A cell-free antigen processing system informs HIV-1 epitope selection and vaccine design.

Distinct CD4+ T cell epitopes have been associated with spontaneous control of HIV-1 replication, but analysis of antigen-dependent factors that influence epitope selection is lacking. To examine these factors, we used a cell-free antigen processing system that incorporates soluble HLA-DR (DR1), HLA-DM (DM), cathepsins, and full-length protein antigens for epitope identification by LC-MS/MS. HIV-1 Gag, Pol, Env, Vif, Tat, Rev, and Nef were examined using this system. We identified 35 novel epitopes, including glycopeptides. Epitopes from smaller HIV-1 proteins mapped to regions of low protein stability and higher solvent accessibility. HIV-1 antigens associated with limited CD4+ T cell responses were processed efficiently, while some protective epitopes were inefficiently processed. 55% of epitopes obtained from cell-free processing induced memory CD4+ T cell responses in HIV-1+ donors, including eight of 19 novel epitopes tested. Thus, an in vitro processing system utilizing the components of Class II processing reveals factors influencing epitope selection of HIV-1 and represents an approach to understanding epitope selection from non-HIV-1 antigens.

A Thermodynamic Atlas of Proteomes Reveals Energetic Innovation across the Tree of Life.

Protein stability is a fundamental molecular property enabling organisms to adapt to their biological niches. How this is facilitated and whether there are kingdom specific or more general universal strategies are unknown. A principal obstacle to addressing this issue is that the vast majority of proteins lack annotation, specifically thermodynamic annotation, beyond the amino acid and chromosome information derived from genome sequencing. To address this gap and facilitate future investigation into large-scale patterns of protein stability and dynamics within and between organisms, we applied a unique ensemble-based thermodynamic characterization of protein folds to a substantial portion of extant sequenced genomes. Using this approach, we compiled a database resource focused on the position-specific variation in protein stability. Interrogation of the database reveals: 1) domains of life exhibit distinguishing thermodynamic features, with eukaryotes particularly different from both archaea and bacteria; 2) the optimal growth temperature of an organism is proportional to the average apolar enthalpy of its proteome; 3) intrinsic disorder content is also proportional to the apolar enthalpy (but unexpectedly not the predicted stability at 25 °C); and 4) secondary structure and global stability information of individual proteins is extractable. We hypothesize that wider access to residue-specific thermodynamic information of proteomes will result in deeper understanding of mechanisms driving functional adaptation and protein evolution. Our database is free for download at https://afc-science.github.io/thermo-env-atlas/ (last accessed January 18, 2022).

Tumor Susceptibility Gene 101 Regulates the Glucocorticoid Receptor through Disorder-Mediated Allostery.

Tumor susceptibility gene 101 (TSG101) is involved in endosomal maturation and has been implicated in the transcriptional regulation of several steroid hormone receptors, although a detailed characterization of such regulation has yet to be conducted. Here we directly measure binding of TSG101 to one steroid hormone receptor, the glucocorticoid receptor (GR). Using biophysical and cellular assays, we show that the coiled-coil domain of TSG101 (1) binds and folds the disordered N-terminal domain of the GR, (2) upon binding improves the DNA binding of the GR in vitro, and (3) enhances the transcriptional activity of the GR in vivo. Our findings suggest that TSG101 is a bona fide transcriptional co-regulator of the GR and reveal how the underlying thermodynamics affect the function of the GR.

Conserved allosteric ensembles in disordered proteins using TROSY/anti-TROSY R2-filtered spectroscopy.

Defining the role of intrinsic disorder in proteins in the myriad of biological processes with which it is involved represents a significant goal in modern biophysics. Toward this end, NMR is uniquely suited for molecular studies of dynamic and disordered regions, but studying these regions in concert with their more structured domains and binding partners presents spectroscopic challenges. Here, we investigate the interactions between the structured and disordered regions of the human glucocorticoid receptor (GR). To do this, we developed an NMR strategy that relies on a novel relaxation filter for the simultaneous study of structured and unstructured regions. Using this approach, we conducted a comparative analysis of three translational isoforms of GR containing a folded DNA-binding domain (DBD) and two disordered regions that flank the DBD, one of which varies in size in the different isoforms. Notably, we were able to assign resonances that had previously been inaccessible because of the spectral complexity of the translational isoforms, which in turn allowed us to 1) identify a region of the structured DBD that undergoes significant changes in the local chemical environment in the presence of the disordered region and 2) determine differences in the conformational ensembles of the disordered regions of the translational isoforms. Furthermore, an ensemble-based thermodynamic analysis of the isoforms reveals conserved patterns of stability within the N-terminal domain of GR that persist despite low sequence conservation. These studies provide an avenue for further investigations of the mechanistic underpinnings of the functional relevance of the translational isoforms of GR while also providing a general NMR strategy for studying systems containing both structured and disordered regions.

