Spontaneous self-organization of solid-binding peptides on single-layer atomic materials offers enormous potential in employing these systems for technological and medical applications from biosensors to logic devices. Molecular self-organization of peptides depends highly on their sequences, which affect their conformational behavior under aqueous conditions. Traditional ways of computationally studying the effect of mutations on the conformation states involves dimension reduction on cosine and sine transformed torsion angles, often represented as Ramachandran plots. Although these studies successfully cluster conformation states, they fail to intuitively characterize the effect of the point mutation(s) directly, necessitating further data analysis. Here, we apply Hilbert Space-Filling-Curve, HSFC, on the torsion angles and demonstrate intuitive visualization for the effect of point mutations on secondary structure dynamics along a reaction coordinate. We perform molecular dynamics simulations on graphene using the graphite-binding dodecapeptide, WT-GrBP5. The 12-AA long peptide was selected by directed evolution using M13 based phage display. The WT-GrBP5 is known to self-organize on graphene under low-neutral pH at room temperature. A rationally designed charge-neutral mutant, M9-GrBP5, assembles at a broader range of pHs widening the range of practical implementations of the peptide. The HSFC shows that the mutated amino acids in M9 do not correlate with the reaction coordinate of pH change, unlike that of WT. Understanding the effect of amino acid φ-ψ pairs that contribute to the changes in the peptide’s conformational space, with changing conditions, will help in analyzing effects of point mutations. The effect of the peptide’s conformational behavior on their self-organization propensities on surfaces would lead to the design of sequences that form soft bio/nano interfaces with controlled molecular interactions towards strategies for practical applications. The research was supported by the DMREF Program at National Science Foundation (NSF) through the MGI platform (Materials Genome Initiative) under grant numbers DMR# 1629071, 1848911, and 1922020.

Self-assembling peptides show great promise as an effective bottom-up technique that can be used to integrate biology and nanoelectronics. Such peptides can form a long-range ordered structure on the surface of Single Layer Atomic Materials (SLAM), such as graphene and transition metal dichalcogenides (e.g. molybdenum disulfide) through a chiral recognition mechanism. As graphene is highly sensitive to the adsorption of molecules, it is an ideal building block for biosensors. However, controlling the self-assembly of biomolecules to attain functional structures remains an engineering challenge. One fundamental question is how does the van der Waals (vdW) interaction between the graphene sheet and the adsorbing peptide, and the resulting strain field, affect the peptide binding footprint and the formation of an ordered phase. To investigate the phenomenon, I used Atomistic Finite Element Modeling (AFEM) to simulate the strain fields that arise during physical adsorption of a peptide to the surface of a graphene sheet. I then compared data from the AFEM simulation to experimental data collected using Scanning Tunneling Microscopy (STM) to determine whether the strain patterns correlate with the experimentally observed chiral assembly directions. Establishing a correlation between adsorption induced strain and chiral recognition would provide a more robust hypothesis concerning the optimal conditions for the generation of long-range ordered biomolecular structures on SLAMs; and, by extension, a step towards the realization of peptide-based biosensors.

Engineered solid binding peptides can be used as molecular tools for a variety of applications, with our focus lying on bio/nano interfaces. Peptide-solid interactions involve surface phenomena such as binding, surface diffusion, and self-organization on atomically flat solids. Each of these phenomena requires the knowledge of peptide folding patterns, which are difficult to study both experimentally and computationally. Molecular dynamics, MD, has been used to computationally model peptide/solid interactions, but without information regarding the energy landscape of peptide conformations, the challenge of predictive design remains. While several methods exist for finding the energy landscapes for single peptide systems, currently no approach is capable of handling multi-peptide/surface systems. Here we use dynamic mode decomposition, DMD, to efficiently explore the energy landscapes of such systems aiming to find accurate linear approximations for predictive design of peptides at bio/nano interfaces. We make use of extended, multiresolution DMD to allow for analysis of various datasets with identical timesteps while minimizing impacts of statistical noise. It is anticipated that some descriptions of conformation will be better suited to describe peptide conformation energy landscapes than others; based on this premise, we examined various descriptions of peptide conformations through DMD. These descriptions include interatomic distances, adjacencies and peptide backbone torsion angles, alongside wavelet, Hilbert, Fourier, and Laplace transformations of select datasets. Through such analyses, it is becoming possible to describe how peptide conformations in multi-peptide/surface systems evolve through the energy landscape and settle into energy minima corresponding to stable conformations. These conformations can then be corroborated to peptide self-assembly on the surface using scanning probe microscopy techniques with sub-A resolutions for the development of predictive design platforms for future applications in biosensors, bioelectronics, and logic devices. 

