Nebojsa Mirkovic, Carles Ferrer-Costa, Marc A. Marti-Renom, Alvaro N.A. Monteiro, Andrej Sali...

Nebojsa Mirkovic, Carles Ferrer-Costa,Marc A. Marti-Renom, Alvaro N.A. Monteiro,Andrej Sali

Functional Consequences of the SNPs :BRCA1, Membrane Transporters and More

Nebojsa Mirkovic

in todays talk, i will present results of two collaborations done by me and MRM, postdoc in the lab, AM from SCRC and CFC, from UB. these projects represent a great portion of my thesis work that is concerned with developing a general approach to understanding and subsequently predicting functional impact of genetic variants in humans.

Single Nucleotide Polymorphisms (SNPs)

• single base pair replacements of appreciable allelic frequency in the population ( >1% );

• predominant form of human genetic variation (90%);

• number predicted to range from one to several million per genome; predicted frequency: 1/1000bp;

• predicted number per gene: 4-12 on average (limited datasets);

• classification by genomic location: ncSNPs, cSNPs (synonymous, non-synonymous: 24,000-40,000 per genome).

Nebojsa Mirkovic

SNPs are the biggest source of genetic variation in humans, accounting for over 90% of all genetic variants, and being encountered, on average, every 1kb. they represent single nucleotide replacements that have population allelic frequency over 1%, due to their neutral or mild phenotypic effects. classical mutations, whose negative impact is more pronounced, are selected against to less than 1% allelic fequency.depending on their position in DNA relative to the coding regions, the SNPs can be non-coding or coding, whereas the SNPs in coding regions can cause amino acid replacements (non-synonymous) or be neutral (synonymous).

• identify gene(s) that underlie numerous genetic disorders and multifactorial traits (eg, pharmacological response);

• SNPs are probably the biggest class of pathogenic changes in the human genome (coding and regulatory regions);

• several genetic variants connected to genetic disorders (E4 allele of APOE with Alzheimer disease, FV Leiden allele with deep-venuos thrombosis, and CCR532 with resistance to HIV infection);

• numerous indirect evidence of functional impact;

• markers of choice in genetic studies.

Significance of SNP Analysis

Nebojsa Mirkovic

With their number in the human genome estimated to be 24-40 thousand nsSNPs, along with SNPs in regulatory regions are likely the biggest class of pathogenic changes in the human genome. Therefore, analysis of SNPs is crucial in uncovering causes of many genetic disorders as well as multifactorial traits. One such trait is the pharmacological response, around which a whole new field, pharmacogenomics, has evolved.In addition to several studies linking SNPs in certain genes to some well known genetic diseases (eg. E4 allele of APO-E with A.d.), there is a line of indirect evidence for functional impact of SNPs that is being selected against. For instance, nsSNPs are encountered at 38% of chance frequency and are enriched in the low-frequency alleles when compared to sSNPs.On top of directly causing genetic disorders, SNPs are markers of choice in genetic studies. Approaches like association studies (form of which is the candidate gene approach), and linkage disequilibrium use them due to the high frequency, low mutation rate per generation and easy manipulation.

Br 32

Br 42

*

Br

Identification of Sequence Variants in Genes of Interest

Nebojsa Mirkovic

Nowadays, individuals at risk for certain genetic disease can easily be genotyped for variants in the gene(s) of interest. However, usually the disease association of uncovered SNPs is unknown. Classical association studies, although being the only certain approach to establishing this connection, are complicated and frequently not feasible.

Project Goal

What is the likelihood that a given SNP destroys the function of a protein?

Approach

• design a classification function that can predict functional impact of a particular SNP and relies on a combination of sequence, structure and genetic properties from a well-characterized set of nsSNPs;

• build ModSNP, a structural database of SNPs, containing protein structure models for all known nsSNPs;

• apply the classification function to a number of specific examples and to all the SNPs in ModSNP.

Nebojsa Mirkovic

Therefore, we set out to try and answer the question of disease association by asking a simpler question: how likely is that a certain SNP has functional impact? We concentrate on SNPs that cause amino acid replacements and use a structural bioinformatics aproach. This approach is fast, cheap, has large-scale capability and prior work in the field showed efficiency of joint structural and statistical approach and was in agreement with generally accpeted structural knowledge.The plan was to design a classification function that can predict functional impact of an isolated nsSNP with intention of applying it to specific cases, possibly in the candidate gene approach.Also, acomprehensive structural database of SNPs, ie. ModSNP is being built that contains protein structure models for all known SNPs. The classification function will ultimately be applied to all the SNPs from ModSNP.

