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Nagarajan Vaidehi, Ph.D. Research

Proteins are allosteric nano-machines whose conformational dynamics controls their functional versatility. Conformational dynamics is important in understanding the allosteric nature of proteins, in identifying allosteric druggable sites as well as in designing drugs with functional specificity.
 
Biophysical experimental methods provide fragmented information on the structure and dynamics and the X-ray crystallography provides a static picture of one of the low energy conformations in an ensemble of states. Therefore computational methods are essential in integrating the experimental information and provide an atomic level detail of the dynamics of proteins. One of the major bottlenecks in using the existing computational methods to study dynamics of proteins is the limitation in time scale and the narrow conformational search afforded by these methods. Thus we need multi-scale computational methods that span a larger range in time and length scale to extend the use of computational methods to large protein complexes. Our laboratory is focused on developing state of the art multi-scale computational methods to study the conformational dynamics of proteins. We are developing coarse grain computational methods to sample the various kinetic states of the protein dynamics, followed by fine grain computational methods to capture the detailed atomic level structural changes and to calculate the thermodynamic properties.
 
Our research projects include:
 
  1. Development of constrained molecular dynamics methods – GNEIMO
  2. Development of coarse grained conformational sampling method for G-protein coupled receptors (GPCRs) – GPCRSimKit
  3. Development of computational method for designing thermostable mutants for GPCRs – LITiConDesign
  4. Development and application of computational methods to identify allosteric sites for drug design in protein-protein complexes – AlloBindSite
  5. Application of these methods to design drugs with functional specificity for GPCRs targeting pancreatic cancer and other cancers – Chemokine
 

GNEIMO

A Hierarchical framework for constrained molecular dynamics method
 
Molecular dynamics (MD) simulation is a powerful computational tool in structural biology, widely used for understanding conformational changes in proteins, and folding of peptides. However MD simulations using Cartesian dynamics model is limited by the total simulation time scale being in tens of nanoseconds for large proteins. Biological processes on the other hand need microseconds of simulation time. We developed the Internal Coordinate Molecular Dynamics (ICMD) algorithms in the early 1990s to enable larger simulation time-steps and they show great promise in long time scale simulations. Despite their promise, ICMD techniques have made little progress due in large part to the additional mathematical complexity of internal coordinate models. As a NIH-NIGMS project and in collaboration with Dr. Abhi Jain at the NASA Jet Propulsion Laboratory at Caltech, we are developing the ICMD methods called Generalized Newton-Euler Inverse Mass Operator (GNEIMO) to enable long time scale and wider conformational search simulations. These simulations have been applied to various biological problems such as
 
  1. Study of large scale conformational dynamics of proteins wherein we showed the NMR based ensemble of conformations of calmodulin was sampled by GNEIMO method.
  2. Structural refinement of homology models of proteins.
  3. Ab initio folding of simple proteins.
 
We are now initiating a collaboration with Lawrence Berkeley laboratory to use the GNEIMO method with the program “PHENIX” that to fit models to X-ray crystallography and low resolution electron microscopy measurements.
The GneimoSim software can be downloaded free of cost for academic use from:
http://dartslab.jpl.nasa.gov/GNEIMO/index.php.

Publications related to this project:
  1. Jain A, Vaidehi N, Rodriguez G A 1993, Fast Recursive Algorithm For Molecular-Dynamics Simulation, J Comput Phys 106: (2) 258-268.
  2. Vaidehi N., Jain A., Goddard III, W.A., 1996, Constant temperature constrained molecular dynamics: The Newton-Euler inverse mass operator method, J Phys Chem-100: (25) 10508-10517.
  3. Bertsch R.A., Vaidehi  N., Chan S.I., et al. 1998,  Kinetic steps for alpha-helix formation Proteins: Structure, Function and Genetics, 33: (3) 343-357.
  4. Vaidehi N., Goddard W.A., 2000, Domain motions in phosphoglycerate kinase using hierarchical NEIMO molecular dynamics simulations,  J Phys. Chem.  A 104: (11) 2375-2383.
  5. Balaraman GS, Park IH, Jain A, Vaidehi N. 2011, Folding of Small Proteins Using Constrained Molecular Dynamics. Journal of Physical Chemistry B. 115(23):7588-96.
  6. Park, I.H., Wagner, J., Jain A, Vaidehi N. 2012, Structure Refinement of Protein Low Resolution Models Using the GNEIMO Constrained Dynamics Method Folding of Small Proteins Using Constrained Molecular Dynamics, J.  Phys. Chem. B, 116, 2365-75.
  7. Jain A, Park IH, Vaidehi N. 2012, Equipartition principle for internal coordinate molecular dynamics,  J Chem Theory Comput. 14;8(8):2581-2587.
  8. Wagner JR, Balaraman GS, Niesen MJ, Larsen AB, Jain A, Vaidehi N. 2013, Advanced techniques for constrained internal coordinate molecular dynamics, J Comput Chem. 34(11):904-14
  9. Gangupomu VK, Wagner JR, Park IH, Jain A and Vaidehi N., 2013, Mapping conformational dynamics of proteins using torsional dynamics simulations, Biophys. J., 104, 1999-2008.
  10. Jain A., Kandel, S., Wagner, JE., Larsen AB, and Vaidehi N., 2013, Fixman compensating potential for general branched molecules, J. Chem. Phys. 139, 244103.
  11. Larsen AB, Wagner, J.R., Jain A and Vaidehi N., 2014, Protein Structure Refinement of CASP target Proteins suing GNEIMO torsional dynamics method, J. Chem. Inf. Model. 2014  24;54(2):508-17.
  12. Larsen, AB, Wagner JE., Kandel S., Salomon-Ferrer R., Vaidehi N., and Jain A, 2014, GneimoSim: A modular Internal Coordinates Molecular Dynamics Simulation Package, J. Comp. Chem. In press.
 

