Structural Bioinformatics - Lehigh University
Structural BioinformaticsLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
All biological systems are nestedand interacting machinesLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Drug DesignBiofuelsBiomaterialsThe leading edge of technologyMedical DiagnosticsAgricultureLehigh University BioS 10: BioSciences in the 21st CenturyCancerBrian Y. Chen
Drug DesignBiofuelsBiomaterialsHow do these biological systems work?Medical DiagnosticsAgricultureLehigh University BioS 10: BioSciences in the 21st CenturyCancerBrian Y. Chen
Structural Biology can help understandthe foundations of these systemsLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Francis CrickRosalind FranklinJames WatsonSource: pbs.orgLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
X-ray diffraction pattern of B-DNA, by R. FranklinB-DNASource: cmgm.stanford.edu, wikimediacommons.orgLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
SOLEIL beamline diagram, ParisJeol Electron MicrosopeSource: synchrotron-soleil.fr, Jeol.com,cnx.org, esrf.eu, jbc.org, salilab.org.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Molecular surfaces can revealthe active sites of proteinsLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Atomic coordinates can show howproteins bind other moleculesLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Protein topology and secondarystructure suggests flexibilityLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Timeline of Nobel Prizes in Structural BiologyRamakrishnan,2009: Steitz, Yonath2006: Kornberg2003: MacKinnon2002: WuthrichStructural biology has madesignificant contributions1997: Walker1991: Ernst1988: Deisenhofer, Huber,Michel1982: Klug1972: Anfinsen1964: Hodgkin1962: Crick, Watson, Wilkins1962: Perutz, Kendrew2009 Nobel Prize Ceremony1946: SumnerSource: nobelprize.orgLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Structural biology has becomedata richNumber of Entries in the Protein Data Bank600005000040000# 97510000Source: www.pdb.orgLehigh University BioS 10: BioSciences in the21stCenturyBrian Y. Chen
Structural bioinformatics adds scaleand ativeMethodsMolecularSimulationDockingLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Many computational fields support csWebTechnologiesGraphicsArtificialIntelligence andRoboticsLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
The Task:Gather, analyze, and integrate data thatcan indicate biological functionLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
The Data: Chains of amino acids in G----HFCGATLIAPNFV-----MSAAHCVANVNVLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Similar sequences imply similar IHQQFVMTAAHCINSRNVLehigh University BioS 10: BioSciences in the 21st CenturyConSurfGlaser, et al. Bioinformatics, 2003.Evolutionary Trace Mihalek, et al. Proteins, 2006.HMAPTang, et al. J. Mol. Biol. 2003.FASTAMackey, et al. Mol. Cell. Prot. 2002.CLUSTALW Larkin et al. Bioinformatics., 2007.BLASTAltschul et al. Nuc. Acid. Res. 1997.Brian Y. Chen
Two fields of Structural ntegrativeMethodsMolecularSimulationDockingLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Similar active sites imply similar functionTargetMotifMatchKnown functionStructureAlignmentSoftwareUnknown functionMASHChen et al, J. Comput. Biol., 2007Combinatorial Extension Jia et al, J. Comput. Biol., 2004Geometric HashingNussinov et al, Proteins, 2001pevoSOARTseng et al, J. Mol. Biol., 2009SkaPetrey et al, Methods Enzymol. 2003.Geometric SievingChen et al, J. Bioinf. Comput. Biol., 2007PINTSStark et al, Nucleic Acids Res, 2003.JESSBarker et al, Bioinformatics, 2003.DaliHolm et al, Bioinformatics, 2008.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Geometric software matches active sites Input: a Motif and Target protein Output: Target atoms corresponding to motif atoms withlowest RMSD (e.g. most geometric similarity) Corresponding atoms must be chemically equivalentMotif (active site)Target (protein)Approximate numberOf matches to test:2505( )7,817,031,300combinationsFew of TheseLots of TheseLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
RMSD is an average of interpoint high University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
An example of motif matching software Seed Matching– Matching for highest ranked 3 motif points– Distance hashing technique makes this efficient– Produces preliminary Seed MatchesInputSeedMatching Augmentation– Extends Seed Matches to include remainingpoints– Hierarchical depth first searchAugmentationOutputAlgorithms for Structural Comparison and Statistical Analysis of 3D Protein Motifs. Brian Y. Chen*, Viacheslav Y. Fofanov*, David M.Kristensen, Marek Kimmel, Olivier Lichtarge, Lydia E. Kavraki. Proc. Pac. Symp. Biocomput. pp. 334-345, 2005.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Seed Matching Isolate Seed: Three highest-ranking motif atomsInput MotifFindSeedAlgorithms for Structural Comparison and Statistical Analysis of 3D Protein Motifs. Brian Y. Chen*, Viacheslav Y. Fofanov*, David M.Kristensen, Marek Kimmel, Olivier Lichtarge, Lydia E. Kavraki. Proc. Pac. Symp. Biocomput. pp. 334-345, 2005.