Sagar D. Khare

Quantitative reactivity profiling predicts functional cysteines in proteomes

Cysteine is the most intrinsically nucleophilic amino acid in proteins, where its reactivity is tuned to perform diverse biochemical functions. The absence of a consensus sequence that defines functional cysteines in proteins has hindered their discovery and characterization. Here we describe a proteomics method to profile quantitatively the intrinsic reactivity of cysteine residues en masse directly in native biological systems. Hyper-reactivity was a rare feature among cysteines and it was found to specify a wide range of activities, including nucleophilic and reductive catalysis and sites of oxidative modification. Hyper-reactive cysteines were identified in several proteins of uncharacterized function, including a residue conserved across eukaryotic phylogeny that we show is required for yeast viability and is involved in iron-sulphur protein biogenesis. We also demonstrate that quantitative reactivity profiling can form the basis for screening and functional assignment of cysteines in computationally designed proteins, where it discriminated catalytically active from inactive cysteine hydrolase designs.

Computational design of ligand-binding proteins with high affinity and selectivity.

The ability to design proteins with high affinity and selectivity for any given small molecule is a rigorous test of our understanding of the physiochemical principles that govern molecular recognition. Attempts to rationally design ligand-binding proteins have met with little success, however, and the computational design of protein–small-molecule interfaces remains an unsolved problem. Current approaches for designing ligand-binding proteins for medical and biotechnological uses rely on raising antibodies against a target antigen in immunized animals and/or performing laboratory-directed evolution of proteins with an existing low affinity for the desired ligand, neither of which allows complete control over the interactions involved in binding. Here we describe a general computational method for designing pre-organized and shape complementary small-molecule-binding sites, and use it to generate protein binders to the steroid digoxigenin (DIG). Of seventeen experimentally characterized designs, two bind DIG; the model of the higher affinity binder has the most energetically favourable and pre-organized interface in the design set. A comprehensive binding-fitness landscape of this design, generated by library selections and deep sequencing, was used to optimize its binding affinity to a picomolar level, and X-ray co-crystal structures of two variants show atomic-level agreement with the corresponding computational models. The optimized binder is selective for DIG over the related steroids digitoxigenin, progesterone and β-oestradiol, and this steroid binding preference can be reprogrammed by manipulation of explicitly designed hydrogen-bonding interactions. The computational design method presented here should enable the development of a new generation of biosensors, therapeutics and diagnostics.

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.

De Novo Enzyme Design Using Rosetta3

The Rosetta de novo enzyme design protocol has been used to design enzyme catalysts for a variety of chemical reactions, and in principle can be applied to any arbitrary chemical reaction of interest, The process has four stages: 1) choice of a catalytic mechanism and corresponding minimal model active site, 2) identification of sites in a set of scaffold proteins where this minimal active site can be realized, 3) optimization of the identities of the surrounding residues for stabilizing interactions with the transition state and primary catalytic residues, and 4) evaluation and ranking the resulting designed sequences. Stages two through four of this process can be carried out with the Rosetta package, while stage one needs to be done externally. Here, we demonstrate how to carry out the Rosetta enzyme design protocol from start to end in detail using for illustration the triosephosphate isomerase reaction.

Rosettascripts: A scripting language interface to the Rosetta Macromolecular modeling suite

Macromolecular modeling and design are increasingly useful in basic research, biotechnology, and teaching. However, the absence of a user-friendly modeling framework that provides access to a wide range of modeling capabilities is hampering the wider adoption of computational methods by non-experts. RosettaScripts is an XML-like language for specifying modeling tasks in the Rosetta framework. RosettaScripts provides access to protocol-level functionalities, such as rigid-body docking and sequence redesign, and allows fast testing and deployment of complex protocols without need for modifying or recompiling the underlying C++ code. We illustrate these capabilities with RosettaScripts protocols for the stabilization of proteins, the generation of computationally constrained libraries for experimental selection of higher-affinity binding proteins, loop remodeling, small-molecule ligand docking, design of ligand-binding proteins, and specificity redesign in DNA-binding proteins.

Computational redesign of a mononuclear zinc metalloenzyme for organophosphate hydrolysis

The ability to redesign enzymes to catalyze noncognate chemical transformations would have wide-ranging applications. We developed a computational method for repurposing the reactivity of metalloenzyme active site functional groups to catalyze new reactions. Using this method, we engineered a zinc-containing mouse adenosine deaminase to catalyze the hydrolysis of a model organophosphate with a catalytic efficiency (k(cat)/K(m)) of ~10(4) M(-1) s(-1) after directed evolution. In the high-resolution crystal structure of the enzyme, all but one of the designed residues adopt the designed conformation. The designed enzyme efficiently catalyzes the hydrolysis of the R(P) isomer of a coumarinyl analog of the nerve agent cyclosarin, and it shows marked substrate selectivity for coumarinyl leaving groups. Computational redesign of native enzyme active sites complements directed evolution methods and offers a general approach for exploring their untapped catalytic potential for new reactivities.

