Application of Metabolic Modeling to Predict the Effects ...FIGURE 2. Manual Verification of Residue...
1
FIGURE 2. Manual Verification of Residue Conservation Yeast delta-aminolevulinic acid dehydratase for S. cerevisiae was queried against the entire human proteome in reciprocal BLAST. The only human hit is also delta-aminolevulinic acid for H. sapiens. The active site, annotated by genbank to occur at human position 221, corresponds to yeast position 232. Here we see conservation not only among the active site residues, but also in the surrounding region. Application of Metabolic Modeling to Predict the Effects of Human nsSNV Orthologs in Yeast Hayley Dingerdissen 1,2 , Dan Weaver 3 , Peter Karp 3 , Yang Pan 1 , Vahan Simonyan 2 , Raja Mazumder 1 1 Department of Biochemistry and Molecular Biology, The George Washington University, Washington, DC, 20037 2 Food and Drug Administration, Center for Biologics Evaluation and Research, Rockville, MD, 20852 3 Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, CA, 94025 We have previously identified the entire set of human genes with single-nucleotide variation causing a nonsynonymous change in the active site residue of the corresponding proteins. 32 of these proteins were shown to be ideal candidates for modeling and laboratory validation based on known biochemical relationships. Reciprocal BLAST followed by manual alignment and pathway comparison between human and yeast orthologs identified 6 of these sets as mutual best hits with conserved active site residues and pathway involvement. 5 of these are implicated in human disease. We hyphothesize that mutation of the active site residues in yeast orthologs should result in an observable change in substrate and product quantities, as compared to wild-type. We attempted to verify this prediction by evaluation of the 6 enzymes using the Yeast 7 FBA model, followed by experimental testing of the YeastCyc/MetaFlux [Latendresse et al. 2012] FBA model. Preliminary findings show 1 enzyme having no growth for the in silico knockout constructs. ABSTRACT FINDING MODELING CANDIDATES FIGURE 1. Schema of candidate identification The method consists of simple steps and queries which could easily be automated for future studies. RESULTS ACKNOWLEDGEMENTS AND REFERENCES Altschul, S. F. et al. (1997) Nucleic acids research. 25, 3389-402. Dingerdissen et al. (2013) The FEBS journal. 280, 1542-62. Forbes, S. A. et al. (2011) Nucleic acids research. 39, D945-50. Heavner, B. D. et al. (2012) BMC systems biology. 6, 55. Karagiannis, et al. (2013) Genomics, proteomics & bioinformatics. 11, 122-6. Karp, P. D. et al. (2010) Briefings in bioinformatics. 11, 40-79. ORTHOLOGOUS ACTIVE SITE CONSERVATION Quer y 15 SSVLAGGYNHPLLRQWQS- ERQLTKNMLI FPLFI SDNPDDFTEI DSLPNI NRI GVNRLKD 73 SVL GY HPLLR WQ+ L + LI +P+F++D PDD I SLP + R GV RL++ Sbj ct 4 QSVLHSGYFHPLLRAWQTATTTLNASNLI YPI FVTDVPDDI QPI TSLPGVARYGVKRLEE 63 Quer y 74 YLKPLVAKGLRSVI LFGVP- LI PGTKDPVGTAADDPAGPVI QGI KFI REYFPELYI I CDV 132 L+PLV +GLR V++FGVP +P KD G+AAD P I+ I +R+ FP L + CDV Sbj ct 64 MLRPLVEEGLRCVLI FGVPSRVP- - KDERGSAADSEESPAI EAI HLLRKTFPNLLVACDV 121 Quer y 133 CLCEYTSHGHCGVLYDDGTI