Abstract
Two different strategies for stabilizing proteins are (i) positive design in which the native state is stabilized and (ii) negative design in which competing non-native conformations are destabilized. Here, the circumstances under which one strategy might be favored over the other are explored in the case of lattice models of proteins and then generalized and discussed with regard to real proteins. The balance between positive and negative design of proteins is found to be determined by their average “contact-frequency”, a property that corresponds to the fraction of states in the conformational ensemble of the sequence in which a pair of residues is in contact. Lattice model proteins with a high average contact-frequency are found to use negative design more than model proteins with a low average contact-frequency. A mathematical derivation of this result indicates that it is general and likely to hold also for real proteins. Comparison of the results of correlated mutation analysis for real proteins with typical contact-frequencies to those of proteins likely to have high contact-frequencies (such as disordered proteins and proteins that are dependent on chaperonins for their folding) indicates that the latter tend to have stronger interactions between residues that are not in contact in their native conformation. Hence, our work indicates that negative design is employed when insufficient stabilization is achieved via positive design owing to high contact-frequencies.
Highlights
Protein stabilization can be achieved via two different strategies: (i) ‘positive design’ in which the native state is stabilized; and (ii) ‘negative design’ in which non-native states are destabilized [1,2,3]
Positive design can be achieved by introducing favorable pairwise interactions between residues that are in contact in the native state whereas negative design can be achieved by introducing unfavorable pairwise interactions between residues that are in contact in non-native conformations of the protein
Computational double-mutant cycle (DMC) analysis can be employed in an exhaustive manner to determine the strength of interaction between all possible residue pairs in a lattice model in contrast with experimental DMC analysis that must be restricted to a relatively small number of residue pairs owing to the prohibitive amount of work involved. Using this computational DMC approach, we previously discovered that the strength of both short- and long-range pairwise interactions changes in a linear fashion with increasing ‘contact-frequency’, a term defined for each pair of residues in a sequence that corresponds to the fraction of states in the conformational ensemble of the sequence in which that pair of residues are in contact [13,14]
Summary
Protein stabilization can be achieved via two different strategies: (i) ‘positive design’ in which the native state is stabilized; and (ii) ‘negative design’ in which non-native states are destabilized [1,2,3]. It is possible that certain features of a protein’s native structure such as its secondary structure content or contact-order [4] bias the choice of which particular strategy is employed. We explore this question with respect to lattice models of proteins and show that the principles that we have discovered apply to real proteins. Such models have the advantage that in certain cases all the possible conformations in the ensemble can be enumerated and, preferential design strategies for certain protein conformations may be identified
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