Structural Elucidation of Ubiquitin via Gas-Phase Ion/Ion Cross-Linking Reactions Using Sodium-Cationized Reagents Coupled with Infrared Multiphoton Dissociation.

Accurate structural determination of proteins is critical to understanding their biological functions and the impact of structural disruption on disease progression. Gas-phase cross-linking mass spectrometry (XL-MS) via ion/ion reactions between multiply charged protein cations and singly charged cross-linker anions has previously been developed to obtain low-resolution structural information on proteins. This method significantly shortens experimental time relative to conventional solution-phase XL-MS but has several technical limitations: (1) the singly deprotonated N-hydroxysulfosuccinimide (sulfo-NHS)-based cross-linker anions are restricted to attachment at neutral amine groups of basic amino acid residues and (2) analyzing terminal cross-linked fragment ions is insufficient to unambiguously localize sites of linker attachment. Herein, we demonstrate enhanced structural information for alcohol-denatured A-state ubiquitin obtained from an alternative gas-phase XL-MS approach. Briefly, singly sodiated ethylene glycol bis(sulfosuccinimidyl succinate) (sulfo-EGS) cross-linker anions enable covalent cross-linking at both ammonium and amine groups. Additionally, covalently modified internal fragment ions, along with terminal b-/y-type counterparts, improve the determination of linker attachment sites. Molecular dynamics simulations validate experimentally obtained gas-phase conformations of denatured ubiquitin. This method has identified four cross-linking sites across 8+ ubiquitin, including two new sites in the N-terminal region of the protein that were originally inaccessible in prior gas-phase XL approaches. The two N-terminal cross-linking sites suggest that the N-terminal half of ubiquitin is more compact in gas-phase conformations. By comparison, the two C-terminal linker sites indicate the signature transformation of this region of the protein from a native to a denatured conformation. Overall, the results suggest that the solution-phase secondary structures of the A-state ubiquitin are conserved in the gas phase. This method also provides sufficient sensitivity to differentiate between two gas-phase conformers of the same charge state with subtle structural variations.

A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries.

Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High-throughput experimental approaches narrow down possible binding partners but often include false positives. Furthermore, they provide no information about what the binding region is (e.g., the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competitive Binding Assay (AF-CBA) to identify proteins that bind a target of interest from a pull-down experiment and the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide-protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies of AF and AF-CBA to help users identify scenarios where the approach will be most useful. Given the method's speed and accuracy, we anticipate its broad applicability to identify binding epitope regions among potential partners, setting the stage for experimental verification.

Revolutionizing Peptide-Based Drug Discovery: Advances in the Post-AlphaFold Era.

Peptide-based drugs offer high specificity, potency, and selectivity. However, their inherent flexibility and differences in conformational preferences between their free and bound states create unique challenges that have hindered progress in effective drug discovery pipelines. The emergence of AlphaFold (AF) and Artificial Intelligence (AI) presents new opportunities for enhancing peptide-based drug discovery. We explore recent advancements that facilitate a successful peptide drug discovery pipeline, considering peptides' attractive therapeutic properties and strategies to enhance their stability and bioavailability. AF enables efficient and accurate prediction of peptide-protein structures, addressing a critical requirement in computational drug discovery pipelines. In the post-AF era, we are witnessing rapid progress with the potential to revolutionize peptide-based drug discovery such as the ability to rank peptide binders or classify them as binders/non-binders and the ability to design novel peptide sequences. However, AI-based methods are struggling due to the lack of well-curated datasets, for example to accommodate modified amino acids or unconventional cyclization. Thus, physics-based methods, such as docking or molecular dynamics simulations, continue to hold a complementary role in peptide drug discovery pipelines. Moreover, MD-based tools offer valuable insights into binding mechanisms, as well as the thermodynamic and kinetic properties of complexes. As we navigate this evolving landscape, a synergistic integration of AI and physics-based methods holds the promise of reshaping the landscape of peptide-based drug discovery.

Sifting Through the Noise: A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries.

Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High-throughput experimental approaches narrow down possible binding partners, but often include false positives. Furthermore, they provide no information about what the binding region is (e.g. the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competition Assay (AF-CBA) to identify proteins that bind a target of interest from a pull-down experiment, along with the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide-protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies to AF and AF-CBA, to help users identify scenarios where the approach will be most useful. Given the speed and accuracy of the methodology, we expect it to be generally applicable to facilitate target selection for experimental verification starting from high-throughput protein libraries.

Outcomes of the EMDataResource Cryo-EM Ligand Modeling Challenge.

