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This upload conatins the raw fastq files for the manuscript "Rugged fitness landscapes minimize promiscuity in the evolution of transcriptional repressors". Deep sequencing was used to evaluate presorted and repressed sorted LacI-GalR Family (LGF) and the extant E. coli LacI deep mutational scanning (DMS) populations. The DNA-binding domain (DBD) was amplified in two stages with Kapa HiFi DNA Polymerase (KAPA Biosystems) for tailed amplicon sequencing. The first PCR reaction was performed with overhang primers that anneal to 5’ and 3’ constant regions surrounding the DBD. The overhangs add a variable N region (to enhance nucleotide diversity), and a portion of the universal Illumina adapter. The first reaction was performed using 14 cycles with 1ng of extracted plasmid DNA (DMS or LGF library, presorted or sorted) as template. The product was purified on a spin column (EZNA Cycle Pure kit from Omega BioTek). A second PCR reaction was used to add the index (for pooled sequencing) and the Illumina ‘stem’. This amplification was performed using 10 cycles with 10ng of the purified product from the first reaction as template. The amplicons were purified and used for deep sequencing. Samples were sequenced on an Illumina MiSeq System, using a MiSeq Reagent Kit v2 (2x250 cycles) following the manufacturer's documentation. Paired-end Illumina sequencing reads were merged with PEAR (Paired-end read merger).Phred scores (Q scores) were used for quality filtering. Reads with an expected number of errors exceeding 1 were removed and total read counts for each DBD variant were used for computing enrichment scores.

Bacteriophages can adapt to new hosts by altering sequence motifs through recombination or convergent evolution. Where such motifs exist and what fitness advantage they confer remains largely unknown. We report a new method, Bacteriophage Library Informed Sequence Scoring (BLISS), to discover sequence motifs in metagenomic datasets governing phage activity. BLISS uses experimental deep mutational scanning data to create sequence profiles to enable deep mining of metagenomes for functional motifs which are otherwise invisible to searches. We experimentally tested 10,073 BLISS-derived sequence motifs for the receptor-binding protein of the T7 phage. The screen revealed hundreds of T7 variants with novel host specificity with functional motifs sourced from distant families besides other major phyla. Position, substitution and location preferences on T7 dictated different specificities. To demonstrate therapeutic utility, we engineered highly active T7 variants against urinary tract pathogens. BLISS is a powerful tool to unlock the functional potential encoded in phage metagenomes.

The ability to regulate circuits and pathways is central to cellular control. The existing toolkit is predominantly comprised of local transcription regulators that are unsuitable for exerting control at a global genome-wide scale. Bacterial sigma factors are ideal global regulators as they direct the RNA polymerase to thousands of transcription sites. Here, we redesigned the promoter specificity of theE. colihousekeeping sigma factor, sigma-70, toward five orthogonal promoter targets not recognized by the native sigma-70. These orthogonal sigma-70s were engineered by screening a pooled library of computationally-guided designs customized toward each promoter target. A combination of conserved interactions with the DNA backbone and target-specific interactions facilitate new promoter recognition. Activity of the top performing redesigned sigma-70s varied across the promoter targets and ranged from 17% to 77% of native sigma-70 on its canonical active promoter. These orthogonal sigma factors represent a new suite of regulators for global transcriptional control.

Bacteriophage research has been vital to fundamental aspects of modern biology. Advances in metagenomics have revealed treasure troves of new and uncharacterized bacteriophages ('phages') that are not yet understood. However, our ability to find new phages has outpaced our understanding of how sequence encodes function in phages. Traditional approaches for characterizing phages are limited in scale and face hurdles in determining how changes in sequence drive function. We describe powerful emerging technologies that can be used to clarify sequence-function relationships in phages through high-throughput genome engineering. Using these approaches, up to 10

A fundamental question in protein science is where allosteric hotspots – residues critical for allosteric signaling – are located, and what properties differentiate them. We carried out deep mutational scanning (DMS) of four homologous bacterial allosteric transcription factors (aTF) to identify hotspots and built a machine learning model with this data to glean the structural and molecular properties of allosteric hotspots. We found hotspots to be distributed protein-wide rather than being restricted to “pathways” linking allosteric and active sites as is commonly assumed. Despite structural homology, the location of hotspots was not superimposable across the aTFs. However, common signatures emerged when comparing hotspots coincident with long-range interactions, suggesting that the allosteric mechanism is conserved among the homologs despite differences in molecular details. Machine learning with our large DMS datasets revealed that global structural and dynamic properties to be a strong predictor of whether a residue is a hotspot than local and physicochemical properties. Furthermore, a model trained on one protein can predict hotspots in a homolog. In summary, the overall allosteric mechanism is embedded in the structural fold of the aTF family, but the finer, molecular details are sequence-specific.

