Clinical Prediction Rule for Patient Outcome after In-Hospital CPR: A New Model, Using Characteristics Present at Hospital Admission, to Identify Patients Unlikely to Benefit from CPR after In-Hospital Cardiac Arrest.

BACKGROUND: Physicians and patients frequently overestimate likelihood of survival after in-hospital cardiopulmonary resuscitation. Discussions and decisions around resuscitation after in-hospital cardiopulmonary arrest often take place without adequate or accurate information. METHODS: We conducted a retrospective chart review of 470 instances of resuscitation after in-hospital cardiopulmonary arrest. Individuals were randomly assigned to a derivation cohort and a validation cohort. Logistic Regression and Linear Discriminant Analysis were used to perform multivariate analysis of the data. The resultant best performing rule was converted to a weighted integer tool, and thresholds of survival and nonsurvival were determined with an attempt to optimize sensitivity and specificity for survival. RESULTS: A 10-feature rule, using thresholds for survival and nonsurvival, was created; the sensitivity of the rule on the validation cohort was 42.7% and specificity was 82.4%. In the Dartmouth Score (DS), the features of age (greater than 70 years of age), history of cancer, previous cardiovascular accident, and presence of coma, hypotension, abnormal PaO2, and abnormal bicarbonate were identified as the best predictors of nonsurvival. Angina, dementia, and chronic respiratory insufficiency were selected as protective features. CONCLUSIONS: Utilizing information easily obtainable on admission, our clinical prediction tool, the DS, provides physicians individualized information about their patients' probability of survival after in-hospital cardiopulmonary arrest. The DS may become a useful addition to medical expertise and clinical judgment in evaluating and communicating an individual's probability of survival after in-hospital cardiopulmonary arrest after it is validated by other cohorts.

OSPREY: protein design with ensembles, flexibility, and provable algorithms.

UNLABELLED: We have developed a suite of protein redesign algorithms that improves realistic in silico modeling of proteins. These algorithms are based on three characteristics that make them unique: (1) improved flexibility of the protein backbone, protein side-chains, and ligand to accurately capture the conformational changes that are induced by mutations to the protein sequence; (2) modeling of proteins and ligands as ensembles of low-energy structures to better approximate binding affinity; and (3) a globally optimal protein design search, guaranteeing that the computational predictions are optimal with respect to the input model. Here, we illustrate the importance of these three characteristics. We then describe OSPREY, a protein redesign suite that implements our protein design algorithms. OSPREY has been used prospectively, with experimental validation, in several biomedically relevant settings. We show in detail how OSPREY has been used to predict resistance mutations and explain why improved flexibility, ensembles, and provability are essential for this application. AVAILABILITY: OSPREY is free and open source under a Lesser GPL license. The latest version is OSPREY 2.0. The program, user manual, and source code are available at www.cs.duke.edu/donaldlab/software.php. CONTACT: osprey@cs.duke.edu.

Efficient a priori identification of drug resistant mutations using Dead-End Elimination and MM-PBSA.

Active site mutations that disrupt drug binding are an important mechanism of drug resistance. Computational methods capable of predicting resistance a priori are poised to become extremely useful tools in the fields of drug discovery and treatment design. In this paper, we describe an approach to predicting drug resistance on the basis of Dead-End Elimination and MM-PBSA that requires no prior knowledge of resistance. Our method utilizes a two-pass search to identify mutations that impair drug binding while maintaining affinity for the native substrate. We use our method to probe resistance in four drug-target systems: isoniazid-enoyl-ACP reductase (tuberculosis), ritonavir-HIV protease (HIV), methotrexate-dihydrofolate reductase (breast cancer and leukemia), and gleevec-ABL kinase (leukemia). We validate our model using clinically known resistance mutations for all four test systems. In all cases, the model correctly predicts the majority of known resistance mutations.

A Bayesian method for 3D macromolecular structure inference using class average images from single particle electron microscopy.

