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MAESTRO--multi agent stability prediction upon point mutations.

Laimer J, Hofer H, Fritz M, Wegenkittl S, Lackner P - BMC Bioinformatics (2015)

Bottom Line: MAESTRO is structure based and distinguishes itself from similar approaches in the following points: (i) MAESTRO implements a multi-agent machine learning system. (ii) It also provides predicted free energy change (Δ ΔG) values and a corresponding prediction confidence estimation. (iii) It provides high throughput scanning for multi-point mutations where sites and types of mutation can be comprehensively controlled. (iv) Finally, the software provides a specific mode for the prediction of stabilizing disulfide bonds.The predictive power of MAESTRO for single point mutations and stabilizing disulfide bonds is comparable to similar methods.MAESTRO is a versatile tool in the field of stability change prediction upon point mutations.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Biology, University of Salzburg, Hellbrunnerstr, Salzburg, 34, 5020, Austria. josef.laimer@stud.sbg.ac.at.

ABSTRACT

Background: Point mutations can have a strong impact on protein stability. A change in stability may subsequently lead to dysfunction and finally cause diseases. Moreover, protein engineering approaches aim to deliberately modify protein properties, where stability is a major constraint. In order to support basic research and protein design tasks, several computational tools for predicting the change in stability upon mutations have been developed. Comparative studies have shown the usefulness but also limitations of such programs.

Results: We aim to contribute a novel method for predicting changes in stability upon point mutation in proteins called MAESTRO. MAESTRO is structure based and distinguishes itself from similar approaches in the following points: (i) MAESTRO implements a multi-agent machine learning system. (ii) It also provides predicted free energy change (Δ ΔG) values and a corresponding prediction confidence estimation. (iii) It provides high throughput scanning for multi-point mutations where sites and types of mutation can be comprehensively controlled. (iv) Finally, the software provides a specific mode for the prediction of stabilizing disulfide bonds. The predictive power of MAESTRO for single point mutations and stabilizing disulfide bonds is comparable to similar methods.

Conclusions: MAESTRO is a versatile tool in the field of stability change prediction upon point mutations. Executables for the Linux and Windows operating systems are freely available to non-commercial users from http://biwww.che.sbg.ac.at/MAESTRO.

No MeSH data available.


Scheme of MAESTRO’s main components and data flow.
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Fig1: Scheme of MAESTRO’s main components and data flow.

Mentions: MAESTRO is a multi-agent prediction system, based on statistical scoring functions (SSFs) and different machine learning approaches. First, we discuss the input values and the design of MAESTRO, followed by a description of the training strategies and data sets used for this work. A scheme of MAESTRO’s components and data flow is shown in Figure 1.Figure 1


MAESTRO--multi agent stability prediction upon point mutations.

Laimer J, Hofer H, Fritz M, Wegenkittl S, Lackner P - BMC Bioinformatics (2015)

Scheme of MAESTRO’s main components and data flow.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4403899&req=5

Fig1: Scheme of MAESTRO’s main components and data flow.
Mentions: MAESTRO is a multi-agent prediction system, based on statistical scoring functions (SSFs) and different machine learning approaches. First, we discuss the input values and the design of MAESTRO, followed by a description of the training strategies and data sets used for this work. A scheme of MAESTRO’s components and data flow is shown in Figure 1.Figure 1

Bottom Line: MAESTRO is structure based and distinguishes itself from similar approaches in the following points: (i) MAESTRO implements a multi-agent machine learning system. (ii) It also provides predicted free energy change (Δ ΔG) values and a corresponding prediction confidence estimation. (iii) It provides high throughput scanning for multi-point mutations where sites and types of mutation can be comprehensively controlled. (iv) Finally, the software provides a specific mode for the prediction of stabilizing disulfide bonds.The predictive power of MAESTRO for single point mutations and stabilizing disulfide bonds is comparable to similar methods.MAESTRO is a versatile tool in the field of stability change prediction upon point mutations.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Biology, University of Salzburg, Hellbrunnerstr, Salzburg, 34, 5020, Austria. josef.laimer@stud.sbg.ac.at.

ABSTRACT

Background: Point mutations can have a strong impact on protein stability. A change in stability may subsequently lead to dysfunction and finally cause diseases. Moreover, protein engineering approaches aim to deliberately modify protein properties, where stability is a major constraint. In order to support basic research and protein design tasks, several computational tools for predicting the change in stability upon mutations have been developed. Comparative studies have shown the usefulness but also limitations of such programs.

Results: We aim to contribute a novel method for predicting changes in stability upon point mutation in proteins called MAESTRO. MAESTRO is structure based and distinguishes itself from similar approaches in the following points: (i) MAESTRO implements a multi-agent machine learning system. (ii) It also provides predicted free energy change (Δ ΔG) values and a corresponding prediction confidence estimation. (iii) It provides high throughput scanning for multi-point mutations where sites and types of mutation can be comprehensively controlled. (iv) Finally, the software provides a specific mode for the prediction of stabilizing disulfide bonds. The predictive power of MAESTRO for single point mutations and stabilizing disulfide bonds is comparable to similar methods.

Conclusions: MAESTRO is a versatile tool in the field of stability change prediction upon point mutations. Executables for the Linux and Windows operating systems are freely available to non-commercial users from http://biwww.che.sbg.ac.at/MAESTRO.

No MeSH data available.