Hidden dynamic signatures drive substrate selectivity in the disordered phosphoproteome.

Phosphorylation sites are hyperabundant in the eukaryotic disordered proteome, suggesting that conformational fluctuations play a major role in determining to what extent a kinase interacts with a particular substrate. In biophysical terms, substrate selectivity may be determined not just by the structural-chemical complementarity between the kinase and its protein substrates but also by the free energy difference between the conformational ensembles that are, or are not, recognized by the kinase. To test this hypothesis, we developed a statistical-thermodynamics-based informatics framework, which allows us to probe for the contribution of equilibrium fluctuations to phosphorylation, as evaluated by the ability to predict Ser/Thr/Tyr phosphorylation sites in the disordered proteome. Essential to this framework is a decomposition of substrate sequence information into two types: vertical information encoding conserved kinase specificity motifs and horizontal information encoding substrate conformational equilibrium that is embedded, but often not apparent, within position-specific conservation patterns. We find not only that conformational fluctuations play a major role but also that they are the dominant contribution to substrate selectivity. In fact, the main substrate classifier distinguishing selectivity is the magnitude of change in local compaction of the disordered chain upon phosphorylation of these mostly singly phosphorylated sites. In addition to providing fundamental insights into the consequences of phosphorylation across the proteome, our approach provides a statistical-thermodynamic strategy for partitioning any sequence-based search into contributions from structural-chemical complementarity and those from changes in conformational equilibrium.

Networked Communication between Polymerase and Exonuclease Active Sites in Human Mitochondrial DNA Polymerase.

High fidelity human mitochondrial DNA polymerase (Pol γ) contains two active sites, a DNA polymerization site (pol) and a 3'-5' exonuclease site (exo) for proofreading. Although separated by 35 Å, coordination between the pol and exo sites is crucial to high fidelity replication. The biophysical mechanisms for this coordination are not completely understood. To understand the communication between the two active sites, we used a statistical-mechanical model of the protein ensemble to calculate the energetic landscape and local stability. We compared a series of structures of Pol γ, complexed with primer/template DNA, and either a nucleotide substrate or a series of nucleotide analogues, which are differentially incorporated and excised by pol and exo activity. Despite the nucleotide or its analogues being bound in the pol, Pol γ residue stability varied across the protein, particularly in the exo domain. This suggests that substrate presence in the pol can be "sensed" in the exo domain. Consistent with this hypothesis, in silico mutations made in one active site mutually perturbed the energetics of the other. To identify specific regions of the polymerase that contributed to this communication, we constructed an allosteric network connectivity map that further demonstrates specific pol-exo cooperativity. Thus, a cooperative network underlies energetic connectivity. We propose that Pol γ and other dual-function polymerases exploit an energetic coupling network that facilitates domain-domain communication to enhance discrimination between correct and incorrect nucleotides.

Dynamic allostery can drive cold adaptation in enzymes.

