Analysis of protein binding sites by computational solvent mapping

Conservation of Hot Spots and Ligand Binding Sites in Protein Models by AlphaFold2

The neural network-based program AlphaFold2 (AF2) provides high accuracy structure prediction for a large fraction of globular proteins. An important question is whether these models are accurate enough for reliably docking small ligands. Several recent papers and the results of CASP15 reveal that local conformational errors reduce the success rates of direct ligand docking. Here, we focus on the ability of the models to conserve the location of binding hot spots, regions on the protein surface that significantly contribute to the binding free energy of the protein-ligand interaction. Clusters of hot spots predict the location and even the druggability of binding sites, and hence are important for computational drug discovery. The hot spots are determined by protein mapping that is based on the distribution of small fragment-sized probes on the protein surface and is less sensitive to local conformation than docking. Mapping models taken from the AlphaFold Protein Structure Database show that identifying binding sites is more reliable than docking, but the success rates are still 5% to 10% lower than based on mapping X-ray structures. The drop in accuracy is particularly large for models of multidomain proteins. However, both the model binding sites and the mapping results can be substantially improved by generating AF2 models for the ligand binding domains of interest rather than the entire proteins and even more if using forced sampling with multiple initial seeds. The mapping of such models tends to reach the accuracy of results obtained by mapping the X-ray structures.

Measuring the rapid kinetics of receptor-ligand interactions in live cells using NanoBRET

The importance of receptor-ligand binding kinetics has often been overlooked during drug development, however, over the past decade it has become increasingly clear that a better understanding of the kinetic parameters is crucial for fully evaluating pharmacological effects of a drug. One technique enabling us to measure the real-time kinetics of receptor-ligand interactions in live cells is NanoBRET, which is a bioluminescence resonance energy transfer (BRET)-based assay that uses Nano luciferase. The assay described here allows the measurement of kinetic parameters of a fluorescent ligand and an unlabeled ligand binding to the same place at the receptor, as well as monitoring the effects of another compound like an allosteric modulator on the ligand binding.

Residue-based pharmacophore approaches to study protein-protein interactions

This review focuses on pharmacophore approaches in researching protein interfaces that bind protein ligands. Pharmacophore descriptions of binding interfaces that employ molecular dynamics simulation can account for effects of solvation and conformational flexibility. In addition, these calculations provide an approximation to entropic considerations and as such, a better approximation of the free energy of binding. Residue-based pharmacophore approaches can facilitate a variety of drug discovery tasks such as the identification of receptor-ligand partners, identifying their binding poses, designing protein interfaces for selectivity, or defining a reduced mutational combinatorial exploration for subsequent experimental engineering techniques by orders of magnitudes.

Interaction Energetics and Druggability of the Protein-Protein Interaction between Kelch-like ECH-Associated Protein 1 (KEAP1) and Nuclear Factor Erythroid 2 Like 2 (Nrf2)

Development of small molecule inhibitors of protein-protein interactions (PPIs) is hampered by our poor understanding of the druggability of PPI target sites. Here, we describe the combined application of alanine-scanning mutagenesis, fragment screening, and FTMap computational hot spot mapping to evaluate the energetics and druggability of the highly charged PPI interface between Kelch-like ECH-associated protein 1 (KEAP1) and nuclear factor erythroid 2 like 2 (Nrf2), an important drug target. FTMap identifies four binding energy hot spots at the active site. Only two of these are exploited by Nrf2, which alanine scanning of both proteins shows to bind primarily through E79 and E82 interacting with KEAP1 residues S363, R380, R415, R483, and S508. We identify fragment hits and obtain X-ray complex structures for three fragments via crystal soaking using a new crystal form of KEAP1. Combining these results provides a comprehensive and quantitative picture of the origins of binding energy at the interface. Our findings additionally reveal non-native interactions that might be exploited in the design of uncharged synthetic ligands to occupy the same site on KEAP1 that has evolved to bind the highly charged DEETGE binding loop of Nrf2. These include π-stacking with KEAP1 Y525 and interactions at an FTMap-identified hot spot deep in the binding site. Finally, we discuss how the complementary information provided by alanine-scanning mutagenesis, fragment screening, and computational hot spot mapping can be integrated to more comprehensively evaluate PPI druggability.

Protein-protein interaction modulators: advances, successes and remaining challenges

Modulating disease-relevant protein-protein interactions (PPIs) using small-molecule inhibitors is a quite indispensable diagnostic and therapeutic strategy in averting pathophysiological cues and disease progression. Over the years, targeting intracellular PPIs as drug design targets has been a challenging task owing to their highly dynamic and expansive interfacial areas (flat, featureless and relatively large). However, advances in PPI-focused drug discovery technology have been reported and a few drugs are already on the market, with some potential drug-like candidates already in clinical trials. In this article, we review the advances, successes and remaining challenges in the application of small molecules as valuable PPI modulators in disease diagnosis and therapeutics.

