that intracellular binding of the scaffold protein p120-catenin can promote extracellular clustering of cadherin and alter its adhesive function. The underlying mechanism, however, is not well understood. To explore possible mechanisms, we designed a new multiscale simulation procedure. Using all-atom molecular dynamics simulations, we found that the conformational dynamics of the cadherin extracellular region can be altered by the intracellular binding of p120-catenin. More intriguingly, by integrating all-atom simulation results into coarse-grained random sampling, we showed that the altered conformational dynamics of cadherin caused by the binding of p120-catenin can increase the probability of lateral interactions between cadherins on the cell surface. These results suggest that p120-catenin could allosterically regulate the cis-dimerization of cadherin through two mechanisms. First, p120-catenin controls the extracellular conformational dynamics of cadherin. Second, p120-catenin oligomerization can further promote cadherin clustering. Our study, therefore, suggests a mechanistic foundation for the inside-out signaling in cadherin-mediated cell adhesion, while the computational framework can be generally applied to other cross-membrane signal transduction systems.

Molecular Characteristics of Bovine Viral Diarrhea Virus Strains Isolated from Persistently Infected Cattle.

In this study, we reported the isolation, identification, and molecular characteristics of nine BVDV strains that were isolated from the serum of persistently infected cattle. The new strains were designated as BVDV TJ2101, TJ2102, TJ2103, TJ2104, TJ2105, TJ2106, TJ2107, TJ2108 and TJ2109. The TJ2102 and TJ2104 strains were found to be cytopathic BVDV, and the other strains were non-cytopathic BVDV. An alignment and phylogenetic analysis showed that the new isolates share 92.2-96.3% homology with the CP7 strain and, thus, were classified as the BVDV-1b subgenotype. A recombination analysis of the genome sequences showed that the new strains could be recombined by the major parent BVDV-1a NADL strain and the minor parent BVDV-1m SD-15 strain. Some genome variations or unique amino acid mutations were found in 5'-UTR, E0 and E2 of these new isolates. In addition, a potential linear B cell epitopes prediction showed that the potential linear B cell epitope at positions 56-61 is highly variable in BVDV-1b. In conclusion, the present study has identified nine strains of BVDV from persistently infected cattle in China. Further studies on the virulence and pathogenesis of these new strains are recommended.

Association of PFN1 Gene Polymorphisms with Bone Mineral Density, Bone Turnover Markers, and Osteoporotic Fractures in Chinese Population.

Recent studies have discovered an association between the PFN1 gene and Paget's disease. However, it is currently unknown whether the PFN1 gene is related to osteoporosis. This study was performed to investigate the association of Single-Nucleotide Polymorphisms (SNPs) in the PFN1 gene with Bone Mineral Density (BMD) as well as bone turnover markers and osteoporotic fractures in Chinese subjects. A total of 2836 unrelated Chinese subjects comprising 1247 healthy subjects and 1589 osteoporotic fractures patients (Fracture group) were enrolled in this study. Seven tagSNPs (rs117337116, rs238243, rs6559, rs238242, rs78224458, rs4790714, and rs13204) of the PFN1 gene were genotyped. The BMD of the lumbar spine 1-4 (L1-4), femoral neck, and total hip as well as bone turnover markers, such as ß-C-Terminal telopeptide of type 1 collagen (ß-CTX) and Procollagen type 1 N-terminal Propeptide (P1NP), were measured. The association between 7 tagSNPs and BMD and bone turnover markers was analyzed in 1247 healthy subjects only. After age matching, we selected 1589 osteoporotic fracture patients (Fracture group) and 756 nonfracture controls (Control group, selected from 1247 healthy subjects) for a case-control study, respectively. For the case-control study, we used logistic regression to investigate the relationship between 7 tagSNPs and osteoporotic fractures risk. In the All group, the PFN1 haplotype GAT was associated with the ß-CTX (P = 0.007). In the Female group, the PFN1 haplotype GAT was associated with the ß-CTX (P = 0.005). In the Male group, the rs13204, the rs78224458, and the PFN1 haplotype GAC were associated with the BMD of the L1-4 (all P = 0.012); the rs13204, the rs78224458, and the PFN1 haplotype GAC were associated with the BMD of the femoral neck (all P = 0.012); the rs13204 and rs78224458 were associated with the BMD of the total hip (both P = 0.015); and the PFN1 haplotype GAT was associated with the ß-CTX (P = 0.013). In the subsequent case-control study, the rs13204 and rs78224458 in the male group were associated with the risk of L1-4 fracture (P = 0.016 and 0.010, respectively) and total hip fracture (P = 0.013 and 0.016, respectively). Our study reveals that PFN1 gene polymorphisms are associated with BMD in Chinese males and ß-CTX in Chinese people and confirmed the relationship between PFN1 gene polymorphisms and Chinese male osteoporotic fractures in a case-control study.

