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G-protein-coupled receptors (GPCRs) are a large group of membrane-bound receptor proteins that are involved in a plethora of diverse processes (e.g., vision, hormone response). In mammals, and particularly in humans, GPCRs are involved in many signal transduction pathways and, as such, are heavily studied for their immense pharmaceutical potential. Indeed, a large fraction of drugs target various GPCRs, and drug-development is often aimed at GPCRs. Therefore, understanding the activation of GPCRs is a challenge of major importance both from fundamental and practical considerations. And yet, despite the remarkable progress in structural understanding, we still do not have a translation of the structural information to an energy-based picture. Here we use coarse-grained (CG) modeling to chart the free-energy landscape of the activation process of the β-2 adrenergic receptor (β2AR) as a representative GPCR. The landscape provides the needed tool for analyzing the processes that lead to activation of the receptor upon binding of the ligand (adrenaline) while limiting constitutive activation. Our results pave the way to better understand the biological mechanisms of action of the β2AR and GPCRs, from a physical chemistry point of view rather than simply by observing the receptor's behavior physiologically.

The omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) characterized by 30 mutations in its spike protein, has rapidly spread worldwide since November 2021, significantly exacerbating the ongoing COVID‐19 pandemic. In order to investigate the relationship between these mutations and the variant's high transmissibility, we conducted a systematic analysis of the mutational effect on spike–angiotensin‐converting enzyme‐2 (ACE2) interactions and explored the structural/energy correlation of key mutations, utilizing a reliable coarse‐grained model. Our study extended beyond the receptor‐binding domain (RBD) of spike trimer through comprehensive modeling of the full‐length spike trimer rather than just the RBD. Our free‐energy calculation revealed that the enhanced binding affinity between the spike protein and the ACE2 receptor is correlated with the increased structural stability of the isolated spike protein, thus explaining the omicron variant's heightened transmissibility. The conclusion was supported by our experimental analyses involving the expression and purification of the full‐length spike trimer. Furthermore, the energy decomposition analysis established those electrostatic interactions make major contributions to this effect. We categorized the mutations into four groups and established an analytical framework that can be employed in studying future mutations. Additionally, our calculations rationalized the reduced affinity of the omicron variant towards most available therapeutic neutralizing antibodies, when compared with the wild type. By providing concrete experimental data and offering a solid explanation, this study contributes to a better understanding of the relationship between theories and observations and lays the foundation for future investigations. [ABSTRACT FROM AUTHOR]

Obtaining a quantitative description of the membrane proteins stability is crucial for understanding many biological processes. However the advance in this direction has remained a major challenge for both experimental studies and molecular modeling. One of the possible directions is the use of coarse-grained models but such models must be carefully calibrated and validated. Here we use a recent progress in benchmark studies on the energetics of amino acid residue and peptide membrane insertion and membrane protein stability in refining our previously developed coarse-grained model (Vicatos et al., Proteins 2014;82:1168). Our refined model parameters were fitted and/or tested to reproduce water/membrane partitioning energetics of amino acid side chains and a couple of model peptides. This new model provides a reasonable agreement with experiment for absolute folding free energies of several β-barrel membrane proteins as well as effects of point mutations on a relative stability for one of those proteins, OmpLA. The consideration and ranking of different rotameric states for a mutated residue was found to be essential to achieve satisfactory agreement with the reference data.

Homomeric dimerization of metabotropic glutamate receptors (mGlus) is essential for the modulation of their functions and represents a promising avenue for the development of novel therapeutic approaches to address central nervous system diseases. Yet, the scarcity of detailed molecular and energetic data on mGlu2 impedes our in-depth comprehension of their activation process. Here, we employ computational simulation methods to elucidate the activation process and key events associated with the mGlu2, including a detailed analysis of its conformational transitions, the binding of agonists, Gi protein coupling, and the guanosine diphosphate (GDP) release. Our results demonstrate that the activation of mGlu2 is a stepwise process and several energy barriers need to be overcome. Moreover, we also identify the rate-determining step of the mGlu2's transition from the agonist-bound state to its active state. From the perspective of free-energy analysis, we find that the conformational dynamics of mGlu2's subunit follow coupled rather than discrete, independent actions. Asymmetric dimerization is critical for receptor activation. Our calculation results are consistent with the observation of cross-linking and fluorescent-labeled blot experiments, thus illustrating the reliability of our calculations. Besides, we also identify potential key residues in the Gi protein binding position on mGlu2, mGlu2 dimer's TM6-TM6 interface, and Gi α5 helix by the change of energy barriers after mutation. The implications of our findings could lead to a more comprehensive grasp of class C G protein-coupled receptor activation. [ABSTRACT FROM AUTHOR]

