chem100 poster-thanh-v3qbq-sm1

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Abstract: Interactions between viral encoded regulatory proteins and RNA target sequences control gene expression of lentiviruses, including human immunodeficiency virus (HIV). Bovine immunodeficiency virus (BIV) provides a simpler model of interaction between the viral transactivator protein (Tat) and trans- activation response RNA element (TAR), using Tat peptides binding to TAR RNA fragments. Besides offering insights into clinical approaches to treatment, this complex offers a simple model system to better understand RNA- protein interactions via theoretical consideration of their flexibility, utilizing literature-based binding assays and NMR. The resulting characterization of the hinge region of BIV TAR-Tat complex has been identified, where K75 and R78 are considered possible residue positions for substitution by glycines. The resulting substitutions may allow alternative structure and RNA-protein contacts. NPDOCK and SCWRL4, molecular modeling programs, most notably indicates that the double substitution G75 and G78 results in a very different 14-mer peptide structure excluded from the major groove of the RNA and the single substituted G75 peptide appears more stable than the native. However these modeling methods are by design constrained, a result of the very extensive search space associated with of RNA-protein complexes. To test the robustness of these initial computational studies more extensive hierarchical modeling is required. This includes the application of statistical potentials describing coarse- grained features in contact with an amino acid as opposed to all-atom descriptions. We have developed an updated set of such potentials using BIV TAR-Tat as an initial model for application and validation. Binding of Mutant Tat Peptides to TAR RNA as a Model for Developing Hierarchical Methods Utilizing RNA-Protein Statistical Potentials Phuc Tran, Thanh Q. Le, Artem Shosnikov, Takayuki Kimura and Brooke Lustig* Chemistry Department, San Jose State University, San Jose, California 95192 *[email protected] References 1. Puglisi, J. D., Chen, L., Blanchard, S., & Frankel, A. D. (1995). Solution structure of a bovine immunodeficiency virus Tat-TAR peptide-RNA complex.Science, 270(5239), 1200-1203. 2. Greenbaum, N. L. (1996). How Tat targets TAR: structure of the BIV peptide–RNA complex. Structure, 4(1), 5-9. 3. Lustig, B., Bahar, I. & Jernigan, R. L. (1998) RNA bulge entropies correlate with peptide binding strengths for HIV-1 and BIV TAR RNA because of improved conformational access. Nucleic Acids Res. 26, 5212-5217 (1998). 4. Tzeng, S.-R. & Kalodimos, C. G. Protein activity regulation by conformational entropy. (2012) Nature 488, 236-240. 5. Hsieh, M., Collins, E. D., Blomquist, T., & Lustig, B. (2002). Flexibility of BIV TAR-Tat: models of peptide binding. Journal of Biomolecular Structure and Dynamics, 20(2), 243-251. 6. Nguyen, L. BIV TAR RNA Binding Glycine Mutant Tat Peptides: An Integrated Modeling and Binding Assay Approach. M.S. Thesis, San Jose State University, 2015. 7. van der Spoel, D.,Lindhal, E., Hess, B. Groenhof, G. Mark, A. E., & van Drunen, R. & Berendsen, H. J. (1995). GROMACS: Fast, Flexible and Free. (2005). J. Computer Chem. 26, 101-1718. 8. Krivov, G. G., Shapovalov, M. V., & Dunbrack, R. L. (2009). Improved prediction of protein side‐chain conformations with SCWRL4. Proteins: Structure, Function, and Bioinformatics, 77(4), 778-795. 9. Tuszynska, I., Magnus, M., Jonak, K., Dawson, W., & Bujnicki, J. M. (2015). NPDock: a web server for protein–nucleic acid docking. Nucleic acids research, 43(W1), W425-W430. 10.Ye, X., Kumar, R. A., & Patel, D. J. (1995). Conclusions Methods Results and Discussion Figure 1: BIV TAR and Tat peptide. A) Secondary structure of RNA BIV TAR fragment. B) Wild type sequence of TAT binding peptide. 1MNB and 1BIV files obtained from rcsb.org. Chimera to prepare files for further modification. SCWRL 4 for glycine mutation. Best Structur es GROMACS to calculate binding energy and Gibbs free energy. Analysis Figure 2: Alignment of 1MNB (1) and 1BIV (10) peptides (11). Blue line represents 1BIV containing 17- amino acids. Red line represents 1MNB containing 14-amino acids. Figure 3: Alignment of 1MNB and 1BIV RNA (12) . Red RNA represents 1BIV while yellow RNA belongs to 1MNB. RMSD is 2.34 angstroms. The two corresponding binding domains appear to be very similar. Figure 4: Binding sites of various BIV Tat peptides. Brown peptide has glycine mutation at position 75. Blue peptide has glycine mutation at position 78. Pink peptide has glycine mutation at positions 75 and 78. Green peptide is the wild type. Figure 5: Energy distribution of some 12 million chains BIV TAR-Tat coarse-grained lattice modeling without glycine mutation and with glycine mutation (at 75 and/or 78 positions) for one move per 2-amino acids (6). A significant decrease in stability can be seen in double mutation at positions 75 and 78 for both all-atom and lattice calculations. Peptide with a single glycine mutation at position 75 shows an increase in binding energy in both all-atom and lattice. Glycine mutation at position 78 peptide has similar binding energy as double glycine mutation peptide in the lattice but energy not yet authoritatively determined in all- atom approach (currently a priority). Future Studies Develop and apply an updated potential to better calculate binding energy. As part of developing hierarchical modeling analyze the 1% of best coarse-grained structures, in order to build plausible all- atom structures to be compared with our current all-atom models. Introduction: Similar to HIV, BIV stays dormant in its hosts for a long period of time. During a long dormancy period, most RNA polymerases disengage from transcription complexes, which inhibits transcriptional elongation and production of viral proteins. Transcription is enhanced by the binding of BIV Tat peptide (1,2) given BIV Tat is an activator that binds to BIV TAR with high affinity and specificity; its specific target is the hairpin located at the 5’ end of the viral mRNA. Many studies have indicated that all-atom simulations do not work well in the case of RNA- protein interactions as RNA is extremely flexible; its flexibility is due to the fact that it is single stranded. Thus, the study of RNA-protein interactions remain a daunting task. Moreover, Lustig proposed that increasing the peptide flexibility likely enhances binding stability (2,3). This included suggested specific substitutions by glycine at potential Tat peptide hinged-adjacent positions. Recent NMR experiments have indicated that protein-DNA binding may be enhanced by increases in conformational entropy (4). Background: The Lustig group has used coarse- grain lattice modeling to study BIV TAR Tat interaction (5,6). This method does exhaustive searching of coarse-grained configurational space, including predicting the flexibility of mutated BIV Tat at certain positions. However, output from this method can involve over 12- million . Updating Statistical Potentials - Currently at least 50 times larger learning set of high-resolution X-ray structures available to calculate statistical potentials than earlier learning set used for potentials applied in Figure 5. - Preliminary results indicate some improvement in properly evaluating coarse-grained structures.

