Robert E. Bruccoleri

antiSMASH 3.0—a comprehensive resource for the genome mining of biosynthetic gene clusters

Abstract Microbial secondary metabolism constitutes a rich source of antibiotics, chemotherapeutics, insecticides and other high-value chemicals. Genome mining of gene clusters that encode the biosynthetic pathways for these metabolites has become a key methodology for novel compound discovery. In 2011, we introduced antiSMASH, a web server and stand-alone tool for the automatic genomic identification and analysis of biosynthetic gene clusters, available at http://antismash.secondarymetabolites.org. Here, we present version 3.0 of antiSMASH, which has undergone major improvements. A full integration of the recently published ClusterFinder algorithm now allows using this probabilistic algorithm to detect putative gene clusters of unknown types. Also, a new dereplication variant of the ClusterBlast module now identifies similarities of identified clusters to any of 1172 clusters with known end products. At the enzyme level, active sites of key biosynthetic enzymes are now pinpointed through a curated pattern-matching procedure and Enzyme Commission numbers are assigned to functionally classify all enzyme-coding genes. Additionally, chemical structure prediction has been improved by incorporating polyketide reduction states. Finally, in order for users to be able to organize and analyze multiple antiSMASH outputs in a private setting, a new XML output module allows offline editing of antiSMASH annotations within the Geneious software.

Finite difference Poisson-Boltzmann electrostatic calculations: Increased accuracy achieved by harmonic dielectric smoothing and charge antialiasing

A common problem in the calculation of electrostatic potentials with the Poisson‐Boltzmann equation using finite difference methods is the effect of molecular position relative to the grid. Previously a uniform charging method was shown to reduce the grid dependence substantially over the point charge model used in commercially available codes. In this article we demonstrate that smoothing the charge and dielectric values on the grid can improve the grid independence, as measured by the spread of calculated values, by another order of magnitude. Calculations of Born ion solvation energies, small molecule solvation energies, the electrostatic field of superoxide dismutase, and protein‐protein binding energies are used to demonstrate that this method yields the same results as the point charge model while reducing the positional errors by several orders of magnitude.

An improved algorithm for nucleic acid secondary structure display

An improved algorithm for the display of nucleic acid secondary structures is presented. It is particularly suitable for large sequence segments and it automatically generates an aesthetically pleasing display of the structure with very limited overlap of strands. Structural similarities in different structures are conserved in the display thus greatly aiding structural homology comparisons. Using the algorithm, we illustrate the effect of ribosome translocation on the secondary structure of a rat neuropeptide messenger RNA.

Application of Systematic Conformational Search to Protein Modeling

Abstract Systematic conformational search is a powerful tool in the modeling of proteins and peptides. As a deterministic method for sampling comformational space, it provides an efficient mechanism for finding global energy minima. The program CONGEN has been developed to use conformational search in conjunction with other modeling methods. The search operators in CONGEN can be combined in arbitrary ways, and therefore, they can applied to a wide variety of problems. Typical applications include homology modeling, construction of protein coordinates from Cα positions, sidechain placement, peptide structure modeling, derivation of three-dimensional structure from NMR constraints, etc. In this paper, a detailed description will be provided of its conformational search capabilities and its previous applications to protein modeling.

Grid positioning independence and the reduction of self-energy in the solution of the Poisson-Boltzmann equation

A common problem in the solution of the Poisson–Boltzmann equation using finite difference methods is the self‐energy of the system, also known as the grid energy. Because atoms are typically modeled as a point charge, the infinite self‐energy of a point charge is likewise modeled. In this article, a simple, alternate treatment of atomic charge is described where each atom is represented as a sphere of uniform charge. Unlike the point charge model, this method converges as the grid spacing is reduced. The uniform charge model generates the same electrostatic field outside the atoms. In addition, the use of fine grids reduces the variations in the potential due to variations in the position of atoms relative to the grid. Calculations of Born ion solvation energies, small‐molecule solvation energies, and the electrostatic field of superoxide dismutase are used to demonstrate that this method yields the same results as the point charge model.

High-resolution solution structure of siamycin II: Novel amphipathic character of a 21-residue peptide that inhibits HIV fusion

SummaryThe 21-amino acid peptides siamycin II (BMY-29303) and siamycin I (BMY-29304), derived from Streptomyces strains AA3891 and AA6532, respectively, have been found to inhibit HIV-1 fusion and viral replication in cell culture. The primary sequence of siamycin II is CLGIGSCNDFAGCGYAIVCFW. Siamycin I differs by only one amino acid; it has a valine residue at position 4. In both peptides, disulfide bonds link Cys1 with Cys13 and Cys7 with Cys19, and the side chain of Asp9 forms an amide bond with the N-terminus. Siamycin II, when dissolved in a 50:50 mixture of DMSO and H2O, yields NOESY spectra with exceptional numbers of cross peaks for a peptide of this size. We have used 335 NOE distance constraints and 13 dihedral angle constraints to generate an ensemble of 30 siamycin II structures; these have average backbone atom and all heavy atom rmsd values to the mean coordinates of 0.24 and 0.52 Å, respectively. The peptide displays an unusual wedge-shaped structure, with one face being predominantly hydrophobic and the other being predominantly hydrophilic. Chemical shift and NOE data show that the siamycin I structure is essentially identical to siamycin II. These peptides may act by preventing oligomerization of the HIV transmembrane glycoprotein gp41, or by interfering with interactions between gp41 and the envelope glycoprotein gp120, the cell membrane or membrane-bound proteins [Frèchet, D. et al. (1994) Biochemistry, 33, 42–50]. The amphipathic nature of siamycin II and siamycin I suggests that a polar (or apolar) site on the target protein may be masked by the apolar (or polar) face of the peptide upon peptide/protein complexation.

