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A simulation study reveals the adsorption behavior ofShewanellamultiheme cytochrome protein MtrF on small-sized α-Fe₂O₃nanoparticles in water and its electron transfer across the adsorbed protein. 

Polyamide membranes are widely used in reverse osmosis (RO) water treatment, yet the mechanism of interfacial polymerization during membrane formation is not fully understood. In this work, we perform atomistic molecular dynamics simulations to explore the cross-linking of trimesoyl chloride (TMC) and m-phenylenediamine (MPD) monomers at the aqueous–organic interface. Our studies show that the solution interface provides a function of “concentration and dispersion” of monomers for cross-linking. The process starts with rapid cross-linking, followed by slower kinetics. Initially, amphiphilic MPD monomers diffuse in water and accumulate at the solution interface to interact with TMC monomers from the organic phase. As cross-linking progresses, a precross-linked thin film forms, reducing monomer diffusion and reaction rates. However, the structural flexibility of the amphiphilic film, influenced by interfacial fluctuations and mixed interactions with water and the organic solvent at the solution interface, promotes further cross-linking. The solubility of MPD and TMC monomers in different organic solvents (cyclohexane versus n-hexane) affects the cross-linking rate and surface homogeneity, leading to slight variations in the structure and size distribution of subnanopores. Our study of the interfacial polymerization process in explicit solvents is essential for understanding membrane formation in various solvents, which will be crucial for optimal polyamide membrane design. 

A comprehensive understanding of the interfacial behaviors of biomolecules holds great significance in the development of biomaterials and biosensing technologies. In this work, we used discontinuous molecular dynamics (DMD) simulations and graphic contrastive learning analysis to study the adsorption of ubiquitin protein on a graphene surface. Our high-throughput DMD simulations can explore the whole protein adsorption process including the protein structural evolution with sufficient accuracy. Contrastive learning was employed to train a protein contact map feature extractor aiming at generating contact map feature vectors. Subsequently, these features were grouped using the k-means clustering algorithm to identify the protein structural transition stages throughout the adsorption process. The machine learning analysis can illustrate the dynamics of protein structural changes, including the pathway and the rate-limiting step. Our study indicated that the protein–graphene surface hydrophobic interactions and the π–π stacking were crucial to the seven-stage adsorption process. Upon adsorption, the secondary structure and tertiary structure of ubiquitin disintegrated. The unfolding stages obtained by contrastive learning-based algorithm were not only consistent with the detailed analyses of protein structures but also provided more hidden information about the transition states and pathway of protein adsorption process and structural dynamics. Our combination of efficient DMD simulations and machine learning analysis could be a valuable approach to studying the interfacial behaviors of biomolecules.