Ligand recognition and allosteric regulation of DRD1-Gs signaling complexes.

Dopamine receptors, including D1- and D2-like receptors, are important therapeutic targets in a variety of neurological syndromes, as well as cardiovascular and kidney diseases. Here, we present five cryoelectron microscopy (cryo-EM) structures of the dopamine D1 receptor (DRD1) coupled to Gs heterotrimer in complex with three catechol-based agonists, a non-catechol agonist, and a positive allosteric modulator for endogenous dopamine. These structures revealed that a polar interaction network is essential for catecholamine-like agonist recognition, whereas specific motifs in the extended binding pocket were responsible for discriminating D1- from D2-like receptors. Moreover, allosteric binding at a distinct inner surface pocket improved the activity of DRD1 by stabilizing endogenous dopamine interaction at the orthosteric site. DRD1-Gs interface revealed key features that serve as determinants for G protein coupling. Together, our study provides a structural understanding of the ligand recognition, allosteric regulation, and G protein coupling mechanisms of DRD1.

Fine-tuning activation specificity of G-protein-coupled receptors via automated path searching.

Physics-based simulation methods can grant atomistic insights into the molecular origin of the function of biomolecules. However, the potential of such approaches has been hindered by their low efficiency, including in the design of selective agonists where simulations of myriad protein-ligand combinations are necessary. Here, we describe an automated input-free path searching protocol that offers (within 14 d using Graphics Processing Unit servers) a minimum free energy path (MFEP) defined in high-dimension configurational space for activating sphingosine-1-phosphate receptors (S1PRs) by arbitrary ligands. The free energy distributions along the MFEP for four distinct ligands and three S1PRs reached a remarkable agreement with Bioluminescence Resonance Energy Transfer (BRET) measurements of G-protein dissociation. In particular, the revealed transition state structures pointed out toward two S1PR3 residues F263/I284, that dictate the preference of existing agonists CBP307 and BAF312 on S1PR1/5. Swapping these residues between S1PR1 and S1PR3 reversed their response to the two agonists in BRET assays. These results inspired us to design improved agonists with both strong polar head and bulky hydrophobic tail for higher selectivity on S1PR1. Through merely three in silico iterations, our tool predicted a unique compound scaffold. BRET assays confirmed that both chiral forms activate S1PR1 at nanomolar concentration, 1 to 2 orders of magnitude less than those for S1PR3/5. Collectively, these results signify the promise of our approach in fine agonist design for G-protein-coupled receptors.

Cryo-EM structure of the human chemerin receptor 1-Gi protein complex bound to the C-terminal nonapeptide of chemerin.

Chemerin is a processed protein that acts on G protein-coupled receptors (GPCRs) for its chemotactic and adipokine activities. The biologically active chemerin (chemerin 21-157) results from proteolytic cleavage of prochemerin and uses its C-terminal peptide containing the sequence YFPGQFAFS for receptor activation. Here we report a high-resolution cryo-electron microscopy (cryo-EM) structure of human chemerin receptor 1 (CMKLR1) bound to the C-terminal nonapeptide of chemokine (C9) in complex with Gi proteins. C9 inserts its C terminus into the binding pocket and is stabilized through hydrophobic interactions involving its Y1, F2, F6, and F8, as well as polar interactions between G4, S9, and several amino acids lining the binding pocket of CMKLR1. Microsecond scale molecular dynamics simulations support a balanced force distribution across the whole ligand-receptor interface that enhances thermodynamic stability of the captured binding pose of C9. The C9 interaction with CMKLR1 is drastically different from chemokine recognition by chemokine receptors, which follow a two-site two-step model. In contrast, C9 takes an "S"-shaped pose in the binding pocket of CMKLR1 much like angiotensin II in the AT1 receptor. Our mutagenesis and functional analyses confirmed the cryo-EM structure and key residues in the binding pocket for these interactions. Our findings provide a structural basis for chemerin recognition by CMKLR1 for the established chemotactic and adipokine activities.

Structural insights into sphingosine-1-phosphate receptor activation.

