p300 is an obligate integrator of combinatorial transcription factor inputs.

Transcription coactivators are proteins or protein complexes that mediate transcription factor (TF) function. However, they lack DNA-binding capacity, prompting the question of how they engage target loci. Three non-exclusive hypotheses have been posited: coactivators are recruited by complexing with TFs, by binding histones through epigenetic reader domains, or by partitioning into condensates through their extensive intrinsically disordered regions. Using p300 as a prototypical coactivator, we systematically mutated its annotated domains and show by single-molecule tracking in live U2OS cells that coactivator-chromatin binding depends entirely on combinatorial binding of multiple TF-interaction domains. Furthermore, we demonstrate that acetyltransferase activity opposes p300-chromatin association and that the N-terminal TF-interaction domains regulate that activity. Single TF-interaction domains are insufficient for chromatin binding and regulation of catalytic activity, implying a principle that we speculate could broadly apply to eukaryotic gene regulation: a TF must act in coordination with other TFs to recruit coactivator activity.

p300 Is an Obligate Integrator of Combinatorial Transcription Factor Inputs.

Transcription coactivators are proteins or protein complexes that mediate transcription factor (TF) function. However, they lack DNA binding capacity, prompting the question of how they engage target loci. Three non-exclusive hypotheses have been posited: coactivators are recruited by complexing with TFs, by binding histones through epigenetic reader domains, or by partitioning into phase-separated compartments through their extensive intrinsically disordered regions (IDRs). Using p300 as a prototypical coactivator, we systematically mutated its annotated domains and show by single-molecule tracking in live cells that coactivator-chromatin binding depends entirely on combinatorial binding of multiple TF-interaction domains. Furthermore, we demonstrate that acetyltransferase activity negatively impacts p300-chromatin association and that the N-terminal TF-interaction domains regulate that activity. Single TF-interaction domains are insufficient for both chromatin binding and regulation of catalytic activity, implying a principle that could broadly inform eukaryotic gene regulation: a TF must act in coordination with other TFs to recruit coactivator activity.

Identification of a Putative α-synuclein Radioligand Using an in silico Similarity Search.

PURPOSE: Previous studies from our lab utilized an ultra-high throughput screening method to identify compound 1 as a small molecule that binds to alpha-synuclein (α-synuclein) fibrils. The goal of the current study was to conduct a similarity search of 1 to identify structural analogs having improved in vitro binding properties for this target that could be labeled with radionuclides for both in vitro and in vivo studies for measuring α-synuclein aggregates. METHODS: Using 1 as a lead compound in a similarity search, isoxazole derivative 15 was identified to bind to α-synuclein fibrils with high affinity in competition binding assays. A photocrosslinkable version was used to confirm binding site preference. Derivative 21, the iodo-analog of 15, was synthesized, and subsequently radiolabeled isotopologs [125I]21 and [11C]21 were successfully synthesized for use in in vitro and in vivo studies, respectively. [125I]21 was used in radioligand binding studies in post-mortem Parkinson's disease (PD) and Alzheimer's disease (AD) brain homogenates. In vivo imaging of an α-synuclein mouse model and non-human primates was performed with [11C]21. RESULTS: In silico molecular docking and molecular dynamic simulation studies for a panel of compounds identified through a similarity search, were shown to correlate with Ki values obtained from in vitro binding studies. Improved affinity of isoxazole derivative 15 for α-synuclein binding site 9 was indicated by photocrosslinking studies with CLX10. Design and successful (radio)synthesis of iodo-analog 21 of isoxazole derivative 15 enabled further in vitro and in vivo evaluation. Kd values obtained in vitro with [125I]21 for α-synuclein and Aß42 fibrils were 0.48 ± 0.08 nM and 2.47 ± 1.30 nM, respectively. [125I]21 showed higher binding in human postmortem PD brain tissue compared with AD tissue, and low binding in control brain tissue. Lastly, in vivo preclinical PET imaging showed elevated retention of [11C]21 in PFF-injected mouse brain. However, in PBS-injected control mouse brain, slow washout of the tracer indicates high non-specific binding. [11C]21 showed high initial brain uptake in a healthy non-human primate, followed by fast washout that may be caused by rapid metabolic rate (21% intact [11C]21 in blood at 5 min p.i.). CONCLUSION: Through a relatively simple ligand-based similarity search, we identified a new radioligand that binds with high affinity (<10 nM) to α-synuclein fibrils and PD tissue. Although the radioligand has suboptimal selectivity for α-synuclein towards Aß and high non-specific binding, we show here that a simple in silico approach is a promising strategy to identify novel ligands for target proteins in the CNS with the potential to be radiolabeled for PET neuroimaging studies.

