Computational approaches to identifying and characterizing protein binding sites for ligand design

Molecular Dynamics Simulations of the Human Ecto-5'-Nucleotidase (h-ecto-5'-NT, CD73): Insights into Protein Flexibility and Binding Site Dynamics

Human ecto-5'-nucleotidase (h-ecto-5'-NT, CD73) is a homodimeric Zn2+-binding metallophosphoesterase that hydrolyzes adenosine 5'-monophosphate (5'-AMP) to adenosine and phosphate. h-Ecto-5'-NT is a key enzyme in purinergic signaling pathways and has been recognized as a promising biological target for several diseases, including cancer and inflammatory, infectious, and autoimmune diseases. Despite its importance as a biological target, little is known about h-ecto-5'-NT dynamics, which poses a considerable challenge to the design of inhibitors of this target enzyme. Here, to explore h-ecto-5'-NT flexibility, all-atom unbiased molecular dynamics (MD) simulations were performed. Remarkable differences in the dynamics of the open (catalytically inactive) and closed (catalytically active) conformations of the apo-h-ecto-5'-NT were observed during the simulations, and the nucleotide analogue inhibitor AMPCP was shown to stabilize the protein structure in the closed conformation. Our results suggest that the large and complex domain motion that enables the h-ecto-5'-NT open/closed conformational switch is slow, and therefore, it could not be completely captured within the time scale of our simulations. Nonetheless, we were able to explore the faster dynamics of the h-ecto-5'-NT substrate binding site, which is mainly located at the C-terminal domain and well conserved among the protein's open and closed conformations. Using the TRAPP ("Transient Pockets in Proteins") approach, we identified transient subpockets close to the substrate binding site. Finally, conformational states of the substrate binding site with higher druggability scores than the crystal structure were identified. In summary, our study provides valuable insights into h-ecto-5'-NT structural flexibility, which can guide the structure-based design of novel h-ecto-5'-NT inhibitors.

3-Amino-Substituted Analogues of Fusidic Acid as Membrane-Active Antibacterial Compounds

Fusidic acid (FA) is an antibiotic with high activity against Staphylococcus aureus; it has been used in clinical practice since the 1960s. However, the narrow antimicrobial spectrum of FA limits its application in the treatment of bacterial infections. In this regard, this work aims both at the study of the antimicrobial effect of a number of FA amines and at the identification of their potential biological targets. In this way, FA analogues containing aliphatic and aromatic amino groups and biogenic polyamine, spermine and spermidine, moieties at the C-3 atom, were synthesized (20 examples). Pyrazinecarboxamide-substituted analogues exhibit a high antibacterial activity against S. aureus (MRSA) with MIC ≤ 0.25 μg/mL. Spermine and spermidine derivatives, along with activity against S. aureus, also inhibit the growth and reproduction of Gram-negative bacteria Escherichia coli, Acinetobacter baumannii, and Pseudomonas aeruginosa, and have a high fungicidal effect against Candida albicans and Cryptococcus neoformans. The study of the membrane activity demonstrated that the spermidine- and spermine-containing compounds are able to immerse into membranes and disorder the lipidsleading to a detergent effect. Moreover, spermine-based compounds are also able to form ion-permeable pores in the lipid bilayers mimicking the bacterial membranes. Using molecular docking, inhibition of the protein synthesis elongation factor EF-G was proposed, and polyamine substituents were shown to make the greatest contribution to the stability of the complexes of fusidic acid derivatives with biological targets. This suggests that the antibacterial effect of the obtained compounds may be associated with both membrane activity and inhibition of the elongation factor EF-G.

The binding pocket properties were fundamental to functional diversification of the GDSL-type esterases/lipases gene family in cotton

Cotton is one of the most important crops in the world. GDSL-type esterases/lipases (GELPs) are widely present in all kingdoms and play an essential role in regulating plant growth, development, and responses to abiotic and biotic stresses. However, the molecular mechanisms underlying this functional diversity remain unclear. Here, based on the identification of the GELP gene family, we applied genetic evolution and molecular simulation techniques to explore molecular mechanisms in cotton species. A total of 1502 GELP genes were identified in 10 cotton species. Segmental duplication and differences in evolutionary rates are the leading causes of the increase in the number and diversity of GELP genes during evolution for ecological adaptation. Structural analysis revealed that the GELP family has high structural diversity. Moreover, molecular simulation studies have demonstrated significant differences in the properties of the binding pockets among cotton GELPs. In the process of adapting to the environment, GELPs not only have segmental duplication but also have different evolutionary rates, resulting in gene diversity. This diversity leads to significant differences in the 3D structure and binding pocket properties and, finally, to functional diversity. These findings provide a reference for further functional analyses of plant GELPs.

In Silico Structure-Based Vaccine Design

Structure-based vaccine design (SBVD) is an important technique in computational vaccine design that uses structural information on a targeted protein to design novel vaccine candidates. This increasing ability to rapidly model structural information on proteins and antibodies has provided the scientific community with many new vaccine targets and novel opportunities for future vaccine discovery. This chapter provides a comprehensive overview of the status of in silico SBVD and discusses the current challenges and limitations. Key strategies in the field of SBVD are exemplified by a case study on design of COVID-19 vaccines targeting SARS-CoV-2 spike protein.

ELIOT: A platform to navigate the E3 pocketome and aid the design of new PROTACs

Proteolysis-targeting chimeras (PROTACs) are novel therapeutics for the treatment of human disease. They exploit the enormous potential of the E3 ligases, a class of proteins that mark a target protein for degradation via the ubiquitin-proteasome system. Despite the existence of several E3 ligase-related databases, the choice of the functioning ligase is limited to only 1.6% of those available, probably due to the fragmentary understanding of their structures and their known ligands; in fact, none of the existing databases report detailed studies covering their 3D structure or their pockets. Here, we report ELIOT (E3 LIgase pocketOme navigaTor), an accurate and complete platform containing the E3 ligase pocketome to enable navigation and selection of new E3 ligases and new ligands for the design of new PROTACs. All E3 ligase pockets were characterized with innovative 3D descriptors including their PROTAC-ability score, and similarity analyses between E3 pockets are presented. Tissue specificity and their degree of involvement in patients with specific cancer types are also annotated for each E3 ligase, enabling appropriate selection for the design of a PROTAC with improved specificity. All data are available at https://eliot.moldiscovery.com.

