A comprehensive computational study of amino acid interactions in membrane proteins

Insights into the role of glycerophospholipids on the iron export function of SLC40A1 and the molecular mechanisms of ferroportin disease

SLC40A1 is the sole iron export protein reported in mammals. In humans, its dysfunction is responsible for ferroportin disease, an inborn error of iron metabolism transmitted as an autosomal dominant trait and observed in different ethnic groups. As a member of the major facilitator superfamily, SLC40A1 requires a series of conformational changes to enable iron translocation across the plasma membrane. The influence of lipids on protein stability and its conformational changes has been little investigated to date. Here, we combine molecular dynamics simulations of SLC40A1 embedded in membrane bilayers with experimental alanine scanning mutagenesis to analyze the specific role of glycerophospholipids. We identify four basic residues (Lys90, Arg365, Lys366, and Arg371) that are located at the membrane-cytosol interface and consistently interact with 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE) molecules. These residues surround a network of salt bridges and hydrogens bonds that play a critical role in stabilizing SLC40A1 in its basal outward-facing conformation. More deeply embedded in the plasma membrane, we identify Arg179 as a charged amino acid residue also tightly interacting with lipid polar heads. This results in a local deformation of the lipid bilayer. Interestingly, Arg179 is adjacent to Arg178, which forms a functionally important salt-bridge with Asp473 and is a recurrently associated with ferroportin disease when mutated to glutamine. We demonstrate that the two p.Arg178Gln and p.Arg179Thr missense variants have similar functional behaviors. These observations provide insights into the role of phospholipids in the formation/disruption of the SLC40A1 inner gate, and give a better understanding of the diversity of molecular mechanisms of ferroportin disease.

KAT6A Condensates Impair PARP1 Trapping of PARP Inhibitors in Ovarian Cancer

Most clinical PARP inhibitors (PARPis) trap PARP1 in a chromatin-bound state, leading to PARPi-mediated cytotoxicity. PARPi resistance impedes the treatment of ovarian cancer in clinical practice. However, the mechanism by which cancer cells overcome PARP1 trapping to develop PARPi resistance remains unclear. Here, it is shown that high levels of KAT6A promote PARPi resistance in ovarian cancer, regardless of its catalytic activity. Mechanistically, the liquid-liquid phase separation (LLPS) of KAT6A, facilitated by APEX1, inhibits the cytotoxic effects of PARP1 trapping during PARPi treatment. The stable KAT6A-PARP1-APEX1 complex reduces the amount of PARP1 trapped at the DNA break sites. In addition, inhibition of KAT6A LLPS, rather than its catalytic activity, impairs DNA damage repair and restores PARPi sensitivity in ovarian cancer both in vivo and in vitro. In conclusion, the findings demonstrate the role of KAT6A LLPS in fostering PARPi resistance and suggest that repressing KAT6A LLPS can be a potential therapeutic strategy for PARPi-resistant ovarian cancer.

Identification of amino acids in transmembrane domains of mutated cytokine receptor-like factor 2 and interleukin-7 receptor α required for constitutive signal transduction

Cytokine receptor-like factor 2 (CRLF2) and interleukin-7 receptor α (IL-7Rα) form a receptor for thymic stromal lymphopoietin (TSLP). A somatic mutation consisting of the substitution of five amino acids (SLLLL) in the transmembrane domain of CRLF2 with three amino acids, including glutamic acid, isoleucine, and methionine (insEIM), which has been identified in acute lymphocytic leukemia, causes the TSLP-independent dimerization with IL-7Rα and activation. However, the dimerization mechanism remains unclear. In this study, we examined the involvement of the amino acids in the transmembrane domains of EIM CRLF2 and IL-7Rα in TSLP-independent activation. HEK293 cells were transfected with vectors encoding CRLF2 and IL-7Rα, or their mutants, in which the amino acid of the transmembrane domain was replaced with alanine. STAT5 phosphorylation was detected using western blotting, and receptor dimerization was analyzed using the NanoBiT assay. The substitution of glutamic acid within the insEIM mutation for alanine failed to cause the STAT5 phosphorylation in the absence of TSLP. Moreover, the alanine substation of the specific leucine residues in the transmembrane domains of both CRLF2 and IL-7Rα abrogated the TSLP-independent signal transduction and dimerization. The mutation of IL-7Rα W264 partially reduced the phosphorylation of STAT5 without affecting receptor dimerization. These results suggest that the amino acids in the transmembrane domains of EIM CRLF2 and IL-7Rα play at least three possible functions: interaction through hydrogen bonds, hydrophobic interaction, and signal transduction. Our findings contribute to a better understanding of the function of the transmembrane domains of cytokine receptors in their dimerization and signal transduction.

Regulation of the Function and Expression of EpCAM

The epithelial cell adhesion molecule (EpCAM) is a single transmembrane protein on the cell surface. Given its strong expression on epithelial cells and epithelial cell-derived tumors, EpCAM has been identified as a biomarker for circulating tumor cells (CTCs) and exosomes and a target for cancer therapy. As a cell adhesion molecule, EpCAM has a crystal structure that indicates that it forms a cis-dimer first and then probably a trans-tetramer to mediate intercellular adhesion. Through regulated intramembrane proteolysis (RIP), EpCAM and its proteolytic fragments are also able to regulate multiple signaling pathways, Wnt signaling in particular. Although great progress has been made, increasingly more findings have revealed the context-specific expression and function patterns of EpCAM and their regulation processes, which necessitates further studies to determine the structure, function, and expression of EpCAM under both physiological and pathological conditions, broadening its application in basic and translational cancer research.

