Fabry:Sequence-based mutation analysis

Fabry Disease » Sequence-based mutation analysis

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The following analyses were performed on the basis of the α-Galactosidase A sequence. Please consult the journal for the commands used to generate the results.

Dataset preparation

Q279E
N215S
I289V
S65T
R356W
V316I
P323T
P40S
R118H
A143T

Amino acid properties

<figtable id="tab:aaProp"> Physicochemical properties of the chosen SNPs and changes of properties between wildtype (wt) and mutant (mt)

SNP	wt AA	wt Side-chain polarity	wt Side-chain charge (pH 7.4)	wt Hydropathy index	wt Residue Mass	wt iP	mt AA	mt Side-chain polarity	mt Side-chain charge (pH 7.4)	mt Hydropathy index	mt Residue Mass	mt iP	change in polarity	change in charge	change in hydropathy	change in residue mass	change in iP
Q279E	Q	polar	neutral	−3.5	128.131	5.65	E	polar	negative	−3.5	129.116	3.15	none	neutral to negative	−3.5-−3.5	0.985000000000014	-2.5
N215S	N	polar	neutral	−3.5	114.104	5.41	S	polar	neutral	−0.8	87.078	5.68	none	none	−0.8-−3.5	-27.026	0.27
I289V	I	nonpolar	neutral	4.5	113.160	6.05	V	nonpolar	neutral	4.2	99.133	6.00	none	none	4.2-4.5	-14.027	-0.0499999999999998
S65T	S	polar	neutral	−0.8	87.078	5.68	T	polar	neutral	−0.7	101.105	5.60	none	none	−0.7-−0.8	14.027	-0.0800000000000001
R356W	R	polar	positive	−4.5	156.188	10.76	W	nonpolar	neutral	−0.9	186.213	5.89	polar to nonpolar	positive to neutral	−0.9-−4.5	30.025	-4.87
V316I	V	nonpolar	neutral	4.2	99.133	6.00	I	nonpolar	neutral	4.5	113.160	6.05	none	none	4.5-4.2	14.027	0.0499999999999998
P323T	P	nonpolar	neutral	−1.6	97.117	6.30	T	polar	neutral	−0.7	101.105	5.60	nonpolar to polar	none	−0.7-−1.6	3.988	-0.7
P40S	P	nonpolar	neutral	−1.6	97.117	6.30	S	polar	neutral	−0.8	87.078	5.68	nonpolar to polar	none	−0.8-−1.6	-10.039	-0.62
R118H	R	polar	positive	−4.5	156.188	10.76	H	polar	pos(10%),neutr(90%)	−3.2	137.142	7.60	none	positive to pos(10%),neutr(90%)	−3.2-−4.5	-19.046	-3.16
A143T	A	nonpolar	neutral	1.8	71.079	6.01	T	polar	neutral	−0.7	101.105	5.60	nonpolar to polar	none	−0.7-1.8	30.026	-0.41

</figtable>

Simple structural analysis

• Now take into consideration where in the protein the mutation occurs and document: Create a picture with PyMOL showing the original and mutated residue in the protein. Use PyMOL for this. More thorough structural analyses will be introduced in the next task.

Location

<figtable id="tab:Location"> Physicochemical properties of the chosen SNPs and changes of properties between wildtype (wt) and mutant (mt)

SNP	SecStruc Psipred	SecStruc Psipred long	SecStruc Reprof	SecStruc Reprof long	SecStruc DSSP	SecStruc DSSP long
Q279E	H	CCCCCCCHHHHHHHHHHHHHH	H	EECCCCCCHHHHHHHHHHHHH	H	CCCCC--HHHHHHHHHHHHHC
N215S	H	CCCCCCCCCCHHHHHCCCCCC	H	CECCCCCCCCHHHHHHHHHHH	H	HHHCCCC---HHHHCCC-CEE
I289V	H	HHHHHHHHHHHCCCEEEECCC	H	HHHHHHHHHHHHHCHHCCCCC	C	HHHHHHHHHHCC--EEE-C-C
S65T	H	CCCCCCCCCCHHHHHHHHHHH	H	CCCCCCCHHHHHHHHHHHHHH	H	CCC-CCCC-CHHHHHHHHHHH
R356W	C	CCEEEEEEECCCCCCEEEEEE	C	HHHHHHHHHHCCCCCCCCHHH	-	CEEEEEEEE---CCC-EEEEE
V316I	H	HHHCCCCHHHHHHCCCCCCCC	E	HHHHCCCCCEEEECCCCCCCC	H	HHHHHH-HHHHHHHC-CC---
P323T	C	HHHHHHCCCCCCCCCEEEEEC	C	CCEEEECCCCCCCCCCEECCC	C	HHHHHHHC-CC----EEEE-C
P40S	C	CCCCCCCCCCCCCCCCCCCCC	C	HHCCCCCCCCCCCHHHHHHEE	-	---CC--CC--EEEECHHHHC
R118H	H	CCCCCCCCHHHHHHHHHHCCC	H	CCCCCCHHHHHHHHHHHCCCC	H	-CCC-CCHHHHHHHHHHHCC-
A143T	C	EECCCCCCCCCCCCCCCCHHH	C	EEECCCCCCCCCCCCCCCCCC	C	EEECCCE-CCCCE--CCCHHH

</figtable>

Substitution matrices

<figtable id="tab:Subsmatr"> Substitution values for all SNPs, both substitution matrices

SNP	Value BLOSUM62	Value PAM250
Q279E	2	2
N215S	1	1
I289V	3	4
S65T	1	1
R356W	-3	2
V316I	3	4
P323T	-1	0
P40S	-1	1
R118H	0	2
A143T	0	1

</figtable>

PSSM

• Getting a bit closer to evolution you will have to create a PSSM (position specific scoring matrix) for your protein sequence using PSI-BLAST (5 iterations). How conserved are the WT residues in your mutant positions? How is the frequency of occurrence (conservation) for the mutant residue type? Anything interesting?

Multiple sequence alignment

• And another step close to evolution: Identify all mammalian homologous sequences. Create a multiple sequence alignment for them with a method of your choice. Using this you can now calculate conservation for WT and mutant residues again. Compare this to the matrix- and PSSM-derived results.

Scoring methods

• Finally, we use three different approaches to score our mutants.
  • SIFT
  • Polyphen2
  • SNAP is installed on the VirtualBox and should be used command-line only. -- As blast is the bottleneck of SNAP, and you are doing that anyway, we might as well look at all possible substitutions in the position of our mutations. This way we can learn much more about the nature of the given mutation: Is our mutation problematic because we introduce an unwanted effect, or because the WT residue is essential and by mutating we remove that?

Results and Conclusion

• Compare ALL results and create an overview table.
• Try to come up with a consensus between all the findings requested above.
• Check whether you are right in the HGMD – were you able to predict a change?