TITLE:

COMPUTATIONAL SCIENCE MASTER THESIS PROPOSAL PRESENTATIONS (No. 19)

TALK 1 : Growth and Control in Escherichia Coli
TALK 2 : Biomechanical boundary conditions delimitation for fracture healing with intramedullary nail fixation.

TALK 3 : Predicting Binding Affinities of Protein-Protein complexes: A tool for Protein Design

DATE:

Friday, May 2nd, 2003

TIME:

3:30 PM

LOCATION:

GMCS 214

SPEAKER:

Speaker 1 : Beltran Rodriguez, MS Student, CSRC
Speaker 2 : Angel Perez, MS Student, CSRC
Speaker 3 : Javier Cuervo, MS Student, CSRC

ABSTRACT:

Abstract 1:
The growth of E. coli is controlled through a series of biochemical processes. Key to this control is ppGpp. Accumulation of ppGpp initiates extensive changes in the cellular metabolism, operating in response to nutrient limitation. A model is developed based on growth data and the known biological mechanisms. This project refines a previous growth model, fits kinetic parameters, and simulates a particular growth medium.

Abstract 2:
Intramedullary nailing favors fracture healing depending in the achievable mechanical stability and load share. In consequence, it is extremely important identify and define the clinical directions for this fixation method. A full understanding of the load-sharing mechanism between Ender’s nail and a fractured femur is the goal of this work. A 3-D Finite Element model of an entire fractured femur stabilized by two non-locking Ender’s nails was build. The mechanical loads in both modeled bone and implant where similar to those observed in a healthy femur under gait. The results should help to identify the mechanical reasons for some reported unfavorable clinical result. This will lead to delimit borderlines indications to assist orthopedic physician in their stabilizing procedures based on biomechanical factors.

Abstract 3:
The scope of this work is to develop a model that allows for the prediction of binding affinities for designed protein-protein complexes. The focus of the Love Lab is to engineer small ‘designer’ proteins to bind target proteins from pathogenic organisms. The first steps entail computational docking and

interfacial mutagenesis. This process generates a large number of candidate

complexes that must be assessed critically. To do so we are extracting from natural complexes interfacial features (“descriptors”) that are correlated to binding affinity. The descriptors will be determined computationally through the use of molecular mechanics force fields that will ensure the physical chemical basis of the model. To achieve this goal we will be perform Molecular Dynamics Simulations of natural complexes as a training set for the model, including the effect of polar solvation by using the Generalized Born Model (GB) and hydrophobic effects using a term related to the Solvent Accessible Surface Area (SA). These parameters will then be used to predict the binding affinities of our ‘designer’ proteins engineered to bind pathogenic targets.

The success of this project will have a major impact on the performance and efficiency of the work carried in the Biochemical Protein Design (Love)

Laboratory.

HOST:

Jose Castillo

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