Protein-ligand Interface Research Articles

An in silico insight on inhibitory potential of short-chain PFHxA and PFHxS against endogenous antioxidant enzymes

Per- and polyfluoroalkyl substances (PFAS) are a large group of synthetic chemicals that are globally used in various commercial products like cloths, carpets, cookware, firefighting foams, cosmetics, electronic products, etc. Inappropriate disposal of these consumer products could lead to environmental contamination with PFAS. Nowadays short-chain PFAS are substituting the long chain PFAS due to their short half-life. However, multiple studies have suggested that short-chain PFAS such as Perfluorohexanoic acid (PFHxA) and Perfluorohexanesulfonic acid (PFHxS) modulate antioxidant enzyme activities, induce Reactive Oxygen Species production, and contribute to oxidative stress in organisms. Notably, experimental set-ups aiming to investigate the mode of action of these compounds on antioxidants are entirely lacking. Hence in the present study, a ligand-docking approach was adopted to investigate the potential intermolecular interactions between short-chain PFAS and major antioxidants such as Glutathione peroxidase-4, Glutathione reductase, Glutathione S-Transferase, Catalase, and Superoxide dismutase-1. Three-dimensional structures of proteins were processed and docked individually with PFHxA and PFHxS using the AutoDock vina.. Results indicated significant binding affinities between various antioxidant proteins and PFAS. Interestingly, non-covalent interactions such as H-bonds, van der Waals, CH bonds, halogen bonds, and alkyl/pi-alkyl were apparent at protein-ligand interface. Hence, these results suggest that both PFHxA and PFHxS may potentially interact with the endogenous antioxidants and this outcome can be detrimental to the redox homeostasis of living organisms.

How E-, L-, and P-Selectins Bind to sLex and PSGL-1: A Quantification of Critical Residue Interactions.

Selectins and their ability to interact with specific ligands are a cornerstone in cell communication. Over the last three decades, a considerable wealth of experimental and molecular modeling insights into their structure and modus operandi were gathered. Nonetheless, explaining the role of individual selectin residues on a quantitative level remained elusive, despite its importance in understanding the structure-function relationship in these molecules and designing their inhibitors. This work explores essential interactions of selectin-ligand binding, employing a multiscale approach that combines molecular dynamics, quantum-chemical calculations, and residue interaction network models. Such an approach successfully reproduces most of the experimental findings. It proves to be helpful, with the potential for becoming an established tool for quantitative predictions of residue contribution to the binding of biomolecular complexes. The results empower us to quantify the importance of particular residues and functional groups in the protein-ligand interface and to pinpoint differences in molecular recognition by the three selectins. We show that mutations in the E-, L-, and P-selectins, e.g., different residues in positions 46, 85, 97, and 107, present a crucial difference in how the ligand is engaged. We assess the role of sulfation of tyrosine residues in PSGL-1 and suggest that TyrSO3- in position 51 interacting with Arg85 in P-selectin is a significant factor in the increased affinity of P-selectin to PSGL-1 compared to E- and L-selectins. We propose an original pharmacophore targeting five essential PSGL-binding sites based on the analysis of the selectin···PSGL-1 interactions.

Structural insight to elucidate the binding specificity of the anti-cortisol Fab fragment with glucocorticoids

Cortisol is a steroid hormone that is produced by the adrenal gland. It is a primary stress hormone that increases glucose levels in the blood stream. High concentrations of cortisol in the body can be used as a biomarker for acute and chronic stress and related mental and physiological disorders. Therefore, the accurate quantification of cortisol levels in body fluids is essential for clinical diagnosis. In this article, we describe the isolation of recombinant anti-cortisol antibodies with high affinity for cortisol and discover their cross-reactivity with other glucocorticoids. To describe the cortisol binding site and elucidate the structural basis for the binding specificity, the high-resolution crystal structures of the anti-cortisol (17) Fab fragment in the absence of glucocorticoid (2.00 Å) and the presence of cortisol (2.26 Å), corticosterone (1.86 Å), cortisone (1.85 Å) and prednisolone (2.00 Å) were determined. To our knowledge, this is the first determined crystal structure of a cortisol-specific antibody. The recognition of cortisol is driven by hydrophobic interactions and hydrogen bonding at the protein–ligand interface coupled with a conformational transition. Comparison of ligand-free and ligand-bound structures showed that the side chains of residues Tyr58-H and Arg56-H can undergo local conformational changes at the binding site, most likely prior to the binding event via a conformational selection mechanism. Compared to other anti-steroid antibody–antigen complexes, (17) Fab possesses a structurally unique steroid binding site, as the H3 loop from the CDR area has only a minor contribution, but framework residues have a prominent contribution to hapten binding.

