Structural adaptation of extreme halophilic proteins through decrease of conserved hydrophobic contact surface

Background

Disrupting protein-protein interactions by small organic molecules is nowadays a promising strategy employed to block protein targets involved in different pathologies. However, structural changes occurring at the binding interfaces make difficult drug discovery processes using structure-based drug design/virtual screening approaches. Here we focused on two homologous calcium binding proteins, calmodulin and human centrin 2, involved in different cellular functions via protein-protein interactions, and known to undergo important conformational changes upon ligand binding.

Results

In order to find suitable protein conformations of calmodulin and centrin for further structure-based drug design/virtual screening, we performed in silico structural/energetic analysis and molecular docking of terphenyl (a mimicking alpha-helical molecule known to inhibit protein-protein interactions of calmodulin) into X-ray and NMR ensembles of calmodulin and centrin. We employed several scoring methods in order to find the best protein conformations. Our results show that docking on NMR structures of calmodulin and centrin can be very helpful to take into account conformational changes occurring at protein-protein interfaces.

Conclusions

NMR structures of protein-protein complexes nowadays available could efficiently be exploited for further structure-based drug design/virtual screening processes employed to design small molecule inhibitors of protein-protein interactions.     

Blind docking of pharmaceutically relevant compounds using RosettaLigand

It is difficult to properly validate algorithms that dock a small molecule ligand into its protein receptor using data from the public domain: the predictions are not blind because the correct binding mode is already known, and public test cases may not be representative of compounds of interest such as drug leads. Here, we use private data from a real drug discovery program to carry out a blind evaluation of the RosettaLigand docking methodology and find that its performance is on average comparable with that of the best commercially available current small molecule docking programs. The strength of RosettaLigand is the use of the Rosetta sampling methodology to simultaneously optimize protein sidechain, protein backbone and ligand degrees of freedom; the extensive benchmark test described here identifies shortcomings in other aspects of the protocol and suggests clear routes to improving the method.     

Retrospective molecular docking study of WY-25105 ligand to β-secretase and bias of the three-dimensional structure flexibility

β-Secretase (BACE) is a very promising target in the search for a treatment for Alzheimer’s disease using a protein–ligand inhibition approach. Given the many published X-ray structures of BACE protein, structure-based drug design has been used extensively to support new inhibitor discovery programs. Due to the high flexibility and large catalytic site of this protein, sampling of the huge conformational space of the binding site is the big challenge to overcome and is the main limitation of the most widely used docking programs. Incorrect treatment of these pitfalls can introduce bias into ligand docking and could affect the results. This is especially the case with the WY-25105 compound reported by the Wyeth Corporation as a BACE ligand that did not fit into any of the known crystal structures. In the present retrospective study, a set of available X-ray enzyme structures was selected and molecular dynamics simulations were conducted to generate more diverse representative BACE protein conformations. These conformations were then used for a docking study of the WY-25105 compound. The results confirmed the need to use an ensemble of structures in protein–ligand docking for identification of new binding modes in structure-based drug design of BACE inhibitors.

Computational approaches for protein function prediction: A combined strategy from multiple sequence alignment to molecular docking-based virtual screening

The functional characterization of proteins represents a daily challenge for biochemical, medical and computational sciences. Although finally proved on the bench, the function of a protein can be successfully predicted by computational approaches that drive the further experimental assays. Current methods for comparative modeling allow the construction of accurate 3D models for proteins of unknown structure, provided that a crystal structure of a homologous protein is available. Binding regions can be proposed by using binding site predictors, data inferred from homologous crystal structures, and data provided from a careful interpretation of the multiple sequence alignment of the investigated protein and its homologs. Once the location of a binding site has been proposed, chemical ligands that have a high likelihood of binding can be identified by using ligand docking and structure-based virtual screening of chemical libraries. Most docking algorithms allow building a list sorted by energy of the lowest energy docking configuration for each ligand of the library. In this review the state-of-the-art of computational approaches in 3D protein comparative modeling and in the study of protein–ligand interactions is provided. Furthermore a possible combined/concerted multistep strategy for protein function prediction, based on multiple sequence alignment, comparative modeling, binding region prediction, and structure-based virtual screening of chemical libraries, is described by using suitable examples. As practical examples, Abl-kinase molecular modeling studies, HPV-E6 protein multiple sequence alignment analysis, and some other model docking-based characterization reports are briefly described to highlight the importance of computational approaches in protein function prediction.     