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein/nucleic-acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: E. coli beta-galactosidase with inhibitor, SARS-CoV-2 RNA-dependent RNA polymerase with covalently bound nucleotide analog, and SARS-CoV-2 ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. We found that (1) the quality of submitted ligand models and surrounding atoms varied, as judged by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics, and contact scores, and (2) a composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

GENERALIST: A latent space based generative model for protein sequence families.

Generative models of protein sequence families are an important tool in the repertoire of protein scientists and engineers alike. However, state-of-the-art generative approaches face inference, accuracy, and overfitting- related obstacles when modeling moderately sized to large proteins and/or protein families with low sequence coverage. Here, we present a simple to learn, tunable, and accurate generative model, GENERALIST: GENERAtive nonLInear tenSor-factorizaTion for protein sequences. GENERALIST accurately captures several high order summary statistics of amino acid covariation. GENERALIST also predicts conservative local optimal sequences which are likely to fold in stable 3D structure. Importantly, unlike current methods, the density of sequences in GENERALIST-modeled sequence ensembles closely resembles the corresponding natural ensembles. Finally, GENERALIST embeds protein sequences in an informative latent space. GENERALIST will be an important tool to study protein sequence variability.

Charge-controlled Pd catalysis enables the meta-C-H activation and olefination of arenes.

The regioselective C-H activation of arenes remains one of the most promising techniques for accessing highly important functionalized motifs. Such functionalizations can generally be achieved through directed and non-directed processes. The directed approach requires a covalently attached directing group (DG) on the substrate to induce reactivity and selectivity and therefore intrinsically leaves a functional group at the point of attachment within the molecule, even after the tailored DG has been removed. Conversely, non-directed methods typically suffer from regioselectivity issues, especially for unbiased substrates. Herein, we report a unique approach that employs weak charge-charge and charge-dipole interactions to enable the meta-selective activation and olefination of arenes to address these challenges in Pd catalysis. The charged moiety can easily be converted to uncharged simple arenes by hydrogenation or cross-coupling. In-depth mechanistic studies prove that the charge is responsible for the observed selectivity. We expect our studies to be generalizable and thereby enable further regioselective transformations.

Hybrid computational methods combining experimental information with molecular dynamics.

A goal of structural biology is to understand how macromolecules carry out their biological roles by identifying their metastable states, mechanisms of action, pathways leading to conformational changes, and the thermodynamic and kinetic relationships between those states. Integrative modeling brings structural insights into systems where traditional structure determination approaches cannot help. We focus on the synergies and challenges of integrative modeling combining experimental data with molecular dynamics simulations.

CryoFold 2.0: Cryo-EM Structure Determination with MELD.

Cryo-electron microscopy data are becoming more prevalent and accessible at higher resolution levels, leading to the development of new computational tools to determine the atomic structure of macromolecules. However, while existing tools adapted from X-ray crystallography are suitable for the highest-resolution maps, new tools are needed for lower-resolution levels and to account for map heterogeneity. In this article, we introduce CryoFold 2.0, an integrative physics-based approach that combines Bayesian inference and the ability to handle multiple data sources with the molecular dynamics flexible fitting (MDFF) approach to determine the structures of macromolecules by using cryo-EM data. CryoFold 2.0 is incorporated into the MELD (modeling employing limited data) plugin, resulting in a pipeline that is more computationally efficient and accurate than running MELD or MDFF alone. The approach requires fewer computational resources and shorter simulation times than the original CryoFold, and it minimizes manual intervention. We demonstrate the effectiveness of the approach on eight different systems, highlighting its various benefits.

Structure Determination of Challenging Protein-Peptide Complexes Combining NMR Chemical Shift Data and Molecular Dynamics Simulations.

Intrinsically disordered regions of proteins often mediate important protein-protein interactions. However, the folding-upon-binding nature of many polypeptide-protein interactions limits the ability of modeling tools to predict the three-dimensional structures of such complexes. To address this problem, we have taken a tandem approach combining NMR chemical shift data and molecular simulations to determine the structures of peptide-protein complexes. Here, we use the MELD (Modeling Employing Limited Data) technique applied to polypeptide complexes formed with the extraterminal domain (ET) of bromo and extraterminal domain (BET) proteins, which exhibit a high degree of binding plasticity. This system is particularly challenging as the binding process includes allosteric changes across the ET receptor upon binding, and the polypeptide binding partners can adopt different conformations (e.g., helices and hairpins) in the complex. In a blind study, the new approach successfully modeled bound-state conformations and binding poses, using only protein receptor backbone chemical shift data, in excellent agreement with experimentally determined structures for moderately tight (Kd ∼100 nM) binders. The hybrid MELD + NMR approach required additional peptide ligand chemical shift data for weaker (Kd ∼250 µM) peptide binding partners. AlphaFold also successfully predicts the structures of some of these peptide-protein complexes. However, whereas AlphaFold can provide qualitative peptide rankings, MELD can directly estimate relative binding affinities. The hybrid MELD + NMR approach offers a powerful new tool for structural analysis of protein-polypeptide complexes involving disorder-to-order transitions upon complex formation, which are not successfully modeled with most other complex prediction methods, providing both the 3D structures of peptide-protein complexes and their relative binding affinities.