GPCRSimKit

A Multiscale computational framework for studying G-protein coupled receptors (GPCRs)
 
G-protein coupled receptors (GPCRs) play an important role in the physiology and in the pathophysiology of many serious diseases. They form the largest superfamily of drug targets. Since GPCRs are membrane bound and are highly dynamic, obtaining three dimensional structural information for GPCRs is a feat and it requires a confluence of various biophysical techniques that include computational methods. The crystal structure is a snapshot in the conformational ensemble that the receptor samples in the absence of any stimulant. We are developing multiscale simulation method suite, GPCRSimKit, that integrates coarse grain simulation method with fine grain techniques. The GPCRSimKit will enable simulation of the dynamics of GPCR conformational ensemble starting from the inactive crystal structures or refine homology models for drug design. The GPCRSimkit will allow calculation of the modulation of the potential energy landscape by full, partial, and inverse agonists. This platform of computational techniques, will lay a theoretical basis and play a crucial role as more crystal structures of GPCRs get published.
 
 
Publications Related to this project:
  1. Vaidehi, N., et al 2002, Structure and Function prediction for G-Protein Coupled Receptors, Proc. Natl. Acad. Sci., USA, 99, 12622-12627.
  2. Bhattacharya S., Hall S.E., Li H., Vaidehi N. Ligand-stabilized conformational states of human beta(2) adrenergic receptor: insight into G-protein-coupled receptor activation. Biophys J. 2008, 94(6):2027-42.
  3. Bhattacharya, S., Hall, S.E. and Vaidehi N., 2008, Agonist induced conformational changes in bovine rhodopsin: Insight into activation of G-protein coupled receptors, J. Mol. Biol. 382, 539-555.
  4. Hall, S.E., Roberts, K., and Vaidehi, N., 2009, Position of helical kinks in membrane protein crystal structures and the accuracy of computational prediction, J. Mol. Graph. & Mod. 27, 944-950.
  5. Hall S.E.,  Mao, A. Nicolaidou, V., Finelli, M., Wise, E.L., Nedjai, B., Kanjanapangka, J., Harirchian, P., Chen, D., Selchau, V., Ribeiro, S.,  Schyler, S.,  Pease, J.E.,  Horuk R., and Vaidehi, N.  2009, Elucidation of binding sites of dual antagonists in the human chemokine receptors CCR2 and CCR5. Mol. Pharm. 75, 1325-1336.
  6. Vaidehi, N., Pease, J. and Horuk R., 2009, Modeling Small Molecule Compound Binding to G-Protein Coupled Receptors, Methods in Enzymology, 460, 263-288.
  7. Bhattacharya S, and Vaidehi N. 2010, Computational Mapping of the Conformational Transitions in Agonist Selective Pathways of a G-Protein Coupled Receptor, J Am Chem Soc. 132(14):5205-14.
  8. Bhattacharya et al 2010, Allosteric Antagonist Binding Sites in Class B GPCRs: Corticotropin Receptor 1, J Comput Aided Mol Des. 8, 659-74.
  9. Vaidehi, N., 2010, Dynamics and Flexibility of G-protein coupled receptor conformations and its relevance to drug design, Drug Discovery Today, 15, 951-957 – invited review. 
  10. Vaidehi N., and Kenakin T., 2010, Conformational Ensembles of Seven Transmembrane Receptors and their Relevance to Functional Selectivity, Curr. Opinion. Pharmacology, 10, 775-781- invited review.
  11. Lam AR, Bhattacharya S, Patel K, Hall SE, Mao A, Vaidehi N. 2011 Importance of receptor flexibility in binding of cyclam compounds to the chemokine receptor CXCR4 J Chem Inf Model. 24;51(1):139-47.
  12. Bhattacharya S, Lam AR, Li H, Balaraman G, Niesen MJ, Vaidehi N. 2013, Critical analysis of the successes and failures of homology models of G protein-coupled receptors, Proteins. 81(5):729-39.
  13. Bhattacharya S. and Vaidehi N., 2012, LITiCon: a discrete conformational sampling computational method for mapping various functionally selective conformational states of transmembrane helical proteins. Methods. Mol. Biol., 914, 167-78.
  14. Lee S, Bhattacharya S, Grisshammer R, Tate C, Vaidehi N. 2014, Dynamic Behavior of the Active and Inactive States of the Adenosine A2A Receptor.J Phys Chem B. 118(12):3355-65.
  15. Vaidehi N, Bhattacharya S, Larsen AB 2014, Structure and dynamics of G-protein coupled receptors, Adv. Exp. Med. Biol., 796, 37-54.
  16. Muppidi, J., Schmitz, Green, J., …………Vaidehi, N., Staudt, L., and Cyster, J., 2014, Loss of signaling via Gα13 in germinal center Bcell derived Lymphoma, Nature,