Receptor–ligand molecular docking

Docking methodology aims to predict the experimental binding modes and affinities of small molecules within the binding site of particular receptor targets and is currently used as a standard computational tool in drug design for lead compound optimisation and in virtual screening studies to find novel biologically active molecules. The basic tools of a docking methodology include a search algorithm and an energy scoring function for generating and evaluating ligand poses. In this review, we present the search algorithms and scoring functions most commonly used in current molecular docking methods that focus on protein–ligand applications. We summarise the main topics and recent computational and methodological advances in protein–ligand docking. Protein flexibility, multiple ligand binding modes and the free-energy landscape profile for binding affinity prediction are important and interconnected challenges to be overcome by further methodological developments in the docking field.     

Insilico analysis and molecular docking of resuscitation promoting factor B (RpfB) protein of Mycobacterium tuberculosis

Invulnerability of Mycobacterium tuberculosis to various drugs and its persistency has stood as a hurdle in the race against eradication of the pathogenecity of the bacteria. Identification of novel antituberculosis compounds is highly demanding as the available drugs are resistant. The ability of the bacteria to surpass the body''s defenses and adapt itself to survive for disease reactivation is contributed by secreted proteins called resuscitating promoting factors (Rpfs). These factors aid in virulence and resuscitation from dormancy of the bacteria. Sequence analysis of RpfB was performed and compounds were first screened for toxicity and high-throughput virtual screening eliminating the toxic compounds. To understand the mechanism of ligand binding and interaction, molecular docking was performed for the compounds passing through the filter resulting with better docking studies predicting the possible binding mode of the inhibitors to the protein. Of all the active residues the binding conformation shows that residues Arg194, Arg196, Glu242, and Asn244 of the RpfB protein play vital role in the enzyme activity and interacts with the ligands. Promising compounds have been identified in the current study, thus holding promise for design of antituberculosis drugs.     

Efficient molecular docking of NMR structures: application to HIV-1 protease

Docking ligands into an ensemble of NMR conformers is essential to structure-based drug discovery if only NMR structures are available for the target. However, sequentially docking ligands into each NMR conformer through standard single-receptor-structure docking, referred to as sequential docking, is computationally expensive for large-scale database screening because of the large number of NMR conformers involved. Recently, we developed an efficient ensemble docking algorithm to consider protein structural variations in ligand binding. The algorithm simultaneously docks ligands into an ensemble of protein structures and achieves comparable performance to sequential docking without significant increase in computational time over single-structure docking. Here, we applied this algorithm to docking with NMR structures. The HIV-1 protease was used for validation in terms of docking accuracy and virtual screening. Ensemble docking of the NMR structures identified 91% of the known inhibitors under the criterion of RMSD < 2.0 A for the best-scored conformation, higher than the average success rate of single docking of individual crystal structures (66%). In the virtual screening test, on average, ensemble docking of the NMR structures obtained higher enrichments than single-structure docking of the crystal structures. In contrast, docking of either the NMR minimized average structure or a single NMR conformer performed less satisfactorily on both binding mode prediction and virtual screening, indicating that a single NMR structure may not be suitable for docking calculations. The success of ensemble docking of the NMR structures suggests an efficient alternative method for standard single docking of crystal structures and for considering protein flexibility.     