Towards rational computational peptide design.

Peptides are prevalent in biology, mediating as many as 40% of protein-protein interactions, and involved in other cellular functions such as transport and signaling. Their ability to bind with high specificity make them promising therapeutical agents with intermediate properties between small molecules and large biologics. Beyond their biological role, peptides can be programmed to self-assembly, and they are already being used for functions as diverse as oligonuclotide delivery, tissue regeneration or as drugs. However, the transient nature of their interactions has limited the number of structures and knowledge of binding affinities available-and their flexible nature has limited the success of computational pipelines that predict the structures and affinities of these molecules. Fortunately, recent advances in experimental and computational pipelines are creating new opportunities for this field. We are starting to see promising predictions of complex structures, thermodynamic and kinetic properties. We believe in the following years this will lead to robust rational peptide design pipelines with success similar to those applied for small molecule drug discovery.

Silver-Free C-H Activation: Strategic Approaches towards Realizing the Full Potential of C-H Activation in Sustainable Organic Synthesis.

The activation of carbon-hydrogen bonds is considered as one of the most attractive techniques in synthetic organic chemistry because it bears the potential to shorten synthetic routes as well as to produce complementary product scopes compared to traditional synthetic strategies. However, many current methods employ silver salts as additives, leading to stoichiometric metal waste and thereby preventing the full potential of C-H activation to be exploited. Therefore, the development of silver-free protocols has recently received increasing attention. Mechanistically, silver can serve various roles in C-H activation and thus, avoiding the use of silver requires different approaches based on the role it serves in a given process. In this Review, we present the comparison of silver-based and silver-free methods. Focusing on the strategic approaches to develop silver-free C-H activation, we provide the reader with the means to develop sustainable methods for C-H activation.

Modelling peptide-protein complexes: docking, simulations and machine learning.

Peptides mediate up to 40% of protein interactions, their high specificity and ability to bind in places where small molecules cannot make them potential drug candidates. However, predicting peptide-protein complexes remains more challenging than protein-protein or protein-small molecule interactions, in part due to the high flexibility peptides have. In this review, we look at the advances in docking, molecular simulations and machine learning to tackle problems related to peptides such as predicting structures, binding affinities or even kinetics. We specifically focus on explaining the number of docking programmes and force fields used in molecular simulations, so a prospective user can have an educated guess as to why choose one modelling tool or another to address their scientific questions.

Simultaneous Assignment and Structure Determination of Proteins From Sparsely Labeled NMR Datasets.

Sparsely labeled NMR samples provide opportunities to study larger biomolecular assemblies than is traditionally done by NMR. This requires new computational tools that can handle the sparsity and ambiguity in the NMR datasets. The MELD (modeling employing limited data) Bayesian approach was assessed to be the best performing in predicting structures from sparsely labeled NMR data in the 13th edition of the Critical Assessment of Structure Prediction (CASP) event-and limitations of the methodology were also noted. In this report, we evaluate the nature and difficulty in modeling unassigned sparsely labeled NMR datasets and report on an improved methodological pipeline leading to higher-accuracy predictions. We benchmark our methodology against the NMR datasets provided by CASP 13.

Palladium-Catalyzed Nondirected Late-Stage C-H Deuteration of Arenes.

We describe a palladium-catalyzed nondirected late-stage deuteration of arenes. Key aspects include the use of D2O as a convenient and easily available deuterium source and the discovery of highly active N,N-bidentate ligands containing an N-acylsulfonamide group. The reported protocol enables high degrees of deuterium incorporation via a reversible C-H activation step and features extraordinary functional group tolerance, allowing for the deuteration of complex substrates. This is exemplified by the late-stage isotopic labeling of various pharmaceutically relevant motifs and related scaffolds. We expect that this method, among other applications, will prove useful as a tool in drug development processes and for mechanistic studies.

CryoFold: determining protein structures and data-guided ensembles from cryo-EM density maps.

