Method, system and computer program product for levinthal process induction from known structure using machine learning

A system for modeling the structure of a macromolecule based on the primary sequence of that macromolecule, the system comprising:

• a memory for storing a training set of known macromolecules, wherein each known macromolecule of the training set has a known structure and a known primary sequence;
• a processor module for:
• a) determining an initialized structure for each known macromolecule of the training set based on its primary sequence;
• b) for each known macromolecule of the training set, defining a corresponding projected folding path comprising a progression of n projected macromolecule states, beginning with the initialized structure and ending with the known structure, wherein n is a positive integer greater than 2, wherein each macromolecule state in the n macromolecule states has a corresponding primary sequence, and a state-specific projected structure;
• c) providing a function operable to, for each known macromolecule of the training set, define a corresponding modeled folding path approximating the corresponding projected folding path, wherein
• i) the corresponding modeled folding path comprises a progression of n modeled macromolecule states, beginning from the initialized structure and ending with the known structure,
• ii) each modeled macromolecule state in the n macromolecule states has the primary sequence and a state-specific modeled structure, and
• iii) the function is operable to, for each modeled macromolecule state progression of n modeled macromolecule states except the last modeled macromolecule state, translate the state-specific structure of any macromolecule state in the corresponding folding path into the state-specific structure of the immediately following macromolecule state in the progression.

A computer program product for configuring a computer system to predict the structure of a macromolecule based on the primary sequence of the macromolecule, the computer program product comprising:

• a recording medium;
• a function saved on the recording medium for predicting the structure of the macromolecule using a training set of macromolecules wherein the function has been generated by a method comprising:
• a) defining an initialized structure for each known macromolecule of the training set based on its primary sequence;
• b) for each known macromolecule of the training set, defining a corresponding projected folding path comprising a progression of n projected macromolecule states, beginning with the initialized structure and ending with the known structure, wherein n is a positive integer greater than 2, wherein each macromolecule state in the n macromolecule states has a corresponding primary sequence and a state-specific projected structure;
• c) providing a function operable to, for each known macromolecule of the training set, define a corresponding modeled folding path approximating the corresponding projected folding path, wherein
• i) the corresponding modeled folding path comprises a progression of n modeled macromolecule states, beginning from the initialized structure and ending with the known structure,
• ii) each modeled macromolecule state in the n macromolecule states has the primary sequence and a state-specific modeled structure, and
• iii) the function is operable to, for each modeled macromolecule state progression of n modeled macromolecule states except the last modeled macromolecule state, translate the state-specific structure of any macromolecule state in the corresponding folding path into the state-specific structure of the immediately following macromolecule state in the progression.