Discovery of new MurA inhibitors using induced-fit simulation and docking

We report on the successful application of ProBiS-CHARMMing web server in the discovery of new inhibitors of MurA, an enzyme that catalyzes the first committed cytoplasmic step of bacterial peptidoglycan synthesis. The available crystal structures of Escherichia coli MurA in the Protein Data Bank have binding sites whose small volume does not permit the docking of drug-like molecules. To prepare the binding site for docking, the ProBiS-CHARMMing web server was used to simulate the induced-fit effect upon ligand binding to MurA, resulting in a larger, more holo-like binding site. The docking of a filtered ZINC compound library to this enlarged binding site was then performed and resulted in three compounds with promising inhibitory potencies against MurA. Compound 1 displayed significant inhibitory potency with IC₅₀ value of 1 μM. All three compounds have novel chemical structures, which could be used for further optimization of small-molecule MurA inhibitors.     

Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery

Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein‐ligand interactions between 296 proteins spanning Escherichia coli''s essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning‐based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true‐positive rate to false‐positive rate. This work indicates that advances in modeling protein‐ligand interactions, particularly using machine learning‐based approaches, are needed to better harness AlphaFold2 for drug discovery.     

First report on exploring structural requirements of alpha and beta thymidine analogs for PfTMPK inhibitory activity using in silico studies

With the emergence of multi-drug resistance of the currently available antimalarial drugs including the “magic bullet” artemisinin derivatives in the market, there is an urgent need for discovery and development of new potent antimalarial molecules. The present work deals with quantitative structure–activity relationship (QSAR) modeling, pharmacophore mapping and docking studies of a series of 35 thymidine analogs as inhibitors of Plasmodium falciparum thymidylate kinase (PfTMPK), an enzyme that catalyzes phosphorylation of thymidine monophosphate (TMP) to thymidine diphosphate (TDP). The models were validated both internally and externally and significant statistical results were obtained, indicating the robustness and reliability of the developed models. The docking study was performed using the LigandFit option of receptor–ligand interactions protocol section available in Discovery Studio 2.1 where lower RMSD values (0.6931 Å) between the co-crystallized ligand and re-docked ligand assured that the ligand was bound in the same binding pocket. The QSAR, pharmacophore mapping and docking studies provide an understanding of important structural requirements or essential molecular properties, or features of molecules, and important binding interactions, and provide an important guidance for the chemist to synthesis of new molecules with improved PfTMPK inhibitory activity profile. This work revealed the importance of –NH-fragment, electrophilicity of the molecules and the number of oxygen atom towards the PfTMPK inhibitory activity of the molecules. To the best of our knowledge, this work presents the first QSAR and pharmacophore report for thymidine analogs which may serve as an efficient tool for the design and synthesis of potent molecules as PfTMPK inhibitors to address the increasing threat of multi-drug resistance against P. falciparum.     

A mining minima approach to exploring the docking pathways of p-nitrocatechol sulfate to YopH

Using the docking of p-nitrocatechol sulfate to Yersinia protein tyrosine phosphatase YopH as an example, we showed that an approach based on mining minima followed by cluster and similarity analysis could generate useful insights into docking pathways. Our simulation treated both the ligand and the protein as flexible molecules so that the coupling between their motion could be properly accounted for. Our simulation identified three docking poses; the one with the lowest energy agreed well with experimental structure. The model also predicted the side-chain conformations of the amino acids lying in the binding pocket correctly with the exception of three residues that appeared to be stabilized by two structural water molecules in the crystal structure. The implicit solvent model employed in the simulation could not capture such effects well. We also found four major pathways leading to these docking poses after the ligand entered the mouth of the binding pocket. In addition, the sulfate group of p-nitrocatechol sulfate was found to be important both in binding the ligand to the pocket and in guiding the ligand to dock into the pocket. The coupling of the motion between the protein and the ligand also played an important role in facilitating ligand loading and unloading.     