"Structure"-function relationships in eukaryotic transcription factors: The role of intrinsically disordered regions in gene regulation.

Many principles of bacterial gene regulation have been foundational to understanding mechanisms of eukaryotic transcription. However, stark structural and functional differences exist between eukaryotic and bacterial transcription factors that complicate inferring properties of the eukaryotic system from that of bacteria. Here, we review those differences, focusing on the impact of intrinsically disordered regions on the thermodynamic and kinetic parameters governing eukaryotic transcription factor interactions-both with other proteins and with chromatin. The prevalence of unstructured domains in eukaryotic transcription factors as well as their known impact on function call for more sophisticated knowledge of what mechanisms they support. Using the evidence available to date, we posit that intrinsically disordered regions are necessary for the complex and integrative functions of eukaryotic transcription factors and that only by understanding their rich biochemistry can we develop a deep molecular understanding of their regulatory mechanisms.

The transcription factor activity gradient (TAG) model: contemplating a contact-independent mechanism for enhancer-promoter communication.

How distal cis-regulatory elements (e.g., enhancers) communicate with promoters remains an unresolved question of fundamental importance. Although transcription factors and cofactors are known to mediate this communication, the mechanism by which diffusible molecules relay regulatory information from one position to another along the chromosome is a biophysical puzzle-one that needs to be revisited in light of recent data that cannot easily fit into previous solutions. Here we propose a new model that diverges from the textbook enhancer-promoter looping paradigm and offer a synthesis of the literature to make a case for its plausibility, focusing on the coactivator p300.

Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine.

The incorporation of unnatural amino acids (Uaas) has provided an avenue for novel chemistries to be explored in biological systems. However, the successful application of Uaas is often hampered by site-specific impacts on protein yield and solubility. Although previous efforts to identify features which accurately capture these site-specific effects have been unsuccessful, we have developed a set of novel Rosetta Custom Score Functions and alternative Empirical Score Functions that accurately predict the effects of acridon-2-yl-alanine (Acd) incorporation on protein yield and solubility. Acd-containing mutants were simulated in PyRosetta, and machine learning (ML) was performed using either the decomposed values of the Rosetta energy function, or changes in residue contacts and bioinformatics. Using these feature sets, which represent Rosetta score function specific and bioinformatics-derived terms, ML models were trained to predict highly abstract experimental parameters such as mutant protein yield and solubility and displayed robust performance on well-balanced holdouts. Model feature importance analyses demonstrated that terms corresponding to hydrophobic interactions, desolvation, and amino acid angle preferences played a pivotal role in predicting tolerance of mutation to Acd. Overall, this work provides evidence that the application of ML to features extracted from simulated structural models allow for the accurate prediction of diverse and abstract biological phenomena, beyond the predictivity of traditional modeling and simulation approaches.

Improved Modeling of Thioamide FRET Quenching by Including Conformational Restriction and Coulomb Coupling.

Thioamide-containing amino acids have been shown to quench a wide range of fluorophores through distinct mechanisms. Here, we quantitatively analyze the mechanism through which the thioamide functional group quenches the fluorescence of p-cyanophenylalanine (Cnf), tyrosine (Tyr), and tryptophan (Trp). By comparing PyRosetta simulations to published experiments performed on polyproline ruler peptides, we corroborate previous findings that both Cnf and Tyr quenching occurs via Förster resonance energy transfer (FRET), while Trp quenching occurs through an alternate mechanism such as Dexter transfer. Additionally, optimization of the peptide sampling scheme and comparison of thioamides attached to the peptide backbone and side chain revealed that the significant conformational restriction associated with the thioamide moiety results in a high sensitivity of the apparent FRET efficiency to underlying conformational differences. Moreover, by computing FRET efficiencies from structural models using a variety of approaches, we find that quantitative accuracy in the role of Coulomb coupling is required to explain contributions to the observed quenching efficiency from individual structures on a detailed level. Last, we demonstrate that these additional considerations improve our ability to predict thioamide quenching efficiencies observed during binding of thioamide-labeled peptides to fluorophore-labeled variants of calmodulin.

