Interleukin-2 druggability is modulated by global conformational transitions controlled by a helical capping switch.

Interleukin-2 (IL-2) is a small α-helical cytokine that regulates immune cell homeostasis through its recruitment to a high-affinity heterotrimeric receptor complex (IL-2Rα/IL-2Rß/γc). IL-2 has been shown to have therapeutic efficacy for immune diseases by preferentially expanding distinct T cell compartments, and several regulatory T cell (Treg)-biasing anti-IL-2 antibodies have been developed for combination therapies. The conformational plasticity of IL-2 plays an important role in its biological actions by modulating the strength of receptor and drug interactions. Through an NMR analysis of milliseconds-timescale dynamics of free mouse IL-2 (mIL-2), we identify a global transition to a sparse conformation which is regulated by an α-helical capping "switch" at the loop between the A and B helices (AB loop). Binding to either an anti-mouse IL-2 monoclonal antibody (mAb) or a small molecule inhibitor near the loop induces a measurable response at the core of the structure, while locking the switch to a single conformation through a designed point mutation leads to a global quenching of core dynamics accompanied by a pronounced effect in mAb binding. By elucidating key details of the long-range allosteric communication between the receptor binding surfaces and the core of the IL-2 structure, our results offer a direct blueprint for designing precision therapeutics targeting a continuum of conformational states.

Computational design of closely related proteins that adopt two well-defined but structurally divergent folds.

The plasticity of naturally occurring protein structures, which can change shape considerably in response to changes in environmental conditions, is critical to biological function. While computational methods have been used for de novo design of proteins that fold to a single state with a deep free-energy minimum [P.-S. Huang, S. E. Boyken, D. Baker, Nature 537, 320-327 (2016)], and to reengineer natural proteins to alter their dynamics [J. A. Davey, A. M. Damry, N. K. Goto, R. A. Chica, Nat. Chem. Biol. 13, 1280-1285 (2017)] or fold [P. A. Alexander, Y. He, Y. Chen, J. Orban, P. N. Bryan, Proc. Natl. Acad. Sci. U.S.A. 106, 21149-21154 (2009)], the de novo design of closely related sequences which adopt well-defined but structurally divergent structures remains an outstanding challenge. We designed closely related sequences (over 94% identity) that can adopt two very different homotrimeric helical bundle conformations-one short (∼66 Šheight) and the other long (∼100 Šheight)-reminiscent of the conformational transition of viral fusion proteins. Crystallographic and NMR spectroscopic characterization shows that both the short- and long-state sequences fold as designed. We sought to design bistable sequences for which both states are accessible, and obtained a single designed protein sequence that populates either the short state or the long state depending on the measurement conditions. The design of sequences which are poised to adopt two very different conformations sets the stage for creating large-scale conformational switches between structurally divergent forms.

HLA3DB: comprehensive annotation of peptide/HLA complexes enables blind structure prediction of T cell epitopes.

The class I proteins of the major histocompatibility complex (MHC-I) display epitopic peptides derived from endogenous proteins on the cell surface for immune surveillance. Accurate modeling of peptide/HLA (pHLA, the human MHC) structures has been mired by conformational diversity of the central peptide residues, which are critical for recognition by T cell receptors. Here, analysis of X-ray crystal structures within a curated database (HLA3DB) shows that pHLA complexes encompassing multiple HLA allotypes present a discrete set of peptide backbone conformations. Leveraging these representative backbones, we employ a regression model trained on terms of a physically relevant energy function to develop a comparative modeling approach for nonamer peptide/HLA structures named RepPred. Our method outperforms the top pHLA modeling approach by up to 19% in terms of structural accuracy, and consistently predicts blind targets not included in our training set. Insights from our work provide a framework for linking conformational diversity with antigen immunogenicity and receptor cross-reactivity.

HLA3DB: comprehensive annotation of peptide/HLA complexes enables blind structure prediction of T cell epitopes.