LITiConDesign

A computational method for designing thermostable mutants for GPCRs
 
G-protein coupled receptors are membrane proteins and play an important part in cellular signal transduction. Solving the three dimensional structures of these proteins is critical and is becoming viable lately. However the biggest bottleneck in obtaining sufficient quantities of the pure protein is that GPCRs are conformationally flexible, and hence aggregate at higher concentrations during purification. A solution to this challenge is to derive thermostable mutants of GPCRs that are amenable to purification techniques. However the experiments involved in identifying the residue positions that lead to thermostability as well as the thermostable mutants is both expensive and time consuming. There are about 300 mutations that need to be done just to be able to identify positions that lead to thermostability. Our goal is to develop a fast computational screening method, LITiConDesign to design and thermally stable mutants of several GPCRs. We will target class A GPCRs in their agonist and antagonist bound structures. This project is in collaboration with Dr. Chris Tate (MRC, Cambridge, UK) and Dr. Reinhard Grisshammer (NINDS).
 
Publications related to this project:
 
  1. Balaraman, G., Bhattacharya, S., and Vaidehi, N., 2010, Structural insights into conformational stability of wild type and mutant β1-adrenergic receptor, BioPhys. J., 99(2):568-77.
  2. Niesen MJM, Bhattacharya S, Grisshammer R, Tate CG and Vaidehi N, 2013, Thermostabilization of the β1-adrenergic receptor correlates with increased entropy of the inactive state, J. Phys. Chem B, 117, 7283-91.
  3. Lee S, Bhattacharya S, Grisshammer R, Tate C, Vaidehi N. 2014, Dynamic Behavior of the Active and Inactive States of the Adenosine A2A Receptor.J Phys Chem B. 118(12):3355-65.
  4. Bhattacharya, S., Lee, S.B., Grisshammer R., Tate, C.G. and Vaidehi N., 2014, Rapid Computational Prediction of Thermostabilizing Mutations for G Protein-Coupled Receptors, J. Chem Theor. & Comp.
 

AlloSteer

Development of computational method to identify allosteric sites for drug design in protein-protein complexes
 
G-protein-coupled receptors (GPCRs) are membrane proteins that allosterically transduce the signal of ligand binding in the extracellular (EC) domain to couple to proteins in the intracellular (IC) domain. However, the complete pathway of allosteric communication from the EC to the IC domain, including the role of individual amino acids in the pathway is not known. Using the correlation in torsion angle movements calculated from microseconds-long molecular-dynamics simulations, we have developed a computational analysis method based on graph theory to elucidate the allosteric pathways in GPCRs. This method is generic and applicable to all proteins. In addition, our analysis shows that mutations that affect the ligand efficacy, but not the binding affinity, are located in the allosteric pipelines. This clarifies the role of such mutations, which has hitherto been unexplained. The residues involved in allosteric communication can be used as “allosteric hubs” that modulate the activity of the protein. We use this information on allosteric hub residues to identify druggable allosteric binding sites in proteins. These potential binding sites can be used to screen for small molecules that act as allosteric modulators or inhibitors to protein-protein interactions.

Publications related to this project:
  • Bhattacharya S., and Vaidehi N., 2014, Differences in allosteric communication pipelines in the inactive and active states of a GPCR, Biophys. J., 107, 422-34.
  • Li H, Kasam V, Tautermann CS, Seeliger D, Vaidehi N. 2014, Computational method to identify druggable binding sites that target protein-protein interactions, J Chem Inf Model. 54(5):1391-400
 

Chemokines

The dynamics of G-protein coupled receptors (GPCRs) and their relevance in drug design G-protein coupled receptors belong to a superfamily of seven helical transmembrane proteins that play a critical role in many physiological processes. They are implicated in the pathology of many diseases such as atherosclerosis, cancer, auto-immune and auto-inflammatory diseases, cancer metastasis, and hence form the biggest class of drug targets. One of the major complexities in drug design for GPCRs, however, is their conformational flexibility. This dynamic flexibility leads to GPCR conformations being in equilibrium between several active and inactive conformational states. Therefore a molecular level understanding of the dynamics is vital to designing functional selective drugs for GPCRs.

Computational methods for studying the dynamics of GPCR conformations: In my laboratory, we have developed novel computational methods to map the potential energy surface and the dynamics of GPCR conformational states and use them for drug design, as seen in the figure, which shows the potential energy surface of an antagonist bound GPCR (right) and the binding site of an antagonist bound to a GPCR used for drug design (left). We have applied these techniques to design drugs for β adrenergic receptors (targets for hypertension and asthma) and chemokine receptors. Using these methods, we also design thermally stable mutant GPCRs for several class A GPCRs that would strongly aid the crystallization of these receptors.
 
 
 
Targeting Chemokine receptors for pancreatic cancer: We are particularly interested in understanding the structural basis of antagonist binding to chemokine receptors. Chemokine receptors belong to class A GPCRs and show versatile function in regulating immune cells. They are also implicated in autoimmune diseases, as well as cancer. Combining computational methods with site directed mutagenesis we have studied antagonist binding for several chemokine receptors such as CCR1, CCR2, CCR3, CCR5, CXCR1, CXCR2, CXCR3, and CXCR4.
 