As a critical sphingolipid metabolite, sphingosine-1-phosphate (S1P) plays an essential role in immune and vascular systems. There are five S1P receptors, designated as S1PR1 to S1PR5, encoded in the human genome, and their activities are governed by endogenous S1P, lipid-like S1P mimics, or nonlipid-like therapeutic molecules. Among S1PRs, S1PR1 stands out due to its nonredundant functions, such as the egress of T and B cells from the thymus and secondary lymphoid tissues, making it a potential therapeutic target. However, the structural basis of S1PR1 activation and regulation by various agonists remains unclear. Here, we report four atomic resolution cryo-electron microscopy (cryo-EM) structures of Gi-coupled human S1PR1 complexes: bound to endogenous agonist d18:1 S1P, benchmark lipid-like S1P mimic phosphorylated Fingolimod [(S)-FTY720-P], or nonlipid-like therapeutic molecule CBP-307 in two binding modes. Our results revealed the similarities and differences of activation of S1PR1 through distinct ligands binding to the amphiphilic orthosteric pocket. We also proposed a two-step "shallow to deep" transition process of CBP-307 for S1PR1 activation. Both binding modes of CBP-307 could activate S1PR1, but from shallow to deep transition may trigger the rotation of the N-terminal helix of Gαi and further stabilize the complex by increasing the Gαi interaction with the cell membrane. We combine with extensive biochemical analysis and molecular dynamic simulations to suggest key steps of S1P binding and receptor activation. The above results decipher the common feature of the S1PR1 agonist recognition and activation mechanism and will firmly promote the development of therapeutics targeting S1PRs.

dbAMP 2.0: updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data.

The last 18 months, or more, have seen a profound shift in our global experience, with many of us navigating a once-in-100-year pandemic. To date, COVID-19 remains a life-threatening pandemic with little to no targeted therapeutic recourse. The discovery of novel antiviral agents, such as vaccines and drugs, can provide therapeutic solutions to save human beings from severe infections; however, there is no specifically effective antiviral treatment confirmed for now. Thus, great attention has been paid to the use of natural or artificial antimicrobial peptides (AMPs) as these compounds are widely regarded as promising solutions for the treatment of harmful microorganisms. Given the biological significance of AMPs, it was obvious that there was a significant need for a single platform for identifying and engaging with AMP data. This led to the creation of the dbAMP platform that provides comprehensive information about AMPs and facilitates their investigation and analysis. To date, the dbAMP has accumulated 26 447 AMPs and 2262 antimicrobial proteins from 3044 organisms using both database integration and manual curation of >4579 articles. In addition, dbAMP facilitates the evaluation of AMP structures using I-TASSER for automated protein structure prediction and structure-based functional annotation, providing predictive structure information for clinical drug development. Next-generation sequencing (NGS) and third-generation sequencing have been applied to generate large-scale sequencing reads from various environments, enabling greatly improved analysis of genome structure. In this update, we launch an efficient online tool that can effectively identify AMPs from genome/metagenome and proteome data of all species in a short period. In conclusion, these improvements promote the dbAMP as one of the most abundant and comprehensively annotated resources for AMPs. The updated dbAMP is now freely accessible at http://awi.cuhk.edu.cn/dbAMP.

miRTarBase update 2022: an informative resource for experimentally validated miRNA-target interactions.

MicroRNAs (miRNAs) are noncoding RNAs with 18-26 nucleotides; they pair with target mRNAs to regulate gene expression and produce significant changes in various physiological and pathological processes. In recent years, the interaction between miRNAs and their target genes has become one of the mainstream directions for drug development. As a large-scale biological database that mainly provides miRNA-target interactions (MTIs) verified by biological experiments, miRTarBase has undergone five revisions and enhancements. The database has accumulated >2 200 449 verified MTIs from 13 389 manually curated articles and CLIP-seq data. An optimized scoring system is adopted to enhance this update's critical recognition of MTI-related articles and corresponding disease information. In addition, single-nucleotide polymorphisms and disease-related variants related to the binding efficiency of miRNA and target were characterized in miRNAs and gene 3' untranslated regions. miRNA expression profiles across extracellular vesicles, blood and different tissues, including exosomal miRNAs and tissue-specific miRNAs, were integrated to explore miRNA functions and biomarkers. For the user interface, we have classified attributes, including RNA expression, specific interaction, protein expression and biological function, for various validation experiments related to the role of miRNA. We also used seed sequence information to evaluate the binding sites of miRNA. In summary, these enhancements render miRTarBase as one of the most research-amicable MTI databases that contain comprehensive and experimentally verified annotations. The newly updated version of miRTarBase is now available at https://miRTarBase.cuhk.edu.cn/.