Rosetta Machine Learning Models Accurately Classify Positional Effects of Thioamides on Proteolysis.

Thioamide substitutions of the peptide backbone have been shown to stabilize therapeutic and imaging peptides toward proteolysis. In order to rationally design thioamide modifications, we have developed a novel Rosetta custom score function to classify thioamide positional effects on proteolysis in substrates of serine and cysteine proteases. Peptides of interest were docked into proteases using the FlexPepDock application in Rosetta. Docked complexes were modified to contain thioamides parametrized through the creation of custom atom types in Rosetta based on ab intio simulations. Thioamide complexes were simulated, and the resultant structural complexes provided features for machine learning classification as the decomposed values of the Rosetta score function. An ensemble, majority voting model was developed to be a robust predictor of previously unpublished thioamide proteolysis holdout data. Theoretical control simulations with pseudo-atoms that modulate only one physical characteristic of the thioamide show differential effects on prediction accuracy by the optimized voting classification model. These pseudo-atom model simulations, as well as statistical analyses of the full thioamide simulations, implicate steric effects on peptide binding as being primarily responsible for thioamide positional effects on proteolytic resistance.

Correction: Rosetta custom score functions accurately predict ΔΔG of mutations at protein-protein interfaces using machine learning.

Correction for 'Rosetta custom score functions accurately predict ΔΔG of mutations at protein-protein interfaces using machine learning' by Sumant R. Shringari et al., Chem. Commun., 2020, 56, 6774-6777, DOI: .

A Unified De Novo Approach for Predicting the Structures of Ordered and Disordered Proteins.

As recognition of the abundance and relevance of intrinsically disordered proteins (IDPs) continues to grow, demand increases for methods that can rapidly predict the conformational ensembles populated by these proteins. To date, IDP simulations have largely been dominated by molecular dynamics (MD) simulations, which require significant compute times and/or complex hardware. Recent developments in MD have afforded methods capable of simulating both ordered and disordered proteins, yet to date, accurate fold prediction from a sequence has been dominated by Monte Carlo (MC)-based methods such as Rosetta. To overcome the limitations of current approaches in IDP simulation using Rosetta while maintaining its utility for modeling folded domains, we developed PyRosetta-based algorithms that allow for the accurate de novo prediction of proteins across all degrees of foldedness along with structural ensembles of disordered proteins. Our simulations have accuracy comparable to state-of-the-art MD with vastly reduced computational demands.

Rosetta custom score functions accurately predict ΔΔG of mutations at protein-protein interfaces using machine learning.

Protein-protein interfaces play essential roles in a variety of biological processes and many therapeutic molecules are targeted at these interfaces. However, accurate predictions of the effects of interfacial mutations to identify "hotspots" have remained elusive despite the myriad of modeling and machine learning methods tested. Here, for the first time, we demonstrate that nonlinear reweighting of energy terms from Rosetta, through the use of machine learning, exhibits improved predictability of ΔΔG values associated with interfacial mutations.

Synthesis and characterization of high affinity fluorogenic α-synuclein probes.

Fluorescent small molecules are powerful tools for imaging α-synuclein pathology in vitro and in vivo. In this work, we explore benzofuranone as a potential scaffold for the design of fluorescent α-synuclein probes. These compounds have high affinity for α-synuclein, show fluorescent turn-on upon binding to fibrils, and display different binding to Lewy bodies, Lewy neurites and glial cytoplasmic inclusion pathologies in post-mortem brain tissue. These studies not only reveal the potential of benzofuranone compounds as α-synuclein specific fluorescent probes, but also have implications for the ways in which α-synucleinopathies are conformationally different and display distinct small molecule binding sites.

