Structures Of Ligand-protein Complexes Research Articles

In silico fragment-based discovery of CIB1-directed anti-tumor agents by FRASE-bot

Chemical probes are an indispensable tool for translating biological discoveries into new therapies, though are increasingly difficult to identify since novel therapeutic targets are often hard-to-drug proteins. We introduce FRASE-based hit-finding robot (FRASE-bot), to expedite drug discovery for unconventional therapeutic targets. FRASE-bot mines available 3D structures of ligand-protein complexes to create a database of FRAgments in Structural Environments (FRASE). The FRASE database can be screened to identify structural environments similar to those in the target protein and seed the target structure with relevant ligand fragments. A neural network model is used to retain fragments with the highest likelihood of being native binders. The seeded fragments then inform ultra-large-scale virtual screening of commercially available compounds. We apply FRASE-bot to identify ligands for Calcium and Integrin Binding protein 1 (CIB1), a promising drug target implicated in triple negative breast cancer. FRASE-based virtual screening identifies a small-molecule CIB1 ligand (with binding confirmed in a TR-FRET assay) showing specific cell-killing activity in CIB1-dependent cancer cells, but not in CIB1-depletion-insensitive cells.

The impact of molecular variants, crystallization conditions and the space group on ligand-protein complexes: a case study on bacterial phosphotriesterase.

A bacterial phosphotriesterase was employed as an experimental paradigm to examine the effects of multiple factors, such as the molecular constructs, the ligands used during protein expression and purification, the crystallization conditions and the space group, on the visualization of molecular complexes of ligands with a target enzyme. In this case, the ligands used were organophosphates that are fragments of the nerve agents and insecticides on which the enzyme acts as a bioscavenger. 12 crystal structures of various phosphotriesterase constructs obtained by directed evolution were analyzed, with resolutions of up to 1.38 Å. Both apo forms and holo forms, complexed with the organophosphate ligands, were studied. Crystals obtained from three different crystallization conditions, crystallized in four space groups, with and without N-terminal tags, were utilized to investigate the impact of these factors on visualizing the organophosphate complexes of the enzyme. The study revealed that the tags used for protein expression can lodge in the active site and hinder ligand binding. Furthermore, the space group in which the protein crystallizes can significantly impact the visualization of bound ligands. It was also observed that the crystallization precipitants can compete with, and even preclude, ligand binding, leading to false positives or to the incorrect identification of lead drug candidates. One of the co-crystallization conditions enabled the definition of the spaces that accommodate the substituents attached to the P atom of several products of organophosphate substrates after detachment of the leaving group. The crystal structures of the complexes of phosphotriesterase with the organophosphate products reveal similar short interaction distances of the two partially charged O atoms of the P-O bonds with the exposed β-Zn2+ ion and the buried α-Zn2+ ion. This suggests that both Zn2+ ions have a role in stabilizing the transition state for substrate hydrolysis. Overall, this study provides valuable insights into the challenges and considerations involved in studying the crystal structures of ligand-protein complexes, highlighting the importance of careful experimental design and rigorous data analysis in ensuring the accuracy and reliability of the resulting phosphotriesterase-organophosphate structures.

Prediction of the Drug–Target Binding Kinetics for Flexible Proteins by Comparative Binding Energy Analysis

There is growing consensus that the optimization of the kinetic parameters for drug-protein binding leads to improved drug efficacy. Therefore, computational methods have been developed to predict kinetic rates and to derive quantitative structure-kinetic relationships (QSKRs). Many of these methods are based on crystal structures of ligand-protein complexes. However, a drawback is that each ligand-protein complex is usually treated as having a single structure. Here, we present a modification of COMparative BINding Energy (COMBINE) analysis, which uses the structures of ligand-protein complexes to predict binding parameters. We introduce the option of using multiple structures to describe each ligand-protein complex in COMBINE analysis and apply this to study the effects of protein flexibility on the derivation of dissociation rate constants (koff) for inhibitors of p38 mitogen-activated protein (MAP) kinase, which has a flexible binding site. Multiple structures were obtained for each ligand-protein complex by performing docking to an ensemble of protein configurations obtained from molecular dynamics simulations. Coefficients to scale ligand-protein interaction energies determined from energy-minimized structures of ligand-protein complexes were obtained by partial least squares regression, and they allowed for the computation of koff values. The QSKR model obtained using single, energy-minimized crystal structures for each ligand-protein complex had higher predictive power than the QSKR model obtained with multiple structures from ensemble docking. However, incorporation of ligand-protein flexibility helped to highlight additional ligand-protein interactions that lead to longer residence times, such as interactions with residues Arg67 and Asp168, which are close to the ligand in many crystal structures. These results show that COMBINE analysis is a promising method to guide the design of compounds that bind to flexible proteins with improved binding kinetics.

Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions.

The ability to design and fine-tune non-covalent interactions between organic ligands and proteins is indispensable to rational drug development. Aromatic stacking has long been recognized as one of the key constituents of ligand-protein interfaces. In this communication, we employ a two-parameter geometric model to conduct a large-scale statistical analysis of aromatic contacts in the experimental and computer-generated structures of ligand-protein complexes, considering various combinations of aromatic amino acid residues and ligand rings. The geometry of interfacial π-π stacking in crystal structures accords with experimental and theoretical data collected for simple systems, such as the benzene dimer. Many contemporary ligand docking programs implicitly treat aromatic stacking with van der Waals and Coulombic potentials. Although this approach generally provides a sufficient specificity to model aromatic interactions, the geometry of π-π contacts in high-scoring docking conformations could still be improved. The comprehensive analysis of aromatic geometries at ligand-protein interfaces lies the foundation for the development of type-specific statistical potentials to more accurately describe aromatic interactions in molecular docking. A Perl script to detect and calculate the geometric parameters of aromatic interactions in ligand-protein complexes is available at https://github.com/michal-brylinski/earomatic. The dataset comprising experimental complex structures and computer-generated models is available at https://osf.io/rztha/.

Interaction studies of Alzheimer's Cathepsin B protein with inhibitors in presence and absence of water

Background The accuracy of ligand-protein docking may be affected by the presence of water molecules on the surface of proteins. Water can form complex bridging networks and can play a critical role in dictating the binding mode of ligands. A recent analysis of high-resolution crystal structures of ligand-protein complexes revealed that 85% of the complexes had one or more water molecules bridging the interaction between ligand and protein. For predicting the binding modes and energies of protein-ligand interactions, molecular docking methods are commonly used. In order to obtain an accurate complex geometry and binding energy estimation, an appropriate method for calculating partial charges is essential. AutoDockTools software, widely used as interface for preparing input files, utilizes the either Gasteiger or Kollman partial charge calculation method for both protein and ligand charge calculations. However, it has already been reported that more accurate partial charge calculation and as a consequence, more accurate docking can be achieved by using quantum chemical methods. In common practice so far, the quantum chemical partial charges were applied to the ligands for docking calculations. The newly developed Mozyme function of MOPAC2009 allows fast partial charge calculation of proteins by quantum mechanical semiempirical methods. Thus, in the current study, we use the semi-empirical quantum-mechanical partial charge calculations to investigate the interaction energies and polarization effects of the various components of the binding pocket on a set of Cathepsin B protein. Results The docking accuracy was computed by using the original AutoDock scoring function with the set of 19 protein ligand complexes using Gasteiger, AM1 and PM3 partial charge calculation methods. This helped us to compare the effect of the partial charge calculation method on docking accuracy. It was seen that the docking accuracy in regard to complex geometry significantly increased when partial charges of the ligands and proteins were calculated with the semi-empirical PM3 method. Our results demonstrate that (i) the energetic of the key water molecule are more favorable for the binding site in the Cathepsin B protein (ii) Water bridging and triangle formation were seen between the key amino acid residue and the ligand (iii) The internal energy is significant factor for the binding modes of various ligands. It was also observed a statistically significant overall increase in accuracy when water molecules are included during docking simulations. Out of the 19 complexes analyzed in the course of our study, the geometry of 17 complexes were accurately calculated using PM3 partial charges, while the use of Gasteiger charges resulted in only 8 accurate geometries. Conclusion Our findings indicate that the inclusion of water molecules in ligand-protein docking results in significant increases in docking accuracy when the use of quantum chemical partial charge assignment on both ligand and proteins for predicting the docking simulations.