PointSite: A Point Cloud Segmentation Tool for Identification of Protein Ligand Binding Atoms

Accurate identification of ligand binding sites (LBS) on a protein structure is critical for understanding protein function and designing structure-based drugs. As the previous pocket-centric methods are usually based on the investigation of pseudo-surface-points outside the protein structure, they cannot fully take advantage of the local connectivity of atoms within the protein, as well as the global 3D geometrical information from all the protein atoms. In this paper, we propose a novel point clouds segmentation method, PointSite, for accurate identification of protein ligand binding atoms, which performs protein LBS identification at the atom-level in a protein-centric manner. Specifically, we first transfer the original 3D protein structure to point clouds and then conduct segmentation through Submanifold Sparse Convolution based U-Net. With the fine-grained atom-level binding atoms representation and enhanced feature learning, PointSite can outperform previous methods in atom Intersection over Union (atom-IoU) by a large margin. Furthermore, our segmented binding atoms, that is, atoms with high probability predicted by our model can work as a filter on predictions achieved by previous pocket-centric approaches, which significantly decreases the false-positive of LBS candidates. Besides, we further directly extend PointSite trained on bound proteins for LBS identification on unbound proteins, which demonstrates the superior generalization capacity of PointSite. Through cascaded filter and reranking aided by the segmented atoms, state-of-the-art performance can be achieved over various canonical benchmarks, CAMEO hard targets, and unbound proteins in terms of the commonly used DCA criteria.

Accelerated Ligand-Mapping Molecular Dynamics Simulations for the Detection of Recalcitrant Cryptic Pockets and Occluded Binding Sites

The identification and characterization of binding sites is a critical component of structure-based drug design (SBDD). Probe-based/cosolvent molecular dynamics (MD) methods that allow for protein flexibility have been developed to predict ligand binding sites. However, cryptic pockets that appear only upon ligand binding and occluded binding sites with no access to the solvent pose significant challenges to these methods. Here, we report the development of accelerated ligand-mapping MD (aLMMD), which combines accelerated MD with LMMD, for the detection of these challenging binding sites. The method was validated on five proteins with what we term "recalcitrant" cryptic pockets, which are deeply buried pockets that require extensive movement of the protein backbone to expose, and three proteins with occluded binding sites. In all the cases, aLMMD was able to detect and sample the binding sites. Our results suggest that aLMMD could be used as a general approach for the detection of such elusive binding sites in protein targets, thus providing valuable information for SBDD.

pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science

BACKGROUND

Biomolecular interactions that modulate biological processes occur mainly in cavities throughout the surface of biomolecular structures. In the data science era, structural biology has benefited from the increasing availability of biostructural data due to advances in structural determination and computational methods. In this scenario, data-intensive cavity analysis demands efficient scripting routines built on easily manipulated data structures. To fulfill this need, we developed pyKVFinder, a Python package to detect and characterize cavities in biomolecular structures for data science and automated pipelines.

RESULTS

pyKVFinder efficiently detects cavities in biomolecular structures and computes their volume, area, depth and hydropathy, storing these cavity properties in NumPy arrays. Benefited from Python ecosystem interoperability and data structures, pyKVFinder can be integrated with third-party scientific packages and libraries for mathematical calculations, machine learning and 3D visualization in automated workflows. As proof of pyKVFinder's capabilities, we successfully identified and compared ADRP substrate-binding site of SARS-CoV-2 and a set of homologous proteins with pyKVFinder, showing its integrability with data science packages such as matplotlib, NGL Viewer, SciPy and Jupyter notebook.

CONCLUSIONS

We introduce an efficient, highly versatile and easily integrable software for detecting and characterizing biomolecular cavities in data science applications and automated protocols. pyKVFinder facilitates biostructural data analysis with scripting routines in the Python ecosystem and can be building blocks for data science and drug design applications.

Challenges in Drug Discovery for Intracellular Bacteria

Novel drugs are needed to treat a variety of persistent diseases caused by intracellular bacterial pathogens. Virulence pathways enable many functions required for the survival of these pathogens, including invasion, nutrient acquisition, and immune evasion. Inhibition of virulence pathways is an established route for drug discovery; however, many challenges remain. Here, we propose the biggest problems that must be solved to advance the field meaningfully. While it is established that we do not yet understand the nature of chemicals capable of permeating into the bacterial cell, this problem is compounded when targeting intracellular bacteria because we are limited to only those chemicals that can permeate through both human and bacterial outer envelopes. Unfortunately, many chemicals that permeate through the outer layers of mammalian cells fail to penetrate the bacterial cytoplasm. Another challenge is the lack of publicly available information on virulence factors. It is virtually impossible to know which virulence factors are clinically relevant and have broad cross-species and cross-strain distribution. In other words, we have yet to identify the best drug targets. Yes, standard genomics databases have much of the information necessary for short-term studies, but the connections with patient outcomes are yet to be established. Without comprehensive data on matters such as these, it is difficult to devise broad-spectrum, effective anti-virulence agents. Furthermore, anti-virulence drug discovery is hindered by the current state of technologies available for experimental investigation. Antimicrobial drug discovery was greatly advanced by the establishment and standardization of broth microdilution assays to measure the effectiveness of antimicrobials. However, the currently available models used for anti-virulence drug discovery are too broad, as they must address varied phenotypes, and too expensive to be generally adopted by many research groups. Therefore, we believe drug discovery against intracellular bacterial pathogens can be advanced significantly by overcoming the above hurdles.

Simultaneous Identification of Multiple Binding Sites in Proteins: A Statistical Mechanics Approach

We present an extension of the Poisson-Boltzmann model in which the solute of interest is immersed in an assembly of self-orienting Langevin water dipoles, anions, cations, and hydrophobic molecules, all of variable densities. Interactions between charges are controlled by electrostatics, while hydrophobic interactions are modeled with a Yukawa potential. We impose steric constraints by assuming that the system is represented on a cubic lattice. We also assume incompressibility; i.e., all sites of the lattice are occupied. This model, which we refer to as the Hydrophobic Dipolar Poisson-Boltzmann Langevin (HDPBL) model, leads to a system of two equations whose solutions give the water dipole, salt, and hydrophobic molecule densities, all of them in the presence of the others in a self-consistent way. We use those to study the organization of the ions, cosolvent, and solvent molecules around proteins. In particular, peaks of densities are expected to reveal, simultaneously, the presence of compatible binding sites of different kinds on a protein. We have tested and validated the ability of HDPBL to detect pockets in proteins that bind to hydrophobic ligands, polar ligands, and charged small probes as well as to characterize the binding sites of lipids for membrane proteins.