DNA Deformation Exerted by Regulatory DNA-Binding Motifs in Human Alkyladenine DNA Glycosylase Promotes Base Flipping.

Human alkyladenine DNA glycosylase (AAG) is a key enzyme that corrects a broad range of alkylated and deaminated nucleobases to maintain genomic integrity. When encountering the lesions, AAG adopts a base-flipping strategy to extrude the target base from the DNA duplex to its active site, thereby cleaving the glycosidic bond. Despite its functional importance, the detailed mechanism of such base extrusion and how AAG distinguishes the lesions from an excess of normal bases both remain elusive. Here, through the Markov state model constructed on extensive all-atom molecular dynamics simulations, we find that the alkylated nucleobase (N3-methyladenine, 3MeA) everts through the DNA major groove. Two key AAG motifs, the intercalation and E131-N146 motifs, play active roles in bending/pressing the DNA backbone and widening the DNA minor groove during 3MeA eversion. In particular, the intercalated residue Y162 is involved in buckling the target site at the early stage of 3MeA eversion. Our traveling-salesman based automated path searching algorithm further revealed that a non-target normal adenine tends to be trapped in an exo site near the active site, which however barely exists for a target base 3MeA. Collectively, these results suggest that the Markov state model combined with traveling-salesman based automated path searching acts as a promising approach for studying complex conformational changes of biomolecules and dissecting the elaborate mechanism of target recognition by this unique enzyme.

Automated Path Searching Reveals the Mechanism of Hydrolysis Enhancement by T4 Lysozyme Mutants.

Bacteriophage T4 lysozyme (T4L) is a glycosidase that is widely applied as a natural antimicrobial agent in the food industry. Due to its wide applications and small size, T4L has been regarded as a model system for understanding protein dynamics and for large-scale protein engineering. Through structural insights from the single conformation of T4L, a series of mutations (L99A,G113A,R119P) have been introduced, which have successfully raised the fractional population of its only hydrolysis-competent excited state to 96%. However, the actual impact of these substitutions on its dynamics remains unclear, largely due to the lack of highly efficient sampling algorithms. Here, using our recently developed travelling-salesman-based automated path searching (TAPS), we located the minimum-free-energy path (MFEP) for the transition of three T4L mutants from their ground states to their excited states. All three mutants share a three-step transition: the flipping of F114, the rearrangement of α0/α1 helices, and final refinement. Remarkably, the MFEP revealed that the effects of the mutations are drastically beyond the expectations of their original design: (a) the G113A substitution not only enhances helicity but also fills the hydrophobic Cavity I and reduces the free energy barrier for flipping F114; (b) R119P barely changes the stability of the ground state but stabilizes the excited state through rarely reported polar contacts S117OG:N132ND2, E11OE1:R145NH1, and E11OE2:Q105NE2; (c) the residue W138 flips into Cavity I and further stabilizes the excited state for the triple mutant L99A,G113A,R119P. These novel insights that were unexpected in the original mutant design indicated the necessity of incorporating path searching into the workflow of rational protein engineering.

Major Postoperative Complications in Esophageal Cancer After Minimally Invasive Esophagectomy Compared With Open Esophagectomy: An Updated Meta-analysis.

BACKGROUND: To evaluate the existing literature comparing cardiopulmonary complications after minimally invasive esophagectomy (MIE) with open esophagectomy (OE) and conduct a meta-analysis based on the relevant studies. METHODS: A systematic search for articles was performed in Medline, Embase, Wiley Online Library, and the Cochrane Library. The relative risks or odds ratios (ORs) were calculated by using fixed or random-effects models. The I2 and X2 tests were used to test for statistical heterogeneity. We performed a metaregression for the pulmonary complications with the adenocarcinoma proportion and tumor stage. Publication bias and small-study effects were assessed using Egger's test and Begg's funnel plot. RESULTS: A total of 30,850 participants were enrolled in the 63 studies evaluated in the meta-analysis. Arrhythmia, pulmonary embolism, pulmonary complications, gastric tip necrosis, anastomotic leakage, and vocal cord palsy were chosen as outcomes. The occurrence rate of arrhythmia was significantly lower in patients receiving MIE than in patients receiving OE (OR = 0.69; 95% CI = 0.53-0.89), with heterogeneity (I2 = 30.7%, P = 0.067). The incidence of pulmonary complications was significantly lower in patients receiving MIE (OR = 0.54, 95% CI = 0.45-0.63) but heterogeneity remained (I2 = 72.1%, P = 0.000). The risk of gastric tip necrosis (OR = 1.48, 95% CI = 1.07-2.05) after OE was lower than that after MIE. Anastomotic leakage, pulmonary embolism, and vocal cord palsy showed no significant differences between the two groups. CONCLUSIONS: MIE has advantages over OE, especially in reducing the incidence of arrhythmia and pulmonary complications. Thus, MIE can be recommended as the preferred alternative surgery method for resectable esophageal cancer.

