ProtoFold: Part II — A Successive Kineto-Static Compliance Method for Protein Conformation Prediction

Author(s):  
Kazem Kazerounian ◽  
Khalid Latif ◽  
Carlos Alvarado

This paper presents an efficient and novel computational protein prediction methodology called Kineto-Static Compliance Method. Successive Kineto-Static Fold Compliance is a methodology for predicting a protein molecule’s motion under the effect of an inter-atomic force field without the need for molecular dynamic simulation. Instead, the chain complies under the Kineto-Static effect of the force field in such a manner that each rotatable joint changes by an amount proportional to the effective torque on that joint. This process successively iterates until all of the joint torques have converged to zero. This configuration is equivalent to a stable, globally optimized potential energy state of the system or, in other words, the final conformation of the protein. This methodology is implemented in a computer software package named ProtoFold. In this paper, we have used Protofold to predict the final conformation of a small peptide chain segment, an alpha helix, and the Triponin protein chains from a denatured configuration. The results show that torques in each joint are minimized to values very close to zero, which demonstrates the method’s effectiveness for protein conformation prediction.

2004 ◽  
Vol 127 (4) ◽  
pp. 712-717 ◽  
Author(s):  
Kazem Kazerounian ◽  
Khalid Latif ◽  
Carlos Alvarado

This paper presents an efficient and novel computational protein prediction methodology called kineto-static compliance method. Successive kineto-static fold compliance is a methodology for predicting a protein molecule’s motion under the effect of an inter-atomic force field without the need for molecular-dynamic simulation. Instead, the chain complies under the kineto-static effect of the force field in such a manner that each rotatable joint changes by an amount proportional to the effective torque on that joint. This process successively iterates until all of the joint torques have converged to a minimum. This configuration is equivalent to a stable, globally optimized potential energy state of the system or, in other words, the final conformation of the protein. This methodology is implemented in a computer software package named PROTOFOLD. In this paper, we have used PROTOFOLD to predict the final conformation of a small peptide chain segment, an alpha helix, and the Triponin protein chains from a denatured configuration. The results show that torques in each joint are minimized to values very close to zero, which demonstrates the method’s effectiveness for protein conformation prediction.


2007 ◽  
Vol 18 (01) ◽  
pp. 91-98 ◽  
Author(s):  
GÖKHAN GÖKOĞLU ◽  
TARIK ÇELİK

We have performed parallel tempering simulations of a 13-residue peptide fragment of ribonuclease-A, c-peptide, in implicit solvent with constant dielectric permittivity. This peptide has a strong tendency to form α-helical conformations in solvent as suggested by circular dichroism (CD) and nuclear magnetic resonance (NMR) experiments. Our results demonstrate that 5th and 8–12 residues are in the α-helical region of the Ramachandran map for global minimum energy state in solvent environment. Effects of salt bridge formation on stability of α-helix structure are discussed.


Author(s):  
Hai-Jun Su ◽  
Jesse Parker ◽  
Kazem Kazerounian ◽  
Horea Ilies

This paper presents an initial comparison of two approaches to energy minimization of protein molecules, namely the Molecular Dynamic Simulation and the Kineto-Static Compliance Method. Both methods are well established and are promising contenders to the seemingly insurmountable task of global optimization in the protein molecules potential energy terrain. The Molecular Dynamic Simulation takes the form of Constrained Multibody Dynamics of interconnected rigid bodies, as implemented at the Virtual Reality Application Center from Iowa State University. The Kineto-Static Compliance Method is implemented in the Protofold Computer package developed in the Mechanical Engineering Department at the University of Connecticut. The simulation results of both methods are compared through the trajectory of potential energy, the Root Mean Square Deviation (RMSD) of the alpha carbons, as well as based on visual observations. The preliminary results indicate that both techniques are very effective in converging the protein structure to a state with significantly less potential energy. At present, the converged solutions for the two methods are, however, different from each other and are very likely different from the global minimum potential energy state.


2019 ◽  
Vol 61 (12) ◽  
pp. 2432
Author(s):  
В.А. Постников ◽  
А.А. Кулишов ◽  
А.А. Островская ◽  
А.С. Степко ◽  
П.В. Лебедев-Степанов

An analysis of the change in the Gibbs free energy ∆G upon the formation of a flat nucleus of a p-terphenyl crystal at the liquid – air interface is presented, taking into account the anisotropy of the surface energy of the faces. The surface energy values of the p-terphenyl crystal faces were calculated by the atomic force field method OPLS, based on structural data. Experimental information on crystal growth from solutions and their surface properties was used to analyze the model.


