Building native protein conformation from NMR backbone chemical shifts using Monte Carlo fragment assembly

A Slice-based 13C-detected NMR Spin System Forming and Resonance Assignment Method

Nuclear magnetic resonance (NMR) spectroscopy is attracting more attention in the field of computational structural biology. Till recently, ¹H-detected experiments are the dominant NMR technique used due to the high sensitivity of ¹H nuclei. However, the current availability of high magnetic fields and cryogenically cooled probe heads allow researchers to overcome the low sensitivity of ¹³C nuclei. Consequently, ¹³C-detected experiments have become a popular technique in different NMR applications especially resonance assignment and structure determination of large proteins. In this paper, we propose the first spin system forming method for ¹³C-detected NMR spectra. Our method is able to accurately form spin systems based on as few as two ¹³C-detected spectra, CBCACON, and CBCANCO. Our method picks slices from the more trusted spectrum and uses them as feedback to direct the slice picking in the less trusted one. This feedback leads to picking the accurate slices that consequently helps to form better spin systems. We tested our method on a real dataset of 'Ubiquitin' and a benchmark simulated dataset consisting of 12 proteins. We fed our spin systems as inputs to a genetic algorithm to generate the chemical shift assignment, and obtained 92 percent correct chemical shift assignment for Ubiquitin. For the simulated dataset, we obtained an average recall of 86 percent and an average precision of 88 percent. Finally, our chemical shift assignment of Ubiquitin was given as an input to CS-ROSETTA server that generated structures close to the experimentally determined structure.

Unraveling the meaning of chemical shifts in protein NMR

Chemical shifts are among the most informative parameters in protein NMR. They provide wealth of information about protein secondary and tertiary structure, protein flexibility, and protein-ligand binding. In this report, we review the progress in interpreting and utilizing protein chemical shifts that has occurred over the past 25years, with a particular focus on the large body of work arising from our group and other Canadian NMR laboratories. More specifically, this review focuses on describing, assessing, and providing some historical context for various chemical shift-based methods to: (1) determine protein secondary and super-secondary structure; (2) derive protein torsion angles; (3) assess protein flexibility; (4) predict residue accessible surface area; (5) refine 3D protein structures; (6) determine 3D protein structures and (7) characterize intrinsically disordered proteins. This review also briefly covers some of the methods that we previously developed to predict chemical shifts from 3D protein structures and/or protein sequence data. It is hoped that this review will help to increase awareness of the considerable utility of NMR chemical shifts in structural biology and facilitate more widespread adoption of chemical-shift based methods by the NMR spectroscopists, structural biologists, protein biophysicists, and biochemists worldwide. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman.

Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics

Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.

Energy landscapes of a hairpin peptide including NMR chemical shift restraints

Methods recently introduced to improve the efficiency of protein structure prediction simulations by adding a restraint potential to a molecular mechanics force field introduce additional input parameters that can affect the performance. Here we investigate the changes in the energy landscape as the relative weight of the two contributions, force field and restraint potential, is systematically altered, for restraint functions constructed from calculated nuclear magnetic resonance chemical shifts. Benchmarking calculations were performed on a 12-residue peptide, tryptophan zipper 1, which features both secondary structure (a β-hairpin) and specific packing of tryptophan sidechains. Basin-hopping global optimization was performed to assess the efficiency with which lowest-energy structures are located, and the discrete path sampling approach was employed to survey the energy landscapes between unfolded and folded structures. We find that inclusion of the chemical shift restraints improves the efficiency of structure prediction because the energy landscape becomes more funnelled and the proportion of local minima classified as native increases. However, the funnelling nature of the landscape is reduced as the relative contribution of the chemical shift restraint potential is increased past an optimal value.