MOTIVATION: Electron cryo-microscopy can be used to infer 3D structures of large macromolecules with high resolution, but the large amounts of data captured necessitate the development of appropriate statistical models to describe the data generation process, and to perform structure inference. We present a new method for performing ab initio inference of the 3D structures of macromolecules from single particle electron cryo-microscopy experiments using class average images. RESULTS: We demonstrate this algorithm on one phantom, one synthetic dataset and three real (experimental) datasets (ATP synthase, V-type ATPase and GroEL). Structures consistent with the known structures were inferred for all datasets. AVAILABILITY: The software and source code for this method is available for download from our website: http://compbio.cs.toronto.edu/cryoem/.

LigAlign: flexible ligand-based active site alignment and analysis.

Ligand-based active site alignment is a widely adopted technique for the structural analysis of protein-ligand complexes. However, existing tools for ligand alignment treat the ligands as rigid objects even though most biological ligands are flexible. We present LigAlign, an automated system for flexible ligand alignment and analysis. When performing rigid alignments, LigAlign produces results consistent with manually annotated structural motifs. In performing flexible alignments, LigAlign automatically produces biochemically reasonable ligand fragmentations and subsequently identifies conserved structural motifs that are not detected by rigid alignment.

Restricted dead-end elimination: protein redesign with a bounded number of residue mutations.

Dead-end elimination (DEE) has emerged as a powerful structure-based, conformational search technique enabling computational protein redesign. Given a protein with n mutable residues, the DEE criteria guide the search toward identifying the sequence of amino acids with the global minimum energy conformation (GMEC). This approach does not restrict the number of permitted mutations and allows the identified GMEC to differ from the original sequence in up to n residues. In practice, redesigns containing a large number of mutations are often problematic when taken into the wet-lab for creation via site-directed mutagenesis. The large number of point mutations required for the redesigns makes the process difficult, and increases the risk of major unpredicted and undesirable conformational changes. Preselecting a limited subset of mutable residues is not a satisfactory solution because it is unclear how to select this set before the search has been performed. Therefore, the ideal approach is what we define as the kappa-restricted redesign problem in which any kappa of the n residues are allowed to mutate. We introduce restricted dead-end elimination (rDEE) as a solution of choice to efficiently identify the GMEC of the restricted redesign (the kappaGMEC). Whereas existing approaches require n-choose-kappa individual runs to identify the kappaGMEC, the rDEE criteria can perform the redesign in a single search. We derive a number of extensions to rDEE and present a restricted form of the A* conformation search. We also demonstrate a 10-fold speed-up of rDEE over traditional DEE approaches on three different experimental systems.

Prediction of sub-cavity binding preferences using an adaptive physicochemical structure representation.

MOTIVATION: The ability to predict binding profiles for an arbitrary protein can significantly improve the areas of drug discovery, lead optimization and protein function prediction. At present, there are no successful algorithms capable of predicting binding profiles for novel proteins. Existing methods typically rely on manually curated templates or entire active site comparison. Consequently, they perform best when analyzing proteins sharing significant structural similarity with known proteins (i.e. proteins resulting from divergent evolution). These methods fall short when used to characterize the binding profile of a novel active site or one for which a template is not available. In contrast to previous approaches, our method characterizes the binding preferences of sub-cavities within the active site by exploiting a large set of known protein-ligand complexes. The uniqueness of our approach lies not only in the consideration of sub-cavities, but also in the more complete structural representation of these sub-cavities, their parametrization and the method by which they are compared. By only requiring local structural similarity, we are able to leverage previously unused structural information and perform binding inference for proteins that do not share significant structural similarity with known systems. RESULTS: Our algorithm demonstrates the ability to accurately cluster similar sub-cavities and to predict binding patterns across a diverse set of protein-ligand complexes. When applied to two high-profile drug targets, our algorithm successfully generates a binding profile that is consistent with known inhibitors. The results suggest that our algorithm should be useful in structure-based drug discovery and lead optimization.

The minimized dead-end elimination criterion and its application to protein redesign in a hybrid scoring and search algorithm for computing partition functions over molecular ensembles.

