A cooperative knock-on mechanism underpins Ca2+-selective cation permeation in TRPV channels

The selective exchange of ions across cellular membranes is a vital biological process. Ca2+-mediated signaling is implicated in a broad array of physiological processes in cells, while elevated intracellular concentrations of Ca2+ are cytotoxic. Due to the significance of this cation, strict Ca2+ concentration gradients are maintained across the plasma and organelle membranes. Therefore, Ca2+ signaling relies on permeation through selective ion channels that control the flux of Ca2+ ions. A key family of Ca2+-permeable membrane channels is the polymodal signal-detecting transient receptor potential (TRP) ion channels. TRP channels are activated by a wide variety of cues including temperature, small molecules, transmembrane voltage, and mechanical stimuli. While most members of this family permeate a broad range of cations non-selectively, TRPV5 and TRPV6 are unique due to their strong Ca2+ selectivity. Here, we address the question of how some members of the TRPV subfamily show a high degree of Ca2+ selectivity while others conduct a wider spectrum of cations. We present results from all-atom molecular dynamics simulations of ion permeation through two Ca2+-selective and two non-selective TRPV channels. Using a new method to quantify permeation cooperativity based on mutual information, we show that Ca2+-selective TRPV channel permeation occurs by a three-binding site knock-on mechanism, whereas a two-binding site knock-on mechanism is observed in non-selective TRPV channels. Each of the ion binding sites involved displayed greater affinity for Ca2+ over Na+. As such, our results suggest that coupling to an extra binding site in the Ca2+-selective TRPV channels underpins their increased selectivity for Ca2+ over Na+ ions. Furthermore, analysis of all available TRPV channel structures shows that the selectivity filter entrance region is wider for the non-selective TRPV channels, slightly destabilizing ion binding at this site, which is likely to underlie mechanistic decoupling.</p

Classification of likely functional class for ligand binding sites identified from fragment screening

Fragment screening is used to identify binding sites and leads in drug discovery, but it is often unclear which binding sites are functionally important. Here, data from 37 experiments, and 1309 protein structures binding to 1601 ligands were analysed. A method to group ligands by binding sites is introduced and sites clustered according to profiles of relative solvent accessibility. This identified 293 unique ligand binding sites, grouped into four clusters (C1-4). C1 includes larger, buried, conserved, and population missense-depleted sites, enriched in known functional sites. C4 comprises smaller, accessible, divergent, missense-enriched sites, depleted in functional sites. A site in C1 is 28 times more likely to be functional than one in C4. Seventeen sites, which to the best of our knowledge are novel, in 13 proteins are identified as likely to be functionally important with examples from human tenascin and 5-aminolevulinate synthase highlighted. A multi-layer perceptron, and K-nearest neighbours model are presented to predict cluster labels for ligand binding sites with an accuracy of 96% and 100%, respectively, so allowing functional classification of sites for proteins not in this set. Our findings will be of interest to those studying protein-ligand interactions and developing new drugs or function modulators

Classification of likely functional class for ligand binding sites identified from fragment screening

Fragment screening is used to identify binding sites and leads in drug discovery, but it is often unclear which binding sites are functionally important. Here, data from 37 experiments, and 1309 protein structures binding to 1601 ligands were analysed. A method to group ligands by binding sites is introduced and sites clustered according to profiles of relative solvent accessibility. This identified 293 unique ligand binding sites, grouped into four clusters (C1-4). C1 includes larger, buried, conserved, and population missense-depleted sites, enriched in known functional sites. C4 comprises smaller, accessible, divergent, missense-enriched sites, depleted in functional sites. A site in C1 is 28 times more likely to be functional than one in C4. Seventeen sites, which to the best of our knowledge are novel, in 13 proteins are identified as likely to be functionally important with examples from human tenascin and 5-aminolevulinate synthase highlighted. A multi-layer perceptron, and K-nearest neighbours model are presented to predict cluster labels for ligand binding sites with an accuracy of 96% and 100%, respectively, so allowing functional classification of sites for proteins not in this set. Our findings will be of interest to those studying protein-ligand interactions and developing new drugs or function modulators