Molecular Origin of Polyglutamine Aggregation in Neurodegenerative Diseases

Expansion of polyglutamine (polyQ) tracts in proteins results in protein aggregation and is associated with cell death in at least nine neurodegenerative diseases. Disease age of onset is correlated with the polyQ insert length above a critical value of 35–40 glutamines. The aggregation kinetics of isolated polyQ peptides in vitro also shows a similar critical-length dependence. While recent experimental work has provided considerable insights into polyQ aggregation, the molecular mechanism of aggregation is not well understood. Here, using computer simulations of isolated polyQ peptides, we show that a mechanism of aggregation is the conformational transition in a single polyQ peptide chain from random coil to a parallel β-helix. This transition occurs selectively in peptides longer than 37 glutamines. In the β-helices observed in simulations, all residues adopt β-strand backbone dihedral angles, and the polypeptide chain coils around a central helical axis with 18.5 ± 2 residues per turn. We also find that mutant polyQ peptides with proline-glycine inserts show formation of antiparallel β-hairpins in their ground state, in agreement with experiments. The lower stability of mutant β-helices explains their lower aggregation rates compared to wild type. Our results provide a molecular mechanism for polyQ-mediated aggregation.

Computational Design of Catalytic Dyads and Oxyanion Holes for Ester Hydrolysis

Nucleophilic catalysis is a general strategy for accelerating ester and amide hydrolysis. In natural active sites, nucleophilic elements such as catalytic dyads and triads are usually paired with oxyanion holes for substrate activation, but it is difficult to parse out the independent contributions of these elements or to understand how they emerged in the course of evolution. Here we explore the minimal requirements for esterase activity by computationally designing artificial catalysts using catalytic dyads and oxyanion holes. We found much higher success rates using designed oxyanion holes formed by backbone NH groups rather than by side chains or bridging water molecules and obtained four active designs in different scaffolds by combining this motif with a Cys-His dyad. Following active site optimization, the most active of the variants exhibited a catalytic efficiency (k(cat)/K(M)) of 400 M(-1) s(-1) for the cleavage of a p-nitrophenyl ester. Kinetic experiments indicate that the active site cysteines are rapidly acylated as programmed by design, but the subsequent slow hydrolysis of the acyl-enzyme intermediate limits overall catalytic efficiency. Moreover, the Cys-His dyads are not properly formed in crystal structures of the designed enzymes. These results highlight the challenges that computational design must overcome to achieve high levels of activity.

Engineering V-type nerve agents detoxifying enzymes using computationally focused libraries.

VX and its Russian (RVX) and Chinese (CVX) analogues rapidly inactivate acetylcholinesterase and are the most toxic stockpile nerve agents. These organophosphates have a thiol leaving group with a choline-like moiety and are hydrolyzed very slowly by natural enzymes. We used an integrated computational and experimental approach to increase Brevundimonas diminuta phosphotriesterases (PTE) detoxification rate of V-agents by 5000-fold. Computational models were built of the complex between PTE and V-agents. On the basis of these models, the active site was redesigned to be complementary in shape to VX and RVX and to include favorable electrostatic interactions with their choline-like leaving group. Small libraries based on designed sequences were constructed. The libraries were screened by a direct assay for V-agent detoxification, as our initial studies showed that colorimetric surrogates fail to report the detoxification rates of the actual agents. The experimental results were fed back to improve the computational models. Overall, five rounds of iterating between experiment and model refinement led to variants that hydrolyze the toxic SP isomers of all three V-agents with kcat/KM values of up to 5 × 10(6) M(-1) min(-1) and also efficiently detoxify G-agents. These new catalysts provide the basis for broad spectrum nerve agent detoxification.

FALS mutations in Cu, Zn superoxide dismutase destabilize the dimer and increase dimer dissociation propensity: A large-scale thermodynamic analysis

Mutations in the dimeric enzyme Cu, Zn superoxide dismutase (SOD1) leading to its aggregation are implicated in the toxicity in familial amyotrophic lateral sclerosis (FALS). We and others have previously shown that aggregation occurs by a pathway involving dimer dissociation, metal-loss from monomers and multimeric assembly of apo-SOD1 monomers. We postulate that FALS mutations cause enhanced aggregation by affecting one or more steps in the pathway, and computationally test this postulate for 75 known mis-sense FALS mutants of SOD1. Based on an extensive thermodynamic analysis of the stability of apo-dimer and apo-monomer forms of these mutants, we classify the mutations into the following groups: 70 out of 75 mutations in SOD1 lead to (i) decreased dimer stability, and/or (ii) increased dimer dissociation, compared to wild type, and four mutations lead to (iii) decreased monomer stability compared to wild type. Our results suggest that enhanced aggregation of SOD1 in FALS occurs due to an increased population of mutant SOD1 apo-monomers compared to wild type. The dissociation of multimeric proteins induced by diverse mutations may be a common theme in several human diseases.