Druggability Assessment for Selected Serine Proteases in a Pharmaceutical Industry Setting

Target druggability assessment is an integral part of the early target characterization and selection process in pharmaceutical industry. Here, we investigate a set of five different serine proteases from the blood coagulation cascade. The aim of this study is twofold. Firstly, leveraging the wealth of available in-house high-throughput screening (HTS) data, we analyze HTS hit rates and discuss their predictive value for the development of small molecule (SMOL) candidates. Purely structure-activity relationship (SAR) based druggability ratings are compared with computational protein-structure based druggability assessments. Secondly, we evaluate the impact of using conformational ensembles from molecular dynamics (MD) simulations instead of single static crystal structures as basis for computational druggability assessments. Based on this study, we recommend incorporating molecular dynamics routinely into the early target characterization process, especially if only a single X-ray structure is available.

Straightforward Incorporation of Multiple Ligand Types into Molecular Dynamics Simulations for Efficient Binding Site Detection and Characterization

Binding site identification and characterization is an important initial step in structure-based drug design. To account for the effects of protein flexibility and solvation, several cosolvent molecular dynamics (MD) simulation methods that incorporate small organic molecules into the protein's solvent box to probe for binding sites have been developed. However, most of these methods are highly inefficient, as they allow for the use of only one probe type at a time, which means that multiple sets of simulations have to be performed to map different types of binding sites. The high probe concentrations used in some of these methods also necessitate the use of artificial repulsive forces to prevent the probes from aggregating. Here, we present multiple-ligand-mapping MD (mLMMD), a method that incorporates multiple types of probes for simultaneous and efficient mapping of different types of binding sites without the need for introduction of artificial forces that may cause unintended mapping artifacts. We validate the method on a diverse set of 10 proteins and show that the mLMMD probes are able to reliably identify hydrophobic, hydrogen-bonding, charged, and cryptic binding sites in all of the test cases. Our results also highlight the potential utility of mLMMD for virtual screening and rational drug design.

Chemical-Shift Perturbations Reflect Bile Acid Binding to Norovirus Coat Protein: Recognition Comes in Different Flavors

Bile acids have been reported as important cofactors promoting human and murine norovirus (NoV) infections in cell culture. The underlying mechanisms are not resolved. Through the use of chemical shift perturbation (CSP) NMR experiments, we identified a low-affinity bile acid binding site of a human GII.4 NoV strain. Long-timescale MD simulations reveal the formation of a ligand-accessible binding pocket of flexible shape, allowing the formation of stable viral coat protein-bile acid complexes in agreement with experimental CSP data. CSP NMR experiments also show that this mode of bile acid binding has a minor influence on the binding of histo-blood group antigens and vice versa. STD NMR experiments probing the binding of bile acids to virus-like particles of seven different strains suggest that low-affinity bile acid binding is a common feature of human NoV and should therefore be important for understanding the role of bile acids as cofactors in NoV infection.

Quantitative Characterization of Binding Pockets and Binding Complementarity by Means of Zernike Descriptors

In this work, we describe the application of the Zernike formalism to quantitatively characterize the binding pockets of two sets of biologically relevant systems. Such an approach, when applied to molecular dynamics trajectories, is able to pinpoint the subtle differences between very similar molecular regions and their impact on the local propensity to ligand binding, allowing us to quantify such differences. The statistical robustness of our procedure suggests that it is very suitable to describe protein binding sites and protein-ligand interactions within a rigorous and well-defined framework.

Exploring the computational methods for protein-ligand binding site prediction

Proteins participate in various essential processes in vivo via interactions with other molecules. Identifying the residues participating in these interactions not only provides biological insights for protein function studies but also has great significance for drug discoveries. Therefore, predicting protein-ligand binding sites has long been under intense research in the fields of bioinformatics and computer aided drug discovery. In this review, we first introduce the research background of predicting protein-ligand binding sites and then classify the methods into four categories, namely, 3D structure-based, template similarity-based, traditional machine learning-based and deep learning-based methods. We describe representative algorithms in each category and elaborate on machine learning and deep learning-based prediction methods in more detail. Finally, we discuss the trends and challenges of the current research such as molecular dynamics simulation based cryptic binding sites prediction, and highlight prospective directions for the near future.

In silico structural elucidation of RNA-dependent RNA polymerase towards the identification of potential Crimean-Congo Hemorrhagic Fever Virus inhibitors

The Crimean-Congo Hemorrhagic Fever virus (CCHFV) is a segmented negative single-stranded RNA virus (-ssRNA) which causes severe hemorrhagic fever in humans with a mortality rate of ~50%. To date, no vaccine has been approved. Treatment is limited to supportive care with few investigational drugs in practice. Previous studies have identified viral RNA dependent RNA Polymerase (RdRp) as a potential drug target due to its significant role in viral replication and transcription. Since no crystal structure is available yet, we report the structural elucidation of CCHFV-RdRp by in-depth homology modeling. Even with low sequence identity, the generated model suggests a similar overall structure as previously reported RdRps. More specifically, the model suggests the presence of structural/functional conserved RdRp motifs for polymerase function, the configuration of uniform spatial arrangement of core RdRp sub-domains, and predicted positively charged entry/exit tunnels, as seen in sNSV polymerases. Extensive pharmacophore modeling based on per-residue energy contribution with investigational drugs allowed the concise mapping of pharmacophoric features and identified potential hits. The combination of pharmacophoric features with interaction energy analysis revealed functionally important residues in the conserved motifs together with in silico predicted common inhibitory binding modes with highly potent reference compounds.

DNAPred: Accurate Identification of DNA-Binding Sites from Protein Sequence by Ensembled Hyperplane-Distance-Based Support Vector Machines

Accurate identification of protein-DNA binding sites is significant for both understanding protein function and drug design. Machine-learning-based methods have been extensively used for the prediction of protein-DNA binding sites. However, the data imbalance problem, in which the number of nonbinding residues (negative-class samples) is far larger than that of binding residues (positive-class samples), seriously restricts the performance improvements of machine-learning-based predictors. In this work, we designed a two-stage imbalanced learning algorithm, called ensembled hyperplane-distance-based support vector machines (E-HDSVM), to improve the prediction performance of protein-DNA binding sites. The first stage of E-HDSVM designs a new iterative sampling algorithm, called hyperplane-distance-based under-sampling (HD-US), to extract multiple subsets from the original imbalanced data set, each of which is used to train a support vector machine (SVM). Unlike traditional sampling algorithms, HD-US selects samples by calculating the distances between the samples and the separating hyperplane of the SVM. The second stage of E-HDSVM proposes an enhanced AdaBoost (EAdaBoost) algorithm to ensemble multiple trained SVMs. As an enhanced version of the original AdaBoost algorithm, EAdaBoost overcomes the overfitting problem. Stringent cross-validation and independent tests on benchmark data sets demonstrated the superiority of E-HDSVM over several popular imbalanced learning algorithms. Based on the proposed E-HDSVM algorithm, we further implemented a sequence-based protein-DNA binding site predictor, called DNAPred, which is freely available at http://csbio.njust.edu.cn/bioinf/dnapred/ for academic use. The computational experimental results showed that our predictor achieved an average overall accuracy of 91.7% and a Mathew's correlation coefficient of 0.395 on five benchmark data sets and outperformed several state-of-the-art sequence-based protein-DNA binding site predictors.