Development of Constrained dynamics methods for long time scale simulations: Molecular dynamics simulations involving all atoms is computationally intensive especially for large proteins or protein-protein complexes and therefore poses a bottleneck for realistic biological simulations. We are using algorithms from robotics in collaboration with NASA-JPL to develop constrained dynamics algorithms. In these methods the protein molecule is modeled as a collection of rigid bodies connected by flexible hinges and the equations of motion are solved in internal coordinates. The major advantage of this method is that it allows large conformational search as well as long time scale simulations.
 
Development of computational methods to identify allosteric sites to disrupt protein-protein interactions: We are developing computational alanine scanning methods
 
SELECTED PUBLICATIONS
  1. Bhattacharya S., Hall S.E., Li H., Vaidehi N. Ligand-stabilized conformational states of human beta(2) adrenergic receptor: insight into G-protein-coupled receptor activation. Biophys J. 2008, 94(6):2027-42.
  2. Bhattacharya, S., Hall, S.E. and Vaidehi N., 2008, Agonist induced conformational changes in bovine rhodopsin: Insight into activation of G-protein coupled receptors, J. Mol. Biol. 382, 539-555.
  3. Hall, S.E., Roberts, K., and Vaidehi, N., 2009, Position of helical kinks in membrane protein crystal structures and the accuracy of computational prediction, J. Mol. Graph. & Mod. 27, 944-950.
  4. Hall S.E.,  Mao, A. Nicolaidou, V., Finelli, M., Wise, E.L., Nedjai, B., Kanjanapangka, J., Harirchian, P., Chen, D., Selchau, V., Ribeiro, S.,  Schyler, S.,  Pease, J.E.,  Horuk R., and Vaidehi, N.  2009, Elucidation of binding sites of dual antagonists in the human chemokine receptors CCR2 and CCR5. Mol. Pharm. 75, 1325-1336.
  5. Vaidehi, N., Pease, J. and Horuk R., 2009, Modeling Small Molecule Compound Binding to G-Protein Coupled Receptors, Methods in Enzymology, 460, 263-288 – invited review.
  6. Lin J, Buettner R, Yuan YC, Yip R, Horne D, Jove R,  Vaidehi N. 2009, Molecular dynamics simulations of the conformational changes in signal transducers and activators of transcription, J Mol Graph Model.  28(4):347-56.
  7. Bhattacharya S, and Vaidehi N. 2010, Computational Mapping of the Conformational Transitions in Agonist Selective Pathways of a G-Protein Coupled Receptor, J Am Chem Soc. 132(14):5205-14.
  8. Balaraman, G., Bhattacharya, S., and Vaidehi, N., 2010, Structural insights into conformational stability of wild type and mutant β1-adrenergic receptor, BioPhys. J., 99(2):568-77.
  9. Bhattacharya et al 2010, Allosteric Antagonist Binding Sites in Class B GPCRs: Corticotropin Receptor 1, J Comput Aided Mol Des. 8, 659-74.
  10. Vaidehi, N., 2010, Dynamics and Flexibility of G-protein coupled receptor conformations and its relevance to drug design, Drug Discovery Today, in press – invited review. 
  11. Vaidehi N. and Kenakin T., 2010, The Role of Conformational Ensembles of Seven Transmembrane Receptors in Functional Selectivity, Curr. Opinion. Pharmacology, in press – invited review.
 

Lab Members

Current Lab members  
 
Nagarajan Vaidehi Ph.D.   
Principal Investigator  
Tel: 626-301-8408
Fax 626-301-8186      
Email: NVaidehi@coh.org               
 
Supriyo Bhattacharya Ph.D.    
Staff Scientist    
Email:sbhattach@coh.org
 
Sangbae Lee   Ph.D    
Postdoctoral Fellow    
Email:vgangupomu@coh.org  
 
Vinod Kasam Ph.D
Postdoctoral Fellow
Email:vkasam@coh.org
 
Saugat Kandel
Research Associate
Email:SKandel@coh.org
 
Adrien Larsen M.S
Research Associate II
Email: ALarsen@coh.org
 
Hubert Li      
Graduate Student     
Email: huli@coh.org
 
Manbir Sandhu
Graduate student
Email: msandu@coh.org
 
Allen Mao     
Systems Manager    
Email:amao@coh.org         
 

Past Members of the Laboratory
 
Spencer Hall PhD
Army Research Lab
 
Jianping Lin Ph.D
Professor, Nankai University, China
 
Gouthaman Balaraman Ph.D
Sr. Quantitative Analyst at Interactive Data Corporation
 
Alfonso Lam Ph.D
UC Irvine
 
Kyle Roberts
Graduate Student, Duke.
 
Bram van Hoof
Graduate student
TuE, Eindhoven, Netherlands.
 
Michael Debertrand
Dutch Navy
 
Daniela Mueller
PD Fellow, Germany
 
Michiel Niesen
Graduate student, Caltech.
 
Vamshi Gangupomu Ph.D
TVS Inc.
 
In-Hee Park
Novartis
 
Romelia Salomon-Ferrer
Pfizer Inc, Cambridge MA.
 
Past summer students
 
Tiffany Chen
UC Berkeley.
 
Rachel Levy
MIT
 
Michael Matthew
Summer undergraduate fellow, UCSD
 
Kevin Patel
Lehigh University
 
Reshma Patel
Drexel University
 
Divya Siddarth
Stanford
 

Laboratory of Nagarajan Vaidehi, Ph.D.

Nagarajan Vaidehi, Ph.D. Research

Proteins are allosteric nano-machines whose conformational dynamics controls their functional versatility. Conformational dynamics is important in understanding the allosteric nature of proteins, in identifying allosteric druggable sites as well as in designing drugs with functional specificity.
 
