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. Therefore, comparing to high zinc concentrations where insulin is insoluble in the crystal form, zinc-deficiency due to loss-of-function mutations of ZnT8 shifts insulin oligomer/crystallization equilibrium toward soluble monomers and dimers, which can efficiently inhibit IAPP aggregation and reduce T2D risk. Currently, we are trying to understand the effects of other granule molecules, including zinc, c-peptide, and their complex, on IAPP aggregation.

3. P. Nedumpully-Govindan, Y. Yang, R. Andorfer, W. Cao, and F. Ding, “Promotion or Inhibition of IAPP Aggregation by Zinc Coordination Depends on Its Relative Concentration”, Biochemistry, in press (2015)

2. P. Nedumpully-Govindan, E.N. Gurzov, P. Chen, E.H. Pilkington, W.J. Stanley, S.A. Litwak, T.P. Davis, P.C. Ke, and F. Ding, “Graphene Oxide Inhibits hIAPP Amyloid Fibrillation and Toxicity in Insulin-Producing NIT-1 Cells”, PCCP, in press (2015)

1. Nedumpully-Govindan P. and Ding F., “Inhibition of IAPP aggregation by insulin depends on the insulin oligomeric state regulated by zinc ion concentration”, Scientific Reports 5, (2015)

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


a) Protein folding thermodynamics and kinetics

b) Protein folding transition states

c) Protein folding pathways

d) Ab initio protein folding



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.


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]

Nov 20

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.

A. MedusaDock–a flexible docking approach. MedusaDock models both ligand and receptor flexibility simultaneously with sets of discrete rotamers. We developed an algorithm to build the ligand rotamer library “on-the-fly” during docking simulations. MedusaDock benchmarks demonstrate a rapid sampling efficiency and high prediction accuracy in both self- (to the co-crystallized state) and cross-docking (to a state co-crystallized with a different ligand), the latter of which mimics the virtual screening procedure in computational drug discovery. We also perform a virtual screening test of four flexible kinase targets, including cyclin-dependent kinase 2, vascular endothelial growth factor receptor 2, HIV reverse transcriptase, and HIV protease. We find significant improvements of virtual screening enrichments when compared to rigid-receptor methods. The predictive power of MedusaDock in cross-docking and preliminary virtual-screening benchmarks highlights the importance to model both ligand and receptor flexibility simultaneously in computational docking.

B. Incorporating backbone flexibility in MedusaDock. We introduce backbone flexibility into MedusaDock by implementing ensemble docking in a sequential manner for a set of distinct receptor backbone conformations. We generate corresponding backbone ensembles to capture backbone changes upon binding to different ligands, as observed experimentally. We develop a simple clustering and ranking approach to select the top poses as blind predictions. We applied our method in the CSAR2011 benchmark exercise. In 28 out of 35 cases (80%) where the ligand-receptor complex structures were released, we were able to predict near-native poses (< 2.5 Å RMSD), the highest success rate reported for CSAR2011. Thisresult highlights the importance of modeling receptor backbone flexibility to the accurate docking of ligands to flexible targets. We expect a broach application of our fully flexible docking approach in biological studies as well as in rational drug design.



C. Protein-peptide binding using DMD simulations. Utilizing sampling efficiency of DMD and Medusa force field in describing interaction between amino acids, we apply DMD simulations to modeling protein-peptide recognition. We find that, in most cases, we recapitulate the native binding sites and native-likeposes of protein-peptide complexes. Inclusion ofelectrostatic interactions in simulations significantly improves the prediction accuracy. Our results alsohighlight the importance of protein conformationalflexibility, especially side-chain movement, which allows the peptide to optimize its conformation. Ourfindings not only demonstrate the importance of sufficient sampling of the protein and peptide conformations, but also reveal the possible effects ofelectrostatics and conformational flexibility on peptide recognition.