Insights into membrane interactions and their therapeutic potential

Recent research into membrane interactions has uncovered a diverse range of therapeutic opportunities through the bioengineering of human and non-human macromolecules. Although the majority of this research is focussed on fundamental developments, emerging studies are showcasing promising new technologies to combat conditions such as cancer, Alzheimer's and inflammatory and immune-based disease, utilising the alteration of bacteriophage, adenovirus, bacterial toxins, type 6 secretion systems, annexins, mitochondrial antiviral signalling proteins and bacterial nano-syringes. To advance the field further, each of these opportunities need to be better understood, and the therapeutic models need to be further optimised. Here, we summarise the knowledge and insights into several membrane interactions and detail their current and potential uses therapeutically.

Discovery of novel IDO1/TDO2 dual inhibitors: a consensus Virtual screening approach with molecular dynamics simulations, and binding free energy analysis

The pursuit of effective cancer immunotherapy drugs remains challenging, with overexpression of indoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan 2,3-dioxygenase 2 (TDO2) allowing cancer cells to evade immune attacks. While several IDO1 inhibitors have undergone clinical testing, only three dual IDO1/TDO2 inhibitors have reached human trials. Hence, this study focuses on identifying novel IDO1/TDO2 dual inhibitors through consensus structure-based virtual screening (SBVS). ZINC15 natural products library was refined based on molecular descriptors, and the selected compounds were docked to the holo form IDO1 and TDO2 using two different software programs and ranked according to their consensus docking scores. The top-scoring compounds underwent in silico evaluations for pharmacokinetics, toxicity, CYP3A4 affinity, molecular dynamics (MD) simulations, and MM-GBSA binding free energy calculations. Five compounds (ZINC00000079405/10, ZINC00004028612/11, ZINC00013380497/12, ZINC00014613023/13, and ZINC00103579819/14) were identified as potential IDO1/TDO2 dual inhibitors due to their high consensus docking scores, key residue interactions with the enzymes, favorable pharmacokinetics, and avoidance of CYP3A4 binding. MD simulations of the top three hits with IDO1 indicated conformational changes and compactness, while MM-GBSA analysis revealed strong binding free energy for compounds 10 (ΔG: -20.13 kcal/mol) and 11 (ΔG: -16.22 kcal/mol). These virtual hits signify a promising initial step in identifying candidates as supplementary therapeutics to immune checkpoint inhibitors in cancer treatment. Their potential to deliver potent dual inhibition of IDO1/TDO2, along with safety and favorable pharmacokinetics, makes them compelling. Validation through in vitro and in vivo assays should be conducted to confirm their activity, selectivity, and preclinical potential as holo IDO1/TDO2 dual inhibitors.Communicated by Ramaswamy H. Sarma.

Differences in Protein Capture by SP3 and SP4 Demonstrate Mechanistic Insights of Proteomics Clean-up Techniques

The goal of proteomics experiments is to identify proteins to observe changes in cellular processes and diseases. One challenge in proteomics is the removal of contaminants following protein extraction, which can limit protein identification. Single-pot, solid-phase-enhanced sample preparation (SP3) is a clean-up technique in which proteins are captured on carboxylate-modified particles through a proposed hydrophilic-interaction-liquid-chromatography (HILIC)-like mechanism. However, recent results have suggested that proteins are captured in SP3 due to a protein-aggregation mechanism. Thus, solvent precipitation, single-pot, solid-phase-enhanced sample preparation (SP4) is a newer clean-up technique that employs protein-aggregation to capture proteins without modified particles. SP4 has previously enriched low-solubility proteins, though differences in protein capture could affect which proteins are detected and identified. We hypothesize that the mechanisms of capture for SP3 and SP4 are distinct. Herein, we assess the proteins identified and enriched using SP3 versus SP4 for MCF7 subcellular fractions and correlate protein capture in each method to protein hydrophobicity. Our results indicate that SP3 captures more hydrophilic proteins through a combination of HILIC-like and protein-aggregation mechanisms, while SP4 captures more hydrophobic proteins through a protein-aggregation mechanism. From these results, we recommend clean-up techniques based on protein-sample hydrophobicity to yield high proteome coverage in biological samples.

Metabolic profiling of bacteria with the application of polypyrrole-MOF SPME fibers and plasmonic nanostructured LDI-MS substrates

Here we present application of innovative lab-made analytical devices such as plasmonic silver nanostructured substrates and polypyrrole-MOF solid-phase microextraction fibers for metabolic profiling of bacteria. For the first time, comprehensive metabolic profiling of both volatile and non-volatile low-molecular weight compounds in eight bacterial strains was carried out with utilization of lab-made devices. Profiles of low molecular weight metabolites were analyzed for similarities and differences using principal component analysis, hierarchical cluster analysis and random forest algorithm. The results showed clear differentiation between Gram positive (G+) and Gram negative (G-) species which were identified as distinct clusters according to their volatile metabolites. In case of non-volatile metabolites, differentiation between G+ and G- species and clustering for all eight species were observed for the chloroform fraction of the Bligh & Dyer extract, while methanolic fraction failed to recover specific ions in the profile. Furthermore, the results showed correlation between volatile and non-volatile metabolites, which suggests that lab-made devices presented in the current study might be complementary and therefore, useful for species differentiation and gaining insights into bacterial metabolic pathways.

Missense3D-TM: Predicting the Effect of Missense Variants in Helical Transmembrane Protein Regions Using 3D Protein Structures

Variant effect predictors assess if a substitution is pathogenic or benign. Most predictors, including those that are structure-based, are designed for globular proteins in aqueous environments and do not consider that the variant residue is located within the membrane. We report Missense3D-TM that provides a structure-based assessment of the impact of a missense variant located within a membrane. On a dataset of 2,078 pathogenic and 1,060 benign variants, spanning 711 proteins from 706 structures, Missense3D-TM achieved an accuracy of 66%, Mathews correlation coefficient of 0.37, sensitivity of 58% and specificity of 81%. Missense3D-TM performed similarly to mCSM-membrane: accuracy 66% vs 61% (p = 0.02) on an unbalanced test set and 70% vs 67% (p = 0.20) on a balanced test set. The Missense3D-TM website provides an analysis of the structural effects of the variant along with its predicted position within the membrane. The web server is available at http://missense3d.bc.ic.ac.uk/.