Allostery is a fundamental regulatory mechanism of protein function. Despite notable advances, understanding the molecular determinants of allostery remains an elusive goal. Our current knowledge of allostery is principally shaped by a structure-centric view which makes it difficult to understand the decentralized character of allostery. We present a function-centric approach using deep mutational scanning to elucidate the molecular basis and underlying functional landscape of allostery. We show that allosteric signaling exhibits a high-degree of functional plasticity and redundancy through myriad mutational pathways. Residues critical for allosteric signaling are surprisingly poorly conserved while those required for structural integrity are highly conserved, suggesting evolutionary pressure to preserve fold over function. Our results suggest multiple solutions to the thermodynamic conditions of cooperativity, in contrast to the common view of a finely-tuned allosteric residue network maintained under selection.

Transcriptional repressors play an important role in regulating complex phage genomes while infecting a bacterial host. Here, we examine how synthetic repressors can be used in bacteriophage T7 to create a dynamic, controllable infectivity switch. We engineered T7 phage by replacing a large region of the early phage genome with combinations of ligand-responsive promoters and ribosome binding sites (RBS) designed to control the phage RNA polymerase, gp1. Phages with the engineered infectivity switch were fully viable compared to wildtype when not repressed, indicating the phage can be engineered without a loss of fitness. The most effective switch used a TetR-responsive promoter and an attenuated RBS, resulting in a twofold increase in latent period and a tenfold decrease in burst size when repressed, with phage activity capable of being further tuned using different inducer concentrations. Our study provides a proof of concept for how a simple synthetic circuitry can enable user control over phage infectivity.

Rapid molecular biosensing is an emerging application area for synthetic biology. Here, we engineer a portable biosensor for cyanuric acid (CYA), an analyte of interest for human and environmental health, using a LysR-type transcription regulator (LTTR) from Pseudomonas within the context of Escherichia coli gene expression machinery. To overcome cross-host portability challenges of LTTRs, we rationally engineered hybrid Pseudomonas-E. coli promoters by integrating DNA elements required for transcriptional activity and ligand-dependent regulation from both hosts, which enabled E. coli to function as whole-cell biosensor for CYA. To alleviate challenges of whole-cell biosensing, we adapted these promoter designs to function within a freeze-dried E. coli cell-free system to sense CYA. This portable, on-demand system robustly detects CYA within an hour from laboratory and real-world samples and works with both fluorescent and colorimetric reporters. This work elucidates general principles to facilitate the engineering of a wider array of LTTR-based environmental sensors.

Epistasis is a major determinant in the emergence of novel protein function. In allosteric proteins, direct interactions between inducer-binding mutations propagate through the allosteric network, manifesting as epistasis at the level of biological function. Elucidating this relationship between local interactions and their global effects is essential to understanding evolution of allosteric proteins. We integrate computational design, structural and biophysical analysis to characterize the emergence of novel inducer specificity in an allosteric transcription factor. Adaptive landscapes of different inducers of the designed mutant show that a few strong epistatic interactions constrain the number of viable sequence pathways, revealing ridges in the fitness landscape leading to new specificity. The structure of the designed mutant shows that a striking change in inducer orientation still retains allosteric function. Comparing biophysical and functional properties suggests a nonlinear relationship between inducer binding affinity and allostery. Our results highlight the functional and evolutionary complexity of allosteric proteins., Epistasis plays an important role in the evolution of novel protein functions because it determines the mutational path a protein takes. Here, the authors combine functional, structural and biophysical analyses to characterize epistasis in a computationally redesigned ligand-inducible allosteric transcription factor and found that epistasis creates distinct biophysical and biological functional landscapes.