Water Networks Repopulate Protein-Ligand Interfaces with Temperature.

High‐resolution crystal structures highlight the importance of water networks in protein–ligand interactions. However, as these are typically determined at cryogenic temperature, resulting insights may be structurally precise but not biologically accurate. By collecting 10 matched room‐temperature and cryogenic datasets of the biomedical target Hsp90α, we identified changes in water networks that impact protein conformations at the ligand binding interface. Water repositioning with temperature repopulates protein ensembles and ligand interactions. We introduce Flipper conformational barcodes to identify temperature‐sensitive regions in electron density maps. This revealed that temperature‐responsive states coincide with ligand‐responsive regions and capture unique binding signatures that disappear upon cryo‐cooling. Our results have implications for discovering Hsp90 selective ligands, and, more generally, for the utility of hidden protein and water conformations in drug discovery.

Prediction of Water Distributions and Displacement at Protein-Ligand Interfaces.

The retention and displacement of water molecules during formation of ligand-protein interfaces play a major role in determining ligand binding. Understanding these effects requires a method for positioning of water molecules in the bound and unbound proteins and for defining water displacement upon ligand binding. We describe an algorithm for water placement and a calculation of ligand-driven water displacement in >9000 protein-ligand complexes. The algorithm predicts approximately 38% of experimental water positions within 1.0 Å and about 83% within 1.5 Å. We further show that the predicted water molecules can complete water networks not detected in crystallographic structures of the protein-ligand complexes. The algorithm was also applied to solvation of the corresponding unbound proteins, and this allowed calculation of water displacement upon ligand binding based on differences in the water network between the bound and unbound structures. We illustrate use of this approach through comparison of water displacement by structurally related ligands at the same binding site. This method for evaluation of water displacement upon ligand binding may be of value for prediction of the effects of ligand modification in drug design.

The small-molecule protein ligand interface stabiliser E7820 induces differential cell line specific responses of integrin \u03b12 expression

BackgroundThe mechanism of small-molecule stabilised protein-protein interactions is of growing interest in the pharmacological discovery process. A plethora of different substances including the aromatic sulphonamide E7820 have been identified to act by such a mechanism. The process of E7820 induced CAPERα degradation and the resultant transcriptional down regulation of integrin α2 expression has previously been described for a variety of different cell lines and been made responsible for E7820’s antiangiogenic activity. Currently the application of E7820 in the treatment of various malignancies including pancreas carcinoma and breast cancer is being investigated in pre-clinical and clinical trials. It has been shown, that integrin α2 deficiency has beneficial effects on bone homeostasis in mice. To transfer E7820 treatment to bone-related pathologies, as non-healing fractures, osteoporosis and bone cancer might therefore be beneficial. However, at present no data is available on the effect of E7820 on osseous cells or skeletal malignancies.MethodsPre-osteoblastic (MC3T3 and Saos-2) cells and endothelial (eEnd2 cells and HUVECs) cells, each of human and murine origin respectively, were investigated. Vitality assay with different concentrations of E7820 were performed. All consecutive experiments were done at a final concentration of 50 ng/ml E7820. The expression and production of integrin α2 and CAPERα were investigated by quantitative real-time PCR and western blotting. Expression of CAPERα splice forms was differentiated by semi-quantitiative reverse transcriptase PCR.ResultsHere we present the first data showing that E7820 can increase integrin α2 expression in the pre-osteoblast MC3T3 cell line whilst also reproducing canonical E7820 activity in HUVECs. We show that the aberrant activity of E7820 in MC3T3 cells is likely due to differential activity of CAPERα at the integrin α2 promoter, rather than due to differential CAPERα degradation or differential expression of CAPERα spliceforms.ConclusionThe results presented here indicate that E7820 may not be suitable to treat certain malignancies of musculoskeletal origin, due to the increase in integrin α2 expression it may induce. Further investigation of the differential functioning of CAPERα and the integrin α2 promoter in cells of various origin would however be necessary to more clearly differentiate between cell lines that will positively respond to E7820 from those that will not.