Increased SIX-1 expression promotes breast cancer metastasis by regulating lncATB-miR-200s-ZEB1 axis.

Patients with advanced breast cancer (BC) showed a higher incidence of regional and distant metastases. Sine oculis homeobox homolog 1 (SIX-1) has been confirmed to be a key tumorigenic and metastatic regulator in BC progression. Yet, molecular mechanisms behind SIX-1-induced BC metastases remain largely unknown. Here we found that SIX-1 was frequently up-regulated in BC and correlated with poor outcomes when tested in human BC tissue microarray. Then, we manipulated the expression of SIX-1 by via shRNA-mediated knockdown and lentivirus-mediated overexpression. Transwell assay in vitro and lung metastases model of nude mice in vivo showed that SIX-1 promoted BC cell invasion and migration in vitro, and facilitated metastases in vivo. Mechanistically, SIX-1 could promote the transcription of lncATB, which exerts critical pro-metastatic role in BC by directly binding to the miR-200 family, especially for miR-200c, to induce EMT and promote metastases. In conclusion, SIX-1 exerts its pro-metastatic role in BC through lncATB/miR-200s axis of EMT signalling pathway and could act as an important diagnostic marker as well as a significant therapeutic target for clinically advanced BC.

Structural dissection of an interaction between transcription initiation and termination factors implicated in promoter-terminator cross-talk.

Functional cross-talk between the promoter and terminator of a gene has long been noted. Promoters and terminators are juxtaposed to form gene loops in several organisms, and gene looping is thought to be involved in transcriptional regulation. The general transcription factor IIB (TFIIB) and the C-terminal domain phosphatase Ssu72, essential factors of the transcription preinitiation complex and the mRNA processing and polyadenylation complex, respectively, are important for gene loop formation. TFIIB and Ssu72 interact both genetically and physically, but the molecular basis of this interaction is not known. Here we present a crystal structure of the core domain of TFIIB in two new conformations that differ in the relative distance and orientation of the two cyclin-like domains. The observed extraordinary conformational plasticity may underlie the binding of TFIIB to multiple transcription factors and promoter DNAs that occurs in distinct stages of transcription, including initiation, reinitiation, and gene looping. We mapped the binding interface of the TFIIB-Ssu72 complex using a series of systematic, structure-guided in vitro binding and site-specific photocross-linking assays. Our results indicate that Ssu72 competes with acidic activators for TFIIB binding and that Ssu72 disrupts an intramolecular TFIIB complex known to impede transcription initiation. We also show that the TFIIB-binding site on Ssu72 overlaps with the binding site of symplekin, a component of the mRNA processing and polyadenylation complex. We propose a hand-off model in which Ssu72 mediates a conformational transition in TFIIB, accounting for the role of Ssu72 in transcription reinitiation, gene looping, and promoter-terminator cross-talk.

TAPS: A traveling-salesman based automated path searching method for functional conformational changes of biological macromolecules.

Locating the minimum free energy paths (MFEPs) between two conformational states is among the most important tasks of biomolecular simulations. For example, knowledge of the MFEP is critical for focusing the effort of unbiased simulations that are used for the construction of Markov state models to the biologically relevant regions of the system. Typically, existing path searching methods perform local sampling around the path nodes in a pre-selected collective variable (CV) space to allow a gradual downhill evolution of the path toward the MFEP. Despite the wide application of such a strategy, the gradual path evolution and the non-trivial a priori choice of CVs are also limiting its overall efficiency and automation. Here we demonstrate that non-local perpendicular sampling can be pursued to accelerate the search, provided that all nodes are reordered thereafter via a traveling-salesman scheme. Moreover, path-CVs can be computed on-the-fly and used as a coordinate system, minimizing the necessary prior knowledge about the system. Our traveling-salesman based automated path searching method achieves a 5-8 times speedup over the string method with swarms-of-trajectories for two peptide systems in vacuum and solution, making it a promising method for obtaining initial pathways when investigating functional conformational changes between a pair of structures.