Author(s):  
Temsiri Sapsaman ◽  
Harvey Lipkin

Since the native conformation or the natural shape of a protein largely determines its function, a prediction of protein conformation can shorten the process of drug discovery. This prediction is an optimization search to locate a configuration associated with the global minimum energy for the molecule. Due to the complexity of the multidimensional energy landscape, the prediction process can be extensive, which leads to very long simulation run times. For example, a high-resolution structure prediction algorithm [1] refining 20,000 to 30,000 models of several 49 to 88 residue long molecules takes about 150 CPU days per molecule. This paper presents the method of modified energy landscape (MEL) that improves the efficiency of the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method by 12.8% on average, and more than 30% in some cases for a representative sample of cases. Since the efficiency improvement allows the probabilistic search to cover more areas of the energy landscape, locating the global minimum is more likely. Also, in a practical protein prediction running coarse refinements on more decoys is more preferable than comprehensively refining few decoys because of the low accuracy of energy functions. Therefore, the MEL can significantly improve the protein prediction simulation even though it yields less average score improvement. The MEL is implemented in a refinement protocol in Rosetta [2].


1986 ◽  
Vol 233 (3) ◽  
pp. 731-736 ◽  
Author(s):  
G J van Holst ◽  
S R Martin ◽  
A K Allen ◽  
D Ashford ◽  
N N Desai ◽  
...  

The structure of potato (Solanum tuberosum) lectin, which is a hydroxyproline-rich glycoprotein, has been investigated by circular dichroism. The spectra of the native lectin, and of the oxidized, reduced and carboxymethylated and deglycosylated derivatives were examined, as was a hydroxyproline-rich glycopeptide and its deglycosylated derivative. It is concluded that the lectin contains about 35% polyproline II conformation, 34% type II beta-turn and 31% irregular conformation. No indications were found for the presence of alpha-helix or beta-sheet conformations. The polyproline II conformation is heat-stable, but is markedly destabilized by deglycosylation. The type II beta-turn is destabilized by cleavage of disulphide bonds.


2011 ◽  
Vol 399-401 ◽  
pp. 1984-1988
Author(s):  
Hai Li Yang ◽  
Li Wu ◽  
Guo Zhang Tang ◽  
Yun Gang Li ◽  
Yu Zhu Zhang

Fe-Si layer was prepared on silicon steel substrate from KCl-NaCl-NaF-SiO2 molten salts by pulse current at different time. The quantitative Si concentration depth profile, surface morphology and phase structure of the layer were studied by glow discharge optical emission spectroscopy, atomic force microscopy and X-ray diffraction. The layer growing process was analyzed from nucleation process, growth pattern and microstructure. It was observed that the Fe-Si alloy nucleated in the way of three dimensional conical shape and initially grew in the orientation of matrix, then gradually adjusted to the lowest energy state. With deposition time going on, the phase structure of the layer changed in the order of -Fe (Si) →α-Fe (Si) +Fe3Si →Fe3Si


2005 ◽  
Vol 277-279 ◽  
pp. 1023-1028
Author(s):  
Sung Ho Hwang ◽  
Jung Il Lee ◽  
Jin Dong Song ◽  
Won Jun Choi ◽  
Il Ki Han ◽  
...  

We report effects of the size and the energy state distribution on the electrical and optical properties in self-assembled InAs quantum dots. The results of characteristics measured using atomic force microscopy, photoluminescence and dark current are analyzed by way of a simulation assuming a Gaussian distribution in size and related energies. The samples investigated in this study are InAs/GaAs quantum dot infrared photodetector structures with an AlGaAs blocking layer grown by molecular beam epitaxy at different growth modes.


Author(s):  
Irina A. Kuz'mina ◽  
Tatiana R. Usacheva ◽  
Mariya A. Volkova ◽  
Natalia V. Belova ◽  
Valentin A. Sharnin

Quantum-chemical calculations of stable conformation of the molecule of 18-crown-6 ether (18C6) were carried for its free state in methanol (MeOH) and acetonitrile (AN) using GAUSSIAN 03 program. The DFT version and cc-pVTZ basic set was used. The values of mean lifetimes of hydrogen bonds between the methanol molecules as well as between 18C6 and methanol in MeOH-MeOH и 18C6-{MeOH-MeOH} systems were calculated by molecular dynamic simulations in NVT- ensemble applying GROMACS 4.5.4 software for OPLS-AA full-atomic force field .


Author(s):  
Bridget Carragher

Structural biologists typically acquire data in the form of a two-dimensional image (or set of images) from which the three-dimensional structure of the object of interest must be inferred. Examples can be found over a range of sizes spanning many orders of magnitude, and covering structures from the macroscopic to the atomic scale. A correspondingly wide range of different instruments is used in the collection of this data, from CT/MRI scanners, through light and electron microscopes, and recently, atomic force instruments. The images which are collected from these instruments may be in the form of a series of 2D slices through the 3D data set (and these may be either physical sections or optical sections) or a series of to mographic 2D projections of the 3D dataset. In either case it is highly likely that computer software tools will be used on the data set eitheras an aid in the qualitative interpretation of the structure or as a means of extracting quantitative morphological measurements.


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