BRCA1 Project

Nebojsa Mirkovic

First protein on which we developed the classification function was human BRCA1, product of the tumor suppressor gene known to play a role in onset of the hereditary breast and ovarian cancers. Other players include similar gene BRCA2, p53, ATM, PTEN, and others.Lifetime risk of acquring the breast cancer is 11% for general population, but 56-85% for the carriers of certain BRCA1 allelic forms. The association of several variants in BRCA1 gene with the early onset breast cancer and unknown significance of numerous other detected nsSNPs, makes this protein an excellent candidate for a structural study.

Human BRCA1 Tandem BRCT Repeats

200 aa

RING NLS BRCT

Globular regions

Nonglobular regions

BRCA1 BRCT repeats

Williams, Green, Glover. Nat.Struct.Biol. 8, 838, 2001

Nebojsa Mirkovic

Human BRCA1 is a nucleoprotein 1.863aa long and consists of several globular domains with intervening non-globular regions as can be seen from that cartoon. The only portion of the protein for which exists stuctural information is the C-terminal region (residues 1649-1859) that harbors two so called BRCT (for BRCA1 C-terminal) domains, presented on the lower picture in blue (for N-terminal BRCT domain) and green (for C-terminal BRCT domain). These tandem repeats are connected by a poorly structured 20aa linker depicted in yellow and represent a structural and functional unit as mentioned in several studies. The BRCT domain can also be present as a single entity or oligomerized in different proteins that are mostly included in the DNA-damage repair or cell-cycle checkpoints. The basic structure a BRCT domain is a alpha-beta-alpha sandwich where a central beta sheet issandwiched between helices A1 and A3 on N-terminal side and A2 on C-terminal side. Repeats interact through a 3-helix bundle formed by the A2 from the N-terminal repeat and A1 and A3 from the C-terminal repeat. The intactness of the inter-repeat surface is crucial for proper functioning of the domains. Although little is known about the function of BRCA1, it has been shown to be a transcription factor and this function resides in the BRCT region. There are numeours observations which point that the transactivation is requred for the tumor suppression function of BRCA1. In our work, we accepted the hypothesis that the loss of transactivation, causes loss of tumor suppression. Our collaborator AM has developed a transcription assay that enabled him to screen a large number of mutations in the BRCT region and therefore functionallt characterize them.

Missense Mutations in BRCT Domains by Function

tran

scrip

tion

activ

atio

n

cancerassociated

V1665MD1692NG1706AD1733GM1775VP1806A

M1652KL1657PE1660GH1686QR1699QK1702E Y1703HF1704S

L1705PS1715NS1722FF1734LG1738EG1743RA1752PF1761I

F1761S M1775E M1775KL1780PI1807SV1833E A1843T

M1652TV1653ML1664P T1685A T1685IM1689R D1692YF1695LV1696LR1699LG1706EW1718C

W1718ST1720AW1730SF1734SE1735KV1736AG1738RD1739ED1739GD1739Y V1741GH1746N

R1751PR1751Q R1758G L1764P I1766S P1771L T1773SP1776SD1778GD1778H D1778NM1783T

A1823TV1833MW1837RW1837GS1841NA1843P T1852SP1856TP1859R

C1787SG1788DG1788VG1803AV1804DV1808AV1809AV1809FV1810GQ1811RP1812SN1819S

C1697RR1699WA1708ES1715RP1749RM1775R

M1652IA1669S

no t

rans

crip

tion

activ

atio

nun

know

n

not cancerassociated unknown

Nebojsa Mirkovic

The mutation set in hte human BRCA1 BRCT region we obtained from AM consists of 94 mutations artificially introduced by site-directed of random mutagenesis as well as germ-line variants found in literature and the Breast Information Core (BIC) database. These mutations were tabulated by function, source of which was either the trnscription assay or association studies. 37 mutations were tested for transcription effect in mammalian and/or yeast cells (8 without transcription assay measurable functional impact and 29 with functional impact). As of now, 57 are unclassified variants. Two classes in the table that are not populated would contain mutations with compromised transcription function but no clinical effect, or with clinical effect and no transcactivation. Although the dataset is relatively small, this finding points that tumor suppression is tightly linked to ability of BRCA1 to activate transcription.As the first step in the analysis, we mapped the mutations onto the crystalographic structure and determined the surface exposure of the positions they occur at. The buried residues are depicetd in red, and the exposed ones in yellow.We first rationalized the observed effect of 37 characterized mutations in structural terms in order to elucidate the rules that govern functional effect of amino acid replacements in our dataset.

