Conservation of Allosteric Ligand Binding Sites in G-Protein Coupled Receptors.

Despite the growing number of G protein-coupled receptor (GPCR) structures, only 39 structures have been cocrystallized with allosteric inhibitors. These structures have been studied by protein mapping using the FTMap server, which determines the clustering of small organic probe molecules distributed on the protein surface. The method has found druggable sites overlapping with the cocrystallized allosteric ligands in 21 GPCR structures. Mapping of Alphafold2 generated models of these proteins confirms that the same sites can be identified without the presence of bound ligands. We then mapped the 394 GPCR X-ray structures available at the time of the analysis (September 2020). Results show that for each of the 21 structures with bound ligands there exist many other GPCRs that have a strong binding hot spot at the same location, suggesting potential allosteric sites in a large variety of GPCRs. These sites cluster at nine distinct locations, and each can be found in many different proteins. However, ligands binding at the same location generally show little or no similarity, and the amino acid residues interacting with these ligands also differ. Results confirm the possibility of specifically targeting these sites across GPCRs for allosteric modulation and help to identify the most likely binding sites among the limited number of potential locations. The FTMap server is available free of charge for academic and governmental use at https://ftmap.bu.edu/.

Exploring the structural origins of cryptic sites on proteins.

Molecular dynamics (MD) simulations of proteins reveal the existence of many transient surface pockets; however, the factors determining what small subset of these represent druggable or functionally relevant ligand binding sites, called "cryptic sites," are not understood. Here, we examine multiple X-ray structures for a set of proteins with validated cryptic sites, using the computational hot spot identification tool FTMap. The results show that cryptic sites in ligand-free structures generally have a strong binding energy hot spot very close by. As expected, regions around cryptic sites exhibit above-average flexibility, and close to 50% of the proteins studied here have unbound structures that could accommodate the ligand without clashes. Nevertheless, the strong hot spot neighboring each cryptic site is almost always exploited by the bound ligand, suggesting that binding may frequently involve an induced fit component. We additionally evaluated the structural basis for cryptic site formation, by comparing unbound to bound structures. Cryptic sites are most frequently occluded in the unbound structure by intrusion of loops (22.5%), side chains (19.4%), or in some cases entire helices (5.4%), but motions that create sites that are too open can also eliminate pockets (19.4%). The flexibility of cryptic sites frequently leads to missing side chains or loops (12%) that are particularly evident in low resolution crystal structures. An interesting observation is that cryptic sites formed solely by the movement of side chains, or of backbone segments with fewer than five residues, result only in low affinity binding sites with limited use for drug discovery.

Benchmark Sets for Binding Hot Spot Identification in Fragment-Based Ligand Discovery.

Binding hot spots are regions of proteins that, due to their potentially high contribution to the binding free energy, have high propensity to bind small molecules. We present benchmark sets for testing computational methods for the identification of binding hot spots with emphasis on fragment-based ligand discovery. Each protein structure in the set binds a fragment, which is extended into larger ligands in other structures without substantial change in its binding mode. Structures of the same proteins without any bound ligand are also collected to form an unbound benchmark. We also discuss a set developed by Astex Pharmaceuticals for the validation of hot and warm spots for fragment binding. The set is based on the assumption that a fragment that occurs in diverse ligands in the same subpocket identifies a binding hot spot. Since this set includes only ligand-bound proteins, we added a set with unbound structures. All four sets were tested using FTMap, a computational analogue of fragment screening experiments to form a baseline for testing other prediction methods, and differences among the sets are discussed.

Molecular Simulation of Conformational Pre-Organization in Cyclic RGD Peptides.

To test the ability of molecular simulations to accurately predict the solution-state conformational properties of peptidomimetics, we examined a test set of 18 cyclic RGD peptides selected from the literature, including the anticancer drug candidate cilengitide, whose favorable binding affinity to integrin has been ascribed to its pre-organization in solution. For each design, we performed all-atom replica-exchange molecular dynamics simulations over several microseconds and compared the results to extensive published NMR data. We find excellent agreement with experimental NOE distance restraints, suggesting that molecular simulation can be a useful tool for the computational design of pre-organized solution-state structure. Moreover, our analysis of conformational populations estimates that, despite the potential for increased flexibility due to backbone amide isomerizaton, N-methylation provides about 0.5 kcal/mol of reduced conformational entropy to cyclic RGD peptides. The combination of pre-organization and binding-site compatibility explains the strong binding affinity of cilengitide to integrin.

