On the importance of composite protein multiple ligand interactions in protein pockets

Implications of the Essential Role of Small Molecule Ligand Binding Pockets in Protein-Protein Interactions

Protein-protein interactions (PPIs) and protein-metabolite interactions play a key role in many biochemical processes, yet they are often viewed as being independent. However, the fact that small molecule drugs have been successful in inhibiting PPIs suggests a deeper relationship between protein pockets that bind small molecules and PPIs. We demonstrate that 2/3 of PPI interfaces, including antibody-epitope interfaces, contain at least one significant small molecule ligand binding pocket. In a representative library of 50 distinct protein-protein interactions involving hundreds of mutations, >75% of hot spot residues overlap with small molecule ligand binding pockets. Hence, ligand binding pockets play an essential role in PPIs. In representative cases, evolutionary unrelated monomers that are involved in different multimeric interactions yet share the same pocket are predicted to bind the same metabolites/drugs; these results are confirmed by examples in the PDB. Thus, the binding of a metabolite can shift the equilibrium between monomers and multimers. This implicit coupling of PPI equilibria, termed "metabolic entanglement", was successfully employed to suggest novel functional relationships among protein multimers that do not directly interact. Thus, the current work provides an approach to unify metabolomics and protein interactomics.

On the possible origin of protein homochirality, structure, and biochemical function

Living systems have chiral molecules, e.g., native proteins that almost entirely contain L-amino acids. How protein homochirality emerged from a background of equal numbers of L and D amino acids is among many questions about life's origin. The origin of homochirality and its implications are explored in computer simulations examining the stability and structural and functional properties of an artificial library of compact proteins containing 1:1 (termed demi-chiral), 3:1, and 1:3 ratios of D:L and purely L or D amino acids generated without functional selection. Demi-chiral proteins have shorter secondary structures and fewer internal hydrogen bonds and are less stable than homochiral proteins. Selection for hydrogen bonding yields a preponderance of L or D amino acids. Demi-chiral proteins have native global folds, including similarity to early ribosomal proteins, similar small molecule ligand binding pocket geometries, and many constellations of L-chiral amino acids with a 1.0-Å RMSD to native enzyme active sites. For a representative subset containing 550 active site geometries matching 457 (2) 4-digit (3-digit) enzyme classification (E.C.) numbers, native active site amino acids were generated at random for 472 of 550 cases. This increases to 548 of 550 cases when similar residues are allowed. The most frequently generated sequences correspond to ancient enzymatic functions, e.g., glycolysis, replication, and nucleotide biosynthesis. Surprisingly, even without selection, demi-chiral proteins possess the requisite marginal biochemical function and structure of modern proteins, but were thermodynamically less stable. If demi-chiral proteins were present, they could engage in early metabolism, which created the feedback loop for transcription and cell formation.

Protein pocket detection via convex hull surface evolution and associated Reeb graph

Motivation

Protein pocket information is invaluable for drug target identification, agonist design, virtual screening and receptor-ligand binding analysis. A recent study indicates that about half holoproteins can simultaneously bind multiple interacting ligands in a large pocket containing structured sub-pockets. Although this hierarchical pocket and sub-pocket structure has a significant impact to multi-ligand synergistic interactions in the protein binding site, there is no method available for this analysis. This work introduces a computational tool based on differential geometry, algebraic topology and physics-based simulation to address this pressing issue.

Results

We propose to detect protein pockets by evolving the convex hull surface inwards until it touches the protein surface everywhere. The governing partial differential equations (PDEs) include the mean curvature flow combined with the eikonal equation commonly used in the fast marching algorithm in the Eulerian representation. The surface evolution induced Morse function and Reeb graph are utilized to characterize the hierarchical pocket and sub-pocket structure in controllable detail. The proposed method is validated on PDBbind refined sets of 4414 protein-ligand complexes. Extensive numerical tests indicate that the proposed method not only provides a unique description of pocket-sub-pocket relations, but also offers efficient estimations of pocket surface area, pocket volume and pocket depth.

