Amino acid classes and the protein folding problem

Abstract

We present and implement a distance-based clustering of amino acids within the framework of a statistically derived interaction matrix and show that the resulting groups faithfully reproduce, for well-designed sequences, thermodynamic stability in and kinetic accessibility to the native state. A simple interpretation of the groups is obtained by eigenanalysis of the interaction matrix.

A method for computing the inter-residue interaction potentials for reduced amino acid alphabet

“…In the map of fi gure 2, the central core can easily be mapped into two groups as shown. These two groups exactly match two groups of Cieplak et al (2001). The exception is K, which Ciplak et al (2001) put in a separate group alone.…”

“…Later they (Wang and Wang 2002) used this metric and an energy landscape technique to classify amino acids into eight to 10 groups. (1983) Phyisco-chemical, structural and mutation data Venn diagram representation Taylor (1986) A special metric called "mismatch" between pairs Appears to be exhaustive enumeration Wang and Wang (1999) BLOSUM50 similarity score Correlation coeffi cient Murphy et al (2000) "Mismatch" Energy landscape based Wang and Wang (2001) Miyazawa-Jernigan matrix Eigenanalysis Cieplak et al (2001) Substitution matrices such as PAM and BLOSUM Branch and bound algorithm Cannata et al (2002) BLOSUM62 similarity score Enumeration and evaluation Li et al (2003) A special metric called "conductance" Perturbation theory applied to Markov matrices…”

“…They also used this scale to separate 20 amino acids into two broad groups. Cieplak et al (2001) extended this eigenanalysis technique and classifi ed amino acids into fi ve groups. Cannata et al (2002) used amino acid substitutions matrices such as PAM and BLOSUM (Henikoff and Henikoff 1992) and exhaustively evaluated all possible simplifi ed alphabets using a branch and bound algorithm.…”

Simplification of complexity in protein molecular systems by grouping amino acids: a view from physics

“…For example, the Miyazawa-Jernigen Matrix (MJ matrix) is a typical statistical potential [72][73][74], which contains the strengths of contact interactions between two kinds of amino acids in protein systems. Through the eigen analysis, this matrix could be represented as M ij ≈ λq i q j , in which M ij is the element of MJ matrix, λ and {q i } are the largest eigen value and the components of the concerned eigen vector [75][76][77][78]. The eigen spectrum is shown in Figure 3(a).…”

“…For the MJ matrix, the HP grouping could be clearly identified based on this method. The simplification could be iteratively carried out based on this index [77,78]. Yet, when smaller differences between amino acids are involved, other eigen components should be considered since the approximation based on the principal components is not precise enough to characterize the differences of interactions.…”