BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

The calculation of the solvation properties of a single water molecule in liquid water is carried out in two ways. In the first, the water molecule is placed in a cavity and the solvent is treated as a dielectric continuum. This model is analyzed by numerically solving the Poisson equation using the DelPhi program. The resulting solvation properties depend sensitively on the shape and size of the cavity. In the second method, the solvent and solute molecules are treated explicitly in molecular dynamics simulations using Ewald boundary conditions. We find a 2 kcal/mole difference in solvation free energies predicted by these two methods when standard cavity radii are used. In addition, dielectric continuum theory assumes that the solvent reacts solely by realigning its electric moments linearly with the strength of the solute's electric field; the results of the molecular simulation show important non-linear effects. Non-linear solvent effects are generally of two types: dielectric saturation, due to solvent-solute hydrogen bonds, and electrostriction, a decrease in the solute cavity due to an increased electrostatic interaction. We find very good agreement between the two methods if the radii defining the solute cavity used in the continuum theory is decreased with the solute charges,

The Aqueous Solvation of Water: A Comparison of Continuum Methods with Molecular Dynamics

Computational Approaches for Protein Function Prediction: A Survey

Forisomes are Ca 2þ -driven, ATP-independent contractile protein bodies that reversibly occlude sieve elements in faboid legumes. They apparently consist of at least three proteins; potential candidates have been described previously as ‘FOR’ proteins. We isolated three genes from Medicago truncatula that correspond to the putative forisome proteins and expressed their green fluorescent protein (GFP) fusion products in Vicia faba and Glycine max using the composite plant methodology. In both species, expression of any of the constructs resulted in homogenously fluorescent forisomes that formed sieve tube plugs upon stimulation; no GFP fluorescence occurred elsewhere. Isolated fluorescent forisomes reacted to Ca 2þ and chelators by contraction and expansion, respectively, and did not lose fluorescence in the process. Wild-type forisomes showed no affinity for free GFP in vitro. The three proteins shared numerous conserved motifs between themselves and with hypothetical proteins derived from the genomes of M. truncatula, Vitis vinifera and Arabidopsis thaliana. However, they showed neither significant similarities to proteins of known function nor canonical metal-binding motifs. We conclude that ‘FOR’-like proteins are components of forisomes that are encoded by a welldefined gene family with relatives in taxa that lack forisomes. Since the mnemonic FOR is already registered and in use for unrelated genes, we suggest the acronym SEO (sieve element occlusion) for this family. The absence of binding sites for divalent cations suggests that the Ca 2þ binding responsible for forisome contraction is achieved either by as yet unidentified additional proteins, or by SEO proteins through a novel, uncharacterized mechanism.

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GFP Tagging of Sieve Element Occlusion (SEO) Proteins Results in Green Fluorescent Forisomes

Structural genomics efforts contribute new protein structures that often lack significant sequence and fold similarity to known proteins. Traditional sequence and structure-based methods may not be sufficient to annotate the molecular functions of these structures. Techniques that combine structural and functional modeling can be valuable for functional annotation. FEATURE is a flexible framework for modeling and recognition of functional sites in macromolecular structures. Here, we present an overview of the main components of the FEATURE framework, and describe the recent developments in its use. These include automating training sets selection to increase functional coverage, coupling FEATURE to structural diversity generating methods such as molecular dynamics simulations and loop modeling methods to improve performance, and using FEATURE in large-scale modeling and structure determination efforts.

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The FEATURE framework for protein function annotation: modeling new functions, improving performance, and extending to novel applications

We report a multiple time step algorithm applied to an atomistic Brownian dynamics simulation for simulating the long time scale dynamics of biomolecules. The algorithm was based on the original multiple time step method; a short time step was used to keep faster motions in local equilibrium. When applied to a 28-mer # # ! folded peptide, the simulation gave stable trajectories and the computation time was reduced by a factor of 160 compared to a conventional molecular dynamics simulation using explicit water molecules. We applied it for the folding simulation of a 13-mer ! -helical peptide, giving a successful folding simulation. These results indicate that the Brownian dynamics with the multiple time step algorithm is useful for studies of biomolecular motions by long time simulation.