Corrigendum

Chemical Biology & Drug DesignVolume 97, Issue 6 p. 1210-1210 CORRIGENDUMFree Access Corrigendum This article corrects the following: The effect of multiple simulation parameters on MM/PBSA performance for binding affinity prediction of CB1 cannabinoid receptor agonists and antagonists Jason S. E. Loo, Amelia Y. Y. Yong, Yen Nee Yong, Volume 96Issue 5Chemical Biology & Drug Design pages: 1244-1254 First Published online: June 25, 2020 First published: 10 May 2021 https://doi.org/10.1111/cbdd.13844AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat The effect of multiple simulation parameters on MM/PBSA performance for binding affinity prediction of CB1 cannabinoid receptor agonists and antagonists. Chem Biol Drug Des. 2020;96:1244–1254. Jason S. E. Loo, Amelia Y. Y. Yong, Yen Nee Yong. The Abstract was incorrectly amended during proofing. The corrected Abstract is reproduced below. Both the inactive- and active-state CB1 receptor crystal structures have now been solved, allowing their application in various structure-based drug design methods. One potential method utilizing these crystal structures is the Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) method of predicting relative binding free energies. However, MM/PBSA performance is sensitive to various simulation parameters and often requires optimization for the system of interest. In this study, we evaluated the effects of various simulation parameters, namely simulation length, choice of dielectric constant, inclusion of explicit water molecules, and inclusion of entropy, on the ability of MM/PBSA to predict the experimental binding free energies of a set of known CB1 agonists and antagonists. MM/PBSA produced r values ranging from 0.410 to 0.688 for the CB1 agonists and from 0.420 to 0.678 for the CB1 antagonists depending on the simulation parameters, which were modestly better than correlations obtained using docking scores. Using fewer replicates with a longer simulation length and a higher solute dielectric constant value had the largest positive effect on performance. Including explicit water molecules at the protein–ligand interface had a minor positive effect on performance at higher dielectric constant values, while the inclusion of entropy had a detrimental effect across most of the dataset. REFERENCE Loo, JSE, Yong, AYY, & Yong, YN (2020). The effect of multiple simulation parameters on MM/PBSA performance for binding affinity prediction of CB1 cannabinoid receptor agonists and antagonists. Chem Biol Drug Des., 96: 1244– 1254. https://doi.org/10.1111/cbdd.13733Wiley Online LibraryCASPubMedWeb of Science®Google Scholar Volume97, Issue6June 2021Pages 1210-1210 ReferencesRelatedInformation

Estimating the accuracy of pharmacophore-based detection of cognate receptor-ligand pairs in the immunoglobulin superfamily.

Secreted and membrane-bound members of the immunoglobulin superfamily (IgSF) encompass a large, diverse array of proteins that play central roles in immune response and neural development, and are implicated in diseases ranging from cancer to rheumatoid arthritis. Despite the potential biomedical benefits of understanding IgSF:IgSF cognate receptor-ligand interactions, relatively little about them is known at a molecular level, and experimentally probing all possible receptor-ligand pairs is prohibitively costly. The Protein Ligand Interface Design (ProtLID) algorithm is a computational pharmacophore-based approach to identify cognate receptor-ligand pairs that was recently validated in a pilot study on a small set of IgSF complexes. Although ProtLID has shown a success rate of 61% at identifying at least one cognate ligand for a given receptor, it currently lacks any form of confidence measure that can prioritize individual receptor-ligand predictions to pursue experimentally. In this study, we expanded the application of ProtLID to cover all IgSF complexes with available structural data. In addition, we introduced an approach to estimate the confidence of predictions made by ProtLID based on a statistical analysis of how the ProtLID-constructed pharmacophore matches the structures of candidate ligands. The confidence score combines the physicochemical compatibility, spatial consistency, and mathematical skewness of the distribution of matches throughout a set of candidate ligands. Our results suggest that a subset of cases meeting stringent confidence criteria will always have at least one successful receptor-ligand prediction.

