Ravinder Abrol

EDUCATION

Ph.D. (Chemistry), California Institute of Technology, Pasadena, CA
M.Sc. (Chemistry), Indian Institute of Technology, Kanpur, India
B.Sc. Honours (Chemistry), University of Delhi, India

POSTDOCTORAL APPOINTMENT

IBM Thomas J. Watson Research Center, NY
California Institute of Technology, CA

COURSES TAUGHT

Chemistry 461L, Biochemistry I Laboratory 
Chemistry 462L, Biochemistry II Laboratory 
Chemistry 464, Principles of Biochemistry
Chemistry 464L, Principles of Biochemistry Laboratory
Chemistry 567, Investigating Protein Structure and Function (co-taught with Dr. Crowhurst)
Chemistry 567L, Investigating Protein Structure and Function Laboratory (co-taught with Dr. Crowhurst)

RESEARCH INTERESTS

Computational Biochemistry and Biophysics

Dr. Ravi Abrol’s research lab is focused on developing and using computational methods to probe how protein structure and biochemical (protein-ligand and protein-protein) interactions of G protein-coupled receptors (GPCRs) determine cellular signaling and physiology, as well as how this knowledge can be used for the rational design of drugs targeting GPCR signaling pathways.

GPCRs are integral membrane proteins that form the largest superfamily in the human genome. The activation of these receptors by a variety of bioactive molecules regulates key physiological processes (e.g., neurotransmission, cellular metabolism, secretion, cell growth, immunity, differentiation), through a balance of G protein-coupled and beta-arrestin-coupled signaling pathways. This has made them targets for ~50% drugs in the clinic. A molecular and structural understanding of these GPCR signaling pathways will have a broad impact on our understanding of cellular signaling and on drug discovery efforts targeting GPCRs.

Research in the Abrol Lab lies at the interface of Chemistry and Biology, where they are using computational biophysics and structural bioinformatics based methods to gain mechanistic insights into the biochemistry of GPCR signaling. The research is following three major themes to connect the sequence, structure, and signaling of GPCRs:

Theme 1: From Structure to Signaling - How do GPCRs behave as allosteric machines and exhibit biased signaling?
There are many challenges in experimental approaches to navigate the multiple conformations of GPCRs that can describe their pleiotropic function. The Abrol Lab is developing the next generation of computational methods to describe the conformational space available to GPCRs (especially the high-energy functionally important conformations) and to predict their effects on intracellular signaling.

Theme 2: From Sequence to Structure - How do GPCRs fold in the membrane?
There are several examples of disease-associated single point mutations in GPCRs, in which the mutant GPCR is not stable enough to escape the quality control of endoplasmic reticulum, but can be rescued by pharmacological chaperones to reach its final membrane destination. These partially stable receptor single-point mutants require looking at the thermodynamics of how they insert and fold in the membrane to gain a mechanistic insight into their instability. The Abrol Lab is developing methods to probe how small alpha-helical membrane proteins get inserted into the lipid bilayers by the Sec61 translocon machinery, to eventually understand this process for GPCRs.

Theme 3: From Sequence to Signaling - How do GPCR sequence variations (receptor paralogs or mutations) lead to observed signaling and disease?
Sequences contain a wealth of functional information, which has led to the development of many computational approaches to extract this information. The Abrol Lab is developing structural bioinformatics tools that combine evolutionary methods using closely-related paralog and ortholog sequences with their structural and functional information to understand the role of specific residues and structural motifs in the functional divergence of GPCRs.

Students from Chemistry, Biochemistry, Biology, Physics, Math, and Computer Science will find highly multi-disciplinary research opportunities in the Abrol Lab, aimed at developing computational methods or applying existing methods or developing and applying new methods to understand the molecular mechanisms behind cellular signaling. Prior experience with computer programming is not necessary, however, students should be open to learning some programming as part of the research. There will also be joint research opportunities combining structural modeling of proteins with biochemical and biophysical experiments.

