Mixed, nonclassical behavior in a classic allosteric protein.

Allostery is a major driver of biological processes requiring coordination. Thus, it is one of the most fundamental and remarkable phenomena in nature, and there is motivation to understand and manipulate it to a multitude of ends. Today, it is often described in terms of two phenomenological models proposed more than a half-century ago involving only T(tense) or R(relaxed) conformations. Here, methyl-based NMR provides extensive detail on a dynamic T to R switch in the classical dimeric allosteric protein, yeast chorismate mutase (CM), that occurs in the absence of substrate, but only with the activator bound. Switching of individual subunits is uncoupled based on direct observation of mixed TR states in the dimer. This unique finding excludes both classic models and solves the paradox of a coexisting hyperbolic binding curve and highly skewed substrate-free T-R equilibrium. Surprisingly, structures of the activator-bound and effector-free forms of CM appear the same by NMR, providing another example of the need to account for dynamic ensembles. The apo enzyme, which has a sigmoidal activity profile, is shown to switch, not to R, but to a related high-energy state. Thus, the conformational repertoire of CM does not just change as a matter of degree depending on the allosteric input, be it effector and/or substrate. Rather, the allosteric model appears to completely change in different contexts, which is only consistent with modern ensemble-based frameworks.

BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening.

Deep learning methods that predict protein-ligand binding have recently been used for structure-based virtual screening. Many such models have been trained using protein-ligand complexes with known crystal structures and activities from the PDBBind data set. However, because PDBbind only includes 20K complexes, models typically fail to generalize to new targets, and model performance is on par with models trained with only ligand information. Conversely, the ChEMBL database contains a wealth of chemical activity information but includes no information about binding poses. We introduce BigBind, a data set that maps ChEMBL activity data to proteins from the CrossDocked data set. BigBind comprises 583 K ligand activities and includes 3D structures of the protein binding pockets. Additionally, we augmented the data by adding an equal number of putative inactives for each target. Using this data, we developed Banana (basic neural network for binding affinity), a neural network-based model to classify active from inactive compounds, defined by a 10 µM cutoff. Our model achieved an AUC of 0.72 on BigBind's test set, while a ligand-only model achieved an AUC of 0.59. Furthermore, Banana achieved competitive performance on the LIT-PCBA benchmark (median EF1% 1.81) while running 16,000 times faster than molecular docking with Gnina. We suggest that Banana, as well as other models trained on this data set, will significantly improve the outcomes of prospective virtual screening tasks.

STOPLIGHT: A Hit Scoring Calculator.

We introduce STOPLIGHT, a web portal to assist medicinal chemists in prioritizing hits from screening campaigns and the selection of compounds for optimization. STOPLIGHT incorporates services to assess 6 physiochemical and structural properties, 6 assay liabilities, and 11 pharmacokinetic properties, for any small molecule represented by its SMILES string. We briefly describe each service and illustrate the utility of this portal with a case study. The STOPLIGHT portal provides a user-friendly tool to guide hit selection in early drug discovery campaigns, whereby compounds with unfavorable properties can be quickly recognized and eliminated.

Accurate ligand-protein docking in CASP15 using the ClusPro LigTBM server.

In the ligand prediction category of CASP15, the challenge was to predict the positions and conformations of small molecules binding to proteins that were provided as amino acid sequences or as models generated by the AlphaFold2 program. For most targets, we used our template-based ligand docking program ClusPro ligTBM, also implemented as a public server available at https://ligtbm.cluspro.org/. Since many targets had multiple chains and a number of ligands, several templates, and some manual interventions were required. In a few cases, no templates were found, and we had to use direct docking using the Glide program. Nevertheless, ligTBM was shown to be a very useful tool, and by any ranking criteria, our group was ranked among the top five best-performing teams. In fact, all the best groups used template-based docking methods. Thus, it appears that the AlphaFold2-generated models, despite the high accuracy of the predicted backbone, have local differences from the x-ray structure that make the use of direct docking methods more challenging. The results of CASP15 confirm that this limitation can be frequently overcome by homology-based docking.