Automatic and Efficient Decomposition of Two-Dimensional Structures of Small Molecules for Fragment-Based High-Throughput Docking

The computer program DAIM (Decomposition and Identification of Molecules) has been developed to automatically break up compounds in small-molecule libraries for fragment-based docking as well as database analysis. Here, DAIM is evaluated on 130 ligands derived from known crystal structures of ligand-protein complexes. The decomposition and a new fingerprint-based identification technique are used to select anchor fragments for docking. The docking results show that the DAIM selection is superior to size-based or random selection of fragments. To evaluate the usefulness for analyzing the fragment composition of a large library, DAIM is applied to a collection of about 1.85 million commercially available compounds. Interestingly, it is found that the set of most frequent cyclic and acyclic fragments originating from the decomposition of the 1.85 million molecules shows a large overlap with the most frequent fragments in a library of 5120 known drugs. DAIM has been successfully used in the in silico screening for inhibitors of beta-secretase and EphB4 kinase by fragment-based high-throughput docking. Possible future applications for de novo ligand design are briefly discussed.

Bcr-Abl Binding Modes of Dasatinib, Imatinib and Nilotinib: An NMR Study.

Following the discovery that point mutations in the kinase domain of Bcr-Abl reduce the binding affinity of imatinib and lead to drug resistance in CML patients, efforts have been directed towards the discovery of new drugs which inhibit these resistant enzymes. Two such agents are dasatinib and nilotinib. Whereas, like imatinib, x-ray analysis of crystal structures of nilotinib in complex with the Abl kinase domain reveal that this agent binds to an inactive, DFG-out conformation of the enzyme, similar studies have shown that dasatinib binds to the catalytically active state of the enzyme (Tokarski et al, Cancer Res. 2006). However, based upon in silico methods using homology models of the imatinib-binding inactive conformation of Abl some reports claim that dasatinib is capable of binding to both the active and inactive forms of the enzyme. To help address this conundrum we have employed nuclear magnetic resonance (NMR) spectroscopy to study the different conformational characteristics and dynamic changes of the Abl protein obtained upon adding ligands.Selectively isotope labelled (15N and 13C) Abl kinase in the unphosphorylated state was produced according to published methods (Strauss et al, J. Biomolecular NMR 2005). Chemical shift data of the protein in solution were recorded by NMR spectroscopy, both with and without ligand. By measuring residual dipolar couplings (RDC) between the back-bone amide nitrogen and hydrogen atoms of amino-acid residues in the vicinity of the conserved DFG-motif (residues 370 – 410), the conformational states and equilibria of the activation loop of the kinase were established. Upon adding imatinib to the unliganded Abl, both chemical shift and RDC data show characteristic signals for residues M388, Y393 and G398, which are entirely consistent with the conformational equilibrium moving to the inactive state, in which the activation loop adopts the DFG-out conformation. In the case of nilotinib, NMR spectroscopy revealed chemical shift patterns and couplings involving the same three residues confirming that the drug binds to the same inactive conformation as imatinib, both in solution as well as in the crystalline state. In contrast, upon adding dasatinib to Abl, NMR data of the complex show distinctly different chemical shifts and RDC values, confirming that the protein assumes a different conformational state, with the activation loop adopting the active conformation, in accordance with the crystallographic evidence. Even upon adding dasatinib to a complex of imatinib with Abl in the inactive conformation, the kinase conformation changed to a state indistinguishable to that observed upon adding dasatinib to unliganded protein.In conclusion, these studies show that the three Abl kinase inhibitors all interact with the protein in solution with the same binding modes as observed in x-ray crystallographic studies, but show no evidence for dasatinib binding to the inactive “DFG-out” conformation. This case study also demonstrates the power of NMR spectroscopy in evaluating solution structures of ligand-protein complexes. Further experiments are in progress evaluating other structurally different Abl kinase inhibitors.

On the detection of multiple-binding modes of ligands to proteins, from biological, structural, and modeling data.

There are several indications that a given compound or a set of related compounds can bind in different modes to a specific binding site of a protein. This is especially evident from X-ray crystallographic structures of ligand-protein complexes. The availability of multiple binding modes of a ligand in a binding site may present an advantage in drug design when simultaneously optimizing several criteria. In the case of the design of anti-HIV compounds we observed that the more active compounds that are also resilient against mutation of the non-nucleoside binding site of HIV1-reverse transcriptase make use of more binding modes than the less active and resilient compounds.

