Rotational dynamics and transition mechanisms of surface-adsorbed proteins

Advancing micro-nano supramolecular assembly mechanisms of natural organic matter by machine learning for unveiling environmental geochemical processes

The nano-self-assembly of natural organic matter (NOM) profoundly influences the occurrence and fate of NOM and pollutants in large-scale complex environments. Machine learning (ML) offers a promising and robust tool for interpreting and predicting the processes, structures and environmental effects of NOM self-assembly. This review seeks to provide a tutorial-like compilation of data source determination, algorithm selection, model construction, interpretability analyses, applications and challenges for big-data-based ML aiming at elucidating NOM self-assembly mechanisms in environments. The results from advanced nano-submicron-scale spatial chemical analytical technologies are suggested as input data which provide the combined information of molecular interactions and structural visualization. The existing ML algorithms need to handle multi-scale and multi-modal data, necessitating the development of new algorithmic frameworks. Interpretable supervised models are crucial owing to their strong capacity of quantifying the structure-property-effect relationships and bridging the gap between simply data-driven ML and complicated NOM assembly practice. Then, the necessity and challenges are discussed and emphasized on adopting ML to understand the geochemical behaviors and bioavailability of pollutants as well as the elemental cycling processes in environments resulting from the NOM self-assembly patterns. Finally, a research framework integrating ML, experiments and theoretical simulation is proposed for comprehensively and efficiently understanding the NOM self-assembly-involved environmental issues.

Mechanisms of Biomolecular Self-Assembly Investigated Through In Situ Observations of Structures and Dynamics

Biomolecular self-assembly of hierarchical materials is a precise and adaptable bottom-up approach to synthesizing across scales with considerable energy, health, environment, sustainability, and information technology applications. To achieve desired functions in biomaterials, it is essential to directly observe assembly dynamics and structural evolutions that reflect the underlying energy landscape and the assembly mechanism. This review will summarize the current understanding of biomolecular assembly mechanisms based on in situ characterization and discuss the broader significance and achievements of newly gained insights. In addition, we will also introduce how emerging deep learning/machine learning-based approaches, multiparametric characterization, and high-throughput methods can boost the development of biomolecular self-assembly. The objective of this review is to accelerate the development of in situ characterization approaches for biomolecular self-assembly and to inspire the next generation of biomimetic materials.

Design of a Hierarchical Assembly at a Solid-Liquid Interface Using an Asymmetric Protein Needle

Design and control of processes for a hierarchical assembly of proteins remain challenging because it requires consideration of design principles with atomic-level accuracy. Previous studies have adopted symmetry-based strategies to minimize the complexity of protein-protein interactions and this has placed constraints on the structures of the resulting protein assemblies. In the present work, we used an anisotropic-shaped protein needle, gene product 5 (gp5) from bacteriophage T4 with a C-terminal hexahistidine-tag (His-tag) (gp5_CHis), to construct a hierarchical assembly with two distinct protein-protein interaction sites. High-speed atomic force microscopy (HS-AFM) measurements reveal that it forms unique tetrameric clusters through its N-terminal head on a mica surface. The clusters further self-assemble into a monolayer through the C-terminal His-tag. The HS-AFM images and displacement analyses show that the monolayer is a network-like structure rather than a crystalline lattice. Our results expand the toolbox for constructing hierarchical protein assemblies based on structural anisotropy.

Discrete Orientations of Interfacial Waters Direct Crystallization of Mica-Binding Proteins

Understanding the basis of templated molecular assembly on a solid surface requires a fundamental comprehension of both short- and long-range aqueous response to the surface under a variety of solution conditions. Herein we provide a detailed picture of how the molecular-scale response to different mica surfaces yields distinct solvent orientations that produce quasi-static directional potentials onto which macromolecules can adsorb. We connect this directionality to observed (a)symmetric epitaxial alignment of designed proteins onto these surfaces, corroborate our findings with 3D atomic force microscopy experiments, and identify slight differences in surface structure as the origin of this effect. Our work provides a detailed picture of the intrinsic electrolyte response in the vicinity of mineral interfaces, with clear predictions for experiment, and highlights the role of solvent on the predictive assembly of hierarchical materials on mineral surfaces.

Protein Desorption Kinetics Depends on the Timescale of Observation

The presence of so-called reversible and irreversible protein adsorption on solid surfaces is well documented in the literature and represents the basis for the development of nanoparticles and implant materials to control interactions in physiological environments. Here, using a series of complementary single-molecule tracking approaches appropriate for different timescales, we show that protein desorption kinetics is much more complex than the traditional reversible-irreversible binary picture. Instead, we find that the surface residence time distribution of adsorbed proteins transitions from power law to exponential behavior when measured over a large range of timescales (10^-2-10⁶ s). A comparison with macroscopic results obtained using a quartz crystal microbalance suggested that macroscopic measurements have generally failed to observe such nonequilibrium phenomena because they are obscured by ensemble-averaging effects. These findings provide new insights into the complex phenomena associated with protein adsorption and desorption.