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A statistical mechanical treatment of biopolymers is presented that includes the sequence information as an internal coordinate. This approach allows an assessment of the contribution of sequence information to the thermodynamic entropy. Even in cases where the sequence composition has no effect on the intersubunit interactions, the sequence composition contributes to the entropy of the system. Using a path integral representation, the canonical partition function can be represented as a product of a polymer configurational path integral and a sequence walk path integral. In most, biological instances the sequence composition influences the potential energy of intersubunit interaction. Consequently, the two path integrals are not separable, but rather "interact" via a sequence-dependent configurational potential. Biological constraints can also be built into the system and these effectively introduce an external potential. In proteins and RNA, the sequence walk occurs in dimensions greater than 3 and, therefore, will be an ideal "polymer". The Markovian nature of this walk can be exploited to show that all the structural information is contained in the sequence. This later effect is a result of the dimensionality of the sequence walk and is not necessarily a result of biological optimization of the system.
Protein modeling could be done on various levels of structural details, from simpli­fied lattice or continuous representations, through high resolution reduced models, employing the united atom representation, to all-atom models of the molecular me­chanics. Here I describe a new high resolution reduced model, its force field and ap­plications in the structural proteomics. The model uses a lattice representation with 800 possible orientations of the virtual alpha carbon-alpha carbon bonds. The sam­pling scheme of the conformational space employs the Replica Exchange Monte Carlo method. Knowledge-based potentials of the force field include: generic pro­tein-like conformational biases, statistical potentials for the short-range confor- mational propensities, a model of the main chain hydrogen bonds and context-de­pendent statistical potentials describing the side group interactions. The model is more accurate than the previously designed lattice models and in many applications it is complementary and competitive in respect to the all-atom techniques. The test applications include: the ab initio structure prediction, multitemplate comparative modeling and structure prediction based on sparse experimental data. Especially, the new approach to comparative modeling could be a valuable tool of the structural proteomics. It is shown that the new approach goes beyond the range of applicability of the traditional methods of the protein comparative modeling.
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