Covalent binders in drug discovery

Covalent modulation of protein function can have multiple utilities including therapeutics, and probes to interrogate biology. While this field is still viewed with scepticism due to the potential for (idiosyncratic) toxicities, significant strides have been made in terms of understanding how to tune electrophilicity to selectively target specific residues. Progress has also been made in harnessing the potential of covalent binders to uncover novel biology and to provide an enhanced utility as payloads for Antibody Drug Conjugates. This perspective covers the tenets and applications of covalent binders.

Protein-protein binding supersites

The lack of a deep understanding of how proteins interact remains an important roadblock in advancing efforts to identify binding partners and uncover the corresponding regulatory mechanisms of the functions they mediate. Understanding protein-protein interactions is also essential for designing specific chemical modifications to develop new reagents and therapeutics. We explored the hypothesis of whether protein interaction sites serve as generic biding sites for non-cognate protein ligands, just as it has been observed for small-molecule-binding sites in the past. Using extensive computational docking experiments on a test set of 241 protein complexes, we found that indeed there is a strong preference for non-cognate ligands to bind to the cognate binding site of a receptor. This observation appears to be robust to variations in docking programs, types of non-cognate protein probes, sizes of binding patches, relative sizes of binding patches and full-length proteins, and the exploration of obligate and non-obligate complexes. The accuracy of the docking scoring function appears to play a role in defining the correct site. The frequency of interaction of unrelated probes recognizing the binding interface was utilized in a simple prediction algorithm that showed accuracy competitive with other state of the art methods.

Allosteric binding sites in Rab11 for potential drug candidates

Rab11 is an important protein subfamily in the RabGTPase family. These proteins physiologically function as key regulators of intracellular membrane trafficking processes. Pathologically, Rab11 proteins are implicated in many diseases including cancers, neurodegenerative diseases and type 2 diabetes. Although they are medically important, no previous study has found Rab11 allosteric binding sites where potential drug candidates can bind to. In this study, by employing multiple clustering approaches integrating principal component analysis, independent component analysis and locally linear embedding, we performed structural analyses of Rab11 and identified eight representative structures. Using these representatives to perform binding site mapping and virtual screening, we identified two novel binding sites in Rab11 and small molecules that can preferentially bind to different conformations of these sites with high affinities. After identifying the binding sites and the residue interaction networks in the representatives, we computationally showed that these binding sites may allosterically regulate Rab11, as these sites communicate with switch 2 region that binds to GTP/GDP. These two allosteric binding sites in Rab11 are also similar to two allosteric pockets in Ras that we discovered previously.

Design, synthesis and anticancer studies of novel aminobenzazolyl pyrimidines as tyrosine kinase inhibitors

Abnormal signalling from the Protein tyrosine kinases (PTKs) like receptor tyrosine kinases and intracellular tyrosine kinases can lead to diseases such as cancer especially non-small cell lung cancer, chronic myeloid leukaemia and gastrointestinal stromal tumours. Various Protein tyrosine kinase inhibitors are available but face poor bioavailability, severe toxicities and recent cases of drug-resistant cancers prompts for development of better drug molecules. In this study we report the design and development of a novel Protein Tyrosine Kinase (PTK) inhibitor on the basis of pharmacophore modelling. Compound 2-(benzo[d]oxazol-2-ylamino)-N-(2-chloro-4-fluorophenyl)-4-methyl-6-(3-nitrophenyl) pyrimidine-5-carboxamide 31 was obtained containing essential pharmacophore structural features. This compound exhibited highest activity against leukaemia cell line (RPMI-8226) at 0.7244 µM, renal cancer cell line (A498) at 0.8511 µM and prostate cancer cell line (PC-3) at 0.7932 µM on the NCI five dose assay test. The PTK assay provides promising activity at IC₅₀ of 0.07 µM in the human breast cancer cell line MDA-MB-468. Compound 31 had good intermolecular interaction with PTK in the molecular docking studies, this ligand-enzyme complex was found to stable in the MM-PBSA study over 100 ns. It had 54.22% oral bioavailability with T_max of 0.60 h which is higher compared to the dasatinib with bioavailability and T_max of 14-34% and 1-1.42 h respectively. Anticancer action of 31 was found to be impressive in pharmacokinetic studies making it a potential lead molecule.