Conformational Transitions upon Ligand Binding: Holo-Structure Prediction from Apo Conformations

Biological function of proteins is frequently associated with the formation of complexes with small-molecule ligands. Experimental structure determination of such complexes at atomic resolution, however, can be time-consuming and costly. Computational methods for structure prediction of protein/ligand complexes, particularly docking, are as yet restricted by their limited consideration of receptor flexibility, rendering them not applicable for predicting protein/ligand complexes if large conformational changes of the receptor upon ligand binding are involved. Accurate receptor models in the ligand-bound state (holo structures), however, are a prerequisite for successful structure-based drug design. Hence, if only an unbound (apo) structure is available distinct from the ligand-bound conformation, structure-based drug design is severely limited. We present a method to predict the structure of protein/ligand complexes based solely on the apo structure, the ligand and the radius of gyration of the holo structure. The method is applied to ten cases in which proteins undergo structural rearrangements of up to 7.1 Å backbone RMSD upon ligand binding. In all cases, receptor models within 1.6 Å backbone RMSD to the target were predicted and close-to-native ligand binding poses were obtained for 8 of 10 cases in the top-ranked complex models. A protocol is presented that is expected to enable structure modeling of protein/ligand complexes and structure-based drug design for cases where crystal structures of ligand-bound conformations are not available.     

Similarity-driven flexible ligand docking

A similarity-driven approach to flexible ligand docking is presented. Given a reference ligand or a pharmacophore positioned in the protein active site, the method allows inclusion of a similarity term during docking. Two different algorithms have been implemented, namely, a similarity-penalized docking (SP-DOCK) and a similarity-guided docking (SG-DOCK). The basic idea is to maximally exploit the structural information about the ligand binding mode present in cases where ligand-bound protein structures are available, information that is usually ignored in standard docking procedures. SP-DOCK and SG-DOCK have been derived as modified versions of the program DOCK 4.0, where the similarity program MIMIC acts as a module for the calculation of similarity indices that correct docking energy scores at certain steps of the calculation. SP-DOCK applies similarity corrections to the set of ligand orientations at the end of the ligand incremental construction process, penalizing the docking energy and, thus, having only an effect on the relative ordering of the final solutions. SG-DOCK applies similarity corrections throughout the entire ligand incremental construction process, thus affecting not only the relative ordering of solutions but also actively guiding the ligand docking. The performance of SP-DOCK and SG-DOCK for binding mode assessment and molecular database screening is discussed. When applied to a set of 32 thrombin ligands for which crystal structures are available, SG-DOCK improves the average RMSD by ca. 1 A when compared with DOCK. When those 32 thrombin ligands are included into a set of 1,000 diverse molecules from the ACD, DIV, and WDI databases, SP-DOCK significantly improves the retrieval of thrombin ligands within the first 10% of each of the three databases with respect to DOCK, with minimal additional computational cost. In all cases, comparison of SP-DOCK and SG-DOCK results with those obtained by DOCK and MIMIC is performed.     

Incorporating replacement free energy of binding‐site waters in molecular docking

Binding‐site water molecules play a crucial role in protein‐ligand recognition, either being displaced upon ligand binding or forming water bridges to stabilize the complex. However, rigorously treating explicit binding‐site waters is challenging in molecular docking, which requires to fully sample ensembles of waters and to consider the free energy cost of replacing waters. Here, we describe a method to incorporate structural and energetic properties of binding‐site waters into molecular docking. We first developed a solvent property analysis (SPA) program to compute the replacement free energies of binding‐site water molecules by post‐processing molecular dynamics trajectories obtained from ligand‐free protein structure simulation in explicit water. Next, we implemented a distance‐dependent scoring term into DOCK scoring function to take account of the water replacement free energy cost upon ligand binding. We assessed this approach in protein targets containing important binding‐site waters, and we demonstrated that our approach is reliable in reproducing the crystal binding geometries of protein‐ligand‐water complexes, as well as moderately improving the ligand docking enrichment performance. In addition, SPA program (free available to academic users upon request) may be applied in identifying hot‐spot binding‐site residues and structure‐based lead optimization. Proteins 2014; 82:1765–1776. © 2014 Wiley Periodicals, Inc.     

