Integrated AlphaFold2 and DEER investigation of the conformational dynamics of a pH-dependent APC antiporter.

The Amino Acid-Polyamine-Organocation (APC) transporter GadC contributes to the survival of pathogenic bacteria under extreme acid stress by exchanging extracellular glutamate for intracellular γ-aminobutyric acid (GABA). Its structure, determined in an inward-facing conformation at alkaline pH, consists of the canonical LeuT-fold with a conserved five-helix inverted repeat, thereby resembling functionally divergent transporters such as the serotonin transporter SERT and the glucose-sodium symporter SGLT1. However, despite this structural similarity, it is unclear if the conformational dynamics of antiporters such as GadC follow the blueprint of these or other LeuT-fold transporters.

Principles of Alternating Access in LeuT-fold Transporters: Commonalities and Divergences.

Found in all domains of life, transporters belonging to the LeuT-fold class mediate the import and exchange of hydrophilic and charged compounds such as amino acids, metals, and sugar molecules. Nearly two decades of investigations on the eponymous bacterial transporter LeuT have yielded a library of high-resolution snapshots of its conformational cycle linked by solution-state experimental data obtained from multiple techniques. In parallel, its topology has been observed in symporters and antiporters characterized by a spectrum of substrate specificities and coupled to gradients of distinct ions.

Modeling of protein conformational changes with Rosetta guided by limited experimental data.

Conformational changes are an essential component of functional cycles of many proteins, but their characterization often requires an integrative structural biology approach. Here, we introduce and benchmark ConfChangeMover (CCM), a new method built into the widely used macromolecular modeling suite Rosetta that is tailored to model conformational changes in proteins using sparse experimental data. CCM can rotate and translate secondary structural elements and modify their backbone dihedral angles in regions of interest.

Sampling alternative conformational states of transporters and receptors with AlphaFold2.

Equilibrium fluctuations and triggered conformational changes often underlie the functional cycles of membrane proteins. For example, transporters mediate the passage of molecules across cell membranes by alternating between inward- and outward-facing states, while receptors undergo intracellular structural rearrangements that initiate signaling cascades. Although the conformational plasticity of these proteins has historically posed a challenge for traditional protein structure prediction pipelines, the recent success of AlphaFold2 (AF2) in CASP14 culminated in the modeling of a transporter in multiple conformations to high accuracy.

Methodology for rigorous modeling of protein conformational changes by Rosetta using DEER distance restraints.

We describe an approach for integrating distance restraints from Double Electron-Electron Resonance (DEER) spectroscopy into Rosetta with the purpose of modeling alternative protein conformations from an initial experimental structure. Fundamental to this approach is a multilateration algorithm that harnesses sets of interconnected spin label pairs to identify optimal rotamer ensembles at each residue that fit the DEER decay in the time domain. Benchmarked relative to data analysis packages, the algorithm yields comparable distance distributions with the advantage that fitting the DEER decay and rotamer ensemble optimization are coupled.

AlphaFold2 predicts the inward-facing conformation of the multidrug transporter LmrP.

As part of the CASP competition, the protein structure prediction algorithm AlphaFold2 generated multiple models of the proton/drug antiporter LmrP. Previous distance restraints from double electron-electron resonance spectroscopy, a technique which reports distance distributions between spin labels attached to proteins, suggest that one of the lower-ranked models may have captured a conformation that has so far eluded experimental structure determination.

Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design.

Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved computational methods for both prediction and design. Constant improvements in performance within the Rosetta software suite for biomolecular modeling have given rise to a greater breadth of structure prediction, including docking and design application cases for antibody and antigen modeling. Here, we present an overview of current protocols for antibody and antigen modeling using Rosetta and exemplify those by detailed tutorials originally developed for a Rosetta workshop at Vanderbilt University.

Efficient Sampling of Protein Loop Regions Using Conformational Hashing Complemented with Random Coordinate Descent.

construction of loop regions is an important problem in computational structural biology. Compared to regions with well-defined secondary structure, loops tend to exhibit significant conformational heterogeneity. As a result, their structures are often ambiguous when determined using experimental data obtained by crystallography, cryo-EM, or NMR.

Characterization of the ExoU activation mechanism using EPR and integrative modeling.

ExoU, a type III secreted phospholipase effector of Pseudomonas aeruginosa, serves as a prototype to model large, dynamic, membrane-associated proteins. ExoU is synergistically activated by interactions with membrane lipids and ubiquitin. To dissect the activation mechanism, structural homology was used to identify an unstructured loop of approximately 20 residues in the ExoU amino acid sequence.

Rapid Simulation of Unprocessed DEER Decay Data for Protein Fold Prediction.

Despite advances in sampling and scoring strategies, Monte Carlo modeling methods still struggle to accurately predict de novo the structures of large proteins, membrane proteins, or proteins of complex topologies. Previous approaches have addressed these shortcomings by leveraging sparse distance data gathered using site-directed spin labeling and electron paramagnetic resonance spectroscopy to improve protein structure prediction and refinement outcomes. However, existing computational implementations entail compromises between coarse-grained models of the spin label that lower the resolution and explicit models that lead to resource-intense simulations.

A method for quantification of exportin-1 (XPO1) occupancy by Selective Inhibitor of Nuclear Export (SINE) compounds.

Selective Inhibitor of Nuclear Export (SINE) compounds are a family of small-molecules that inhibit nuclear export through covalent binding to cysteine 528 (Cys528) in the cargo-binding pocket of Exportin 1 (XPO1/CRM1) and promote cancer cell death. Selinexor is the lead SINE compound currently in phase I and II clinical trials for advanced solid and hematological malignancies. In an effort to understand selinexor-XPO1 interaction and to establish whether cancer cell response is a function of drug-target engagement, we developed a quantitative XPO1 occupancy assay.