SCide: identification of stabilization centers 
in proteins

Dosztányi, Zsuzsanna; Magyar, Csaba; Tusnády, Gábor; Simon, István

doi:10.1093/bioinformatics/btg110

Cited by 97 publications

(83 citation statements)

References 12 publications

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“…SCide [Dosztanyi et al, 2003] and Scpred [Dosztanyi et al, 1997], which predict stability centers, as well as SRide [Gromiha and Selvaraj, 2004], which predicts stabilizing residues, can predict only destabilizing effects caused by mutations. The other programs evaluate both stabilizing and destabilizing changes.…”

Section: Resultsmentioning

confidence: 99%

“…The programs SCide [Dosztanyi et al, 2003], Scpred [Dosztanyi et al, 1997], and SRide [Magyar et al, 2005] identify stability centers from sequence data. Mutations found at stability centers were considered by us to be destabilizing and thus deleterious.…”

Section: Prediction Methodsmentioning

confidence: 99%

“…For the empirical-potential approach [Cheng et al, 2006;Guerois et al, 2002;Parthiban et al, 2006], the energy function is a combination of the weighted physical and statistical energy terms and structural descriptors. Machine-learning methods [Capriotti et al, 2005[Capriotti et al, , 2008Cheng et al, 2006;Dosztanyi et al, 1997Dosztanyi et al, , 2003Shen et al, 2008] are first trained using examples of proteins and their mutants for which the DDGs have been experimentally measured. Recently a combination of these approaches has been developed [Masso and Vaisman, 2008].…”

Section: Introductionmentioning

confidence: 99%

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Performance of protein stability predictors

2010

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Stability is a fundamental property affecting function, activity, and regulation of biomolecules. Stability changes are often found for mutated proteins involved in diseases. Stability predictors computationally predict protein-stability changes caused by mutations. We performed a systematic analysis of 11 online stability predictors' performances. These predictors are CUPSAT, Dmutant, FoldX, I-Mutant2.0, two versions of IMutant3.0 (sequence and structure versions), MultiMutate, MUpro, SCide, Scpred, and SRide. As input, 1,784 single mutations found in 80 proteins were used, and these mutations did not include those used for training. The programs' performances were also assessed according to where the mutations were found in the proteins, that is, in secondary structures and on the surface or in the core of a protein, and according to protein structure type. The extents to which the mutations altered the occupied volumes at the residue sites and the charge interactions were also characterized. The predictions of all programs were in line with the experimental data. I-Mutant3.0 (utilizing structural information), Dmutant, and FoldX were the most reliable predictors. The stability-center predictors performed with similar accuracy. However, at best, the predictions were only moderately accurate ($60%) and significantly better tools would be needed for routine analysis of mutation effects. Hum Mutat 31:675-684,

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Section: Resultsmentioning

confidence: 99%

Section: Prediction Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance of protein stability predictors

2010

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“…The correlation coefficient for experimentally studied and predicted changes was shown to be 0.67 [Zhou and Zhou, 2002]. Stabilizing amino acids can be predicted based on long-range interactions in protein structures (Sride, Scide), and hydrophobicity and conservation of amino acid residues (Sride) [Dosztányi et al, 2003a;Magyar et al, 2005]. The Scpred method is based on differences in sequential neighborhood [Dosztányi et al, 2003b].…”

Section: Discussionmentioning

confidence: 99%

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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“…Residues can be considered part of stabilization centers if they are involved in medium or long range interactions and if two supporting residues can be selected from both of their flanking tetra peptides, which together with the central residues form at least seven out of the nine possible contacts. We used the server which is available at http:// www.enzim.hu/scide (Dosztanyi et al, 2003) for this purpose.…”

Section: Computation Of Stabilization Centermentioning

confidence: 99%

Role of the Cation-π Interaction in Therapeutic Proteins: A Comparative Study with Conventional Stabilizing Forces

Shanthi¹,

Ramanathan²

2009

J Comput Sci Syst Biol

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The cation-π interaction is an important, general force for molecular recognition in biological receptors. In this study, we have analyzed the energy contribution resulting from cation-π interactions in the set of therapeutic proteins. The contribution of cation-π interacting residues in secondary structure involvement, solvent accessibility, stabilization centers, stabilizing residues and conservation score has been evaluated. Secondary structure of the cation-π involving residues shows that, Arg and Lys prefers to be in strand. Among the π residues, Phe prefer to be in coil, Tyr prefers to be in strand and Trp prefer to be in helix. Among the cation-π interacting residues Arg and Lys were in the exposed regions. Phe and Tyr were in the partially buried region and Trp in the fully buried region. Stabilization centers for these proteins showed that all the five residues found in cation-π interactions are important in locating one or more of such centers. The contribution of stabilizing residues in the cation-π interactions was analyzed. Further, the study shows that, 43 percent of the amino acid residues that are involved in cation-π interactions might be conserved in therapeutic proteins. The comparison between the conventional and nonconventional interactions in the data set, clearly depict the significance of cation-π interaction in the stability of therapeutic proteins. On the whole, the results presented in this work will be very useful for understanding the contribution of cation-π interaction to the stability of therapeutic proteins.

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SCide: identification of stabilization centers in proteins

Abstract: SCide is available on the web at http://www.enzim.hu/scide. The source code can be obtained from the authors on request.

Cited by 97 publications

References 12 publications

Performance of protein stability predictors

Performance of protein stability predictors

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

Role of the Cation-π Interaction in Therapeutic Proteins: A Comparative Study with Conventional Stabilizing Forces

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