Lipid-dependent conformational landscape of the ErbB2 growth factor receptor dimers

Assessing Protein-Protein Docking Protocols: Case Studies of G-Protein-Coupled Receptor Interactions

The interactions of G-protein-coupled receptors (GPCRs) with other proteins are critical in several cellular processes but resolving their structural dynamics remains challenging. An increasing number of GPCR complexes have been experimentally resolved but others including receptor variants are yet to be characterized, necessitating computational predictions of their interactions. Although integrative approaches with multi-scale simulations would provide rigorous estimates of their conformational dynamics, protein-protein docking remains a first tool of choice of many researchers due to the availability of open-source programs and easy to use web servers with reasonable predictive power. Protein-protein docking algorithms have limited ability to consider protein flexibility, environment effects, and entropy contributions and are usually a first step towards more integrative approaches. The two critical steps of docking: the sampling and scoring algorithms have improved considerably and their performance has been validated against experimental data. In this chapter, we provide an overview and generalized protocol of a few docking protocols using GPCRs as test cases. In particular, we demonstrate the interactions of GPCRs with extracellular protein ligands and an intracellular protein effectors (G-protein) predicted from docking approaches and test their limitations. The current chapter will help researchers critically assess docking protocols and predict experimentally testable structures of GPCR complexes.

Transmembrane dimers of type 1 receptors sample alternate configurations: MD simulations using coarse grain Martini 3 versus AlphaFold2 Multimer

Structures and dynamics of transmembrane (TM) receptor regions are key to understanding their signaling mechanism across membranes. Here we examine configurations of TM region dimers, assembled using the recent Martini 3 force field for coarse-grain (CG) molecular dynamics simulations. At first glance, our results show only a reasonable agreement with ab initio predictions using PREDDIMER and AlphaFold2 Multimer and with nuclear magnetic resonance (NMR)-derived structures. 5 of 11 CG TM structures are similar to the NMR structures (within <3.5 Å root-mean-square deviation [RMSD]) compared with 10 and 9 using PREDDIMER and AlphaFold2, respectively (with 8 structures of the later within 1.5 Å). Surprisingly, AlphaFold2 predictions are closer to NMR structures when the 2001 instead of 2020 database is used for training. The CG simulations reveal that alternative configurations of TM dimers readily interconvert with a predominant population. The implications for transmembrane signaling are discussed, including for the development of peptide-based pharmaceuticals.

Excavation and characterization of key circRNAs for milk fat percentage in Holstein cattle

Milk fat percentage is one of the significant indicators governing the price and quality of milk and is regulated by a variety of non-coding RNAs. We used RNA sequencing (RNA-seq) techniques and bioinformatics approaches to explore potential candidate circular RNAs (circRNAs) regulating milk fat metabolism. After analysis, compared with low milk fat percentage (LMF) cows, 309 circRNAs were significantly differentially expressed in high milk fat percentage (HMF) cows. Functional enrichment and pathway analysis revealed that the main functions of the parental genes of differentially expressed circRNAs (DE-circRNAs) were related to lipid metabolism. We selected four circRNAs (Novel_circ_0000856, Novel_circ_0011157, novel_circ_0011944, and Novel_circ_0018279) derived from parental genes related to lipid metabolism as key candidate DE-circRNAs. Their head-to-tail splicing was demonstrated by linear RNase R digestion experiments and Sanger sequencing. However, the tissue expression profiles showed that only Novel_circ_0000856, Novel_circ_0011157, and Novel_circ_0011944 were expressed with high abundance in breast tissue. Based on the subcellular localization found that Novel_circ_0000856, Novel_circ_0011157, and Novel_circ_0011944 mainly function as competitive endogenous RNAs (ceRNAs) in the cytoplasm. Therefore, we constructed their ceRNA regulatory networks, and the five hub target genes (CSF1, TET2, VDR, CD34, and MECP2) in ceRNAs were obtained by CytoHubba and MCODE plugins in Cytoscape, as well as tissue expression profiles analysis of target genes. These genes play a key role as important target genes in lipid metabolism, energy metabolism, and cellular autophagy. The Novel_circ_0000856, Novel_circ_0011157, and Novel_circ_0011944 regulate the expression of hub target genes through interaction with miRNAs and constitute key regulatory networks that may be involved in milk fat metabolism. The circRNAs obtained in this study may act as miRNA sponges and thus influence mammary gland development and lipid metabolism in cows, which improves our understanding of the role of circRNAs in cow lactation.

Lipid-Mediated Association of the Slg1 Transmembrane Domains in Yeast Plasma Membranes

Clustering of transmembrane proteins underlies a multitude of fundamental biological processes at the plasma membrane (PM) such as receptor activation, lateral domain formation, and mechanotransduction. The self-association of the respective transmembrane domains (TMDs) has also been suggested to be responsible for the micron-scaled patterns seen for integral membrane proteins in the budding yeast PM. However, the underlying interplay between the local lipid composition and the TMD identity is still not mechanistically understood. In this work, we combined coarse-grained molecular dynamics simulations of simplified bilayer systems with high-resolution live-cell microscopy to analyze the distribution of a representative helical yeast TMD from the PM sensor Slg1 within different lipid environments. In our simulations, we specifically evaluated the effects of acyl chain saturation and anionic lipid head groups on the association of two TMDs. We found that weak lipid-protein interactions significantly affect the configuration of TMD dimers and the free energy of association. Increased amounts of unsaturated phospholipids (PLs) strongly reduced the helix-helix interaction, while the presence of anionic phosphatidylserine (PS) hardly affected the dimer formation. We could experimentally confirm this surprising lack of effect of PS using the network factor, a mesoscopic measure of PM pattern formation in yeast cells. Simulations also showed that the formation of TMD dimers in turn increased the order parameter of the surrounding lipids and induced long-range perturbations in lipid organization. In summary, our results shed new light on the mechanisms of lipid-mediated dimerization of TMDs in complex lipid mixtures.

Role of Cholesterol in Transmembrane Dimerization of the ErbB2 Growth Factor Receptor

The association of ErbB2 growth factor receptors is critical for cell growth and potentiates tumor proliferation in several cancer types. An important aspect in ErbB2 association is the role of lipids such as cholesterol, especially since their metabolism is often reprogrammed in cancer cells. Here, we have coupled metadynamics with coarse-grain simulations to identify cholesterol effects in the transmembrane dimerization of ErbB2 receptors. Overall, cholesterol interactions are observed with the receptor that directly tunes the association energetics. Several dimer conformations are identified both in the presence and absence of cholesterol, although the dimer regime appears to be more favorable in the presence of cholesterol. We observe an overall modulation of the underlying energy profile and the symmetric active and inactive conformational states are not distinguished in the presence of cholesterol. We show that cholesterol binds to the receptor transmembrane domain at a site (CRAC motif) that overlaps with the dimer interface (SmXXXSm motif). The competition between the transmembrane interactions and cholesterol interactions decides the final conformational landscape. Our work is an important step toward characterizing cholesterol effects in ErbB2 membrane receptor function.

Guardians of the Cell: State-of-the-Art of Membrane Proteins from a Computational Point-of-View

Membrane proteins (MPs) encompass a large family of proteins with distinct cellular functions, and although representing over 50% of existing pharmaceutical drug targets, their structural and functional information is still very scarce. Over the last years, in silico analysis and algorithm development were essential to characterize MPs and overcome some limitations of experimental approaches. The optimization and improvement of these methods remain an ongoing process, with key advances in MPs' structure, folding, and interface prediction being continuously tackled. Herein, we discuss the latest trends in computational methods toward a deeper understanding of the atomistic and mechanistic details of MPs.