Nanomechanical Characterization of the Triple β-Helix Domain in the Cell Puncture Needle of Bacteriophage T4 Virus

Generative design of de novo proteins based on secondary structure constraints using an attention-based diffusion model

We report two generative deep learning models that predict amino acid sequences and 3D protein structures based on secondary structure design objectives via either overall content or per-residue structure. Both models are robust regarding imperfect inputs and offer de novo design capacity as they can discover new protein sequences not yet discovered from natural mechanisms or systems. The residue-level secondary structure design model generally yields higher accuracy and more diverse sequences. These findings suggest unexplored opportunities for protein designs and functional outcomes within the vast amino acid sequences beyond known proteins. Our models, based on an attention-based diffusion model and trained on a dataset extracted from experimentally known 3D protein structures, offer numerous downstream applications in conditional generative design of various biological or engineering systems. Future work may include additional conditioning, and an exploration of other functional properties of the generated proteins for various properties beyond structural objectives.

End-to-End Deep Learning Model to Predict and Design Secondary Structure Content of Structural Proteins

Structural proteins are the basis of many biomaterials and key construction and functional components of all life. Further, it is well-known that the diversity of proteins' function relies on their local structures derived from their primary amino acid sequences. Here, we report a deep learning model to predict the secondary structure content of proteins directly from primary sequences, with high computational efficiency. Understanding the secondary structure content of proteins is crucial to designing proteins with targeted material functions, especially mechanical properties. Using convolutional and recurrent architectures and natural language models, our deep learning model predicts the content of two essential types of secondary structures, the α-helix and the β-sheet. The training data are collected from the Protein Data Bank and contain many existing protein geometries. We find that our model can learn the hidden features as patterns of input sequences that can then be directly related to secondary structure content. The α-helix and β-sheet content predictions show excellent agreement with training data and newly deposited protein structures that were recently identified and that were not included in the original training set. We further demonstrate the features of the model by a search for de novo protein sequences that optimize max/min α-helix/β-sheet content and compare the predictions with folded models of these sequences based on AlphaFold2. Excellent agreement is found, underscoring that our model has predictive potential for rapidly designing proteins with specific secondary structures and could be widely applied to biomedical industries, including protein biomaterial designs and regenerative medicine applications.

Protein Needles Designed to Self-Assemble through Needle Tip Engineering

The dynamic process of formation of protein assemblies is essential to form highly ordered structures in biological systems. Advances in structural and synthetic biology have led to the construction of artificial protein assemblies. However, development of design strategies exploiting the anisotropic shape of building blocks of protein assemblies has not yet been achieved. Here, the 2D assembly pattern of protein needles (PNs) is controlled by regulating their tip-to-tip interactions. The PN is an anisotropic needle-shaped protein composed of β-helix, foldon, and His-tag. Three different types of tip-modified PNs are designed by deleting the His-tag and foldon to change the protein-protein interactions. Observing their assembly by high-speed atomic force microscopy (HS-AFM) reveals that PN, His-tag deleted PN, and His-tag and foldon deleted PN form triangular lattices, the monomeric state with nematic order, and fiber assemblies, respectively, on a mica surface. Their assembly dynamics are observed by HS-AFM and analyzed by the theoretical models. Monte Carlo (MC) simulations indicate that the mica-PN interactions and the flexible and multipoint His-tag interactions cooperatively guide the formation of the triangular lattice. This work is expected to provide a new strategy for constructing supramolecular protein architectures by controlling directional interactions of anisotropic shaped proteins.

