Kinematic Flexibility Analysis: Hydrogen Bonding Patterns Impart a Spatial Hierarchy of Protein Motion

Elastic network models (ENM) and constraint-based, topological rigidity analysis are two distinct, coarse-grained approaches to study conformational flexibility of macromolecules. In the two decades since their introduction, both have contributed significantly to insights into protein molecular mechanisms and function. However, despite a shared purpose of these approaches, the topological nature of rigidity analysis, and thereby the absence of motion modes, has impeded a direct comparison. Here, we present an alternative, kinematic approach to rigidity analysis, which circumvents these drawbacks. We introduce a novel protein hydrogen bond network spectral decomposition, which provides an orthonormal basis for collective motions modulated by non-covalent interactions, analogous to the eigenspectrum of normal modes, and decomposes proteins into rigid clusters identical to those from topological rigidity. Our kinematic flexibility analysis bridges topological rigidity theory and ENM, and enables a detailed analysis of motion modes obtained from both approaches. Our analysis reveals that collectivity of protein motions, reported by the Shannon entropy, is significantly lower for rigidity theory versus normal mode approaches. Strikingly, kinematic flexibility analysis suggests that the hydrogen bonding network encodes a protein-fold specific, spatial hierarchy of motions, which goes nearly undetected in ENM. This hierarchy reveals distinct motion regimes that rationalize protein stiffness changes observed from experiment and molecular dynamics simulations. A formal expression for changes in free energy derived from the spectral decomposition indicates that motions across nearly 40% of modes obey enthalpy-entropy compensation. Taken together, our analysis suggests that hydrogen bond networks have evolved to modulate protein structure and dynamics

Fluid Dynamics of Cell Printing

Cell printing is an emerging technology that uses droplets to deliver cells to desired positions with resolution potentially comparable to the size of single cells. In particular, ink–jet based cell printing technique has been successfully used to build simple bio–constructs and has shown a promise in building complex bio–structures or even organs. Two important issues in ink–jet based cell printing are the moderate survival rate of delicate cells and the limited cell placement resolution. Resolving these issues is critical for the ink–jet based cell printing techniques to realize their full potential. In this work, we use numerical simulations to reconstruct the impact of a droplet loaded with a single cell onto a pool of viscous fluids to gain insights into the droplet and cell dynamics during cell printing. We developed a mathematical model for this process: the droplet, pool and air are modeled as Newtonian fluids, and their flow is modeled as a laminar flow governed by the Navier–Stokes equation. The cell is modeled as an axisymmetric solid object governed by the neo–Hookean law and also has a shear viscosity that is the same as that of its host droplet. To numerically solve the coupled fluid and cell motion, we used a hybrid method in which fluid flow is solved on a fixed Cartesian grid and the deformation of solid body is solved on a Lagrangian mesh. We also developed a new full Eulerian method, termed the solid level set (SLS) method, to simulate cell printing. The key idea is to track the deformation of the solid body using four level set functions on a fixed Cartesian grid instead of using a Lagrangian mesh. The SLS method is easy to implement and addresses several challenges in simulations of fluid–tructure interactions using hybrid Eulerian/Lagrangian meshes. Using codes developed based on the above methods, we systematically investigated the fluid and cell dynamics during the cell printing process. We studied how the droplet penetration depth, droplet lateral spreading, cell stress and cell surface area change are affected by printing conditions such as impact velocity, pool depth, and cell stiffness. Our simulations indicate that cell experiences significant stress (∼20kPa) and local surface area dilation (∼100%) during the impact process. The latter suggests that cell membrane is temporally ruptured during the printing process, and is consistent with the gene transfection observed during cell printing. We speculate that the survival of cell through the rather violent cell printing process may be related to the briefness of the impact process, which only lasts about 0.1 milliseconds. Based on our simulation results, several strategies have been proposed to reduce the stress and membrane dilation of cells during cell printing

Technology 2001: The Second National Technology Transfer Conference and Exposition, volume 1

Papers from the technical sessions of the Technology 2001 Conference and Exposition are presented. The technical sessions featured discussions of advanced manufacturing, artificial intelligence, biotechnology, computer graphics and simulation, communications, data and information management, electronics, electro-optics, environmental technology, life sciences, materials science, medical advances, robotics, software engineering, and test and measurement

Molecular dynamics simulation and machine learning study of biological processes

In this dissertation, I use computational techniques especially molecular dynamics (MD) and machine learning to study important biological processes. MD simulations can effectively be used to understand and investigate biologically relevant systems with lengths and timescales that are otherwise inaccessible to experimental techniques. These include but are not limited to thermodynamics and kinetics of protein folding, protein-ligand binding free energies, interaction of proteins with membranes, and designing new therapeutics for diseases with rational design strategies. The first chapter includes a detailed description of the computational methods including MD, Markov state modeling and deep learning. In the second chapter, we studied membrane active peptides using MD simulation and machine learning. Two cell penetrating peptides MPG and Hst5 were simulated in the presence of membrane. We showed that MPG enters the model membrane through its N-terminal hydrophobic residues while Hst5 remains attached to the phosphate layer. Formation of helical conformation for MPG helps its deeper insertion into membrane. Natural language processing (NLP) and deep generative modeling using a variational attention based variational autoencoder (VAE) was used to generate novel antimicrobial peptides. These in silico generated peptides have a high quality with similar physicochemical properties to real antimicrobial peptides. In the third chapter, we studied kinetics of protein folding using Markov state models and machine learning. We studied the kinetics of misfolding in β2-microglobulin using MSM analysis which gave us insights about the metastable states of β2m where the outer strands are unfolded and the hydrophobic core gets exposed to solvent and is highly amyloidogenic. In the next part of this chapter, we propose a machine learning model Gaussian mixture variational autoencoder (GMVAE) for simultaneous dimensionality reduction and clustering of MD simulations. The last part of this chapter is about a novel machine learning model GraphVAMPNet which uses graph neural networks and variational approach to markov processes for kinetic modeling of protein folding. In the last chapter, we study two membrane proteins, spike protein of SARS-COV-2 and EAG potassium channel using MD simulations. Binding free energy calculations using MMPBSA showed a higher binding affinity of receptor binding domain in SARS-COV-2 to its receptor ACE2 than SARS-COV which is one of the major reason for its higher infection rate. Hotspots of interaction were also identified at the interface. Glycans on the spike protein shield the spike from antibodies. Our MD simulation on the full length spike showed that glycan dynamics gives the spike protein an effective shield. However, breaches were found in the RBD at the open state for therapeutics using network analysis. In the last section, we study ligand binding to the PAS domain of EAG potassium channel and show that a residue Tyr71 blocks the binding pocket. Ligand binding inhibits the current through EAG channel