Xavier de la Cruz Translational Bioinformatics, VHIR

+Translational Bioinformatics

Casandra Riera

Natàlia Padilla

Òscar Marín

Elena Álvarez de la Campa

‘Amics del VHIR’

 Context: where are we going?  The prediction score: ◦  role ◦  components: stability, MSA

 Going beyond  Take home points...

SC SC

Xue  et  al.,  Gene,cs  in  Medicine,  2014  

Dixon-Salazar et al., Sci. Transl. Med., 2012

Identifying molecular damage

P N …KKRHCSG…

Y

PREDICTOR: PolyPhen-2,

SIFT, etc

PATHOLOGICAL NEUTRAL

MUTATION: F367V SC=1.3

PATHOLOGICAL

NEUTRAL

SCORE MUTATION: F367V SC=1.3

PATHOLOGICAL NEUTRAL

SCORE MUTATION: F367V SC=1.3

PATHOLOGICAL NEUTRAL

SCORE ERROR MUTATION: F367V SC=1.3

 What should we consider when we want to use pathogenicity predictions in a biomedical decision process

 Why in some cases we can successfully interpret the pathogenicity of sequence variants, and why we fail in others

CRITICAL UNDERSTANDING OF THE PRINCIPLES UNDERLYING THE PREDICTION OF PATHOLOGICAL MUTATIONS

F367V

SC = ??????????????????????????????????????????????????

MUTATION MOLECULAR IMPACT

F367V

SC = Change in function ≈ Change in stability

MUTATION MOLECULAR IMPACT

Yue et al., J Mol Biol, 2005

Fersht, Matouschek & Serrano J Mol Biol, 1992

FoldX: Guerois et al., J Mol Biol, 2002

APPROXIMATION: (i) eliminate terms (ii) fit to in vitro exper. data

SC = SC(stability)

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-8 -6 -4 -2 0 2 4

Rela

tive

Act

ivit

y

Stability (Kcal/mol)

Bromber & Rost, BMC Bioinformatics, 2009

Phage T4 Lysozyme mutants r=0.3

  There is not an exact relationship between function and stability

  SC(stability) is an approximation   In vitro data are used to model stability in Foldx, etc

Wt-Wt rmsd=0.16Å

Wt-A98V rmsd=0.20Å, 5Kcal/mol destab.

Experimental error: 0.5Å

  There is not an exact relationship between function and stability

  SC(stability) is an approximation   In vitro data are used to model stability in Foldx, etc   Structure gives a qualitative view of stability change   Manual analysis of 3D structures does not provide

quantitative information

F367V

SC = SC(stability) + SC(function)

MUTATION MOLECULAR IMPACT

Yue et al., J Mol Biol, 2005

SC(function) = wAcS.AcS + wCons1.Cons1 + wCons2.Cons2 + ...

Are we hitting the active site? (Yes/No)

Do we know the active site? Usually No

We can use residue conservation

How to quantify it? Use MSA and some conservation measure

APPROXIMATIONS: (i) annotations may be imprecise (ii) what conservation measure (iii) building MSAs is computationally unsolved problem (NP-hard) (iv) the weighs are empirical

SC(function) = wAcS.AcS + wCons1.Cons1 + wCons2.Cons2 + ...

Imprecise annotations (Yue et al., J Mol Biol, 2005)

MSA are suboptimal

Several conservation measures

  STABILITY: ◦  There is not an exact relationship between function and stability ◦  SC(stability) is an approximation ◦  In vitro data are used to model stability in Foldx, etc ◦  Structure gives a qualitative view of stability change ◦  Manual analysis of 3D structures does not provide quantitative

information

  FUNCTION: ◦  SC(function) is an approximation ◦  functional information is indirectly obtained from MSA:

prediction depends on MSA (and these are an approximation)

Protein stability Functional interactions

Unspecific cellular interactions

…KKRHCSGWL…

Y

Change in function

SC = SC(stability) + SC(function)

SC = SC(stability) + SC(function) MUTATION MOLECULAR IMPACT

PATHOLOGICAL NEUTRAL

 Combine several strategies: ◦ Only use the best predictions, however,... ◦ Use several methods ◦ Manual analysis (better through

collaborations)

PATHOLOGICAL NEUTRAL

NO PREDICTION

• BETTER ACCURACY • LESS PREDICTIONS

Consistency at low scores may serve to indicate mild pathological mutations

  STABILITY: ◦  There is not an exact relationship between function and stability ◦  SC(stability) is an approximation ◦  In vitro data are used to model stability in Foldx, etc ◦  Structure gives a qualitative view of stability change ◦  Manual analysis of 3D structures does not provide quantitative

information

  FUNCTION: ◦  SC(function) is an approximation ◦  functional information is indirectly obtained from MSA:

prediction depends on MSA (and these are an approximation)

  GOING BEYOND: ◦  take only the best predictions ◦  use several methods ◦  look for unexpected effects