NADH and NAD+ cofactors drive hundreds of biochemical reactions, and their ratio is a key metabolic marker of cellular state. Traditional assays to measure the NADH/NAD+ ratio is laborious, prone to inaccuracies, and not suitable for high-throughput screening. We report a genetically encoded ratiometric biosensor for NADH/NAD+ based on redox-responsive bacterial transcription factor Rex that overcomes these limitations. We engineered a Rex-regulated E. coli promoter with improved biosensor characteristics by tuning the affinity of Rex and the operator site. Since NADH is oxidized during aerobic respiration, we used the biosensor-reporter to investigate the effect of removing respiratory chain enzymes on NADH/NAD+ ratio during aerobiosis. We found that the NADH/NAD+ signal increased in five of the nine mutants by over 3-fold compared to wildtype, including an NADH dehydrogenase double mutant with 6-fold elevation. We also found that among several common carbon sources, E. coli grown on acetate exhibited higher NADH/NAD+ compared to E. coli grown on glucose. As a proof-of-concept for high-throughput redox screening, we were able to enrich high NADH mutants present at 1 in 10 000 among wildtype cells by biosensor-guided pooled screen. Thus, our Rex biosensor-reporter enables facile, noninvasive, high-throughput redox measurement to understand and engineer redox metabolism.

SummaryViruses influence the fate of nutrients and human health by killing microorganisms and altering metabolic processes. Organosulfur metabolism and biologically-derived hydrogen sulfide play dynamic roles in manifestation of diseases, infrastructure degradation, and essential biological processes. While microbial organosulfur metabolism is well-studied, the role of viruses in organosulfur metabolism is unknown. Here we report the discovery of 39 gene families involved in organosulfur metabolism encoded by 3,749 viruses from diverse ecosystems, including human microbiomes. The viruses infect organisms from all three domains of life. Six gene families encode for enzymes that degrade organosulfur compounds into sulfide, while others manipulate organosulfur compounds and may influence sulfide production. We show that viral metabolic genes encode key enzymatic domains, are translated into protein, are maintained after recombination, and that sulfide provides a fitness advantage to viruses. Our results reveal viruses as drivers of organosulfur metabolism with important implications for human and environmental health.

Epistasis is a major determinant in the emergence of novel protein function. In allosteric proteins, direct interactions between inducer-binding mutations propagate through the allosteric network, manifesting as epistasis at the level of biological function. Elucidating this relationship between local interactions and their global effects is essential to understanding evolution of allosteric proteins. We integrate computational design, structural and biophysical analysis to characterize the emergence of novel inducer specificity in an allosteric transcription factor. Adaptive landscapes of different inducers of the engineered mutant show that a few strong epistatic interactions constrain the number of viable sequence pathways, revealing ridges in the fitness landscape leading to new specificity. Crystallographic evidence shows a single mutation drives specificity by reshaping the binding pocket. Comparison of biophysical and functional landscapes emphasizes the nonlinear relationship between local inducer affinity and global function (allostery). Our results highlight the functional and evolutionary complexity of allosteric proteins.

Splitting bioactive proteins into conditionally reconstituting fragments is a powerful strategy for building tools to study and control biological systems. However, split proteins often exhibit a high propensity to reconstitute, even without the conditional trigger, limiting their utility. Current approaches for tuning reconstitution propensity are laborious, context-specific or often ineffective. Here, we report a computational design strategy grounded in fundamental protein biophysics to guide experimental evaluation of a sparse set of mutants to identify an optimal functional window. We hypothesized that testing a limited set of mutants would direct subsequent mutagenesis efforts by predicting desirable mutant combinations from a vast mutational landscape. This strategy varies the degree of interfacial destabilization while preserving stability and catalytic activity. We validate our method by solving two distinct split protein design challenges, generating both design and mechanistic insights. This new technology will streamline the generation and use of split protein systems for diverse applications.

The study of allostery has a central place in biology because of the myriad roles of allosteric proteins in cellular function. As technologies for probing the spatiotemporal resolution of biomolecules have become increasingly sophisticated, so has our understanding of the diverse structural and molecular mechanisms of allosteric proteins. Studies have shown that the allosteric signal is transmitted a through a network of residue-residue interactions connecting distal sites on a protein. Linking structural and dynamical changes to the functional role of individual residues will give a more complete molecular view of allostery. In this work, we highlight new mutational technologies that enable a systems-level, quantitative description of allostery that dissect the role of individual residues through large-scale functional screens. A molecular model for predicting allosteric hot spots can be developed by applying statistical tools on the resulting large sequence-structure-function data sets. Design of allosteric proteins with new function is essential for engineering biological systems. Previous design efforts demonstrate that the allosteric network is a powerful functional constraint in the design of novel or enhanced allosteric proteins. We discuss how a priori knowledge of an allosteric network could improve rational design by facilitating better navigation of the design space. Understanding the molecular "rules" governing allostery would elucidate the molecular basis of dysfunction in disease-associated allosteric proteins, provide a means for designing tailored therapeutics, and enable the design of new sensors and enzymes for synthetic biology.

