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1. Coarse-Grained Modeling and Molecular Dynamics Simulations of Ca2+-Calmodulin

   https://doi.org/10.3389/fmolb.2021.661322  

   Nde, Jules; Zhang, Pengzhi; Ezerski, Jacob C.; Lu, Wei; Knapp, Kaitlin; Wolynes, Peter G.; Cheung, Margaret S. (August 2021, Frontiers in Molecular Biosciences)

   Calmodulin (CaM) is a calcium-binding protein that transduces signals to downstream proteins through target binding upon calcium binding in a time-dependent manner. Understanding the target binding process that tunes CaM’s affinity for the calcium ions (Ca 2+ ), or vice versa, may provide insight into how Ca 2+ -CaM selects its target binding proteins. However, modeling of Ca 2+ -CaM in molecular simulations is challenging because of the gross structural changes in its central linker regions while the two lobes are relatively rigid due to tight binding of the Ca 2+ to the calcium-binding loops where the loop forms a pentagonal bipyramidal coordination geometry with Ca 2+ . This feature that underlies the reciprocal relation between Ca 2+ binding and target binding of CaM, however, has yet to be considered in the structural modeling. Here, we presented a coarse-grained model based on the Associative memory, Water mediated, Structure, and Energy Model (AWSEM) protein force field, to investigate the salient features of CaM. Particularly, we optimized the force field of CaM and that of Ca 2+ ions by using its coordination chemistry in the calcium-binding loops to match with experimental observations. We presented a “community model” of CaM that is capable of sampling various conformations of CaM, incorporating various calcium-binding states, and carrying the memory of binding with various targets, which sets the foundation of the reciprocal relation of target binding and Ca 2+ binding in future studies. 

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2. Intramolecularly enhanced molecular tweezers with unusually strong binding for aromatic guests in unfavorable solvents

   https://doi.org/10.1039/C8OB00786A  

   Xing, Xiaoyu; Zhao, Yan (January 2018, Organic & Biomolecular Chemistry)

   Molecular tweezers using aromatic interactions for binding normally work best in polar instead of nonpolar solvents due to the strong solvophobic effect in the binding. Inspired by biological receptors that utilize “delocalized binding interactions” remote from the binding interface to strengthen guest-binding, we constructed molecular tweezers that have a reversed solvent effect. As the direct aromatic binding interactions were weakened by nonpolar solvent, guest-triggered intrahost interactions between two strategically placed carboxylic acids became stronger and contributed to the binding. 

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3. Absolute Protein Binding Free Energy Simulations for Ligands with Multiple Poses, a Thermodynamic Path That Avoids Exhaustive Enumeration of the Poses

   https://doi.org/10.1002/jcc.26078  

   Sakae, Yoshitake; Zhang, Bin W.; Levy, Ronald M.; Deng, Nanjie (October 2019, Journal of Computational Chemistry)

   We propose a free energy calculation method for receptor–ligand binding, which have multiple binding poses that avoids exhaustive enumeration of the poses. For systems with multiple binding poses, the standard procedure is to enumerate orientations of the binding poses, restrain the ligand to each orientation, and then, calculate the binding free energies for each binding pose. In this study, we modify a part of the thermodynamic cycle in order to sample a broader conformational space of the ligand in the binding site. This modification leads to more accurate free energy calculation without performing separate free energy simulations for each binding pose. We applied our modification to simple model host–guest systems as a test, which have only two binding poses, by using a single decoupling method (SDM) in implicit solvent. The results showed that the binding free energies obtained from our method without knowing the two binding poses were in good agreement with the benchmark results obtained by explicit enumeration of the binding poses. Our method is applicable to other alchemical binding free energy calculation methods such as the double decoupling method (DDM) in explicit solvent. We performed a calculation for a protein–ligand system with explicit solvent using our modified thermodynamic path. The results of the free energy simulation along our modified path were in good agreement with the results of conventional DDM, which requires a separate binding free energy calculation for each of the binding poses of the example of phenol binding to T4 lysozyme in explicit solvent. © 2019 Wiley Periodicals, Inc. 

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4. Transcription factors perform a 2-step search of the nucleus

   https://doi.org/10.1093/genetics/iyac111  

   Staller, Max Valentín; Rine, ed., J. (August 2022, Genetics)

   Abstract Transcription factors regulate gene expression by binding to regulatory DNA and recruiting regulatory protein complexes. The DNA-binding and protein-binding functions of transcription factors are traditionally described as independent functions performed by modular protein domains. Here, I argue that genome binding can be a 2-part process with both DNA-binding and protein-binding steps, enabling transcription factors to perform a 2-step search of the nucleus to find their appropriate binding sites in a eukaryotic genome. I support this hypothesis with new and old results in the literature, discuss how this hypothesis parsimoniously resolves outstanding problems, and present testable predictions. 

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5. Free Energy-Based Computational Methods for the Study of Protein-Peptide Binding Equilibria

   https://doi.org/10.1007/978-1-0716-1855-4_15  

   Gallicchio, Emilio (May 2021, Methods in molecular biology)

   Simonson, Thomas (Ed.)

   This chapter discusses the theory and application of physics-based free energy methods to estimate protein-peptide binding free energies. It presents a statistical mechanics formulation of molecular binding, which is then specialized in three methodologies: (i) alchemical absolute binding free energy estimation with implicit solvation, (ii) alchemical relative binding free energy estimation with explicit solvation, and (iii) potential of mean force binding free energy estimation. Case studies of protein-peptide binding application taken from the recent literature are discussed for each method. 

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