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Page 1: Chem100 poster-Thanh-V3qBq-SM1

Abstract: Interactions between viral encoded regulatory proteins and RNA target sequences control gene expression of lentiviruses, including human immunodeficiency virus (HIV). Bovine immunodeficiency virus (BIV) provides a simpler model of interaction between the viral transactivator protein (Tat) and trans-activation response RNA element (TAR), using Tat peptides binding to TAR RNA fragments. Besides offering insights into clinical approaches to treatment, this complex offers a simple model system to better understand RNA-protein interactions via theoretical consideration of their flexibility, utilizing literature-based binding assays and NMR. The resulting characterization of the hinge region of BIV TAR-Tat complex has been identified, where K75 and R78 are considered possible residue positions for substitution by glycines. The resulting substitutions may allow alternative structure and RNA-protein contacts. NPDOCK and SCWRL4, molecular modeling programs, most notably indicates that the double substitution G75 and G78 results in a very different 14-mer peptide structure excluded from the major groove of the RNA and the single substituted G75 peptide appears more stable than the native. However these modeling methods are by design constrained, a result of the very extensive search space associated with of RNA-protein complexes. To test the robustness of these initial computational studies more extensive hierarchical modeling is required. This includes the application of statistical potentials describing coarse-grained features in contact with an amino acid as opposed to all-atom descriptions. We have developed an updated set of such potentials using BIV TAR-Tat as an initial model for application and validation.

Binding of Mutant Tat Peptides to TAR RNA as a Model for Developing Hierarchical Methods Utilizing

RNA-Protein Statistical Potentials 

Phuc Tran, Thanh Q. Le, Artem Shosnikov, Takayuki Kimura and Brooke Lustig*Chemistry Department, San Jose State University, San Jose, California 95192

*[email protected]

References1. Puglisi, J. D., Chen, L., Blanchard, S., & Frankel, A. D. (1995). Solution

structure of a bovine immunodeficiency virus Tat-TAR peptide-RNA complex.Science, 270(5239), 1200-1203.

2. Greenbaum, N. L. (1996). How Tat targets TAR: structure of the BIV peptide–RNA complex. Structure, 4(1), 5-9.

3. Lustig, B., Bahar, I. & Jernigan, R. L. (1998) RNA bulge entropies correlate with peptide binding strengths for HIV-1 and BIV TAR RNA because of improved conformational access. Nucleic Acids Res. 26, 5212-5217 (1998).

4. Tzeng, S.-R. & Kalodimos, C. G. Protein activity regulation by conformational entropy. (2012) Nature 488, 236-240.

5. Hsieh, M., Collins, E. D., Blomquist, T., & Lustig, B. (2002). Flexibility of BIV TAR-Tat: models of peptide binding. Journal of Biomolecular Structure and Dynamics, 20(2), 243-251.

6. Nguyen, L. BIV TAR RNA Binding Glycine Mutant Tat Peptides: An Integrated Modeling and Binding Assay Approach. M.S. Thesis, San Jose State University, 2015.