5. Nedumpully-Govindan P., Domen J., and Ding F., “CSAR Benchmark of flexible MedusaDock in affinity prediction and native-like binding pose selection”, Journal of Chemical Information and Modeling, in press (2015)

4. Fourche, D., Muratov, E., Ding, F., Dokholyan, N. V., Tropsha, A. “Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches”, Journal of Chemical Information and Modeling, in press (2013)

3. F. Ding. and Dokholyan, N. V., “Incorporating backbone flexibility in MedusaDock improves ligand binding pose prediction in the CSAR2011 docking benchmark”, Journal of Chemical Information and Modeling, in press, (2012)[download]

2. Dagliyan, O., Proctor, E. A., D’Auria, K., F. Ding* and Dokholyan N. V.* “Structural and Dynamic Determinants of Protein-peptide Recognition”, Structure, 19:1837 (2011) [download]

1. F. Ding, Yin, S., and Dokholyan, N. V. “Rapid flexible docking using a stochastic rotamer library of ligands”, Journal of Chemical Information and Modeling, 50:1623-32 (2010)[download]

Nov 12

Nanoparticle-Protein Corona

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.

Upon entering biological systems, NPs form molecular complexes with their encountered proteins, termed as the NP-protein corona. Protein corona shields the surface of an exogenous NP and determines its biological identifies. On the other hand, interactions with NP can alter the structure, dynamics, and function of the NP-bound proteins, which may further impact recognition by membrane receptors and the immune system. We hypothesize that the conformational changes of NP-bound proteins are an important determinant of nanotoxicity in additional to other accepted factors, such as ion dissolution and ability for lipid membrane translocation. We apply multiscale computer simulations to understand the structure and dynamics of NP-protein corona, and to evaluate the corresponding conformational changes of the NP-bound proteins. Correlation between computational studies of NP-protein corona and experimental evaluations of NP cytotoxicity will help the development of a predictive metric of nanotoxicity for advancing nanomedicine and environmental remediation of the hazardous by-products of the petroleum industry.

12. P. Nedumpully-Govindan, E.N. Gurzov, P. Chen, E.H. Pilkington, W.J. Stanley, S.A. Litwak, T.P. Davis, P.C. Ke, and F. Ding, “Graphene Oxide Inhibits hIAPP Amyloid Fibrillation and Toxicity in Insulin-Producing NIT-1 Cells”, PCCP, in press (2015)

11. Wang B, Geitner NK, Davis TP, Ke PC, Ladner DL and Ding F, “Deviation from the Unimolecular Micelle Paradigm of PAMAM Dendrimers Induced by Strong Inter-Ligand Interactions”, Journal of Physical Chemistry C, in press (2015)

10. Ge XW, Ke PC, Davis TP and Ding F, “A Thermodynamics Model for the Emergence of a Stripe-like Binary SAM on a Nanoparticle Surface”, Small, in press (2015)

9. DeFever R., Geitner N., Bhattacharya P., Ding F., Ke P.C., Sarupria S., “PAMAM dendrimers and graphene: Materials for removing aromatic contaminants from water”, Environmental Science & Technology, in press (2015)

8. Geitner N., Wang B., Andorfer R.; Ladner D.; Ke P.K.; Ding F., “The structure-function relationship of PAMAM dendrimers as robust oil dispersants”, Environmental Science & Technology, 48(21):12868-75, (2014)

7. Wang B., Seabrook S.A., Nedumpully-Govindan P., Chen P., Yin H., Waddington L., Epa V.C., Winkler D.A., Kirby J.K., Ding F., Ke P.C., “Thermostability and Reversibility of Silver Nanoparticle-Protein Binding”, Physical Chemistry Chemical Physics, in press (2014)

6. S. Radic, P. Nedumpully-Govindan,R. Chen, E. Salonen, J.M. Brown, P.C. Ke, and F. Ding, “Effect of Fullerenol Surface Chemistry on Nanoparticle Binding-induced Protein Misfolding”, Nanoscale, in press (2014)

5. Y. Wen, N.K. Geitner, R. Chen, F. Ding, P. Chen, R.E. Andorfer, P.N. Govindan, and P.C. Ke, Binding of Cytoskeletal Proteins with Silver Nanoparticles, RSC Adcances, in press, (2013).