Mechanical unfolding of alpha- and beta-helical protein motifs

Alpha-helices and beta-sheets are the two most common secondary structure motifs in proteins. Beta-helical structures merge features of the two motifs, containing two or three beta-sheet faces connected by loops or turns in a single protein. Beta-helical structures form the basis of proteins with diverse mechanical functions such as bacterial adhesins, phage cell-puncture devices, antifreeze proteins, and extracellular matrices. Alpha-helices are commonly found in cellular and extracellular matrix components, whereas beta-helices such as curli fibrils are more common as bacterial and biofilm matrix components. It is currently not known whether it may be advantageous to use one helical motif over the other for different structural and mechanical functions. To better understand the mechanical implications of using different helix motifs in networks, here we use Steered Molecular Dynamics (SMD) simulations to mechanically unfold multiple alpha- and beta-helical proteins at constant velocity at the single molecule scale. We focus on the energy dissipated during unfolding as a means of comparison between proteins and work normalized by protein characteristics (initial and final length, # H-bonds, # residues, etc.). We find that although alpha-helices such as keratin and beta-helices CsgA and CsgB can require similar amounts of work to unfold, the normalized work per hydrogen bond, initial end to end length, and number of residues is greater for beta-helices at the same pulling rate. To explain this, we analyze the orientation of the backbone alpha carbons and backbone hydrogen bonds during unfolding. We find that the larger width and shorter height of beta-helices results in smaller angles between the protein backbone and the pulling direction during unfolding. As subsequent strands are separated from the beta-helix core, the angle between the backbone and the pulling direction diminishes. This marks a transition where beta-sheet hydrogen bonds become loaded predominantly in a collective shearing mode, which requires a larger rupture force. This finding underlines the importance of geometry in optimizing resistance to mechanical unfolding in proteins. The helix radius is identified here as an important parameter that governs how much sacrificial energy dissipation capacity can be stored in protein networks, where beta-helices offer unique properties.

Artificial bio-nanomachines based on protein needles derived from bacteriophage T4

Bacteriophage T4 is a natural bio-nanomachine which achieves efficient infection of host cells via cooperative motion of specific three-dimensional protein architectures. The relationships between the protein structures and their dynamic functions have recently been clarified. In this review we summarize the design principles for fabrication of nanomachines using the component proteins of bacteriophage T4 based on these recent advances. We focus on the protein needle known as gp5, which is located at the center of the baseplate at the end of the contractile tail of bacteriophage T4. This protein needle plays a critical role in directly puncturing host cells, and analysis has revealed that it contains a common motif used for cell puncture in other known injection systems, such as T6SS. Our artificial needle based on the β-helical domain of gp5 retains the ability to penetrate cells and can be engineered to deliver various cargos into living cells. Thus, the unique components of bacteriophage T4 and other natural nanomachines have great potential for use as molecular scaffolds in efforts to fabricate new bio-nanomachines.

The vibration of nanosprings affected by van der Waals interactions

The vibration of tightly helical nanosprings affected by van der Waals (vdW) interactions is investigated based on continuum modelling. Explicit solutions are derived to clarify the influence of initial pitch, stiffness and the number of nanosprings on the period, frequency and amplitude of the vibration. Unlike classic linear/nonlinear springs, the waveform of the vibration is always asymmetric for tightly helical nanosprings due to the asymmetry of vdW attraction and repulsion. The at most three equilibrium positions for the nanosprings strongly depend on the deformation due to competition between the vdW interactions and the elastic energy of the nanosprings. This study provides physical insights into the origin of the novel dynamic properties of such nanosprings.

Plasma membrane translocation of a protein needle based on a triple-stranded β-helix motif

Plasma membrane translocation is challenging due to the barrier of the cell membrane. Contrary to the synthetic cell-penetrating materials, tailed bacteriophages use cell-puncturing protein needles to puncture the cell membranes as an initial step of the DNA injection process. Cell-puncturing protein needles are thought to remain functional in the native phages. In this paper, we found that a bacteriophage T4 derived protein needle of 16 nm length spontaneously translocates through the living cell membrane. The β-helical protein needle (β-PN) internalizes into human red blood cells that lack endocytic machinery. By comparing the cellular uptake of β-PNs with modified surface charge, it is shown that the uptake efficiency is maximum when it has a negative charge corresponding to a zeta potential value of -16 mV. In HeLa cells, uptake of β-PN incorporates endocytosis independent mechanisms with partial macropinocytosis dependence. The endocytosis dependence of the uptake increases when the surface charges of β-PNs are modified to positive or negative. Thus, these results suggest that natural DNA injecting machinery can serve as an inspiration to design new class of cell-penetrating materials with a tailored mechanism.

