Welcome to Ding Lab

We are a biophysics group in the Department of Physics and Astronomy at Clemson University, College of Engineering and Science. We apply concepts and methods in Physics, especially Statistical Physics, to study biological systems, and hopefully learn new physics emerging from the complex biological systems. The major theme throughout our research is to integrate dynamics into the study of structure-function relationship of biomolecules and molecular complexes. We believe that uncovering the interrelationship between structure, dynamics, and function of biomolecules can help better understand biology and human diseases, and accelerate biomedical research.

Our present research focus is multiscale modeling of biomolecules and molecular complexes. Experimental characterization of biological systems is often hindered by the limited ability to cover a wide range of time and length scales, associated with many biological processes. Multiscale molding, which innovatively combines atomic and coarse-grained simulations, provides a unique opportunity to bridge the gaps of time and length scales between experimental observation and underlying molecular systems. We apply the multiscale modeling approach to study structure, dynamics, and function of large biomolecules, formation of molecular complexes, and also interaction between nanomaterials and biological systems.

We are always interested in enthusiastic colleagues to join our lab.


Inhibition of islet amyloid polypeptide aggregation in type-II diabetes

Accumulating evidence suggests that the aggregation of islet amyloid polypeptide (IAPP, a.k.a. amylin) is associated with β-cell death in type 2 diabetes (T2D). IAPP is co-secreted with insulin by pancreatic beta-cells, and also works together with insulin to control the serum glucose level. In vitro studies suggest that IAPP is one of the most amyloidogenic peptide, which forms amyloid fibrils within hours at micromolar concentrations. However, no apparent IAPP amyloid aggregates are observed in healthy individuals where IAPP is stored in β-cell granules at milimolar concentrations for hours before being secreted to the blood stream. Therefore, physiological conditions of β-cell granules natively inhibit the amyloid aggregation of IAPP.

The cellular environment of β-cell granules is unique in its high concentrations of Zn2+, insulin and proinsulin c-peptide in addition to IAPP. C-peptide is the co-product of insulin synthesis, which connects insulin A- and B-chains in the precursor proinsulin and is co-secreted with insulin in equal molar. A high concentration of Zn2+, maintained by a β-cell specific zinc transporter – ZnT8, is believed to be important for the efficient storage of insulin: zinc coordinates the formation of insulin hexamers, which form crystals in the dense core of β-cell granules. ). We hypothesize that intermolecular interactions with insulin, zinc and c-peptide are important for the native inhibition of IAPP aggregation inside β-cell granules. Using state-of-the-art discrete molecular dynamics (DMD) simulations, we showed that both insulin monomers and dimers could bind IAPP monomer and inhibit IAPP self-association by competing with same amyloidogenic regions, subsequently preventing aggregation. Read the rest of this entry »

Interactions between Nanoparticles and Biomolecules at the Nano-Bio interface

The advancement of nanomedicine and increasing applications of nanoparticles (NPs) in consumer products have led to administered biological exposure and unintentional environmental accumulation of NPs, causing concerns over the safety and sustainability of nanotechnology. Other sources of NP pollutions include combustion processes from human activities, such as petroleum-fueled vehicles. Therefore, there is a crucial need to understand the molecular mechanism of nanotoxicity to ensure safe nanotechnology and enable the vast applications of nanomedicine.

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Protein folding, misfolding, and aggregation

Most proteins fold into specific three-dimensional structures, which determine their functions. The folding process can be described by the free energy landscape as in a first order phase transition. The native state features the lowest free energy and correspond to the most stable and most populated species in physiological conditions. However, due to either environmental changes or mutations, the native states are destabilized. The intermediate(s) and unfolded states are promoted, where protein exposes their hydrophobic core and un-satisfied hydrogen bond donors and acceptors. These non-native species are sticky in nature and tends to aggregate under high concentrations. The aggregation is a nucleation process, and the final aggregates can adopt a fibrillar shape depending on the structural and dynamical properties of the aggregation precursor species. Read the rest of this entry »

