Metodi Traykov

A New Heuristic Algorithm for Protein Folding in the HP Model

This article presents an efficient heuristic for protein folding. The protein folding problem is to predict the compact three-dimensional structure of a protein based on its amino acid sequence. The focus is on an original integer programming model derived from a platform used for Contact Map Overlap problem.

Protein Folding Prediction in a Cubic Lattice in Hydrophobic-Polar Model

The tertiary structure of the proteins determines their functions. Therefore, the predicting of proteins tertiary structure, based on the primary amino acid sequence from long time, is the most important and challenging subject in biochemistry, molecular biology, and biophysics. One of the most popular protein structure prediction methods, called Hydrophobic-Polar (HP) model, is based on the observation that in polar environment hydrophobic amino acids are in the core of the molecule-in contact between them and more polar amino acids are in contact with the polar environment. In this study, we present a new mixed integer programming formulation, exact algorithm, and two heuristic algorithms to solve the protein folding problem stated as a combinatorial optimization problem in a simple cubic lattice. The results from computational runs on a set of benchmarks are favorably compared to known algorithms for solving the 3D lattice HP model as genetic algorithms, ant colony optimization algorithm, and Monte Carlo algorithm.

A Model for HP Folding Prediction Using Increasing Constrain for Spreading in the Process of Making Conformations

The 3D structure of proteins is the major factor that determines their biological activity. The synthesis of new proteins and the crystallographic analysis of their 3D structure is very slow and very expensive process. If we can predict the 3D structure of many proteins, than only proteins with expected properties have to be synthesized. The main idea, implemented in our research, is not to use lattice cube with constant size to make possible conformations in this space, but to use flexible constrain for spreading away from the center of the formed molecule, which constrain has a coefficient that can vary in the process of folding according the percentage of failing to make possible conformation, caused by lack of space. Less space allowed causes difficulties to make the conformations but the achieved forms are more compact and with lower energy. More space causes making many useless random conformations and more computational time is needed to find the best conformation and to make the same one more times in this random process in order to have bigger probability that it is the best one. This method may be used in every other model for protein folding prediction to improve the computational time and the probability of finding the accurate 3D structure – our results show that advantage.

3D Visualization of the Biological Structures

In this paper we aim to present a visualization of 3D model of a protein using data stored in a molecular structure file. All our examples will be realized with geometry and texturing in Maya. It will also be used a very practical application of MEL scripting to automate a modeling. It will be presented a simulations of DNA permeation through nanopores using NAMD, VMD and Maya. In this work we will briefly describe this technique and the possibilities for its use.