Visualizing and quantifying molecular goodness-of-fit: small-probe contact dots with explicit hydrogen atoms 1 1Edited by J. Thornton

Jm, Word; Lovell, Simon C.; LaBean, Thomas H.; Hc, Taylor; Me, Zalis; Bk, Presley; Js, Richardson; Dc, Richardson

doi:10.1006/jmbi.1998.2400

Cited by 516 publications

(480 citation statements)

References 95 publications

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“…The structure was visualized and the mutations were modeled by PyMOL [DeLano, 2002]. Hydrogen atoms were added to the structures using Reduce [Word et al, 1999a]. Mutant amino acid side chain w angles were rotated at intervals of 101 by the Autobondrot function in PROBE [Word et al, 1999b and the best rotamers were selected for further analysis.…”

Section: Methodsmentioning

confidence: 99%

“…Hydrogen atoms were added to the structures using Reduce [Word et al, 1999a]. Mutant amino acid side chain w angles were rotated at intervals of 101 by the Autobondrot function in PROBE [Word et al, 1999b and the best rotamers were selected for further analysis. The acceptable conformations for a mutated side chain have a total score above -1.0, allowing for small local perturbations in the structure .…”

Section: Methodsmentioning

confidence: 99%

“…The best rotamers were used in the analyses. Most of the mutated side chains do not fit into the structure without deleterious changes to protein scaffolding, as determined both computationally by the Probe score [Word et al, 1999b and by visual inspection. Of the 23 mutations screened, excluding mutations introducing proline side chains, as well as mutations not positioned at the structurally determined trypsin domain, only seven gave an acceptable score above -1.0, which allows for small local perturbations to take place in the structure .…”

Section: Structural Mutationsmentioning

confidence: 99%

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Bioinformatic analysis of protein structure–function relationships: case study of leukocyte elastase (ELA2) missense mutations

Thusberg

Vihinen

2006

Hum. Mutat.

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For the Immunogenetics Special IssueCyclic and congenital neutropenia are caused by mutations in the human neutrophil elastase (HNE) gene (ELA2), leading to an immunodeficiency characterized by decreased or oscillating levels of neutrophils in the blood. The HNE mutations presumably cause loss of enzyme activity, consequently leading to compromised immune system function. To understand the structural basis for the disease, we implemented methods from bioinformatics to analyze all the known HNE missense mutations at both the sequence and structural level. Our results demonstrate that the 32 different mutations have diverse effects on HNE structure and function, affecting structural disorder and aggregation tendencies, stability maintaining contacts, and electrostatic properties. A large proportion of the mutations are located at conserved amino acids, which are usually essential in determining protein structure and function. The majority of the disease-causing HNE missense mutations lead to major structural changes and loss of stability in the protein. A few mutations also affect functional residues, leading into decreased catalytic activity or altered ligand binding. Our analysis reveals the putative effects of all known missense mutations in HNE, thus allowing the structural basis of cyclic and congenital neutropenia to be elucidated. We have employed and analyzed a set of some 30 different methods for predicting the effects of amino acid substitutions. We present results and experience from the analysis of the applicability of these methods in the analysis of numerous genes, proteins, and diseases to reveal protein structure-function relationships and disease genotype-phenotype correlations.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Structural Mutationsmentioning

confidence: 99%

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Bioinformatic analysis of protein structure–function relationships: case study of leukocyte elastase (ELA2) missense mutations

Thusberg

Vihinen

2006

Hum. Mutat.

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“…Selection emphasizes data quality by removing structures not meeting criteria such as B-factor, clash score, and main chain torsion angles (Lovell et al 2003). Hydrogen atoms were added as needed using the Reduce program (Word et al 1999a) and the program was also used to correct side-chain orientations (Word et al 1999b). Hydrogen bond definition is based on the method recommended by Baker and Hubbard (1984).…”

Section: Methodsmentioning

confidence: 99%

Mimicry by asx‐ and ST‐turns of the four main types of β‐turn in proteins

et al. 2004

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Hydrogen-bonded ␤-turns in proteins occur in four categories: type I (the most common), type II, type II', and type I'. Asx-turns resemble ␤-turns, in that both have an NH . . . OC hydrogen bond forming a ring of 10 atoms. Serine and threonine side chains also commonly form hydrogen-bonded turns, here called ST-turns. Asx-turns and ST-turns can be categorized into four classes, based on side chain rotamers and the conformation of the central turn residue, which are geometrically equivalent to the four types of ␤-turns. We propose asx-and ST-turns be named using the type I, II, I', and II' ␤-turn nomenclature. Using this, the frequency of occurrence of both asx-and ST-turns is: type II' > type I > type II > type I', whereas for ␤-turns it is type I > type II > type I' > type II'. Almost all type II asx-turns occur as a recently described three residue feature named an asx-nest.

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“…(B) Stereo of stack switches and dented backbones produced by two 1e suite conformations on opposite strands of the ar0038/1KD5 duplex (Kacer et al 2003). The 1e suites are highlighted, and all-atom contact dots (Word et al 1999) show van der Waals contacts for the base stacking (blue and green) and Hbonds for the base pairs (brown). Note the two positions where one base stack ends and the other divides.…”

Section: Structural Roles Of Specific Conformersmentioning

confidence: 99%

RNA backbone: Consensus all-angle conformers and modular string nomenclature (an RNA Ontology Consortium contribution)

Richardson

Schneider²,

Murray³

et al. 2008

RNA

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438

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A consensus classification and nomenclature are defined for RNA backbone structure using all of the backbone torsion angles. By a consensus of several independent analysis methods, 46 discrete conformers are identified as suitably clustered in a qualityfiltered, multidimensional dihedral angle distribution. Most of these conformers represent identifiable features or roles within RNA structures. The conformers are given two-character names that reflect the seven-angle dezabgd combinations empirically found favorable for the sugar-to-sugar ''suite'' unit within which the angle correlations are strongest (e.g., 1a for A-form, 5z for the start of S-motifs). Since the half-nucleotides are specified by a number for dez and a lowercase letter for abgd, this modular system can also be parsed to describe traditional nucleotide units (e.g., a1) or the dinucleotides (e.g., a1a1) that are especially useful at the level of crystallographic map fitting. This nomenclature can also be written as a string with two-character suite names between the uppercase letters of the base sequence (N1aG1gN1aR1aA1cN1a for a GNRA tetraloop), facilitating bioinformatic comparisons. Cluster means, standard deviations, coordinates, and examples are made available, as well as the Suitename software that assigns suite conformer names and conformer match quality (suiteness) from atomic coordinates. The RNA Ontology Consortium will combine this new backbone system with others that define base pairs, base-stacking, and hydrogen-bond relationships to provide a full description of RNA structural motifs.

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Visualizing and quantifying molecular goodness-of-fit: small-probe contact dots with explicit hydrogen atoms 1 1Edited by J. Thornton

Cited by 516 publications

References 95 publications

Bioinformatic analysis of protein structure–function relationships: case study of leukocyte elastase (ELA2) missense mutations

Bioinformatic analysis of protein structure–function relationships: case study of leukocyte elastase (ELA2) missense mutations

Mimicry by asx‐ and ST‐turns of the four main types of β‐turn in proteins

RNA backbone: Consensus all-angle conformers and modular string nomenclature (an RNA Ontology Consortium contribution)

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