Molecular Recognition

A major challenge in modeling molecular recognition is the conformational flexibility. The structures of the receptors in the bound and un-bound states are often different, known as the induced-fit problem. In these cases, a rigid docking approach will fail to achieve accurate predictions. Existing flexible methods for modeling molecular recognition or docking often adopt the ensemble docking approach, where an ensemble of receptor and/or ligand conformations are pre-constructed. Rigid or semi-rigid docking is performed onto these structures in the ensemble. The predicted binding poses are then selected from the grand ensemble of poses. In another words, the conformational flexibility of ligands and receptors are modeled in a decoupled or loose-coupled manner. Once the bound-conformation is not included in the pre-constructed ensemble, accurate prediction will be difficult. Alternatively, we fully integrate the conformational flexibility into the modeling of molecular recognition.

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Post-translational modifications

The structural and energetic determinants of TPST enzyme specificity

Many PTM enzymes have strong sequence preferences in the targeted substrate proteins/peptides while others do not. What are the molecular mechanism of such drastic differences in PTM enzyme specificities? It is expected that the binding affinity between substrate and enzyme plays an important role in defining the specificities. Interestingly, in the case of tyrosylprotein sulfotransferase proteins (TPST) affinity alone cannot explain the differences in sulfated and non-sulfated sequences. We found that the structural properties of the peptide in the host protein also play an important role in determining the TPST specificity. We are trying to extend this idea to other PTM enzymes.

1. P. Nedumpully-Govindan, L. Li, E.G. Alexov, M.A. Blenner, and F. Ding, “Structural and energetic determinants of tyrosylprotein sulfotransferase sulfation specificity”, Bioinformatics, in press (2014)

Protein folding

Topics:

a) Protein folding thermodynamics and kinetics

b) Protein folding transition states

c) Protein folding pathways

d) Ab initio protein folding

 

References:

15. Shirvanyants, D., F. Ding, Tsao, D., Ramachandran, S. and Dokholyan, N. V. “DMD: an efficient and versatile simulation method for fine protein characterization”, Journal of Physical Chemistry B, in press [download] (2012)

14. F. Ding, D. Tsao, H. Nie, and N. V. Dokholyan, “Ab initio folding of proteins using all-atom discrete molecular dynamics” Structure, 16: 1010-1018 (2008) [download]

13. A. R. Lam, J. M. Borreguero, F. Ding, N. V. Dokholyan, S. V. Buldyrev, E. I. Shakhnovich, H. E. Stanley, “Parallel folding pathways in the Src SH3 domain” Journal of Molecular Biology, 373: 1348-1360 (2007)[download]

12. Y. Chen, F. Ding, H. Nie, A. W. Serohijos, S. Sharma, K. C. Wilcox, S. Yin, and N. V. Dokholyan, “Protein folding: then and now” Archives of Biochemistry and Biophysics, 469: 4-19 (2008)[download]

11. S. Sharma, F. Ding, H. Nie, D. Watson, A. Unnithan, J. Lopp, D. Pozefsky, and N. V. Dokholyan, “iFold: A platform for interactive folding simulations of proteins” Bioinformatics, 22: 2693-2694 (2006) [download] [service]

10. F. Ding, W. Guo, N. V. Dokholyan, E. I. Shakhnovich, and J.-E. Shea, “Reconstruction of the src-SH3 protein domain transition state ensemble using multiscale molecular dynamics simulations”, J. Mol. Biol., 350: 1035-1050 (2005). [download]

9. F. Ding, R. K. Jha, and N. V. Dokholyan, “Scaling behavior and structure of denatured proteins”, Structure, 13: 1047-1054 (2005).[download]

8. F. Ding, S. V. Buldyrev, and N. V. Dokholyan, “Folding Trp-cage to NMR resolution native structure using a coarse-grained protein model”, Biophys. J., 88: 147-155 (2005). [download]