Mapping of the Mutations on the Surface of BRCA1 BRCT Domains

R1699Q/W

G1743R

K1702F

L1657P

D1692ND1773G

E1660G

Nebojsa Mirkovic

Mapping of the exposed mutations onto the BRCT domains molecular surface revealed that the mutations which abrogate transcription activity, depicted in red, segregate from the mutations that do not affect it, depicted in yellow. We then produced the structural alignment of 8 known BRCT domain structures and the phylogenetic alignment with 6 orthologs. Upon calculation of the degrees of structural and evolutionary conservation of each position, we colored the molecular surface so that the intensity of blue color corresponds to the level of conservation. Structural conservation is plotted on the left side and phylogenetic on the right.4 mutations at positions 1699, 1702 and 1657 cluster in a groove on the molecular surface. We propose that those mutations fall into a protein-protein binding site important for transcription. Functional importance would explain why those positions are not conserved structurally but are preserved in evolution.

Location of Putative BRCT-Protein Interaction Site

RMSMVVSGLTPEEFMLVYKFARKHHITLTNLITEETTHVVMKTDAEFVCERTLKYFLGIAGGKWVVSYFWVTQSIKERKMLNEHDFEVRGDVVNGRNHQGPKRARESQDRKIFRGLEICCYGPFTNMPTDQLEWMVQLCGASVVKELSSFTLGTGVHPIVVVQPDAWTEDNGFHAIGQMCEAPVVTREWVLDSVALYQCQELDTYLI

PQIP

RMSMVVSGLTPEEFMLVYKFARKHHITLTNLITEETTHVVMKTDAEFVCERTLKYFLGIAGGKWVVSYFWVTQSIKERKMLNEHDFEVRGDVVNGRNHQGPKRARESQDRKIFRGLEICCYGPFTNMPTDQLEWMVQLCGASVVKELSSFTLGTGVHPIVVVQPDAWTEDNGFHAIGQMCEAPVVTREWVLDSVALYQCQELDTYLI

PQIP

Nebojsa Mirkovic

The binding site stretches over a longitudinal groove covering surface in both BRCT domains and consisting of residues quite separated iin sequence. Large changes in any of these positions is expected to cause impact of transcription activity of BRCA1, as may be the case with mutations Q1811R and P1812S from our set of unclassified variants.

Mutation Features

Mutation Mutation likelihood

Phylo- genetic entropy

Accessibility

Residue rigidity

Neighor-

hood rigidity

Percent relative volume change

Polarity change

Percent relative

ASA

change

Charge change

M1652I 1 0.27 B -0.53 -1.25 2 0.1

0 0 00

00

M1652K -1 0.27 B -0.53 -1.25 3

0.2

-2 0 00

10

V1653M 1 0.00 B -1.17 -0.83 16 0.8

0 0 00

00

L1657P -1 0.00 E -0.65 -1.05 -32 -1.8

-1 6 00

00

E1660G -1 0.43 E 0.18 -0.67 -57 -2.6

1 -7 00

00

V1665M 1 0.00 B -0.95 -0.87 16 0.8

0 0 00

00

Y1703H 0 0.00 B -0.35 -0.58 -21 -1.4

-2 0 00

10

Nebojsa Mirkovic

Upon mapping, we built comparative structure models for every mutation from our set, based on the crystalographic structure of the wild-type form. Subsequently, we calculated a set of sequence and structure based features. The features were chosen based on generally accepted structural knowledge about the factors that impact protein folding and stability. Changes in their values for a sample sset of mutations are depicted in the table.