One of the main challenges for protein redesign is the efficient evaluation of a combinatorial number of candidate structures. The modeling of protein flexibility, typically by using a rotamer library of commonly-observed low-energy side-chain conformations, further increases the complexity of the redesign problem. A dominant algorithm for protein redesign is dead-end elimination (DEE), which prunes the majority of candidate conformations by eliminating rigid rotamers that provably are not part of the global minimum energy conformation (GMEC). The identified GMEC consists of rigid rotamers (i.e., rotamers that have not been energy-minimized) and is thus referred to as the rigid-GMEC. As a postprocessing step, the conformations that survive DEE may be energy-minimized. When energy minimization is performed after pruning with DEE, the combined protein design process becomes heuristic, and is no longer provably accurate: a conformation that is pruned using rigid-rotamer energies may subsequently minimize to a lower energy than the rigid-GMEC. That is, the rigid-GMEC and the conformation with the lowest energy among all energy-minimized conformations (the minimized-GMEC) are likely to be different. While the traditional DEE algorithm succeeds in not pruning rotamers that are part of the rigid-GMEC, it makes no guarantees regarding the identification of the minimized-GMEC. In this paper we derive a novel, provable, and efficient DEE-like algorithm, called minimized-DEE (MinDEE), that guarantees that rotamers belonging to the minimized-GMEC will not be pruned, while still pruning a combinatorial number of conformations. We show that MinDEE is useful not only in identifying the minimized-GMEC, but also as a filter in an ensemble-based scoring and search algorithm for protein redesign that exploits energy-minimized conformations. We compare our results both to our previous computational predictions of protein designs and to biological activity assays of predicted protein mutants. Our provable and efficient minimized-DEE algorithm is applicable in protein redesign, protein-ligand binding prediction, and computer-aided drug design.

Redesigning the PheA domain of gramicidin synthetase leads to a new understanding of the enzyme's mechanism and selectivity.

The PheA domain of gramicidin synthetase A, a non-ribosomal peptide synthetase, selectively binds phenylalanine along with ATP and Mg2+ and catalyzes the formation of an aminoacyl adenylate. In this study, we have used a novel protein redesign algorithm, K*, to predict mutations in PheA that should exhibit improved binding for tyrosine. Interestingly, the introduction of two predicted mutations to PheA did not significantly improve KD, as measured by equilibrium fluorescence quenching. However, the mutations improved the specificity of the enzyme for tyrosine (as measured by kcat/KM), primarily driven by a 56-fold improvement in KM, although the improvement did not make tyrosine the preferred substrate over phenylalanine. Using stopped-flow fluorometry, we examined binding of different amino acid substrates to the wild-type and mutant enzymes in the pre-steady state in order to understand the improvement in KM. Through these investigations, it became evident that substrate binding to the wild-type enzyme is more complex than previously described. These experiments show that the wild-type enzyme binds phenylalanine in a kinetically selective manner; no other amino acids tested appeared to bind the enzyme in the early time frame examined (500 ms). Furthermore, experiments with PheA, phenylalanine, and ATP reveal a two-step binding process, suggesting that the PheA-ATP-phenylalanine complex may undergo a conformational change toward a catalytically relevant intermediate on the pathway to adenylation; experiments with PheA, phenylalanine, and other nucleotides exhibit only a one-step binding process. The improvement in KM for the mutant enzyme toward tyrosine, as predicted by K*, may indicate that redesigning the side-chain binding pocket allows the substrate backbone to adopt productive conformations for catalysis but that further improvements may be afforded by modeling an enzyme:ATP:substrate complex, which is capable of undergoing conformational change.

Improved Pruning algorithms and Divide-and-Conquer strategies for Dead-End Elimination, with application to protein design.

MOTIVATION: Structure-based protein redesign can help engineer proteins with desired novel function. Improving computational efficiency while still maintaining the accuracy of the design predictions has been a major goal for protein design algorithms. The combinatorial nature of protein design results both from allowing residue mutations and from the incorporation of protein side-chain flexibility. Under the assumption that a single conformation can model protein folding and binding, the goal of many algorithms is the identification of the Global Minimum Energy Conformation (GMEC). A dominant theorem for the identification of the GMEC is Dead-End Elimination (DEE). DEE-based algorithms have proven capable of eliminating the majority of candidate conformations, while guaranteeing that only rotamers not belonging to the GMEC are pruned. However, when the protein design process incorporates rotameric energy minimization, DEE is no longer provably-accurate. Hence, with energy minimization, the minimized-DEE (MinDEE) criterion must be used instead. RESULTS: In this paper, we present provably-accurate improvements to both the DEE and MinDEE criteria. We show that our novel enhancements result in a speedup of up to a factor of more than 1000 when applied in redesign for three different proteins: Gramicidin Synthetase A, plastocyanin, and protein G. AVAILABILITY: Contact authors for source code.