Structural analysis of protein tyrosine phosphatase 1B reveals potentially druggable allosteric binding sites

Catalytic proteins such as human protein tyrosine phosphatase 1B (PTP1B), with conserved and highly polar active sites, warrant the discovery of druggable nonactive sites, such as allosteric sites, and potentially, therapeutic small molecules that can bind to these sites. Catalyzing the dephosphorylation of numerous substrates, PTP1B is physiologically important in intracellular signal transduction pathways in diverse cell types and tissues. Aberrant PTP1B is associated with obesity, diabetes, cancers, and neurodegenerative disorders. Utilizing clustering methods (based on root mean square deviation, principal component analysis, nonnegative matrix factorization, and independent component analysis), we have examined multiple PTP1B structures. Using the resulting representative structures in different conformational states, we determined consensus clustroids and used them to identify both known and novel binding sites, some of which are potentially allosteric. We report several lead compounds that could potentially bind to the novel PTP1B binding sites and can be further optimized. Considering the possibility for drug repurposing, we discovered homologous binding sites in other proteins, with ligands that could potentially bind to the novel PTP1B binding sites.

The use of fake ligands from computational solvent mapping in ligand and structure-based virtual screening

AIM

Virtual screening selects compounds that resemble a known modulator or compounds that fit into the binding site of a target protein. Computational solvent mapping defines important chemical features for binding to a target protein. Results/methodology: We have tested the ability to use solvent mapping for generating a 'fake' ligand that is a negative image of the binding site. We used this fake ligand as a query for the program ROCS and to define the search space of the docking programs FRED and HYBRID.

CONCLUSION

The fake ligands perform comparably to or better than the ligands from crystal structures across a set of ten targets. Thus, the approach is suitable for guiding virtual screening and hit-to-lead optimization.

Targeting Bacterial Cell Wall Peptidoglycan Synthesis by Inhibition of Glycosyltransferase Activity

Synthesis of bacterial cell wall peptidoglycan requires glycosyltransferase enzymes that transfer the disaccharide-peptide from lipid II onto the growing glycan chain. The polymerization of the glycan chain precedes cross-linking by penicillin-binding proteins and is essential for growth for key bacterial pathogens. As such, bacterial cell wall glycosyltransferases are an attractive target for antibiotic drug discovery. However, significant challenges to the development of inhibitors for these targets include the development of suitable assays and chemical matter that is suited to the nature of the binding site. We developed glycosyltransferase enzymatic activity and binding assays using the natural products moenomycin and vancomycin as model inhibitors. In addition, we designed a library of disaccharide compounds based on the minimum moenomycin fragment with peptidoglycan glycosyltransferase inhibitory activity and based on a more drug-like and synthetically versatile disaccharide building block. A subset of these disaccharide compounds bound and inhibited the glycosyltransferase enzymes, and these compounds could serve as chemical entry points for antibiotic development.

The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins

FTMap is a computational mapping server that identifies binding hot spots of macromolecules-i.e., regions of the surface with major contributions to the ligand-binding free energy. To use FTMap, users submit a protein, DNA or RNA structure in PDB (Protein Data Bank) format. FTMap samples billions of positions of small organic molecules used as probes, and it scores the probe poses using a detailed energy expression. Regions that bind clusters of multiple probe types identify the binding hot spots in good agreement with experimental data. FTMap serves as the basis for other servers, namely FTSite, which is used to predict ligand-binding sites, FTFlex, which is used to account for side chain flexibility, FTMap/param, used to parameterize additional probes and FTDyn, for mapping ensembles of protein structures. Applications include determining the druggability of proteins, identifying ligand moieties that are most important for binding, finding the most bound-like conformation in ensembles of unliganded protein structures and providing input for fragment-based drug design. FTMap is more accurate than classical mapping methods such as GRID and MCSS, and it is much faster than the more-recent approaches to protein mapping based on mixed molecular dynamics. By using 16 probe molecules, the FTMap server finds the hot spots of an average-size protein in <1 h. As FTFlex performs mapping for all low-energy conformers of side chains in the binding site, its completion time is proportionately longer.

Improvements, trends, and new ideas in molecular docking: 2012-2013 in review

Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.

In silico design of small molecule inhibitors of CDK9/cyclin T1 interaction

In order to design a small molecule which potentially may interfere with CDK9/cyclin T1 complex formation and therefore influence its physiological role, a computational study of dynamics and druggability of CDK9 binding surface was conducted. Druggability estimates and pocket opening analyses indicated binding regions of cyclin T1 residues, Phe 146 and Lys 6, as starting points for the design of small molecules with the potential to inhibit the CDK9/cyclin T1 association. A pharmacophore model was created, based on these two residues and used to select potential inhibitor structures. Binding energies of the inhibitors were estimated with MM-GBSA. A good correlation of MM-GBSA energies and FTMap druggability predictions was observed. Amongst studied compounds a derivative of 2-amino-8-hydroxyquinoline was identified as the best potential candidate to inhibit CDK9/cyclin T1 interactions.