Underlying features for the enhanced electrostatic strength of the extremophilic malate dehydrogenase interface salt-bridge compared to the mesophilic one

Malate dehydrogenase (MDH) exists in multimeric form in normal and extreme solvent conditions where residues of the interface are involved in specific interactions. The interface salt-bridge (ISB) and its microenvironment (ME) residues may have a crucial role in the stability and specificity of the interface. To gain insight into this, we have analyzed 218 ISBs from 42 interfaces of 15 crystal structures along with their sequences. Comparative analyses demonstrate that the ISB strength is ∼30 times greater in extremophilic cases than that of the normal one. To this end, the interface residue propensity, ISB design and pair selection, and ME-residue's types, i.e., type-I and type-II, are seen to be intrinsically involved. Although Type-I is a common type, Type-II appears to be extremophile-specific, where the net ME-residue count is much lower with an excessive net ME-energy contribution, which seems to be a novel interface compaction strategy. Furthermore, the interface strength can be enhanced by selecting the desired mutant from the net-energy profile of all possible mutations of an unfavorable ME-residue. The study that applies to other similar systems finds applications in protein-protein interaction and protein engineering.Communicated by Ramaswamy H. Sarma.

Cardiolipin occupancy profiles of YidC paralogs reveal the significance of respective TM2 helix residues in determining paralog-specific phenotypes

YidC belongs to an evolutionarily conserved family of insertases, YidC/Oxa1/Alb3, in bacteria, mitochondria, and chloroplasts, respectively. Unlike Gram-negative bacteria, Gram-positives including Streptococcus mutans harbor two paralogs of YidC. The mechanism for paralog-specific phenotypes of bacterial YidC1 versus YidC2 has been partially attributed to the differences in their cytoplasmic domains. However, we previously identified a W138R gain-of-function mutation in the YidC1 transmembrane helix 2. YidC1W138R mostly phenocopied YidC2, yet the mechanism remained unknown. Primary sequence comparison of streptococcal YidCs led us to identify and mutate the YidC1W138 analog, YidC2S152 to W/A, which resulted in a loss of YidC2- and acquisition of YidC1-like phenotype. The predicted lipid-facing side chains of YidC1W138/YidC2S152 led us to propose a role for membrane phospholipids in specific-residue dependent phenotypes of S. mutans YidC paralogs. Cardiolipin (CL), a prevalent phospholipid in the S. mutans cytoplasmic membrane during acid stress, is encoded by a single gene, cls. We show a concerted mechanism for cardiolipin and YidC2 under acid stress based on similarly increased promoter activities and similar elimination phenotypes. Using coarse grain molecular dynamics simulations with the Martini2.2 Forcefield, YidC1 and YidC2 wild-type and mutant interactions with CL were assessed in silico. We observed substantially increased CL interaction in dimeric versus monomeric proteins, and variable CL occupancy in YidC1 and YidC2 mutant constructs that mimicked characteristics of the other wild-type paralog. Hence, paralog-specific amino acid- CL interactions contribute to YidC1 and YidC2-associated phenotypes that can be exchanged by point mutation at positions 138 or 152, respectively.

Membrane protein synthesis: no cells required

Despite advances in membrane protein (MP) structural biology and a growing interest in their applications, these proteins remain challenging to study. Progress has been hindered by the complex nature of MPs and innovative methods will be required to circumvent technical hurdles. Cell-free protein synthesis (CFPS) is a burgeoning technique for synthesizing MPs directly into a membrane environment using reconstituted components of the cellular transcription and translation machinery in vitro. We provide an overview of CFPS and how this technique can be applied to the synthesis and study of MPs. We highlight numerous strategies including synthesis methods and folding environments, each with advantages and limitations, to provide a survey of how CFPS techniques can advance the study of MPs.

Effect of Cholesterol on Biomimetic Membrane Curvature and Coronavirus Fusion Peptide Encapsulation

Biomimetic cubic phases can be used for protein encapsulation in a variety of applications such as biosensors and drug delivery. Cubic phases with a high concentration of cholesterol and phospholipids were obtained herein. It is shown that the cubic phase structure can be maintained with a higher concentration of biomimetic membrane additives than has been reported previously. Opposing effects on the curvature of the membrane were observed upon the addition of phospholipids and cholesterol. Furthermore, the coronavirus fusion peptide significantly increased the negative curvature of the biomimetic membrane with cholesterol. We show that the viral fusion peptide can undergo structural changes leading to the formation of hydrophobic α-helices that insert into the lipid bilayer. This is of high importance, as a fusion peptide that induces increased negative curvature as shown by the formation of inverse hexagonal phases allows for greater contact area between two membranes, which is required for viral fusion to occur. The cytotoxicity assay showed that the toxicity toward HeLa cells was dramatically decreased when the cholesterol or peptide level in the nanoparticles increased. This suggests that the addition of cholesterol can improve the biocompatibility of the cubic phase nanoparticles, making them safer for use in biomedical applications. As the results, this work improves the potential for the biomedical end-use applications of the nonlamellar lipid nanoparticles and shows the need of systematic formulation studies due to the complex interplay of all components.

Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications

Membrane proteins mediate a wide spectrum of biological processes, such as signal transduction and cell communication. Due to the arduous and costly nature inherent to the experimental process, membrane proteins have long been devoid of well-resolved atomic-level tertiary structures and, consequently, the understanding of their functional roles underlying a multitude of life activities has been hampered. Currently, computational tools dedicated to furthering the structure-function understanding are primarily focused on utilizing intelligent algorithms to address a variety of site-wise prediction problems (e.g., topology and interaction sites), but are scattered across different computing sources. Moreover, the recent advent of deep learning techniques has immensely expedited the development of computational tools for membrane protein-related prediction problems. Given the growing number of applications optimized particularly by manifold deep neural networks, we herein provide a review on the current status of computational strategies mainly in membrane protein type classification, topology identification, interaction site detection, and pathogenic effect prediction. Meanwhile, we provide an overview of how the entire prediction process proceeds, including database collection, data pre-processing, feature extraction, and method selection. This review is expected to be useful for developing more extendable computational tools specific to membrane proteins.

In silico investigation of uncoupling protein function in avian genomes

Introduction

The uncoupling proteins (UCPs) are involved in lipid metabolism and belong to a family of mitochondrial anionic transporters. In poultry, only one UCP homologue has been identified and experimentally shown to be associated with growth, feed conversion ratio, and abdominal fat according to its predominant expression in bird muscles. In endotherm birds, cell metabolic efficiency can be tuned by the rate of mitochondrial coupling. Thus, avUCP may be a key contributor to controlling metabolic rate during particular environmental changes.

Methods

This study aimed to perform a set of in-silico investigations primarily focused on the structural, biological, and biomimetic functions of avUCP. Thereby, using in silico genome analyses among 8 avian species (chicken, turkey, swallow, manakin, sparrow, wagtail, pigeon, and mallard) and a series of bioinformatic approaches, we provide phylogenetic inference and comparative genomics of avUCPs and investigate whether sequence variation can alter coding sequence characteristics, the protein structure, and its biological features. Complementarily, a combination of literature mining and prediction approaches was also applied to predict the gene networks of avUCP to identify genes, pathways, and biological crosstalk associated with avUCP function.

Results

The results showed the evolutionary alteration of UCP proteins in different avian species. Uncoupling proteins in avian species are highly conserved trans membrane proteins as seen by sequence alignment, physio-chemical parameters, and predicted protein structures. Taken together, avUCP has the potential to be considered a functional marker for the identification of cell metabolic state, thermogenesis, and oxidative stress caused by cold, heat, fasting, transfer, and other chemical stimuli stresses in birds. It can also be deduced that avUCP, in migrant or domestic birds, may increase heat stress resistance by reducing fatty acid transport/b-oxidation and thermoregulation alongside antioxidant defense mechanisms. The predicted gene network for avUCP highlighted a cluster of 21 genes involved in response to stress and 28 genes related to lipid metabolism and the proton buffering system. Finally, among 11 enriched pathways, crosstalk of 5 signaling pathways including MAPK, adipocytokine, mTOR, insulin, ErbB, and GnRH was predicted, indicating a possible combination of positive or negative feedback among pathways to regulate avUCP functions.

Discussion

Genetic selection for fast-growing commercial poultry has unintentionally increased susceptibility to many kinds of oxidative stress, and so avUCP could be considered as a potential candidate gene for balancing energy expenditure and reactive oxygen species production, especially in breeding programs. In conclusion, avUCP can be introduced as a pleiotropic gene that requires the contribution of regulatory genes, hormones, pathways, and genetic crosstalk to allow its finely-tuned function.

Not sorcery after all: Roles of multiple charged residues in membrane insertion of gasdermin-A3

Gasdermins execute programmatory cell death, known as pyroptosis, by forming medium-sized membrane pores. Recently, the molecular structure of those pores as well as the diversity in their shape and size have been revealed by cryoTEM and atomic force microscopy, respectively. Even though a growth of smaller to larger oligomers and reshaping from slits to rings could be documented, the initiation of the gasdermin pore formation remains a mystery. In one hypothesis, gasdermin monomers insert into membranes before associating into oligomeric pores. In the other hypothesis, gasdermin oligomers preassemble on the membrane surface prior to membrane insertion. Here, by studying the behavior of monomeric membrane-inserted gasdermin-A3 (GSDMA3), we unveil that a monomeric gasdermin prefers the membrane-adsorbed over the membrane-inserted state. Our results thus support the hypothesis of oligomers preassembling on the membrane surface before membrane penetration. At the same time, our simulations of small membrane-inserted arcs of GSDMA3 suggest that the inserting oligomer can be small and does not have to comprise a full ring of approximately 26–30 subunits. Moreover, our simulations have revealed an astonishingly large impact of salt-bridge formation and protein surroundings on the transmembrane passage of charged residues, reducing the energetic cost by up to 53% as compared to their free forms. The here observed free energy barrier of mere 5.6 kcal/mol for the membrane insertion of monomeric GSDMA3 explains the surprising ability of gasdermins to spontaneously self-insert into cellular membranes.

Thermostability engineering of industrial enzymes through structure modification

Thermostability is an essential requirement of enzymes in the industrial processes to catalyze the reactions at high temperatures; thus, enzyme engineering through directed evolution, semi-rational design and rational design are commonly employed to construct desired thermostable mutants. Several strategies are implemented to fulfill enzymes' thermostability demand including decreasing the entropy of the unfolded state through substitutions Gly → Xxx or Xxx → Pro, hydrogen bond, salt bridge, introducing two different simultaneous interactions through single mutant, hydrophobic interaction, filling the hydrophobic cavity core, decreasing surface hydrophobicity, truncating loop, aromatic-aromatic interaction and introducing positively charged residues to enzyme surface. In the current review, horizons about compatibility between secondary structures and substitutions at preferable structural positions to generate the most desirable thermostability in industrial enzymes are broadened. KEY POINTS: • Protein engineering is a powerful tool for generating thermostable industrial enzymes. • Directed evolution and rational design are practical approaches in enzyme engineering. • Substitutions in preferable structural positions can increase thermostability.

