Structures of Human Amylin peptide as discovered by our group
Human amylin in the presence of membrane
Islet amyloid polypeptide (IAPP, also known as amylin) is responsible for pancreatic amyloid deposits in type II diabetes. The deposits, as well as intermediates in their assembly, are cytotoxic to pancreatic ß-cells and contribute to the loss of ß-cell mass associated with type II diabetes. The factors that trigger islet amyloid deposition in vivo are not well understood, but peptide membrane interactions have been postulated to play an important role in islet amyloid formation. To better understand the mechanism and cause of such aggregation, we use molecular simulations with explicit and implicit solvent models with a variety of novel, enhanced sampling and free-energy calculation techniques, including Metadynamics, replica exchange and bias-exchange to compare monomer structure and the early aggregation mechanisms in different environments, including water, 35% (wt) aqueous trehalose, charged membranes.
The aim of this project is to study, at a molecular detail, the folding and aggregation of long polyglutamine (PolyQ) chains. Under some circumstances, these peptides fold onto themselves adopting a metastable conformation that is believed to be the nucleus for subsequent polymerization of additional chains. Our work is motivated by a desire to understand the molecular mechanisms behind such a nucleated growth polymerization process, and by the fact that polyglutamine is of central importance in a number of neurodegenerative disorders, most notably Huntington's Disease; literature studies concur in that a better understanding of the thermodynamics and kinetics of PolyQ folding and aggregation will accelerate the development of therapeutic treatments for Huntington's and other expanded PolyQ diseases.
With this goal in mind, the project has been divided into several sub-projects involving:
- the development of new simulation techniques that will enable efficient study of the PolyQ folding and aggregation processes, including a detailed analysis of the relevant transition states
- the study of the folding process of individual PolyQ chains into plausible, metastable candidate folded structures
- the study of the polymerization or aggregation process of multiple chains into stable oligomeric aggregates
- the study of the effects of additives (solutes) and site mutations on the folding of individual chains and the aggregation of multiple chains
A detailed, atomic-level understanding of the pathways through which polyglutamin folds and polymerizes will emerge out of these studies, as well as methodological and fundamental advances that will have a positive, wide-ranging impact on the scientific community's ability to understand protein misfolding and aggregation. Finally, the insights into PolyQ structure and dynamics under a variety of conditions will be of considerable use for development of therapeutic strategies.
Nanoparticle/block-copolymer self-assembled on patterned substrates
Example of morphologies obtained with an ABC triblock: (a) lamellae, (b) core-shell cylinders, (c) tetragonal cylinders, (d) spheres
Block copolymers are made of two or more polymeric blocks, each a sequence of identical monomers, attached by covalent bonds. At a low enough temperature, the incompatibility between different blocks leads to a micro-phase separation, and the copolymer self-assembles into an ordered morphology, such as lamellae, cylinders or spheres. The extraordinary variety of possible morphologies and their molecular dimension (5-100 nm) make block copolymers ideal materials to create structures at the nanoscale. To predict the self-assembly of block-copolymers, we have developed Monte Carlo simulations of a coarse-grained model . We focus on three different lines of research, often in close contact with the experimental work done in Prof. Nealey's group.
- The directed assembly of block copolymers is a promising approach to extend lithographic processes and fabricate devices with critical dimensions below 10 nm. However, morphologies produced through spontaneous self-assembly usually lack the long-range order which is desirable for applications. The use of patterned substrates, either chemically or topographically, are two proposed solutions to enforce long-range order. We use our simulation to compare those strategies and identify under which conditions a defect-free assembly is obtained.
- Incorporating nanoparticles into self-assembling copolymers could prove useful for design of new functional materials. We have investigated how a mixture of nanoparticles (8 nm in diameter) and copolymers self-assemble when deposited in a thin film over patterned substrate. The distribution of nanoparticles predicted by simulation is in good agreement with the experimental result.
- Our simulations are used to predict the phase diagram of triblock copolymers, which remains largely unexplored.
Our research group uses coarse grain models to study the biophysics of DNA. Recent efforts have included:
(1) using advanced sampling techniques to probe the single-strand to double-strand transition to identify mechanistic pathways and intermediate states;
(2) extending coarse-grain representations of DNA to include explicit ionic species and using these models to explore the effect of ions on the behavior of DNA;
(3) coupling our coarse-grain DNA models with coarse-grain protein models to explore DNA-protein interactions;
(4) examining the effect of DNA confinement in the context of DNA packaging in a viral capsid.
Our work includes model development and parametrization as well as development of the methods required to explore the biophysical problems we are interested in. These methods include Density of States methods such as Metadynamics, enhanced sampling methods such as Parallel Tempering, and many others when needed.
Glasses are materials that have the microscopically disordered structure of a liquid and the mechanical properties of a solids. Hence, such materials find vital usage in a variety of engineering applications. Our research focuses on understanding the dynamics and engineering of such materials for different purposes.
One of uses of glassy materials is in the long-term storage for biological materials, a problem which presents interesting challenges. Using statistical mechanics and molecular modeling techniques, we work on developing certain key factors that contribute to maintaining protein structure and function over extended storage times. Gaining a better understanding of these systems will result in the more intelligent design of storage materials.Longer storage times will enhance the availability of biological materials like drugs and vaccines.
Liquid crystals (LCs) are a phase of matter that flows like a liquid, but the orientations of the molecules are highly ordered over a very long range. This presence of long-range orientation results in interesting behavior of systems that employ LCs. In our group, we model LCs on multiple scales in an effort to engineer new applications for the laboratory and industry. At the atomistic level, we investigate the behavior of LCs near surfaces to determine the types and strength of anchoring present at different surfaces. At a mesoscale, we study systems mixtures and determine the accessibility different phases of LCs. On the largest scales, we investigate the behavior of particles, from the nanometer to micron scale, and observe their behavior in an LC solvent; the presence of defects in the LC has a marked effect on particle behavior, so by controlling the defect with fields (flow, electric, magnetic,etc.), we can dictate particle behavior in a well controlled manner.
Molecular simulations are typically limited by the time scale of sampling. With reasonable amount of computational resources one can only simulate on the order of hundreds of nanoseconds. On the other hand in real complex systems most of the phenomena of interest occur at orders-of-magnitudes longer time scales. To solve such problems of sampling, we develop new advanced sampling algorithms that can accelerate sampling of these systems in both Monte Carlo (MC) and Molecular Dynamics (MD) simulations.