Lattice protein
[2] Because proteins are such large molecules, there are severe computational limits on the simulated timescales of their behaviour when modeled in all-atom detail.
The millisecond regime for all-atom simulations was not reached until 2010,[3] and it is still not possible to fold all real proteins on a computer.
Simplification significantly reduces the computational effort in handling the model, although even in this simplified scenario the protein folding problem is NP-complete.
[5] The energy function mimics the interactions between amino acids in real proteins, which include steric, hydrophobic and hydrogen bonding effects.
The relative positions of the beads in the native state constitute the lattice protein's tertiary structure[citation needed].
[11] Attempts to resolve Levinthal paradox in protein folding are another efforts made in the field.
As an example, study conducted by Fiebig and Dill examined searching method involving constraints in forming residue contacts in lattice protein to provide insights to the question of how a protein finds its native structure without global exhaustive searching.
The lattice statistical model seeks to recreate protein folding by minimizing the free energy of the contacts between hydrophobic amino acids.
[5] Different lattice types and algorithms were used to study protein folding with HP model.
Attempts to address this include the HPNX model which classifies amino acids as hydrophobic (H), positive (P), negative (N), or neutral (X) according to the charge of the amino acid,[15] adding additional parameters to reduce the number of low energy conformations and allowing for more realistic protein simulations.
Another issue with lattice models is that they generally don't take into account the space taken up by amino acid side chains, instead considering only the α-carbon.
