Intrinsically Disordered N-terminal Domain (NTD) of p53 Interacts with Mitochondrial PTP Regulator Cyclophilin D

Zhao, Jing; Blayney, Alan J.; Zhang, Yumeng; Gandy, Lauren; Mirsky, Paige Olivia; Smith, Nathan; Zhang, Fuming; Linhardt, Robert J.; Chen, Jianhan; Baines, Christopher P.; Loh, Stewart N.; Wang, Qianqian

doi:10.1016/j.jmb.2022.167552

Cited by 14 publications

(15 citation statements)

References 34 publications

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“…Examples are also emerging to show that IDPs can remain unstructured, even in specific complexes and functional assemblies [ 83 , 84 , 85 , 86 , 87 , 88 , 89 ]. Figure 1 D illustrates how the N-terminal transactivation domain of tumor suppressor p53 remains highly dynamic in the specific complex with cyclophilin D, a key regulator of the mitochondrial permeability transition pore (PTP) [ 90 ]. Such a dynamic mode of specific protein interactions seems much more prevalent than previously thought [ 91 , 92 , 93 ].…”

Section: A Rich Continuum Of Protein Structures and Dynamics For Func...mentioning

confidence: 99%

Machine Learning Generation of Dynamic Protein Conformational Ensembles

Chen

2023

Molecules

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Machine learning has achieved remarkable success across a broad range of scientific and engineering disciplines, particularly its use for predicting native protein structures from sequence information alone. However, biomolecules are inherently dynamic, and there is a pressing need for accurate predictions of dynamic structural ensembles across multiple functional levels. These problems range from the relatively well-defined task of predicting conformational dynamics around the native state of a protein, which traditional molecular dynamics (MD) simulations are particularly adept at handling, to generating large-scale conformational transitions connecting distinct functional states of structured proteins or numerous marginally stable states within the dynamic ensembles of intrinsically disordered proteins. Machine learning has been increasingly applied to learn low-dimensional representations of protein conformational spaces, which can then be used to drive additional MD sampling or directly generate novel conformations. These methods promise to greatly reduce the computational cost of generating dynamic protein ensembles, compared to traditional MD simulations. In this review, we examine recent progress in machine learning approaches towards generative modeling of dynamic protein ensembles and emphasize the crucial importance of integrating advances in machine learning, structural data, and physical principles to achieve these ambitious goals.

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Section: A Rich Continuum Of Protein Structures and Dynamics For Func...mentioning

confidence: 99%

Machine Learning Generation of Dynamic Protein Conformational Ensembles

Chen

2023

Molecules

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“…103 It was also able to describe the dynamic interaction between p53-TAD and protein cyclophilin D, predicting structure features that are highly consistent with NMR titration and binding experiments. 105 Therefore, HyRes may provide an appropriate CG protein model for the direct simulation of the peptide backbone and secondary structures in the phase separation of IDPs.…”

Section: ■ Introductionmentioning

confidence: 99%

“…The latest version, HyRes II, has been shown to provide a realistic description of a range of nontrivial local and long-range structure features of IDPs, including residual helicity and transient long-range contacts, at a level comparable to atomistic simulations for the N-terminal transactivation domain (TAD) of tumor suppressor p53 . It was also able to describe the dynamic interaction between p53-TAD and protein cyclophilin D, predicting structure features that are highly consistent with NMR titration and binding experiments . Therefore, HyRes may provide an appropriate CG protein model for the direct simulation of the peptide backbone and secondary structures in the phase separation of IDPs.…”

Section: Introductionmentioning

confidence: 99%

Toward Accurate Simulation of Coupling between Protein Secondary Structure and Phase Separation