Harnessing complexity in molecular self-assembly using computer simulations.

In molecular self-assembly, hundreds of thousands of freely-diffusing molecules associate to form ordered and functional architectures in the absence of an actuator. This intriguing phenomenon plays a critical role in biology and has become a powerful tool for the fabrication of advanced nanomaterials. Due to the limited spatial and temporal resolutions of current experimental techniques, computer simulations offer a complementary strategy to explore self-assembly with atomic resolution. Here, we review recent computational studies focusing on both thermodynamic and kinetic aspects. As we shall see, thermodynamic approaches based on modeling and statistical mechanics offer initial guidelines to design nanostructures with modest computational effort. Computationally more intensive analyses based on molecular dynamics simulations and kinetic network models (KNMs) reach beyond it, opening the door to the rational design of self-assembly pathways. Current limitations of these methodologies are discussed. We anticipate that the synergistic use of thermodynamic and kinetic analyses based on computer simulations will provide an important contribution to the de novo design of self-assembly.

Improving the productivity of monodisperse polyhedral cages by the rational design of kinetic self-assembly pathways.

Hollow polyhedral cages hold great potential for application in nanotechnological and biomedical fields. Understanding the formation mechanism of these self-assembled structures could provide guidance for the rational design of the desired polyhedral cages. Here, by constructing kinetic network models from extensive coarse-grained molecular dynamics simulations, we elucidated the formation mechanism of the dodecahedral cage, which is formed by the self-assembly of patchy particles. We found that the dodecahedral cage is formed through increasing the aggregate size followed by structure rearrangement. Based on this mechanistic understanding, we improved the productivity of the dodecahedral cage through the rational design of the patch arrangement of patchy particles, which promotes the structural rearrangement process. Our results demonstrate that it should be a feasible strategy to achieve the rational design of the desired nanostructures via the kinetic analysis. We anticipate that this methodology could be extended to other self-assembly systems for the fabrication of functional nanomaterials.

Site-Mutation of Hydrophobic Core Residues Synchronically Poise Super Interleukin 2 for Signaling: Identifying Distant Structural Effects through Affordable Computations.

A superkine variant of interleukin-2 with six site mutations away from the binding interface developed from the yeast display technique has been previously characterized as undergoing a distal structure alteration which is responsible for its super-potency and provides an elegant case study with which to get insight about how to utilize allosteric effect to achieve desirable protein functions. By examining the dynamic network and the allosteric pathways related to those mutated residues using various computational approaches, we found that nanosecond time scale all-atom molecular dynamics simulations can identify the dynamic network as efficient as an ensemble algorithm. The differentiated pathways for the six core residues form a dynamic network that outlines the area of structure alteration. The results offer potentials of using affordable computing power to predict allosteric structure of mutants in knowledge-based mutagenesis.

Adaptive partitioning by local density-peaks: An efficient density-based clustering algorithm for analyzing molecular dynamics trajectories.

We present an efficient density-based adaptive-resolution clustering method APLoD for analyzing large-scale molecular dynamics (MD) trajectories. APLoD performs the k-nearest-neighbors search to estimate the density of MD conformations in a local fashion, which can group MD conformations in the same high-density region into a cluster. APLoD greatly improves the popular density peaks algorithm by reducing the running time and the memory usage by 2-3 orders of magnitude for systems ranging from alanine dipeptide to a 370-residue Maltose-binding protein. In addition, we demonstrate that APLoD can produce clusters with various sizes that are adaptive to the underlying density (i.e., larger clusters at low-density regions, while smaller clusters at high-density regions), which is a clear advantage over other popular clustering algorithms including k-centers and k-medoids. We anticipate that APLoD can be widely applied to split ultra-large MD datasets containing millions of conformations for subsequent construction of Markov State Models. © 2016 Wiley Periodicals, Inc.

Path lumping: An efficient algorithm to identify metastable path channels for conformational dynamics of multi-body systems.

