Differential Gene Expression Profiles between N-Terminal Domain and Ligand-Binding Domain Inhibitors of Androgen Receptor Reveal Ralaniten Induction of Metallothionein by a Mechanism Dependent on MTF1

Hormonal therapies for prostate cancer target the androgen receptor (AR) ligand-binding domain (LBD). Clinical development for inhibitors that bind to the N-terminal domain (NTD) of AR has yielded ralaniten and its analogues. Ralaniten acetate is well tolerated in patients at 3600 mgs/day. Clinical trials are ongoing with a second-generation analogue of ralaniten. Binding sites on different AR domains could result in differential effects on AR-regulated gene expression. Here, we provide the first comparison between AR-NTD inhibitors and AR-LBD inhibitors on androgen-regulated gene expression in prostate cancer cells using cDNA arrays, GSEA, and RT-PCR. LBD inhibitors and NTD inhibitors largely overlapped in the profile of androgen-induced genes that they each inhibited. However, androgen also represses gene expression by various mechanisms, many of which involve protein–protein interactions. De-repression of the transcriptome of androgen-repressed genes showed profound variance between these two classes of inhibitors. In addition, these studies revealed a unique and strong induction of expression of the metallothionein family of genes by ralaniten by a mechanism independent of AR and dependent on MTF1, thereby suggesting this may be an off-target. Due to the relatively high doses that may be encountered clinically with AR-NTD inhibitors, identification of off-targets may provide insight into potential adverse events, contraindications, or poor efficacy.

Critical reagents for ligand-binding assays: process development methodologies to enable high-quality reagents

Development of biotherapeutics require pharmacokinetic/pharmacodynamic (PK/PD) and immunogenicity assays that are frequently in a ligand-binding assay (LBA) format. Conjugated critical reagents for LBAs are generated conjugation of the biotherapeutic drug or anti-drug molecule with a label. Since conjugated critical reagent quality impacts LBA performance, control of the generation process is essential. Our perspective is that process development methodologies should be integrated into critical reagent production to understand the impact of conjugation reactions, purification techniques and formulation conditions on the quality of the reagent. In this article, case studies highlight our approach to developing process conditions for different molecular classes of critical reagents including antibodies and a peptide. This development approach can be applied to the generation of future conjugated critical reagents.

A cell-free approach to identify binding hotspots in plant immune receptors

Plant immune receptors are often difficult to express heterologously, hindering study of direct interactions between these receptors and their targets with traditional biochemical approaches. The cell-free method ribosome display (RD) enables expression of such recalcitrant proteins by keeping each nascent polypeptide chain tethered to its ribosome, which can enhance protein folding by virtue of its size and solubility. Moreover, in contrast to an in planta readout of receptor activity such as a hypersensitive response that conflates binding and signaling, RD enables direct probing of the interaction between plant immune receptors and their targets. Here, we demonstrate the utility of this approach using tomato recognition of Trichoderma viride ethylene-inducing xylanase (EIX) as a case study. Leveraging the modular nature of the tomato LeEIX2 and LeEIX1 leucine-rich repeat (LRR) receptors, we applied an entropy-informed algorithm to maximize the information content in our receptor segmentation RD experiments to identify segments implicated in EIX binding. Unexpectedly, two distinct EIX-binding hotspots were discovered on LeEIX2 and both hotspots are shared with decoy LeEIX1, suggesting that their contrasting receptor functions are not due to differential modes of ligand binding. Given that most plant immune receptors are thought to engage targets via their LRR sequences, this approach should be of broad utility in rapidly identifying their binding hotspots.

New insights into the phosphorylation of the threonine motif of the β1 integrin cytoplasmic domain

Integrins require an activation step before ligand binding and signaling that is mediated by talin and kindlin binding to the β integrin cytosolic domain (β-tail). Conflicting reports exist about the contribution of phosphorylation of a conserved threonine motif in the β1-tail (β1-pT788/pT789) to integrin activation. We show that widely used and commercially available antibodies against β1-pT788/pT789 integrin do not detect specific β1-pT788/pT789 integrin signals in immunoblots of several human and mouse cell lysates but bind bi-phosphorylated threonine residues in numerous proteins, which were identified by mass spectrometry experiments. Furthermore, we found that fibroblasts and epithelial cells expressing the phospho-mimicking β1-TT788/789DD integrin failed to activate β1 integrins and displayed reduced integrin ligand binding, adhesion initiation and cell spreading. These cellular defects are specifically caused by the inability of kindlin to bind β1-tail polypeptides carrying a phosphorylated threonine motif or phospho-mimicking TT788/789DD substitutions. Our findings indicate that the double-threonine motif in β1-class integrins is not a major phosphorylation site but if phosphorylated would curb integrin function.

