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 ␣ , 13 C ␤ , 13 C , 15 N, 1 H ␣ 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 Å 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. molecular fragment replacement ͉ protein structure prediction ͉ ROSETTA ͉ structural genomics

Summary Naturally occurring, pharmacologically active peptides constrained with covalent crosslinks generally have shapes evolved to fit precisely into binding pockets on their targets. Such peptides can have excellent pharmaceutical properties, combining the stability and tissue penetration of small molecule drugs with the specificity of much larger protein therapeutics. The ability to design constrained peptides with precisely specified tertiary structures would enable the design of shape-complementary inhibitors of arbitrary targets. Here we describe the development of computational methods for de novo design of conformationally-restricted peptides, and the use of these methods to design 15–50 residue disulfide-crosslinked and heterochiral N-C backbone-cyclized peptides. These peptides are exceptionally stable to thermal and chemical denaturation, and twelve experimentally-determined X-ray and NMR structures are nearly identical to the computational models. The computational design methods and stable scaffolds presented here provide the basis for development of a new generation of peptide-based drugs.

Conventional protein structure determination from nuclear magnetic resonance data relies heavily on side-chain proton-proton distances. The necessary side-chain resonance assignment, however, is labor intensive and prone to error. Here we show that structures can be accurately determined without NMR information on the sidechains for proteins up to 25 kDa by incorporating backbone chemical shifts, residual dipolar couplings, and amide proton distances into the Rosetta protein structure modelling methodology. These data, which are too sparse for conventional methods, serve only to guide conformational search towards the lowest energy conformations in the folding landscape; the details of the computed models are determined by the physical chemistry implicit in the Rosetta all atom energy function. The new method is not hindered by the deuteration required to suppress nuclear relaxation processes for proteins greater than 15 kDa, and should enable routine NMR structure determination for larger proteins.The first step in protein structure determination by NMR is chemical shift assignment for the backbone atoms. In contrast to the subsequent assignment of the sidechains, this is now rapid, reliable, and largely automated (1-5). Global backbone structural information complementing the local structure information provided by backbone chemical shift assignments (6,7), can be obtained from H N -H N NOESY, residual dipolar coupling (RDC) (8), and other (9,10) experiments. For larger proteins, deuteration becomes necessary to circumvent the efficient spin relaxation properties resulting from their higher rotational correlation times (11,12), but removing protons also eliminates long range NOESY information from sidechains except for selectively protonated sidechain moieties (13). The difficulty in determining accurate structures with no or limited sidechain information is a # To whom correspondence should be addressed. Phone: (206) 543-1295, Fax: (206) Here we show that structures of proteins up to 200 residues (23 kDa) can be determined using information from backbone (H N , N, C α , C β , C') NMR data by taking advantage of the conformational sampling and all atom energy function in the Rosetta structure prediction methodology, which for small proteins in favorable cases can produce atomic accuracy models starting from sequence information alone(15). Structure prediction in Rosetta proceeds in two steps; first a low resolution exploration phase using Monte-Carlo fragment assembly and a coarse-grained energy function, and second a computationally expensive refinement phase which cycles between combinatorial sidechain optimization and gradientbased minimization of all torsional degrees of freedom in a physically-realistic all-atom forcefield(15). The primary obstacle to Rosetta structure prediction from amino acid sequence information alone is conformational sampling; native structures almost always have lower energies than non-native conformations, but are very seldom sampled in unbiased trajectories. Incorpora...

A general method to enhance the sensitivity of the multidimensional NMR experiments performed at high-polarizing magnetic field via the significant reduction of the longitudinal proton relaxation times is described. The method is based on the use of two vast pools of "thermal bath" 1H spins residing on hydrogens covalently attached to carbon and oxygen atoms in 13C,15N labeled and fully protonated or fractionally deuterated proteins to uniformly enhance longitudinal relaxation of the 1HN spins and concomitantly the sensitivity of multipulse NMR experiments. The proposed longitudinal relaxation optimization is implemented in the 2D [15N,1H]-LTROSY, 2D [15N,1H]-LHSQC and 3D LTROSY-HNCA experiments yielding the factor 2-2.5 increase of the maximal signal-to-noise ratio per unit time at 600 MHz. At 900 MHz, the predicted decrease of the 1HN longitudinal relaxation times can be as large as one order of magnitude, making the proposed method an important tool for protein NMR at high magnetic fields.

Natural recombination combines pieces of pre-existing proteins to create new tertiary structures and functions. We describe a computational protocol, called SEWING, which is inspired by this process and builds new proteins from connected or disconnected pieces of existing structures. Helical proteins designed with SEWING contain structural features absent from other de novo designed proteins and in some cases remain folded to over 100 °C. High resolution structures of the designed proteins CA01 and DA05R1 were solved by X-ray crystallography (2.2 Å resolution) and NMR respectively, and there was excellent agreement with the design models. This method provides a new strategy to rapidly create large numbers of diverse and designable protein scaffolds.