In Silico Measurements of Twist and Bend Moduli for β-Solenoid Protein Self-Assembly Units

We compute potentials of mean force for bend and twist deformations via force pulling and umbrella sampling experiments for four β-solenoid proteins (BSPs) that show promise in nanotechnology applications. In all cases, we find quasi-Hooke's law behavior until the point of rupture. Bending moduli show modest anisotropy for two-sided and three-sided BSPs, and little anisotropy for a four-sided BSP. There is a slight clockwise/counterclockwise asymmetry in the twist potential of mean force, showing greater stiffness when the applied twist follows the intrinsic twist. When we extrapolate to beam theory appropriate for amyloid fibrils of the BSPs, we find bend/twist moduli which are somewhat smaller than those in the literature for other amyloid fibrils. Twist persistence lengths are on the order of a micron, and bend persistence lengths are several microns. Provided the intrinsic twist can be reversed, these results support the usage of BSPs in biomaterials applications.

Persistence length and stochastic fragmentation of supramolecular nanotubes under mechanical force

Cyclic peptide nanotubes (CPNs) exhibit impressive structural, mechanical and chemical properties in resemblance to beta-sheet proteins found in silks and amyloids, and show potential as supramolecular nanotubes that can be utilized to generate novel nanocomposites and nanoporous thin films. Quantifying the persistence length and thermomechanical fragmentation of CPNs is of great importance for establishing a theoretical basis of how to generate rectilinear nanostructures with controlled aspect ratio and rigidity. However, factors governing the elasticity and dynamical breaking of these supramolecular nanostructures remain to be fully understood. Here we present a statistical analysis of the Young's modulus and persistence length of CPNs using fully-atomistic molecular dynamic simulations in explicit solvent. We show that the measured properties exhibit a dependence on the magnitude of the shear force applied, and extrapolation to the quasi-static deformation case yields 0.46 μm for the persistence length and 7.8 GPa for the Young's modulus, in agreement with our experimental observations from TEM images. We establish a theoretical model for the spatial and temporal distribution of stochastic fracture, which we use to explain the simulation-based observations of spontaneous fragmentation under an applied shear force. Our methodology, blending theory, simulation and experiments provide a framework that can be utilized to investigate the mechanical behavior of self-assembling protein materials, paving the way for their design towards biological and industrial applications.

Flow-induced dynamics of carbon nanotubes

The high aspect ratio and bending resilience of a carbon nanotube (CNT) enables it to have remarkable responses to fluid flow. The structural deformation and vibration of a CNT under fluid flow are discussed in this paper, closely tied to their applications in mechanosensing and energy harvesting. We perform molecular dynamics (MD) simulations and a theoretical analysis based on the elastic beam theory, and find that the performance of these applications is critically defined by thermal noise at low flow speeds and flow-induced elastic instabilities at high speeds. We provide a map of operating mechanisms as defined by the properties of both nanostructures and fluid. The results and understanding obtained here could shed some light on the design of nanomechanical devices operating in fluidic environments.

Mechanical energy transfer and dissipation in fibrous beta-sheet-rich proteins

Mechanical properties of structural protein materials are crucial for our understanding of biological processes and disease states. Through utilization of molecular simulation based on stress wave tracking, we investigate mechanical energy transfer processes in fibrous beta-sheet-rich proteins that consist of highly ordered hydrogen bond (H-bond) networks. By investigating four model proteins including two morphologies of amyloids, beta solenoids, and silk beta-sheet nanocrystals, we find that all beta-sheet-rich protein fibrils provide outstanding elastic moduli, where the silk nanocrystal reaches the highest value of ≈40 GPa. However, their capacities to dissipate mechanical energy differ significantly and are controlled strongly by the underlying molecular structure of H-bond network. Notably, silk beta-sheet nanocrystals feature a ten times higher energy damping coefficient than others, owing to flexible intrastrand motions in the transverse directions. The results demonstrate a unique feature of silk nanocrystals, their capacity to simultaneously provide extreme stiffness and energy dissipation capacity. Our results could help one to explain the remarkable properties of silks from an atomistic and molecular perspective, in particular its great toughness and energy dissipation capacity, and may enable the design of multifunctional nanomaterials with outstanding stiffness, strength, and impact resistance.