The inverse protein folding problem: self consistent mean field optimisation of a structure specific mutation matrix

Area of Science:

• Computational Biology
• Protein Structure Prediction
• Bioinformatics

Background:

• The inverse folding problem aims to identify protein sequences that fold into a specific 3D structure.
• Exhaustive exploration of sequence space is computationally intractable due to the vast number of possible amino acid combinations, especially when considering two-body interactions.

Purpose of the Study:

• To develop an efficient computational method for the inverse folding problem.
• To predict protein sequences compatible with a known backbone structure without prior sequence information.

Main Methods:

• A novel approach is proposed where multiple copies of each possible amino acid side-chain type are attached to C-alpha positions.
• Sequence matrix weights are refined using mean field theory, where each side-chain copy interacts with the mean field of neighboring positions.
• Amino acid pair potentials of mean force are used to calculate potential energy.

Main Results:

• The method converges rapidly to a self-consistent solution within a few cycles.
• The refined sequence matrix is independent of the starting point, effectively removing memory effects.
• Computer experiments demonstrate that the method can retrieve significant sequence information from backbone structure alone.

Conclusions:

• The proposed mean field theory-based method offers an efficient solution to the inverse folding problem.
• This approach successfully predicts compatible sequences from protein backbone structures, highlighting the principle of structure-recognizes-sequence.
• The method's ability to retrieve sequence information without initial sequence data is a significant advancement.