In vitro combination effects of plant-derived quercetin with synthetic bicalutamide on prostate cancer and normal cell lines: in silico comparison

Prostate cancer is the second most frequent and the fifth greatest cause of death in men. Although diet has been connected to the prevalence of cancer in addition to other factors, the relation between cancer and prevention is weak. Treatment options are at risk due to cell resistance. To identify new combinations, we tried plant-derived quercetin with bicalutamide on cell lines. To determine the cytotoxicity and apoptotic potential of plant-derived quercetin and its combination, MTT [3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyl tetrazolium bromide] and dual stain assays were performed. In silico protein-ligand interaction was performed to support the in vitro findings. A thin layer, column, and high-performance chromatography were used to purify quercetin along with an authentic sample. In the cytotoxic study, quercetin was minimized by 80% similar to bicalutamide and a combination of quercetin and bicalutamide by 50% when compared to controls by 2%. Quercetin and bicalutamide showed a similar binding affinity for androgen receptors (9.7 and 9.8), hub genes (10.8 and 10.0), and a few other PCa-related genes (9.4 and 9.1). We propose to conclude that the combination of quercetin plus bicalutamide can be used for chemotherapy if additional in vivo studies are conducted. The intake of foods high in polyphenolic compounds can help to prevent prostate cancer. Examination of quercetin on several cell lines will provide a definite conclusion to combat cancers.

Structural Analysis of Proteins from Bacterial Secretion Systems and Their Assemblies by NMR Spectroscopy

Bacterial secretion systems are built up from proteins with different physicochemical characteristics, such as highly hydrophobic transmembrane polypeptides, and soluble periplasmic or intracellular domains. A single complex can be composed of more than ten proteins with distinct features, spreading through different cellular compartments. The membrane and multicompartment nature of the proteins, and their large molecular weight make their study challenging. However, information on their structure and assemblies is required to understand their mechanisms and interfere with them. An alternative strategy is to work with soluble domains and peptides corresponding to the regions of interest of the proteins.Here, we describe a simple and fast protocol to evaluate the stability, folding, and interaction of protein sub-complexes by using solution-state Nuclear Magnetic Resonance (NMR) spectroscopy. This technique is widely used for protein structure and protein-ligand interaction analysis in solution.

Mce1R of Mycobacterium tuberculosis prefers long-chain fatty acids as specific ligands: a computational study

The mce1 operon of Mycobacterium tuberculosis, which codes the Mce1 transporter, facilitates the transport of fatty acids. Fatty acids are one of the major sources for carbon and energy for the pathogen during its intracellular survival and pathogenicity. The mce1 operon is transcriptionally regulated by Mce1R, a VanR-type regulator, which could bind specific ligands and control the expression of the mce1 operon accordingly. This work reports computational identification of Mce1R-specific ligands. Initially by employing cavity similarity search algorithm by the ProBis server, the cavities of the proteins similar to that of Mce1R and the bound ligands were identified from which fatty acids were selected as the potential ligands. From the earlier-generated monomeric structure, the dimeric structure of Mce1R was then modeled by the GalaxyHomomer server and validated computationally to use in molecular docking and molecular dynamics simulation analysis. The fatty acid ligands were found to dock within the cavity of Mce1R and the docked complexes were subjected to molecular dynamics simulation to explore their stabilities and other dynamic properties. The data suggest that Mce1R preferably binds to long-chain fatty acids and undergoes distinct structural changes upon binding.

Extracting prime protein targets as possible drug candidates: machine learning evaluation

Extracting "high ranking" or "prime protein targets" (PPTs) as potent MRSA drug candidates from a given set of ligands is a key challenge in efficient molecular docking. This study combines protein-versus-ligand matching molecular docking (MD) data extracted from 10 independent molecular docking (MD) evaluations - ADFR, DOCK, Gemdock, Ledock, Plants, Psovina, Quickvina2, smina, vina, and vinaxb to identify top MRSA drug candidates. Twenty-nine active protein targets (APT) from the enhanced DUD-E repository ( http://DUD-E.decoys.org ) are matched against 1040 ligands using "forward modeling" machine learning for initial "data mining and modeling" (DDM) to extract PPTs and the corresponding high affinity ligands (HALs). K-means clustering (KMC) is then performed on 400 ligands matched against 29 PTs, with each cluster accommodating HALs, and the corresponding PPTs. Performance of KMC is then validated against randomly chosen head, tail, and middle active ligands (ALs). KMC outcomes have been validated against two other clustering methods, namely, Gaussian mixture model (GMM) and density based spatial clustering of applications with noise (DBSCAN). While GMM shows similar results as with KMC, DBSCAN has failed to yield more than one cluster and handle the noise (outliers), thus affirming the choice of KMC or GMM. Databases obtained from ADFR to mine PPTs are then ranked according to the number of the corresponding HAL-PPT combinations (HPC) inside the derived clusters, an approach called "reverse modeling" (RM). From the set of 29 PTs studied, RM predicts high fidelity of 5 PPTs (17%) that bind with 76 out of 400, i.e., 19% ligands leading to a prediction of next-generation MRSA drug candidates: PPT2 (average HPC is 41.1%) is the top choice, followed by PPT14 (average HPC 25.46%), and then PPT15 (average HPC 23.12%). This algorithm can be generically implemented irrespective of pathogenic forms and is particularly effective for sparse data.

