Probing allosteric regulations with coevolution-driven molecular simulations

Are Allosteric Mechanisms Conserved Among Homologues?

Conservation of allosteric mechanisms among homologues is often assumed but seldom tested. This assumption underpins key concepts like coevolution of residues involved in allosteric mechanisms and the comparison of structures of two different homologues to gain insights into allosteric mechanisms. As an initial assessment of whether allosteric mechanisms are conserved among homologues, this work reviews what is known about the allosteric mechanisms of liver pyruvate kinase (LPYK) vs. skeletal muscle pyruvate kinase (M1PYK), framed within a two-ligand allosteric energy cycle description of allosteric regulation. Selective observations from other PYK homologues are included when relevant. The primary focus of this review is on functional data, while expressing caution regarding the interpretation of allosteric mechanisms based solely on available X-ray crystallographic structures. Additionally, this review considers types of data that are currently lacking for these two PYK homologues, highlighting potential techniques that could be valuable for evaluating the conservation of allosteric mechanisms among homologues. In particular, a hybrid tetramer technique that has been used to study bacterial phosphofructokinases 1 is summarized. Interestingly, despite a high degree of similarity (66.5% sequence identity) between the LPYK and rM1PYK proteins, the available functional comparisons do not provide strong evidence for conserved allosteric mechanisms. Lastly, we consider whether insights into native allosteric mechanisms are relevant to allosteric mechanisms associated with allosteric drug designs.

Advancing Molecular Simulations: Merging Physical Models, Experiments, and AI to Tackle Multiscale Complexity

Proteins and protein complexes form adaptable networks that regulate essential biochemical pathways and define cell phenotypes through dynamic mechanisms and interactions. Advances in structural biology and molecular simulations have revealed how protein systems respond to changes in their environments, such as ligand binding, stress conditions, or perturbations like mutations and post-translational modifications, influencing signal transduction and cellular phenotypes. Here, we discuss how computational approaches, ranging from molecular dynamics (MD) simulations to AI-driven methods, are instrumental in studying protein dynamics from isolated molecules to large assemblies. These techniques elucidate conformational landscapes, ligand-binding mechanisms, and protein-protein interactions and are starting to support the construction of multiscale realistic representations of highly complex systems, ranging up to whole cell models. With cryo-electron microscopy, cryo-electron tomography, and AlphaFold accelerating the structural characterization of protein networks, we suggest that integrating AI and Machine Learning with multiscale MD methods will enhance fundamental understating for systems of ever-increasing complexity, usher in exciting possibilities for predictive modeling of the behavior of cell compartments or even whole cells. These advances are indeed transforming biophysics and chemical biology, offering new opportunities to study biomolecular mechanisms at atomic resolution.

Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery

Lipid-mediated delivery of active pharmaceutical ingredients (API) opened new possibilities in advanced therapies. By encapsulating an API into a lipid nanocarrier (LNC), one can safely deliver APIs not soluble in water, those with otherwise strong adverse effects, or very fragile ones such as nucleic acids. However, for the rational design of LNCs, a detailed understanding of the composition-structure-function relationships is missing. This review presents currently available computational methods for LNC investigation, screening, and design. The state-of-the-art physics-based approaches are described, with the focus on molecular dynamics simulations in all-atom and coarse-grained resolution. Their strengths and weaknesses are discussed, highlighting the aspects necessary for obtaining reliable results in the simulations. Furthermore, a machine learning, i.e., data-based learning, approach to the design of lipid-mediated API delivery is introduced. The data produced by the experimental and theoretical approaches provide valuable insights. Processing these data can help optimize the design of LNCs for better performance. In the final section of this Review, state-of-the-art of computer simulations of LNCs are reviewed, specifically addressing the compatibility of experimental and computational insights.