Equilibrium simulations of proteins using molecular fragment replacement and NMR chemical shifts

Methods of protein structure determination based on NMR chemical shifts are becoming increasingly common. The most widely used approaches adopt the molecular fragment replacement strategy, in which structural fragments are repeatedly reassembled into different complete conformations in molecular simulations. Although these approaches are effective in generating individual structures consistent with the chemical shift data, they do not enable the sampling of the conformational space of proteins with correct statistical weights. Here, we present a method of molecular fragment replacement that makes it possible to perform equilibrium simulations of proteins, and hence to determine their free energy landscapes. This strategy is based on the encoding of the chemical shift information in a probabilistic model in Markov chain Monte Carlo simulations. First, we demonstrate that with this approach it is possible to fold proteins to their native states starting from extended structures. Second, we show that the method satisfies the detailed balance condition and hence it can be used to carry out an equilibrium sampling from the Boltzmann distribution corresponding to the force field used in the simulations. Third, by comparing the results of simulations carried out with and without chemical shift restraints we describe quantitatively the effects that these restraints have on the free energy landscapes of proteins. Taken together, these results demonstrate that the molecular fragment replacement strategy can be used in combination with chemical shift information to characterize not only the native structures of proteins but also their conformational fluctuations.

Bayesian weighting of statistical potentials in NMR structure calculation

The use of statistical potentials in NMR structure calculation improves the accuracy of the final structure but also raises issues of double counting and possible bias. Because statistical potentials are averaged over a large set of structures, they may not reflect the preferences of a particular structure or data set. We propose a Bayesian method to incorporate a knowledge-based backbone dihedral angle potential into an NMR structure calculation. To avoid bias exerted through the backbone potential, we adjust its weight by inferring it from the experimental data. We demonstrate that an optimally weighted potential leads to an improvement in the accuracy and quality of the final structure, especially with sparse and noisy data. Our findings suggest that no universally optimal weight exists, and that the weight should be determined based on the experimental data. Other knowledge-based potentials can be incorporated using the same approach.

Modeling proteins using a super-secondary structure library and NMR chemical shift information

A remaining challenge in protein modeling is to predict structures for sequences with no sequence similarity to any experimentally solved structure. Based on earlier observations, the library of protein backbone supersecondary structure motifs (Smotifs) saturated about a decade ago. Therefore, it should be possible to build any structure from a combination of existing Smotifs with the help of limited experimental data that are sufficient to relate the backbone conformations of Smotifs between target proteins and known structures. Here, we present a hybrid modeling algorithm that relies on an exhaustive Smotif library and on nuclear magnetic resonance chemical shift patterns without any input of primary sequence information. In a test of 102 proteins, the algorithm delivered 90 homology-model-quality models, among them 24 high-quality ones, and a topologically correct solution for almost all cases. The current approach opens a venue to address the modeling of larger protein structures for which chemical shifts are available.

Solution nuclear magnetic resonance spectroscopy

Solution nuclear magnetic resonance (NMR) spectroscopy has come a long way in characterizing the structure and function of biological molecules since the first one-dimensional spectrum of protein was recorded about 30 years ago. To date (September 1, 2012), there are 9,521 solution NMR structures in the Protein Data Bank, compared to 74,009 determined by crystallographic methods. Unlike X-ray and electron microscopy (EM) methods, which are based on the concepts of Fourier optics and image reconstruction, structure determination by NMR involves measuring structural restraints and finding structural solutions that satisfy the restraints. Although the NMR approach is much less direct in a physical sense, it has proven itself over the years to be capable of de novo structure determination at high precision. Moreover, the method is highly versatile and can be used in a variety of ways for addressing mechanistic questions. NMR measurements of protein internal dynamics and protein-protein or protein-ligand interaction are directly relevant to function in vivo because the molecules are often in physiological buffer conditions. The method can also be applied to investigate protein-folding intermediates, conformational changes, as well as intrinsically unfolded proteins. Recently, along with X-ray and EM, solution NMR has entered a state of rapid growth for structural studies of membrane proteins, already demonstrating its feasibility in de novo structure determination of membrane-embedded ion channels and receptors. As the hardware advances rapidly, especially in cryogenic probes that have much higher sensitivity, the sample concentration required for solution NMR investigation is decreasing, hopefully soon to a concentration level at which nonspecific protein aggregation is no longer an issue. After three decades of improvement in spectrometer technology, NMR pulse experiments, isotope labeling schemes, and structure determination software, we believe that solution NMR will truly enter the production phase in the next decade to answer biological questions of high impact, and to become more versatile than ever in complementing X-ray and EM in investigating protein structure and function.

