Zahra Shahbazi

Hydrogen Bonds and Kinematic Mobility of Protein Molecules

Modeling protein molecules as kinematic chains provides the foundation for developing powerful approaches to the design, manipulation, and fabrication of peptide based molecules and devices. Nevertheless, these models possess a high number of degrees of freedom (DOFs) with considerable computational implications. On the other hand, real protein molecules appear to exhibit a much lower mobility during the folding process than what is suggested by existing kinematic models. The key contributor to the lower mobility of real proteins is the formation of hydrogen bonds during the folding process. In this paper, we explore the pivotal role of hydrogen bonds in determining the structure and function of the proteins from the point of view of mechanical mobility. The existing geometric criteria on the formation of hydrogen bonds are reviewed and a new set of geometric criteria is proposed. We show that the new criteria better correlate the number of predicted hydrogen bonds with those established by biological principles than other existing criteria. Furthermore, we employ established tools in kinematics mobility analysis to evaluate the internal mobility of protein molecules and to identify the rigid and flexible segments of the proteins. Our results show that the developed procedure significantly reduces the DOF of the protein models, with an average reduction of 94%. Such a dramatic reduction in the number of DOF can have enormous computational implications in protein folding simulations.

Stress Analysis Along Tree Branches

Efforts have been made to develop various models to calculate the stress due to weight throughout tree branches. Most studies assumed a uniform modulus of elasticity throughout the branch as well as analyzing the branch as a tapered cantilever beam orientated horizontally or at an angle. However, previous studies show that branches located lower on the tree have a greater variance of modulus of elasticity values in the radial direction and that branches located lower on a tree are more curved. Also, different tree species have different morphologies, some with curvy branches. In this work we have developed a model which considers the curved shape and varying modulus of elasticity values in order to determine stress across the tree branches more accurately. To do this the cross sectional area was divided into rings and each ring was assigned a different modulus of elasticity. Next, the area of the rings was transformed according to their modulus of elasticity. We then considered the curved shape of the branch by generating a best fit line for the diameter of the tree branch in terms of distance from the end of the branch. The generated diameter equation was used in the stress calculations to provide more realistic results. Based on equations developed in this work, we have created a Graphical User Interface (GUI) in Matlab, which can be used as a tool to calculate stress within tree branches by biologists without getting involved with the mathematical and mechanical calculations. We also created a Finite Element Model (FEM) in Abaqus and compared results.© 2014 ASME

An Optimized Kinematic Mobility Analysis of Protein Molecules

Understanding the 3D structure and consequently the motion of protein molecules contributes to simulate their function. Modeling protein molecules as kinematic chains has been used to predict protein molecules flexible and rigid regions as well as their degrees of freedom to predict their mobility. However, high computational cost for relatively large molecules is one of the major challenges in this field.In this paper we have combined our previously developed rigidity analysis (ProtoFold) with pebble game thus improving computational cost of our simulation. Here, we have determined the required time for all steps of ProtoFold and subsequently the most time consuming step. Results have shown that finding rigid loops inside the protein structure using graph theory and Grubler-Kutzbach criterion is the slowest part of the procedure, taking an average of 75% of the time required for the rigidity analysis. Therefore we have replaced this step with pebble game. The modified method has been applied to a random group of protein molecules and its efficiency in significantly improving the simulation speed has been verified.Copyright

Kinematic Motion Constraints of the Protein Molecule Chains

The function of protein molecules is defined by their 3-D geometry, as well as their internal mobility, which is heavily influenced by the internal hydrogen bonds. The correct identification of these hydrogen bonds and the prediction of their effect on the mobility of protein molecules can provide an invaluable mechanism to understand protein behavior. Applications of this study ranges from nano-engineering to new drug design. We are extending our recent approach from identifying main-chain main-chain hydrogen bonds to all types of hydrogen bonds that occur in protein structures, such as α-helices and β-sheets. We use the Grubler-Kutzbach kinematic mobility criterion to determine the degrees of freedom of all closed loops (rigid loops as well as closed loops of one or more degrees of freedom) formed by Hydrogen bonds. Furthermore, we systematically develop constraint equations for non-rigid closed loops. Several examples of protein molecules from PDB are used to show that these additions both improve the accuracy of mobility analysis and enable us to study a broader range of the motion of protein molecules. This approach offers theoretical insight as well as extensive numerical efficiencies in protein simulations.Copyright

Protein Molecules as Natural Nano Bio Devices: Mobility Analysis

Proteins are nature’s nano-robots in the form of functional molecular components of living cells. The function of these natural nano-robots often requires conformational transitions between two or more native conformations that are made possible by the intrinsic mobility of the proteins. Understanding these transitions is essential to the understanding of how proteins function, as well as to the ability to design and manipulate protein-based nano-mechanical systems [1]. Modeling protein molecules as kinematic chains provides the foundation for developing powerful approaches to the design, manipulation and fabrication of peptide based molecules and devices. Nevertheless, these models possess a high number of degrees of freedom (DOF) with considerable computational implications. On the other hand, real protein molecules appear to exhibits a much lower mobility during the folding process than what is suggested by existing kinematic models. The key contributor to the lower mobility of real proteins is the formation of Hydrogen bonds during the folding process.Copyright

On Hydrogen Bonds and Mobility of Protein Molecules

Modeling protein molecules as kinematic chains provides the foundation for developing powerful approaches to the design, manipulation and fabrication of peptide based molecules and devices. Nevertheless, these models possess a high number of degrees of freedom (DOF) with considerable computational implications. On the other hand, real protein molecules appear to exhibits a much lower mobility during the folding process than what is suggested by existing kinematic models. The key contributor to the lower mobility of real proteins is the formation of Hydrogen bonds during the folding process. In this paper we explore the pivotal role of Hydrogen bonds in determining the structure and function of the proteins from the point of view of mechanical mobility. The existing geometric criteria on the formation of Hydrogen bonds are reviewed and a new set of geometric criteria are proposed. We show that the new criteria better correlate the number of predicted Hydrogen bonds with those established by biological principles than other existing criteria. Furthermore, we employ established tools in kinematics mobility analysis to evaluate the internal mobility of protein molecules, and to identify the rigid and flexible segments of the proteins. Our results show that the developed procedure significantly reduces the DOF of the protein models, with an average reduction of 94%. Such a dramatic reduction in the number of DOF can have has enormous computational implications in protein folding simulations.© 2009 ASME