7. R. D. S. Dixon, Y. Chen, F. Ding, S. D. Khare, K. C. Prutzman, M. D. Schaller, S. L. Campbell, and N. V. Dokholyan, “New insights into FAK signaling and localization based on detection of a FAT domain folding intermediate” Structure, 12: 2161-2171 (2004). [download]

6. J. M. Borreguero, F. Ding, S. V. Buldyrev, H. E. Stanley, and N. V. Dokholyan, “Multiple folding pathways of the SH3 domain.” Biophys. J. 87: 521-533 (2004). [download]

5. S. D. Khare, F. Ding and N. V. Dokholyan, “Folding of Cu,Zn superoxide dismutase and Familial Amyotrophic Lateral Sclerosis.” J. Mol. Biol, 334, 515-525, 2003 [download]

4. Dokholyan N.V., Borreguero J.M., Buldyrev S.V., F. Ding, Stanley H.E. and Shakhnovich E.I., Identifying the importance of amino acids for protein folding from crystal structures, Methods in Enzymology Vol. 374: pp 618-640 Macromolecular crystallography D. Editors: C. W. Carter Jr. and R. M. Sweet (2003) [download]

3. F. Ding, Borreguero J.M., Buldyrev S.V., Stangley H.E. and Dokholyan N.V., A mechanism for alpha-helix to beta-hairpin transition, Proteins: Structure, Function and Genetics, 53:220-228 (2003)[download]

2. Dokholyan N.V., Li L., F. Ding, et al. Topological determinants of protein folding P NATL ACAD SCI USA, 99 (13): 8637-8641 JUN 25 2002 [download]

1. F. Ding, Dokholyan V.N., Buldyrev V.S., Stanley H.E. and Shakhnovich E.I. Direct molecular dynamics observation of protein folding transition state ensemble. Biophys J., 86 (6): December 2002 [download]

Protein Design and Protein Engineering

The ability to accurately manipulate a protein’s structure, dynamics and function, and eventually to create de novo proteins with specific functions is the holy grail of modern biology. The success in protein design & engineering requires the accurate knowledge of inter-residue interactions, protein folding, protein-ligand and protein-protein recognitions, and also the molecular mechanism of catalysis. For example, the design of an enzyme needs the sub-angstrom precision in strategically positioning residues important for catalysis. Therefore, computational modeling plays a crucial role in protein design. Despite some recent successes in protein design, we are still far from perfect, requiring better understanding of inter-molecular interactions and faster sampling approaches.

Medusa-Protein

We designed a molecular modeling suite, named Medusa. Medusa features physical-based force field to model inter-residue interactions, and a Monte-Carlo based simulated annealing approach to sample the sequence and sidechain structural spaces. The orientation of amino acid is usually modeled by rotamer library, a set of discrete sidechain conformations tabulated from the protein databank. One of the salient features of Medusa algorithm is the continuous sampling of sub-rotamer space. As result, we were able to accurately model the sidechain packing, and estimate the interaction energies. The applications of Medusa include prediction of the optimal amino acid sequence compatible to the structure of a given protein or protein-ligand and protein-protein complex. Hence, Medusa can be used to design a novel protein with low energy by itself (high stability), or that can bind to a specific partner, including small-molecule ligands and proteins (strong affinity), thus novel functions.

In Medusa, protein backbone can be perturbed to model subtle protein backbone changes upon mutations or binding with partners. We model the major protein backbone changes using discrete molecular dynamics (DMD), or event-driven molecular dynamics. We developed an all-atom protein model for DMD simulations, where the inter-atomic interactions were adopted from Medusa forcefield. Using all-atom DMD, we were able to fold a few proteins into their near-native states. Combining Medusa and all-atom DMD, we can efficiently sample both structural and sequence spaces, allowing us to design proteins with novel functions.