Tyrosinase-Catalyzed Peptide Macrocyclization for mRNA Display.

mRNA display of macrocyclic peptides has proven itself to be a powerful technique to discover high-affinity ligands for a protein target. However, only a limited number of cyclization chemistries are known to be compatible with mRNA display. Tyrosinase is a copper-dependent oxidase that oxidizes tyrosine phenol to an electrophilic o-quinone, which is readily attacked by cysteine thiol. Here we show that peptides containing tyrosine and cysteine are rapidly cyclized upon tyrosinase treatment. Characterization of the cyclization reveals it to be widely applicable to multiple macrocycle sizes and scaffolds. We combine tyrosinase-mediated cyclization with mRNA display to discover new macrocyclic ligands targeting melanoma-associated antigen A4 (MAGE-A4). These macrocycles potently inhibit the MAGE-A4 binding axis with nanomolar IC50 values. Importantly, macrocyclic ligands show clear advantage over noncyclized analogues with ∼40-fold or greater decrease in IC50 values.

Mapping co-regulatory interactions among ligand-binding sites in ryanodine receptor 1.

Ryanodine receptor 1 (RyR1) is an intracellular calcium ion (Ca2+ ) release channel required for skeletal muscle contraction. Although cryo-electron microscopy identified binding sites of three coactivators Ca2+ , ATP, and caffeine (CFF), the mechanism of co-regulation and synergy of these activators is unknown. Here, we report allosteric connections among the three ligand-binding sites and pore region in (i) Ca2+ bound-closed, (ii) ATP/CFF bound-closed, (iii) Ca2+ /ATP/CFF bound-closed, and (iv) Ca2+ /ATP/CFF bound-open RyR1 states. We identified two dominant networks of interactions that mediate communication between the Ca2+ -binding site and pore region in Ca2+ bound-closed state, which partially overlapped with the pore communications in ATP/CFF bound-closed RyR1 state. In Ca2+ /ATP/CFF bound-closed and -open RyR1 states, co-regulatory interactions were analogous to communications in the Ca2+ bound-closed and ATP/CFF bound-closed states. Both ATP- and CFF-binding sites mediate communication between the Ca2+ -binding site and the pore region in Ca2+ /ATP/CFF bound-open RyR1 structure. We conclude that Ca2+ , ATP, and CFF propagate their effects to the pore region through a network of overlapping interactions that mediate allosteric control and molecular synergy in channel regulation.

Integrated approach to elucidate metal-implant related adverse outcome pathways.

Exogenous metal particles and ions from implant devices are known to cause severe toxic events with symptoms ranging from adverse local tissue reactions to systemic toxicities, potentially leading to the development of cancers, heart conditions, and neurological disorders. Toxicity mechanisms, also known as Adverse Outcome Pathways (AOPs), that explain these metal-induced toxicities are severely understudied. Therefore, we deployed in silico structure- and knowledge-based approaches to identify proteome-level perturbations caused by metals and pathways that link these events to human diseases. We captured 177 structure-based, 347 knowledge-based, and 402 imputed metal-gene/protein relationships for chromium, cobalt, molybdenum, nickel, and titanium. We prioritized 72 proteins hypothesized to directly contact implant surfaces and contribute to adverse outcomes. Results of this exploratory analysis were formalized as structured AOPs. We considered three case studies reflecting the following possible situations: (i) the metal-protein-disease relationship was previously known; (ii) the metal-protein, protein-disease, and metal-disease relationships were individually known but were not linked (as a unified AOP); and (iii) one of three relationships was unknown and was imputed by our methods. These situations were illustrated by case studies on nickel-induced allergy/hypersensitivity, cobalt-induced heart failure, and titanium-induced periprosthetic osteolysis, respectively. All workflows, data, and results are freely available in https://github.com/DnlRKorn/Knowledge_Based_Metallomics/. An interactive view of select data is available at the ROBOKOP Neo4j Browser at http://robokopkg.renci.org/browser/.

Structure of prion ß-oligomers as determined by short-distance crosslinking constraint-guided discrete molecular dynamics simulations.

The conversion of the native monomeric cellular prion protein (PrPC ) into an aggregated pathological ß-oligomeric form (PrPß ) and an infectious form (PrPSc ) is the central element in the development of prion diseases. The structure of the aggregates and the molecular mechanisms of the conformational changes involved in the conversion are still unknown. We applied mass spectrometry combined with chemical crosslinking, hydrogen/deuterium exchange, limited proteolysis, and surface modification for the differential characterization of the native and the urea+acid-converted prion ß-oligomer structures to obtain insights into the mechanisms of conversion and aggregation. For the determination of the structure of the monomer and the dimer unit of the ß-oligomer, we applied a recently-developed approach for de novo protein structure determination which is based on the incorporation of zero-length and short-distance crosslinking data as intra- and inter-protein constraints in discrete molecular dynamics simulations (CL-DMD). Based on all of the structural-proteomics experimental data and the computationally predicted structures of the monomer units, we propose the potential mode of assembly of the ß-oligomer. The proposed ß-oligomer assembly provides a clue on the ß-sheet nucleation site, and how template-based conversion of the native prion molecule occurs, growth of the prion aggregates, and maturation into fibrils may occur.