A Census of Human Methionine-Rich Prion-like Domain-Containing Proteins

Methionine-rich prion-like proteins can regulate liquid–liquid phase separation processes in response to stresses. To date, however, very few proteins have been identified as methionine-rich prion-like. Herein, we have performed a computational survey of the human proteome to search for methionine-rich prion-like domains. We present a census of 51 manually curated methionine-rich prion-like proteins. Our results show that these proteins tend to be modular in nature, with molecular sizes significantly greater than those we would expect due to random sampling effects. These proteins also exhibit a remarkably high degree of spatial compaction when compared to average human proteins, even when protein size is accounted for. Computational evidence suggests that such a high degree of compactness might be due to the aggregation of methionine residues, pointing to a potential redox regulation of compactness. Gene ontology and network analyses, performed to shed light on the biological processes in which these proteins might participate, indicate that methionine-rich and non-methionine-rich prion-like proteins share gene ontology terms related to the regulation of transcription and translation but, more interestingly, these analyses also reveal that proteins from the methionine-rich group tend to share more gene ontology terms among them than they do with their non-methionine-rich prion-like counterparts.

Application of Coarse-Grained (CG) Models to Explore Conformational Pathway of Large-Scale Protein Machines

Protein machines are clusters of protein assemblies that function in order to control the transfer of matter and energy in cells. For a specific protein machine, its working mechanisms are not only determined by the static crystal structures, but also related to the conformational transition dynamics and the corresponding energy profiles. With the rapid development of crystallographic techniques, the spatial scale of resolved structures is reaching up to thousands of residues, and the concomitant conformational changes become more and more complicated, posing a great challenge for computational biology research. Previously, a coarse-grained (CG) model aiming at conformational free energy evaluation was developed and showed excellent ability to reproduce the energy profiles by accurate electrostatic interaction calculations. In this study, we extended the application of the CG model to a series of large-scale protein machine systems. The spike protein trimer of SARS-CoV-2, ATP citrate lyase (ACLY) tetramer, and P4-ATPases systems were carefully studied and discussed as examples. It is indicated that the CG model is effective to depict the energy profiles of the conformational pathway between two endpoint structures, especially for large-scale systems. Both the energy change and energy barrier between endpoint structures provide reasonable mechanism explanations for the associated biological processes, including the opening of receptor binding domain (RBD) of spike protein, the phospholipid transportation of P4-ATPase, and the loop translocation of ACLY. Taken together, the CG model provides a suitable alternative in mechanistic studies related to conformational change in large-scale protein machines.

Fast and Accurate Quantum Mechanical Modeling of Large Molecular Systems Using Small Basis Set Hartree-Fock Methods Corrected with Atom-Centered Potentials

There has been significant interest in developing fast and accurate quantum mechanical methods for modeling large molecular systems. In this work, by utilizing a machine learning regression technique, we have developed new low-cost quantum mechanical approaches to model large molecular systems. The developed approaches rely on using one-electron Gaussian-type functions called atom-centered potentials (ACPs) to correct for the basis set incompleteness and the lack of correlation effects in the underlying minimal or small basis set Hartree-Fock (HF) methods. In particular, ACPs are proposed for ten elements common in organic and bioorganic chemistry (H, B, C, N, O, F, Si, P, S, and Cl) and four different base methods: two minimal basis sets (MINIs and MINIX) plus a double-ζ basis set (6-31G*) in combination with dispersion-corrected HF (HF-D3/MINIs, HF-D3/MINIX, HF-D3/6-31G*) and the HF-3c method. The new ACPs are trained on a very large set (73 832 data points) of noncovalent properties (interaction and conformational energies) and validated additionally on a set of 32 048 data points. All reference data are of complete basis set coupled-cluster quality, mostly CCSD(T)/CBS. The proposed ACP-corrected methods are shown to give errors in the tenths of a kcal/mol range for noncovalent interaction energies and up to 2 kcal/mol for molecular conformational energies. More importantly, the average errors are similar in the training and validation sets, confirming the robustness and applicability of these methods outside the boundaries of the training set. In addition, the performance of the new ACP-corrected methods is similar to complete basis set density functional theory (DFT) but at a cost that is orders of magnitude lower, and the proposed ACPs can be used in any computational chemistry program that supports effective-core potentials without modification. It is also shown that ACPs improve the description of covalent and noncovalent bond geometries of the underlying methods and that the improvement brought about by the application of the ACPs is directly related to the number of atoms to which they are applied, allowing the treatment of systems containing some atoms for which ACPs are not available. Overall, the ACP-corrected methods proposed in this work constitute an alternative accurate, economical, and reliable quantum mechanical approach to describe the geometries, interaction energies, and conformational energies of systems with hundreds to thousands of atoms.

Synthesis and Evaluation of a Library of Alternating Amphipathic Copolymers to Solubilize and Study Membrane Proteins

Amphipathic copolymers such as poly(styrene-maleic acid) (SMA) are promising tools for the facile extraction of membrane proteins (MPs) into native nanodiscs. Here, we designed and synthesized a library of well-defined alternating copolymers of SMA analogues in order to elucidate polymer properties that are important for MP solubilization and stability. MP extraction efficiency was determined using KcsA from E. coli membranes, and general solubilization efficiency was investigated via turbidimetry experiments on membranes of E. coli, yeast mitochondria, and synthetic lipids. Remarkably, halogenation of SMA copolymers dramatically improved solubilization efficiency in all systems, while substituents on the copolymer backbone improved resistance to Ca²⁺. Relevant polymer properties were found to include hydrophobic balance, size and positioning of substituents, rigidity, and electronic effects. The library thus contributes to the rational design of copolymers for the study of MPs.