Constructing Markov state models from large-scale molecular dynamics simulation trajectories is a promising approach to dissect the kinetic mechanisms of complex chemical and biological processes. Combined with transition path theory, Markov state models can be applied to identify all pathways connecting any conformational states of interest. However, the identified pathways can be too complex to comprehend, especially for multi-body processes where numerous parallel pathways with comparable flux probability often coexist. Here, we have developed a path lumping method to group these parallel pathways into metastable path channels for analysis. We define the similarity between two pathways as the intercrossing flux between them and then apply the spectral clustering algorithm to lump these pathways into groups. We demonstrate the power of our method by applying it to two systems: a 2D-potential consisting of four metastable energy channels and the hydrophobic collapse process of two hydrophobic molecules. In both cases, our algorithm successfully reveals the metastable path channels. We expect this path lumping algorithm to be a promising tool for revealing unprecedented insights into the kinetic mechanisms of complex multi-body processes.

Markov State Models Reveal a Two-Step Mechanism of miRNA Loading into the Human Argonaute Protein: Selective Binding followed by Structural Re-arrangement.

Argonaute (Ago) proteins and microRNAs (miRNAs) are central components in RNA interference, which is a key cellular mechanism for sequence-specific gene silencing. Despite intensive studies, molecular mechanisms of how Ago recognizes miRNA remain largely elusive. In this study, we propose a two-step mechanism for this molecular recognition: selective binding followed by structural re-arrangement. Our model is based on the results of a combination of Markov State Models (MSMs), large-scale protein-RNA docking, and molecular dynamics (MD) simulations. Using MSMs, we identify an open state of apo human Ago-2 in fast equilibrium with partially open and closed states. Conformations in this open state are distinguished by their largely exposed binding grooves that can geometrically accommodate miRNA as indicated in our protein-RNA docking studies. miRNA may then selectively bind to these open conformations. Upon the initial binding, the complex may perform further structural re-arrangement as shown in our MD simulations and eventually reach the stable binary complex structure. Our results provide novel insights in Ago-miRNA recognition mechanisms and our methodology holds great potential to be widely applied in the studies of other important molecular recognition systems.

Elucidation of the conformational dynamics of multi-body systems by construction of Markov state models.

Constructing Markov State Models (MSMs) based on short molecular dynamics simulations is a powerful computational technique to complement experiments in predicting long-time kinetics of biomolecular processes at atomic resolution. Even though the MSM approach has been widely applied to study one-body processes such as protein folding and enzyme conformational changes, the majority of biological processes, e.g. protein-ligand recognition, signal transduction, and protein aggregation, essentially involve multiple entities. Here we review the attempts at constructing MSMs for multi-body systems, point out the challenges therein and discuss recent algorithmic progresses that alleviate these challenges. In particular, we describe an automatic kinetics based partitioning method that achieves optimal definition of the conformational states in a multi-body system, and discuss a novel maximum-likelihood approach that efficiently estimates the slow uphill kinetics utilizing pre-computed equilibrium populations of all states. We expect that these new algorithms and their combinations may boost investigations of important multi-body biological processes via the efficient construction of MSMs.

Elucidating dominant pathways of the nano-particle self-assembly process.

Self-assembly processes play a key role in the fabrication of functional nano-structures with widespread application in drug delivery and micro-reactors. In addition to the thermodynamics, the kinetics of the self-assembled nano-structures also play an important role in determining the formed structures. However, as the self-assembly process is often highly heterogeneous, systematic elucidation of the dominant kinetic pathways of self-assembly is challenging. Here, based on mass flow, we developed a new method for the construction of kinetic network models and applied it to identify the dominant kinetic pathways for the self-assembly of star-like block copolymers. We found that the dominant pathways are controlled by two competing kinetic parameters: the encounter time Te, characterizing the frequency of collision and the transition time Tt for the aggregate morphology change from rod to sphere. Interestingly, two distinct self-assembly mechanisms, diffusion of an individual copolymer into the aggregate core and membrane closure, both appear at different stages (with different values of Tt) of a single self-assembly process. In particular, the diffusion mechanism dominates the middle-sized semi-vesicle formation stage (with large Tt), while the membrane closure mechanism dominates the large-sized vesicle formation stage (with small Tt). Through the rational design of the hydrophibicity of the copolymer, we successfully tuned the transition time Tt and altered the dominant self-assembly pathways.