A two-lane mechanism for selective biological ammonium transport

The transport of charged molecules across biological membranes faces the dual problem of accommodating charges in a highly hydrophobic environment while maintaining selective substrate translocation. This has been the subject of a particular controversy for the exchange of ammonium across cellular membranes, an essential process in all domains of life. Ammonium transport is mediated by the ubiquitous Amt/Mep/Rh transporters that includes the human Rhesus factors. Here, using a combination of electrophysiology, yeast functional complementation and extended molecular dynamics simulations, we reveal a unique two-lane pathway for electrogenic NH4+ transport in two archetypal members of the family, the transporters AmtB from Escherichia coli and Rh50 from Nitrosomonas europaea. The pathway underpins a mechanism by which charged H+ and neutral NH3 are carried separately across the membrane after NH4+ deprotonation. This mechanism defines a new principle of achieving transport selectivity against competing ions in a biological transport process

Classification of likely functional state for ligand binding sites identified from fragment screening

Fragment screening data from 37 experiments, and 1,309 protein structures binding to 1,601 ligands were analysed. A new method to group ligands by binding sites was developed and sites clustered according to profiles of relative solvent accessibility. This identified 293 unique ligand binding sites, which are grouped into four clusters (C1-4). C1 presents larger, more buried, conserved, and missense-depleted sites, and is enriched in known functional sites. C4 comprises smaller highly accessible, more divergent, missense-enriched sites, and is depleted in functional sites. A site in C1 is 28 times more likely to be functional than a site in C4. 17 novel sites in 13 proteins are identified as likely to be functionally important with examples from human tenascin and 5-aminolevulinate synthase discussed in more detail. An artificial neural network, and a K-nearest neighbours model are presented to predict cluster labels for new ligand binding sites with an accuracy of 96% and 100%, respectively so allowing functional classification of sites for proteins not in this set. Our findings will be of interest to those studying protein-ligand interactions and developing new drugs or function modulators

Role of N343 glycosylation on the SARS-CoV-2 S RBD structure and co-receptor binding across variants of concern

Glycosylation of the SARS-CoV-2 spike (S) protein represents a key target for viral evolution because it affects both viral evasion and fitness. Successful variations in the glycan shield are difficult to achieve though, as protein glycosylation is also critical to folding and structural stability. Within this framework, the identification of glycosylation sites that are structurally dispensable can provide insight into the evolutionary mechanisms of the shield and inform immune surveillance. In this work, we show through over 45 μs of cumulative sampling from conventional and enhanced molecular dynamics (MD) simulations, how the structure of the immunodominant S receptor binding domain (RBD) is regulated by N-glycosylation at N343 and how this glycan's structural role changes from WHu-1, alpha (B.1.1.7), and beta (B.1.351), to the delta (B.1.617.2), and omicron (BA.1 and BA.2.86) variants. More specifically, we find that the amphipathic nature of the N-glycan is instrumental to preserve the structural integrity of the RBD hydrophobic core and that loss of glycosylation at N343 triggers a specific and consistent conformational change. We show how this change allosterically regulates the conformation of the receptor binding motif (RBM) in the WHu-1, alpha, and beta RBDs, but not in the delta and omicron variants, due to mutations that reinforce the RBD architecture. In support of these findings, we show that the binding of the RBD to monosialylated ganglioside co-receptors is highly dependent on N343 glycosylation in the WHu-1, but not in the delta RBD, and that affinity changes significantly across VoCs. Ultimately, the molecular and functional insight we provide in this work reinforces our understanding of the role of glycosylation in protein structure and function and it also allows us to identify the structural constraints within which the glycosylation site at N343 can become a hotspot for mutations in the SARS-CoV-2 S glycan shield.</p

Predicting glycan structure from tandem mass spectrometry via deep learning

&lt;p&gt;Curated set of LC-MS/MS data from glycomics studies. Used for training and applying CandyCrunch, a deep learning model to predict glycan structure from LC-MS/MS data, described in Urban et al., bioRxiv, 2023 and https://github.com/BojarLab/CandyCrunch.&lt;/p&gt; &lt;p&gt;Files:&lt;/p&gt; &lt;p&gt;full_dataset.xlsx: Full dataset with all annotated LC-MS/MS glycan spectra&lt;/p&gt; &lt;p&gt;X_train.pkl: spectra and metadata from our training set&lt;/p&gt; &lt;p&gt;y_train.pkl: labels from our training set&lt;/p&gt; &lt;p&gt;X_test.pkl: spectra and metadata from our independent test set&lt;/p&gt; &lt;p&gt;y_test.pkl: labels from our independent test set&lt;/p&gt