Mining anion-aromatic interactions in the Protein Data Bank

Mutual positioning and non-covalent interactions in anion–aromatic motifs are crucial for functional performance of biological systems. In this context, regular, comprehensive Protein Data Bank (PDB) screening that involves various scientific points of view and individual critical analysis is of utmost importance. Analysis of anions in spheres with radii of 5 Å around all 5- and 6-membered aromatic rings allowed us to distinguish 555 259 unique anion–aromatic motifs, including 92 660 structures out of the 171 588 structural files in the PDB. The use of a scarcely exploited (x, h) coordinate system led to (i) identification of three separate areas of motif accumulation: A – over the ring, B – over the ring-substituent bonds, and C – roughly in the plane of the aromatic ring, and (ii) unprecedented simultaneous comparative description of various anion–aromatic motifs located in these areas. Of the various residues considered, i.e. aminoacids, nucleotides, and ligands, the latter two exhibited a considerable tendency to locate in region Avia archetypal anion–π contacts. The applied model not only enabled statistical quantitative analysis of space around the ring, but also enabled discussion of local intermolecular arrangements, as well as detailed sequence and secondary structure analysis, e.g. anion–π interactions in the GNRA tetraloop in RNA and protein helical structures. As a purely practical issue of this work, the new code source for the PDB research was produced, tested and made freely available at https://github.com/chemiczny/PDB_supramolecular_search.

The comprehensive analysis of non-redundant PDB macromolecular structures investigating anion distributions around all aromatic molecules in available biosystems is presented.

Charge Engineering Reveals the Roles of Ionizable Side Chains in Electrospray Ionization Mass Spectrometry

In solution, the charge of a protein is intricately linked to its stability, but electrospray ionization distorts this connection, potentially limiting the ability of native mass spectrometry to inform about protein structure and dynamics. How the behavior of intact proteins in the gas phase depends on the presence and distribution of ionizable surface residues has been difficult to answer because multiple chargeable sites are present in virtually all proteins. Turning to protein engineering, we show that ionizable side chains are completely dispensable for charging under native conditions, but if present, they are preferential protonation sites. The absence of ionizable side chains results in identical charge state distributions under native-like and denaturing conditions, while coexisting conformers can be distinguished using ion mobility separation. An excess of ionizable side chains, on the other hand, effectively modulates protein ion stability. In fact, moving a single ionizable group can dramatically alter the gas-phase conformation of a protein ion. We conclude that although the sum of the charges is governed solely by Coulombic terms, their locations affect the stability of the protein in the gas phase.

BRANEart: Identify Stability Strength and Weakness Regions in Membrane Proteins

Understanding the role of stability strengths and weaknesses in proteins is a key objective for rationalizing their dynamical and functional properties such as conformational changes, catalytic activity, and protein-protein and protein-ligand interactions. We present BRANEart, a new, fast and accurate method to evaluate the per-residue contributions to the overall stability of membrane proteins. It is based on an extended set of recently introduced statistical potentials derived from membrane protein structures, which better describe the stability properties of this class of proteins than standard potentials derived from globular proteins. We defined a per-residue membrane propensity index from combinations of these potentials, which can be used to identify residues which strongly contribute to the stability of the transmembrane region or which would, on the contrary, be more stable in extramembrane regions, or vice versa. Large-scale application to membrane and globular proteins sets and application to tests cases show excellent agreement with experimental data. BRANEart thus appears as a useful instrument to analyze in detail the overall stability properties of a target membrane protein, to position it relative to the lipid bilayer, and to rationally modify its biophysical characteristics and function. BRANEart can be freely accessed from http://babylone.3bio.ulb.ac.be/BRANEart.

Prediction of Metal Ion Binding Sites of Transmembrane Proteins

The metal ion binding of transmembrane proteins (TMPs) plays a fundamental role in biological processes, pharmaceutics, and medicine, but it is hard to extract enough TMP structures in experimental techniques to discover their binding mechanism comprehensively. To predict the metal ion binding sites for TMPs on a large scale, we present a simple and effective two-stage prediction method TMP-MIBS, to identify the corresponding binding residues using TMP sequences. At present, there is no specific research on the metal ion binding prediction of TMPs. Thereby, we compared our model with the published tools which do not distinguish TMPs from water-soluble proteins. The results in the independent verification dataset show that TMP-MIBS has superior performance. This paper explores the interaction mechanism between TMPs and metal ions, which is helpful to understand the structure and function of TMPs and is of great significance to further construct transport mechanisms and identify potential drug targets.

Tryptophan, an Amino-Acid Endowed with Unique Properties and Its Many Roles in Membrane Proteins

Tryptophan is an aromatic amino acid with unique physico-chemical properties. It is often encountered in membrane proteins, especially at the level of the water/bilayer interface. It plays a role in membrane protein stabilization, anchoring and orientation in lipid bilayers. It has a hydrophobic character but can also engage in many types of interactions, such as π–cation or hydrogen bonds. In this review, we give an overview of the role of tryptophan in membrane proteins and a more detailed description of the underlying noncovalent interactions it can engage in with membrane partners.