Graphical abstract, Highlights • Engineered bacteriophages are promising tools for use in food biotechnology. • Diverse natural bacteriophages can be leveraged by engineering for specificity and infectivity. • Engineered bacteriophages are potent tools for pathogen biocontrol. • Engineered bacteriophages can be used for targeted delivery vectors and pathogen detection., Bacteriophages (or ‘phages’) can be potent biocontrol agents but their potential has not been fully realized due to inherent limitations of natural phages. By leveraging new tools in synthetic biology, natural phages can be engineered to overcome these limitations to markedly improve their efficacy and programmability. Engineered phages can be used for targeted detection and removal of pathogens, in situ microbiome editing, gene delivery and programmable control of phage-bacterial interactions. In this mini review we examine different ways natural phages can be engineered as effective biocontrol agents through a design-build-test-learn platform and identify novel applications of engineered phages in food biotechnology.

Ligand-responsive allosteric transcription factors (aTF) play a vital role in genetic circuits and high-throughput screening because they transduce biochemical signals into gene expression changes. Programmable control of gene expression from aTF-regulated promoter is important because different downstream effector genes function optimally at different expression levels. However, tuning gene expression of native promoters is difficult due to complex layers of homeostatic regulation encoded within them. We engineered synthetic promoters de novo by embedding operator sites with varying affinities and radically reshaped binding preferences within a minimal, constitutive Escherichia coli promoter. Multiplexed cell-based screening of promoters for three TetR-like aTFs generated with this approach gave rich diversity of gene expression levels, dynamic ranges and ligand sensitivities and were 50- to 100-fold more active over their respective native promoters. Machine learning on our dataset revealed that relative position of the core motif and bases flanking the core motif play an important role in modulating induction response. Our generalized approach yields customizable and programmable aTF-regulated promoters for engineering cellular pathways and enables the discovery of new small molecule biosensors.

Characterization and standardization of inducible transcriptional regulators has transformed how scientists approach biology by allowing precise and tunable control of gene expression. Despite their utility, only a handful of well-characterized regulators exist, limiting the complexity of engineered biological systems. We apply a characterization pipeline to four genetically encoded sensors that respond to acrylate, glucarate, erythromycin and naringenin. We evaluate how the concentration of the inducing chemical relates to protein expression, how the extent of induction affects protein expression kinetics, and how the activation behavior of single cells relates to ensemble measurements. We show that activation of each sensor is orthogonal to the other sensors, and to other common inducible systems. We demonstrate independent control of three fluorescent proteins in a single cell, chemically defining eight unique transcriptional states. To demonstrate biosensor utility in metabolic engineering, we apply the glucarate biosensor to monitor product formation in a heterologous glucarate biosynthesis pathway and identify superior enzyme variants. Doubling the number of well-characterized inducible systems makes more complex synthetic biological circuits accessible. Characterizing sensors that transduce the intracellular concentration of valuable metabolites into fluorescent readouts enables high-throughput screening of biological catalysts and alleviates the primary bottleneck of the metabolic engineering design-build-test cycle.

Allosteric proteins have great potential in synthetic biology, but our limited understanding of the molecular underpinnings of allostery has hindered the development of designer molecules, including transcription factors with new DNA-binding or ligand-binding specificities that respond appropriately to inducers. Such allosteric proteins could function as novel switches in complex circuits, metabolite sensors, or as orthogonal regulators for independent, inducible control of multiple genes. Advances in DNA synthesis and next-generation sequencing technologies have enabled the assessment of millions of mutants in a single experiment, providing new opportunities to study allostery. Using the classic LacI protein as an example, we describe a genetic selection system using a bidirectional reporter to capture mutants in both allosteric states, allowing the positions most crucial for allostery to be identified. This approach is not limited to bacterial transcription factors, and could reveal new mechanistic insights and facilitate engineering of other major classes of allosteric proteins such as nuclear receptors, two-component systems, G protein-coupled receptors, and protein kinases.