The class I proteins of the major histocompatibility complex (MHC-I) display epitopic peptides derived from endogenous proteins on the cell surface for immune surveillance. Accurate modeling of peptides bound to the human MHC, HLA, has been mired by conformational diversity of the central peptide residues, which are critical for recognition by T cell receptors. Here, analysis of X-ray crystal structures within our curated database (HLA3DB) shows that pHLA complexes encompassing multiple HLA allotypes present a discrete set of peptide backbone conformations. Leveraging these backbones, we employ a regression model trained on terms of a physically relevant energy function to develop a comparative modeling approach for nonamer pHLA structures named RepPred. Our method outperforms the top pHLA modeling approach by up to 19% in structural accuracy, and consistently predicts blind targets not included in our training set. Insights from our work may be applied towards predicting antigen immunogenicity, and receptor cross-reactivity.

Global protein dynamics as communication sensors in peptide synthetase domains.

Biological activity is governed by the timely redistribution of molecular interactions, and static structural snapshots often appear insufficient to provide the molecular determinants that choreograph communication. This conundrum applies to multidomain enzymatic systems called nonribosomal peptide synthetases (NRPSs), which assemble simple substrates into complex metabolites, where a dynamic domain organization challenges rational design to produce new pharmaceuticals. Using a nuclear magnetic resonance (NMR) atomic-level readout of biochemical transformations, we demonstrate that global structural fluctuations help promote substrate-dependent communication and allosteric responses, and impeding these global dynamics by a point-site mutation hampers allostery and molecular recognition. Our results establish global structural dynamics as sensors of molecular events that can remodel domain interactions, and they provide new perspectives on mechanisms of allostery, protein communication, and NRPS synthesis.

Backbone-independent NMR resonance assignments of methyl probes in large proteins.

Methyl-specific isotope labeling is a powerful tool to study the structure, dynamics and interactions of large proteins and protein complexes by solution-state NMR. However, widespread applications of this methodology have been limited by challenges in obtaining confident resonance assignments. Here, we present Methyl Assignments Using Satisfiability (MAUS), leveraging Nuclear Overhauser Effect cross-peak data, peak residue type classification and a known 3D structure or structural model to provide robust resonance assignments consistent with all the experimental inputs. Using data recorded for targets with known assignments in the 10-45 kDa size range, MAUS outperforms existing methods by up to 25,000 times in speed while maintaining 100% accuracy. We derive de novo assignments for multiple Cas9 nuclease domains, demonstrating that the methyl resonances of multi-domain proteins can be assigned accurately in a matter of days, while reducing biases introduced by manual pre-processing of the raw NOE data. MAUS is available through an online web-server.

Structure-Based Modeling of SARS-CoV-2 Peptide/HLA-A02 Antigens.

SARS-CoV-2-specific CD4 and CD8 T cells have been shown to be present in individuals with acute, mild, and asymptomatic Coronavirus disease (COVID-19). Toward the development of diagnostic and therapeutic tools to fight COVID-19, it is important to predict and characterize T cell epitopes expressed by SARS-CoV-2. Here, we use RosettaMHC, a comparative modeling approach which leverages existing structures of peptide/MHC complexes available in the Protein Data Bank, to derive accurate 3D models for putative SARS-CoV-2 CD8 epitopes. We outline an application of our method to model 8-10 residue epitopic peptides predicted to bind to the common allele HLA-A*02:01, and we make our models publicly available through an online database (https://rosettamhc.chemistry.ucsc.edu). We further compare electrostatic surfaces with models of homologous peptide/HLA-A*02:01 complexes from human common cold coronavirus strains to identify epitopes which may be recognized by a shared pool of cross-reactive TCRs. As more detailed studies on antigen-specific T cell recognition become available, RosettaMHC models can be used to understand the link between peptide/HLA complex structure and surface chemistry with immunogenicity, in the context of SARS-CoV-2 infection.