Solution conformation of a cyclic pentapeptide endothelin antagonist : comparison of structures obtained from constrained dynamics and conformational search

The structure of a cyclic pentapeptide, cyclo‐(d‐Trp‐d‐Asp‐l‐Pro‐d‐Val‐l‐Leu), that has high selectivity for the endothelin ETAA receptor has been determined by NMR spectroscopy using constrained molecular dynamics and conformational search procedures. Structures obtained using two methods of refinement, namely (i) constrained molecular dynamics; and (ii) systematic searches of conformational space for optimal satisfaction of distance constraints, were compared to those obtained from systematic searches of conformational space without NMR data. The two different procedures of refinement produce similar conformations that are consistent with the NMR distance constraints. Conformational searches for optimal energy without any NMR distance constraints produced several low‐energy structures, two of which have essentially the same backbone as those structures derived from distance‐constrained procedures and one of these even reproduces several side‐chain positions well. The pentapeptide backbone consists of a linked γ and β‐turn conformation, with the leucine and tryptophan as corner residues of the type II β‐turn. The side chains are highly ordered both in aqueous solvent and in dimethyl sulfoxide. In aqueous media the leucine side chain is directed towards the indole ring, presumably to reduce the non‐polar surface exposure, producing unusual upfield shifts for the methyls (and particularly Hγ). This structural feature was reproduced in one of the structures obtained from conformational searches performed without NMR data. Exhaustive conformational searches appear to provide an alternative method for structure generation for cyclic peptides.

Characterization of an anti-digoxin antibody binding site by site-directed in vitro mutagenesis☆

In vitro mutagenesis and immunoglobulin gene transfection were used to investigate the binding site of a monoclonal antibody, 2610, that binds to digoxin, a cardiac glycoside. A computer model was generated in order to select sites in the complementarity determining regions (CDR) that would participate in binding. Residues in the CDR segments were chosen that possess high solvent exposure and were located in a putative cleft. The cloned heavy and light chain variable regions were subjected to in vitro mutagenesis at these sites. The mutated variable regions in M13 were then subcloned into expression vectors and transfected. The affinities and specificity binding properties of the resultant expressed antibodies were measured. Many of the mutants of the putative contact residues showed significant but not major alterations of binding properties. Since most of the residues in the binding site are non-polar and aromatic and since many of the mutations resulted in only modest binding changes, we theorize that much of the high affinity binding (> 10(9)/M) is the cumulation of many weak interactions, arising from dispersion forces and hydrophobic effects in the pocket. Preliminary mutagenesis of two L chain positions proposed to bind to the lactone end of digoxin have larger binding effects. Specificity studies show that the mutants more frequently possess altered binding to the lactone ring of digoxin that altered binding to other digoxin moieties. The data are most suggestive of a model in which lactone is at the bottom of a binding pocket, followed by the steroid nucleus and then by the sugar moiety extruding out of the pocket. The binding information may be useful in understanding the immune response to large, hydrophobic haptens.

Transcriptional Profiling of the Dose Response: A More Powerful Approach for Characterizing Drug Activities

The dose response curve is the gold standard for measuring the effect of a drug treatment, but is rarely used in genomic scale transcriptional profiling due to perceived obstacles of cost and analysis. One barrier to examining transcriptional dose responses is that existing methods for microarray data analysis can identify patterns, but provide no quantitative pharmacological information. We developed analytical methods that identify transcripts responsive to dose, calculate classical pharmacological parameters such as the EC50, and enable an in-depth analysis of coordinated dose-dependent treatment effects. The approach was applied to a transcriptional profiling study that evaluated four kinase inhibitors (imatinib, nilotinib, dasatinib and PD0325901) across a six-logarithm dose range, using 12 arrays per compound. The transcript responses proved a powerful means to characterize and compare the compounds: the distribution of EC50 values for the transcriptome was linked to specific targets, dose-dependent effects on cellular processes were identified using automated pathway analysis, and a connection was seen between EC50s in standard cellular assays and transcriptional EC50s. Our approach greatly enriches the information that can be obtained from standard transcriptional profiling technology. Moreover, these methods are automated, robust to non-optimized assays, and could be applied to other sources of quantitative data.

SDRS – an algorithm for analyzing large scale dose response data

Summary: Dose–response information is critical to understanding drug effects, yet analytical methods for dose–response assays cannot cope with the dimensionality of large-scale screening data such as the microarray profiling data. To overcome this limitation, we developed and implemented the Sigmoidal Dose Response Search (SDRS) algorithm, a grid search-based method designed to handle large-scale dose–response data. This method not only calculates the pharmacological parameters for every assay, but also provides built-in statistic that enables downstream systematic analyses, such as characterizing dose response at the transcriptome level. Availability: Bio::SDRS is freely available from CPAN (www.cpan.org). Contacts: ruiruji@gmail.com; bruc@acm.org Supplementary Information: Supplementary data is available at Bioinformatics online.