Grand: A Python Module for Grand Canonical Water Sampling in OpenMM.

Networks of water molecules can play a critical role at the protein-ligand interface and can directly influence drug-target interactions. Grand canonical methods aid in the sampling of these water molecules, where conventional molecular dynamics equilibration times are often long, by allowing waters to be inserted and deleted from the system, according to the chemical potential. Here, we present our open source Python module, grand (https://github.com/essex-lab/grand), which allows molecular dynamics simulations to be performed in conjunction with grand canonical Monte Carlo sampling, using the OpenMM simulation engine. We demonstrate the accuracy of this module by reproducing the density of bulk water observed from constant pressure simulations. Application of this code to the bovine pancreatic trypsin inhibitor protein reproduces three buried crystallographic water sites that are poorly sampled using conventional molecular dynamics.

VisGReMLIN: graph mining-based detection and visualization of conserved motifs at 3D protein-ligand interface at the atomic level

BackgroundInteractions between proteins and non-proteic small molecule ligands play important roles in the biological processes of living systems. Thus, the development of computational methods to support our understanding of the ligand-receptor recognition process is of fundamental importance since these methods are a major step towards ligand prediction, target identification, lead discovery, and more. This article presents visGReMLIN, a web server that couples a graph mining-based strategy to detect motifs at the protein-ligand interface with an interactive platform to visually explore and interpret these motifs in the context of protein-ligand interfaces.ResultsTo illustrate the potential of visGReMLIN, we conducted two cases in which our strategy was compared with previous experimentally and computationally determined results. visGReMLIN allowed us to detect patterns previously documented in the literature in a totally visual manner. In addition, we found some motifs that we believe are relevant to protein-ligand interactions in the analyzed datasets.ConclusionsWe aimed to build a visual analytics-oriented web server to detect and visualize common motifs at the protein-ligand interface. visGReMLIN motifs can support users in gaining insights on the key atoms/residues responsible for protein-ligand interactions in a dataset of complexes.

The Binding of Drug Molecules to Serum Albumin: The Effect of Drug Hydrophobicity on Binding Strength and Protein Desolvation.

In this work, dual polarization interferometry (DPI) and quartz crystal microgravimetry with dissipation monitoring (QCM-D) were used to examine the binding characteristics and structure-activity relationships of 12 common drugs on a model bovine serum albumin (BSA) film. By taking advantage of the different hydration sensitivities of DPI and QCM-D, we were able to quantify changes in the solvent state upon drug binding to BSA. Quantifying the changes in water mass within binding pockets and upon drug-protein binding allows for a more complete understanding of binding phenomena between drug molecules and serum proteins. For the drugs tested, a quantitative structure-activity relationship (QSAR) was used to establish a correlation between drug binding (KD) and hydrophobicity (ClogP), with the latter being related to the drug's ability to desolvate the BSA upon binding. Understanding these relationships provides insight into the role of water at the protein-ligand interface and is of particular importance in the area of ligand binding within the field of drug design. This study underscores the importance of hydrophobicity to drug binding kinetics and may be used to further understand and improve drug design and delivery protocols.

