Structural Information-Based Method for the Efficient and Reliable Prediction of Oligopeptide Conformations

Genetic Algorithm Embedded with a Search Space Dimension Reduction Scheme for Efficient Peptide Structure Predictions

The computational determination of peptide conformations is a challenging task of finding minima in a high dimensional space. By combining the sampling efficiency of the genetic algorithm (GA) and the dimensionality reduction resulted from the backbone dihedral angle correlations, named as the path matrix (PM) method, a new searching algorithm, parallel microgenetic algorithm (PMGA), is proposed. Meanwhile, PMGA employs the density functional theory based energy as the fitness function and performs local geometry optimizations to enhance the reliability of its GA encoding strategy. Tests on peptides with up to eight amino-acid residues show PMGA is quite efficient for providing high-quality conformational coverages. The computational cost of the PMGA search increases slowly with the number of amino-acid residues in a peptide, with no sign of deterioration on the searching results for the increased length of the peptide. The PMGA method should therefore be useful for determining the conformations of oligopeptide, studying the protein-ligand interactions, and designing the peptide-based drugs.

A random forest learning assisted "divide and conquer" approach for peptide conformation search

Computational determination of peptide conformations is challenging as it is a problem of finding minima in a high-dimensional space. The "divide and conquer" approach is promising for reliably reducing the search space size. A random forest learning model is proposed here to expand the scope of applicability of the "divide and conquer" approach. A random forest classification algorithm is used to characterize the distributions of the backbone φ-ψ units ("words"). A random forest supervised learning model is developed to analyze the combinations of the φ-ψ units ("grammar"). It is found that amino acid residues may be grouped as equivalent "words", while the φ-ψ combinations in low-energy peptide conformations follow a distinct "grammar". The finding of equivalent words empowers the "divide and conquer" method with the flexibility of fragment substitution. The learnt grammar is used to improve the efficiency of the "divide and conquer" method by removing unfavorable φ-ψ combinations without the need of dedicated human effort. The machine learning assisted search method is illustrated by efficiently searching the conformations of GGG/AAA/GGGG/AAAA/GGGGG through assembling the structures of GFG/GFGG. Moreover, the computational cost of the new method is shown to increase rather slowly with the peptide length.

Computational study on single molecular spectroscopy of tyrosin-glycine, tryptophane-glycine and glycine-tryptophane

Quantum chemistry calculations play a fundamental role in revealing the molecular structures observed in gas-phase spectroscopic measurements. The supersonic jet cooling widely used in single molecular spectroscopy experiment is a non-equilibrium process and often causes confusion on the theoretical and experimental comparison. A computational approach is proposed here to account for the effect of the non-equilibrium cooling on the experimental spectra and applied to the cases of tyrosin-glycine (YG), tryptophane-glycine (WG) and glycine-tryptophane (GW). The low energy conformers of YG, WG and GW are obtained through thorough conformational searches. The structural features and equilibrium distributions of conformations and the energy barriers for conformer conversions are then determined. Three classes of transition energy barriers, high, medium and low, are found for the conversions among conformers with distinctly different, similar and the same structural types, respectively. The final conformation populations are determined by assuming an initial temperature of about 450 K and allowing for only the conformation conversion with a low energy barrier to occur during the rapid cooling process. The results provide a natural explanation for the numbers of YG, WG and GW conformations observed experimentally. The theoretical conformation assignments are also in good agreement with the experimental IR data.