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Publications and source records attributed to George D Rose.
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Many single-domain proteins exhibit two-state folding kinetics, with folding rates that span more than six orders of magnitude. A quantity of much recent interest for such proteins is their contact order, the average separation in sequence between contacting residue pairs. Numerous studies have reached the surprising conclusion that contact order is well-correlated with the logarithm of the folding rate for these small, well-characterized molecules. Here, we investigate the physico-chemical basis for this finding by asking whether contact order is actually a composite number that measures the fraction of local secondary structure in the protein; viz. turns, helices, and hairpins. To pursue this question, we calculated the secondary structure content for 24 two-state proteins and obtained coefficients that predict their folding rates. The predicted rates correlate strongly with experimentally determined rates, comparable to the correlation with contact order. Further, these predicted folding rates are correlated strongly with contact order. Our results suggest that the folding rate of two-state proteins is a function of their local secondary structure content, consistent with the hierarchic model of protein folding. Accordingly, it should be possible to utilize secondary structure prediction methods to predict folding rates from sequence alone.
RNABase is a unified database of all three-dimensional structures containing RNA deposited in either the Protein Data Bank (PDB) or Nucleic Acid Data Base (NDB). For each structure, RNABase contains a brief summary as well as annotation of conformational parameters, identification of possible model errors, Ramachandran-style conformational maps and classification of ribonucleotides into conformers. These same analyses can also be performed on structures submitted by users. To facilitate access, structures are automatically placed into a variety of functional and structural categories, including: ribozymes, pseudoknots, etc. RNABase can be freely accessed on the web at http://www.rnabase.org. We are committed to maintaining this database indefinitely.
Most often, the unfolded state of peptides and proteins has been modeled as a statistical random coil. Here, we suggest an alternative model based on the presence of a significant, temperature-dependent conformational bias in the unfolded population. Conformational bias is suggested by our calculations [Proc. Natl. Acad. Sci. USA 96 (1999) 14258-14263], and it is found in recent studies of both proteins and peptides. The imposition of even a modest bias would transform our assessment of the folding problem.
A sequence of seven alanine residues-too short to form an alpha-helix and whose side chains do not interact with each other-is a particularly simple model for testing the common description of denatured proteins as structureless random coils. The (3)J(HN alpha) coupling constants of individual alanine residues have been measured from 2 to 56 degrees C by using isotopically labeled samples. The results display a thermal transition between different backbone conformations, which is confirmed by CD spectra. The NMR results suggest that polyproline II is the dominant conformation at 2 degrees C and the content of beta strand is increased by approximately 10% at 55 degrees C relative to that at 2 degrees C. The polyproline II conformation is consistent with recent studies of short alanine peptides, including structure prediction by ab initio quantum mechanics and solution structures for both a blocked alanine dipeptide and an alanine tripeptide. CD and other optical spectroscopies have found structure in longer "random coil" peptides and have implicated polyproline II, which is a major backbone conformation in residues within loop regions of protein structures. Our result suggests that the backbone conformational entropy in alanine peptides is considerably smaller than estimated by the random coil model. New thermodynamic data confirm this suggestion: the entropy loss on alanine helix formation is only 2.2 entropy units per residue.
LINUS is an ab initio method for the prediction of protein structure based on simple physical principles. Here we report the performance of LINUS at CASP4, a community wide experiment to predict protein structure in which participants are blinded to the structures they seek to predict. We submitted 13 predictions for this experiment. The best four are described in detail, together with an assessment of secondary structure prediction for the entire set. Coordinates for all predictions are available from the CASP web site (http://predictioncenter.llnl.gov). It should be emphasized that our use of the descriptor "ab initio" is unequivocal: the sole input into these simulations is the amino acid sequence.
We report the identification and characterization of a novel cytokine-like gene family using structure-based methods to search for novel four-helix-bundle cytokines in genomics databases. There are four genes in this family, FAM3A, FAM3B, FAM3C, and FAM3D, each encoding a protein (224-235 amino acids) with a hydrophobic leader sequence. Northern analysis indicates that FAM3B is highly expressed in pancreas, FAM3D in placenta, and FAM3A and FAM3C in almost all tissues. Immunohistochemistry showed that FAM3A is expressed prominently in the vascular endothelium, particularly capillaries. We found that FAM3A and FAM3B protein were both localized to the islets of Langerhans of the endocrine pancreas. Recombinant FAM3B protein has delayed effects on beta-cell function, inhibiting basal insulin secretion from a beta-cell line in a dose-dependent manner.
The striking similarity between observed circular dichroism spectra of nonprolyl homopolymers and that of regular left-handed polyproline II (P(II)) helices prompted Tiffany and Krimm to propose in 1968 that unordered peptides and unfolded proteins are built of P(II) segments linked by sharp bends. A large body of experimental evidence, accumulated over the past three decades, provides compelling evidence in support of the original hypothesis of Tiffany and Krimm. Of particular interest are the recent experiments of Shi et al. who find significant P(II) structure in a short unfolded alanine-based peptide. What is the physical basis for P(II) helices in peptide and protein unfolded states? The widely accepted view is that favorable chain-solvent hydrogen bonds lead to a preference for dynamical fluctuations about noncooperative P(II) helices in water. Is this preference simply a consequence of hydrogen bonding or is it a manifestation of a more general trend for unfolded states which are appropriately viewed as chains in a good solvent? The prevalence of closely packed interiors in folded proteins suggests that under conditions that favor folding, water-which is a better solvent for itself than for any polypeptide chain-expels the chain from its midst, thereby maximizing chain packing. Implicit in this view is a complementary idea: under conditions that favor unfolding, chain-solvent interactions are preferred and in a so-called good solvent, chain packing density is minimized. In this work we show that minimization of chain packing density leads to preferred fluctuations for short polyalanyl chains around canonical, noncooperative P(II)-like conformations. Minimization of chain packing is modeled using a purely repulsive soft-core potential between polypeptide atoms. Details of chain-solvent interactions are ignored. Remarkably, the simple model captures the essential physics behind the preference of short unfolded alanine-based peptides for P(II) helices. Our results are based on a detailed analysis of the potential energy landscape which determines the system's structural and thermodynamic preferences. We use the inherent structure formalism of Stillinger and Weber, according to which the energy landscape is partitioned into basins of attraction around local minima. We find that the landscape for the experimentally studied seven-residue alanine-based peptide is dominated by fluctuations about two noncooperative structures: the left-handed polyproline II helix and its symmetry mate.
The domain is a fundamental unit of protein structure. Numerous studies have analyzed folding patterns in protein domains of known structure to gain insight into the underlying protein folding process. Are such patterns a haphazard assortment or are they similar to sentences in a language, which can be generated by an underlying grammar? Specifically, can a small number of intuitively sensible rules generate a large class of folds, including feasible new folds? In this paper, we explore the extent to which four simple rules can generate the known all-beta folds, using tools from graph theory. As a control, an exhaustive set of beta-sandwiches was tested and found to be largely incompatible with such a grammar. The existence of a protein grammar has potential implications for both the mechanism of folding and the evolution of domains.