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Seed Matching Isolate Seed: Three highest-ranking motif atoms Record inter-point distances as red, blue, greenInput MotifRecordDistancesAlgorithms for Structural Comparison and Statistical Analysis of 3D Protein Motifs. Brian Y. Chen*, Viacheslav Y. Fofanov*, David M.Kristensen, Marek Kimmel, Olivier Lichtarge, Lydia E. Kavraki. Proc. Pac. Symp. Biocomput. pp. 334-345, 2005.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Seed Matching Isolate Seed: Three highest-ranking motif atoms Record inter-point distances as red, blue, green Find compatible target points at similar distancesInput MotifFindTarget PointsAlgorithms for Structural Comparison and Statistical Analysis of 3D Protein Motifs. Brian Y. Chen*, Viacheslav Y. Fofanov*, David M.Kristensen, Marek Kimmel, Olivier Lichtarge, Lydia E. Kavraki. Proc. Pac. Symp. Biocomput. pp. 334-345, 2005.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Seed Matching Isolate Seed: Three highest-ranking motif atomsRecord inter-point distances as red, blue, greenFind compatible target points at similar distancesSearch resulting graph for 3-color trianglesInput MotifFindRed EdgesAlgorithms for Structural Comparison and Statistical Analysis of 3D Protein Motifs. Brian Y. Chen*, Viacheslav Y. Fofanov*, David M.Kristensen, Marek Kimmel, Olivier Lichtarge, Lydia E. Kavraki. Proc. Pac. Symp. Biocomput. pp. 334-345, 2005.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Seed Matching Isolate Seed: Three highest-ranking motif atomsRecord inter-point distances as red, blue, greenFind compatible target points at similar distancesSearch resulting graph for 3-color trianglesInput MotifFindBlue EdgesAlgorithms for Structural Comparison and Statistical Analysis of 3D Protein Motifs. Brian Y. Chen*, Viacheslav Y. Fofanov*, David M.Kristensen, Marek Kimmel, Olivier Lichtarge, Lydia E. Kavraki. Proc. Pac. Symp. Biocomput. pp. 334-345, 2005.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Seed Matching Isolate Seed: Three highest-ranking motif atomsRecord inter-point distances as red, blue, greenFind compatible target points at similar distancesSearch resulting graph for 3-color trianglesInput MotifFindGreen EdgesAlgorithms for Structural Comparison and Statistical Analysis of 3D Protein Motifs. Brian Y. Chen*, Viacheslav Y. Fofanov*, David M.Kristensen, Marek Kimmel, Olivier Lichtarge, Lydia E. Kavraki. Proc. Pac. Symp. Biocomput. pp. 334-345, 2005.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Seed Matching Isolate Seed: Three highest-ranking motif atomsRecord inter-point distances as red, blue, greenFind compatible target points at similar distancesSearch resulting graph for 3-color trianglesAlign all Seed Matches by LRMSD, and store in a stackOutput match stackInput MotifOutputTrianglesAlgorithms for Structural Comparison and Statistical Analysis of 3D Protein Motifs. Brian Y. Chen*, Viacheslav Y. Fofanov*, David M.Kristensen, Marek Kimmel, Olivier Lichtarge, Lydia E. Kavraki. Proc. Pac. Symp. Biocomput. pp. 334-345, 2005.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Augmentation Input: Stack populated with Seed Matches.Algorithms for Structural Comparison and Statistical Analysis of 3D Protein Motifs. Brian Y. Chen*, Viacheslav Y. Fofanov*, David M.Kristensen, Marek Kimmel, Olivier Lichtarge, Lydia E. Kavraki. Proc. Pac. Symp. Biocomput. pp. 334-345, 2005.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Augmentation Input: Stack populated with Seed Matches. Pop off a match, get highest ranked unmatched atom PPAlgorithms for Structural Comparison and Statistical Analysis of 3D Protein Motifs. Brian Y. Chen*, Viacheslav Y. Fofanov*, David M.Kristensen, Marek Kimmel, Olivier Lichtarge, Lydia E. Kavraki. Proc. Pac. Symp. Biocomput. pp. 334-345, 2005.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Augmentation Input: Stack populated with Seed Matches. Pop off a match, get highest ranked unmatched atom P Find compatible target atoms in the vicinity of PPPAlgorithms for Structural Comparison and Statistical Analysis of 3D Protein Motifs. Brian Y. Chen*, Viacheslav Y. Fofanov*, David M.Kristensen, Marek Kimmel, Olivier Lichtarge, Lydia E. Kavraki. Proc. Pac. Symp. Biocomput. pp. 334-345, 2005.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Augmentation Input: Stack populated with Seed Matches. Pop off a match, get highest ranked unmatched atom P Find compatible target atoms in the vicinity of P32PPP1Algorithms for Structural Comparison and Statistical Analysis of 3D Protein Motifs. Brian Y. Chen*, Viacheslav Y. Fofanov*, David M.Kristensen, Marek Kimmel, Olivier Lichtarge, Lydia E. Kavraki. Proc. Pac. Symp. Biocomput. pp. 334-345, 2005.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Augmentation Input: Stack populated with Seed Matches.Pop off a match, get highest ranked unmatched atom PFind compatible target atoms in the vicinity of PTest alignments with each atom compatible with PGoodP32PP1P1P3BadAlgorithms for Structural Comparison and Statistical Analysis of 3D Protein Motifs. Brian Y. Chen*, Viacheslav Y. Fofanov*, David M.Kristensen, Marek Kimmel, Olivier Lichtarge, Lydia E. Kavraki. Proc. Pac. Symp. Biocomput. pp. 334-345, 2005.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
