Structural Modeling of Cytokine-Receptor-JAK2 Signaling Complexes Using AlphaFold Multimer

Proline cis/trans Conformational Selection Controls 14-3-3 Binding

Intrinsically disordered protein regions (IDRs) are structurally dynamic yet functional, often interacting with other proteins through short linear motifs (SLiMs). Proline residues in IDRs introduce conformational heterogeneity on a uniquely slow time scale arising from cis/trans isomerization of the Xaa-Pro peptide bond. Here, we explore the role of proline isomerization in the interaction between the prolactin receptor (PRLR) and 14-3-3. Using NMR spectroscopy, thermodynamic profiling, and molecular dynamics (MD) simulations, we uncover a unique proline isomer-dependent binding, with a cis conformation affinity 3 orders of magnitude higher than the trans. MD simulations identify structural constraints in the narrow 14-3-3 binding groove that provide an explanation for the observed isomer selectivity. The cis preference of WT PRLR introduces a slow kinetic component relevant to signal propagation and a steric component that impacts chain direction. Proline isomerization constitutes a previously unrecognized selective component relevant to the ubiquitous 14-3-3 interactome. Given the prevalence of prolines in IDRs and SLiMs, our study highlights the importance of considering the distinct properties of proline isomers in experimental design and data interpretation to fully comprehend IDR functionality.

Exploring structure-function relationships in engineered receptor performance using computational structure prediction

Engineered receptors play increasingly important roles in transformative cell-based therapies. However, the structural mechanisms that drive differences in performance across receptor designs are often poorly understood. Recent advances in protein structural prediction tools have enabled the modeling of virtually any user-defined protein, but how these tools might build understanding of engineered receptors has yet to be fully explored. In this study, we employed structural modeling tools to perform post hoc analyses to investigate whether predicted structural features might explain observed functional variation. We selected a recently reported library of receptors derived from natural cytokine receptors as a case study, generated structural models, and from these predictions quantified a set of structural features that plausibly impact receptor performance. Encouragingly, for a subset of receptors, structural features explained considerable variation in performance, and trends were largely conserved across structurally diverse receptor sets. This work indicates potential for structure prediction-guided synthetic receptor engineering.

CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed

Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published. They include the following: new faster simulation engines, accessible user interfaces for convenient workflows, and a vast array of simulation and analysis methods that encompass quantum mechanical, atomistic, and coarse-grained levels, as well as extensive coverage of force fields. In addition to providing the current snapshot of the CHARMM development, this review may serve as a starting point for exploring relevant theories and computational methods for tackling contemporary and emerging problems in biomolecular systems. CHARMM is freely available for academic and nonprofit research at https://academiccharmm.org/program.

PPIscreenML: Structure-based screening for protein-protein interactions using AlphaFold

Protein-protein interactions underlie nearly all cellular processes. With the advent of protein structure prediction methods such as AlphaFold2 (AF2), models of specific protein pairs can be built extremely accurately in most cases. However, determining the relevance of a given protein pair remains an open question. It is presently unclear how to use best structure-based tools to infer whether a pair of candidate proteins indeed interact with one another: ideally, one might even use such information to screen amongst candidate pairings to build up protein interaction networks. Whereas methods for evaluating quality of modeled protein complexes have been co-opted for determining which pairings interact (e.g., pDockQ and iPTM), there have been no rigorously benchmarked methods for this task. Here we introduce PPIscreenML, a classification model trained to distinguish AF2 models of interacting protein pairs from AF2 models of compelling decoy pairings. We find that PPIscreenML out-performs methods such as pDockQ and iPTM for this task, and further that PPIscreenML exhibits impressive performance when identifying which ligand/receptor pairings engage one another across the structurally conserved tumor necrosis factor superfamily (TNFSF). Analysis of benchmark results using complexes not seen in PPIscreenML development strongly suggest that the model generalizes beyond training data, making it broadly applicable for identifying new protein complexes based on structural models built with AF2.