Conformational ensemble of native α-synuclein in solution as determined by short-distance crosslinking constraint-guided discrete molecular dynamics simulations.

Combining structural proteomics experimental data with computational methods is a powerful tool for protein structure prediction. Here, we apply a recently-developed approach for de novo protein structure determination based on the incorporation of short-distance crosslinking data as constraints in discrete molecular dynamics simulations (CL-DMD) for the determination of conformational ensemble of the intrinsically disordered protein α-synuclein in the solution. The predicted structures were in agreement with hydrogen-deuterium exchange, circular dichroism, surface modification, and long-distance crosslinking data. We found that α-synuclein is present in solution as an ensemble of rather compact globular conformations with distinct topology and inter-residue contacts, which is well-represented by movements of the large loops and formation of few transient secondary structure elements. Non-amyloid component and C-terminal regions were consistently found to contain ß-structure elements and hairpins.

Inter-Active Site Communication Mediated by the Dimer Interface ß-Sheet in the Half-the-Sites Enzyme, Thymidylate Synthase.

Thymidylate synthase (TS) is a dimeric enzyme conserved in all life forms that exhibits the allosteric feature of half-the-sites activity. Neither the reason for nor the mechanism of this phenomenon is understood. We used a combined nuclear magnetic resonance (NMR) and molecular dynamics approach to study a stable intermediate preceding hydride transfer, which is the rate-limiting and half-the-sites step. In NMR titrations with ligands leading to this intermediate, we measured chemical shifts of the apoenzyme (lig0), the saturated holoenzyme (lig2), and the typically elusive singly bound (lig1) states. Approximately 40 amides showed quartet patterns providing direct NMR evidence of coupling between the active site and probes >30 Å away in the distal subunit. Quartet peak patterns have symmetrical character, indicating reciprocity in communicating the first and second binding events to the distal protomer. Quartets include key catalytic residues and map to the dimer interface ß-sheet, which also represents the shortest path between the two active sites. Simulations corroborate the coupling observed in solution in that there is excellent overlap between quartet residues and main-chain atoms having intersubunit cross-correlated motions. Simulations identify five hot spot residues, three of which lie at the kink in the unique ß-bulge abutting the active sites on either end of the sheet. Interstrand cross-correlated motions become more organized and pronounced as the enzyme progresses from lig0 to lig1 and ultimately lig2. Coupling in the apparently symmetrical complex has implications for half-the-sites reactivity and potentially resolves the paradox of inequivalent TS active sites despite the vast majority of X-ray structures appearing to be symmetrical.

Mapping allosteric linkage to channel gating by extracellular domains in the human epithelial sodium channel.

The epithelial sodium channel (ENaC) mediates sodium absorption in lung, kidney, and colon epithelia. Channels in the ENaC/degenerin family possess an extracellular region that senses physicochemical changes in the extracellular milieu and allosterically regulates the channel opening. Proteolytic cleavage activates the ENaC opening, by the removal of specific segments in the finger domains of the α- and γ ENaC-subunits. Cleavage causes perturbations in the extracellular region that propagate to the channel gate. However, it is not known how the channel structure mediates the propagation of activation signals through the extracellular sensing domains. Here, to identify the structure-function determinants that mediate allosteric ENaC activation, we performed MD simulations, thiol modification of residues substituted by cysteine, and voltage-clamp electrophysiology recordings. Our simulations of an ENaC heterotetramer, α1ßα2γ, in the proteolytically cleaved and uncleaved states revealed structural pathways in the α-subunit that are responsible for ENaC proteolytic activation. To validate these findings, we performed site-directed mutagenesis to introduce cysteine substitutions in the extracellular domains of the α-, ß-, and γ ENaC-subunits. Insertion of a cysteine at the α-subunit Glu557 site, predicted to stabilize a closed state of ENaC, inhibited ENaC basal activity and retarded the kinetics of proteolytic activation by 2-fold. Our results suggest that the lower palm domain of αENaC is essential for ENaC activation. In conclusion, our integrated computational and experimental approach suggests key structure-function determinants for ENaC proteolytic activation and points toward a mechanistic model for the allosteric communication in the extracellular domains of the ENaC/degenerin family channels.

High-speed atomic force microscopy reveals structural dynamics of α-synuclein monomers and dimers.