Strong and Weak Hydrogen Bonds in Protein–Ligand Recognition

The hydrogen bond has justifiably been termed the ‘master key of molecular recognition’. It is an interaction that is weaker than the covalent bond and stronger than the van der Waals interaction. The ubiquity and flexibility of hydrogen bonds make them the most important physical interaction in systems of biomolecules in aqueous solution. Hydrogen bonding plays a significant role in many chemical and biological processes, including ligand binding and enzyme catalysis. In biological processes, both specificity and reversibility are important. Weaker interactions can be made and broken more easily than stronger interactions. In this context, it is of interest to assess the relative significance of strong and weak interactions in the macromolecular recognition processes. Is protein–ligand binding governed by conventional, that is, electrostatic N–H…O and O–H…O hydrogen bonds, or do weaker interactions with a greater dispersive component such as C–H…O hydrogen bonds also play a role? If so, to what extent are they significant? Most proteins, involving as they do, main chains, side chains, and differently bound forms of water, do not really have a static fixed structure, but rather have a dynamic, breathing nature. This tendency may to some extent be lessened by the ligands which are small molecules, but in the end, it is reasonable to expect that the strong and weak hydrogen bonds inside the protein and also at the protein–ligand interface will also have dynamic character; arguably, the weaker the hydrogen bond, the greater its dynamic character. These are often central to the much debated mechanisms of binding such as conformational selection and induced fit. All protein–ligand interactions must compete with interactions with water; both the protein and the ligand are solvated before complexation and lose their solvation shell on complex formation. Conversely, the entropic cost of trapping highly mobile water molecules in the binding site is large. However, in favorable cases, these losses are suitably compensated by the enthalpic gain resulting from water-mediated hydrogen bonds. In effect, the enthalpy–entropy balance is a fine one, and for a water molecule to be able to contribute to binding affinity, it has to be in a binding site that provides the maximum number of hydrogen-bond partners at the optimum distance and orientation. In summary, hydrogen bonds are crucial to the recognition of ligands by proteins. Integration of knowledge gained from more high-quality protein–ligand structures into theoretical and computational molecular models will be an exciting challenge in the coming years.

DeepBindRG: a deep learning based method for estimating effective protein-ligand affinity.

Proteins interact with small molecules to modulate several important cellular functions. Many acute diseases were cured by small molecule binding in the active site of protein either by inhibition or activation. Currently, there are several docking programs to estimate the binding position and the binding orientation of protein–ligand complex. Many scoring functions were developed to estimate the binding strength and predict the effective protein–ligand binding. While the accuracy of current scoring function is limited by several aspects, the solvent effect, entropy effect, and multibody effect are largely ignored in traditional machine learning methods. In this paper, we proposed a new deep neural network-based model named DeepBindRG to predict the binding affinity of protein–ligand complex, which learns all the effects, binding mode, and specificity implicitly by learning protein–ligand interface contact information from a large protein–ligand dataset. During the initial data processing step, the critical interface information was preserved to make sure the input is suitable for the proposed deep learning model. While validating our model on three independent datasets, DeepBindRG achieves root mean squared error (RMSE) value of pKa (−logKd or −logKi) about 1.6–1.8 and R value around 0.5–0.6, which is better than the autodock vina whose RMSE value is about 2.2–2.4 and R value is 0.42–0.57. We also explored the detailed reasons for the performance of DeepBindRG, especially for several failed cases by vina. Furthermore, DeepBindRG performed better for four challenging datasets from DUD.E database with no experimental protein–ligand complexes. The better performance of DeepBindRG than autodock vina in predicting protein–ligand binding affinity indicates that deep learning approach can greatly help with the drug discovery process. We also compare the performance of DeepBindRG with a 4D based deep learning method “pafnucy”, the advantage and limitation of both methods have provided clues for improving the deep learning based protein–ligand prediction model in the future.

Targeting the Bacterial Transglycosylase: Antibiotic Development from a Structural Perspective.

One of the major threats to human life nowadays is widespread antibiotic resistance. Antibiotics are used to treat bacterial infections by targeting their essential pathways, such as the biosynthesis of bacterial cell walls. Bacterial transglycosylase, particularly glycosyltransferase family 51 (GT51), is one critical player in the cell wall biosynthesis and has long been known as a promising yet challenging target for antibiotic development. Here, we review the structural studies of this protein and summarize recent progress in developing its specific inhibitors, including synthetic substrate analogs and novel compounds identified from high-throughput screens. A detailed analysis of the protein-ligand interface has also provided us with valuable insights into the future antibiotic development against the bacterial transglycosylase.