Extensive Reliability Evaluation of Docking-Based Target-Fishing Strategies

The development of target-fishing approaches, aimed at identifying the possible protein targets of a small molecule, represents a hot topic in medicinal chemistry. A successful target-fishing approach would allow for the elucidation of the mechanism of action of all therapeutically interesting compounds for which the actual target is still unknown. Moreover, target-fishing would be essential for preventing adverse effects of drug candidates, by predicting their potential off-targets, and it would speed up drug repurposing campaigns. However, due to the huge number of possible protein targets that a small-molecule might interact with, experimental target-fishing approaches are out of reach. In silico target-fishing represents a valuable alternative, and examples of receptor-based approaches, exploiting the large number of crystallographic protein structures determined to date, have been reported in the literature. To the best of our knowledge, no proper evaluation of such approaches is, however, reported yet. In the present work, we extensively assessed the reliability of docking-based target-fishing strategies. For this purpose, a set of X-ray structures belonging to different targets was selected, and a dataset of compounds, including 10 experimentally active ligands for each target, was created. A target-fishing benchmark database was then obtained, and used to assess the performance of 13 different docking procedures, in identifying the correct target of the dataset ligands. Moreover, a consensus docking-based target-fishing strategy was developed and evaluated. The analysis highlighted that specific features of the target proteins could affect the reliability of the protocol, which however, proved to represent a valuable tool in the proper applicability domain. Our study represents the first extensive performance assessment of docking-based target-fishing approaches, paving the way for the development of novel efficient receptor-based target fishing strategies.

P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure

Background

Ligand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery. It often represents only one step of many in complex computational drug design efforts. Although many methods have been published to date, only few of them are suitable for use in automated pipelines or for processing large datasets. These use cases require stability and speed, which disqualifies many of the recently introduced tools that are either template based or available only as web servers.

Results

We present P2Rank, a stand-alone template-free tool for prediction of ligand binding sites based on machine learning. It is based on prediction of ligandability of local chemical neighbourhoods that are centered on points placed on the solvent accessible surface of a protein. We show that P2Rank outperforms several existing tools, which include two widely used stand-alone tools (Fpocket, SiteHound), a comprehensive consensus based tool (MetaPocket 2.0), and a recent deep learning based method (DeepSite). P2Rank belongs to the fastest available tools (requires under 1 s for prediction on one protein), with additional advantage of multi-threaded implementation.

Conclusions

P2Rank is a new open source software package for ligand binding site prediction from protein structure. It is available as a user-friendly stand-alone command line program and a Java library. P2Rank has a lightweight installation and does not depend on other bioinformatics tools or large structural or sequence databases. Thanks to its speed and ability to make fully automated predictions, it is particularly well suited for processing large datasets or as a component of scalable structural bioinformatics pipelines.

Electronic supplementary material

The online version of this article (10.1186/s13321-018-0285-8) contains supplementary material, which is available to authorized users.

Nitazoxanide inhibits paramyxovirus replication by targeting the Fusion protein folding: role of glycoprotein-specific thiol oxidoreductase ERp57

Paramyxoviridae, a large family of enveloped viruses harboring a nonsegmented negative-sense RNA genome, include important human pathogens as measles, mumps, respiratory syncytial virus (RSV), parainfluenza viruses, and henipaviruses, which cause some of the deadliest emerging zoonoses. There is no effective antiviral chemotherapy for most of these pathogens. Paramyxoviruses evolved a sophisticated membrane-fusion machine consisting of receptor-binding proteins and the fusion F-protein, critical for virus infectivity. Herein we identify the antiprotozoal/antimicrobial nitazoxanide as a potential anti-paramyxovirus drug targeting the F-protein. We show that nitazoxanide and its circulating-metabolite tizoxanide act at post-entry level by provoking Sendai virus and RSV F-protein aggregate formation, halting F-trafficking to the host plasma membrane. F-protein folding depends on ER-resident glycoprotein-specific thiol-oxidoreductase ERp57 for correct disulfide-bond architecture. We found that tizoxanide behaves as an ERp57 non-competitive inhibitor; the putative drug binding-site was located at the ERp57-b/b′ non-catalytic domains interface. ERp57-silencing mimicked thiazolide-induced F-protein alterations, suggesting an important role of this foldase in thiazolides anti-paramyxovirus activity. Nitazoxanide is used in the clinic as a safe and effective antiprotozoal/antimicrobial drug; its antiviral activity was shown in patients infected with hepatitis-C virus, rotavirus and influenza viruses. Our results now suggest that nitazoxanide may be effective also against paramyxovirus infection.

Electrostatic recognition in substrate binding to serine proteases

Serine proteases of the Chymotrypsin family are structurally very similar but have very different substrate preferences. This study investigates a set of 9 different proteases of this family comprising proteases that prefer substrates containing positively charged amino acids, negatively charged amino acids, and uncharged amino acids with varying degree of specificity. Here, we show that differences in electrostatic substrate preferences can be predicted reliably by electrostatic molecular interaction fields employing customized GRID probes. Thus, we are able to directly link protease structures to their electrostatic substrate preferences. Additionally, we present a new metric that measures similarities in substrate preferences focusing only on electrostatics. It efficiently compares these electrostatic substrate preferences between different proteases. This new metric can be interpreted as the electrostatic part of our previously developed substrate similarity metric. Consequently, we suggest, that substrate recognition in terms of electrostatics and shape complementarity are rather orthogonal aspects of substrate recognition. This is in line with a 2‐step mechanism of protein‐protein recognition suggested in the literature.