NRERSVSRLAAVAVNYAKAGAHCVAPSDMI DGRI RDI KRG 192 CLC YTSHGHCG+L ++G E S RLA VA+ YAKAG VAPSDM+DGR+ IK Sbj ct 122 CLCPYTSHGHCGLLSENGAFRAEESRQRLAEVALAYAKAGCQVVAPSDMMDGRVEAI KEA 181 Quer y 193 LI NANLAHKTFVLSYAAKFSGNLYGPFRDAACSAPSNGD R KCYQLPPAGRGLARRALERD 252 L+ L ++ V+SY+AKF+ YGPFRDAA S+P+ GD R +CYQLPP RGLA RA++RD Sbj ct 182 LMAHGLGNRVSVMSYSAKFASCFYGPFRDAAKSSPAFGD R RCYQLPPGARGLALRAVDRD 241 Quer y 253 MSEGADGI I VKPSTFYLDI MRDASEI CKDLPI CAYHVSGEYAMLHAAAEKGVVDLKTI AF 312 + EGAD ++VKP YLDI +R+ + DLP+ YHVSGE+AML A+ G DLK Sbj ct 242 VREGADMLMVKPGMPYLDI VREVKDKHPDLPLAVYHVSGEFAMLWHGAQAGAFDLKAAVL 301 Quer y 313 ESHQGFLRAGARLI I TYLAPEFLDWLDEE 341 E+ F RAGA +I I TY P+ L WL EE Sbj ct 302 EAMTAFRRAGADI I I TYYTPQLLQWLKEE 330 CONCLUSIONS • Preliminary FBA modeling shows predictive support to our hypothesis that nsSNV at the active site should disrupt enzyme function and can induce disease • 5 enzymes with known deleterious effects have no SNP linked to OMIM and may be candidates for disease discovery • Laboratory validation may enable development of biochemical assays to test for the presence of active site variation using yeast as a model. Human Yeast Reaction Tested Yeast 7 FBA Results YeastCyc/MetaFlux FBA Results Knock-out growth Explanation Knock-out growth Explanation P13176 P05373 Porphobilinogen synthesis WT growth Protoheme IX / siroheme not in biomass equation No growth Protoheme IX and siroheme block P22830 P16622 Protoheme ferrochelatase WT growth Protoheme IX not present in Yeast 7 biomass equation No growth Protoheme IX block P48637 Q08220 Glutathione synthesis* WT growth 0 flux through reaction in WT WT growth No flux through reaction in current model P49247 Q12189 Ribose-5-phosphate isomerization No growth No growth Phosphoribosyl- pyrophosphate block Q96GX9 P47095 Methylthioribulose 1- phosphate dehydratase No growth Cofactor synthesis blocked Q9Y2Z4 P48527 Mitochondrial tyrosine- tRNA-ligase reaction WT growth Mitochondrial protein synthesis not represented WT growth No flux through reaction in current model* Of all the knockouts modeled, Q12189 is the only enzyme to predict no growth in both models. However, the Yeast 7 model does not currently model heme synthesis, and neither model represents mitochondrial protein synthesis requirements. P47095 impact could not be modeled as the Yeast 7 model contains no corresponding gene. *These reactions are not fully accounted for in the current MetaFlux model so results cannot be considered reliable. Latendresse, M. et al. (2012) Bioinformatics. 28, 388-96. Sherry, S. T. et al. (2001) Nucleic acids research. 29, 308-11. Tanabe, M. & Kanehisa, M. (2012) Current protocols in bioinformatics. Ch 1, Unit1-12. Uniprot Consortium. (2012) Nucleic acids research. 40, D71-5. Walker, B.N.., Antonakos, C., Retterer, S.T. & Vertes, A. (2013) Angewandte Chemie. 125, 3738-3741 We would like to thank Akos Vertes, Ph.D., Professor of Chemistry, Biochemistry and Molecular Biology at the George Washington University, for discussion and insight in the direction of this effort.
Transcript of Application of Metabolic Modeling to Predict the Effects ...FIGURE 2. Manual Verification of Residue...