Structure-based modeling of SARS-CoV-2 peptide/HLA-A02 antigens.

As a first step toward the development of diagnostic and therapeutic tools to fight the Coronavirus disease (COVID-19), it is important to characterize CD8+ T cell epitopes in the SARS-CoV-2 peptidome that can trigger adaptive immune responses. Here, we use RosettaMHC, a comparative modeling approach which leverages existing high-resolution X-ray structures from peptide/MHC complexes available in the Protein Data Bank, to derive physically realistic 3D models for high-affinity SARS-CoV-2 epitopes. We outline an application of our method to model 439 9mer and 279 10mer predicted epitopes displayed by the common allele HLA-A*02:01, and we make our models publicly available through an online database ( https://rosettamhc.chemistry.ucsc.edu ). As more detailed studies on antigen-specific T cell recognition become available, RosettaMHC models of antigens from different strains and HLA alleles can be used as a basis to understand the link between peptide/HLA complex structure and surface chemistry with immunogenicity, in the context of SARS-CoV-2 infection.

CS-ROSETTA.

Chemical Shift-Rosetta (CS-Rosetta) is an automated method that employs NMR chemical shifts to model protein structures de novo. In this chapter, we introduce the terminology and central concepts of CS-Rosetta. We describe the architecture and functionality of automatic NOESY assignment (AutoNOE) and structure determination protocols (Abrelax and RASREC) within the CS-Rosetta framework. We further demonstrate how CS-Rosetta can discriminate near-native structures against a large conformational search space using restraints obtained from NMR data, and/or sequence and structure homology. We highlight how CS-Rosetta can be combined with alternative automated approaches to (i) model oligomeric systems and (ii) create NMR-based structure determination pipeline. To show its practical applicability, we emphasize on the computational requirements and performance of CS-Rosetta for protein targets of varying molecular weight and complexity. Finally, we discuss the current Python interface, which enables easy execution of protocols for rapid and accurate high-resolution structure determination.

An order-to-disorder structural switch activates the FoxM1 transcription factor.

Intrinsically disordered transcription factor transactivation domains (TADs) function through structural plasticity, adopting ordered conformations when bound to transcriptional co-regulators. Many transcription factors contain a negative regulatory domain (NRD) that suppresses recruitment of transcriptional machinery through autoregulation of the TAD. We report the solution structure of an autoinhibited NRD-TAD complex within FoxM1, a critical activator of mitotic gene expression. We observe that while both the FoxM1 NRD and TAD are primarily intrinsically disordered domains, they associate and adopt a structured conformation. We identify how Plk1 and Cdk kinases cooperate to phosphorylate FoxM1, which releases the TAD into a disordered conformation that then associates with the TAZ2 or KIX domains of the transcriptional co-activator CBP. Our results support a mechanism of FoxM1 regulation in which the TAD undergoes switching between disordered and different ordered structures.

Chemical shift-based methods in NMR structure determination.

Chemical shifts are highly sensitive probes harnessed by NMR spectroscopists and structural biologists as conformational parameters to characterize a range of biological molecules. Traditionally, assignment of chemical shifts has been a labor-intensive process requiring numerous samples and a suite of multidimensional experiments. Over the past two decades, the development of complementary computational approaches has bolstered the analysis, interpretation and utilization of chemical shifts for elucidation of high resolution protein and nucleic acid structures. Here, we review the development and application of chemical shift-based methods for structure determination with a focus on ab initio fragment assembly, comparative modeling, oligomeric systems, and automated assignment methods. Throughout our discussion, we point out practical uses, as well as advantages and caveats, of using chemical shifts in structure modeling. We additionally highlight (i) hybrid methods that employ chemical shifts with other types of NMR restraints (residual dipolar couplings, paramagnetic relaxation enhancements and pseudocontact shifts) that allow for improved accuracy and resolution of generated 3D structures, (ii) the utilization of chemical shifts to model the structures of sparsely populated excited states, and (iii) modeling of sidechain conformations. Finally, we briefly discuss the advantages of contemporary methods that employ sparse NMR data recorded using site-specific isotope labeling schemes for chemical shift-driven structure determination of larger molecules. With this review, we aim to emphasize the accessibility and versatility of chemical shifts for structure determination of challenging biological systems, and to point out emerging areas of development that lead us towards the next generation of tools.