Biophysical experimental methods provide fragmented information on the structure and dynamics and the X-ray crystallography provides a static picture of one of the low energy conformations in an ensemble of states. Therefore computational methods are essential in integrating the experimental information and provide an atomic level detail of the dynamics of proteins. One of the major bottlenecks in using the existing computational methods to study dynamics of proteins is the limitation in time scale and the narrow conformational search afforded by these methods. Thus we need multi-scale computational methods that span a larger range in time and length scale to extend the use of computational methods to large protein complexes. Our laboratory is focused on developing state of the art multi-scale computational methods to study the conformational dynamics of proteins. We are developing coarse grain computational methods to sample the various kinetic states of the protein dynamics, followed by fine grain computational methods to capture the detailed atomic level structural changes and to calculate the thermodynamic properties.
 
Our research projects include:
 
  1. Development of constrained molecular dynamics methods – GNEIMO
  2. Development of coarse grained conformational sampling method for G-protein coupled receptors (GPCRs) – GPCRSimKit
  3. Development of computational method for designing thermostable mutants for GPCRs – LITiConDesign
  4. Development and application of computational methods to identify allosteric sites for drug design in protein-protein complexes – AlloBindSite
  5. Application of these methods to design drugs with functional specificity for GPCRs targeting pancreatic cancer and other cancers – Chemokine
 

GNEIMO

GNEIMO

A Hierarchical framework for constrained molecular dynamics method
 
Molecular dynamics (MD) simulation is a powerful computational tool in structural biology, widely used for understanding conformational changes in proteins, and folding of peptides. However MD simulations using Cartesian dynamics model is limited by the total simulation time scale being in tens of nanoseconds for large proteins. Biological processes on the other hand need microseconds of simulation time. We developed the Internal Coordinate Molecular Dynamics (ICMD) algorithms in the early 1990s to enable larger simulation time-steps and they show great promise in long time scale simulations. Despite their promise, ICMD techniques have made little progress due in large part to the additional mathematical complexity of internal coordinate models. As a NIH-NIGMS project and in collaboration with Dr. Abhi Jain at the NASA Jet Propulsion Laboratory at Caltech, we are developing the ICMD methods called Generalized Newton-Euler Inverse Mass Operator (GNEIMO) to enable long time scale and wider conformational search simulations. These simulations have been applied to various biological problems such as
 
  1. Study of large scale conformational dynamics of proteins wherein we showed the NMR based ensemble of conformations of calmodulin was sampled by GNEIMO method.
  2. Structural refinement of homology models of proteins.
  3. Ab initio folding of simple proteins.
 
We are now initiating a collaboration with Lawrence Berkeley laboratory to use the GNEIMO method with the program “PHENIX” that to fit models to X-ray crystallography and low resolution electron microscopy measurements.
The GneimoSim software can be downloaded free of cost for academic use from:
http://dartslab.jpl.nasa.gov/GNEIMO/index.php.

Publications related to this project:
  1. Jain A, Vaidehi N, Rodriguez G A 1993, Fast Recursive Algorithm For Molecular-Dynamics Simulation, J Comput Phys 106: (2) 258-268.
  2. Vaidehi N., Jain A., Goddard III, W.A., 1996, Constant temperature constrained molecular dynamics: The Newton-Euler inverse mass operator method, J Phys Chem-100: (25) 10508-10517.
  3. Bertsch R.A., Vaidehi  N., Chan S.I., et al. 1998,  Kinetic steps for alpha-helix formation Proteins: Structure, Function and Genetics, 33: (3) 343-357.
  4. Vaidehi N., Goddard W.A., 2000, Domain motions in phosphoglycerate kinase using hierarchical NEIMO molecular dynamics simulations,  J Phys. Chem.  A 104: (11) 2375-2383.
  5. Balaraman GS, Park IH, Jain A, Vaidehi N. 2011, Folding of Small Proteins Using Constrained Molecular Dynamics. Journal of Physical Chemistry B. 115(23):7588-96.
  6. Park, I.H., Wagner, J., Jain A, Vaidehi N. 2012, Structure Refinement of Protein Low Resolution Models Using the GNEIMO Constrained Dynamics Method Folding of Small Proteins Using Constrained Molecular Dynamics, J.  Phys. Chem. B, 116, 2365-75.
  7. Jain A, Park IH, Vaidehi N. 2012, Equipartition principle for internal coordinate molecular dynamics,  J Chem Theory Comput. 14;8(8):2581-2587.
  8. Wagner JR, Balaraman GS, Niesen MJ, Larsen AB, Jain A, Vaidehi N. 2013, Advanced techniques for constrained internal coordinate molecular dynamics, J Comput Chem. 34(11):904-14
  9. Gangupomu VK, Wagner JR, Park IH, Jain A and Vaidehi N., 2013, Mapping conformational dynamics of proteins using torsional dynamics simulations, Biophys. J., 104, 1999-2008.
  10. Jain A., Kandel, S., Wagner, JE., Larsen AB, and Vaidehi N., 2013, Fixman compensating potential for general branched molecules, J. Chem. Phys. 139, 244103.
  11. Larsen AB, Wagner, J.R., Jain A and Vaidehi N., 2014, Protein Structure Refinement of CASP target Proteins suing GNEIMO torsional dynamics method, J. Chem. Inf. Model. 2014  24;54(2):508-17.
  12. Larsen, AB, Wagner JE., Kandel S., Salomon-Ferrer R., Vaidehi N., and Jain A, 2014, GneimoSim: A modular Internal Coordinates Molecular Dynamics Simulation Package, J. Comp. Chem. In press.
 