REPRESENTATIVE PUBLICATIONS 

1. "A beetle antifreeze protein protects lactate dehydrogenase under freeze-thawing"; Rodriguez C, Sajjadi S, Abrol R, Wen X (2019), International Journal of Biological Macromolecules, 136(1):1153-1160. [PMID: 31226372]
2. "Chiral graphs: Reduced representations of ligand scaffolds for stereoselective biomolecular recognition, drug design, and enhanced exploration of chemical structure space"; Mikhael S, Abrol R (2019), ChemMedChem, DOI:10.1002/cmdc.201800761.
3. “Identifying multiple active conformations in the G protein-coupled receptor activation landscape using computational methods”; Dong S, Goddard WA 3rd, Abrol R (2017), G Protein Coupled Receptors Part A (Methods in Cell Biology Book Series), Ed. Arun K. Shukla (Elsevier), Vol.142, pp. 173-186. [PMID: 28964335]
4. "Conformational and Thermodynamic Landscape of GPCR Activation From Theory and Computation"; Dong SS, Goddard WA 3rd, Abrol R (2016), Biophysical Journal, 110(12):2618-29. [PMID: 27332120]
5. "Structure-Based Sequence Alignment of the Transmembrane Domains of all Human GPCRs: Phylogenetic, Structural and Functional Implications"; Cvicek V, Goddard WA 3rd, Abrol R (2016), PLoS Computational Biology, 12(3):e1004805. [PMID: 27028541]
6. "Computational Prediction and Biochemical Analyses of New Inverse Agonists for the CB1 Receptor"; Scott CE, Ahn KH, Graf ST, Goddard WA 3rd, Kendall DA, Abrol R (2016). J Chem Inf Model, 56(1):201-212. [PMID: 26633590]
7. "FOXC1 Activates Smoothened-Independent Hedgehog Signaling in Basal-like Breast Cancer"; Han B, Qu Y, Jin Y, Yu Y, Deng N, Wawrowsky K, Zhang X, Li N, Bose S, Wang Q, Sakkiah S, Abrol R, Jensen TW, Benjamin B, Tanaka H, Johnson J, Gao B, Hao J, Liu Z, Buttyan R, Ray PS, Hung MC, Giuliano AE, Cui X (2015). Cell Reports, 13(5):1046-58. [PMID: 26565916]
8. "The interaction of N-glycans in Fcγ receptor I α-chain with Escherichia coli K1 outer membrane protein A for entry into macrophages: experimental and computational analysis"; Krishnan S, Liu F, Abrol R, Hodges J, Goddard WA 3rd, Prasadarao NV (2014). J Biol Chem, 289(45):30937-49. [PMID: 25231998]
9. "Ligand- and mutation-induced conformational selection in the CCR5 chemokine G protein-coupled receptor"; Abrol R, Trzaskowski B, Goddard WA 3rd, Nesterov A, Olave I, Irons C (2014). Proc Natl Acad Sci, 111(36):13040-5. [PMID: 25157173]
10. "The SuperBiHelix Method for Predicting the Pleiotropic Ensemble of G-Protein Coupled Receptor Conformations"; Bray JK, Abrol R, Goddard WA 3rd, Trzaskowski B, Scott CE (2013). Proc Nat Acad Sci, 111(1):E72-8. [PMID: 24344284]
11. "The glove-like structure of the conserved membrane protein TatC provides insight into signal sequence recognition in twin-arginine translocation"; Ramasamy S, Abrol R, Suloway CJ, Clemons WM Jr. (2013). Structure 21(5):777-88. [PMID: 23583035]
12. "Computationally-predicted CB1 cannabinoid receptor mutants show distinct patterns of salt-bridges that correlate with their level of constitutive activity reflected in G protein coupling levels, thermal stability, and ligand binding"; Ahn KH, Scott CE, Abrol R, Goddard WA 3rd, Kendall DA (2013). Proteins, 81(8):1304-17. [PMID: 23408552]
13. "Conformational Ensemble View of G Protein-Coupled Receptors and the Effect of Mutations and Ligand Binding"; Abrol R, Kim SK, Bray JK, Trzaskowski B, Goddard WA 3rd (2013). G Protein-Coupled Receptors (Methods in Enzymology), Ed. Conn PM (Elsevier, Oxford), Vol.520, pp. 31-48. [PMID: 23332694] (Invited Chapter)
14. "Molecular basis for dramatic changes in cannabinoid CB1 G protein-coupled receptor activation upon single and double point mutations"; Scott CE, Abrol R, Ahn KH, Kendall DA, Goddard WA 3rd (2013). Protein Science, 22(1):101-13. [PMID: 23184890]
15. "The Predicted Structure of Agonist-Bound Glucagon-Like Peptide 1 Receptor, a Class B G Protein-Coupled Receptor"; Kirkpatrick A, Heo J, Abrol R, Goddard WA 3rd (2012). Proc Nat Acad Sci, 109(49):19988-93. [PMID: 23169631]
16. "Structure Prediction of G Protein-Coupled Receptors and Their Ensemble of Functionally Important Conformations"; Abrol R, Griffith AR, Bray JK, Goddard WA 3rd (2012). Membrane Protein Structure: Methods and Protocols (Methods in Molecular Biology), Eds. Vaidehi N and Klein-Seetharaman J (Humana, New York), Vol.914, pp. 237-54. [PMID: 22976032] (Invited Paper)
17. "The 3D structure prediction of TAS2R38 bitter receptors bound to agonists phenylthiocarbamide (PTC) and 6-n-Propylthiouracil (PROP)"; Tan J, Abrol R, Trzaskowski B, Goddard WA 3rd (2012). J Chem Inf Model, 52:1875-85. [PMID: 22656649]
18. "Molecular basis for the interplay of apoptosis and proliferation mediated by Bcl-xL:Bim interactions in pancreatic cancer cells"; Abrol R, Edderkaoui M, Goddard WA 3rd, Pandol SJ (2012). Biochem Biophys Res Commun, 422(4):596-601. [PMID: 22609401]
19. "BiHelix: Towards de novo Structure Prediction of an Ensemble of G-Protein Coupled Receptor Conformations"; Abrol R, Bray JK, Goddard WA 3rd (2012). Prot Struc Func Bioinf, 80(2):505-18. [PMID: 22173949]
20. "Characterizing and Predicting the Functional and Conformational Diversity of Seven-Transmembrane Proteins"; Abrol R, Kim SK, Bray JK, Griffith AR, Goddard WA 3rd (2011). Methods, 55(4):405-14. [PMID: 22197575] (Invited Paper)
21. "Experimental validation of the predicted binding site of Escherichia coli K1 outer membrane protein A to human brain microvascular endothelial cells: identification of critical mutations that prevent E. coli meningitis"; Pascal TA, Abrol R, Mittal R, Wang Y, Prasadarao NV, Goddard WA 3rd (2010). J Biol Chem, 285(48):37753-61. [PMID: 20851887]
22. "Biological Chiral Recognition: A Substrate's Perspective"; Sundaresan V, Abrol R (2005). Chirality, 17(Suppl):S30-9. [PMID: 15736174]
23. "Towards a general model for protein-substrate stereoselectivity"; Sundaresan V, Abrol R (2002). Protein Sci, 11(6): 1330-9. [PMID: 12021432]