Engineering biosynthetic pathways for chemical production requires extensive optimization of the host cellular metabolic machinery. Because it is challenging to specify a priori an optimal design, metabolic engineers often need to construct and evaluate a large number of variants of the pathway. We report a general strategy that combines targeted genome-wide mutagenesis to generate pathway variants with evolution to enrich for rare high producers. We convert the intracellular presence of the target chemical into a fitness advantage for the cell by using a sensor domain responsive to the chemical to control a reporter gene necessary for survival under selective conditions. Because artificial selection tends to amplify unproductive cheaters, we devised a negative selection scheme to eliminate cheaters while preserving library diversity. This scheme allows us to perform multiple rounds of evolution (addressing ∼10(9) cells per round) with minimal carryover of cheaters after each round. Based on candidate genes identified by flux balance analysis, we used targeted genome-wide mutagenesis to vary the expression of pathway genes involved in the production of naringenin and glucaric acid. Through up to four rounds of evolution, we increased production of naringenin and glucaric acid by 36- and 22-fold, respectively. Naringenin production (61 mg/L) from glucose was more than double the previous highest titer reported. Whole-genome sequencing of evolved strains revealed additional untargeted mutations that likely benefit production, suggesting new routes for optimization.

Determination of high-quality small protein structures by nuclear magnetic resonance (NMR) methods generally requires acquisition and analysis of an extensive set of structural constraints. The process generally demands extensive backbone and sidechain resonance assignments, and weeks or even months of data collection and interpretation. Here we demonstrate rapid and high-quality protein NMR structure generation using CS-Rosetta with a perdeuterated protein sample made at a significantly reduced cost using new bacterial culture condensation methods. Our strategy provides the basis for a high-throughput approach for routine, rapid, high-quality structure determination of small proteins. As an example, we demonstrate the determination of a high-quality 3D structure of a small 8 kDa protein, E. coli cold shock protein A (CspA), using4 days of data collection and fully automated data analysis methods together with CS-Rosetta. The resulting CspA structure is highly converged and in excellent agreement with the published crystal structure, with a backbone RMSD value of 0.5 Å, an all atom RMSD value of 1.2 Å to the crystal structure for well-defined regions, and RMSD value of 1.1 Å to crystal structure for core, non-solvent exposed sidechain atoms. Cross validation of the structure with (15)N- and (13)C-edited NOESY data obtained with a perdeuterated (15)N, (13)C-enriched (13)CH(3) methyl protonated CspA sample confirms that essentially all of these independently-interpreted NOE-based constraints are already satisfied in each of the 10 CS-Rosetta structures. By these criteria, the CS-Rosetta structure generated by fully automated analysis of data for a perdeuterated sample provides an accurate structure of CspA. This represents a general approach for rapid, automated structure determination of small proteins by NMR.

Conventional NMR structure determination requires nearly complete assignment of the cross peaks of a refined NOESY peak list. Depending on the size of the protein and quality of the spectral data, this can be a time-consuming manual process requiring several rounds of peak list refinement and structure determination. Programs such as Aria, CYANA, and AutoStructure can generate models using unassigned NOESY data but are very sensitive to the quality of the input peak lists and can converge to inaccurate structures if the signal-to-noise of the peak lists is low. Here, we show that models with high accuracy and reliability can be produced by combining the strengths of the high-resolution structure prediction program Rosetta with global measures of the agreement between structure models and experimental data. A first round of models generated using CS-Rosetta (Rosetta supplemented with backbone chemical shift information) are filtered on the basis of their goodness-of-fit with unassigned NOESY peak lists using the DP-score, and the best fitting models are subjected to high resolution refinement with the Rosetta rebuild-and-refine protocol. This hybrid approach uses both local backbone chemical shift and the unassigned NOESY data to direct Rosetta trajectories toward the native structure and produces more accurate models than AutoStructure/CYANA or CS-Rosetta alone, particularly when using raw unedited NOESY peak lists. We also show that when accurate manually refined NOESY peak lists are available, Rosetta refinement can consistently increase the accuracy of models generated using CYANA and AutoStructure.

