4.3 Article

Salt bridges: Geometrically specific, designable interactions

Journal

PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
Volume 79, Issue 3, Pages 898-915

Publisher

WILEY-BLACKWELL PUBLISHING, INC
DOI: 10.1002/prot.22927

Keywords

structural bioinformatics; protein design; protein structure; protein stability; protein folding; arginine; lysine; histidine; side chain rotamers; networked salt bridges

Funding

  1. NIGMS NIH HHS [R37 GM054616, R37 GM054616-16] Funding Source: Medline

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Salt bridges occur frequently in proteins, providing conformational specificity and contributing to molecular recognition and catalysis. We present a comprehensive analysis of these interactions in protein structures by surveying a large database of protein structures. Salt bridges between Asp or Glu and His, Arg, or Lys display extremely well-defined geometric preferences. Several previously observed preferences are confirmed, and others that were previously unrecognized are discovered. Salt bridges are explored for their preferences for different separations in sequence and in space, geometric preferences within proteins and at protein protein interfaces, co-operativity in networked salt bridges, inclusion within metal-binding sites, preference for acidic electrons, apparent conformational side chain entropy reduction on formation, and degree of burial. Salt bridges occur far more frequently between residues at close than distant sequence separations, but, at close distances, there remain strong preferences for salt bridges at specific separations. Specific types of complex salt bridges, involving three or more members, are also discovered. As we observe a strong relationship between the propensity to form a salt bridge and the placement of salt-bridging residues in protein sequences, we discuss the role that salt bridges might play in kinetically influencing protein folding and thermodynamically stabilizing the native conformation. We also develop a quantitative method to select appropriate crystal structure resolution and B-factor cutoffs. Detailed knowledge of these geometric and sequence dependences should aid de novo design and prediction algorithms.

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