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Sefika Banu Ozkan
Arizona State University-Tempe Campus
$1,067,328
Attributed
$2,134,655
Total exposure
2
Grants
1
Lead (contact PI)
Attributed= this PI's even-split share of every grant they're on (the fair, additive number). Exposure = full size of all those grants.
Funding over time
peak $504.1K · FY2016–25$1M$750K$500K$250K$0
'16
'17
'18
'19
'20
'21
'22
'23
'24
'25
Funding mix
By agency
NIH$2,134,655 · 2
By mechanism
R01$1,755,030 · 1
R21$379,625 · 1
Top collaborators
- Liskin Swint-Kruse5 shared
- Giovanna Ghirlanda2 shared
Most similar at Arizona State University-Tempe Campus
Same institution · by research overlap
- Melissa A Wilson$3,336,690
- Petr Sulc$432,531
- Giovanna Ghirlanda$189,813
- Chao Wang$2,852,096
Others in their field
Top investigators on “Binding Sites”
- Barton F Haynes · Duke University$271,615,822
- Michael P Snyder · Yale University$102,650,300
- David Heimbrook · Leidos Biomedical Research, Inc.$93,692,184
- Wafaa M. El-Sadr · Columbia University Health Sciences$91,263,426
- Myron S Cohen · Family Health International$91,263,426
- Paul S. Aisen · Cognition Therapeutics, Inc.$72,776,809
Research focus
Binding SitesComputerized ToolsSpecificityAffinityVariantComputersPositioning AttributeProteinsViralAmino AcidsHomologous ProteinBindingPropertyFlexibilityPredictive ToolsAmino Acid SubstitutionEscherichia ColiEquilibriumHuman GeneticsCollaborationsCyclic Amp Receptor ProteinFunctional OutcomesCoupledDistal
Grant awards (7)
Using dynamic network models to quantitatively predict changes in binding affinity/specificity that arise from long-range amino acid substitutions$416,686
R01 · FY2025 · GM · contact PI
Using dynamic network models to quantitatively predict changes in binding affinity/specificity that arise from long-range amino acid substitutions$410,129
R01 · FY2024 · GM · contact PI
Using dynamic network models to quantitatively predict changes in binding affinity/specificity that arise from long-range amino acid substitutions$410,129
R01 · FY2023 · GM · contact PI
Using dynamic network models to quantitatively predict changes in binding affinity/specificity that arise from long-range amino acid substitutions$93,975
R01 · FY2023 · GM · contact PI
Using dynamic network models to quantitatively predict changes in binding affinity/specificity that arise from long-range amino acid substitutions$424,111
R01 · FY2022 · GM · contact PI
New Tools for Glycoscience: Engineering Targeted Lectins by Computer-Guided Directed Evolution$172,624
R21 · FY2017 · AI
New Tools for Glycoscience: Engineering Targeted Lectins by Computer-Guided Directed Evolution$207,001
R21 · FY2016 · AI