Mapping ligand binding pockets in chloride ClC-1 channels through an integrated in silico and experimental approach using anthracene-9-carboxylic acid and niflumic acid

BACKGROUND AND PURPOSE

Although chloride channels are involved in several physiological processes and acquired diseases, the availability of compounds selectively targeting CLC proteins is limited. ClC-1 channels are responsible for sarcolemma repolarization after an action potential in skeletal muscle and have been associated with myotonia congenita and myotonic dystrophy as well as with other muscular physiopathological conditions. To date only a few ClC-1 blockers have been discovered, such as anthracene-9-carboxylic acid (9-AC) and niflumic acid (NFA), whereas no activator exists. The absence of a ClC-1 structure and the limited information regarding the binding pockets in CLC channels hamper the identification of improved modulators.

EXPERIMENTAL APPROACH

Here we provide an in-depth characterization of drug binding pockets in ClC-1 through an integrated in silico and experimental approach. We first searched putative cavities in a homology model of ClC-1 built upon an eukaryotic CLC crystal structure, and then validated in silico data by measuring the blocking ability of 9-AC and NFA on mutant ClC-1 channels expressed in HEK 293 cells.

KEY RESULTS

We identified four putative binding cavities in ClC-1. 9-AC appears to interact with residues K231, R421 and F484 within the channel pore. We also identified one preferential binding cavity for NFA and propose R421 and F484 as critical residues.

CONCLUSIONS AND IMPLICATIONS

This study represents the first effort to delineate the binding sites of ClC-1. This information is fundamental to discover compounds useful in the treatment of ClC-1-associated dysfunctions and might represent a starting point for specifically targeting other CLC proteins.

ATPbind: Accurate Protein-ATP Binding Site Prediction by Combining Sequence-Profiling and Structure-Based Comparisons

Protein-ATP interactions are ubiquitous in a wide variety of biological processes. Correctly locating ATP binding sites from protein information is an important but challenging task for protein function annotation and drug discovery. However, there is no method that can optimally identify ATP binding sites for different proteins. In this study, we report a new composite predictor, ATPbind, for ATP binding sites by integrating the outputs of two template-based predictors (i.e., S-SITE and TM-SITE) and three discriminative sequence-driven features of proteins: position specific scoring matrix, predicted secondary structure, and predicted solvent accessibility. In ATPbind, we assembled multiple support vector machines (SVMs) based on a random undersampling technique to cope with the serious imbalance phenomenon between the numbers of ATP binding sites and of non-ATP binding sites. We also constructed a new gold-standard benchmark data set consisting of 429 ATP binding proteins from the PDB database to evaluate and compare the proposed ATPbind with other existing predictors. Starting from a query sequence and predicted I-TASSER models, ATPbind can achieve an average accuracy of 72%, covering 62% of all ATP binding sites while achieving a Matthews correlation coefficient value that is significantly higher than that of other state-of-the-art predictors.

POVME 3.0: Software for Mapping Binding Pocket Flexibility

We present a substantial update to the open-source POVME binding pocket analysis software. New capabilities of POVME 3.0 include a flexible chemical coloring scheme for feature identification, postanalysis tools for comparing large ensembles of pockets (e.g., from molecular dynamics simulations), and the introduction of scripts and methods that facilitate binding pocket comparison and analysis. We envision the use of this software for visualization of binding pocket dynamics, selection of representative structures for ensemble docking, and incorporation of molecular dynamics results into ligand design efforts.

Bioinformatics in translational drug discovery

Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse 'big data' that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications.

Mapping the 3D structures of small molecule binding sites

Background

Analysis of the 3D structures of protein–ligand binding sites can provide valuable insights for drug discovery. Binding site comparison (BSC) studies can be employed to elucidate the function of orphan proteins or to predict the potential for polypharmacology. Many previous binding site analyses only consider binding sites surrounding an experimentally observed bound ligand.

Results

To encompass potential protein–ligand binding sites that do not have ligands known to bind, we have incorporated fpocket cavity detection software and assessed the impact of this inclusion on BSC performance. Using fpocket, we generated a database of ligand-independent potential binding sites and applied the BSC tool, SiteHopper, to analyze similarity relationships between protein binding sites. We developed a method for clustering potential binding sites using a curated dataset of structures for six therapeutically relevant proteins from diverse protein classes in the protein data bank. Two clustering methods were explored; hierarchical clustering and a density-based method adept at excluding noise and outliers from a dataset. We introduce circular plots to visualize binding site structure space. From the datasets analyzed in this study, we highlight a structural relationship between binding sites of cationic trypsin and prothrombin, protein targets known to bind structurally similar small molecules, exemplifying the potential utility of objectively and holistically mapping binding site space from the structural proteome.

Conclusions

We present a workflow for the objective mapping of potential protein–ligand binding sites derived from the currently available structural proteome. We show that ligand-independent binding site detection tools can be introduced without excessive penalty on BSC performance. Clustering combined with intuitive visualization tools can be applied to map relationships between the 3D structures of protein binding sites.Graphical abstract

Benzene Probes in Molecular Dynamics Simulations Reveal Novel Binding Sites for Ligand Design

Protein flexibility poses a major challenge in binding site identification. Several computational pocket detection methods that utilize small-molecule probes in molecular dynamics (MD) simulations have been developed to address this issue. Although they have proven hugely successful at reproducing experimental structural data, their ability to predict new binding sites that are yet to be identified and characterized has not been demonstrated. Here, we report the use of benzenes as probe molecules in ligand-mapping MD (LMMD) simulations to predict the existence of two novel binding sites on the surface of the oncoprotein MDM2. One of them was serendipitously confirmed by biophysical assays and X-ray crystallography to be important for the binding of a new family of hydrocarbon stapled peptides that were specifically designed to target the other putative site. These results highlight the predictive power of LMMD and suggest that predictions derived from LMMD simulations can serve as a reliable basis for the identification of novel ligand binding sites in structure-based drug design.

Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms

Predicting ligand binding sites (LBSs) on protein structures, which are obtained either from experimental or computational methods, is a useful first step in functional annotation or structure-based drug design for the protein structures. In this work, the structure-based machine learning algorithm ISMBLab-LIG was developed to predict LBSs on protein surfaces with input attributes derived from the three-dimensional probability density maps of interacting atoms, which were reconstructed on the query protein surfaces and were relatively insensitive to local conformational variations of the tentative ligand binding sites. The prediction accuracy of the ISMBLab-LIG predictors is comparable to that of the best LBS predictors benchmarked on several well-established testing datasets. More importantly, the ISMBLab-LIG algorithm has substantial tolerance to the prediction uncertainties of computationally derived protein structure models. As such, the method is particularly useful for predicting LBSs not only on experimental protein structures without known LBS templates in the database but also on computationally predicted model protein structures with structural uncertainties in the tentative ligand binding sites.

