Results of the 3D Virtual Comparison Microscopy Error Rate (VCMER) Study for firearm forensics.

The digital examination of scanned or measured 3D surface topography is referred to as Virtual Comparison Microscopy (VCM). Within the discipline of firearm and toolmark examination, VCM enables review and comparison of microscopic toolmarks on fired ammunition components. In the coming years, this technique may supplement and potentially replace the light comparison microscope as the primary instrument used for firearm and toolmark examination.

Development and Validation of a Virtual Examination Tool for Firearm Forensics,

The transition from 2D imaging to 3D scanning in the discipline of firearms and toolmark analysis is likely to provide examiners an unprecedented view of microscopic surface topography. The digital examination of measured 3D surface topographies has been referred to as virtual microscopy (VM). The approach offers several potential advantages over traditional comparison microscopy.

Network-analysis-guided synthesis of weisaconitine D and liljestrandinine.

General strategies for the chemical synthesis of organic compounds, especially of architecturally complex natural products, are not easily identified. Here we present a method to establish a strategy for such syntheses, which uses network analysis. This approach has led to the identification of a versatile synthetic intermediate that facilitated syntheses of the diterpenoid alkaloids weisaconitine D and liljestrandinine, and the core of gomandonine.

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.

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.

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.

Normalizing molecular docking rankings using virtually generated decoys.

Drug discovery research often relies on the use of virtual screening via molecular docking to identify active hits in compound libraries. An area for improvement among many state-of-the-art docking methods is the accuracy of the scoring functions used to differentiate active from nonactive ligands. Many contemporary scoring functions are influenced by the physical properties of the docked molecule.

Virtual decoy sets for molecular docking benchmarks.

Virtual docking algorithms are often evaluated on their ability to separate active ligands from decoy molecules. The current state-of-the-art benchmark, the Directory of Useful Decoys (DUD), minimizes bias by including decoys from a library of synthetically feasible molecules that are physically similar yet chemically dissimilar to the active ligands. We show that by ignoring synthetic feasibility, we can compile a benchmark that is comparable to the DUD and less biased with respect to physical similarity.

A structure-based approach for mapping adverse drug reactions to the perturbation of underlying biological pathways.

Adverse drug reactions (ADR), also known as side-effects, are complex undesired physiologic phenomena observed secondary to the administration of pharmaceuticals. Several phenomena underlie the emergence of each ADR; however, a dominant factor is the drug's ability to modulate one or more biological pathways. Understanding the biological processes behind the occurrence of ADRs would lead to the development of safer and more effective drugs.

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.

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).

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.

Predicting multiple ligand binding modes using self-consistent pharmacophore hypotheses.

The ability to predict ligand binding modes without the aid of wet-lab experiments may accelerate and reduce the cost of drug discovery research. Despite significant recent progress, virtual screening has not yet eliminated the need for wet-lab experiments. For example, after a lead compound has been identified, the precise binding mode is still typically determined by experimental structural biology.

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.

The protein-small-molecule database, a non-redundant structural resource for the analysis of protein-ligand binding.

Motivation: An enabling resource for drug discovery and protein function prediction is a large, accurate and actively maintained collection of protein/small-molecule complex structures. Models of binding are typically constructed from these structural libraries by generalizing the observed interaction patterns. Consequently, the quality of the model is dependent on the quality of the structural library.

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).

Allosteric inhibition of the protein-protein interaction between the leukemia-associated proteins Runx1 and CBFbeta.

The two subunits of core binding factor (Runx1 and CBFbeta) play critical roles in hematopoiesis and are frequent targets of chromosomal translocations found in leukemia. The binding of the CBFbeta-smooth muscle myosin heavy chain (SMMHC) fusion protein to Runx1 is essential for leukemogenesis, making this a viable target for treatment. We have developed inhibitors with low micromolar affinity which effectively block binding of Runx1 to CBFbeta.

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.

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.

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.

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.

A polynomial-time nuclear vector replacement algorithm for automated NMR resonance assignments.

High-throughput NMR structural biology can play an important role in structural genomics. We report an automated procedure for high-throughput NMR resonance assignment for a protein of known structure, or of a homologous structure. These assignments are a prerequisite for probing protein-protein interactions, protein-ligand binding, and dynamics by NMR.

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.