Availability and implementation

Source code available at https://github.com/rdzhao/ProteinPocketDetection. Webserver available at http://weilab.math.msu.edu/PPD/.

Why Some Targets Benefit from beyond Rule of Five Drugs

Beyond rule-of-five (bRo5) compounds are increasingly used in drug discovery. Here we analyze 37 target proteins that have bRo5 drugs or clinical candidates. Targets can benefit from bRo5 drugs if they have "complex" hot spot structure with four or more hots spots, including some strong ones. Complex I targets show positive correlation between binding affinity and molecular weight. These targets are conventionally druggable, but reaching additional hot spots enables improved pharmaceutical properties. Complex II targets, mostly protein kinases, also have strong hot spots but show no correlation between affinity and ligand molecular weight, and the primary motivation for creating larger drugs is to increase selectivity. Each target considered as complex III has some specific reason for requiring bRo5 drugs. Finally, targets with "simple" hot spot structure, i.e., three or fewer weak hot spots, must use larger compounds that interact with surfaces beyond the hot spot region to achieve acceptable affinity.

PocketPipe: A computational pipeline for integratedPocketome prediction and comparison

Functional characterisation of proteins often depends on specific interactions with other molecules. In the drug discovery scenario, the ability of a protein to bind with drug-like molecule with a high affinity is referred as druggability. Deciphering such druggable binding pockets on proteins plays an important role in structure-based drug designing studies. Moreover, availability of plethora of structural data poses a need automated pipelines which can efficiently integrate robust algorithms towards large-scale pocket identification and comparison. These pipelines have direct applicability on off-target analysis, drug repurposing and structural prioritization of drug targets in pathogenic microbes. However, currently there is a paucity of such efficient pipelines. Hence, by this study a highly optimized shell script based pipeline (PocketPipe) has been developed with seamless integration of robust algorithms namely, P2Rank (predicts binding sites based on machine learning) and PocketMatch-v2.1 (compares binding pockets by residue-based method), for pocketome generation and comparison, respectively. The process of input workflow and various steps carried out by PocketPipe and the output results are well documented in the operating manual. On execution, the pipeline features seamless operability, high scalability, dynamic file handling and results parsing. PocketPipe is distributed freely under GNU GPL license and can be downloaded at https://github.com/inpacdb/PocketPipe

Belinostat, at Its Clinically Relevant Concentrations, Inhibits Rifampicin-Induced CYP3A4 and MDR1 Gene Expression

Activation of human pregnane X receptor (hPXR) has been associated with induction of chemoresistance. It has been proposed that such chemoresistance via cytochrome P450/drug transporters can be reversed with the use of antagonists that specifically abrogate agonist-mediated hPXR activation. Unfortunately, proposed antagonists lack the specificity and appropriate pharmacological characteristics that allow these features to be active in the clinic. We propose that, ideally, an hPXR antagonist would be a cancer drug itself that is part of a "cancer drug cocktail" and effective as an hPXR antagonist at therapeutic concentrations. Belinostat (BEL), a histone deacetylase inhibitor approved for the treatment of relapsed/refractory peripheral T-cell lymphoma, and often used in combination with chemotherapy, is an attractive candidate based on its hPXR ligand-like features. We sought to determine whether these features of BEL might allow it to behave as an antagonist in combination chemotherapy regimens that include hPXR activators. BEL represses agonist-activated hPXR target gene expression at its therapeutic concentrations in human primary hepatocytes and LS174T human colon cancer cells. BEL repressed rifampicin-induced gene expression of CYP3A4 and multidrug resistance protein 1, as well as their respective protein activities. BEL decreased rifampicin-induced resistance to SN-38, the active metabolite of irinotecan, in LS174T cells. This finding indicates that BEL could suppress hPXR agonist-induced chemoresistance. BEL attenuated the agonist-induced steroid receptor coactivator-1 interaction with hPXR, and, together with molecular docking studies, the study suggests that BEL directly interacts with multiple sites on hPXR. Taken together, our results suggest that BEL, at its clinically relevant therapeutic concentration, can antagonize hPXR agonist-induced gene expression and chemoresistance.