α-Synuclein (α-syn) is the major component of the intraneuronal inclusions called Lewy bodies, which are the pathological hallmark of Parkinson's disease. α-Syn is capable of self-assembly into many different species, such as soluble oligomers and fibrils. Even though attempts to resolve the structures of the protein have been made, detailed understanding about the structures and their relationship with the different aggregation steps is lacking, which is of interest to provide insights into the pathogenic mechanism of Parkinson's disease. Here we report the structural flexibility of α-syn monomers and dimers in an aqueous solution environment as probed by single-molecule time-lapse high-speed AFM. In addition, we present the molecular basis for the structural transitions using discrete molecular dynamics (DMD) simulations. α-Syn monomers assume a globular conformation, which is capable of forming tail-like protrusions over dozens of seconds. Importantly, a globular monomer can adopt fully extended conformations. Dimers, on the other hand, are less dynamic and show a dumbbell conformation that experiences morphological changes over time. DMD simulations revealed that the α-syn monomer consists of several tightly packed small helices. The tail-like protrusions are also helical with a small ß-sheet, acting as a "hinge". Monomers within dimers have a large interfacial interaction area and are stabilized by interactions in the non-amyloid central (NAC) regions. Furthermore, the dimer NAC-region of each α-syn monomer forms a ß-rich segment. Moreover, NAC-regions are located in the hydrophobic core of the dimer.

HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries.

Recent rapid expansion of make-on-demand, purchasable, chemical libraries comprising dozens of billions or even trillions of molecules has challenged the efficient application of traditional structure-based virtual screening methods that rely on molecular docking. We present a novel computational methodology termed HIDDEN GEM (HIt Discovery using Docking ENriched by GEnerative Modeling) that greatly accelerates virtual screening. This workflow uniquely integrates machine learning, generative chemistry, massive chemical similarity searching and molecular docking of small, selected libraries in the beginning and the end of the workflow. For each target, HIDDEN GEM nominates a small number of top-scoring virtual hits prioritized from ultra-large chemical libraries. We have benchmarked HIDDEN GEM by conducting virtual screening campaigns for 16 diverse protein targets using Enamine REAL Space library comprising 37 billion molecules. We show that HIDDEN GEM yields the highest enrichment factors as compared to state of the art accelerated virtual screening methods, while requiring the least computational resources. HIDDEN GEM can be executed with any docking software and employed by users with limited computational resources.

An Improved Metric and Benchmark for Assessing the Performance of Virtual Screening Models.

Structure-based virtual screening (SBVS) is a key workflow in computational drug discovery. SBVS models are assessed by measuring the enrichment of known active molecules over decoys in retrospective screens. However, the standard formula for enrichment cannot estimate model performance on very large libraries. Additionally, current screening benchmarks cannot easily be used with machine learning (ML) models due to data leakage. We propose an improved formula for calculating VS enrichment and introduce the BayesBind benchmarking set composed of protein targets that are structurally dissimilar to those in the BigBind training set. We assess current models on this benchmark and find that none perform appreciably better than a KNN baseline. We publicly release the BayesBind benchmark at https://github.com/molecularmodelinglab/bigbind.

Utilizing Low-Dimensional Molecular Embeddings for Rapid Chemical Similarity Search.

Nearest neighbor-based similarity searching is a common task in chemistry, with notable use cases in drug discovery. Yet, some of the most commonly used approaches for this task still leverage a brute-force approach. In practice this can be computationally costly and overly time-consuming, due in part to the sheer size of modern chemical databases. Previous computational advancements for this task have generally relied on improvements to hardware or dataset-specific tricks that lack generalizability. Approaches that leverage lower-complexity searching algorithms remain relatively underexplored. However, many of these algorithms are approximate solutions and/or struggle with typical high-dimensional chemical embeddings. Here we evaluate whether a combination of low-dimensional chemical embeddings and a k-d tree data structure can achieve fast nearest neighbor queries while maintaining performance on standard chemical similarity search benchmarks. We examine different dimensionality reductions of standard chemical embeddings as well as a learned, structurally-aware embedding-SmallSA-for this task. With this framework, searches on over one billion chemicals execute in less than a second on a single CPU core, five orders of magnitude faster than the brute-force approach. We also demonstrate that SmallSA achieves competitive performance on chemical similarity benchmarks.

Synthesis of 5-Benzylamino and 5-Alkylamino-Substituted Pyrimido[4,5-c]quinoline Derivatives as CSNK2A Inhibitors with Antiviral Activity.