Adaptation of organisms to environmental niches is a hallmark of evolution. One prevalent example is that of thermal adaptation, in which two descendants evolve at different temperature extremes1,2. Underlying the physiological differences between such organisms are changes in enzymes that catalyse essential reactions 3 , with orthologues from each organism undergoing adaptive mutations that preserve similar catalytic rates at their respective physiological temperatures4,5. The sequence changes responsible for these adaptive differences, however, are often at surface-exposed sites distant from the substrate-binding site, leaving the active site of the enzyme structurally unperturbed6,7. How such changes are allosterically propagated to the active site, to modulate activity, is not known. Here we show that entropy-tuning changes can be engineered into distal sites of Escherichia coli adenylate kinase, allowing us to quantitatively assess the role of dynamics in determining affinity, turnover and the role in driving adaptation. The results not only reveal a dynamics-based allosteric tuning mechanism, but also uncover a spatial separation of the control of key enzymatic parameters. Fluctuations in one mobile domain (the LID) control substrate affinity, whereas dynamic attenuation in the other domain (the AMP-binding domain) affects rate-limiting conformational changes that govern enzyme turnover. Dynamics-based regulation may thus represent an elegant, widespread and previously unrealized evolutionary adaptation mechanism that fine-tunes biological function without altering the ground state structure. Furthermore, because rigid-body conformational changes in both domains were thought to be rate limiting for turnover8,9, these adaptation studies reveal a new model for understanding the relationship between dynamics and turnover in adenylate kinase.

Ensemble allosteric model: energetic frustration within the intrinsically disordered glucocorticoid receptor.

Allostery is an important regulatory phenomenon enabling precise control of biological function. Initial understanding of allostery was gained from seminal work on conformational changes exhibited by structured proteins. Within the last decade, protein allostery has also been demonstrated to occur within intrinsically disordered proteins. This emerging concept of disorder-mediated allostery can be usefully understood in the context of a thermodynamic ensemble. The advantage of this ensemble allosteric model is that it unifies the explanations of allostery occurring within both structured and disordered proteins. One central finding from this model is that energetic coupling, the transmission of a signal between separate regions (or domains) of a protein, is maximized when one or more domains are disordered. This is due to a disorder-order transition that contributes additional coupling energy to the allosteric system through formation of a molecular interaction surface or interface. A second key finding is that multiple interfaces may constructively or destructively interfere with each other, resulting in a new form of allosteric regulation called 'energetic frustration'. Articulating protein allostery in terms of the thermodynamic ensemble permits formulation of experimentally testable hypotheses which can increase fundamental understanding and direct drug-design efforts. These ideas are illustrated here with the specific case of human glucocorticoid receptor, a medically important multi-domain allosteric protein that contains both structured and disordered regions and exemplifies 'energetic frustration'.This article is part of a discussion meeting issue 'Allostery and molecular machines'.

Genetically tunable frustration controls allostery in an intrinsically disordered transcription factor.

Intrinsically disordered proteins (IDPs) present a functional paradox because they lack stable tertiary structure, but nonetheless play a central role in signaling, utilizing a process known as allostery. Historically, allostery in structured proteins has been interpreted in terms of propagated structural changes that are induced by effector binding. Thus, it is not clear how IDPs, lacking such well-defined structures, can allosterically affect function. Here, we show a mechanism by which an IDP can allosterically control function by simultaneously tuning transcriptional activation and repression, using a novel strategy that relies on the principle of 'energetic frustration'. We demonstrate that human glucocorticoid receptor tunes this signaling in vivo by producing translational isoforms differing only in the length of the disordered region, which modulates the degree of frustration. We expect this frustration-based model of allostery will prove to be generally important in explaining signaling in other IDPs.

The role of negative selection in protein evolution revealed through the energetics of the native state ensemble.

Knowing the determinants of conformational specificity is essential for understanding protein structure, stability, and fold evolution. To address this issue, a novel statistical measure of energetic compatibility between sequence and structure was developed using an experimentally validated model of the energetics of the native state ensemble. This approach successfully matched sequences from a diverse subset of the human proteome to their respective folds. Unexpectedly, significant energetic compatibility between ostensibly unrelated sequences and structures was also observed. Interrogation of these matches revealed a general framework for understanding the origins of conformational specificity within a proteome: specificity is a complex function of both the ability of a sequence to adopt folds other than the native, and ability of a fold to accommodate sequences other than the native. The regional variation in energetic compatibility indicates that the compatibility is dominated by incompatibility of sequence for alternative fold segments, suggesting that evolution of protein sequences has involved substantial negative selection, with certain segments serving as "gatekeepers" that presumably prevent alternative structures. Beyond these global trends, a size dependence exists in the degree to which the energetic compatibility is determined from negative selection, with smaller proteins displaying more negative selection. This partially explains how short sequences can adopt unique folds, despite the higher probability in shorter proteins for small numbers of mutations to increase compatibility with other folds. In providing evolutionary ground rules for the thermodynamic relationship between sequence and fold, this framework imparts valuable insight for rational design of unique folds or fold switches.

