Environment-dependent residue contact energies for proteins - PubMed (original) (raw)

Environment-dependent residue contact energies for proteins

C Zhang et al. Proc Natl Acad Sci U S A. 2000.

Abstract

We examine the interactions between amino acid residues in the context of their secondary structural environments (helix, strand, and coil) in proteins. Effective contact energies for an expanded 60-residue alphabet (20 aa x three secondary structural states) are estimated from the residue-residue contacts observed in known protein structures. Similar to the prototypical contact energies for 20 aa, the newly derived energy parameters reflect mainly the hydrophobic interactions; however, the relative strength of such interactions shows a strong dependence on the secondary structural environment, with nonlocal interactions in beta-sheet structures and alpha-helical structures dominating the energy table. Environment-dependent residue contact energies outperform existing residue pair potentials in both threading and three-dimensional contact prediction tests and should be generally applicable to protein structure prediction.

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Figures

Figure 1

Figure 1

Secondary structural ERCE (in RT units). The energy parameters are divided into six groups, α-α, β-β, α-β, α-C, β-C, and_C_-C, where α, β, and C represent helix, strand, and coil states, respectively.

Figure 2

Figure 2

Contact prediction using three different potentials: MJ, JS, and ERCE. ERCE predictions based on predicted (ERCE-Pred) and real secondary structures (ERCE-Real) both are shown. The x axis indicates the fraction of experimentally determined contacts that are correctly predicted (defined here as the coverage). Each coverage value_x_ corresponds to an energy cutoff such that the percentage of native contacts whose energies are lower than the cutoff happens to be x. There are other residue pairs that do not form contacts in the native protein structure but also have energies lower than the cutoff. The fraction of predicted contacts that correspond to actually observed contacts define the accuracy. Here we compare different prediction methods with a common standard: the random prediction. In theory, the accuracy of a random prediction averaged over many trials equals the ratio of native contacts to the number of all possible residue pairs in a protein. This value varies with protein size and contact density. For each method, the y axis indicates how much more accurate the prediction is relative to that from a random method.

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