YEScharge change

+

buriedness

YES

NO

<30A3 ≥60A3

<90A3

≥90A3

rigid (<-0.7)

rigid (<-0.7)

non-rigid (≥-0.7)

non-rigid (≥-0.7)

exposed buried

residue rigidity

volume change

volume changevolume change

functional site

---

-

-0 or 1 class

phylogenetic entropy

polarity change

0

<0

-

NO

non 0

≥0

YES

+-

“Decision” Tree for Predicting

Functional Impact

of Genetic

Variants

NO

2 class

<60A3

≥30A3

neighborhood rigidity

buriedness

residue rigidity

volume change

charge change

polarity change


other information(helix breaker, turn

breaker)


breaker) +

mutation likelihoodmutation likelihood

volume change

-

residue rigidity

volume change

polarity change



breaker) +

mutation likelihood

functional site

buriednessburiedness

STAR

T



charge changecharge change

Nebojsa Mirkovic

Based on the sequence-structure features and the notion that a change in any of them, if only big enough, can cause functional impact, we designed a decision tree that predicts functional impact of a mutation of interest. If the changes in mutation features are not big, the mutation will end up at the + leaf, otherwise, it will stop at any of the - leafs. The architecture of the tree is fixed, but threshold for the feature changes have been designed so that the decision tree rationalize the characterized mutation set as well as possible.

Rationalization of Functionally Characterized Mutants

M1652I ++ Likely replacement, ‘I’ allowed in multiple sequence alignment.

L1657P ?- Predicted binding site, large volume change in rigid neighborhood, unlikely replacement at completely preserved position.

C1697R -- Two class polarity change, unlikely replacement at completely preserved position.

S1715N ?- Not explained. Likely replacement.

Prediction of Function of Uncharacterized Mutants

M1652T - Large volume change in rigid neighborhood.

V1653M + Likely replacement.

G1738R - Very large volume change at flexible position, unlikely replacement at completely preserved position.

T1773S + Likely replacement, predicted binding site.

Nebojsa Mirkovic

This slide shows a sample output of the decision tree with the mutations the functional classes they belong to and the decision tree output, listing all possible reasons why a mutation is considered to be deleterious. We were able to rationalize 31/37 muutations, and 4 more upon manual analysis, which leaves some space for improvement of the classification procedure. After these promissing results, we predicted functional impact for remaining 57 unclassified variants, and subsequently, our prediction was confirmed for one of them experimentally.

SNP Web Server

Nebojsa Mirkovic

The decision tree has been implemented in the Web server called SNPWeb, that upon providing the identifier for a protein and the mutation, gives prediction and explanation for it. As of now, the server only works for the BRCA1 BRCT region.

Membrane Transporter Project

Nebojsa Mirkovic

Next set of genetic variants that we used in our work was kindly provided by our collaborators from UCSF, who work on membrane transporter superfamily, with focus on the ABC transporter family. Disfunction in ABC transporters is connected to many syndromes (eg, cystic fibrosis) and their overproduction causes resistance to cytotoxic agents. The generic representative is human p-glycoprotein, or MDR1, known for pumping out of the cell lipophilic substances, among them many cytostatics.

Chang, Roth. Science 293, 1793, 2001

Eco-msbA dimer, 1jsq

Domain Organization and Structure of E.Coli ABC Transporter MsbA

1 582

crystalized

not crystalized

A BC

A Walker motif A (375-383)

B Walker motif B (501-505)

C ABC transporter signature (481-489)

transmembrane nucleotide binding

transmembrane

intracellular

extracellular

Nebojsa Mirkovic

ABC transporters are the largest family of membrane transporters, that are present in all kingdoms of life and are highly conserved despite different substrates and mechanisms of their membrane translocation.On this slide, there is depicted domain architecture and crystallographic structure of E.Coli homolog of MDR1, MsbA flipase. The mechanism of the ABC transporter action is dependent on ATP hydrolysis, located in the ATP-binding cassette (or ABC domain). MsbA contains one TM and one ABC region, but in humans, the core consists of two TM regions and two ABC domains, due to the gene duplication. Therefore, human MDR1 is equivalent to the MsbA dimer whose structure is represented in the lower picture.

PMT Database

• a database of in-site detected and/or verified polymorphisms in various mammalian membrane transporters with ample population genetics data (allelic frequencies, ethnic distribution);

• 59 proteins divided in two groups (24 and 25 proteins respectively) depending on the SNP-detection approach;

• 10 ABC transporters, 5 with SNPs reported so far (BSEP, MDR1, MDR3, MRP1, MRP2).