A correct alignment is an essential requirement in homology modeling. Yet in order to bridge the structural gap between template and target, which may not only involve loop rearrangements, but also shifts of secondary structure elements and repacking of core residues, high-resolution refinement methods with full atomic details are needed. Here, we describe four approaches that address this "last mile of the protein folding problem" and have performed well during CASP8, yielding physically realistic models: YASARA, which runs molecular dynamics simulations of models in explicit solvent, using a new partly knowledge-based all atom force field derived from Amber, whose parameters have been optimized to minimize the damage done to protein crystal structures. The LEE-SERVER, which makes extensive use of conformational space annealing to create alignments, to help Modeller build physically realistic models while satisfying input restraints from templates and CHARMM stereochemistry, and to remodel the side-chains. ROSETTA, whose high resolution refinement protocol combines a physically realistic all atom force field with Monte Carlo minimization to allow the large conformational space to be sampled quickly. And finally UNDERTAKER, which creates a pool of candidate models from various templates and then optimizes them with an adaptive genetic algorithm, using a primarily empirical cost function that does not include bond angle, bond length, or other physics-like terms.

Examining the Backbone Determination of tertiary protein structures by nuclear magnetic resonance (NMR) currently relies heavily on side-chain NMR data. The assignment of side-chain atoms is challenging. In addition, proteins larger than 15 kilodaltons (kD) must be deuterated to improve resolution and this eliminates the possibility of measuring long-range interproton distance constraints. Now Raman et al. (p. 1014 , published online 4 February) use backbone-only NMR data—chemical shifts, residual dipolar coupling, and backbone amide proton distances—available from highly deuterated proteins to guide conformational searching in the Rosetta structure prediction protocol. Using this new protocol, they were able to generate accurate structures for proteins of up to 25 kD.

A correct alignment is an essential requirement in homology modeling. Yet in order to bridge the structural gap between template and target, which may not only involve loop rearrangements, but also shifts of secondary structure elements and repacking of core residues, high-resolution refinement methods with full atomic details are needed. Here, we describe four approaches that address this "last mile of the protein folding problem" and have performed well during CASP8, yielding physically realistic models: YASARA, which runs molecular dynamics simulations of models in explicit solvent, using a new partly knowledge-based all atom force field derived from Amber, whose parameters have been optimized to minimize the damage done to protein crystal structures. The LEE-SERVER, which makes extensive use of conformational space annealing to create alignments, to help Modeller build physically realistic models while satisfying input restraints from templates and CHARMM stereochemistry, and to remodel the side-chains. ROSETTA, whose high resolution refinement protocol combines a physically realistic all atom force field with Monte Carlo minimization to allow the large conformational space to be sampled quickly. And finally UNDERTAKER, which creates a pool of candidate models from various templates and then optimizes them with an adaptive genetic algorithm, using a primarily empirical cost function that does not include bond angle, bond length, or other physics-like terms.

We describe predictions made using the Rosetta structure prediction methodology for the Eighth Critical Assessment of Techniques for Protein Structure Prediction. Aggressive sampling and all-atom refinement were carried out for nearly all targets. A combination of alignment methodologies was used to generate starting models from a range of templates, and the models were then subjected to Rosetta all atom refinement. For the 64 domains with readily identified templates, the best submitted model was better than the best alignment to the best template in the Protein Data Bank for 24 cases, and improved over the best starting model for 43 cases. For 13 targets where only very distant sequence relationships to proteins of known structure were detected, models were generated using the Rosetta de novo structure prediction methodology followed by all-atom refinement; in several cases the submitted models were better than those based on the available templates. Of the 12 refinement challenges, the best submitted model improved on the starting model in seven cases. These improvements over the starting template-based models and refinement tests demonstrate the power of Rosetta structure refinement in improving model accuracy.