Proteins are the workhorses in biology and their functions are affected by their amino acid domains in a modular fashion. When placed on solid surfaces, such as single layer graphene, certain short-sequence solid-binding peptides possess specific conformation propensities that aid in the self-organization into long range ordered peptide nanowires on atomically flat crystal lattices, such as single atomic layer solids. The self-assembly of nanowires on two-dimensional solids create electronic junctions through the biological doping of the underlying material and, e.g., by ordering the dipole moments. The utilization of these self-assembled nanowires with unique electrical capabilities may be a significant step towards the advancement of biologically compatible electronic devices such as biosensors. One of the key aspects of the design, namely the driving force for self-assembly of peptides on inorganic crystals, is currently unknown. To further understand the surface phenomena, our goal is to apply theoretical computational modeling to simulate the behavior of peptides and analyze how various conditions, such as temperature, pH, concentration and molecular conformations impact self-assembly. Peptides are computationally simple and only act based on local information such as the conformation of their immediate neighbors on a shared surface. Therefore, the self-assembly behavior can be simulated by fully distributed and asynchronous Markov chain algorithms, which have previously not been applied to peptide folding behavior interacting with a substrate. From these simulation algorithms, we determined the ideal physical conditions and the driving-forces for self-assembled peptide-based bioelectronic networks interfaced with single layer materials. Understanding how to better form these bioelectronic networks could constitute the critical foundation to advance bio-nanotechnology through the creation of biosensors or other biologically compatible electronic devices.

The long term goal of this project is to study and mathematically characterize how the mammalian (mainly Homo sapiens) brain creates and maintains meaningful neuronal connections and organizes into connectomes and cortexes, and to compare what we learn to an existing (patented and under development at GEMSEC) brain-like computational neural network. A key point in understanding the formation, organization and long-term existence of new brain neural networks is the fundamental relationship between the geometric entropy of the physical network embedding, and information entropy of the network adjacency, connectivity. To our knowledge, there is currently no study in the literature that focuses on understanding biological neural networks through the entropy of their connections. Here we pose the question whether entropy related learning rules emerge from biological network connections or are the driving force for these connections. We also ask whether such learning rules can be imposed on artificial neural networks for enhanced functionality. To find the information entropy analogue of the geometric entropy term from a network point of view, we need to define the information entropy of the neuronal adjacency matrix. We define the information entropy of the neuronal adjacency, with random numbers between 0 and 1 symbolizing strength of the connections, i.e., synaptic plasticity, as the Shannon’s entropy, i.e., information entropy, of the spectral distribution of the neuronal adjacency matrix. To aid us in this study, two public neuronal datasets have been found: the Janelia FlyEM research group’s Hemibrain, and the NeuralEnsemble simulated spike train data. We test the entropy of the inferred neuronal connections from these datasets toward achieving our goal of the mechanism of formation of neural connections and connectomes. This project is supported by the UW Computational Neuroscience Center, and the DMREF Program of NSF through the MGI platform under DMR# 1629071, 1848911, and 1922020.

The ease of data retrieval, analysis, and distribution can accelerate the pace of scientific research. A difference in philosophies and methodologies in the manner of data-collection and storage leads to a lack of semantic-consistency in naming-conventions across disciplines. Previous studies have opened this discussion within various research disciplines, however there has yet to be a study accomplished within a Convergence Science Lab such ours, Genetically Engineered Materials Science and Engineering Center (GEMSEC). Well-defined naming-conventions, clear-cut data standards, and set programmatic interfaces are required to provide improved data access to researchers and the public. This consistency is necessary to create a robust database with the ability to query large amounts of related information. The key point is that integrating design (of variables, research questions, etc.) with traditional, empirical research approaches in the natural sciences will result in more robust data and clearer analysis. Here we discuss the need for standardized protocol and metadata standards for data-collection and storage. We began by creating a data analysis and storage pipeline for two computationally involved experiments, Molecular Dynamics (MD) and Next Generation Sequencing (NGS), within GEMSEC. A database schema storing metadata associated with raw, cleaned, and analyzed files was created. We found homogenous collections of metadata with inconsistencies, especially in peptide naming conventions, between experiments. The target-database includes those from other labs some of which may store unconventional file types, use other naming schemes for key variables such as sequences, and have different standards for what metadata is stored and what is computationally retrieved at a later time. This schema is formed via interviewing various other researchers, determining similarities and differences within metadata, and creating a standard for all to use based on this collected information.