Functional energy of protin
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Explanation:Proteins are polymers assembled from 20 naturally occurring amino acids, which fold to unique, biologically active, three-dimensional conformations called native structures. Their biological functions are governed by their three-dimensional structures, which in turn are fully determined by their amino acid sequences. Predicting the native structure of a protein from its amino acid sequence is one of the most important and challenging scientific problems in contemporary biology and chemistry [1]. The capability to reliably make such predictions would allow biochemists to design drugs more efficiently, understand biological processes in detail, and answer fundamental questions about biological systems, diseases, immune response, and more.
The experimental determination of protein structure is a time consuming and expensive process. Hence, computational methods play an essential role in the prediction of the native structures of proteins. There are three classes of computational approaches to protein structure prediction: homology modeling, threading, and ab initio folding. Homology modeling and threading methods utilize proteins with known structure that are evolutionarily related to the target protein with unknown structure [2]. If one can not find such proteins in the available library of experimentally resolved protein structures, the only remaining approach to predicting the native structure is ab initio folding.
Ab initio folding attempts to find the native structure of a protein “from scratch”. The fundamental assumption in ab initio folding is the existence of a free energy function that assigns an energy value to each three-dimensional structure the protein can in principle assume. The native structure is assumed to be the one with the lowest energy [3]. Thus, there are two main ingredients in ab initio folding: The design of a reliable energy function, and the development of an efficient approach to search the space of all possible conformations for the one with the lowest energy. In this paper, we focus on the first problem.
The energy functions used in ab initio folding are physics-based: for a given three dimensional configuration of a protein, one first calculates various terms contributing to the total energy such as electrostatic energy, covalent bonding energy, Van der Waals energy, etc., and then adds these terms to obtain the total energy [4]. While these terms are based on physics, their functional forms are sometimes approximate, and the coefficients that appear are obtained by various fitting procedures. In this work, we represent the total energy of a configuration as a linear combination of these physics-based energy terms, and optimize the coefficients.
The fitness of a given energy function for a given protein can be visually inspected by plotting the total energy versus the structural dissimilarity from the native structure. In order to do this, one generates many possible conformations and computes the total energy and dissimilarity from the native structure for each. 1 Fig. 1 shows such a plot for a desirable energy function. As can be seen, the energy value is higher for conformations that have large dissimilarities from the native structure, with a roughly monotonic trend. Due to the monotonic trend, reducing the energy corresponds to getting closer to the native structure during ab initio folding procedure. If one can construct an energy function that has energy vs. structural dissimilarity plots like that of Fig. 1, one can hope to reproduce a similar trend for proteins with unknown structure.