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Insights into Rational Design of a New Class of Allosteric Effectors with Molecular Dynamics Markov State Models and Network Theory.

ABSTRACT: The development of drugs to restore protein function has been a major advance facilitated by molecular medicine. Allosteric regulation, a phenomenon widely observed in nature, in which a molecule binds to control a distance active site, holds great promise for regulating proteins, yet how to rationally design such a molecule remains a mystery. Over the past few years, we and others have developed several techniques based on molecular dynamics (MD) simulations: MD-Markov state models to capture global conformational substates, and network theory approach utilizing the interaction energy within the protein to confer local allosteric control. We focus on the key case study of the p53 Y220C mutation restoration by PK11000, a compound experimentally shown to reactivate p53 native function in Y220C mutant present tumors. We gain insights into the mutation and allosteric reactivation of the protein, which we anticipate will be applicable to de novo design to engineer new compounds not only for this mutation, but in other macromolecular systems as well.

SUBMITTER: Han ISM

PROVIDER: S-EPMC8792916 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Insights into Rational Design of a New Class of Allosteric Effectors with Molecular Dynamics Markov State Models and Network Theory.

Han In Sub M ISM Abramson Dylan D Thayer Kelly M KM

ACS omega 20220113 3

The development of drugs to restore protein function has been a major advance facilitated by molecular medicine. Allosteric regulation, a phenomenon widely observed in nature, in which a molecule binds to control a distance active site, holds great promise for regulating proteins, yet how to rationally design such a molecule remains a mystery. Over the past few years, we and others have developed several techniques based on molecular dynamics (MD) simulations: MD-Markov state models to capture g ...[more]

PMID: 35097279

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Project description:CONSPECTUS: Protein function is inextricably linked to protein dynamics. As we move from a static structural picture to a dynamic ensemble view of protein structure and function, novel computational paradigms are required for observing and understanding conformational dynamics of proteins and its functional implications. In principle, molecular dynamics simulations can provide the time evolution of atomistic models of proteins, but the long time scales associated with functional dynamics make it difficult to observe rare dynamical transitions. The issue of extracting essential functional components of protein dynamics from noisy simulation data presents another set of challenges in obtaining an unbiased understanding of protein motions. Therefore, a methodology that provides a statistical framework for efficient sampling and a human-readable view of the key aspects of functional dynamics from data analysis is required. The Markov state model (MSM), which has recently become popular worldwide for studying protein dynamics, is an example of such a framework. In this Account, we review the use of Markov state models for efficient sampling of the hierarchy of time scales associated with protein dynamics, automatic identification of key conformational states, and the degrees of freedom associated with slow dynamical processes. Applications of MSMs for studying long time scale phenomena such as activation mechanisms of cellular signaling proteins has yielded novel insights into protein function. In particular, from MSMs built using large-scale simulations of GPCRs and kinases, we have shown that complex conformational changes in proteins can be described in terms of structural changes in key structural motifs or "molecular switches" within the protein, the transitions between functionally active and inactive states of proteins proceed via multiple pathways, and ligand or substrate binding modulates the flux through these pathways. Finally, MSMs also provide a theoretical toolbox for studying the effect of nonequilibrium perturbations on conformational dynamics. Considering that protein dynamics in vivo occur under nonequilibrium conditions, MSMs coupled with nonequilibrium statistical mechanics provide a way to connect cellular components to their functional environments. Nonequilibrium perturbations of protein folding MSMs reveal the presence of dynamically frozen glass-like states in their conformational landscape. These frozen states are also observed to be rich in β-sheets, which indicates their possible role in the nucleation of β-sheet rich aggregates such as those observed in amyloid-fibril formation. Finally, we describe how MSMs have been used to understand the dynamical behavior of intrinsically disordered proteins such as amyloid-β, human islet amyloid polypeptide, and p53. While certainly not a panacea for studying functional dynamics, MSMs provide a rigorous theoretical foundation for understanding complex entropically dominated processes and a convenient lens for viewing protein motions.

Dataset Information

Insights into Rational Design of a New Class of Allosteric Effectors with Molecular Dynamics Markov State Models and Network Theory.

Publications

Insights into Rational Design of a New Class of Allosteric Effectors with Molecular Dynamics Markov State Models and Network Theory.

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