Exploring Multi-Subsite Binding Pockets in Proteins: DEEP-STD NMR Fingerprinting and Molecular Dynamics Unveil a Cryptic Subsite at the GM1 Binding Pocket of Cholera Toxin B

Glycosaminoglycans: What Remains To Be Deciphered?

Glycosaminoglycans (GAGs) are complex polysaccharides exhibiting a vast structural diversity and fulfilling various functions mediated by thousands of interactions in the extracellular matrix, at the cell surface, and within the cells where they have been detected in the nucleus. It is known that the chemical groups attached to GAGs and GAG conformations comprise "glycocodes" that are not yet fully deciphered. The molecular context also matters for GAG structures and functions, and the influence of the structure and functions of the proteoglycan core proteins on sulfated GAGs and vice versa warrants further investigation. The lack of dedicated bioinformatic tools for mining GAG data sets contributes to a partial characterization of the structural and functional landscape and interactions of GAGs. These pending issues will benefit from the development of new approaches reviewed here, namely (i) the synthesis of GAG oligosaccharides to build large and diverse GAG libraries, (ii) GAG analysis and sequencing by mass spectrometry (e.g., ion mobility-mass spectrometry), gas-phase infrared spectroscopy, recognition tunnelling nanopores, and molecular modeling to identify bioactive GAG sequences, biophysical methods to investigate binding interfaces, and to expand our knowledge and understanding of glycocodes governing GAG molecular recognition, and (iii) artificial intelligence for in-depth investigation of GAGomic data sets and their integration with proteomics.

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2

Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic's evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (Mpro), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. This work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic "warhead" to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of Mpro with micromolar affinities (KI of 5.27 μM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering KI and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.

Exploring multivalent carbohydrate-protein interactions by NMR

Nuclear Magnetic Resonance (NMR) has been widely employed to assess diverse features of glycan-protein molecular recognition events. Different types of qualitative and quantitative information at different degrees of resolution and complexity can be extracted from the proper application of the available NMR-techniques. In fact, affinity, structural, kinetic, conformational, and dynamic characteristics of the binding process are available. Nevertheless, except in particular cases, the affinity of lectin-sugar interactions is weak, mostly at the low mM range. This feature is overcome in biological processes by using multivalency, thus augmenting the strength of the binding. However, the application of NMR methods to monitor multivalent lectin-glycan interactions is intrinsically challenging. It is well known that when large macromolecular complexes are formed, the NMR signals disappear from the NMR spectrum, due to the existence of fast transverse relaxation, related to the large size and exchange features. Indeed, at the heart of the molecular recognition event, the associated free-bound chemical exchange process for both partners takes place in a particular timescale. Thus, these factors have to be considered and overcome. In this review article, we have distinguished, in a subjective manner, the existence of multivalent presentations in the glycan or in the lectin. From the glycan perspective, we have also considered whether multiple epitopes of a given ligand are presented in the same linear chain of a saccharide (i.e., poly-LacNAc oligosaccharides) or decorating different arms of a multiantennae scaffold, either natural (as in multiantennae N-glycans) or synthetic (of dendrimer or polymer nature). From the lectin perspective, the presence of an individual binding site at every monomer of a multimeric lectin may also have key consequences for the binding event at different levels of complexity.

Inter-Ligand STD NMR: An Efficient 1D NMR Approach to Probe Relative Orientation of Ligands in a Multi-Subsite Protein Binding Pocket

In recent years, Saturation Transfer Difference NMR (STD NMR) has been proven to be a powerful and versatile ligand-based NMR technique to elucidate crucial aspects in the investigation of protein-ligand complexes. Novel STD NMR approaches relying on “multi-frequency” irradiation have enabled us to even elucidate specific ligand-amino acid interactions and explore the binding of fragments in previously unknown binding subsites. Exploring multi-subsite protein binding pockets is especially important in Fragment Based Drug Discovery (FBDD) to design leads of increased specificity and efficacy. We hereby propose a novel multi-frequency STD NMR approach based on direct irradiation of one of the ligands in a multi-ligand binding process, to probe the vicinity and explore the relative orientation of fragments in adjacent binding sub-sites, which we called Inter-Ligand STD NMR (IL-STD NMR). We proved its applicability on (i) a standard protein-ligand system commonly used for ligand-observed NMR benchmarking: Naproxen as bound to Bovine Serum Albumin, and (ii) the biologically relevant system of Cholera Toxin Subunit B and two inhibitors adjacently bound within the GM1 binding site. Relative to Inter-Ligand NOE (ILOE), the current state-of-the-art methodology to probe relative orientations of adjacent ligands, IL-STD NMR requires about one tenth of the experimental time and protein consumption, making it a competitive methodology with the potential to be applied in the pharmaceutical industries.

Investigation of protein-ligand complexes by ligand-based NMR methods

Molecular recognition is at the base of all biological events and its knowledge at atomic level is pivotal in the development of new drug design approaches. NMR spectroscopy is one of the most widely used technique to detect and characterize transient ligand-receptor interactions in solution. In particular, ligand-based NMR approaches, including NOE-based NMR techniques, diffusion experiments and relaxation methods, are excellent tools to investigate how ligands interact with their receptors. Here we describe the key structural information that can be achieved on binding processes thanks to the combined used of advanced NMR and computational methods. Saturation Transfer Difference NMR (STD-NMR), WaterLOGSY, diffusion- and relaxation-based experiments, together with tr-NOE techniques allow, indeed, to investigate the ligand behavior when bound to a receptor, determining, among others, the epitope map of the ligand and its bioactive conformation. The combination of these NMR techniques with computational methods, including docking, molecular dynamics and CORCEMA-ST analysis, permits to define and validate an accurate 3D model of protein-ligand complexes.