Understanding the General Principles of T Cell Engagement by Multiscale Computational Simulations.

The use of bispecific antibodies as T cell engagers can bypass the normal TCR-MHC interaction, redirect the cytotoxic activity of T-cells, and lead to highly efficient tumor cell killing. However, this immunotherapy also causes significant on-target off-tumor toxicologic effects, especially when they were used to treat solid tumors. In order to avoid these adverse events, it is necessary to understand the fundamental mechanisms during the physical process of T cell engagement. We developed a multiscale computational framework to reach this goal. The framework combines simulations on the intercellular and multicellular levels. On the intercellular level, we simulated the spatial-temporal dynamics of three-body interactions among bispecific antibodies, CD3 and TAA. The derived number of intercellular bonds formed between CD3 and TAA were further transferred into the multicellular simulations as the input parameter of adhesive density between cells. Through the simulations under various molecular and cellular conditions, we were able to gain new insights of how to adopt the most appropriate strategy to maximize the drug efficacy and avoid the off-target effect. For instance, we discovered that the low antibody binding affinity resulted in the formation of large clusters at the cell-cell interface, which could be important to control the downstream signaling pathways. We also tested different molecular architectures of the bispecific antibody and suggested the existence of an optimal length in regulating the T cell engagement. Overall, the current multiscale simulations serve as a prove-of-concept study to help the future design of new biological therapeutics. SIGNIFICANCE: T-cell engagers are a class of anti-cancer drugs that can directly kill tumor cells by bringing T cells next to them. However, current treatments using T-cell engagers can cause serious side-effects. In order to reduce these effects, it is necessary to understand how T cells and tumor cells interact together through the connection of T-cell engagers. Unfortunately, this process is not well studied due to the limitations in current experimental techniques. We developed computational models on two different scales to simulate the physical process of T cell engagement. Our simulation results provide new insights into the general properties of T cell engagers. The new simulation methods can therefore serve as a useful tool to design novel antibodies for cancer immunotherapy.

Fine-tuning spatial-temporal dynamics and surface receptor expression support plasma cell-intrinsic longevity.

Durable serological memory following vaccination is critically dependent on the production and survival of long-lived plasma cells (LLPCs). Yet, the factors that control LLPC specification and survival remain poorly resolved. Using intra-vital two-photon imaging, we find that in contrast to most plasma cells in the bone marrow, LLPCs are uniquely sessile and organized into clusters that are dependent on April, an important survival factor. Using deep, bulk RNA sequencing, and surface protein flow-based phenotyping, we find that LLPCs express a unique transcriptome and proteome compared to bulk PCs, fine tuning expression of key cell surface molecules, CD93, CD81, CXCR4, CD326, CD44 and CD48, important for adhesion and homing, and phenotypically label LLPCs within mature PC pool. Conditional deletion of Cxcr4 in PCs following immunization leads to rapid mobilization from the BM, reduced survival of antigen-specific PCs, and ultimately accelerated decay of antibody titer. In naïve mice, the endogenous LLPCs BCR repertoire exhibits reduced diversity, reduced somatic mutations, and increased public clones and IgM isotypes, particularly in young mice, suggesting LLPC specification is non-random. As mice age, the BM PC compartment becomes enriched in LLPCs, which may outcompete and limit entry of new PC into the LLPC niche and pool. HIGHLIGHTS: LLPCs have reduced motility and increased clustering in the BMLLPCs accumulate in the BM PC pool, with mouse ageLLPCs have unique surfaceome, transcriptome, and BCR clonalityCXCR4 controls maintenance of PCs and antibody titers.

