Srivatsan Raman

Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8.

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. Proteins 2009.

High-resolution structure prediction and the crystallographic phase problem

The energy-based refinement of low-resolution protein structure models to atomic-level accuracy is a major challenge for computational structural biology. Here we describe a new approach to refining protein structure models that focuses sampling in regions most likely to contain errors while allowing the whole structure to relax in a physically realistic all-atom force field. In applications to models produced using nuclear magnetic resonance data and to comparative models based on distant structural homologues, the method can significantly improve the accuracy of the structures in terms of both the backbone conformations and the placement of core side chains. Furthermore, the resulting models satisfy a particularly stringent test: they provide significantly better solutions to the X-ray crystallographic phase problem in molecular replacement trials. Finally, we show that all-atom refinement can produce de novo protein structure predictions that reach the high accuracy required for molecular replacement without any experimental phase information and in the absence of templates suitable for molecular replacement from the Protein Data Bank. These results suggest that the combination of high-resolution structure prediction with state-of-the-art phasing tools may be unexpectedly powerful in phasing crystallographic data for which molecular replacement is hindered by the absence of sufficiently accurate previous models.

Structure prediction for CASP8 with all-atom refinement using Rosetta

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. Proteins 2009.

NMR structure determination for larger proteins using backbone-only data.

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. Protein structures can be determined by using the limited nuclear magnetic resonance information obtainable for larger proteins. Conventional protein structure determination from nuclear magnetic resonance data relies heavily on side-chain proton-to-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 nuclear magnetic resonance (NMR) information on the side chains for proteins up to 25 kilodaltons by incorporating backbone chemical shifts, residual dipolar couplings, and amide proton distances into the Rosetta protein structure modeling methodology. These data, which are too sparse for conventional methods, serve only to guide conformational search toward 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 kilodaltons and should enable routine NMR structure determination for larger proteins.

Structure prediction for CASP7 targets using extensive all-atom refinement with Rosetta@home

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. Proteins 2007.

Evolution-guided optimization of biosynthetic pathways

Significance Microbes can be made to produce industrially valuable chemicals in high quantities by engineering their central metabolic pathways. This process may require evaluating billions of cells, each containing a unique pathway design, to identify the rare cells with high production phenotypes. We mutated targeted locations across the genome to modify several genes identified as key players. We used sensory proteins responsive to a number of target chemicals to couple the concentration of the target chemical in each cell to individual cell fitness. This coupling of chemical production to fitness allows us to harness evolution to progressively enrich superior pathway designs. Through iterations of genetic diversification and selection, we increased the production of naringenin and glucaric acid 36- and 22-fold, respectively. 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 ∼109 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.

Engineering an allosteric transcription factor to respond to new ligands.

Genetic regulatory proteins inducible by small molecules are useful synthetic biology tools as sensors and switches. Bacterial allosteric transcription factors (aTFs) are a major class of regulatory proteins, but few aTFs have been redesigned to respond to new effectors beyond natural aTF-inducer pairs. Altering inducer specificity in these proteins is difficult because substitutions that affect inducer binding may also disrupt allostery. We engineered an aTF, the Escherichia coli lac repressor, LacI, to respond to one of four new inducer molecules: fucose, gentiobiose, lactitol and sucralose. Using computational protein design, single-residue saturation mutagenesis or random mutagenesis, along with multiplex assembly, we identified new variants comparable in specificity and induction to wild-type LacI with its inducer, isopropyl β-D-1-thiogalactopyranoside (IPTG). The ability to create designer aTFs will enable applications including dynamic control of cell metabolism, cell biology and synthetic gene circuits.

Synthetic biosensors for precise gene control and real-time monitoring of metabolites

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.

Improving NMR Protein Structure Quality by Rosetta Refinement: A Molecular Replacement Study

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. Proteins 2009.

CASD-NMR: Critical Assessment of Automated Structure Determination by NMR

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