GPCRSimkit

GPCRSimKit

A Multiscale computational framework for studying G-protein coupled receptors (GPCRs)
 
G-protein coupled receptors (GPCRs) play an important role in the physiology and in the pathophysiology of many serious diseases. They form the largest superfamily of drug targets. Since GPCRs are membrane bound and are highly dynamic, obtaining three dimensional structural information for GPCRs is a feat and it requires a confluence of various biophysical techniques that include computational methods. The crystal structure is a snapshot in the conformational ensemble that the receptor samples in the absence of any stimulant. We are developing multiscale simulation method suite, GPCRSimKit, that integrates coarse grain simulation method with fine grain techniques. The GPCRSimKit will enable simulation of the dynamics of GPCR conformational ensemble starting from the inactive crystal structures or refine homology models for drug design. The GPCRSimkit will allow calculation of the modulation of the potential energy landscape by full, partial, and inverse agonists. This platform of computational techniques, will lay a theoretical basis and play a crucial role as more crystal structures of GPCRs get published.
 
 
Publications Related to this project:
  1. Vaidehi, N., et al 2002, Structure and Function prediction for G-Protein Coupled Receptors, Proc. Natl. Acad. Sci., USA, 99, 12622-12627.
  2. Bhattacharya S., Hall S.E., Li H., Vaidehi N. Ligand-stabilized conformational states of human beta(2) adrenergic receptor: insight into G-protein-coupled receptor activation. Biophys J. 2008, 94(6):2027-42.
  3. Bhattacharya, S., Hall, S.E. and Vaidehi N., 2008, Agonist induced conformational changes in bovine rhodopsin: Insight into activation of G-protein coupled receptors, J. Mol. Biol. 382, 539-555.
  4. Hall, S.E., Roberts, K., and Vaidehi, N., 2009, Position of helical kinks in membrane protein crystal structures and the accuracy of computational prediction, J. Mol. Graph. & Mod. 27, 944-950.
  5. Hall S.E.,  Mao, A. Nicolaidou, V., Finelli, M., Wise, E.L., Nedjai, B., Kanjanapangka, J., Harirchian, P., Chen, D., Selchau, V., Ribeiro, S.,  Schyler, S.,  Pease, J.E.,  Horuk R., and Vaidehi, N.  2009, Elucidation of binding sites of dual antagonists in the human chemokine receptors CCR2 and CCR5. Mol. Pharm. 75, 1325-1336.
  6. Vaidehi, N., Pease, J. and Horuk R., 2009, Modeling Small Molecule Compound Binding to G-Protein Coupled Receptors, Methods in Enzymology, 460, 263-288.
  7. Bhattacharya S, and Vaidehi N. 2010, Computational Mapping of the Conformational Transitions in Agonist Selective Pathways of a G-Protein Coupled Receptor, J Am Chem Soc. 132(14):5205-14.
  8. Bhattacharya et al 2010, Allosteric Antagonist Binding Sites in Class B GPCRs: Corticotropin Receptor 1, J Comput Aided Mol Des. 8, 659-74.
  9. Vaidehi, N., 2010, Dynamics and Flexibility of G-protein coupled receptor conformations and its relevance to drug design, Drug Discovery Today, 15, 951-957 – invited review. 
  10. Vaidehi N., and Kenakin T., 2010, Conformational Ensembles of Seven Transmembrane Receptors and their Relevance to Functional Selectivity, Curr. Opinion. Pharmacology, 10, 775-781- invited review.
  11. Lam AR, Bhattacharya S, Patel K, Hall SE, Mao A, Vaidehi N. 2011 Importance of receptor flexibility in binding of cyclam compounds to the chemokine receptor CXCR4 J Chem Inf Model. 24;51(1):139-47.
  12. Bhattacharya S, Lam AR, Li H, Balaraman G, Niesen MJ, Vaidehi N. 2013, Critical analysis of the successes and failures of homology models of G protein-coupled receptors, Proteins. 81(5):729-39.
  13. Bhattacharya S. and Vaidehi N., 2012, LITiCon: a discrete conformational sampling computational method for mapping various functionally selective conformational states of transmembrane helical proteins. Methods. Mol. Biol., 914, 167-78.
  14. Lee S, Bhattacharya S, Grisshammer R, Tate C, Vaidehi N. 2014, Dynamic Behavior of the Active and Inactive States of the Adenosine A2A Receptor.J Phys Chem B. 118(12):3355-65.
  15. Vaidehi N, Bhattacharya S, Larsen AB 2014, Structure and dynamics of G-protein coupled receptors, Adv. Exp. Med. Biol., 796, 37-54.
  16. Muppidi, J., Schmitz, Green, J., …………Vaidehi, N., Staudt, L., and Cyster, J., 2014, Loss of signaling via Gα13 in germinal center Bcell derived Lymphoma, Nature,

LitiConDesign

LITiConDesign

A computational method for designing thermostable mutants for GPCRs
 
G-protein coupled receptors are membrane proteins and play an important part in cellular signal transduction. Solving the three dimensional structures of these proteins is critical and is becoming viable lately. However the biggest bottleneck in obtaining sufficient quantities of the pure protein is that GPCRs are conformationally flexible, and hence aggregate at higher concentrations during purification. A solution to this challenge is to derive thermostable mutants of GPCRs that are amenable to purification techniques. However the experiments involved in identifying the residue positions that lead to thermostability as well as the thermostable mutants is both expensive and time consuming. There are about 300 mutations that need to be done just to be able to identify positions that lead to thermostability. Our goal is to develop a fast computational screening method, LITiConDesign to design and thermally stable mutants of several GPCRs. We will target class A GPCRs in their agonist and antagonist bound structures. This project is in collaboration with Dr. Chris Tate (MRC, Cambridge, UK) and Dr. Reinhard Grisshammer (NINDS).
 
