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,


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