prediction tools for mutations: mcsm and maestro
TRANSCRIPT
Prediction tools for mutations: Prediction tools for mutations: mCSM and MAESTROmCSM and MAESTRO
Alex [email protected]
Professor: Karina Machado
UNIVERSIDADE FEDERAL DO RIO GRANDEPROGRAMA DE PÓS-GRADUAÇÃO EM COMPUTAÇÃO
GRUPO DE PESQUISA EM BIOLOGIA COMPUTACIONAL
May/2015
Goals of this study
Why study prediction tools for mutations?
Gain an understanding of the range of problems being tackled by bioinformatics.
Be able to interpret the output of bioinformatics programs and understand their limitations.
Learn more about impacts of mutations in proteins.
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
Overview
The need of powerful in-silico methods to predict the effect of point mutation on the protein stability is obvious. (Josef Laimer)
mCSM: predicting the effects of mutations in proteins using graph-based signatures.available at http://structure.bioc.cam.ac.uk/mcsm
MAESTRO: multi-agent stability prediction upon point mutationsavailable at http://che.sbg.ac.at/MAESTRO
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
mCSM: predicting the effects of mutations in proteins using graph-based signatures
mCSM
Mutations play fundamental roles in evolution by introducing diversity into genomes.
The ability to predict the impact of mutations on protein stability and interactions is of significant value.
Propose a novel approach to the study of missense mutations which relies on graph-based signatures.nodes are the atoms and the edges are the physicochemical interactions
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
mCSM
Mutations play fundamental roles in evolution by introducing diversity into genomes.
The ability to predict the impact of mutations on protein stability and interactions is of significant value.
Propose a novel approach to the study of missense mutations which relies on graph-based signatures.nodes are the atoms and the edges are the physicochemical interactions
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
mCSM
Mutations play fundamental roles in evolution by introducing diversity into genomes.
The ability to predict the impact of mutations on protein stability and interactions is of significant value.
Propose a novel approach to the study of missense mutations which relies on graph-based signatures.nodes are the atoms and the edges are the physicochemical interactions
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
mCSM
Each mutation is represented as a signature vector that is used to train and test predictive machine learning methods.
Regression
Classification
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
mCSM
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
mCSM
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
mCSM
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
MAESTRO: multi-agent stability prediction upon point mutations
MAESTRO
Point mutations can have a strong impact on protein stability. A change in stability may subsequently lead to dysfunction and finally cause diseases.
Contribute a novel method for predicting changes in stability upon point mutation in proteins.
Implements a multi-agent machine learning system.predicted free energy change (∆ ∆G).
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
MAESTRO
MAESTRO includes prediction agents on the basis of three different machine learning approaches, namely:
artificial neural networks (ANN);use OpenNN, an open source C++ library
support vector machines (SVM);employs libSVM and use an e-SVR with a Gaussian kernel
multiple linear regression (MLR);agents are computed using the MLR GNU Scientific Library
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
MAESTRO
MAESTRO includes prediction agents on the basis of three different machine learning approaches, namely:
artificial neural networks (ANN);ANN agents use OpenNN, an open source C++ library
support vector machines (SVM);SVM agents employ libSVM and use an e-SVR with a Gaussian kernel
multiple linear regression (MLR);Multiple linear regression agents are computed using the MLR GNU Scientific Library
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
MAESTRO
MAESTRO performs a prediction in three main steps:
the computation of the SSF based score and the other input values;
the calculation of a consensus prediction and a corresponding confidence score.
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
MAESTRO
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
MAESTRO
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
MAESTRO
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
Discussion
mCSM:
Can predict stability changes of a wide range of mutations occurring in the tumour suppressor protein p53 (PDB: 2OCJ)
In classification tasks, for both data sets the predictive models trained with the mCSM signatures were able to achieve accuracies of 82%.
HydroPaCe >> Protein cutoff scanning >> aCSM >> mCSM
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
Discussion
MAESTRO:
The basic idea behind a multi-agent prediction was to improve the prediction power in relation to a single machine learning method.
This technical reduces the risk of outliers and overfitting.
The performance is comparable to mCSM, our main competitor method.
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
References
[1] Laimer, Josef, et al. "MAESTRO-multi agent stability prediction upon point mutations." BMC bioinformatics 16.1 (2015): 116.
[2] Pires, Douglas EV, David B. Ascher, and Tom L. Blundell. "mCSM: predicting the effects of mutations in proteins using graph-based signatures." Bioinformatics 30.3 (2014): 335-342.
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo
Acknowledgements
Prof. Karina MachadoProf. Adriano Werhli
This work is supported by:
Prediction tools for mutations: mCSM and MAESTRO Alex Camargo