FIGURE 2. Manual Verification of Residue Conservation Yeast delta-aminolevulinic acid dehydratase for S. cerevisiae was queried against the entire human proteome in reciprocal BLAST. The only human hit is also delta-aminolevulinic acid for H. sapiens. The active site, annotated by genbank to occur at human position 221, corresponds to yeast position 232. Here we see conservation not only among the active site residues, but also in the surrounding region.
Application of Metabolic Modeling to Predict the Effects of Human nsSNV Orthologs in Yeast
Hayley Dingerdissen1,2, Dan Weaver3, Peter Karp3 , Yang Pan1, Vahan Simonyan2, Raja Mazumder1
1Department of Biochemistry and Molecular Biology, The George Washington University, Washington, DC, 20037
2Food and Drug Administration, Center for Biologics Evaluation and Research, Rockville, MD, 20852 3Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, CA, 94025
We have previously identified the entire set of human genes with single-nucleotide variation causing a nonsynonymous change in the active site residue of the corresponding proteins. 32 of these proteins were shown to be ideal candidates for modeling and laboratory validation based on known biochemical relationships. Reciprocal BLAST followed by manual alignment and pathway comparison between human and yeast orthologs identified 6 of these sets as mutual best hits with conserved active site residues and pathway involvement. 5 of these are implicated in human disease. We hyphothesize that mutation of the active site residues in yeast orthologs should result in an observable change in substrate and product quantities, as compared to wild-type. We attempted to verify this prediction by evaluation of the 6 enzymes using the Yeast 7 FBA model, followed by experimental testing of the YeastCyc/MetaFlux [Latendresse et al. 2012] FBA model. Preliminary findings show 1 enzyme having no growth for the in silico knockout constructs.
ABSTRACT
FINDING MODELING CANDIDATES
FIGURE 1. Schema of candidate identification The method consists of simple steps and queries which could easily be automated for future studies.
RESULTS
ACKNOWLEDGEMENTS AND REFERENCES
Altschul, S. F. et al. (1997) Nucleic acids research. 25, 3389-402. Dingerdissen et al. (2013) The FEBS journal. 280, 1542-62. Forbes, S. A. et al. (2011) Nucleic acids research. 39, D945-50. Heavner, B. D. et al. (2012) BMC systems biology. 6, 55. Karagiannis, et al. (2013) Genomics, proteomics & bioinformatics. 11, 122-6. Karp, P. D. et al. (2010) Briefings in bioinformatics. 11, 40-79.
ORTHOLOGOUS ACTIVE SITE CONSERVATION Quer y 15 SSVLAGGYNHPLLRQWQS- ERQLTKNMLI FPLFI SDNPDDFTEI DSLPNI NRI GVNRLKD 73 SVL GY HPLLR WQ+ L + LI +P+F++D PDD I SLP + R GV RL++ Sbj ct 4 QSVLHSGYFHPLLRAWQTATTTLNASNLI YPI FVTDVPDDI QPI TSLPGVARYGVKRLEE 63 Quer y 74 YLKPLVAKGLRSVI LFGVP- LI PGTKDPVGTAADDPAGPVI QGI KFI REYFPELYI I CDV 132 L+PLV +GLR V++FGVP +P KD G+AAD P I + I +R+ FP L + CDV Sbj ct 64 MLRPLVEEGLRCVLI FGVPSRVP- - KDERGSAADSEESPAI EAI HLLRKTFPNLLVACDV 121 Quer y 133 CLCEYTSHGHCGVLYDDGTI NRERSVSRLAAVAVNYAKAGAHCVAPSDMI DGRI RDI KRG 192 CLC YTSHGHCG+L ++G E S RLA VA+ YAKAG VAPSDM+DGR+ I K Sbj ct 122 CLCPYTSHGHCGLLSENGAFRAEESRQRLAEVALAYAKAGCQVVAPSDMMDGRVEAI