Identification and understanding of allostery hotspots in proteins: Integration of deep mutational scanning and multi-faceted computational analyses

Motivated by recent deep mutational scanning (DMS) experiments, we have carried out a diverse set of computations to better understand the distribution and contributions of allostery hotspot residues in a transcription factor, TetR. These include extensive atomistic simulations and free energy computations for different functional states of TetR, machine learning analysis of the DMS data and a statistical thermodynamic model for the experimental induction data for the WT protein and a handful of hotspot mutants. Collectively, these computations provided insights into the structural and energetic basis of allostery in TetR, and the distinct contributions of allostery hotspots. The results highlight that the allostery function (i.e., the induction activity) of TetR can be modulated by perturbing both inter-domain coupling and intra-domain properties, such as the population of the binding-competent conformation of each domain. This mechanistic degeneracy qualitatively explains the broad distribution of allostery hotspots across the protein structure observed in the DMS experiments, and also informs the design of strategies aimed at identifying allostery hotspots. The mechanistic framework and the multi-faceted computational approaches are expected to be applicable to the analysis of other allostery systems, especially those sharing the similar two-domain structural topology, and to the design of allostery modulators.

Exploring the Binding Mechanism of ADGRG2 Through Metadynamics and Biochemical Analysis

G protein-coupled receptors (GPCRs) play essential roles in numerous physiological processes and are key targets for drug development. Among them, adhesion GPCRs (aGPCRs) stand out for their unique domain structures and diverse functions. ADGRG2 is a member of the aGPCR family and is involved in the regulation of various systems in the human body, including reproductive, nervous, cardiovascular, and endocrine systems. Investigating ADGRG2 antagonists enhances our understanding of its regulatory roles in diverse physiological processes, yet their precise mechanisms of action remain unclear. To address this, we investigated the antagonistic mechanism of ADGRG2 by examining its interactions with various antagonists, including short peptides (F601D, F601E) and small molecules (deoxycorticosterone, DOC). Using advanced metadynamics simulation, ligand binding assay and cAMP assay, we elucidated the binding modes of these antagonists. We identified five distinct F601D-ADGRG2 complex states, four F601E-ADGRG2 complex states, and three DOC-ADGRG2 complex states, which were each characterized by specific hydrogen bonds or polar interactions with their respective ligands. Although the ADGRG2 binding pocket consists of both polar and hydrophobic residues, our biochemical experiments revealed that mutations in polar amino acids significantly reduce the efficacy of the antagonists. Our results show that F601D, F601E, and DOC induce the formation of Y758ECL2-N7755.32-N8607.46 polar networks within ADGRG2, effectively stabilizing its inactive state. Additionally, we compared the active and inactive states of ADGRG2, highlighting the structural changes induced by antagonist-stabilized polar networks and their impact on receptor conformation. These findings provide important insights into the biology of aGPCRs and provide theoretical support for the rational design of therapeutic drugs targeting ADGRG2.

Precise redesign for improving enzyme robustness based on coevolutionary analysis and multidimensional virtual screening

Natural enzymes are able to function effectively under optimal physiological conditions, but the intrinsic performance often fails to meet the demands of industrial production. Existing strategies are based mainly on the evaluation and subsequent combination of single-point mutations; however, this approach often suffers from a limited number of designable residues and from low accuracy. Here, we propose a strategy (Co-MdVS) based on coevolutionary analysis and multidimensional virtual screening for precise design to improve enzyme robustness, employing nattokinase as a model. Using this strategy, we efficiently screened 8 dual mutants with enhanced thermostability from a virtual mutation library containing 7980 mutants. After further iterative combination, the optimal mutant M6 exhibited a 31-fold increase in half-life at 55 °C, significantly enhanced acid resistance, and improved catalytic efficiency with different substrates. Molecular dynamics simulations indicated that the reduced flexibility of thermal and acid-sensitive regions resulted in a significantly increased robustness of M6. Furthermore, the potential of multidimensional virtual screening in enhancing design precision has been validated on l-rhamnose isomerase and PETase. Therefore, the Co-MdVS strategy introduced in this research may offer a viable approach for developing enzymes with enhanced robustness.