A novel ensemble-based scoring and search algorithm for protein redesign and its application to modify the substrate specificity of the gramicidin synthetase a phenylalanine adenylation enzyme.

Realization of novel molecular function requires the ability to alter molecular complex formation. Enzymatic function can be altered by changing enzyme-substrate interactions via modification of an enzyme's active site. A redesigned enzyme may either perform a novel reaction on its native substrates or its native reaction on novel substrates. A number of computational approaches have been developed to address the combinatorial nature of the protein redesign problem. These approaches typically search for the global minimum energy conformation among an exponential number of protein conformations. We present a novel algorithm for protein redesign, which combines a statistical mechanics-derived ensemble-based approach to computing the binding constant with the speed and completeness of a branch-and-bound pruning algorithm. In addition, we developed an efficient deterministic approximation algorithm, capable of approximating our scoring function to arbitrary precision. In practice, the approximation algorithm decreases the execution time of the mutation search by a factor of ten. To test our method, we examined the Phe-specific adenylation domain of the nonribosomal peptide synthetase gramicidin synthetase A (GrsA-PheA). Ensemble scoring, using a rotameric approximation to the partition functions of the bound and unbound states for GrsA-PheA, is first used to predict binding of the wildtype protein and a previously described mutant (selective for leucine), and second, to switch the enzyme specificity toward leucine, using two novel active site sequences computationally predicted by searching through the space of possible active site mutations. The top scoring in silico mutants were created in the wetlab and dissociation/binding constants were determined by fluorescence quenching. These tested mutations exhibit the desired change in specificity from Phe to Leu. Our ensemble-based algorithm, which flexibly models both protein and ligand using rotamer-based partition functions, has application in enzyme redesign, the prediction of protein-ligand binding, and computer-aided drug design.

High-throughput inference of protein-protein interfaces from unassigned NMR data.

SUMMARY: We cast the problem of identifying protein-protein interfaces, using only unassigned NMR spectra, into a geometric clustering problem. Identifying protein-protein interfaces is critical to understanding inter- and intra-cellular communication, and NMR allows the study of protein interaction in solution. However it is often the case that NMR studies of a protein complex are very time-consuming, mainly due to the bottleneck in assigning the chemical shifts, even if the apo structures of the constituent proteins are known. We study whether it is possible, in a high-throughput manner, to identify the interface region of a protein complex using only unassigned chemical shifts and residual dipolar coupling (RDC) data. We introduce a geometric optimization problem where we must cluster the cells in an arrangement on the boundary of a 3-manifold, where the arrangement is induced by a spherical quadratic form [corrected] The arrangement is induced by a spherical quadratic form, which in turn is parameterized by a SO(3)xR2. We show that this formalism derives directly from the physics of RDCs. We present an optimal algorithm for this problem that runs in O(n3 log n) time for an n-residue protein. We then use this clustering algorithm as a subroutine in a practical algorithm for identifying the interface region of a protein complex from unassigned NMR data. We present the results of our algorithm on NMR data for seven proteins from five protein complexes, and show that our approach is useful for high-throughput applications in which we seek to rapidly identify the interface region of a protein complex. AVAILABILITY: Contact authors for source code.

Optimization and dynamics of protein-protein complexes using B-splines.

