We discuss a model of protein conformations where conformations are combinations of  fragments from some small set. For these fragments we consider a distribution of frequencies of occurrence of pairs (sequence of amino acids, conformation), averaged over some balls in the spaces of sequences and conformations. These frequencies can be estimated due to smallness of the epsilon-entropy of the set of conformations of protein fragments. We consider statistical potentials for protein fragments which describe the mentioned frequencies of occurrence and discuss a model of the free energy of a protein where the free energy is equal to a sum of statistical potentials of the fragments. We discuss application of this model to the problem of prediction of the native conformation of a protein from its primary structure and to the description of the dynamics of a protein.
We discuss a model of protein conformations where conformations are combinations of short fragments from some small set. For these fragments we consider a distribution of frequencies of occurrence of pairs (sequence of amino acids, conformation), averaged over some balls in the spaces of sequences and conformations. These frequencies can be estimated due to smallness of the epsilon-entropy of the set of conformations of protein fragments. We consider statistical potentials for protein fragments which describe the mentioned frequencies of occurrence and discuss model of free energy of a protein where the free energy is equal to a sum of statistical potentials of the fragments. We also consider contribution of contacts of fragments to the energy of protein conformation, and contribution from statistical potentials of some hierarchical set of larger protein fragments. This set of fragments is constructed using the distribution of frequencies of occurrence of short fragments. We discuss analogy between this approach and deep learning methods. We discuss applications of this model to problem of prediction of the native conformation of a protein from its primary structure and to description of dynamics of a protein.
A
short
R
a model of the
model of
R
discuss application
also consider contribution of contacts of fragments to the energy of protein conformation, and contribution from statistical potentials of some hierarchical set of larger protein fragments. This set of fragments is constructed using the distribution of frequencies of occurrence of short fragments. We discuss analogy between this approach and deep learning methods. We discuss applications
D
the
R
the description of the
description of