Protein flexibility is an important component of structure-based drug discovery.

Receptor-based drug discovery can increase the novelty of a hit list over ligand-based models that are dependent on known inhibitors. It is important to explore new conformational and chemical space, but it is difficult to predict the plasticity of the binding site. Receptor-based methods are usually based on crystal structures of ligand-protein complexes, and hit lists can be restricted to the size and shape of the receptor model. Many improvements that accommodate protein flexibility in computer-aided drug design are being developed. These methods are reviewed with the focus being techniques that move beyond the rotation of side chains. The use of multiple protein structures is emerging as the best choice for including more realistic changes in protein conformation, but the optimal way to using these structures is still unclear.

Deciphering common failures in molecular docking of ligand-protein complexes.

Common failures in predicting crystal structures of ligand-protein complexes are investigated for three ligand-protein systems by a combined thermodynamic and kinetic analysis of the binding energy landscapes. Misdocked predictions in ligand-protein docking are classified as 'soft' and 'hard' failures. While a soft failure arises when the search algorithm is unable to find the global energy minimum corresponding to the crystal structure, a hard failure results from a flaw of the energy function to qualify the crystal structure as the predicted lowest energy conformation in docking simulations. We find that neither the determination of a single structure with the lowest energy nor finding the most common binding mode is sufficient to predict crystal structures of the complexes, which belong to the category of hard failures. In a proposed hierarchical approach, structural similarity clustering of the conformations, generated from equilibrium simulations with the simplified energy function, is followed by energy refinement with the AMBER force field. This protocol, that involves a hierarchy of energy functions, resolves some common failures in ligand-protein docking and detects crystallographic binding modes that were not found during docking simulations.

Predicting structural effects in HIV-1 protease mutant complexes with flexible ligand docking and protein side-chain optimization.

We present a computational approach for predicting structures of ligand-protein complexes and analyzing binding energy landscapes that combines Monte Carlo simulated annealing technique to determine the ligand bound conformation with the dead-end elimination algorithm for side-chain optimization of the protein active site residues. Flexible ligand docking and optimization of mobile protein side-chains have been performed to predict structural effects in the V32I/I47V/V82I HIV-1 protease mutant bound with the SB203386 ligand and in the V82A HIV-1 protease mutant bound with the A77003 ligand. The computational structure predictions are consistent with the crystal structures of these ligand-protein complexes. The emerging relationships between ligand docking and side-chain optimization of the active site residues are rationalized based on the analysis of the ligand-protein binding energy landscape.

Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming

Background: An important prerequisite for computational structure-based drug design is prediction of the structures of ligand-protein complexes that have not yet been experimentally determined by X-ray crystallography or NMR. For this task, docking of rigid ligands is inadequate because it assumes knowledge of the conformation of the bound ligand. Docking of flexible ligands would be desirable, but requires one to search an enormous conformational space. We set out to develop a strategy for flexible docking by, combining a simple model of ligand-protein interactions for molecular recognition with an evolutionary programming search technique. Results: We have developed an intermolecular energy function that incorporates steric and hydrogen-bonding terms. The parameters in this function were obtained by docking in three different protein systems. The effectiveness of this method was demonstrated by conformationally flexible docking of the inhibitor AG-1343, a potential new drug against AIDS, into HIV-1 protease. For this molecule, which has nine rotatable bonds, the crystal structure was reproduced within 1.5 Å rootmean-square deviation 34 times in 100 simulations, each requiring eight minutes on a Silicon Graphics R4400 workstation. The energy function correctly evaluates the crystal structure as the global energy minimum. Conclusions: We believe that a solution of the docking problem may be achieved by matching a simple model of molecular recognition with an efficient search procedure. The necessary ingredients of a molecular recognition model include only steric and hydrogen-bond interaction terms. Although these terms are not necessarily sufficient to predict binding affinity, they describe ligand-protein interactions faithfully enough to enable a docking program to predict the structure of the bound ligand. This docking strategy thus provides an important tool for the interdisciplinary field of rational drug design.