The ensemble nature of allostery.

Allostery is the process by which biological macromolecules (mostly proteins) transmit the effect of binding at one site to another, often distal, functional site, allowing for regulation of activity. Recent experimental observations demonstrating that allostery can be facilitated by dynamic and intrinsically disordered proteins have resulted in a new paradigm for understanding allosteric mechanisms, which focuses on the conformational ensemble and the statistical nature of the interactions responsible for the transmission of information. Analysis of allosteric ensembles reveals a rich spectrum of regulatory strategies, as well as a framework to unify the description of allosteric mechanisms from different systems.

A horizontal alignment tool for numerical trend discovery in sequence data: application to protein hydropathy.

An algorithm is presented that returns the optimal pairwise gapped alignment of two sets of signed numerical sequence values. One distinguishing feature of this algorithm is a flexible comparison engine (based on both relative shape and absolute similarity measures) that does not rely on explicit gap penalties. Additionally, an empirical probability model is developed to estimate the significance of the returned alignment with respect to randomized data. The algorithm's utility for biological hypothesis formulation is demonstrated with test cases including database search and pairwise alignment of protein hydropathy. However, the algorithm and probability model could possibly be extended to accommodate other diverse types of protein or nucleic acid data, including positional thermodynamic stability and mRNA translation efficiency. The algorithm requires only numerical values as input and will readily compare data other than protein hydropathy. The tool is therefore expected to complement, rather than replace, existing sequence and structure based tools and may inform medical discovery, as exemplified by proposed similarity between a chlamydial ORFan protein and bacterial colicin pore-forming domain. The source code, documentation, and a basic web-server application are available.

Conformational heterogeneity within the LID domain mediates substrate binding to Escherichia coli adenylate kinase: function follows fluctuations.

Proteins exist as dynamic ensembles of molecules, implying that protein amino acid sequences evolved to code for both the ground state structure as well as the entire energy landscape of excited states. Accumulating theoretical and experimental evidence suggests that enzymes use such conformational fluctuations to facilitate allosteric processes important for substrate binding and possibly catalysis. This phenomenon can be clearly demonstrated in Escherichia coli adenylate kinase, where experimentally observed local unfolding of the LID subdomain, as opposed to a more commonly postulated rigid-body opening motion, is related to substrate binding. Because "entropy promoting" glycine mutations designed to increase specifically the local unfolding of the LID domain also affect substrate binding, changes in the excited energy landscape effectively tune the function of this enzyme without changing the ground state structure or the catalytic site. Thus, additional thermodynamic information, above and beyond the single folded structure of an enzyme-substrate complex, is likely required for a full and quantitative understanding of how enzymes work.

Structural and energetic basis of allostery.

Allostery is a biological phenomenon of fundamental importance in regulation and signaling, and efforts to understand this process have led to the development of numerous models. In spite of individual successes in understanding the structural determinants of allostery in well-documented systems, much less success has been achieved in identifying a set of quantitative and transferable ground rules that provide an understanding of how allostery works. Are there organizing principles that allow us to relate structurally different proteins, or are the determinants of allostery unique to each system? Using an ensemble-based model, we show that allosteric phenomena can be formulated in terms of conformational free energies of the cooperative elements in a protein and the coupling interactions between them. Interestingly, the resulting allosteric ground rules provide a framework to reconcile observations that challenge purely structural models of site-to-site coupling, including (a) allostery in the absence of pathways of structural distortions, (b) allostery in the absence of any structural change, and (c) the ability of allosteric ligands to act as agonists under some circumstances and antagonists under others. The ensemble view of allostery that emerges provides insights into the energetic prerequisites of site-to-site coupling and thus into how allostery works.

The role of protein conformational fluctuations in allostery, function, and evolution.