Chemical space of Escherichia coli dihydrofolate reductase inhibitors: New approaches for discovering novel drugs for old bugs

Escherichia coli Dihydrofolate reductase is an important enzyme that is essential for the survival of the Gram-negative microorganism. Inhibitors designed against this enzyme have demonstrated application as antibiotics. However, either because of poor bioavailability of the small-molecules resulting from their inability to cross the double membrane in Gram-negative bacteria or because the microorganism develops resistance to the antibiotics by mutating the DHFR target, discovery of new antibiotics against the enzyme is mandatory to overcome drug-resistance. This review summarizes the field of DHFR inhibition with special focus on recent efforts to effectively interface computational and experimental efforts to discover novel classes of inhibitors that target allosteric and active-sites in drug-resistant variants of EcDHFR.

Pocket detection and interaction-weighted ligand-similarity search yields novel high-affinity binders for Myocilin-OLF, a protein implicated in glaucoma

Traditional structure and ligand based virtual screening approaches rely on the availability of structural and ligand binding information. To overcome this limitation, hybrid approaches were developed that relied on extraction of ligand binding information from proteins sharing similar folds and hence, evolutionarily relationship. However, they cannot target a chosen pocket in a protein. To address this, a pocket centric virtual ligand screening approach is required. Here, we employ a new, iterative implementation of a pocket and ligand-similarity based approach to virtual ligand screening to predict small molecule binders for the olfactomedin domain of human myocilin implicated in glaucoma. Small-molecule binders of the protein might prevent the aggregation of the protein, commonly seen during glaucoma. First round experimental assessment of the predictions using differential scanning fluorimetry with myoc-OLF yielded 7 hits with a success rate of 12.7%; the best hit had an apparent dissociation constant of 99nM. By matching to the key functional groups of the best ligand that were likely involved in binding, the affinity of the best hit was improved by almost 10,000 fold from the high nanomolar to the low picomolar range. Thus, this study provides preliminary validation of the methodology on a medically important glaucoma associated protein.

Rational Design of Novel Allosteric Dihydrofolate Reductase Inhibitors Showing Antibacterial Effects on Drug-Resistant Escherichia coli Escape Variants

In drug discovery, systematic variations of substituents on a common scaffold and bioisosteric replacements are often used to generate diversity and obtain molecules with better biological effects. However, this could saturate the small-molecule diversity pool resulting in drug resistance. On the other hand, conventional drug discovery relies on targeting known pockets on protein surfaces leading to drug resistance by mutations of critical pocket residues. Here, we present a two-pronged strategy of designing novel drugs that target unique pockets on a protein's surface to overcome the above problems. Dihydrofolate reductase, DHFR, is a critical enzyme involved in thymidine and purine nucleotide biosynthesis. Several classes of compounds that are structural analogues of the substrate dihydrofolate have been explored for their antifolate activity. Here, we describe 10 novel small-molecule inhibitors of Escherichia coli DHFR, EcDHFR, belonging to the stilbenoid, deoxybenzoin, and chalcone family of compounds discovered by a combination of pocket-based virtual ligand screening and systematic scaffold hopping. These inhibitors show a unique uncompetitive or noncompetitive inhibition mechanism, distinct from those reported for all known inhibitors of DHFR, indicative of binding to a unique pocket distinct from either substrate or cofactor-binding pockets. Furthermore, we demonstrate that rescue mutants of EcDHFR, with reduced affinity to all known classes of DHFR inhibitors, are inhibited at the same concentration as the wild-type. These compounds also exhibit antibacterial activity against E. coli harboring the drug-resistant variant of DHFR. This discovery is the first report on a novel class of inhibitors targeting a unique pocket on EcDHFR.