AugmentationInput: Stack populated with Seed Matches.Pop off a match, get highest ranked unmatched atom PFind compatible target atoms in the vicinity of PTest alignments with each atom compatible with PPut successful alignments back on stack, or store completedmatchesGoodSuccessful AlignmentP32PP1P1PBad3Completed Matches Algorithms for Structural Comparison and Statistical Analysis of 3D Protein Motifs. Brian Y. Chen*, Viacheslav Y. Fofanov*, David M.Kristensen, Marek Kimmel, Olivier Lichtarge, Lydia E. Kavraki. Proc. Pac. Symp. Biocomput. pp. 334-345, 2005.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Matching Output Matching atoms with greatest geometric similarity Corresponding atoms with similar chemical propertiesMotif (active site)Input MotifTarget (protein)MatchingAtomsAlgorithms for Structural Comparison and Statistical Analysis of 3D Protein Motifs. Brian Y. Chen*, Viacheslav Y. Fofanov*, David M.Kristensen, Marek Kimmel, Olivier Lichtarge, Lydia E. Kavraki. Proc. Pac. Symp. Biocomput. pp. 334-345, 2005.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Matching can suggest unknown function Matching suggests that BioHperforms a carboxylesterasefunctionMOTIF: CatalyticTriad, LipaseTARGET:E. Coli BioHIntegrating structure, bioinformatics, and enzymology to discover function:BioH, a new carboxylesterase from Escherichia coli.Sanishvili R, et al. J. Biol. Chem. 278(28):26039-45, 2003.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Matching can suggest unknown function Matching suggests that BioHperforms a carboxylesterasefunctionMatching TriadMOTIF: CatalyticTriad, LipaseTARGET:E. Coli BioHIntegrating structure, bioinformatics, and enzymology to discover function:BioH, a new carboxylesterase from Escherichia coli.Sanishvili R, et al. J. Biol. Chem. 278(28):26039-45, 2003.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Two fields of Structural ntegrativeMethodsMolecularSimulationDockingLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Matching doesn’t tell us everythingHow does this protein fit in the system?What parts of the protein make it work?FunctionalSiteComparisonLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Specificity is preferential bindingSpecificity is an aspect of functionLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Cavity shape influences specificityLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Proteins with the same function can havedifferent specificityLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
VASP isolates differences in cavity shape tofind influences on specificityVASP: Volumetric Analysis of the Surfaces of Proteins Identify amino acids that alter cavity shape Identify subcavities that alter cavity shapeVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
The VASP procedureInput:A icComparisonOf CavitiesOutput:VolumetricDifferencesVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
The VASP procedureInput:A icComparisonOf CavitiesOutput:VolumetricDifferencesSkaPetrey D, Honig B. GRASP2: visualization, surface properties, and electrostatics ofmacromolecular structures and sequences. Methods Enzymol. 374:492-509. 2003.VASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
The VASP procedureInput:A icComparisonOf CavitiesOutput:VolumetricDifferencesComputational Solid GeometryVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Computational Solid Geometry (CSG)VASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
CSG was originally for modeling partsVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Computational Solid Geometry (CSG)AABBBoolean SetOperationsUnionIntersectionDifferenceVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Using CSG with protein structuresVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
The VASP procedureInput:A icComparisonOf CavitiesOutput:VolumetricDifferencesVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Begin with the molecular surfaceSchematicVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Compute an envelope surfaceSchematicVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Find the interior surfaceSchematicVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Identify nearby amino acidsSchematicVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Compute the convex hullSchematicBarber, C.B., Dobkin, D.P., and Huhdanpaa, H.T., ACM T Math Software, 22(4):469-483Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
CSG hull minus molecular surfaceSchematicVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
CSG intersection with the envelope surfaceSchematicVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Remove disconnected piecesSchematicVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