Protein structure prediction using global optimization by basin-hopping with NMR shift restraints

Computational methods that utilize chemical shifts to produce protein structures at atomic resolution have recently been introduced. In the current work, we exploit chemical shifts by combining the basin-hopping approach to global optimization with chemical shift restraints using a penalty function. For three peptides, we demonstrate that this approach allows us to find near-native structures from fully extended structures within 10,000 basin-hopping steps. The effect of adding chemical shift restraints is that the α and β secondary structure elements form within 1000 basin-hopping steps, after which the orientation of the secondary structure elements, which produces the tertiary contacts, is driven by the underlying protein force field. We further show that our chemical shift-restraint BH approach also works for incomplete chemical shift assignments, where the information from only one chemical shift type is considered. For the proper implementation of chemical shift restraints in the basin-hopping approach, we determined the optimal weight of the chemical shift penalty energy with respect to the CHARMM force field in conjunction with the FACTS solvation model employed in this study. In order to speed up the local energy minimization procedure, we developed a function, which continuously decreases the width of the chemical shift penalty function as the minimization progresses. We conclude that the basin-hopping approach with chemical shift restraints is a promising method for protein structure prediction.

Uncovering symmetry-breaking vector and reliability order for assigning secondary structures of proteins from atomic NMR chemical shifts in amino acids

Unravelling the complex correlation between chemical shifts of (13) C (α), (13) C (β), (13) C', (1) H (α), (15) N, (1) H ( N ) atoms in amino acids of proteins from NMR experiment and local structural environments of amino acids facilitates the assignment of secondary structures of proteins. This is an important impetus for both determining the three-dimensional structure and understanding the biological function of proteins. The previous empirical correlation scores which relate chemical shifts of (13) C (α), (13) C (β), (13) C', (1) H (α), (15) N, (1) H ( N ) atoms to secondary structures resulted in progresses toward assigning secondary structures of proteins. However, the physical-mathematical framework for these was elusive partly due to both the limited and orthogonal exploration of higher-dimensional chemical shifts of hetero-nucleus and the lack of physical-mathematical understanding underlying those correlation scores. Here we present a simple multi-dimensional hetero-nuclear chemical shift score function (MDHN-CSSF) which captures systematically the salient feature of such complex correlations without any references to a random coil state of proteins. We uncover the symmetry-breaking vector and its reliability order not only for distinguishing different secondary structures of proteins but also for capturing the delicate sensitivity interplayed among chemical shifts of (13) C (α), (13) C (β), (13) C', (1) H (α), (15) N, (1) H ( N ) atoms simultaneously, which then provides a straightforward framework toward assigning secondary structures of proteins. MDHN-CSSF could correctly assign secondary structures of training (validating) proteins with the favourable (comparable) Q3 scores in comparison with those from the previous correlation scores. MDHN-CSSF provides a simple and robust strategy for the systematic assignment of secondary structures of proteins and would facilitate the de novo determination of three-dimensional structures of proteins.

Partial high-resolution structure of phosphorylated and non-phosphorylated leucine-rich amelogenin protein adsorbed to hydroxyapatite

The formation of biogenic materials requires the interaction of organic molecules with the mineral phase. In forming enamel, the amelogenin proteins contribute to the mineralization of hydroxyapatite (HAp). Leucine-rich amelogenin protein (LRAP) is a naturally occurring splice variant of amelogenin that comprises amelogenin's predicted HAp binding domains. We determined the partial structure of phosphorylated and non-phosphorylated LRAP variants bound to HAp using combined solid-state NMR (ssNMR) and ssNMR-biased computational structure prediction. New ssNMR measurements in the N-terminus indicate a largely extended structure for both variants, though some measurements are consistent with a partially helical N-terminal segment. The N-terminus of the phosphorylated variant is found to be consistently closer to the HAp surface than the non-phosphorylated variant. Structure prediction was biased using 21 ssNMR measurements in the N- and C-terminus at five HAp crystal faces. The predicted fold of LRAP is similar at all HAp faces studied, regardless of phosphorylation. Largely consistent with experimental observations, LRAP's predicted structure is relatively extended with a helix-turn-helix motif in the N-terminal domain and some helix in the C-terminal domain, and the N-terminal domain of the phosphorylated variant binds HAp more closely than the N-terminal domain of the non-phosphorylated variant. Predictions for both variants show some potential binding specificity for the {010} HAp crystal face, providing further support that amelogenins block crystal growth on the a and b faces to allow elongated crystals in the c-axis.