We report the completion of the first comparison of automated NMR protein structure calculation methods and announce its continuation in the form of an ongoing, community-wide experiment: CASD-NMR (Critical Assessment of Automated Structure Determination of Proteins by NMR). CASD-NMR is open for any laboratory to participate and/or to submit targets. NMR spectroscopy is the only technique for the determination of the solution structure of biological macromolecules. This typically requires both the assignment of resonances and a labor-intensive analysis of multidimensional NOESY spectra, where peaks are matched to assigned resonances. Software tools for the full automation of the NOESY assignment and the structure calculation steps have the potential to boost the efficiency, reproducibility and reliability of NMR structures. Within the e-NMR project (www.e-nmr.eu), which is funded by the European Commission (Project number 213010), we are developing an approach to assess whether such automated methods can indeed produce structures that closely match those manually refined using the same experimental data (the “reference structures”). The concept closely resembles that of other community-wide experiments, such as CASP, the Critical Assessment of Techniques for Protein Structure Prediction1, and CAPRI, the Critical Assessment of Prediction of Interactions2. At variance with both CASP and CAPRI, CASD-NMR is entirely based on experimental data, presenting special issues in assembling, organizing, and distributing these data among participants. We provided seven research teams in the field with ten experimental data sets for various protein systems of known structure and two sets for protein structures not yet publicly available (“blind tests”), courtesy of the NorthEast Structural Genomics consortium (NESG). We then met in Florence, Italy on May 4–6, 2009 to analyze the structures generated (Fig. 1), by comparison to the reference structures and by using software tools for structure validation. This first experiment indicated that while most submissions had correct overall folds, on certain targets some programs failed to calculate accurate packing and length of secondary structure elements. The root mean square deviations (RMSDs) of the backbone coordinates from the manually-solved structures were typically in the 1–2 A range, but reached values as high as 9 A in some cases. Figure 1 Performance of various automated structure calculation methods

The structure of human protein HSPC034 has been determined by both solution nuclear magnetic resonance (NMR) spectroscopy and X-ray crystallography. Refinement of the NMR structure ensemble, using a Rosetta protocol in the absence of NMR restraints, resulted in significant improvements not only in structure quality, but also in molecular replacement (MR) performance with the raw X-ray diffraction data using MOLREP and Phaser. This method has recently been shown to be generally applicable with improved MR performance demonstrated for eight NMR structures refined using Rosetta (Qian et al., Nature 2007;450:259-264). Additionally, NMR structures of HSPC034 calculated by standard methods that include NMR restraints have improvements in the RMSD to the crystal structure and MR performance in the order DYANA, CYANA, XPLOR-NIH, and CNS with explicit water refinement (CNSw). Further Rosetta refinement of the CNSw structures, perhaps due to more thorough conformational sampling and/or a superior force field, was capable of finding alternative low energy protein conformations that were equally consistent with the NMR data according to the Recall, Precision, and F-measure (RPF) scores. On further examination, the additional MR-performance shortfall for NMR refined structures as compared with the X-ray structure were attributed, in part, to crystal-packing effects, real structural differences, and inferior hydrogen bonding in the NMR structures. A good correlation between a decrease in the number of buried unsatisfied hydrogen-bond donors and improved MR performance demonstrates the importance of hydrogen-bond terms in the force field for improving NMR structures. The superior hydrogen-bond network in Rosetta-refined structures demonstrates that correct identification of hydrogen bonds should be a critical goal of NMR structure refinement. Inclusion of nonbivalent hydrogen bonds identified from Rosetta structures as additional restraints in the structure calculation results in NMR structures with improved MR performance.

One of the key challenges in computational biology is prediction of three-dimensional protein structures from amino-acid sequences. For most proteins, the "native state" lies at the bottom of a free-energy landscape. Protein structure prediction involves varying the degrees of freedom of the protein in a constrained manner until it approaches its native state. In the Rosetta protein structure prediction protocols, a large number of independent folding trajectories are simulated, and several lowest-energy results are likely to be close to the native state. The availability of hundred-teraflop, and shortly, petaflop, computing resources is revolutionizing the approaches available for protein structure prediction. Here, we discuss issues involved in utilizing such machines efficiently with the Rosetta code, including an overview of recent results of the Critical Assessment of Techniques for Protein Structure Prediction 7 (CASP7) in which the computationally demanding structure-refinement process was run on 16 racks of the IBM Blue Gene/L (TM) system at the IBM T. J. Watson Research Center. We highlight recent advances in high-performance computing and discuss,future development paths that make use of the next-generation petascale (> 10(12) floating-point operations per second) machines.

We describe predictions made using the Rosetta structure prediction methodology for both template-based modeling and free modeling categories in the Seventh Critical Assessment of Techniques for Protein Structure Prediction. For the first time, aggressive sampling and all-atom refinement could be carried out for the majority of targets, an advance enabled by the Rosetta@home distributed computing network. Template-based modeling predictions using an iterative refinement algorithm improved over the best existing templates for the majority of proteins with less than 200 residues. Free modeling methods gave near-atomic accuracy predictions for several targets under 100 residues from all secondary structure classes. These results indicate that refinement with an all-atom energy function, although computationally expensive, is a powerful method for obtaining accurate structure predictions.