Multiple Time Step Brownian Dynamics for Long Time Simulation of Biomolecules

We report the implementation of an atomistic Brownian dynamics simulation of proteins. A protein was described by united-atom model with AMBER91 force field. The solvent was treated by distance-dependent dielectric/surface area model. The computation time of the Brownian dynamics and the calculated results on structure and dynamics of 28-mer ββα fold peptide with the implicit solvent model were compared with those of molecular dynamics simulation with explicit solvent. The Brownian dynamics simulation was 53 times faster than molecular dynamics simulation with explicit solvent. The simulation was stable and the artifacts often observed in simulations at vacuum condition were reduced. The results indicate that the Brownian dynamics with the implicit solvent model can be widely used in studies of protein dynamics by long time simulation in future.

Development of an Atomistic Brownian Dynamics Algorithm with Implicit Solvent Model for Long Time Simulation

We applied an atomistic Brownian dynamics (BD) simulation with multiple time step method for the folding simulation of a 13-mer α-helical peptide and a 12-mer β-hairpin peptide, giving successful folding simulations. In this model, the driving energy contribution towards folding came from both electrostatic and van der Waals interactions for the α-helical peptide and from van der Waals interactions for the β-hairpin peptide. Although, many non-native structures having the same or lower energy than that of native structure were observed, the folded states formed the most populated cluster when the structures obtained by the BD simulations were subjected to the cluster analysis based on distance-based root mean square deviation of side-chains between different structures. This result indicates that we can predict the native structures from conformations sampled by BD simulation.

Free energy landscapes of two model peptides: α-helical and β-hairpin peptides explored with Brownian dynamics simulation

A new simple implicit solvent model, effective charge (EC) model, was introduced into the Brownian dynamics algorithm based on AMBER united-atom force field. In the EC model, an atomic charge was decreased as a function of solvent-accessible surface area of the atom. We carried out the Brownian dynamics simulations of a 28-mer ββα fold peptide using four implicit solvent models: a generalized Born/solvent-accessible surface area (GB/SA) model, a solvent-accessible surface area (SA) based solvent model, a SA in combination with distance-dependent dielectric (DD/SA) and the EC combined with DD/SA (DD/SA/EC) model; and the calculated results on structure and dynamics of the peptide were compared with those of molecular dynamics simulation using explicit solvent model. Several artifacts were observed in the simulation using the GB/SA model. On the other hand, simulation using the DD/SA and DD/SA/EC implicit solvent models were free from such artifacts. Especially BD with the DD/SA/EC model gave the most stable trajectory as judged by root mean square deviations from the initial structure without large computational cost.

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A New Implicit Solvent Model for Brownian Dynamics Simulation: Solvent-Accessible Surface Area Dependent Effective Charge Model

Three-dimensional local structures located near active sites are responsible for protein functions. To predict protein functions, we developed a method to extract three-dimensional structural characteristics of functions. We chose EF-hand calcium binding proteins as the model target. We defined a local structure as the structure within 12A from an arbitrary Asp(Cα). Analysis of the amino acid propensities in the active sites showed that there were clear preferences for certain amino acids to locate at the binding sites. Furthermore, the analysis of the distributions of the distances between Asp(Cα) and any other amino acids(Cα) showed that there were preferred distances for respective amino acids. Using such structural data, we derived a DB (distance based) score and a DPB (distance and propensity based) score for the prediction of functional sites. A search for EF-hand calcium binding sites was performed to test the method. EF-hand calcium binding sites were successfully predicted. Key Words: protein local structures, calcium binding protein, EF-hand calcium binding site, amino acid propensity, distance distribution

Tadashi Ando

Papers

Multiple Time Step Brownian Dynamics for Long Time Simulation of Biomolecules

Development of an Atomistic Brownian Dynamics Algorithm with Implicit Solvent Model for Long Time Simulation

Free energy landscapes of two model peptides: α-helical and β-hairpin peptides explored with Brownian dynamics simulation

A New Implicit Solvent Model for Brownian Dynamics Simulation: Solvent-Accessible Surface Area Dependent Effective Charge Model

Development of a structure based protein function prediction method: Calcium binding protein