Mapping the binding sites of challenging drug targets.

An increasing number of medically important proteins are challenging drug targets because their binding sites are too shallow or too polar, are cryptic and thus not detectable without a bound ligand or located in a protein-protein interface. While such proteins may not bind druglike small molecules with sufficiently high affinity, they are frequently druggable using novel therapeutic modalities. The need for such modalities can be determined by experimental or computational fragment based methods. Computational mapping by mixed solvent molecular dynamics simulations or the FTMap server can be used to determine binding hot spots. The strength and location of the hot spots provide very useful information for selecting potentially successful approaches to drug discovery.

API Development Increases Access to Shared Computing Resources at Boston University.

Within the last few decades, increases in computational resources have contributed enormously to the progress of science and engineering (S & E). To continue making rapid advancements, the S & E community must be able to access computing resources. One way to provide such resources is through High-Performance Computing (HPC) centers. Many academic research institutions offer their own HPC Centers but struggle to make the computing resources easily accessible and user-friendly. Here we present SHABU, a RESTful Web API framework that enables S & E communities to access resources from Boston University's Shared Computing Center (SCC). The SHABU requirements are derived from the use cases described in this work.

Use of Crystallography and Molecular Modeling for the Inhibition of the Botulinum Neurotoxin A Protease.

Botulinum neurotoxins (BoNTs) are extremely toxic and have been deemed a Tier 1 potential bioterrorism agent. The most potent and persistent of the BoNTs is the "A" serotype, with strategies to counter its etiology focused on designing small-molecule inhibitors of its light chain (LC), a zinc-dependent metalloprotease. The successful structure-based drug design of inhibitors has been confounded as the LC is highly flexible with significant morphological changes occurring upon inhibitor binding. To achieve greater success, previous and new cocrystal structures were evaluated from the standpoint of inhibitor enantioselectivity and their effect on active-site morphology. Based upon these structural insights, we designed inhibitors that were predicted to take advantage of π-π stacking interactions present in a cryptic hydrophobic subpocket. Structure-activity relationships were defined, and X-ray crystal structures and docking models were examined to rationalize the observed potency differences between inhibitors.

Analysis of tractable allosteric sites in G protein-coupled receptors.

Allosteric modulation of G protein-coupled receptors represent a promising mechanism of pharmacological intervention. Dramatic developments witnessed in the structural biology of membrane proteins continue to reveal that the binding sites of allosteric modulators are widely distributed, including along protein surfaces. Here we restrict consideration to intrahelical and intracellular sites together with allosteric conformational locks, and show that the protein mapping tools FTMap and FTSite identify 83% and 88% of such experimentally confirmed allosteric sites within the three strongest sites found. The methods were also able to find partially hidden allosteric sites that were not fully formed in X-ray structures crystallized in the absence of allosteric ligands. These results confirm that the intrahelical sites capable of binding druglike allosteric modulators are among the strongest ligand recognition sites in a large fraction of GPCRs and suggest that both FTMap and FTSite are useful tools for identifying allosteric sites and to aid in the design of such compounds in a range of GPCR targets.

Cryptic binding sites on proteins: definition, detection, and druggability.

Many proteins in their unbound structures lack surface pockets appropriately sized for drug binding. Hence, a variety of experimental and computational tools have been developed for the identification of cryptic sites that are not evident in the unbound protein but form upon ligand binding, and can provide tractable drug target sites. The goal of this review is to discuss the definition, detection, and druggability of such sites, and their potential value for drug discovery. Novel methods based on molecular dynamics simulations are particularly promising and yield a large number of transient pockets, but it has been shown that only a minority of such sites are generally capable of binding ligands with substantial affinity. Based on recent studies, current methodology can be improved by combining molecular dynamics with fragment docking and machine learning approaches.