The Prevention Effect of Lactobacillus plantarum 17-5 on Escherichia coli-Induced Mastitis in Mice.

Mastitis is the most economically important disease affecting the dairy industry worldwide. Lactobacillus plantarum, an important probiotic with a wide range of applications, has potential anti-inflammatory properties and has become a currently strong candidate for mastitis therapies. In the current study, we evaluated the prevention effect of Lactobacillus plantarum 17-5 on Escherichia coli-induced mastitis in mice. The results showed that pretreatment with L. plantarum 17-5 maintained the integrity of tight junctions; improved inflammatory injury; decreased MPO activity and the mRNA expression levels of IL1ß, IL6, and TNFα; and inhibited the NF-κB and MAPK signaling pathways in mice mammary tissue. The results indicated that Lactobacillus plantarum 17-5 had excellent anti-inflammatory activities and could be developed into microecological preparation for clinical use to prevent mastitis.

Computational analyses of the interactome between TNF and TNFR superfamilies.

Proteins in the tumor necrosis factor (TNF) superfamily (TNFSF) regulate diverse cellular processes by interacting with their receptors in the TNF receptor (TNFR) superfamily (TNFRSF). Ligands and receptors in these two superfamilies form a complicated network of interactions, in which the same ligand can bind to different receptors and the same receptor can be shared by different ligands. In order to study these interactions on a systematic level, a TNFSF-TNFRSF interactome was constructed in this study by searching the database which consists of both experimentally measured and computationally predicted protein-protein interactions (PPIs). The interactome contains a total number of 194 interactions between 18 TNFSF ligands and 29 TNFRSF receptors in human. We modeled the structure for each ligand-receptor interaction in the network. Their binding affinities were further computationally estimated based on modeled structures. Our computational outputs, which are all publicly accessible, serve as a valuable addition to the currently limited experimental resources to study TNF-mediated cell signaling.

How does the same ligand activate signaling of different receptors in TNFR superfamily: a computational study.

TNFα is a highly pleiotropic cytokine inducing inflammatory signaling pathways. It is initially presented on plasma membrane of cells (mTNFα), and also exists in a soluble variant (sTNFα) after cleavage. The ligand is shared by two structurally similar receptors, TNFR1 and TNFR2. Interestingly, while sTNFα preferentially stimulates TNFR1, TNFR2 signaling can only be activated by mTNFα. How can two similar receptors respond to the same ligand in such a different way? We employed computational simulations in multiple scales to address this question. We found that both mTNFα and sTNFα can trigger the clustering of TNFR1. The size of clusters induced by sTNFα is constantly larger than the clusters induced by mTNFα. The systems of TNFR2, on the other hand, show very different behaviors. Only when the interactions between TNFR2 are very weak, mTNFα can trigger the receptors to form very large clusters. Given the same weak binding affinity, only small oligomers were obtained in the system of sTNFα. Considering that TNF-mediated signaling is modulated by the ligand-induced clustering of receptors on cell surface, our study provided the mechanistic foundation to the phenomenon that different isoforms of the ligand can lead to highly distinctive signaling patterns for its receptors.

Dissecting the general mechanisms of protein cage self-assembly by coarse-grained simulations.

The development of artificial protein cages has recently gained massive attention due to their promising application prospect as novel delivery vehicles for therapeutics. These nanoparticles are formed through a process called self-assembly, in which individual subunits spontaneously arrange into highly ordered patterns via non-covalent but specific interactions. Therefore, the first step toward the design of novel engineered protein cages is to understand the general mechanisms of their self-assembling dynamics. Here we have developed a new computational method to tackle this problem. Our method is based on a coarse-grained model and a diffusion-reaction simulation algorithm. Using a tetrahedral cage as test model, we showed that self-assembly of protein cage requires of a seeding process in which specific configurations of kinetic intermediate states are identified. We further found that there is a critical concentration to trigger self-assembly of protein cages. This critical concentration allows that cages can only be successfully assembled under a persistently high concentration. Additionally, phase diagram of self-assembly has been constructed by systematically testing the model across a wide range of binding parameters. Finally, our simulations demonstrated the importance of protein's structural flexibility in regulating the dynamics of cage assembly. In summary, this study throws lights on the general principles underlying self-assembly of large cage-like protein complexes and thus provides insights to design new nanomaterials.