Computer-aided identification of novel 3,5-substituted rhodanine derivatives with activity against Staphylococcus aureus DNA gyrase

Methicillin resistant Staphylococcus aureus (MRSA) is among the major drug resistant bacteria that persist in both the community and clinical settings due to resistance to commonly used antimicrobials. This continues to fuel the need for novel compounds that are active against this organism. For this purpose we have targeted the type IIA bacterial topoisomerase, DNA gyrase, an essential enzyme involved in bacterial replication, through the ATP-dependent supercoiling of DNA. The virtual screening tool Shape Signatures was applied to screen a large database for agents with shape similar to Novobiocin, a known gyrase B inhibitor. The binding energetics of the top hits from this initial screen were further validated by molecular docking. Compounds with the highest score on available crystal structure of homologous DNA gyrase from Thermus thermophilus were selected. From this initial set of compounds, several rhodanine-substituted derivatives had the highest antimicrobial activity against S. aureus, as determined by minimal inhibitory concentration assays, with Novobiocin as the positive control. Further activity validation of the rhodanine compounds through biochemical assays confirmed their inhibition of both the supercoiling and the ATPase activity of DNA gyrase. Subsequent docking and molecular dynamics on the crystal structure of DNA gyrase from S. aureus when it became available, provides further rationalization of the observed biochemical activity and understanding of the receptor–ligand interactions. A regression model for MIC prediction against S. aureus is generated based on the current molecules studied as well as other rhodanines derivatives found in the literature.     

Analysis of the structure and dynamics of human serum albumin

A computational approach to designing a peptide-based ligand for the purification of human serum albumin (HSA) was undertaken using molecular docking and molecular dynamics (MD) simulation. A three-step procedure was performed to design a specific ligand for HSA. Based on the candidate pocket structure of HSA (warfarin binding site), a peptide library was built. These peptides were then docked into the pocket of HSA using the GOLD program. The GOLDscore values were used to determine the affinity of peptides for HSA. Consequently, the dipeptide Trp–Trp, which shows a high GOLDscore value, was selected and linked to a spacer arm of Lys[CO(CH₂)₅NH] on the surface of ECH-lysine sepharose 4 gel. For further evaluation, the Autodock Vina program was used to dock the linked compound into the pocket of HSA. The docking simulation was performed to obtain a first guess of the binding structure of the spacer–Trp–Trp–HSA complex and subsequently analyzed by MD simulations to assess the reliability of the docking results. These MD simulations indicated that the ligand–HSA complex remains stable, and water molecules can bridge between the ligand and the protein by hydrogen bonds. Finally, absorption spectroscopic studies were performed to illustrate the appropriateness of the binding affinity of the designed ligand toward HSA. These studies demonstrate that the designed dipeptide can bind preferentially to the warfarin binding site. Graphical Abstract

Three-step computational approach to the design of a dipeptide ligand for human serum albumin purification exploiting structure-based docking and molecular dynamics simulation     

Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking

One approach to incorporate protein flexibility in molecular docking is the use of an ensemble consisting of multiple protein structures. Sequentially docking each ligand into a large number of protein structures is computationally too expensive to allow large-scale database screening. It is challenging to achieve a good balance between docking accuracy and computational efficiency. In this work, we have developed a fast, novel docking algorithm utilizing multiple protein structures, referred to as ensemble docking, to account for protein structural variations. The algorithm can simultaneously dock a ligand into an ensemble of protein structures and automatically select an optimal protein structure that best fits the ligand by optimizing both ligand coordinates and the conformational variable m, where m represents the m-th structure in the protein ensemble. The docking algorithm was validated on 10 protein ensembles containing 105 crystal structures and 87 ligands in terms of binding mode and energy score predictions. A success rate of 93% was obtained with the criterion of root-mean-square deviation <2.5 A if the top five orientations for each ligand were considered, comparable to that of sequential docking in which scores for individual docking are merged into one list by re-ranking, and significantly better than that of single rigid-receptor docking (75% on average). Similar trends were also observed in binding score predictions and enrichment tests of virtual database screening. The ensemble docking algorithm is computationally efficient, with a computational time comparable to that for docking a ligand into a single protein structure. In contrast, the computational time for the sequential docking method increases linearly with the number of protein structures in the ensemble. The algorithm was further evaluated using a more realistic ensemble in which the corresponding bound protein structures of inhibitors were excluded. The results show that ensemble docking successfully predicts the binding modes of the inhibitors, and discriminates the inhibitors from a set of noninhibitors with similar chemical properties. Although multiple experimental structures were used in the present work, our algorithm can be easily applied to multiple protein conformations generated by computational methods, and helps improve the efficiency of other existing multiple protein structure(MPS)-based methods to accommodate protein flexibility.     

Computational investigation of the molecular conformation-dependent binding mode of (<Emphasis Type="Italic">E</Emphasis>)-<Emphasis Type="Italic">β</Emphasis>-farnesene analogs with a heterocycle to aphid odorant-binding proteins

Odorant-binding proteins (OBPs) play an important role as ligand-transfer filters in olfactory recognition in insects. (E)-β-farnesene (EBF) is the main component of the aphid alarm pheromone and could keep aphids away from crops to prevent damage. Computational insight into the molecular binding mode of EBF analogs containing a heterocycle based on the structure of Megoura viciae OBP 3 (MvicOBP3) was obtained by molecular docking and molecular dynamics simulations. The results showed that high affinity EBF analogs substituted with an aromatic ring present a unique binding conformation in the surface cavity of MvicOBP3. A long EBF chain was located inside the cavity and was surrounded by many hydrophobic residues, while the substituted aromatic ring was exposed to the outside due to limitations from the formation of multiple hydrogen bonds. However, the low activity EBF analogs displayed an exactly inverted binding pose, with EBF loaded on the external side of the protein cavity. The affinity of the recently synthesized EBF analogs containing a triazine ring was evaluated in silico based on the binding modes described above and in vitro through fluorescence competitive binding assay reported later. Compound N1 not only showed a similar binding conformation to that of the high affinity analogs but was also found to have a much higher docking score and binding affinity than the other analogs. In addition, the docking score results correlated well with the predicted logP values for these EBF analogs, suggesting highly hydrophobic interactions between the protein and ligand. These studies provide an in silico screening model for the binding affinity of EBF analogs in order to guide their rational design based on aphid OBPs.     

Macromolecular docking restrained by a small angle X-ray scattering profile

While many structures of single protein components are becoming available, structural characterization of their complexes remains challenging. Methods for modeling assembly structures from individual components frequently suffer from large errors, due to protein flexibility and inaccurate scoring functions. However, when additional information is available, it may be possible to reduce the errors and compute near-native complex structures. One such type of information is a small angle X-ray scattering (SAXS) profile that can be collected in a high-throughput fashion from a small amount of sample in solution. Here, we present an efficient method for protein–protein docking with a SAXS profile (FoXSDock): generation of complex models by rigid global docking with PatchDock, filtering of the models based on the SAXS profile, clustering of the models, and refining the interface by flexible docking with FireDock. FoXSDock is benchmarked on 124 protein complexes with simulated SAXS profiles, as well as on 6 complexes with experimentally determined SAXS profiles. When induced fit is less than 1.5 Å interface C_α RMSD and the fraction residues of missing from the component structures is less than 3%, FoXSDock can find a model close to the native structure within the top 10 predictions in 77% of the cases; in comparison, docking alone succeeds in only 34% of the cases. Thus, the integrative approach significantly improves on molecular docking alone. The improvement arises from an increased resolution of rigid docking sampling and more accurate scoring.