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1. Predicting multiple conformations of ligand binding sites in proteins suggests that AlphaFold2 may remember too much

   https://doi.org/10.1073/pnas.2412719121  

   Lazou, Maria; Khan, Omeir; Nguyen, Thu; Padhorny, Dzmitry; Kozakov, Dima; Joseph-McCarthy, Diane; Vajda, Sandor (November 2024, Proceedings of the National Academy of Sciences)

   The goal of this paper is predicting the conformational distributions of ligand binding sites using the AlphaFold2 (AF2) protein structure prediction program with stochastic subsampling of the multiple sequence alignment (MSA). We explored the opening of cryptic ligand binding sites in 16 proteins, where the closed and open conformations define the expected extreme points of the conformational variation. Due to the many structures of these proteins in the Protein Data Bank (PDB), we were able to study whether the distribution of X-ray structures affects the distribution of AF2 models. We have found that AF2 generates both a cluster of open and a cluster of closed models for proteins that have comparable numbers of open and closed structures in the PDB and not too many other conformations. This was observed even with default MSA parameters, thus without further subsampling. In contrast, with the exception of a single protein, AF2 did not yield multiple clusters of conformations for proteins that had imbalanced numbers of open and closed structures in the PDB, or had substantial numbers of other structures. Subsampling improved the results only for a single protein, but very shallow MSA led to incorrect structures. The ability of generating both open and closed conformations for six out of the 16 proteins agrees with the success rates of similar studies reported in the literature. However, we showed that this partial success is due to AF2 “remembering” the conformational distributions in the PDB and that the approach fails to predict rarely seen conformations. 

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2. Elucidation of a dynamic interplay between a beta-2 adrenergic receptor, its agonist, and stimulatory G protein

   https://doi.org/10.1073/pnas.2215916120  

   Han, Yanxiao; Dawson, John R.; DeMarco, Kevin R.; Rouen, Kyle C.; Bekker, Slava; Yarov-Yarovoy, Vladimir; Clancy, Colleen E.; Xiang, Yang K.; Vorobyov, Igor (March 2023, Proceedings of the National Academy of Sciences)

   G protein-coupled receptors (GPCRs) represent the largest group of membrane receptors for transmembrane signal transduction. Ligand-induced activation of GPCRs triggers G protein activation followed by various signaling cascades. Understanding the structural and energetic determinants of ligand binding to GPCRs and GPCRs to G proteins is crucial to the design of pharmacological treatments targeting specific conformations of these proteins to precisely control their signaling properties. In this study, we focused on interactions of a prototypical GPCR, beta-2 adrenergic receptor (β 2 AR), with its endogenous agonist, norepinephrine (NE), and the stimulatory G protein (G s ). Using molecular dynamics (MD) simulations, we demonstrated the stabilization of cationic NE, NE(+), binding to β 2 AR by G s protein recruitment, in line with experimental observations. We also captured the partial dissociation of the ligand from β 2 AR and the conformational interconversions of G s between closed and open conformations in the NE(+)–β 2 AR–G s ternary complex while it is still bound to the receptor. The variation of NE(+) binding poses was found to alter G s α subunit (G s α) conformational transitions. Our simulations showed that the interdomain movement and the stacking of G s α α1 and α5 helices are significant for increasing the distance between the G s α and β 2 AR, which may indicate a partial dissociation of G s α The distance increase commences when G s α is predominantly in an open state and can be triggered by the intracellular loop 3 (ICL3) of β 2 AR interacting with G s α, causing conformational changes of the α5 helix. Our results help explain molecular mechanisms of ligand and GPCR-mediated modulation of G protein activation. 

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3. Ligand-Dependent Conformational Transitions in Molecular Dynamics Trajectories of GPCRs Revealed by a New Machine Learning Rare Event Detection Protocol

   https://doi.org/10.3390/molecules26103059  

   Plante, Ambrose; Weinstein, Harel (May 2021, Molecules)

   null (Ed.)

   Central among the tools and approaches used for ligand discovery and design are Molecular Dynamics (MD) simulations, which follow the dynamic changes in molecular structure in response to the environmental condition, interactions with other proteins, and the effects of ligand binding. The need for, and successes of, MD simulations in providing this type of essential information are well documented, but so are the challenges presented by the size of the resulting datasets encoding the desired information. The difficulty of extracting information on mechanistically important state-to-state transitions in response to ligand binding and other interactions is compounded by these being rare events in the MD trajectories of complex molecular machines, such as G-protein-coupled receptors (GPCRs). To address this problem, we have developed a protocol for the efficient detection of such events. We show that the novel Rare Event Detection (RED) protocol reveals functionally relevant and pharmacologically discriminating responses to the binding of different ligands to the 5-HT2AR orthosteric site in terms of clearly defined, structurally coherent, and temporally ordered conformational transitions. This information from the RED protocol offers new insights into specific ligand-determined functional mechanisms encoded in the MD trajectories, which opens a new and rigorously reproducible path to understanding drug activity with application in drug discovery. 

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4. Computational Ways to Enhance Protein Inhibitor Design

   https://doi.org/10.3389/fmolb.2020.607323  

   Jernigan, Robert L.; Sankar, Kannan; Jia, Kejue; Faraggi, Eshel; Kloczkowski, Andrzej (February 2021, Frontiers in Molecular Biosciences)

   null (Ed.)

   Two new computational approaches are described to aid in the design of new peptide-based drugs by evaluating ensembles of protein structures from their dynamics and through the assessing of structures using empirical contact potential. These approaches build on the concept that conformational variability can aid in the binding process and, for disordered proteins, can even facilitate the binding of more diverse ligands. This latter consideration indicates that such a design process should be less restrictive so that multiple inhibitors might be effective. The example chosen here focuses on proteins/peptides that bind to hemagglutinin (HA) to block the large-scale conformational change for activation. Variability in the conformations is considered from sets of experimental structures, or as an alternative, from their simple computed dynamics; the set of designe peptides/small proteins from the David Baker lab designed to bind to hemagglutinin, is the large set considered and is assessed with the new empirical contact potentials. 

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5. Structural predictions of protein–DNA binding: MELD-DNA

   https://doi.org/10.1093/nar/gkad013  

   Esmaeeli, Reza; Bauzá, Antonio; Perez, Alberto (February 2023, Nucleic Acids Research)

   Abstract Structural, regulatory and enzymatic proteins interact with DNA to maintain a healthy and functional genome. Yet, our structural understanding of how proteins interact with DNA is limited. We present MELD-DNA, a novel computational approach to predict the structures of protein–DNA complexes. The method combines molecular dynamics simulations with general knowledge or experimental information through Bayesian inference. The physical model is sensitive to sequence-dependent properties and conformational changes required for binding, while information accelerates sampling of bound conformations. MELD-DNA can: (i) sample multiple binding modes; (ii) identify the preferred binding mode from the ensembles; and (iii) provide qualitative binding preferences between DNA sequences. We first assess performance on a dataset of 15 protein–DNA complexes and compare it with state-of-the-art methodologies. Furthermore, for three selected complexes, we show sequence dependence effects of binding in MELD predictions. We expect that the results presented herein, together with the freely available software, will impact structural biology (by complementing DNA structural databases) and molecular recognition (by bringing new insights into aspects governing protein–DNA interactions). 

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