Publications related to this project:
 
  1. Balaraman, G., Bhattacharya, S., and Vaidehi, N., 2010, Structural insights into conformational stability of wild type and mutant β1-adrenergic receptor, BioPhys. J., 99(2):568-77.
  2. Niesen MJM, Bhattacharya S, Grisshammer R, Tate CG and Vaidehi N, 2013, Thermostabilization of the β1-adrenergic receptor correlates with increased entropy of the inactive state, J. Phys. Chem B, 117, 7283-91.
  3. Lee S, Bhattacharya S, Grisshammer R, Tate C, Vaidehi N. 2014, Dynamic Behavior of the Active and Inactive States of the Adenosine A2A Receptor.J Phys Chem B. 118(12):3355-65.
  4. Bhattacharya, S., Lee, S.B., Grisshammer R., Tate, C.G. and Vaidehi N., 2014, Rapid Computational Prediction of Thermostabilizing Mutations for G Protein-Coupled Receptors, J. Chem Theor. & Comp.
 

Allosteer

AlloSteer

Development of computational method to identify allosteric sites for drug design in protein-protein complexes
 
G-protein-coupled receptors (GPCRs) are membrane proteins that allosterically transduce the signal of ligand binding in the extracellular (EC) domain to couple to proteins in the intracellular (IC) domain. However, the complete pathway of allosteric communication from the EC to the IC domain, including the role of individual amino acids in the pathway is not known. Using the correlation in torsion angle movements calculated from microseconds-long molecular-dynamics simulations, we have developed a computational analysis method based on graph theory to elucidate the allosteric pathways in GPCRs. This method is generic and applicable to all proteins. In addition, our analysis shows that mutations that affect the ligand efficacy, but not the binding affinity, are located in the allosteric pipelines. This clarifies the role of such mutations, which has hitherto been unexplained. The residues involved in allosteric communication can be used as “allosteric hubs” that modulate the activity of the protein. We use this information on allosteric hub residues to identify druggable allosteric binding sites in proteins. These potential binding sites can be used to screen for small molecules that act as allosteric modulators or inhibitors to protein-protein interactions.

Publications related to this project:
  • Bhattacharya S., and Vaidehi N., 2014, Differences in allosteric communication pipelines in the inactive and active states of a GPCR, Biophys. J., 107, 422-34.
  • Li H, Kasam V, Tautermann CS, Seeliger D, Vaidehi N. 2014, Computational method to identify druggable binding sites that target protein-protein interactions, J Chem Inf Model. 54(5):1391-400
 

Chemokines

Chemokines

The dynamics of G-protein coupled receptors (GPCRs) and their relevance in drug design G-protein coupled receptors belong to a superfamily of seven helical transmembrane proteins that play a critical role in many physiological processes. They are implicated in the pathology of many diseases such as atherosclerosis, cancer, auto-immune and auto-inflammatory diseases, cancer metastasis, and hence form the biggest class of drug targets. One of the major complexities in drug design for GPCRs, however, is their conformational flexibility. This dynamic flexibility leads to GPCR conformations being in equilibrium between several active and inactive conformational states. Therefore a molecular level understanding of the dynamics is vital to designing functional selective drugs for GPCRs.

Computational methods for studying the dynamics of GPCR conformations: In my laboratory, we have developed novel computational methods to map the potential energy surface and the dynamics of GPCR conformational states and use them for drug design, as seen in the figure, which shows the potential energy surface of an antagonist bound GPCR (right) and the binding site of an antagonist bound to a GPCR used for drug design (left). We have applied these techniques to design drugs for β adrenergic receptors (targets for hypertension and asthma) and chemokine receptors. Using these methods, we also design thermally stable mutant GPCRs for several class A GPCRs that would strongly aid the crystallization of these receptors.
 
 
 
Targeting Chemokine receptors for pancreatic cancer: We are particularly interested in understanding the structural basis of antagonist binding to chemokine receptors. Chemokine receptors belong to class A GPCRs and show versatile function in regulating immune cells. They are also implicated in autoimmune diseases, as well as cancer. Combining computational methods with site directed mutagenesis we have studied antagonist binding for several chemokine receptors such as CCR1, CCR2, CCR3, CCR5, CXCR1, CXCR2, CXCR3, and CXCR4.
 
Development of Constrained dynamics methods for long time scale simulations: Molecular dynamics simulations involving all atoms is computationally intensive especially for large proteins or protein-protein complexes and therefore poses a bottleneck for realistic biological simulations. We are using algorithms from robotics in collaboration with NASA-JPL to develop constrained dynamics algorithms. In these methods the protein molecule is modeled as a collection of rigid bodies connected by flexible hinges and the equations of motion are solved in internal coordinates. The major advantage of this method is that it allows large conformational search as well as long time scale simulations.
 