Cryo-electron microscopy (EM) requires molecular modeling to refine structural details from data. Ensemble models arrive at low free-energy molecular structures, but are computationally expensive and limited to resolving only small proteins that cannot be resolved by cryo-EM. Here, we introduce CryoFold - a pipeline of molecular dynamics simulations that determines ensembles of protein structures directly from sequence by integrating density data of varying sparsity at 3-5 Å resolution with coarse-grained topological knowledge of the protein folds. We present six examples showing its broad applicability for folding proteins between 72 to 2000 residues, including large membrane and multi-domain systems, and results from two EMDB competitions. Driven by data from a single state, CryoFold discovers ensembles of common low-energy models together with rare low-probability structures that capture the equilibrium distribution of proteins constrained by the density maps. Many of these conformations, unseen by traditional methods, are experimentally validated and functionally relevant. We arrive at a set of best practices for data-guided protein folding that are controlled using a Python GUI.

Catalyst-Controlled Regiodivergent C-H Alkynylation of Thiophenes*.

Alkynes are highly attractive motifs in organic synthesis due to their presence in natural products and bioactive molecules as well as their versatility in a plethora of subsequent transformations. A common procedure to insert alkynes into (hetero)arenes, such as the thiophenes studied herein, consists of a halogenation followed by a Sonogashira cross-coupling. The regioselectivity of this approach depends entirely on the halogenation step. Similarly, direct alkynylations of thiophenes have been described that follow the same regioselectivity patterns. Herein we report the development of a palladium catalyzed C-H activation/alkynylation of thiophenes. The method is applicable to a broad range of thiophene substrates. For 3-substituted substrates where controlling the regioselectivity between the C2 and C5 position is particularly challenging, two sets of reaction conditions enable a regiodivergent reaction, giving access to each regioisomer selectively. Both protocols use the thiophene as limiting reagent and show a broad scope, rendering our method suitable for late-stage modification.

Palladium-Catalyzed meta-C-H Allylation of Arenes: A Unique Combination of a Pyrimidine-Based Template and Hexafluoroisopropanol.

Controlling remote selectivity and delivering novel functionalities at distal positions in arenes are an important endeavor in contemporary organic synthesis. In this vein, template engineering and mechanistic understanding of new functionalization strategies are essential for enhancing the scope of such methods. Herein, meta-C-H allylation of arenes has been achieved with the aid of a palladium catalyst, pyrimidine-based auxiliary, and allyl phosphate. 1,1,1,3,3,3-Hexafluoroisopropanol (HFIP) was found as a critical solvent in this transformation. The role of HFIP throughout the catalytic cycle has been systematically studied. A broad substrate scope with phenethyl ether, phenol, benzylsulfonyl ester, phenethylsulfonyl ester, phenylacetic acid, hydrocinnamic acid, and 2-phenylbenzoic acid derivatives has been demonstrated. Interestingly, conformationally flexible arenes have also been selectively allylated at the meta-position using allyl phosphate. A combination of 1H NMR, 31P NMR, ESI-MS, kinetic experiments, and density functional theory (DFT) computations suggested that reaction proceeds through a ligand-assisted meta-C-H activation, allyl addition forming a Pd-π-allyl complex which is then followed by a turnover determining the C-C bond formation step leading to the meta-allylated product.

Ligand-Enabled γ-C(sp3 )-H Olefination of Free Carboxylic Acids.

We report the ligand-enabled C-H activation/olefination of free carboxylic acids in the γ-position. Through an intramolecular Michael addition, δ-lactones are obtained as products. Two distinct ligand classes are identified that enable the challenging palladium-catalyzed activation of free carboxylic acids in the γ-position. The developed protocol features a wide range of acid substrates and olefin reaction partners and is shown to be applicable on a preparatively useful scale. Insights into the underlying reaction mechanism obtained through kinetic studies are reported.

Sterically Controlled Late-Stage C-H Alkynylation of Arenes.

Phenylacetylenes are key structural motifs in organic chemistry, which find widespread applications in bioactive molecules, synthetic intermediates, functional materials, and reagents. These molecules are typically prepared from prefunctionalized starting materials, e.g. using the Sonogashira coupling, or using directing group-based C-H activation strategies. While highly efficient, these approaches remain limited by their inherent selectivities for specific regioisomers. Herein we present a complementary approach based on an arene-limited nondirected C-H activation. The reaction is predominantly controlled by steric rather than electronic factors and thereby gives access to a complementary product spectrum with respect to traditional methods. A broad scope as well as the suitability of this protocol for late-stage functionalization are demonstrated.