Complementary Steric Engineering at the Protein-Ligand Interface for Analogue-Sensitive TET Oxygenases.

Ten-eleven translocation (TET) enzymes employ O2, earth-abundant iron, and 2-ketoglutarate (2KG) to perform iterative C-H oxidation of 5-methylcytosine in DNA to control expression of the mammalian genome. Given that more than 60 such C-H oxygenases are present in humans, determining context-dependent functions of each of these enzymes is a pivotal challenge. In an effort to tackle the problem, we developed analogue-sensitive TET enzymes to perturb the activity of a specific member. We rationally engineered the TET2-2KG interface to develop TET2 variants with an expanded active site that can be specifically inhibited by the N-oxalylglycine (NOG) derivatives carrying a complementary steric "bump". Herein, we describe the identification and engineering of a bulky gatekeeper residue for TET proteins, characterize the orthogonal mutant-inhibitor pairs, and show generality of the approach. Employing cell-permeable NOG analogues, we show that the TET2 mutant can be specifically inhibited to conditionally modulate cytosine methylation in chromosomal DNA in intact human cells. Finally, we demonstrate application of the orthogonal mutant-inhibitor pair to probe transcriptional activity of a specific TET member in cells. Our work provides a general platform for developing analogue-sensitive 2KG-dependent oxygenases to unravel their functions in diverse signaling processes.

Inhibitor and peptide binding to calmodulin characterized by high pressure Fourier transform infrared spectroscopy and Förster resonance energy transfer

We compare the binding of an inhibitor with that of a natural peptide to Ca2+ saturated calmodulin (holo-CaM). As inhibitor we have chosen trifluoperazine (TFP) that is inducing a huge conformational change of holo-CaM from the open dumbbell-shaped to the closed globular conformation upon binding. On the other hand, melittin is used as model peptide, which is a well-known natural binding partner of holo-CaM. The experiments are carried out as a function of pressure to reveal the contribution of volume or packing effects to the stability of the calmodulin-ligand complexes. From high-pressure Fourier transform infrared (FTIR) spectroscopy, we find that the holo-CaM/TFP complex has a much higher pressure stability than the holo-CaM/melittin complex. Although the analysis of the secondary structure of holo-CaM (without and with ligand) indicates no major changes up to several kbar, pressure-induced exposure of α-helices to water is most pronounced for holo-CaM without ligand, followed by holo-CaM/melittin and then holo-CaM/TFP. Moreover, structural pressure resistance of the holo-CaM/TFP complex in comparison with the holo-CaM/melittin complex is also clearly visible by higher Ca2+ affinity. Förster resonance energy transfer (FRET) from the Tyr residues of holo-CaM to the Trp residue of melittin even suggests some partial dissociation of the complex under pressure which points to void volumes at the protein-ligand interface and to electrostatic binding. Thus, all results of this study show that the inhibitor TFP binds to holo-CaM with higher packing density than the peptide melittin enabling a favorable volume contribution to the inhibitor efficiency.

Structure determination of protein-ligand complexes by NMR in solution.

In this paper, we discuss methods for determining structures of protein-ligand complexes by NMR in solution. Our discussion is based on small ligands (<2 kDa) as for example drugs, metabolites or oligo-peptides, but most of the considerations also apply to more general cases. In NMR in solution, the kinetics of association and dissociation of the complex - the exchange rate - determines the optimal sample preparation and the NMR experimental approach. Additionally, depending on the part of the complex that will be studied (only the bound ligand, the protein, the protein-ligand interface or the entire protein-ligand complex structure), different types of NMR experiments are needed. Therefore, the choice of a combination of the appropriate experiment and a suitable sample preparation in terms of ligand to protein ratios are discussed in detail. Also, considerations for practically preparing samples of protein-ligand complexes and carrying out experiments including trouble shooting are described. For structure determination, the scope of this paper is limited to NOE-based methods and some of the most recent approaches will be covered.

