Polarity as a criterion in protein design

Abstract

Hypothetical proteins can be tested computationally by determining whether or not the designed sequence-structure pair has the characteristics of a typical globular protein. We have developed such a test by deriving quantities with approximately constant value for all globular proteins, based on empirical analysis of the exposed and buried surfaces of 128 structurally known proteins. The characteristic quantities that best appear to segregate badly designed or deliberately misfolded proteins from their properly folded natural relatives are the polar fraction of side chains on the protein surface and, independently, in the protein interior. Three of the seven hypothetical structures tested here can be rejected as having too many polar side-chain groups in the interior or too few on the protein surface. In addition, a recently designed nutritional protein is identified as being very much unlike globular proteins. These database-derived characteristic quantities are useful in screening designed proteins prior to experiment and may be useful in screening experimentally determined (X-ray, NMR) protein structures for possible errors.

Correlation functions as a tool for protein modeling and structure analysis

“…In the present study, the effective charged surface area is introduced as a new concept similar to the one described by Baumann et al (1989). It describes the effective electrostatic interaction of bordering surfaces and is calculated for every atom by the point charge of an atom, multiplied by the surface area associated with the atom.…”

“…The most significant ones are (1) the ratio of the solvent-accessible surfaces of apolar and polar side chains, (2) empirical free energy functions, and (3) the number of buried charged atoms. Further studies including three-dimensional profile scores allow localization of differences between correctly and wrongly folded or mistraced structures without providing an unambiguous assessment (Baumann et al, 1989; Liithy et al, 1992).The correlation between the size of typical globular proteins and their respective surfaces has been proposed by Miller and coworkers (1987). More recent studies revealed that there is a strong correlation between the size of a protein and the solvation free energy of its folding (Chiche et al, 1990); this correlation should therefore be a useful criterion for the detection of incorrect model structures.…”

“…The most significant ones are (1) the ratio of the solvent-accessible surfaces of apolar and polar side chains, (2) empirical free energy functions, and (3) the number of buried charged atoms. Further studies including three-dimensional profile scores allow localization of differences between correctly and wrongly folded or mistraced structures without providing an unambiguous assessment (Baumann et al, 1989;Liithy et al, 1992).…”

Verification of protein structures: Patterns of nonbonded atomic interactions

“…O f these, the Ramachandran analysis of peptide dihedral angles (Ramachandran & Sasisekharan, 1968) was one of the first to classify allowed and nonallowed conformations; most grossly misfolded structures can be identified in this fashion. Several other methods that physiochemicallycharacterize a protein structure have been described, including the energetics methods of Novotny et al (1984), the atomic solvation parameter of Eisenberg and McLachlan (1986), and the evaluation of surface polarity (Baumann et al, 1989). Other empirical approaches include analysis of the pairwise likelihood of neighboring residues (Tanaka & Scheraga, 1976;Hendlich et al, 1990), the three-dimensional-one-dimensional profile method of Liithy et al (1992), and the fragment matching methods of Jones et al (1991).…”

Prediction of ligand binding site and functionally important residues based on fuzzy-oil-drop model

“…According to this model, the hydrophobic residues tend to be placed in the central part of the protein molecule and in hydrophilic residues on the protein's surface [38][39][40]. Even the recognition of native versus nonnative protein structures can be to some extent differentiated on the basis of spatial distribution of amino acid hydrophobicity [41][42][43]. The importance of hydrophobicity distribution has been emphasized, particularly for Rosetta development, when the description of the hydrophobic core significantly increased the performance of the Rosetta program [44].…”