Advances in automated NMR protein structure determination

Around half of all protein structures solved nowadays using solution-state nuclear magnetic resonance (NMR) spectroscopy have been because of automated data analysis. The pervasiveness of computational approaches in general hides, however, a more nuanced view in which the full variety and richness of the field appears. This review is structured around a comparison of methods associated with three NMR observables: classical nuclear Overhauser effect (NOE) constraint gathering in contrast with more recent chemical shift and residual dipole coupling (RDC) based protocols. In each case, the emphasis is placed on the latest research, covering mainly the past 5 years. By describing both general concepts and representative programs, the objective is to map out a field in which--through the very profusion of approaches--it is all too easy to lose one's bearings.

Trends in template/fragment-free protein structure prediction

Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward.

Toward a structure determination method for biomineral-associated protein using combined solid- state NMR and computational structure prediction

Protein-biomineral interactions are paramount to materials production in biology, including the mineral phase of hard tissue. Unfortunately, the structure of biomineral-associated proteins cannot be determined by X-ray crystallography or solution nuclear magnetic resonance (NMR). Here we report a method for determining the structure of biomineral-associated proteins. The method combines solid-state NMR (ssNMR) and ssNMR-biased computational structure prediction. In addition, the algorithm is able to identify lattice geometries most compatible with ssNMR constraints, representing a quantitative, novel method for investigating crystal-face binding specificity. We use this method to determine most of the structure of human salivary statherin interacting with the mineral phase of tooth enamel. Computation and experiment converge on an ensemble of related structures and identify preferential binding at three crystal surfaces. The work represents a significant advance toward determining structure of biomineral-adsorbed protein using experimentally biased structure prediction. This method is generally applicable to proteins that can be chemically synthesized.

De novo structure generation using chemical shifts for proteins with high-sequence identity but different folds

Proteins with high-sequence identity but very different folds present a special challenge to sequence-based protein structure prediction methods. In particular, a 56-residue three-helical bundle protein (GA(95)) and an alpha/beta-fold protein (GB(95)), which share 95% sequence identity, were targets in the CASP-8 structure prediction contest. With only 12 out of 300 submitted server-CASP8 models for GA(95) exhibiting the correct fold, this protein proved particularly challenging despite its small size. Here, we demonstrate that the information contained in NMR chemical shifts can readily be exploited by the CS-Rosetta structure prediction program and yields adequate convergence, even when input chemical shifts are limited to just amide (1)H(N) and (15)N or (1)H(N) and (1)H(alpha) values.

Predicting continuous local structure and the effect of its substitution for secondary structure in fragment-free protein structure prediction

Local structures predicted from protein sequences are used extensively in every aspect of modeling and prediction of protein structure and function. For more than 50 years, they have been predicted at a low-resolution coarse-grained level (e.g., three-state secondary structure). Here, we combine a two-state classifier with real-value predictor to predict local structure in continuous representation by backbone torsion angles. The accuracy of the angles predicted by this approach is close to that derived from NMR chemical shifts. Their substitution for predicted secondary structure as restraints for ab initio structure prediction doubles the success rate. This result demonstrates the potential of predicted local structure for fragment-free tertiary-structure prediction. It further implies potentially significant benefits from using predicted real-valued torsion angles as a replacement for or supplement to the secondary-structure prediction tools used almost exclusively in many computational methods ranging from sequence alignment to function prediction.

DNA structures from phosphate chemical shifts

For B-DNA, the strong linear correlation observed by nuclear magnetic resonance (NMR) between the (31)P chemical shifts (deltaP) and three recurrent internucleotide distances demonstrates the tight coupling between phosphate motions and helicoidal parameters. It allows to translate deltaP into distance restraints directly exploitable in structural refinement. It even provides a new method for refining DNA oligomers with restraints exclusively inferred from deltaP. Combined with molecular dynamics in explicit solvent, these restraints lead to a structural and dynamical view of the DNA as detailed as that obtained with conventional and more extensive restraints. Tests with the Jun-Fos oligomer show that this deltaP-based strategy can provide a simple and straightforward method to capture DNA properties in solution, from routine NMR experiments on unlabeled samples.