7. van der Spoel, D.,Lindhal, E., Hess, B. Groenhof, G. Mark, A. E., & van Drunen, R. & Berendsen, H. J. (1995). GROMACS: Fast, Flexible and Free. (2005). J. Computer Chem. 26, 101-1718.

8. Krivov, G. G., Shapovalov, M. V., & Dunbrack, R. L. (2009). Improved prediction of protein side‐chain conformations with SCWRL4. Proteins: Structure, Function, and Bioinformatics, 77(4), 778-795.

9. Tuszynska, I., Magnus, M., Jonak, K., Dawson, W., & Bujnicki, J. M. (2015). NPDock: a web server for protein–nucleic acid docking. Nucleic acids research, 43(W1), W425-W430. 

10. Ye, X., Kumar, R. A., & Patel, D. J. (1995). Molecular recognition in the bovine immunodeficiency virus Tat peptide-TAR RNA complex. Chemistry & biology, 2(12), 827-840.

11. Ye, Y., & Godzik, A. (2003). Flexible structure alignment by chaining aligned fragment pairs allowing twists. Bioinformatics, 19(suppl 2), ii246-ii255.

12. PyMOL Molecular Graphics System, Version 1.8 Schrödinger, LLC.

Conclusions

Methods

Results and Discussion

Figure 1: BIV TAR and Tat peptide. A) Secondary structure of RNA BIV TAR fragment.B) Wild type sequence of TAT binding peptide.

1MNB and 1BIV files obtained from rcsb.org.

Chimera to prepare files for further modification.

SCWRL 4 for glycine mutation.

Best Structures

GROMACS to calculate binding energy and Gibbs free energy.

Analysis

Figure 2: Alignment of 1MNB (1) and 1BIV (10) peptides (11). Blue line represents 1BIV containing 17-amino acids. Red line represents 1MNB containing 14-amino acids.

Figure 3: Alignment of 1MNB and 1BIV RNA (12) . Red RNA represents 1BIV while yellow RNA belongs to 1MNB. RMSD is 2.34 angstroms. The two corresponding binding domains appear to be very similar.

Figure 4: Binding sites of various BIV Tat peptides. Brown peptide has glycine mutation at position 75. Blue peptide has glycine mutation at position 78. Pink peptide has glycine mutation at positions 75 and 78. Green peptide is the wild type.

Figure 5: Energy distribution of some 12 million chains BIV TAR-Tat coarse-grained lattice modeling without glycine mutation and with glycine mutation (at 75 and/or 78 positions) for one move per 2-amino acids (6).

• A significant decrease in stability can be seen in double mutation at positions 75 and 78 for both all-atom and lattice calculations.

• Peptide with a single glycine mutation at position 75 shows an increase in binding energy in both all-atom and lattice.

• Glycine mutation at position 78 peptide has similar binding energy as double glycine mutation peptide in the lattice but energy not yet authoritatively determined in all-atom approach (currently a priority).

Future Studies• Develop and apply an updated potential to better calculate

binding energy.• As part of developing hierarchical modeling analyze the 1%

of best coarse-grained structures, in order to build plausible all-atom structures to be compared with our current all-atom models.

Introduction: Similar to HIV, BIV stays dormant in its hosts for a long period of time. During a long dormancy period, most RNA polymerases disengage from transcription complexes, which inhibits transcriptional elongation and production of viral proteins. Transcription is enhanced by the binding of BIV Tat peptide (1,2) given BIV Tat is an activator that binds to BIV TAR with high affinity and specificity; its specific target is the hairpin located at the 5’ end of the viral mRNA. Many studies have indicated that all-atom simulations do not work well in the case of RNA-protein interactions as RNA is extremely flexible; its flexibility is due to the fact that it is single stranded. Thus, the study of RNA-protein interactions remain a daunting task. Moreover, Lustig proposed that increasing the peptide flexibility likely enhances binding stability (2,3). This included suggested specific substitutions by glycine at potential Tat peptide hinged-adjacent positions. Recent NMR experiments have indicated that protein-DNA binding may be enhanced by increases in conformational entropy (4).

Background: The Lustig group has used coarse-grain lattice modeling to study BIV TAR Tat interaction (5,6). This method does exhaustive searching of coarse-grained configurational space, including predicting the flexibility of mutated BIV Tat at certain positions. However, output from this method can involve over 12- million structures. Our approach utilizes GROMACS (7) and other all-atom approaches (8,9) to calculate binding energy between BIV TAR and mutated Tat. Tat peptides. In this study, we noted a significant decrease in stability of a doubly glycine mutation at positions 75 and 78 peptide and single glycine mutation at position 78. Peptide with glycine mutation at position 75 showed an increase in stability and binding.

.

Updating Statistical Potentials

- Currently at least 50 times larger learning set of high-resolution X-ray structures available to calculate statistical potentials than earlier learning set used for potentials applied in Figure 5.

- Preliminary results indicate some improvement in properly evaluating coarse-grained structures.