Prospecting the therapeutic edge of a novel compound (B12) over berberine in the selective targeting of Retinoid X Receptor in colon cancer

The Retinoid X Receptor (RXR) is an attractive target in the treatment of colon cancer. Different therapeutic binders with high potency have been used to specifically target RXR. Among these compounds is a novel analogue of berberine, B12. We provided structural and molecular insights into the therapeutic activity properties of B12 relative to its parent compound, berberine, using force field estimations and thermodynamic calculations. Upon binding of B12 to RXR, the high instability elicited by RXR was markedly reduced; similar observation was seen in the berberine-bound RXR. However, our analysis revealed that B12 could have a more stabilizing effect on RXR when compared to berberine. Interestingly, the mechanistic behaviour of B12 in the active site of RXR opposed its impact on RXR protein. This disparity could be due to the bond formation and breaking elicited between B12/berberine and the active site residues. We observed that B12 and berberine could induce a disparate conformational change in regions Gly250-Asp258 located on the His-RXRα/LBD domain. Comparatively, the high agonistic and activation potential reported for B12 compared to berberine might be due to its superior binding affinity as evidenced in the thermodynamic estimations. The total affinity for B12 (-25.76 kcal/mol) was contributed by electrostatic interactions from Glu243 and Glu239. Also, Arg371, which plays a crucial role in the activity of RXR, formed a strong hydrogen bond with B12; however, a weak interaction was elicited between Arg371 and berberine. Taken together, our study has shown the RXRα activating potential of B12, and findings from this study could provide a framework in the future design of RXRα binders specifically tailored in the selective treatment of colon cancer.

AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches

Antiviral peptide (AVP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against virus infection. Machine learning-based prediction with a computational biology approach can facilitate the development of the novel therapeutic agents. In this study, we proposed a double-stage classification scheme, named AVPIden, for predicting the AVPs and their functional activities against different viruses. The first stage is to distinguish the AVP from a broad-spectrum peptide collection, including not only the regular peptides (non-AMP) but also the AMPs without antiviral functions (non-AVP). The second stage is responsible for characterizing one or more virus families or species that the AVP targets. Imbalanced learning is utilized to improve the performance of prediction. The AVPIden uses multiple descriptors to precisely demonstrate the peptide properties and adopts explainable machine learning strategies based on Shapley value to exploit how the descriptors impact the antiviral activities. Finally, the evaluation performance of the proposed model suggests its ability to predict the antivirus activities and their potential functions against six virus families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight kinds of virus (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). The AVPIden gives an option for reinforcing the development of AVPs with the computer-aided method and has been deployed at http://awi.cuhk.edu.cn/AVPIden/.

Plausible blockers of Spike RBD in SARS-CoV2-molecular design and underlying interaction dynamics from high-level structural descriptors

Abstract

COVID-19 is characterized by an unprecedented abrupt increase in the viral transmission rate (SARS-CoV-2) relative to its pandemic evolutionary ancestor, SARS-CoV (2003). The complex molecular cascade of events related to the viral pathogenicity is triggered by the Spike protein upon interacting with the ACE2 receptor on human lung cells through its receptor binding domain (RBD_Spike). One potential therapeutic strategy to combat COVID-19 could thus be limiting the infection by blocking this key interaction. In this current study, we adopt a protein design approach to predict and propose non-virulent structural mimics of the RBD_Spike which can potentially serve as its competitive inhibitors in binding to ACE2. The RBD_Spike is an independently foldable protein domain, resilient to conformational changes upon mutations and therefore an attractive target for strategic re-design. Interestingly, in spite of displaying an optimal shape fit between their interacting surfaces (attributed to a consequently high mutual affinity), the RBD_Spike–ACE2 interaction appears to have a quasi-stable character due to a poor electrostatic match at their interface. Structural analyses of homologous protein complexes reveal that the ACE2 binding site of RBD_Spike has an unusually high degree of solvent-exposed hydrophobic residues, attributed to key evolutionary changes, making it inherently “reaction-prone.” The designed mimics aimed to block the viral entry by occupying the available binding sites on ACE2, are tested to have signatures of stable high-affinity binding with ACE2 (cross-validated by appropriate free energy estimates), overriding the native quasi-stable feature. The results show the apt of directly adapting natural examples in rational protein design, wherein, homology-based threading coupled with strategic “hydrophobic ↔ polar” mutations serve as a potential breakthrough.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s00894-021-04779-0.

MENSAdb: a thorough structural analysis of membrane protein dimers

Membrane proteins (MPs) are key players in a variety of different cellular processes and constitute the target of around 60% of all Food and Drug Administration-approved drugs. Despite their importance, there is still a massive lack of relevant structural, biochemical and mechanistic information mainly due to their localization within the lipid bilayer. To help fulfil this gap, we developed the MEmbrane protein dimer Novel Structure Analyser database (MENSAdb). This interactive web application summarizes the evolutionary and physicochemical properties of dimeric MPs to expand the available knowledge on the fundamental principles underlying their formation. Currently, MENSAdb contains features of 167 unique MPs (63% homo- and 37% heterodimers) and brings insights into the conservation of residues, accessible solvent area descriptors, average B-factors, intermolecular contacts at 2.5 Å and 4.0 Å distance cut-offs, hydrophobic contacts, hydrogen bonds, salt bridges, π-π stacking, T-stacking and cation-π interactions. The regular update and organization of all these data into a unique platform will allow a broad community of researchers to collect and analyse a large number of features efficiently, thus facilitating their use in the development of prediction models associated with MPs. Database URL: http://www.moreiralab.com/resources/mensadb.

Structural determinants of cholesterol recognition in helical integral membrane proteins

Cholesterol is an integral component of mammalian membranes. It has been shown to modulate membrane fluidity and dynamics and alter integral membrane protein function. However, understanding the molecular mechanisms of how cholesterol impacts protein function is complicated by limited and conflicting structural data. Because of the nature of the crystallization and cryo-EM structure determination, it is difficult to distinguish between specific and biologically relevant interactions and a nonspecific association. The only widely recognized search algorithm for cholesterol-integral-membrane-protein interaction sites is sequence based, i.e., searching for the so-called "Cholesterol Recognition/interaction Amino acid Consensus" motif. Although these motifs are present in numerous integral membrane proteins, there is inconclusive evidence to support their necessity or sufficiency for cholesterol binding. Here, we leverage the increasing number of experimental cholesterol-integral-membrane-protein structures to systematically analyze putative interaction sites based on their spatial arrangement and evolutionary conservation. This analysis creates three-dimensional representations of general cholesterol interaction sites that form clusters across multiple integral membrane protein classes. We also classify cholesterol-integral-membrane-protein interaction sites as either likely-specific or nonspecific. Information gleaned from our characterization will eventually enable a structure-based approach to predict and design cholesterol-integral-membrane-protein interaction sites.