Calmodulin (CaM) is a key signaling protein that triggers several cellular and physiological processes inside the cell. Upon binding with calcium ion, CaM undergoes large scale conformational transition from a closed state to an open state that facilitates its interaction with various target protein and regulates their activity. This work explores the origin of the energetic and structural variation of the wild type and mutated CaM and explores the molecular origin for the structural differences between them. We first calculated the sequential calcium binding energy to CaM using the PDLD/S‐LRA/β approach. This study shows a very good correlation with experimental calcium binding energies. Next we calculated the calcium binding energies to the wild type CaM and several mutated CaM systems which were reported experimentally. On the structural aspect, it has been reported experimentally that certain mutation (Q41L‐K75I) in calcium bound CaM leads to complete conformational transition from an open to a closed state. By using equilibrium molecular dynamics simulation, free energy calculation and contact frequency map analysis, we have shown that the formation of a cluster of long‐range hydrophobic contacts, initiated by the Q41L‐K75I CaM variant is the driving force behind its closing motion. This study unravels the energetics and structural aspects behind calcium ion induced conformational changes in wild type CaM and its variant. [ABSTRACT FROM AUTHOR]

Physics-based simulation methods can grant atomistic insights into the molecular origin of the function of biomolecules. However, the potential of such approaches has been hindered by their low efficiency, including in the design of selective agonists where simulations of myriad protein-ligand combinations are necessary. Here, we describe an automated input-free path searching protocol that offers (within 14 d using Graphics Processing Unit servers) a minimum free energy path (MFEP) defined in high-dimension configurational space for activating sphingosine-1-phosphate receptors (S1PRs) by arbitrary ligands. The free energy distributions along the MFEP for four distinct ligands and three S1PRs reached a remarkable agreement with Bioluminescence Resonance Energy Transfer (BRET) measurements of G-protein dissociation. In particular, the revealed transition state structures pointed out toward two S1PR3 residues F263/I284, that dictate the preference of existing agonists CBP307 and BAF312 on S1PR1/5. Swapping these residues between S1PR1 and S1PR3 reversed their response to the two agonists in BRET assays. These results inspired us to design improved agonists with both strong polar head and bulky hydrophobic tail for higher selectivity on S1PR1. Through merely three in silico iterations, our tool predicted a unique compound scaffold. BRET assays confirmed that both chiral forms activate S1PR1 at nanomolar concentration, 1 to 2 orders of magnitude less than those for S1PR3/5. Collectively, these results signify the promise of our approach in fine agonist design for G-protein-coupled receptors. [ABSTRACT FROM AUTHOR]

Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. Generative models could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, catalytic activity and stability, rationalizing the laboratory evolution of de novo enzymes, and decoding protein sequence semantics and their application in enzyme engineering. Notably, the prediction of catalytic activity and stability of enzymes using natural protein sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall, we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts. [ABSTRACT FROM AUTHOR]

The availability of natural protein sequences synergized with generative AI provides new paradigms to engineer enzymes. Although active enzyme variants with numerous mutations have been designed using generative models, their performance often falls short of their wild type counterparts. Additionally, in practical applications, choosing fewer mutations that can rival the efficacy of extensive sequence alterations is usually more advantageous. Pinpointing beneficial single mutations continues to be a formidable task. In this study, using the generative maximum entropy model to analyze Renilla luciferase (RLuc) homologs, and in conjunction with biochemistry experiments, we demonstrated that natural evolutionary information could be used to predictively improve enzyme activity and stability by engineering the active center and protein scaffold, respectively. The success rate to improve either luciferase activity or stability of designed single mutants is ~50%. This finding highlights nature's ingenious approach to evolving proficient enzymes, wherein diverse evolutionary pressures are preferentially applied to distinct regions of the enzyme, ultimately culminating in an overall high performance. We also reveal an evolutionary preference in RLuc toward emitting blue light that holds advantages in terms of water penetration compared to other light spectra. Taken together, our approach facilitates navigation through enzyme sequence space and offers effective strategies for computer-aided rational enzyme engineering. [ABSTRACT FROM AUTHOR]