Prediction of Ordered Water Molecules in Protein Binding Sites from Molecular Dynamics Simulations: The Impact of Ligand Binding on Hydration Networks.

Water plays a major role in ligand binding and is attracting increasing attention in structure-based drug design. Water molecules can make large contributions to binding affinity by bridging protein–ligand interactions or by being displaced upon complex formation, but these phenomena are challenging to model at the molecular level. Herein, networks of ordered water molecules in protein binding sites were analyzed by clustering of molecular dynamics (MD) simulation trajectories. Locations of ordered waters (hydration sites) were first identified from simulations of high resolution crystal structures of 13 protein–ligand complexes. The MD-derived hydration sites reproduced 73% of the binding site water molecules observed in the crystal structures. If the simulations were repeated without the cocrystallized ligands, a majority (58%) of the crystal waters in the binding sites were still predicted. In addition, comparison of the hydration sites obtained from simulations carried out in the absence of ligands to those identified for the complexes revealed that the networks of ordered water molecules were preserved to a large extent, suggesting that the locations of waters in a protein–ligand interface are mainly dictated by the protein. Analysis of >1000 crystal structures showed that hydration sites bridged protein–ligand interactions in complexes with different ligands, and those with high MD-derived occupancies were more likely to correspond to experimentally observed ordered water molecules. The results demonstrate that ordered water molecules relevant for modeling of protein–ligand complexes can be identified from MD simulations. Our findings could contribute to development of improved methods for structure-based virtual screening and lead optimization.

PLHINT: A knowledge-driven computational approach based on the intermolecular H bond interactions at the protein-ligand interface from docking solutions

The tendency of docking scoring functions to generate crystal close conformations of ligands bound to protein structures face limitations in not reproducing the exact crystal intermolecular contacts in dock poses. Intermolecular H bond contacts enumerated at the protein-docked ligand interface can be used to train scoring models and improve virtual screening performance. There is a need to incorporate additional knowledge of protein-ligand H bond contacts in extension to crystal contacts from docking solutions within the reproducibility efficiency of the docking program. A computational approach PLHINT (Protein-ligand H bond interaction pattern) is presented here which extracts intermolecular H bond interactions from native-like docked ligand poses, transform into the scoring scheme and apply over the virtual screening results of database molecules. The basic premise of the PLHINT approach is to score the most observed H bond patterns with the high score to achieve high recovery rates. Tested on ten diverse DUD-E benchmark datasets, the approach has demonstrated better overall performance and ligand enrichment competency over virtual screening results generated by three genetic algorithm-based docking programs viz. AutoDock Vina, FlexAID and PLANTS. Furthermore, the approach has successfully recovered the poor and random virtual screening results with better enrichments.

Impact of Azidohomoalanine Incorporation on Protein Structure and Ligand Binding.

The impact of the incorporation of a non-natural amino acid (NNAA) on protein structure, dynamics, and ligand binding has not been studied rigorously so far. NNAAs are regularly used to modify proteins post-translationally in vivo and in vitro through click chemistry. Herein, structural characterisation of the impact of the incorporation of azidohomoalanine (AZH) into the model protein domain PDZ3 is examined by means of NMR spectroscopy and X-ray crystallography. The structure and dynamics of the apo state of AZH-modified PDZ3 remain mostly unperturbed. Furthermore, the binding of two PDZ3 binding peptides are unchanged upon incorporation of AZH. The interface of the AZH-modified PDZ3 and an azulene-linked peptide for vibrational energy transfer studies has been mapped by means of chemical shift perturbations and NOEs between the unlabelled azulene-linked peptide and the isotopically labelled protein. Co-crystallisation and soaking failed for the peptide-bound holo complex. NMR spectroscopy, however, allowed determination of the protein-ligand interface. Although the incorporation of AZH was minimally invasive for PDZ3, structural analysis of NNAA-modified proteins through the methodology presented herein should be performed to ensure structural integrity of the studied target.