Human Aquaporin-4 and Molecular Modeling: Historical Perspective and View to the Future

Among the different aquaporins (AQPs), human aquaporin-4 (hAQP4) has attracted the greatest interest in recent years as a new promising therapeutic target. Such a membrane protein is, in fact, involved in a multiple sclerosis-like immunopathology called Neuromyelitis Optica (NMO) and in several disorders resulting from imbalanced water homeostasis such as deafness and cerebral edema. The gap of knowledge in its functioning and dynamics at the atomistic level of detail has hindered the development of rational strategies for designing hAQP4 modulators. The application, lately, of molecular modeling has proved able to fill this gap providing a breeding ground to rationally address compounds targeting hAQP4. In this review, we give an overview of the important advances obtained in this field through the application of Molecular Dynamics (MD) and other complementary modeling techniques. The case studies presented herein are discussed with the aim of providing important clues for computational chemists and biophysicists interested in this field and looking for new challenges.

Artonin E and Structural Analogs from Artocarpus Species Abrogates Estrogen Receptor Signaling in Breast Cancer

The increasing rate of mortality ensued from breast cancer has encouraged research into safer and efficient therapy. The human Estrogen receptor α has been implicated in the majority of reported breast cancer cases. Molecular docking employing Glide, Schrodinger suite 2015, was used to study the binding affinities of small molecules from the Artocarpus species after their drug-like properties were ascertained. The structure of the ligand-binding domain of human Estrogen receptor α was retrieved from Protein Data Bank while the structures of compounds were collected from PubChem database. The binding interactions of the studied compounds were reported as well as their glide scores. The best glide scored ligand, was Artonin E with a score of -12.72 Kcal when compared to other studied phytomolecules and it evoked growth inhibition of an estrogen receptor positive breast cancer cells in submicromolar concentration (3.8-6.9 µM) in comparison to a reference standard Tamoxifen (18.9-24.1 µM) within the tested time point (24-72 h). The studied ligands, which had good interactions with the target receptor, were also drug-like when compared with 95% of orally available drugs with the exception of Artoelastin, whose predicted physicochemical properties rendered it less drug-like. The in silico physicochemical properties, docking interactions and growth inhibition of the best glide scorer are indications of the anti-breast cancer relevance of the studied molecules.

AutoSite: an automated approach for pseudo-ligands prediction-from ligand-binding sites identification to predicting key ligand atoms

MOTIVATION

The identification of ligand-binding sites from a protein structure facilitates computational drug design and optimization, and protein function assignment. We introduce AutoSite: an efficient software tool for identifying ligand-binding sites and predicting pseudo ligand corresponding to each binding site identified. Binding sites are reported as clusters of 3D points called fills in which every point is labelled as hydrophobic or as hydrogen bond donor or acceptor. From these fills AutoSite derives feature points: a set of putative positions of hydrophobic-, and hydrogen-bond forming ligand atoms.

RESULTS

We show that AutoSite identifies ligand-binding sites with higher accuracy than other leading methods, and produces fills that better matches the ligand shape and properties, than the fills obtained with a software program with similar capabilities, AutoLigand In addition, we demonstrate that for the Astex Diverse Set, the feature points identify 79% of hydrophobic ligand atoms, and 81% and 62% of the hydrogen acceptor and donor hydrogen ligand atoms interacting with the receptor, and predict 81.2% of water molecules mediating interactions between ligand and receptor. Finally, we illustrate potential uses of the predicted feature points in the context of lead optimization in drug discovery projects.

AVAILABILITY AND IMPLEMENTATION

http://adfr.scripps.edu/AutoDockFR/autosite.html CONTACT: sanner@scripps.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Protein Binding Pocket Dynamics

The dynamics of protein binding pockets are crucial for their interaction specificity. Structural flexibility allows proteins to adapt to their individual molecular binding partners and facilitates the binding process. This implies the necessity to consider protein internal motion in determining and predicting binding properties and in designing new binders. Although accounting for protein dynamics presents a challenge for computational approaches, it expands the structural and physicochemical space for compound design and thus offers the prospect of improved binding specificity and selectivity. A cavity on the surface or in the interior of a protein that possesses suitable properties for binding a ligand is usually referred to as a binding pocket. The set of amino acid residues around a binding pocket determines its physicochemical characteristics and, together with its shape and location in a protein, defines its functionality. Residues outside the binding site can also have a long-range effect on the properties of the binding pocket. Cavities with similar functionalities are often conserved across protein families. For example, enzyme active sites are usually concave surfaces that present amino acid residues in a suitable configuration for binding low molecular weight compounds. Macromolecular binding pockets, on the other hand, are located on the protein surface and are often shallower. The mobility of proteins allows the opening, closing, and adaptation of binding pockets to regulate binding processes and specific protein functionalities. For example, channels and tunnels can exist permanently or transiently to transport compounds to and from a binding site. The influence of protein flexibility on binding pockets can vary from small changes to an already existent pocket to the formation of a completely new pocket. Here, we review recent developments in computational methods to detect and define binding pockets and to study pocket dynamics. We introduce five different classes of protein pocket dynamics: (1) appearance/disappearance of a subpocket in an existing pocket; (2) appearance/disappearance of an adjacent pocket on the protein surface in the direct vicinity of an already existing pocket; (3) pocket breathing, which may be caused by side-chain fluctuations or backbone or interdomain vibrational motion; (4) opening/closing of a channel or tunnel, connecting a pocket inside the protein with solvent, including lid motion; and (5) the appearance/disappearance of an allosteric pocket at a site on a protein distinct from an already existing pocket with binding of a ligand to the allosteric binding site affecting the original pocket. We suggest that the class of pocket dynamics, as well as the type and extent of protein motion affecting the binding pocket, should be factors considered in choosing the most appropriate computational approach to study a given binding pocket. Furthermore, we examine the relationship between pocket dynamics classes and induced fit, conformational selection, and gating models of ligand binding on binding kinetics and thermodynamics. We discuss the implications of protein binding pocket dynamics for drug design and conclude with potential future directions for computational analysis of protein binding pocket dynamics.

Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design

Modern rational drug design not only deals with the search for ligands binding to interesting and promising validated targets but also aims to identify the function and ligands of yet uncharacterized proteins having impact on different diseases. Additionally, it contributes to the design of inhibitors with distinct selectivity patterns and the prediction of possible off-target effects. The identification of similarities between binding sites of various proteins is a useful approach to cope with those challenges. The main scope of this perspective is to describe applications of different protein binding site comparison approaches to outline their applicability and impact on molecular design. The article deals with various substantial application domains and provides some outstanding examples to show how various binding site comparison methods can be applied to promote in silico drug design workflows. In addition, we will also briefly introduce the fundamental principles of different protein binding site comparison methods.

CryptoSite: Expanding the Druggable Proteome by Characterization and Prediction of Cryptic Binding Sites

Many proteins have small-molecule binding pockets that are not easily detectable in the ligand-free structures. These cryptic sites require a conformational change to become apparent; a cryptic site can therefore be defined as a site that forms a pocket in a holo structure, but not in the apo structure. Because many proteins appear to lack druggable pockets, understanding and accurately identifying cryptic sites could expand the set of drug targets. Previously, cryptic sites were identified experimentally by fragment-based ligand discovery and computationally by long molecular dynamics simulations and fragment docking. Here, we begin by constructing a set of structurally defined apo-holo pairs with cryptic sites. Next, we comprehensively characterize the cryptic sites in terms of their sequence, structure, and dynamics attributes. We find that cryptic sites tend to be as conserved in evolution as traditional binding pockets but are less hydrophobic and more flexible. Relying on this characterization, we use machine learning to predict cryptic sites with relatively high accuracy (for our benchmark, the true positive and false positive rates are 73% and 29%, respectively). We then predict cryptic sites in the entire structurally characterized human proteome (11,201 structures, covering 23% of all residues in the proteome). CryptoSite increases the size of the potentially "druggable" human proteome from ~40% to ~78% of disease-associated proteins. Finally, to demonstrate the utility of our approach in practice, we experimentally validate a cryptic site in protein tyrosine phosphatase 1B using a covalent ligand and NMR spectroscopy. The CryptoSite Web server is available at http://salilab.org/cryptosite.

Recognition Dynamics of p53 and MDM2: Implications for Peptide Design

Peptides that inhibit MDM2 and attenuate MDM2-p53 interactions, thus activating p53, are currently being pursued as anticancer drug leads for tumors harboring wild type p53. The thermodynamic determinants of peptide-MDM2 interactions have been extensively studied. However, a detailed understanding of the dynamics that underlie these interactions is largely missing. In this study, we explore the kinetics of the binding of a set of peptides using Brownian dynamics simulations. We systematically investigate the effect of peptide C-terminal substitutions (Ser, Ala, Asn, Pro) of a Q16ETFSDLWKLLP27 p53-based peptide and a M1PRFMDYWEGLN12 12/1 phage-derived peptide on their interaction dynamics with MDM2. The substitutions modulate peptide residence times around the MDM2 protein. In particular, the highest affinity peptide, Q16ETFSDLWKLLS27, has the longest residence time (t ∼ 25 μs) around MDM2, suggesting its potentially important contribution to binding affinity. The binding of the p53-based peptides appears to be kinetically driven while that of the phage-derived series appears to be thermodynamically driven. The phage-derived peptides were found to adopt distinctly different modes of interaction with the MDM2 protein compared to their p53-based counterparts. The p53-based peptides approach the N-terminal region of the MDM2 protein with the peptide C-terminal end oriented toward the protein, while the M1PRFMDYWEGLN12-based peptides adopt the reverse orientation. To probe the determinants of this switch in orientation, a designed mutant of the phage-derived peptide, R3E (M1PEFMDYWEGLN12), was simulated and found to adopt the orientation adopted by the p53-based peptides and also to result in almost a 5-fold increase in the peptide residence time (∼120 μs) relative to the p53-based peptides. On this basis, we suggest that the R3E mutant phage-derived peptide has a higher affinity for MDM2 than the p53-based peptides and would therefore, competitively inhibit MDM2-p53. The study, therefore, provides a novel computational framework for kinetics-based lead optimization for anticancer drug development strategies.

Rosetta and the Design of Ligand Binding Sites

Proteins that bind small molecules (ligands) can be used as biosensors, signal modulators, and sequestering agents. When naturally occurring proteins for a particular target ligand are not available, artificial proteins can be computationally designed. We present a protocol based on RosettaLigand to redesign an existing protein pocket to bind a target ligand. Starting with a protein structure and the structure of the ligand, Rosetta can optimize both the placement of the ligand in the pocket and the identity and conformation of the surrounding sidechains, yielding proteins that bind the target compound.

A spatiotemporal characterization of the effect of p53 phosphorylation on its interaction with MDM2

The interaction of p53 and MDM2 is modulated by the phosphorylation of p53. This mechanism is key to activating p53, yet its molecular determinants are not fully understood. To study the spatiotemporal characteristics of this molecular process we carried out Brownian dynamics simulations of the interactions of the MDM2 protein with a p53 peptide in its wild type state and when phosphorylated at Thr18 (pThr18) and Ser20 (pSer20). We found that p53 phosphorylation results in concerted changes in the topology of the interaction landscape in the diffusively bound encounter complex domain. These changes hinder phosphorylated p53 peptides from binding to MDM2 well before reaching the binding site. The underlying mechanism appears to involve shift of the peptide away from the vicinity of the MDM2 protein, peptide reorientation, and reduction in peptide residence time relative to wild-type p53 peptide. pThr18 and pSr20 p53 peptides experience reduction in residence times by factors of 13.6 and 37.5 respectively relative to the wild-type p53 peptide, indicating a greater role for Ser20 phosphorylation in abrogating p53 MDM2 interactions. These detailed insights into the effect of phosphorylation on molecular interactions are not available from conventional experimental and theoretical approaches and open up new avenues that incorporate molecular interaction dynamics, for stabilizing p53 against MDM2, which is a major focus of anticancer drug lead development.

To Hit or Not to Hit, That Is the Question - Genome-wide Structure-Based Druggability Predictions for Pseudomonas aeruginosa Proteins

Pseudomonas aeruginosa is a Gram-negative bacterium known to cause opportunistic infections in immune-compromised or immunosuppressed individuals that often prove fatal. New drugs to combat this organism are therefore sought after. To this end, we subjected the gene products of predicted perturbative genes to structure-based druggability predictions using DrugPred. Making this approach suitable for large-scale predictions required the introduction of new methods for calculation of descriptors, development of a workflow to identify suitable pockets in homologous proteins and establishment of criteria to obtain valid druggability predictions based on homologs. We were able to identify 29 perturbative proteins of P. aeruginosa that may contain druggable pockets, including some of them with no or no drug-like inhibitors deposited in ChEMBL. These proteins form promising novel targets for drug discovery against P. aeruginosa.