KEA 181 Quer y 193 LI NANLAHKTFVLSYAAKFSGNLYGPFRDAACSAPSNGDRKCYQLPPAGRGLARRALERD 252 L+ L ++ V+SY+AKF+ YGPFRDAA S+P+ GDR+CYQLPP RGLA RA++RD Sbj ct 182 LMAHGLGNRVSVMSYSAKFASCFYGPFRDAAKSSPAFGDRRCYQLPPGARGLALRAVDRD 241 Quer y 253 MSEGADGI I VKPSTFYLDI MRDASEI CKDLPI CAYHVSGEYAMLHAAAEKGVVDLKTI AF 312 + EGAD ++VKP YLDI +R+ + DLP+ YHVSGE+AML A+ G DLK Sbj ct 242 VREGADMLMVKPGMPYLDI VREVKDKHPDLPLAVYHVSGEFAMLWHGAQAGAFDLKAAVL 301 Quer y 313 ESHQGFLRAGARLI I TYLAPEFLDWLDEE 341 E+ F RAGA +I I TY P+ L WL EE Sbj ct 302 EAMTAFRRAGADI I I TYYTPQLLQWLKEE 330
CONCLUSIONS • Preliminary FBA modeling shows predictive support to our
hypothesis that nsSNV at the active site should disrupt enzyme function and can induce disease
• 5 enzymes with known deleterious effects have no SNP linked to OMIM and may be candidates for disease discovery
• Laboratory validation may enable development of biochemical assays to test for the presence of active site variation using yeast as a model.
Human Yeast Reaction Tested Yeast 7 FBA Results YeastCyc/MetaFlux FBA Results Knock-out growth
Explanation Knock-out growth
Explanation
P13176 P05373 Porphobilinogen synthesis
WT growth Protoheme IX / siroheme not in biomass equation
No growth Protoheme IX and siroheme block
P22830 P16622 Protoheme ferrochelatase
WT growth Protoheme IX not present in Yeast 7 biomass equation
No growth Protoheme IX block
P48637 Q08220 Glutathione synthesis* WT growth 0 flux through reaction in WT
WT growth No flux through reaction in current model
P49247 Q12189 Ribose-5-phosphate isomerization
No growth No growth Phosphoribosyl-pyrophosphate block
Q96GX9 P47095 Methylthioribulose 1-phosphate dehydratase
No growth Cofactor synthesis blocked
Q9Y2Z4 P48527 Mitochondrial tyrosine-tRNA-ligase reaction
WT growth Mitochondrial protein synthesis not represented
WT growth No flux through reaction in current model*
Of all the knockouts modeled, Q12189 is the only enzyme to predict no growth in both models. However, the Yeast 7 model does not currently model heme synthesis, and neither model represents mitochondrial protein synthesis requirements. P47095 impact could not be modeled as the Yeast 7 model contains no corresponding gene. *These reactions are not fully accounted for in the current MetaFlux model so results cannot be considered reliable.
Latendresse, M. et al. (2012) Bioinformatics. 28, 388-96. Sherry, S. T. et al. (2001) Nucleic acids research. 29, 308-11. Tanabe, M. & Kanehisa, M. (2012) Current protocols in bioinformatics. Ch 1, Unit1-12. Uniprot Consortium. (2012) Nucleic acids research. 40, D71-5. Walker, B.N.., Antonakos, C., Retterer, S.T. & Vertes, A. (2013) Angewandte Chemie. 125,
3738-3741
We would like to thank Akos Vertes, Ph.D., Professor of Chemistry, Biochemistry and Molecular Biology at the George Washington University, for discussion and insight in the direction of this effort.
![The Dehydratase ADT3 Affects ROS Homeostasis and … · The Dehydratase ADT3 Affects ROS Homeostasis and Cotyledon Development1[OPEN] Alessia Para, DurreShahwar Muhammad, Danielle](https://static.fdocuments.net/doc/165x107/5cca391988c993570d8d8d21/the-dehydratase-adt3-affects-ros-homeostasis-and-the-dehydratase-adt3-affects.jpg)