Zhang,

Li,

Gong

et al. 2023

J. Am. Chem. Soc.

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Intrinsically disordered proteins (IDPs) frequently mediate phase separation that underlies the formation of a biomolecular condensate. Together with theory and experiment, efficient coarse-grained (CG) simulations have been instrumental in understanding the sequence-specific phase separation of IDPs. However, the widely used Cα-only models are limited in capturing the peptide nature of IDPs, particularly backbone-mediated interactions and effects of secondary structures, in phase separation. Here, we describe a hybrid resolution (HyRes) protein model toward a more accurate description of the backbone and transient secondary structures in phase separation. With an atomistic backbone and coarse-grained side chains, HyRes can semiquantitatively capture the residue helical propensity and overall chain dimension of monomeric IDPs. Using GY-23 as a model system, we show that HyRes is efficient enough for the direct simulation of spontaneous phase separation and, at the same time, appears accurate enough to resolve the effects of single His to Lys mutations. HyRes simulations also successfully predict increased β-structure formation in the condensate, consistent with available experimental CD data. We further utilize HyRes to study the phase separation of TPD-43, where several disease-related mutants in the conserved region (CR) have been shown to affect residual helicities and modulate the phase separation propensity as measured by the saturation concentration. The simulations successfully recapitulate the effect of these mutants on the helicity and phase separation propensity of TDP-43 CR. Analyses reveal that the balance between backbone and side chain-mediated interactions, but not helicity itself, actually determines phase separation propensity. These results support that HyRes represents an effective protein model for molecular simulation of IDP phase separation and will help to elucidate the coupling between transient secondary structures and phase separation.

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“…HyRes is able to semiquantitatively capture the secondary structure details, while qualitatively describing dynamic long-range interactions of IDPs. The model has been applied to simulate the dynamic interactions of p53 TAD with cyclophilin D (CypD), successfully predicting key molecular features that agree well with NMR experiments . Nonetheless, HyRes was not originally designed for stand-alone simulations of IDPs, but for driving more efficient atomistic sampling within the framework of multiscaled enhanced sampling (MSES), where accurate descriptions of nonspecific interactions are not crucial.…”

Section: Introductionmentioning

confidence: 99%

“…The model has been applied to simulate the dynamic interactions of p53 TAD with cyclophilin D (CypD), successfully predicting key molecular features that agree well with NMR experiments. 75 Nonetheless, HyRes was not originally designed for stand-alone simulations of IDPs, but for driving more efficient atomistic sampling within the framework of multiscaled enhanced sampling (MSES), where accurate descriptions of nonspecific interactions are not crucial. Indeed, further analysis of the structural properties of p53 TAD in unbound and CypD-bound states revealed some important limitations of HyRes for independent simulation of IDPs.…”

Section: ■ Introductionmentioning

confidence: 99%

Toward Accurate Coarse-Grained Simulations of Disordered Proteins and Their Dynamic Interactions

Liu

Chen

2022

J. Chem. Inf. Model.

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Intrinsically disordered proteins (IDPs) play crucial roles in cellular regulatory networks and are now recognized to often remain highly dynamic even in specific interactions and assemblies. Accurate description of these dynamic interactions is extremely challenging using atomistic simulations because of the prohibitive computational cost. Efficient coarse-grained approaches could offer an effective solution to overcome this bottleneck if they could provide an accurate description of key local and global properties of IDPs in both unbound and bound states. The recently developed hybrid-resolution (HyRes) protein model has been shown to be capable of providing a semiquantitative description of the secondary structure propensities of IDPs. Here, we show that greatly improved description of global structures and transient interactions can be achieved by introducing a solvent-accessible surface area-based implicit solvent term followed by reoptimization of effective interaction strengths. The new model, termed HyRes II, can semiquantitatively reproduce a wide range of local and global structural properties of a set of IDPs of various lengths and complexities. It can also distinguish the level of compaction between folded proteins and IDPs. In particular, applied to the disordered N-terminal transactivation domain (TAD) of tumor suppressor p53, HyRes II is able to recapitulate various nontrivial structural properties compared to experimental results, some of them to a level of accuracy that is almost comparable to results from atomistic explicit solvent simulations. Furthermore, we demonstrate that HyRes II can be used to simulate the dynamic interactions of TAD with the DNA-binding domain of p53, generating structural ensembles that are highly consistent with existing NMR data. We anticipate that HyRes II will provide an efficient and relatively reliable tool toward accurate coarse-grained simulations of dynamic protein interactions.

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Intrinsically Disordered N-terminal Domain (NTD) of p53 Interacts with Mitochondrial PTP Regulator Cyclophilin D

Cited by 14 publications

References 34 publications

Machine Learning Generation of Dynamic Protein Conformational Ensembles

Machine Learning Generation of Dynamic Protein Conformational Ensembles

Toward Accurate Simulation of Coupling between Protein Secondary Structure and Phase Separation

Toward Accurate Coarse-Grained Simulations of Disordered Proteins and Their Dynamic Interactions

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