The VASP procedureInput:A esVolumetricComparisonOf CavitiesOutput:VolumetricDifferences Amino Acids affecting cavity shape Subcavities affecting cavity shapeVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Finding amino acids that affect cavity shapeVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Finding amino acids that affect cavity shapeVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Finding amino acids that affect cavity shape12546783910Å301 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Amino AcidsVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Three proteins with different preferences{ Tyr, Phe, Trp }{ Arg, Lys }{ Ala, Gly, Val, . } ChymotrypsinTrypsinElastaseVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
VASP finds amino acids in trypsinsthat influence specificityAmino Acid Sequence NumberChymotrypsinTrypsinSteitz T.A., Henderson R., Blow D.M. Structure of crystalline alpha-chymotrypsin. 3. Crystallographic studies ofsubstrates and inhibitors bound to the active site of alpha-chymotrypsin. J. Mol. Biol. 46(2): 337-348. 1969.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
VASP finds amino acids in elastasethat influence specificityAmino Acid Sequence NumberChymotrypsinElastaseShotton D.M., Watson H.C. Three-dimensional structure of tosyl-elastase.Nature 225(5235): 811-816. 1970.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
The VASP procedureInput:A es VolumetricComparisonOf CavitiesOutput:VolumetricDifferencesAmino Acids affecting cavity shapeSubcavities affecting cavity shapeVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
What makes A cavities different from B?ABVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
What is common in A?ABIntersectionVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
What is the maximum extent of B?ABIntersectionUnionVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
All parts of A that are not in any part of BABUnionIntersectionDifferenceoutputVASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand BindingSpecificity. Brian Chen and Barry Honig. PLOS Computational Biology. 6(8): e1000881.Lehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
VASP finds subcavities in trypsins andelastases that influence specificityTrypsin IntersectionElastase UnionLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Computers can help us understandprotein tiveMethodsMolecularSimulationDockingLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Protein function forms a basis foranalyzing bigger systems andharder biological problemsLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Drug DesignBiofuelsBiomaterialsProtein functions drive technologyMedical DiagnosticsAgricultureLehigh University BioS 10: BioSciences in the 21st CenturyCancerBrian Y. Chen
Drug DesignBiofuelsBiomaterialsBioinformatics impacts many problemsMedical DiagnosticsAgricultureLehigh University BioS 10: BioSciences in the 21st CenturyCancerBrian Y. Chen
Spring 2011: Introduction to Bioinformatics An introduction to combining computation with biology to solvebiological problems Recommended for BioS, BioE, CSE, and Math students. No programming experience required Semester Project on a Genome or Algorithm of your choice– Extra Credit for Collaborative Interdisciplinary Projects Topics include:– Sequence Alignment, Multiple Sequence Alignment– Phylogenetic Trees and Reticulate Evolution– DNA Sequencing and DNA Microarrays– Gene Regulatory Networks– Genome Annotation, The Cancer Genome Atlas– Transcription Factor Binding Site PredictionLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Fall: Structural Bioinformatics A survey of geometric algorithms for understanding proteinfunctions from structure Recommended for BioS, BioE, CSE, Math seniors, grad students. No programming experience required Semester Project on finding similar functional sites– Interdisciplinary Collaboration with experts in other fields Topics include:– Whole structure alignment and the Space of Protein Folds– Protein surfaces, cavities, and electrostatics– Protein-protein, Protein-DNA interfaces, interactions– Protein Structure Prediction, Simulation, Docking– Structural Bioinformatics in Pharmaceutical discovery– Function annotation, active site prediction, geometric matchingLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
QuestionsLehigh University BioS 10: BioSciences in the 21st CenturyBrian Y. Chen
Structural bioinformatics adds scale and precision Structural Bioinformatics Structure Prediction Integrative Methods Molecular Simulation Structure Alignment Functional Site Comparison Docking . Lehigh University BioS 10: BioSciences in the 21st Century Brian Y. Chen Many computational fields support Structural Bioinformatics Structural
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Bioinformatics Crash Course Ian Misner Ph.D. Bioinformatics Coordinator UMD Bioinformatics Core . Bioinformatics!Core The Plan Monday – Introductions – Linux and Python Hands-on Training Tuesday – NGS Introduction – RNAseq with Sailfish (Dr. Steve Mount, CBCB) – RNAse
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Introduction Origami is the art of folding 2D materials, such as a flat sheet of paper, into 3D objects with desired shapes. Since early 1980s, origami has evolved into a fertile scientific field connecting diverse disciplines, creating an