A series of 5-benzylamine-substituted pyrimido[4,5-c]quinoline derivatives of the CSNK2A chemical probe SGC-CK2-2 were synthesized with the goal of improving kinase inhibitor cellular potency and antiviral phenotypic activity while maintaining aqueous solubility. Among the range of analogs, those bearing electron-withdrawing (4c and 4g) or donating (4f) substituents on the benzyl ring as well as introduction of non-aromatic groups such as the cyclohexylmethyl (4t) were shown to maintain CSNK2A activity. The CSNK2A activity was also retained with N-methylation of SGC-CK2-2, but α-methyl substitution of the benzyl substituent led to a 10-fold reduction in potency. CSNK2A inhibition potency was restored with indene-based compound 4af, with activity residing in the S-enantiomer (4ag). Analogs with the highest CSNK2A potency showed good activity for inhibition of Mouse Hepatitis Virus (MHV) replication. Conformational analysis indicated that analogs with the best CSNK2A inhibition (4t, 4ac, and 4af) exhibited smaller differences between their ground state conformation and their predicted binding pose. Analogs with reduced activity (4ad, 4ae, and 4ai) required more substantial conformational changes from their ground state within the CSNK2A protein pocket.

Structure Activity of ß-Amidomethyl Vinyl Sulfones as Covalent Inhibitors of Chikungunya nsP2 Cysteine Protease with Anti-alphavirus Activity.

Despite their widespread impact on human health there are no approved drugs for combating alphavirus infections. The heterocyclic ß-aminomethyl vinyl sulfone RA-0002034 (1a) is a potent irreversible covalent inhibitor of the alphavirus nsP2 cysteine protease with broad spectrum antiviral activity. Analogs of 1a that varied each of three regions of the molecule were synthesized to establish structure-activity relationships for inhibition of Chikungunya (CHIKV) nsP2 protease and viral replication. The covalent warhead was highly sensitive to modifications of the sulfone or vinyl substituents. However, numerous alterations to the core 5-membered heterocycle and its aryl substituent were well tolerated and several analogs were identified that enhanced CHIKV nsP2 binding. For example, the 4-cyanopyrazole analog 8d exhibited a kinact /Ki ratio >10,000 M-1s-1. 3-Arylisoxazole was identified an isosteric replacement for the 5-membered heterocycle, which circumvented the intramolecular cyclization that complicated the synthesis of pyrazole-based inhibitors like 1a. The accumulated structure-activity data was used to build a ligand-based model of the enzyme active site, which can be used to guide the design of covalent nsP2 protease inhibitors as potential therapeutics against alphaviruses.

Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles.

Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.

PLANTAIN: Diffusion-inspired Pose Score Minimization for Fast and Accurate Molecular Docking.

Molecular docking aims to predict the 3D pose of a small molecule in a protein binding site. Traditional docking methods predict ligand poses by minimizing a physics-inspired scoring function. Recently, a diffusion model has been proposed that iteratively refines a ligand pose. We combine these two approaches by training a pose scoring function in a diffusion-inspired manner. In our method, PLANTAIN, a neural network is used to develop a very fast pose scoring function. We parameterize a simple scoring function on the fly and use L-BFGS minimization to optimize an initially random ligand pose. Using rigorous benchmarking practices, we demonstrate that our method achieves state-of-the-art performance while running ten times faster than the next-best method. We release PLANTAIN publicly and hope that it improves the utility of virtual screening workflows.

Conserved coronavirus proteins as targets of broad-spectrum antivirals.

Coronaviruses are a class of single-stranded, positive-sense RNA viruses that have caused three major outbreaks over the past two decades: Middle East respiratory syndrome-related coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus (SARS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). All outbreaks have been associated with significant morbidity and mortality. In this study, we have identified and explored conserved binding sites in the key coronavirus proteins for the development of broad-spectrum direct acting anti-coronaviral compounds and validated the significance of this conservation for drug discovery with existing experimental data. We have identified four coronaviral proteins with highly conserved binding site sequence and 3D structure similarity: PLpro, Mpro, nsp10-nsp16 complex(methyltransferase), and nsp15 endoribonuclease. We have compiled all available experimental data for known antiviral medications inhibiting these targets and identified compounds active against multiple coronaviruses. The identified compounds representing potential broad-spectrum antivirals include: GC376, which is active against six viral Mpro (out of six tested, as described in research literature); mycophenolic acid, which is active against four viral PLpro (out of four); and emetine, which is active against four viral RdRp (out of four). The approach described in this study for coronaviruses, which combines the assessment of sequence and structure conservation across a viral family with the analysis of accessible chemical structure - antiviral activity data, can be explored for the development of broad-spectrum drugs for multiple viral families.