4. E. Salonen, F. Ding and P.C. Ke, “Fate, Behavior and Biophysical Modeling of Nanoparticles in Living Systems”, Volume IV, for Biophysico-Chemical Processes in Environmental Systems, Wiley-IUPAC (2014)

3. A. Kakinen, F. Ding, P. Chen, A. Kahru*, and P.C. Ke, “Interaction of Silver Nanoparticles and Firefly Luciferase and Its Impact on Enzyme Luminescence”, Nanotechnology, in press. (2013)

2. S. Radic, N. Geitner, R. Podila, A. Kakinen, P. Chen, P.C. Ke*, and F. Ding*, “Competitive Binding of Natural Amphiphiles with Graphene Derivatives”, Scientific Reports, in press. (2013)

1. F. Ding*, S. Radic, R. Chen, P. Chen, J.M. Brown and P.C. Ke*, “Direct observation of silver nanoparticle-ubiquitin corona formation”, Nanoscale, in press (2012)


Oct 17

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. For a long time, the fibrillar aggregation, a.k.a. amyloid fibrils, has been thought to cause a long list of amyloid diseases, including Alzheimers’, Parkinson’s, Lou Gehrig’s diseases. Recent experiments have suggested that the smaller, soluble oligomers are more toxic to cells. We are applying computational modeling approaches to uncover the molecular mechanism of misfolding, to determine  driving forces underlying aggregation, to characterize structures of the oligomers and fibril aggregates, and to design therapeutics against aggregation.


15. Nedumpully-Govindan P. and Ding F., “Inhibition of IAPP aggregation by insulin depends on the insulin oligomeric state regulated by zinc ion concentration”, Scientific Reports, in press (2015)

14. F. Ding, Y. Furukawa, N. Nukina, and Dokholyan, N.V., “Local unfolding of Cu, Zn superoxide Dismutase monomer determines the morphology of fibrillar aggregates”, Journal of Molecular Biology, 421:548-560 (2012) [download]

13. Proctor, E. A., F. Ding, and Dokholyan, N. V. “Structural and thermodynamic effects of post-translational modifications in mutant and wild type Cu, Zn superoxide dismutase”, Journal of Molecular Biology, 408:555-567 (2011). [download]

12. V. V. Lakhani, F. Ding and N. V. Dokholyan, “Poly-glutamine induced misfolding of huntingtin exon1 is modulated by the flanking sequences” Public Library of Science Computational Biology:e1000772 (2010) [download]

11. F. Ding and N. V. Dokholyan, “Dynamical roles of metal ions and the disulfide bond in Cu, Zn superoxide dismutase folding and aggregation” Proceedings of the National Academy of Sciences USA, 105:19696-19701 (2008) [download]

10. S. Sharma, F. Ding, and N. V. Dokholyan, “Probing protein aggregation using simplified models and discrete molecular dynamics” Frontiers in Bioscience, 13: 4795-4808 (2008) [download]

9. S. Barton, R. Jacak, S. D. Khare, F. Ding*, and N. V. Dokholyan*, “The length dependence of the polyQ-mediated protein aggregation” Journal of Biological Chemistry, 282: 25487-25492 (2007)[download]

8. F. Ding, K. C. Prutzman, S. L. Campbell, and N. V. Dokholyan, “Topological determinants of protein domain swapping”, Structure, 14: 5-14 (2005). [download][service]

7. F. Ding, J. J. LaRocque, and N. V. Dokholyan, “Direct observation of protein folding, aggregation and a prion-like conformational transition”, Journal of Biological Chemistry, 280: 40235-40240 (2005)[download]

6. S. D. Khare, F. Ding, K. N. Gwanmesia, and N. V. Dokholyan, “Molecular origin of polyglutamine-mediated protein aggregation in neurodegenerative diseases”, PLoS Computational Biology, 1, e30 (2005). [download]

5. B. Urbanc, L. Cruz, F. Ding, D. Sammond, S. Khare, S. V. Buldyrev, H. E. Stanley, and N. V. Dokholyan, “Molecular dynamics simulation of Amyloid beta dimer formation” Biophys. J., 87: 2310-2321 (2004). [download]

4. S. Peng, F. Ding, B. Urbanc, S. V. Buldyrev, L. Cruz , H. E. Stanley, and N. V. Dokholyan, “Discrete molecular dynamics simulations of peptide aggregation” Phys. Rev. E 69: 041908 (2004)[download]

3. 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]

2. 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]

1. F. Ding, Dokholyan N.V., Buldyrev S.V., Stanley H.E. and Shakhnovich E.I. Molecular dynamics simulation of C-Src SH3 aggregation suggests a generic amyloidogenesis mechanism J Mol Biol, 324:851-857 (2002) [download]

Oct 17

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]