Identification of a nanomolar affinity α-synuclein fibril imaging probe by ultra-high throughput in silico screening.

Small molecules that bind with high affinity and specificity to fibrils of the α-synuclein (αS) protein have the potential to serve as positron emission tomography (PET) imaging probes to aid in the diagnosis of Parkinson's disease and related synucleinopathies. To identify such molecules, we employed an ultra-high throughput in silico screening strategy using idealized pseudo-ligands termed exemplars to identify compounds for experimental binding studies. For the top hit from this screen, we used photo-crosslinking to confirm its binding site and studied the structure-activity relationship of its analogs to develop multiple molecules with nanomolar affinity for αS fibrils and moderate specificity for αS over Aß fibrils. Lastly, we demonstrated the potential of the lead analog as an imaging probe by measuring binding to αS-enriched homogenates from mouse brain tissue using a radiolabeled analog of the identified molecule. This study demonstrates the validity of our powerful new approach to the discovery of PET probes for challenging molecular targets.

Fluorescent Probes for Studying Thioamide Positional Effects on Proteolysis Reveal Insight into Resistance to Cysteine Proteases.

Thioamide substitutions of the peptide backbone have been shown to reduce proteolytic degradation, and this property can be used to generate competitive protease inhibitors and to stabilize peptides toward degradation in vivo. Here, we present a straightforward sensor design that allows a systematic study of the positional effects of thioamide substitution by using real-time fluorescence. Thioamide scanning in peptide substrates of five papain family cysteine proteases demonstrates that a thioamide at or near the scissile bond can slow proteolysis in all cases, but that the magnitude of the effects varies with position and protease in spite of high sequence homology. Mechanistic investigation of papain proteolysis reveals that the thioamide effects derive from reductions in both affinity (KM ) and turnover number (kcat ). Computational modeling allows these effects to be understood based on disruption of key enzyme-substrate hydrogen bonds, providing a model for future rational use of thioamides to confer cysteine protease resistance.

Cyclized NDGA modifies dynamic α-synuclein monomers preventing aggregation and toxicity.

Growing evidence implicates α-synuclein aggregation as a key driver of neurodegeneration in Parkinson's disease (PD) and other neurodegenerative disorders. Herein, the molecular and structural mechanisms of inhibiting α-synuclein aggregation by novel analogs of nordihydroguaiaretic acid (NDGA), a phenolic dibenzenediol lignan, were explored using an array of biochemical and biophysical methodologies. NDGA analogs induced modest, progressive compaction of monomeric α-synuclein, preventing aggregation into amyloid-like fibrils. This conformational remodeling preserved the dynamic adoption of α-helical conformations, which are essential for physiological membrane interactions. Oxidation-dependent NDGA cyclization was required for the interaction with monomeric α-synuclein. NDGA analog-pretreated α-synuclein did not aggregate even without NDGA-analogs in the aggregation mixture. Strikingly, NDGA-pretreated α-synuclein suppressed aggregation of naïve untreated aggregation-competent monomeric α-synuclein. Further, cyclized NDGA reduced α-synuclein-driven neurodegeneration in Caenorhabditis elegans. The cyclized NDGA analogs may serve as a platform for the development of small molecules that stabilize aggregation-resistant α-synuclein monomers without interfering with functional conformations yielding potential therapies for PD and related disorders.

Systematic Evaluation of Soluble Protein Expression Using a Fluorescent Unnatural Amino Acid Reveals No Reliable Predictors of Tolerability.

Improvements in genetic code expansion have made preparing proteins with diverse functional groups almost routine. Nonetheless, unnatural amino acids (Uaas) pose theoretical burdens on protein solubility, and determinants of position-specific tolerability to Uaas remain underexplored. To broadly examine associations, we systematically assessed the effect of substituting the fluorescent Uaa, acridonylalanine, at more than 50 chemically, evolutionarily, and structurally diverse residues in two bacterial proteins: LexA and RecA. Surprisingly, properties that ostensibly contribute to Uaa tolerability-such as conservation, hydrophobicity, or accessibility-demonstrated no consistent correlations with resulting protein solubility. Instead, solubility is closely dependent on the location of the substitution within the overall tertiary structure, suggesting that intrinsic properties of protein domains, and not individual positions, are stronger determinants of Uaa tolerability. Consequently, those who seek to install Uaas in new target proteins should consider broadening, rather than narrowing, the types of residues screened for Uaa incorporation.