Development of computational methods to identify allosteric sites to disrupt protein-protein interactions: We are developing computational alanine scanning methods
 
SELECTED PUBLICATIONS
  1. Bhattacharya S., Hall S.E., Li H., Vaidehi N. Ligand-stabilized conformational states of human beta(2) adrenergic receptor: insight into G-protein-coupled receptor activation. Biophys J. 2008, 94(6):2027-42.
  2. Bhattacharya, S., Hall, S.E. and Vaidehi N., 2008, Agonist induced conformational changes in bovine rhodopsin: Insight into activation of G-protein coupled receptors, J. Mol. Biol. 382, 539-555.
  3. Hall, S.E., Roberts, K., and Vaidehi, N., 2009, Position of helical kinks in membrane protein crystal structures and the accuracy of computational prediction, J. Mol. Graph. & Mod. 27, 944-950.
  4. Hall S.E.,  Mao, A. Nicolaidou, V., Finelli, M., Wise, E.L., Nedjai, B., Kanjanapangka, J., Harirchian, P., Chen, D., Selchau, V., Ribeiro, S.,  Schyler, S.,  Pease, J.E.,  Horuk R., and Vaidehi, N.  2009, Elucidation of binding sites of dual antagonists in the human chemokine receptors CCR2 and CCR5. Mol. Pharm. 75, 1325-1336.
  5. Vaidehi, N., Pease, J. and Horuk R., 2009, Modeling Small Molecule Compound Binding to G-Protein Coupled Receptors, Methods in Enzymology, 460, 263-288 – invited review.
  6. Lin J, Buettner R, Yuan YC, Yip R, Horne D, Jove R,  Vaidehi N. 2009, Molecular dynamics simulations of the conformational changes in signal transducers and activators of transcription, J Mol Graph Model.  28(4):347-56.
  7. Bhattacharya S, and Vaidehi N. 2010, Computational Mapping of the Conformational Transitions in Agonist Selective Pathways of a G-Protein Coupled Receptor, J Am Chem Soc. 132(14):5205-14.
  8. Balaraman, G., Bhattacharya, S., and Vaidehi, N., 2010, Structural insights into conformational stability of wild type and mutant β1-adrenergic receptor, BioPhys. J., 99(2):568-77.
  9. Bhattacharya et al 2010, Allosteric Antagonist Binding Sites in Class B GPCRs: Corticotropin Receptor 1, J Comput Aided Mol Des. 8, 659-74.
  10. Vaidehi, N., 2010, Dynamics and Flexibility of G-protein coupled receptor conformations and its relevance to drug design, Drug Discovery Today, in press – invited review. 
  11. Vaidehi N. and Kenakin T., 2010, The Role of Conformational Ensembles of Seven Transmembrane Receptors in Functional Selectivity, Curr. Opinion. Pharmacology, in press – invited review.
 

Lab Members

Lab Members

Current Lab members  
 
Nagarajan Vaidehi Ph.D.   
Principal Investigator  
Tel: 626-301-8408
Fax 626-301-8186      
Email: NVaidehi@coh.org               
 
Supriyo Bhattacharya Ph.D.    
Staff Scientist    
Email:sbhattach@coh.org
 
Sangbae Lee   Ph.D    
Postdoctoral Fellow    
Email:vgangupomu@coh.org  
 
Vinod Kasam Ph.D
Postdoctoral Fellow
Email:vkasam@coh.org
 
Saugat Kandel
Research Associate
Email:SKandel@coh.org
 
Adrien Larsen M.S
Research Associate II
Email: ALarsen@coh.org
 
Hubert Li      
Graduate Student     
Email: huli@coh.org
 
Manbir Sandhu
Graduate student
Email: msandu@coh.org
 
Allen Mao     
Systems Manager    
Email:amao@coh.org         
 

Past Members of the Laboratory
 
Spencer Hall PhD
Army Research Lab
 
Jianping Lin Ph.D
Professor, Nankai University, China
 
Gouthaman Balaraman Ph.D
Sr. Quantitative Analyst at Interactive Data Corporation
 
Alfonso Lam Ph.D
UC Irvine
 
Kyle Roberts
Graduate Student, Duke.
 
Bram van Hoof
Graduate student
TuE, Eindhoven, Netherlands.
 
Michael Debertrand
Dutch Navy
 
Daniela Mueller
PD Fellow, Germany
 
Michiel Niesen
Graduate student, Caltech.
 
Vamshi Gangupomu Ph.D
TVS Inc.
 
In-Hee Park
Novartis
 
Romelia Salomon-Ferrer
Pfizer Inc, Cambridge MA.
 
Past summer students
 
Tiffany Chen
UC Berkeley.
 
Rachel Levy
MIT
 
Michael Matthew
Summer undergraduate fellow, UCSD
 
Kevin Patel
Lehigh University
 
Reshma Patel
Drexel University
 
Divya Siddarth
Stanford
 
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City of Hope is one of only 41 Comprehensive Cancer Centers in the country, the highest designation awarded by the National Cancer Institute to institutions that lead the way in cancer research, treatment, prevention and professional education.
Learn more about City of Hope's institutional distinctions, breakthrough innovations and collaborations.
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