De novo design of a non-local ß-sheet protein with high stability and accuracy.

ß-sheet proteins carry out critical functions in biology, and hence are attractive scaffolds for computational protein design. Despite this potential, de novo design of all-ß-sheet proteins from first principles lags far behind the design of all-α or mixed-αß domains owing to their non-local nature and the tendency of exposed ß-strand edges to aggregate. Through study of loops connecting unpaired ß-strands (ß-arches), we have identified a series of structural relationships between loop geometry, side chain directionality and ß-strand length that arise from hydrogen bonding and packing constraints on regular ß-sheet structures. We use these rules to de novo design jellyroll structures with double-stranded ß-helices formed by eight antiparallel ß-strands. The nuclear magnetic resonance structure of a hyperthermostable design closely matched the computational model, demonstrating accurate control over the ß-sheet structure and loop geometry. Our results open the door to the design of a broad range of non-local ß-sheet protein structures.

Automated NMR resonance assignments and structure determination using a minimal set of 4D spectra.

Automated methods for NMR structure determination of proteins are continuously becoming more robust. However, current methods addressing larger, more complex targets rely on analyzing 6-10 complementary spectra, suggesting the need for alternative approaches. Here, we describe 4D-CHAINS/autoNOE-Rosetta, a complete pipeline for NOE-driven structure determination of medium- to larger-sized proteins. The 4D-CHAINS algorithm analyzes two 4D spectra recorded using a single, fully protonated protein sample in an iterative ansatz where common NOEs between different spin systems supplement conventional through-bond connectivities to establish assignments of sidechain and backbone resonances at high levels of completeness and with a minimum error rate. The 4D-CHAINS assignments are then used to guide automated assignment of long-range NOEs and structure refinement in autoNOE-Rosetta. Our results on four targets ranging in size from 15.5 to 27.3 kDa illustrate that the structures of proteins can be determined accurately and in an unsupervised manner in a matter of days.

A Recurrent Mutation in Anaplastic Lymphoma Kinase with Distinct Neoepitope Conformations.

The identification of recurrent human leukocyte antigen (HLA) neoepitopes driving T cell responses against tumors poses a significant bottleneck in the development of approaches for precision cancer therapeutics. Here, we employ a bioinformatics method, Prediction of T Cell Epitopes for Cancer Therapy, to analyze sequencing data from neuroblastoma patients and identify a recurrent anaplastic lymphoma kinase mutation (ALK R1275Q) that leads to two high affinity neoepitopes when expressed in complex with common HLA alleles. Analysis of the X-ray structures of the two peptides bound to HLA-B*15:01 reveals drastically different conformations with measurable changes in the stability of the protein complexes, while the self-epitope is excluded from binding due to steric hindrance in the MHC groove. To evaluate the range of HLA alleles that could display the ALK neoepitopes, we used structure-based Rosetta comparative modeling calculations, which accurately predict several additional high affinity interactions and compare our results with commonly used prediction tools. Subsequent determination of the X-ray structure of an HLA-A*01:01 bound neoepitope validates atomic features seen in our Rosetta models with respect to key residues relevant for MHC stability and T cell receptor recognition. Finally, MHC tetramer staining of peripheral blood mononuclear cells from HLA-matched donors shows that the two neoepitopes are recognized by CD8+ T cells. This work provides a rational approach toward high-throughput identification and further optimization of putative neoantigen/HLA targets with desired recognition features for cancer immunotherapy.