Considerations of Protein Subpockets in Fragment-Based Drug Design

While the fragment-based drug design approach continues to gain importance, gaps in the tools and methods available in the identification and accurate utilization of protein subpockets have limited the scope. The importance of these features of small molecule-protein recognition is highlighted with several examples. A generalized solution for the identification of subpockets and corresponding chemical fragments remains elusive, but there are numerous advancements in methods that can be used in combination to address subpockets. Finally, additional examples of approaches that consider the relative importance of small-molecule co-dependence of protein conformations are highlighted to emphasize an increased significance of subpockets, especially at protein interfaces.

Improving protein-ligand binding site prediction accuracy by classification of inner pocket points using local features

BACKGROUND

Protein-ligand binding site prediction from a 3D protein structure plays a pivotal role in rational drug design and can be helpful in drug side-effects prediction or elucidation of protein function. Embedded within the binding site detection problem is the problem of pocket ranking - how to score and sort candidate pockets so that the best scored predictions correspond to true ligand binding sites. Although there exist multiple pocket detection algorithms, they mostly employ a fairly simple ranking function leading to sub-optimal prediction results.

RESULTS

We have developed a new pocket scoring approach (named PRANK) that prioritizes putative pockets according to their probability to bind a ligand. The method first carefully selects pocket points and labels them by physico-chemical characteristics of their local neighborhood. Random Forests classifier is subsequently applied to assign a ligandability score to each of the selected pocket point. The ligandability scores are finally merged into the resulting pocket score to be used for prioritization of the putative pockets. With the used of multiple datasets the experimental results demonstrate that the application of our method as a post-processing step greatly increases the quality of the prediction of Fpocket and ConCavity, two state of the art protein-ligand binding site prediction algorithms.

CONCLUSIONS

The positive experimental results show that our method can be used to improve the success rate, validity and applicability of existing protein-ligand binding site prediction tools. The method was implemented as a stand-alone program that currently contains support for Fpocket and Concavity out of the box, but is easily extendible to support other tools. PRANK is made freely available at http://siret.ms.mff.cuni.cz/prank.

BioGPS descriptors for rational engineering of enzyme promiscuity and structure based bioinformatic analysis

A new bioinformatic methodology was developed founded on the Unsupervised Pattern Cognition Analysis of GRID-based BioGPS descriptors (Global Positioning System in Biological Space). The procedure relies entirely on three-dimensional structure analysis of enzymes and does not stem from sequence or structure alignment. The BioGPS descriptors account for chemical, geometrical and physical-chemical features of enzymes and are able to describe comprehensively the active site of enzymes in terms of “pre-organized environment” able to stabilize the transition state of a given reaction. The efficiency of this new bioinformatic strategy was demonstrated by the consistent clustering of four different Ser hydrolases classes, which are characterized by the same active site organization but able to catalyze different reactions. The method was validated by considering, as a case study, the engineering of amidase activity into the scaffold of a lipase. The BioGPS tool predicted correctly the properties of lipase variants, as demonstrated by the projection of mutants inside the BioGPS “roadmap”.

Enhancing protein-vitamin binding residues prediction by multiple heterogeneous subspace SVMs ensemble

Background

Vitamins are typical ligands that play critical roles in various metabolic processes. The accurate identification of the vitamin-binding residues solely based on a protein sequence is of significant importance for the functional annotation of proteins, especially in the post-genomic era, when large volumes of protein sequences are accumulating quickly without being functionally annotated.

Results

In this paper, a new predictor called TargetVita is designed and implemented for predicting protein-vitamin binding residues using protein sequences. In TargetVita, features derived from the position-specific scoring matrix (PSSM), predicted protein secondary structure, and vitamin binding propensity are combined to form the original feature space; then, several feature subspaces are selected by performing different feature selection methods. Finally, based on the selected feature subspaces, heterogeneous SVMs are trained and then ensembled for performing prediction.

Conclusions

The experimental results obtained with four separate vitamin-binding benchmark datasets demonstrate that the proposed TargetVita is superior to the state-of-the-art vitamin-specific predictor, and an average improvement of 10% in terms of the Matthews correlation coefficient (MCC) was achieved over independent validation tests. The TargetVita web server and the datasets used are freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetVita or http://www.csbio.sjtu.edu.cn/bioinf/TargetVita.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-297) contains supplementary material, which is available to authorized users.

Computational and experimental prediction of human C-type lectin receptor druggability

Mammalian C-type lectin receptors (CTLRS) are involved in many aspects of immune cell regulation such as pathogen recognition, clearance of apoptotic bodies, and lymphocyte homing. Despite a great interest in modulating CTLR recognition of carbohydrates, the number of specific molecular probes is limited. To this end, we predicted the druggability of a panel of 22 CTLRs using DoGSiteScorer. The computed druggability scores of most structures were low, characterizing this family as either challenging or even undruggable. To further explore these findings, we employed a fluorine-based nuclear magnetic resonance screening of fragment mixtures against DC-SIGN, a receptor of pharmacological interest. To our surprise, we found many fragment hits associated with the carbohydrate recognition site (hit rate = 13.5%). A surface plasmon resonance-based follow-up assay confirmed 18 of these fragments (47%) and equilibrium dissociation constants were determined. Encouraged by these findings we expanded our experimental druggability prediction to Langerin and MCL and found medium to high hit rates as well, being 15.7 and 10.0%, respectively. Our results highlight limitations of current in silico approaches to druggability assessment, in particular, with regard to carbohydrate-binding proteins. In sum, our data indicate that small molecule ligands for a larger panel of CTLRs can be developed.

Virtual Screening of compounds to 1-deoxy-Dxylulose 5-phosphate reductoisomerase (DXR) from Plasmodium falciparum

The 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR) protein (Gen Bank ID AAN37254.1) from Plasmodium falciparum is a potential drug target. Therefore, it is of interest to screen DXR against a virtual library of compounds (at the ZINC database) for potential binders as possible inhibitors. This exercise helped to choose 10 top ranking molecules with ZINC00200163 [N-(2,2di methoxy ethyl)-6-methyl-2, 3, 4, 9-tetrahydro-1H-carbazol-1-amine] a having good fit (-6.43 KJ/mol binding energy) with the target protein. Thus, ZINC00200163 is identified as a potential molecule for further comprehensive characterization and in-depth analysis.