Constructing a protein mutant database

Nishikawa, Ken; Ishino, Sonoko; Takenaka, Hitomi; Norioka, Naoko; Hirai, Takeshi; Yao, Takeshi; Seto, Yoshiki

doi:10.1093/protein/7.5.733

Cited by 30 publications

(24 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Single amino acid substitutions were extracted from the Protein Mutant Database (1,2). PMD is an extensive literature-derived database containing experimental information about protein mutations and their effects.…”

Section: Methodsmentioning

confidence: 99%

SNAP: predict effect of non-synonymous polymorphisms on function

Bromberg

Rost

2007

Nucleic Acids Research

766

704

View full text Add to dashboard Cite

Many genetic variations are single nucleotide polymorphisms (SNPs). Non-synonymous SNPs are ‘neutral’ if the resulting point-mutated protein is not functionally discernible from the wild type and ‘non-neutral’ otherwise. The ability to identify non-neutral substitutions could significantly aid targeting disease causing detrimental mutations, as well as SNPs that increase the fitness of particular phenotypes. Here, we introduced comprehensive data sets to assess the performance of methods that predict SNP effects. Along we introduced SNAP (screening for non-acceptable polymorphisms), a neural network-based method for the prediction of the functional effects of non-synonymous SNPs. SNAP needs only sequence information as input, but benefits from functional and structural annotations, if available. In a cross-validation test on over 80 000 mutants, SNAP identified 80% of the non-neutral substitutions at 77% accuracy and 76% of the neutral substitutions at 80% accuracy. This constituted an important improvement over other methods; the improvement rose to over ten percentage points for mutants for which existing methods disagreed. Possibly even more importantly SNAP introduced a well-calibrated measure for the reliability of each prediction. This measure will allow users to focus on the most accurate predictions and/or the most severe effects. Available at http://www.rostlab.org/services/SNAP

show abstract

Section: Methodsmentioning

confidence: 99%

SNAP: predict effect of non-synonymous polymorphisms on function

Bromberg

Rost

2007

Nucleic Acids Research

766

704

View full text Add to dashboard Cite

show abstract

“…21,32 Specifically, we used entries containing reference to single-point mutations in proteins at least 70% identical with our test cases, annotated as either causing a complete loss of protein …”

Section: Key Residues In the Test Setmentioning

confidence: 99%

A Family of Evolution–Entropy Hybrid Methods for Ranking Protein Residues by Importance

Mihalek

Reš

Lichtarge

2004

Journal of Molecular Biology

281

319

View full text Add to dashboard Cite

In order to identify the amino acids that determine protein structure and function it is useful to rank them by their relative importance. Previous approaches belong to two groups; those that rely on statistical inference, and those that focus on phylogenetic analysis. Here, we introduce a class of hybrid methods that combine evolutionary and entropic information from multiple sequence alignments. A detailed analysis in insulin receptor kinase domain and tests on proteins that are well-characterized experimentally show the hybrids' greater robustness with respect to the input choice of sequences, as well as improved sensitivity and specificity of prediction. This is a further step toward proteome scale analysis of protein structure and function.

show abstract

“…Mutational Data-Functional data on trace residues were gathered from the tGRAP mutant data base (32), the protein mutant database (33), and the literature. For global trace residues we considered mutations at cognate residues across all class A receptors, but for opsinspecific trace residues we used opsin mutations only.…”

Section: Gpcr Family Alignments-blastmentioning

confidence: 99%

Evolutionary Trace of G Protein-coupled Receptors Reveals Clusters of Residues That Determine Global and Class-specific Functions

Madabushi

Gross

Philippi

et al. 2004

Journal of Biological Chemistry

185

171

View full text Add to dashboard Cite

G protein-coupled receptor (GPCR) activation mediated by ligand-induced structural reorganization of its helices is poorly understood. To determine the universal elements of this conformational switch, we used evolutionary tracing (ET) to identify residue positions commonly important in diverse GPCRs. When mapped onto the rhodopsin structure, these trace residues cluster into a network of contacts from the retinal binding site to the G protein-coupling loops. Their roles in a generic transduction mechanism were verified by 211 of 239 published mutations that caused functional defects. When grouped according to the nature of the defects, these residues subdivided into three striking sub-clusters: a trigger region, where mutations mostly affect ligand binding, a coupling region near the cytoplasmic interface to the G protein, where mutations affect G protein activation, and a linking core in between where mutations cause constitutive activity and other defects. Differential ET analysis of the opsin family revealed an additional set of opsin-specific residues, several of which form part of the retinal binding pocket, and are known to cause functional defects upon mutation. To test the predictive power of ET, we introduced novel mutations in bovine rhodopsin at a globally important position, Leu-79, and at an opsin-specific position, Trp-175. Both were functionally critical, causing constitutive G protein activation of the mutants and rapid loss of regeneration after photobleaching. These results define in GPCRs a canonical signal transduction mechanism where ligand binding induces conformational changes propagated through adjacent trigger, linking core, and coupling regions.The profusion and diversity of G protein-coupled receptors (GPCRs) 1 give them a central role in health and disease. In humans, over 1000 genes encode these receptors (1), each of which responds to a single or few ligands by activating G proteins, which then modulate enzymes and channels to initiate highly amplified signaling cascades. Such cascades control sight, taste, smell, slow neurotransmission and the responses to most water-soluble hormones and chemokines. In fact, GPCRs are so ubiquitous that, although they are the targets of nearly 50% of current drugs (2), this is still a small fraction of their pharmacological potential (3).Some of the major questions relevant to GPCR pharmacology include the following: What residues are critical for ligand binding and G protein activation? What do different receptor families have in common with regard to their activation mechanism? From a structural perspective, it is known that all GPCRs form a seven transmembrane (TM) ␣-helical bundle, connected by three intracellular and three extracellular loops, with an extracellular N terminus and an intracellular C terminus (4, 5). However, low overall sequence identity of 25% even within class A GPCRs (6 -8) suggests that significant deviations can occur in ligand binding pockets and in interhelical contacts that stabilize or mediate the transition between a...

show abstract

Constructing a protein mutant database

Cited by 30 publications

References 0 publications

SNAP: predict effect of non-synonymous polymorphisms on function

SNAP: predict effect of non-synonymous polymorphisms on function

A Family of Evolution–Entropy Hybrid Methods for Ranking Protein Residues by Importance

Evolutionary Trace of G Protein-coupled Receptors Reveals Clusters of Residues That Determine Global and Class-specific Functions

Contact Info

Product

Resources

About