Recent advances in computational prediction of molecular properties in food chemistry

The combination of food chemistry and computational simulation has brought many impacts to food research, moving from experimental chemistry to computer chemistry. This paper will systematically review in detail the important role played by computational simulations in the development of the molecular structure of food, mainly from the atomic, molecular, and multicomponent dimension. It will also discuss how different computational chemistry models can be constructed and analyzed to obtain reliable conclusions. From the calculation principle to case analysis, this paper focuses on the selection and application of quantum mechanics, molecular mechanics and coarse-grained molecular dynamics in food chemistry research. Finally, experiments and computations of food chemistry are compared and summarized to obtain the best balance between them. The above review and outlook will provide an important reference for the intersection of food chemistry and computational chemistry, and is expected to provide innovative thinking for structural research in food chemistry.

Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation

Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.

Exploring the Alternative Conformation of a Known Protein Structure Based on Contact Map Prediction

The rapid development of deep learning-based methods has considerably advanced the field of protein structure prediction. The accuracy of predicting the 3D structures of simple proteins is comparable to that of experimentally determined structures, providing broad possibilities for structure-based biological studies. Another critical question is whether and how multistate structures can be predicted from a given protein sequence. In this study, analysis of tens of two-state proteins demonstrated that deep learning-based contact map predictions contain structural information on both states, which suggests that it is probably appropriate to change the target of deep learning-based protein structure prediction from one specific structure to multiple likely structures. Furthermore, by combining deep learning- and physics-based computational methods, we developed a protocol for exploring alternative conformations from a known structure of a given protein, by which we successfully approached the holo-state conformations of multiple representative proteins from their apo-state structures.

Computing allostery: from the understanding of biomolecular regulation and the discovery of cryptic sites to molecular design

The concept of allostery has become a central tenet in the study of biological systems. In parallel, the discovery of allosteric drugs is generating new opportunities to selectively modulate difficult targets involved in pathologic mechanisms. Molecular simulations can provide atomistically detailed insight into the processes involved in allosteric regulation and signaling, and at the same time, they have the potential to unveil regulatory hotspots or cryptic sites that are not immediately evident from the analysis of static structures. In this context, computational approaches should be able to connect the study of allosteric regulation at different scales to the possibility of leveraging this knowledge to expand the chemical space of new, active drugs. Here, we will discuss recent advances in the study of allosteric regulation via computational methods and connect the mechanistic knowledge generated to the possibility of designing new small molecules that can tweak the functions of their receptors.

Molecular Dynamics Simulations Establish the Molecular Basis for the Broad Allostery Hotspot Distributions in the Tetracycline Repressor

It is imperative to identify the network of residues essential to the allosteric coupling for the purpose of rationally engineering allostery in proteins. Deep mutational scanning analysis has emerged as a function-centric approach for identifying such allostery hotspots in a comprehensive and unbiased fashion, leading to observations that challenge our understanding of allostery at the molecular level. Specifically, a recent deep mutational scanning study of the tetracycline repressor (TetR) revealed an unexpectedly broad distribution of allostery hotspots throughout the protein structure. Using extensive molecular dynamics simulations (up to 50 μs) and free energy computations, we establish the molecular and energetic basis for the strong anticooperativity between the ligand and DNA binding sites. The computed free energy landscapes in different ligation states illustrate that allostery in TetR is well described by a conformational selection model, in which the apo state samples a broad set of conformations, and specific ones are selectively stabilized by either ligand or DNA binding. By examining a range of structural and dynamic properties of residues at both local and global scales, we observe that various analyses capture different subsets of experimentally identified hotspots, suggesting that these residues modulate allostery in distinct ways. These results motivate the development of a thermodynamic model that qualitatively explains the broad distribution of hotspot residues and their distinct features in molecular dynamics simulations. The multifaceted strategy that we establish here for hotspot evaluations and our insights into their mechanistic contributions are useful for modulating protein allostery in mechanistic and engineering studies.