Structure and Dynamics of GPCRs in Lipid Membranes: Physical Principles and Experimental Approaches

Over the past decade, the vast amount of information generated through structural and biophysical studies of GPCRs has provided unprecedented mechanistic insight into the complex signalling behaviour of these receptors. With this recent information surge, it has also become increasingly apparent that in order to reproduce the various effects that lipids and membranes exert on the biological function for these allosteric receptors, in vitro studies of GPCRs need to be conducted under conditions that adequately approximate the native lipid bilayer environment. In the first part of this review, we assess some of the more general effects that a membrane environment exerts on lipid bilayer-embedded proteins such as GPCRs. This is then followed by the consideration of more specific effects, including stoichiometric interactions with specific lipid subtypes. In the final section, we survey a range of different membrane mimetics that are currently used for in vitro studies, with a focus on NMR applications.

EpCAM cellular functions in adhesion and migration, and potential impact on invasion: A critical review

EpCAM has long been known as a cell surface protein highly expressed in carcinomas. It has since become one of the key cancer biomarkers. Despite its high fame, its actual role in cancer development is still controversial. Beyond a flurry of correlative studies, which point either to a positive or a negative link with tumour progression, there has been surprisingly few studies on the actual cellular mechanisms of EpCAM and on their functional consequences. Clearly, EpCAM plays multiple important roles, in cell proliferation as well as in cell adhesion and migration. The two latter functions, directly relevant for metastasis, are the focus of this review. We attempt here to bring together the available experimental data to build a global coherent view of EpCAM functions. We also include in this overview EpCAM2/Trop2, the close relative of EpCAM. At the core of EpCAM (and EpCAM2/Trop2) function stands the ability to repress contractility of the actomyosin cell cortex. This activity appears to involve direct inhibition by EpCAM of members of the novel PKC family and of a specific downstream PKD-Erk cascade. We will discuss how this activity can result in a variety of adhesive and migratory phenotypes, thus potentially explaining at least part of the apparent inconsistencies between different studies. The picture remains fragmented, and we will highlight some of the conflicting evidence and the many unsolved issues, starting with the controversy around its original description as a cell-cell adhesion molecule.

EpCAM as Modulator of Tissue Plasticity

The Epithelial Cell Adhesion Molecule or EpCAM is a well-known marker highly expressed in carcinomas and showing a strong correlation with poor cancer prognosis. While its name relates to its proposed function as a cell adhesion molecule, EpCAM has been shown to have various signalling functions. In particular, it has been identified as an important positive regulator of cell adhesion and migration, playing an essential role in embryonic morphogenesis as well as intestinal homeostasis. This activity is not due to its putative adhesive function, but rather to its ability to repress myosin contractility by impinging on a PKC signalling cascade. This mechanism confers EpCAM the unique property of favouring tissue plasticity. I review here the currently available data, comment on possible connections with other properties of EpCAM, and discuss the potential significance in the context of cancer invasion.

An information gain-based approach for evaluating protein structure models

For three decades now, knowledge-based scoring functions that operate through the "potential of mean force" (PMF) approach have continuously proven useful for studying protein structures. Although these statistical potentials are not to be confused with their physics-based counterparts of the same name-i.e. PMFs obtained by molecular dynamics simulations-their particular success in assessing the native-like character of protein structure predictions has lead authors to consider the computed scores as approximations of the free energy. However, this physical justification is a matter of controversy since the beginning. Alternative interpretations based on Bayes' theorem have been proposed, but the misleading formalism that invokes the inverse Boltzmann law remains recurrent in the literature. In this article, we present a conceptually new method for ranking protein structure models by quality, which is (i) independent of any physics-based explanation and (ii) relevant to statistics and to a general definition of information gain. The theoretical development described in this study provides new insights into how statistical PMFs work, in comparison with our approach. To prove the concept, we have built interatomic distance-dependent scoring functions, based on the former and new equations, and compared their performance on an independent benchmark of 60,000 protein structures. The results demonstrate that our new formalism outperforms statistical PMFs in evaluating the quality of protein structural decoys. Therefore, this original type of score offers a possibility to improve the success of statistical PMFs in the various fields of structural biology where they are applied. The open-source code is available for download at https://gitlab.rpbs.univ-paris-diderot.fr/src/ig-score.

Mutations in transmembrane proteins: diseases, evolutionary insights, prediction and comparison with globular proteins

Membrane proteins are unique in that they interact with lipid bilayers, making them indispensable for transporting molecules and relaying signals between and across cells. Due to the significance of the protein's functions, mutations often have profound effects on the fitness of the host. This is apparent both from experimental studies, which implicated numerous missense variants in diseases, as well as from evolutionary signals that allow elucidating the physicochemical constraints that intermembrane and aqueous environments bring. In this review, we report on the current state of knowledge acquired on missense variants (referred to as to single amino acid variants) affecting membrane proteins as well as the insights that can be extrapolated from data already available. This includes an overview of the annotations for membrane protein variants that have been collated within databases dedicated to the topic, bioinformatics approaches that leverage evolutionary information in order to shed light on previously uncharacterized membrane protein structures or interaction interfaces, tools for predicting the effects of mutations tailored specifically towards the characteristics of membrane proteins as well as two clinically relevant case studies explaining the implications of mutated membrane proteins in cancer and cardiomyopathy.