Resolving Protein Conformational Plasticity and Substrate Binding Through the Lens of Machine-Learning

A long-standing target in elucidating the biomolecular recognition process is the identification of binding-competent conformations of the receptor protein. However, protein conformational plasticity and the stochastic nature of the recognition processes often preclude the assignment of a specific protein conformation to an individual ligand-bound pose. In particular, we consider multi-microsecond long Molecular dynamics simulation trajectories of ligand recognition process in solvent-inaccessible cavity of two archtypal systems: L99A mutant of T4 Lysozyme and Cytochrome P450. We first show that if the substrate-recognition occurs via long-lived intermediate, the protein conformations can be automatically classified into substrate-bound and unbound state through an unsupervised dimensionality reduction technique. On the contrary, if the recognition process is mediated by selection of transient protein conformation by the ligand, a clear correspondence between protein conformation and binding-competent macrostates can only be established via a combination of supervised machine learning (ML) and unsupervised dimension reduction approach. In such scenario, we demonstrate that a priori random forest based supervised classification of the simulated trajectories recognition process would help characterize key amino-acid residue-pairs of the protein that are deemed sensitive for ligand binding. A subsequent unsupervised dimensional reduction via time-lagged independent component analysis of the selected residue-pairs would delineate a conformational landscape of protein which is able to demarcate ligand-bound pose from the unbound ones. As a key breakthrough, the ML-based protocol would identify distal protein locations which would be allosterically important for ligand binding and characterise their roles in recognition pathways.

PDBspheres - a method for finding 3D similarities in local regions in proteins

We present a structure-based method for finding and evaluating structural similarities in protein regions relevant to ligand binding. PDBspheres comprises an exhaustive library of protein structure regions (spheres) adjacent to complexed ligands derived from the Protein Data Bank (PDB), along with methods to find and evaluate structural matches between a protein of interest and spheres in the library. Currently, PDBspheres library contains more than 2 million spheres, organized to facilitate searches by sequence and/or structure similarity of protein-ligand binding sites or interfaces between interacting molecules. PDBspheres uses the LGA structure alignment algorithm as the main engine for detecting structure similarities between the protein of interest and library spheres. An all-atom structure similarity metric ensures that sidechain placement is taken into account in the PDBspheres primary assessment of confidence in structural matches. In this paper, we (1) describe the PDBspheres method, (2) demonstrate how PDBspheres can be used to detect and characterize binding sites in protein structures, (3) compare PDBspheres use for binding site prediction with seven other binding site prediction methods using a curated dataset of 2,528 ligand-bound and ligand-free crystal structures, and (4) use PDBspheres to cluster pockets and assess structural similarities among protein binding sites of the 4,876 structures in the refined set of PDBbind 2019 dataset. The PDBspheres library is made publicly available for download at https://proteinmodel.org/AS2TS/PDBspheres

Cryo-EM structure of human somatostatin receptor 2 complex with its agonist somatostatin delineates the ligand binding specificity

Somatostatin is a peptide hormone regulating endocrine systems through binding to G-protein-coupled somatostatin receptors. somatostatin receptor 2 (SSTR2) is one of the human somatostatin receptors and highly implicated in cancers and neurological disorders. Here, we report the high resolution cryo-EM structure of full-length human SSTR2 bound to the agonist somatostatin (SST-14) complex with inhibitory G (Gi) proteins. Our structure shows that seven transmembrane helices form a deep pocket for ligand binding and that the highly conserved Trp-Lys motif of SST-14 positions at the bottom of the pocket. Furthermore, our sequence analysis combined with AlphaFold modeled structures of other SSTR isoforms provide how SSTR family proteins specifically interact with their cognate ligands. This work provides the first glimpse into the molecular recognition of somatostatin receptor and crucial resource to develop therapeutics targeting somatostatin receptors.

Impact of membrane lipid polyunsaturation on dopamine D2 receptor ligand binding and signaling

The heterogenous and dynamic constitution of the membrane fine-tunes signal transduction. In particular, the polyunsaturated fatty acid (PUFA) tails of phospholipids influence the biophysical properties of the membrane, production of second messengers, or membrane partitioning. Few evidence mostly originating from studies of rhodopsin suggest that PUFAs directly modulate the conformational dynamic of transmembrane proteins. However, whether such properties translate to other G protein-coupled receptors remains unclear. We focused on the dopamine D2 receptor (D2R), a main target of antipsychotics. Membrane enrichment in n-3, but not n-6, PUFAs potentiates ligand binding. Molecular dynamics simulations show that the D2R preferentially interacts with n-3 over n-6 PUFAs. Furthermore, even though this mildly affects signalling in heterologous systems, in vivo n-3 PUFA deficiency blunts the effects of D2R ligands. These results suggest that n-3 PUFAs act as allosteric modulators of the D2R and provide a putative mechanism for their potentiating effect on antipsychotic efficacy.

Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors

The realization of many protein functions is inseparable from the interaction with ligands; in particular, the combination of protein and metal ion ligands performs an important biological function. Currently, it is a challenging work to identify the metal ion ligand-binding residues accurately by computational approaches. In this study, we proposed an improved method to predict the binding residues of 10 metal ion ligands (Zn2+, Cu2+, Fe2+, Fe3+, Co2+, Mn2+, Ca2+, Mg2+, Na+, and K+). Based on the basic feature parameters of amino acids, and physicochemical and predicted structural information, we added another two features of amino acid correlation information and binding residue propensity factors. With the optimized parameters, we used the GBM algorithm to predict metal ion ligand-binding residues. In the obtained results, the Sn and MCC values were over 10.17% and 0.297, respectively. Besides, the Sn and MCC values of transition metals were higher than 34.46% and 0.564, respectively. In order to test the validity of our model, another method (Random Forest) was also used in comparison. The better results of this work indicated that the proposed method would be a valuable tool to predict metal ion ligand-binding residues.