 

10. Dagliyan, O., Shirvanyants, D., Karginov, A., F. Ding, L. Fee, Chandransekaran, S.N., Freisinger, C.M., Smolen, G., Huttenlocher, A., Hahn, K.M., and Dokholyan, N.V., “Rational design of a novel protein for allosteric control of kinases in living organisms”, Proceedings of the National Academy of Sciences USA, in press (2013)

9. A. Karginov, F. Ding, P. Kota, Dokholyan, N.V. and K. Hahn, “Engineered allosteric regulation of kinases in living cells”, Nature Biotechnology, 28:743-748, (2010) [download]

8. M. P. Torres, M. J. Lee, F. Ding, C. Purbeck, B. Kuhlman, N. V. Dokholyan, and H. G. Dohlman, “G Protein Mono-Ubiquitination By the RSP5 Ubiquitin Ligase”, Journal of Biological Chemistry, 284: 8940-8950 (2009)[download]

7. Yin, S., F. Ding and Dokholyan, N. V. “Modeling mutations in proteins using Medusa and discrete molecular dynamics” in “Protein Structure Prediction: Method and Algorithms”, Editors: Rangwala, H. and Karypis, G. Wiley & Sons, (2009)

6. S. Yin, F. Ding, and N. V. Dokholyan, “Computational evaluation of protein stability change upon mutations using Eris.” in “In Vitro Mutagenesis Protocols” Editor: J. Braman. Humana Press (2009)

5. F. Ding, D. Tsao, H. Nie, and N. V. Dokholyan, “Ab initio folding of proteins using all-atom discrete molecular dynamics” Structure, 16: 1010-1018 (2008) [download]

4. S. Yin, F. Ding, and N. V. Dokholyan, “Modeling backbone flexibility improves protein stability estimation”, Structure, 15: 1567-1576 (2007) [download]

3. S. Yin, F. Ding, and N. V. Dokholyan, “Eris: An automated estimator of protein stability” Nature Methods, 4: 466-467 (2007) [download][service]

2. V. V. Demidov, N. V. Dokholyan, C. Witte-Hoffman, P. Chalasani, H.-W. Yiu, F. Ding, Y. Yu, C. R. Cantor, N. E. Broude, “Fast complementation of split fluorescent protein triggered by DNA hybridization”, Proceedings of the National Academy of Sciences USA, 103: 2052-2056 (2006).[download]

1. F. Ding and N. V. Dokholyan, “Emergence of protein fold families through rational design” Public Library of Science Computational Biology, 2: e85 (2006) [download]

Modeling RNA 3D structure using experimental constraints

RNA structure determination is one of the major challenges in structural biology. Many RNAs are not amenable to high-resolution structure characterization by either x-ray or NMR methods because of their conformational flexibility or large size. Recently, novel computational methods to determine RNA structures have begun to emerge, but have often been limited to small RNAs with simple topologies due to either sampling problems or inaccuracy in force field. We developed a discrete molecular dynamics (DMD)-based RNA modeling approach, which allowed robust recapitulation of 3D structure of small RNA structures (< 50 nts). To fold large RNAs with complex 3D structures, we proposed to incorporate experimentally-derived structural information into modeling. Using various types experimentally-derived structural information to bias DMD simulations, we were able to recapitulate 3D structure of RNAs with complex topologies and lengths up to 230 nts. We expect a broad application of our experimentally-driven RNA modeling approach for generating robust structural hypotheses that are useful for guiding explorations of structure-function relationships in RNA.

