PubMed Health⌕ Search

Biomedical subjects

Rajgopal Srinivasan

Publications and source records attributed to Rajgopal Srinivasan.

7 recordsLinked to original sources
PubMed2004

A novel method reveals that solvent water favors polyproline II over beta-strand conformation in peptides and unfolded proteins: conditional hydrophobic accessible surface area (CHASA).

In aqueous solution, the ensemble of conformations sampled by peptides and unfolded proteins is largely determined by their interaction with water. It has been a long-standing goal to capture these solute-water energetics accurately and efficiently in calculations. Historically, accessible surface area (ASA) has been used to estimate these energies, but this method breaks down when applied to amphipathic peptides and proteins. Here we introduce a novel method in which hydrophobic ASA is determined after first positioning water oxygens in hydrogen-bonded orientations proximate to all accessible peptide/protein backbone N and O atoms. This conditional hydrophobic accessible surface area is termed CHASA. The CHASA method was validated by predicting the polyproline-II (P(II)) and beta-strand conformational preferences of non-proline residues in the coil library (i.e., non-alpha-helix, non-beta-strand, non-beta-turn library derived from X-ray elucidated structures). Further, the method successfully rationalizes the previously unexplained solvation energies in polyalanyl peptides and compares favorably with published experimentally determined P(II) residue propensities. We dedicate this paper to Frederic M. Richards.

Hydrogen Bonding↗
PubMed2004

Polyproline II helix is the preferred conformation for unfolded polyalanine in water.

Does aqueous solvent discriminate among peptide conformers? To address this question, we computed the solvation free energy of a blocked, 12-residue polyalanyl-peptide in explicit water and analyzed its solvent structure. The peptide was modeled in each of 4 conformers: alpha-helix, antiparallel beta-strand, parallel beta-strand, and polyproline II helix (P(II)). Monte Carlo simulations in the canonical ensemble were performed at 300 K using the CHARMM 22 forcefield with TIP3P water. The simulations indicate that the solvation free energy of P(II) is favored over that of other conformers for reasons that defy conventional explanation. Specifically, in these 4 conformers, an almost perfect correlation is found between a residue's solvent-accessible surface area and the volume of its first solvent shell, but neither quantity is correlated with the observed differences in solvation free energy. Instead, solvation free energy tracks with the interaction energy between the peptide and its first-shell water. An additional, previously unrecognized contribution involves the conformation-dependent perturbation of first-shell solvent organization. Unlike P(II), beta-strands induce formation of entropically disfavored peptide:water bridges that order vicinal water in a manner reminiscent of the hydrophobic effect. The use of explicit water allows us to capture and characterize these dynamic water bridges that form and dissolve during our simulations.

Computer Simulation↗
PubMed2003

Local secondary structure content predicts folding rates for simple, two-state proteins.

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.

Kinetics↗
PubMed2002

Methinks it is like a folding curve.

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.

Amino Acid Sequence↗
PubMed2002

Ab initio prediction of protein structure using LINUS.

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.

Animals↗
PubMed2002

Recursive domains in proteins.

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.

Dimerization↗