It is now well-known that proteins exist at equilibrium as ensembles of conformational states rather than as unique static structures. Here we review from an ensemble perspective important biological effects of such spontaneous fluctuations on protein allostery, function, and evolution. However, rather than present a thorough literature review on each subject, we focus instead on connecting these phenomena through the ensemble-based experimental, theoretical, and computational investigations from our laboratory over the past decade. Special emphasis is given to insights that run counter to some of the prevailing ideas that have emerged over the past 40 years of structural biology research. For instance, when proteins are viewed as conformational ensembles rather than as single structures, the commonly held notion of an allosteric pathway as an obligate series of individual structural distortions loses its meaning. Instead, allostery can result from energetic linkage between distal sites as one Boltzmann distribution of states transitions to another. Additionally, the emerging principles from this ensemble view of proteins have proven surprisingly useful in describing the role of intrinsic disorder in inter-domain communication, functional adaptation mediated by mutational control of fluctuations, and evolutionary conservation of the energetics of protein stability.

Investigating homology between proteins using energetic profiles.

Accumulated experimental observations demonstrate that protein stability is often preserved upon conservative point mutation. In contrast, less is known about the effects of large sequence or structure changes on the stability of a particular fold. Almost completely unknown is the degree to which stability of different regions of a protein is generally preserved throughout evolution. In this work, these questions are addressed through thermodynamic analysis of a large representative sample of protein fold space based on remote, yet accepted, homology. More than 3,000 proteins were computationally analyzed using the structural-thermodynamic algorithm COREX/BEST. Estimated position-specific stability (i.e., local Gibbs free energy of folding) and its component enthalpy and entropy were quantitatively compared between all proteins in the sample according to all-vs.-all pairwise structural alignment. It was discovered that the local stabilities of homologous pairs were significantly more correlated than those of non-homologous pairs, indicating that local stability was indeed generally conserved throughout evolution. However, the position-specific enthalpy and entropy underlying stability were less correlated, suggesting that the overall regional stability of a protein was more important than the thermodynamic mechanism utilized to achieve that stability. Finally, two different types of statistically exceptional evolutionary structure-thermodynamic relationships were noted. First, many homologous proteins contained regions of similar thermodynamics despite localized structure change, suggesting a thermodynamic mechanism enabling evolutionary fold change. Second, some homologous proteins with extremely similar structures nonetheless exhibited different local stabilities, a phenomenon previously observed experimentally in this laboratory. These two observations, in conjunction with the principal conclusion that homologous proteins generally conserved local stability, may provide guidance for a future thermodynamically informed classification of protein homology.

An energetic representation of protein architecture that is independent of primary and secondary structure.

Protein fold classification often assumes that similarity in primary, secondary, or tertiary structure signifies a common evolutionary origin. However, when similarity is not obvious, it is sometimes difficult to conclude that particular proteins are completely unrelated. Clearly, a set of organizing principles that is independent of traditional classification could be valuable in linking different structural motifs and identifying common ancestry from seemingly disparate folds. Here, a four-dimensional ensemble-based energetic space spanned by a diverse set of proteins was defined and its characteristics were contrasted with those of Cartesian coordinate space. Eigenvector decomposition of this energetic space revealed the dominant physical processes contributing to the more or less stable regions of a protein. Unexpectedly, those processes were identical for proteins with different secondary structure content and were also identical among different amino-acid types. The implications of these results are twofold. First, it indicates that excited conformational states comprising the protein native state ensemble, largely invisible upon inspection of the high-resolution structure, are the major determinant of the energetic space. Second, it suggests that folds dissimilar in sequence or structure could nonetheless be energetically similar if their respective excited conformational states are considered, one example of which was observed in the N-terminal region of the Arc repressor switch mutant. Taken together, these results provide a surface area-based framework for understanding folds in energetic terms, a framework that may eventually yield a means of identifying common ancestry among structurally dissimilar proteins.

Energetic profiling of protein folds.

Current protein classification methods treat high-resolution structures as static entities. However, experiments have well documented the dynamic nature of proteins. With knowledge that thermodynamic fluctuations around the high-resolution structure contribute to a more physically accurate and biologically meaningful picture of a protein, the concept of a protein's energetic profile is introduced. It is demonstrated on a large scale that energetic profiles are both diagnostic of a protein fold and evolutionarily relevant. Development of Structural Thermodynamic Ensemble-based Protein Homology (STEPH), an algorithm that searches for local similarities between energetic profiles, constitutes a first step towards a long-term goal of our laboratory to integrate thermodynamic information into protein-fold classification approaches.