Peng Tao
CD4 project:
Deciphering Protein Allostery
Protein allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity, and is considered as the “second secret of life”. Specific mutations of certain amino acid residues can alter distribution of a protein’s conformation or dynamics, and regulate its allosteric function. Some unnatural mutants could potential drive proteins to reach conformational space or sustain dynamical distribution (referred to as "hidden states") that are inaccessible for natural variants. Investigation and understanding of protein allostery in these hidden states, which are approachable only through computational means, could serve as a novel approach in protein engineering. We are exploiting hidden conformational spaces and dynamical states of proteins and developing theoretical models of protein allostery and functions.
Enzymatic Mechanism Evolution
The goal of this project is to develop new computational methods to model and predict the evolution of β-lactamase enzymatic reaction mechanisms that lead to dangerous resistant strains. β-Lactamases are proteins that hydrolyze β-lactam antibiotics and are one of the main causes of antimicrobial resistance. Understanding and predicting β-lactamase evolution is crucial for the future development of antibiotics with low resistance. Current protein evolution studies focus on protein sequences and three-dimensional structures. Little is known about how these changes impact the evolution of enzymes' catalytic mechanisms. We are developing novel computational methods combining machine learning that link changes in protein structure to changes in catalytic mechanism. This methods will enable us to predict how new mutations could lead to changes in catalytic mechanisms.
Recent related publications:
With collaboration with other SMU faculty members:
42. Wang, F.; Zhou, H.; Wang, X.; Tao, P.* Dynamical Behavior of β-Lactamases and Penicillin-Binding Proteins in Different Functional States and Its Potential Role in Evolution. 2019, Entropy, 21(11), 1130 DOI: 10.3390/e21111130
41. Zhou, H.*; Wang, F.; Bennett, D. I. G.; Tao, P.* Directed kinetic transition network model. 2019, J. Chem. Phys. 151, 144112 DOI: 10.1063/1.5110896
39. Wang, F.; Shen, L.; Zhou, H.; Wang, S.; Wang, X.; Tao, P.* Machine Learning Classification Model for Functional Binding Modes of TEM-1 β-Lactamase. 2019, Front. Mol. Biosci. 6:47 DOI: 10.3389/fmolb.2019.00047
38. Zhou, H.; Dong, Z.; Verkhivker, G.; Zoltowski, B. D.; Tao, P.*; Allosteric mechanism of the circadian protein Vivid resolved through Markov state model and machine learning analysis. PLoS Comput Biol. 2019, 15(2): e1006801 DOI: 10.1371/journal.pcbi.1006801
29. Zhou, H.; Zoltowski, B. D.; Tao, P. Revealing Hidden Conformational Space of LOV Protein VIVID Through Rigid Residue Scan Simulations. Scientific Reports 2017, 7, 46626 DOI: 10.1038/srep46626
25. Cao, J.; Lopez, R.; Thacker J. M.; Moon, J. Y.; Jiang, C.; Morris, S. N. S.; Bauer, J. H.; Tao, P.; Mason, R. P.; Lippert, A. R.* "Chemiluminescent Probes for Imaging H2S in Living Animals." Chem. Sci. 2015, 6, 1979-1985. DOI: 10.1039/C4SC03516J
Collaborations from outside of SMU:
40. Hayatshahi, H. S.; Ahuactzin, E.; Tao, P.; Wang, S.; Liu, J.* Probing Protein Allostery as a Residue-Specific Concept via Residue Response Maps. 2019, J. Chem. Inf. Model. 59, 4691-4705 DOI: 10.1021/acs.jcim.9b00447
34. Zahler, C. T.; Zhou, H.; Abdolvdahabi, A.; Holden, R. L.; Rasouli, S.; Tao, P.; Shaw, B. F. Direct Measurement of Charge Regulation in Metalloprotein ElectronTransfer, Angew. Chem. Int. Ed. 2018, 57, 5364-5368 DOI: 10.1002/anie.201712306
32. Wang, F.; Zhou, H.; Olademehin, O. P.; Kim, S. J.; Tao, P. Insights into Key Interactions between Vancomycin and Bacterial Wall Structures. ACS Omega 2018, 3, 37−45 DOI: 10.1021/acsomega.7b01483
27. Chang, J.; Zhou, H.; Preobrazhenskaya, M.; Tao, P.;Kim, S. J. The Carboxyl Terminus of Eremomycin Facilitates Binding to the Non-d-Ala-d-Ala Segment of the Peptidoglycan Pentapeptide Stem. Biochemistry 2016, 55, 3383–3391. DOI: 10.1021/acs.biochem.6b00188
26. Kalescky, R.; Zhou, H.; Liu, J.; Tao, P. (2016) Rigid Residue Scan Simulations Systematically Reveal Residue Entropic Roles in Protein Allostery. PLoS Comput Biol 12(4): e1004893. DOI: 10.1371/ journal.pcbi.1004893
24. Kalesky, R.; Liu, J.; Tao, P.; “Identifying Key Residues for Protein Allostery through Rigid Residue Scan”. J. Phys. Chem. A 2015, 119, 1689-1700. DOI: 10.1021/jp5083455
Within Tao group
45. Tian, H.; Tao, P*, ivis Dimensionality Reduction Framework for Biomacromolecular Simulations, J. Chem. Inf. Model. 2020, https://doi.org/10.1021/acs.jcim.0c00485
44. Tian, H.; Tao, P*, Deciphering the protein motion of S1 subunit in SARS-CoV-2 spike glycoprotein through integrated computational methods, 2020, Journal of Biomolecular Structure and Dynamics, DOI: 10.1080/07391102.2020.1802338
43. Wang, F.; Tao, P.* Exploring free energy profile of petroleum thermal cracking mechanisms. 2020, J. Mol. Mod., 26:15 DOI: 10.1007/s00894-019-4273-3
37. Zhou, H.; Tao, P.*; REDAN: relative entropy-based dynamical allosteric network model. Mol. Phys. 2018 DOI: 10.1080/00268976.2018.1543904
36. Zhou, H.; Wang, F.; Tao, P.*; t-Distributed Stochastic Neighbor Embedding (t-SNE) Method with the Least Information Loss for Macromolecular Simulations. J. Chem. Theory Comput. 2018, 14, 5499-5510. DOI: 10.1021/acs.jctc.8b00652
33. Zhou, H.; Dong, Z.; Tao, P.*; Novel Application of Machine Learning Approaches on the Recognition of Protein States and Identification of Function-Related Residues. J. Comput. Chem., 2018, 39, 1481-1490 DOI: 10.1002/jcc.25218
31. Zhou, H.; Tao, P. Dynamics Sampling in Transition Pathway Space. J. Chem. Theory Comput. 2018, 14, 14−29 DOI: 10.1021/acs.jctc.7b00606
30. Dong, Z.; Zhou, H.; Tao, P. Combining protein sequence, structure, and dynamics: A novel approach for functional evolution analysis of PAS domain superfamily. Protein Science 2017, 27, 421-430 DOI: 10.1002/pro.3329