Utilizing the charge field effect on amide (15)N chemical shifts for protein structure validation

Of all the nuclei in proteins, the nuclear magnetic resonance (NMR) chemical shifts of nitrogen are the theoretically least well understood. In this study, quantum chemical methods are used in combination with polarizable-continuum models in order to show that consideration of the effective electric field, including charge screening due to solvation, improves considerably the consistencies of statistical relationships between experimental and computed amide (15)N shifts between various sets of charged and uncharged oligopeptides and small organic molecules. A single conversion scheme between shielding parameters from first principles using density functional theory (DFT) and experimental shifts is derived that holds for all classes of compounds examined here. This relationship is then used to test the accuracy of such (15)N chemical shift predictions in the cyclic decapeptide antibiotic gramicidin S (GS). GS has previously been studied in great detail, both by NMR and X-ray crystallography. It adopts a well-defined backbone conformation, and hence, only a few discrete side chain conformational states need to be considered. Moreover, a charge-relay effect of the two cationic ornithine side chains to the protein backbone has been described earlier by NMR spectroscopy. Here, DFT-derived backbone amide nitrogen chemical shifts were calculated for multiple conformations of GS. Overall, the structural dynamics of GS is revisited in view of chemical shift behavior along with energetic considerations. Together, the study demonstrates proof of concept that (15)N chemical shift information is particularly useful in the analysis and validation of protein conformational states in a charged environment.

De novo protein structure generation from incomplete chemical shift assignments

NMR chemical shifts provide important local structural information for proteins. Consistent structure generation from NMR chemical shift data has recently become feasible for proteins with sizes of up to 130 residues, and such structures are of a quality comparable to those obtained with the standard NMR protocol. This study investigates the influence of the completeness of chemical shift assignments on structures generated from chemical shifts. The Chemical-Shift-Rosetta (CS-Rosetta) protocol was used for de novo protein structure generation with various degrees of completeness of the chemical shift assignment, simulated by omission of entries in the experimental chemical shift data previously used for the initial demonstration of the CS-Rosetta approach. In addition, a new CS-Rosetta protocol is described that improves robustness of the method for proteins with missing or erroneous NMR chemical shift input data. This strategy, which uses traditional Rosetta for pre-filtering of the fragment selection process, is demonstrated for two paramagnetic proteins and also for two proteins with solid-state NMR chemical shift assignments.

The effect of a DeltaK280 mutation on the unfolded state of a microtubule-binding repeat in Tau

Tau is a natively unfolded protein that forms intracellular aggregates in the brains of patients with Alzheimer's disease. To decipher the mechanism underlying the formation of tau aggregates, we developed a novel approach for constructing models of natively unfolded proteins. The method, energy-minima mapping and weighting (EMW), samples local energy minima of subsequences within a natively unfolded protein and then constructs ensembles from these energetically favorable conformations that are consistent with a given set of experimental data. A unique feature of the method is that it does not strive to generate a single ensemble that represents the unfolded state. Instead we construct a number of candidate ensembles, each of which agrees with a given set of experimental constraints, and focus our analysis on local structural features that are present in all of the independently generated ensembles. Using EMW we generated ensembles that are consistent with chemical shift measurements obtained on tau constructs. Thirty models were constructed for the second microtubule binding repeat (MTBR2) in wild-type (WT) tau and a DeltaK280 mutant, which is found in some forms of frontotemporal dementia. By focusing on structural features that are preserved across all ensembles, we find that the aggregation-initiating sequence, PHF6*, prefers an extended conformation in both the WT and DeltaK280 sequences. In addition, we find that residue K280 can adopt a loop/turn conformation in WT MTBR2 and that deletion of this residue, which can adopt nonextended states, leads to an increase in locally extended conformations near the C-terminus of PHF6*. As an increased preference for extended states near the C-terminus of PHF6* may facilitate the propagation of beta-structure downstream from PHF6*, these results explain how a deletion at position 280 can promote the formation of tau aggregates.