Alpha Synuclein Fibrils Contain Multiple Binding Sites for Small Molecules.

The fibrillary aggregation of the protein alpha synuclein (Asyn) is a hallmark of Parkinson's disease, and the identification of small molecule binding sites on fibrils is essential to the development of diagnostic imaging probes. A series of molecular modeling, photoaffinity labeling, mass spectrometry, and radioligand binding studies were conducted on Asyn fibrils. The results of these studies revealed the presence of three different binding sites within fibrillar Asyn capable of binding small molecules with moderate to high affinity. A knowledge of the amino acid residues in these binding sites will be important in the design of high affinity probes capable of imaging fibrillary species of Asyn.

Dithioamide substitutions in proteins: effects on thermostability, peptide binding, and fluorescence quenching in calmodulin.

Thioamide substitutions in the backbones of proteins can modulate their structure and thermostability, or serve as spectroscopic probes in fluorescence quenching experiments. Using native chemical ligation, we have produced the first examples of a protein (calmodulin) containing two thioamides. Dithioamide variants were made to explore the effects of combining stabilizing, neutral, and destabilizing single thioamide substitutions. One of the dithioamide calmodulin variants exhibited stabilization greater than any monothioamide variant, although the effect could not easily be anticipated from the results of single substitutions. Each of the calmodulin variants retained the ability to bind a target peptide, and the dithioamide proteins exhibited an increase in fluorescence quenching of tryptophan relative to their single thioamide counterparts. These results show that multiply thioamidated proteins can be synthesized, and that properly placed thioamides can be used to increase protein thermostability or enhance fluorecsence quenching in peptide binding experiments.

Using a FRET Library with Multiple Probe Pairs To Drive Monte Carlo Simulations of α-Synuclein.

We describe a strategy for experimentally-constraining computational simulations of intrinsically disordered proteins (IDPs), using α-synuclein, an IDP with a central role in Parkinson's disease pathology, as an example. Previously, data from single-molecule Förster Resonance Energy Transfer (FRET) experiments have been effectively utilized to generate experimentally constrained computational models of IDPs. However, the fluorophores required for single-molecule FRET experiments are not amenable to the study of short-range (<30 Å) interactions. Using ensemble FRET measurements allows one to acquire data from probes with multiple distance ranges, which can be used to constrain Monte Carlo simulations in PyRosetta. To appropriately employ ensemble FRET data as constraints, we optimized the shape and weight of constraining potentials to afford ensembles of structures that are consistent with experimental data. We also used this approach to examine the structure of α-synuclein in the presence of the compacting osmolyte trimethylamine-N-oxide. Despite significant compaction imparted by 2 M trimethylamine-N-oxide, the underlying ensemble of α-synuclein remains largely disordered and capable of aggregation, also in agreement with experimental data. These proof-of-concept experiments demonstrate that our modeling protocol enables one to efficiently generate experimentally constrained models of IDPs that incorporate atomic-scale detail, allowing one to study an IDP under a variety of conditions.

Improving the Fluorescent Probe Acridonylalanine Through a Combination of Theory and Experiment.

Acridonylalanine (Acd) is a useful fluorophore for studying proteins by fluorescence spectroscopy, but it can potentially be improved by being made longer wavelength or brighter. Here, we report the synthesis of Acd core derivatives and their photophysical characterization. We also performed ab initio calculations of the absorption and emission spectra of Acd derivatives, which agree well with experimental measurements. The amino acid aminoacridonylalanine (Aad) was synthesized in forms appropriate for genetic incorporation and peptide synthesis. We show that Aad is a superior FRET acceptor to Acd in a peptide cleavage assay, and that Aad can be activated by an aminoacyl tRNA synthetase for genetic incorporation. Together, these results show that we can use computation to design enhanced Acd derivatives which can be used in peptides and proteins.