 

13. Miao Z, Adamiak RW, Blanchet MF, Boniecki M, Bujnicki JM, Chen SJ, Cheng C, Chojnowski G, Chou FC, Cordero P, Cruz JA, Ferré-D’amaré AR, Das R, Ding F, Dokholyan NV, Dunin-Horkawicz S, Kladwang W, Krokhotin A, Lach G, Magnus M, Major F, Mann TH, Masquida B, Matelska D, Meyer M, Peselis A, Popenda M, Purzycka KJ, Serganov A, Stasiewicz J, Szachniuk M, Tandon A, Tian S, Wang J, Xiao Y, Xu X, Zhang J, Zhao P, Zok T, Westhof E., “RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures”, RNA, in press, (2015) [download]

12. Homan, P., Tandon, A., Rice, G. M., Ding, F., Dokholyan, N. V., and Weeks, K. M. “RNA tertiary structure analysis and refinement by 2′-hydroxyl molecular interference”, Biochemistry, 53(43):6825-33 (2014)

11. Ramachandran, S., Ding, F., Weeks, K., and Dokholyan, N. V. “Statistical analysis of SHAPE-directed RNA secondary structure modeling”, Biochemistry, in press (2013)

10. D.I. Cole, J.D. Legassie, L.N. Bonifacio, V.G. Sekaran, F. Ding, N.V.  Dokholyan & M. B. Jarstfer, “New Models of Tetrahymena Telomerase RNA from Experimen- tally Derived Constraints and Modeling”, JACS, in press (2012)[download]

9. F. Ding, C. Lavendar, K.M. Weeks, and N.V. Dokholyan, “Three-Dimensional RNA Structure Refinement by Hydroxyl Radical Probing”, Nature Methods, 9(6):603-608 (2012)[download]
*Accompanied by a news and review paper: Behrouzi, R. and Woodson, S.R., “Rendering RNA in 3D”, Nature Methods, 9(6):552-3 (2012)

8. Cruz, J. A., Blanchet, M-F., Boniecki, M., Bujnicki, J. M., Chen, S-J., Cao, S., Das, R., F. Ding, Dokholyan, N. V., Flores, S. C., Lavender C. A., Lisi, V., Major, F., Mikolajczak, K., Philips, A., Puton, T., Santalucia, J., Siyenji, F., Hermann, T., Rother, K., Rother, M., Serganov, S., Skorupski, M., Soltysinski, T., Sripakdeevong, P., Tuszynska, I., Weeks, K. M., Waldsich, C., Wildauer, M., Leontis, N. B. and Westhof, E. “RNA-Puzzles: A CASP-like evaluation of RNA three-dimensional structure prediction”, RNA, 18:610-625[download] (2012)

7. F. Ding. and Dokholyan, N. V., “RNA three-dimensional structure determination using experimental constraints”, in “RNA Nanotechnology and Therapeutics” edited by Dr. Peixuan Guo, in press, (2012)

6. F. Ding and Dokholyan N.V., “Multiscale modeling of RNA Structure and Dynamics” in “RNA 3D Structure Analysis and Prediction”, Edited by Leontis, N. and Westhof, E. Springer, 2012 [download]

5. C. Lavender, F. Ding, Dokholyan, N.V., K.M. Weeks, “Robust and Generic RNA Modeling Using Inferred Constraints: A Structure for the Hepatitis C Virus IRES Pseudoknot Domain, Biochemistry, 49:4931-4933 (2010)[download]

4. C. Hajdin, F. Ding, N. V. Dokholyan, and K. M. Weeks, “How good is that RNA tertiary structure prediction?” RNA, 16:1340-1349 (2010)[download][service]

3. C. M. Gherghe, C. W. Leonard, F. Ding, N. V. Dokholyan, and K. M. Weeks, “Native-like RNA tertiary structures using a sequence-encoded cleavage agent and refinement by discrete molecular dynamics” Journal of the American Chemical Society , 131:2541-2546(2009)[download]

2. S. Sharma, F. Ding, and N. V. Dokholyan, “iFoldRNA: Three-dimensional RNA structure prediction and folding” Bioinformatics, 24:1951-1952 (2008) [download] [service]

1. F. Ding, S. Sharma, P. Chalasani, V. V. Demidov, N. E. Broude, and N. V. Dokholyan, “Large scale simulations of 3D RNA folding by discrete molecular dynamics: From structure prediction to folding mechanisms” RNA, 14: (2008) [download]