Nebojsa Mirkovic

Since there is no experimental structural information on human membrane transporters, we started the SNP analysis by constructing their protein structure models. THese models served as templates for the structural analysis of SNPs from the UCSF Pharmacogenetics of Membrane Transporters (PMT) database. PMT is a database of in-site detected and/or verified polymorphisms in various mammalian membrane transporters with ample population genetics data (allelic frequencies, ethnic distribution). It contains 10 ABC transporters, 5 with SNPs (BSEP, MDR1, MDR3, MRP1, MRP2).

http://guitar.rockefeller.edu/modbasePieper et al., Nucl. Acids Res. 2002.

http://guitar.rockefeller.edu/modbase

Nebojsa Mirkovic

The protein structure models were produced by automated comparative modeling pipeline called ModPipe and deposited in our local database of protein structure models. ModBase is accessible on the URL written in the upper right corner and a user can query it by different criteria, take a look at model coverage of the protein of interest, modelling alignments, different statistical parameters and model itself in 3D through ModView, our integrated sequence-strucure viewer.- Calculate a set of sequence and structure-based features for each model.Predict functional impact of the SNPs in the dataset based on the feature values and clinical data.

ModPipe Results for Transmembrane Proteins from the PMT Database

• 49 proteins submitted;

• 11 proteins could not be modeled;

• 15 proteins have bad fold assessment;

• 8 proteins have good fold assessment, but bad model score;

• 15 proteins have good fold assessment and good model score.

Protein BSEP MDR1 MRP2 MDR3 MRP1

Coverage 6/9 8/13 3/18 2/7 0/5

Coverage of ABC Transporters

Nebojsa Mirkovic

Upon submission of all the membrane transporter sequences to Mod Pipe (49 of them), for 15 proteins we obtained reliable models covering parts of the sequences. As far as ABC transporters are concerned, we were able to reliably model only the ABC domains, since the only experimental structure containing the TM region, ie. the one of MsbA was not of sufficient quality to serve as a modeling template. Therefore, we covered only 19 out of 52 SNPs (37%).

Modeled regions of MDR1

Predicted Domain Organization and Structure Model of Human ABC Transporter MDR1

1 1280

modeled

not modeled

repeat 1 repeat 2

intracellular, extracellular

ATP-binding loop

transmembrane helix

ATP-binding cassette

1jj7A (383-625) 1g291 (1034-1274)

383

6251034

1274

Nebojsa Mirkovic

On this slide, you can see a graphics representation of the coverage of MDR1, with the models of the 2 ABC domains superposed onto the MsbA dimer.

MDR1

(ABC-B1)Numb. of

models

Seq. Id.

[%]

E-value Coverage

(aa)

Mapped

SNPs

ABC1 6 43-18 -94 -63 ~240 3

ABC2 1 24 -62 ~240 5

SNP ASASecondary

StructureHydrophilicity Volume

Blosum

scoreEnergy

S400N Half Coil - 0.770 +23 + 1 - 3.01

L662R Half Coil - 3.690 +24 - 2 - 4.42

R669C Exposed Helix + 2.710 - 62 - 3 - 14.11

P1051A Half Strand - 0.560 - 23 - 1 - 3.28

W1108R Half Coil - 4.440 - 15 - 3 + 2.80

S1141T Exposed Coil + 0.400 +20 + 1 - 3.72

V1251I Half Strand + 0.800 + 19 + 3 - 1.24

T1256K Half Coil - 1.700 + 42 - 1 - 4.87

Sequence and structure features of SNPs

Nebojsa Mirkovic

Next, we calculated a similar set of sequence and structure-based features for each model. On this slide you can see a sample set of 8 modeled SNPs from MDR1.In predicting functional impact based on the feature values and clinical data we will be using the decision tree described previously as well as neural networks.This is the work in progress.

Significance

• a comprehensive structural study of the functional impact of mutations focused on a particular protein family;

• a large and informative mutation set used;

• rationalization of the characterized mutation set used to develop a predictive tool;

• possible contribution to genetic studies (eg, candidate gene approach) and medical practice (with other methods).

Nebojsa Mirkovic, Carles Ferrer-Costa, Marc A. Marti-Renom, Alvaro N.A. Monteiro, Andrej Sali...

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