CS23D: a web server for rapid protein structure generation using NMR chemical shifts and sequence data

CS23D (chemical shift to 3D structure) is a web server for rapidly generating accurate 3D protein structures using only assigned nuclear magnetic resonance (NMR) chemical shifts and sequence data as input. Unlike conventional NMR methods, CS23D requires no NOE and/or J-coupling data to perform its calculations. CS23D accepts chemical shift files in either SHIFTY or BMRB formats, and produces a set of PDB coordinates for the protein in about 10-15 min. CS23D uses a pipeline of several preexisting programs or servers to calculate the actual protein structure. Depending on the sequence similarity (or lack thereof) CS23D uses either (i) maximal subfragment assembly (a form of homology modeling), (ii) chemical shift threading or (iii) shift-aided de novo structure prediction (via Rosetta) followed by chemical shift refinement to generate and/or refine protein coordinates. Tests conducted on more than 100 proteins from the BioMagResBank indicate that CS23D converges (i.e. finds a solution) for >95% of protein queries. These chemical shift generated structures were found to be within 0.2-2.8 A RMSD of the NMR structure generated using conventional NOE-base NMR methods or conventional X-ray methods. The performance of CS23D is dependent on the completeness of the chemical shift assignments and the similarity of the query protein to known 3D folds. CS23D is accessible at http://www.cs23d.ca.

Consistent blind protein structure generation from NMR chemical shift data

Protein NMR chemical shifts are highly sensitive to local structure. A robust protocol is described that exploits this relation for de novo protein structure generation, using as input experimental parameters the (13)C(alpha), (13)C(beta), (13)C', (15)N, (1)H(alpha) and (1)H(N) NMR chemical shifts. These shifts are generally available at the early stage of the traditional NMR structure determination process, before the collection and analysis of structural restraints. The chemical shift based structure determination protocol uses an empirically optimized procedure to select protein fragments from the Protein Data Bank, in conjunction with the standard ROSETTA Monte Carlo assembly and relaxation methods. Evaluation of 16 proteins, varying in size from 56 to 129 residues, yielded full-atom models that have 0.7-1.8 A root mean square deviations for the backbone atoms relative to the experimentally determined x-ray or NMR structures. The strategy also has been successfully applied in a blind manner to nine protein targets with molecular masses up to 15.4 kDa, whose conventional NMR structure determination was conducted in parallel by the Northeast Structural Genomics Consortium. This protocol potentially provides a new direction for high-throughput NMR structure determination.

Assessing the solvent-dependent surface area of unfolded proteins using an ensemble model

We present a physically rigorous method to calculate solvent-dependent accessible surface areas (ASAs) of amino acid residues in unfolded proteins. ASA values will be larger in a good solvent, where solute-solvent interactions dominate and promote chain extension. Conversely, they will be smaller in a poor solvent, where solute-solute interactions dominate and promote chain collapse. In the method described here, these solvent-dependent effects are modeled by Boltzmann-weighting a simulated ensemble for solvent quality-good or poor. Solvent quality is parameterized as intramolecular hydrogen bond strength, using a "hydrogen bond dial" that can be varied from "off" to "high" (i.e., from 0 to -6 kcal/mol per hydrogen bond). When plotted as a function of hydrogen bond strength, the Boltzmann-weighted distribution of conformers describes a sigmoidal curve, with a transition midpoint near 1.5 kcal/mol per hydrogen bond. ASA tables for the 20 residues are provided under good solvent conditions and at this transition midpoint. For the backbone, these midpoint ASA values are found to be in good agreement with the earlier estimate of unfolded state ASA given by the mean of Creamer's upper and lower bounds [Creamer TP, et al. (1997) Biochemistry 36:2832-2835], a gratifying result in that cosolvents of experimental interest, such as urea (good solvent) and trimethylamine N-oxide (poor solvent), are known to affect the backbone predominantly. Unanticipated results from our simulations predict that a significant population of three-residue, hydrogen-bonded turns (inverse gamma-turns) will be detectable in blocked polyalanyl heptamers in poor solvent-an experimentally verifiable conjecture.