A moving-grid approach for optimization and dynamics of protein-protein complexes is introduced, which utilizes cubic B-spline interpolation for rapid energy and force evaluation. The method allows for the efficient use of full electrostatic potentials joined smoothly to multipoles at long distance so that multiprotein simulation is possible. Using a recently published benchmark of 58 protein complexes, we examine the performance and quality of the grid approximation, refining cocrystallized complexes to within 0.68 A RMSD of interface atoms, close to the optimum 0.63 A produced by the underlying MMFF94 force field. We quantify the theoretical statistical advantage of using minimization in a stochastic search in the case of two rigid bodies, and contrast it with the underlying cost of conjugate gradient minimization using B-splines. The volumes of conjugate gradient minimization basins of attraction in cocrystallized systems are generally orders of magnitude larger than well volumes based on energy thresholds needed to discriminate native from nonnative states; nonetheless, computational cost is significant. Molecular dynamics using B-splines is doubly efficient due to the combined advantages of rapid force evaluation and large simulation step sizes. Large basins localized around the native state and other possible binding sites are identifiable during simulations of protein-protein motion. In addition to providing increased modeling detail, B-splines offer new algorithmic possibilities that should be valuable in refining docking candidates and studying global complex behavior.

A subgroup algorithm to identify cross-rotation peaks consistent with non-crystallographic symmetry.

Molecular replacement (MR) often plays a prominent role in determining initial phase angles for structure determination by X-ray crystallography. In this paper, an efficient quaternion-based algorithm is presented for analyzing peaks from a cross-rotation function in order to identify model orientations consistent with proper non-crystallographic symmetry (NCS) and to generate proper NCS-consistent orientations missing from the list of cross-rotation peaks. The algorithm, CRANS, analyzes the rotation differences between each pair of cross-rotation peaks to identify finite subgroups. Sets of rotation differences satisfying the subgroup axioms correspond to orientations compatible with the correct proper NCS. The CRANS algorithm was first tested using cross-rotation peaks computed from structure-factor data for three test systems and was then used to assist in the de novo structure determination of dihydrofolate reductase-thymidylate synthase (DHFR-TS) from Cryptosporidium hominis. In every case, the CRANS algorithm runs in seconds to identify orientations consistent with the observed proper NCS and to generate missing orientations not present in the cross-rotation peak list. The CRANS algorithm has application in every molecular-replacement phasing effort with proper NCS.

Phylogenetic classification of protozoa based on the structure of the linker domain in the bifunctional enzyme, dihydrofolate reductase-thymidylate synthase.

We have determined the crystal structure of dihydrofolate reductase-thymidylate synthase (DHFR-TS) from Cryptosporidium hominis, revealing a unique linker domain containing an 11-residue alpha-helix that has extensive interactions with the opposite DHFR-TS monomer of the homodimeric enzyme. Analysis of the structure of DHFR-TS from C. hominis and of previously solved structures of DHFR-TS from Plasmodium falciparum and Leishmania major reveals that the linker domain primarily controls the relative orientation of the DHFR and TS domains. Using the tertiary structure of the linker domains, we have been able to place a number of protozoa in two distinct and dissimilar structural families corresponding to two evolutionary families and provide the first structural evidence validating the use of DHFR-TS as a tool of phylogenetic classification. Furthermore, the structure of C. hominis DHFR-TS calls into question surface electrostatic channeling as the universal means of dihydrofolate transport between TS and DHFR in the bifunctional enzyme.

Probabilistic disease classification of expression-dependent proteomic data from mass spectrometry of human serum.

We have developed an algorithm called Q5 for probabilistic classification of healthy versus disease whole serum samples using mass spectrometry. The algorithm employs principal components analysis (PCA) followed by linear discriminant analysis (LDA) on whole spectrum surface-enhanced laser desorption/ionization time of flight (SELDI-TOF) mass spectrometry (MS) data and is demonstrated on four real datasets from complete, complex SELDI spectra of human blood serum. Q5 is a closed-form, exact solution to the problem of classification of complete mass spectra of a complex protein mixture. Q5 employs a probabilistic classification algorithm built upon a dimension-reduced linear discriminant analysis. Our solution is computationally efficient; it is noniterative and computes the optimal linear discriminant using closed-form equations. The optimal discriminant is computed and verified for datasets of complete, complex SELDI spectra of human blood serum. Replicate experiments of different training/testing splits of each dataset are employed to verify robustness of the algorithm. The probabilistic classification method achieves excellent performance. We achieve sensitivity, specificity, and positive predictive values above 97% on three ovarian cancer datasets and one prostate cancer dataset. The Q5 method outperforms previous full-spectrum complex sample spectral classification techniques and can provide clues as to the molecular identities of differentially expressed proteins and peptides.