Does protein pretrained language model facilitate the prediction of protein-ligand interaction?

Protein-ligand interaction (PLI) is a critical step for drug discovery. Recently, protein pretrained language models (PLMs) have showcased exceptional performance across a wide range of protein-related tasks. However, a significant heterogeneity exists between the PLM and PLI tasks, leading to a degree of uncertainty. In this study, we propose a method that quantitatively assesses the significance of protein PLMs in PLI prediction. Specifically, we analyze the performance of three widely-used protein PLMs (TAPE, ESM-1b, and ProtTrans) on three PLI tasks (PDBbind, Kinase, and DUD-E). The model with pre-training consistently achieves improved performance and decreased time cost, demonstrating that enhance both the accuracy and efficiency of PLI prediction. By quantitatively assessing the transferability, the optimal PLM for each PLI task is identified without the need for costly transfer experiments. Additionally, we examine the contributions of PLMs on the distribution of feature space, highlighting the improved discriminability after pre-training. Our findings provide insights into the mechanisms underlying PLMs in PLI prediction and pave the way for the design of more interpretable and accurate PLMs in the future. Code and data are freely available at https://github.com/brian-zZZ/PLM-PLI.

Defining A Global Map of Functional Group-based 3D Ligand-binding Motifs

Uncovering conserved 3D protein-ligand binding patterns on the basis of functional groups (FGs) shared by a variety of small molecules can greatly expand our knowledge of protein-ligand interactions. Despite that conserved binding patterns for a few commonly used FGs have been reported in the literature, large-scale identification and evaluation of FG-based 3D binding motifs are still lacking. Here, we propose a computational method, Automatic FG-based Three-dimensional Motif Extractor (AFTME), for automatic mapping of 3D motifs to different FGs of a specific ligand. Applying our method to 233 naturally-occurring ligands, we define 481 FG-binding motifs that are highly conserved across different ligand-binding pockets. Systematic analysis further reveals four main classes of binding motifs corresponding to distinct sets of FGs. Combinations of FG-binding motifs facilitate the binding of proteins to a wide spectrum of ligands with various binding affinities. Finally, we show that our FG-motif map can be used to nominate FGs that potentially bind to specific drug targets, thus providing useful insights and guidance for rational design of small-molecule drugs.

Molecular docking studies of natural immunomodulators indicate their interactions with the CD40L of CD40/CD40L pathway and CSF1R kinase domain of microglia

Natural products have proved beneficial in reducing neuroinflammation in neurological diseases. Their impacts have also been associated with the activities of microglia, responsible for brain-specific immunity. Recent studies have shown the involvement of the number of microglia-specific proteins in the regulation of brain-specific immunity. However, molecular targets of natural products and their mechanism of interaction with microglia-specific proteins are elusive. Since the genetic signature of microglia offers many potential targets for drug discovery, molecular docking followed by molecular dynamics (MD) simulations of cluster of differentiation 40 ligand (CD40L) and colony-stimulating factor 1 receptor (CSF1R) kinase domain protein with some known neuro-immunomodulators (Curcumin, Cannabidiol, Ginsenoside Rg1, Resveratrol, and Sulforaphane) has been evaluated. Curcumin and cannabidiol were observed likely to modulate CD40L and expression of cytokines and entry of inflammatory cells. Resveratrol and cannabidiol may affect the CSF1R kinase domain and activation of microglia. Our finding suggests that curcumin, cannabidiol, and resveratrol may serve specific drug ligands in regulating microglia-mediated brain immunity.

Structural insights into inhibition of PRRSV Nsp4 revealed by structure-based virtual screening, molecular dynamics, and MM-PBSA studies

BACKGROUND

Porcine reproductive and respiratory syndrome respiratory sickness in weaned and growing pigs, as well as sow reproductive failure, and its infection is regarded as one of the most serious swine illnesses worldwide. Given the current lack of an effective treatment, in this study, we identified natural compounds capable of inhibiting non-structural protein 4 (Nsp4) of the virus, which is involved in their replication and pathogenesis.

RESULTS

We screened natural compounds (n = 97,999) obtained from the ZINC database against Nsp4 and selected the top 10 compounds for analysing protein-ligand interactions and physicochemical properties. The five compounds demonstrating strong binding affinity were then subjected to molecular dynamics simulations (100 ns) and binding free energy calculations. Based on analysis, we identified four possible lead compounds that represent potentially effective drug-like inhibitors.

CONCLUSIONS

These methods identified that these natural compounds are capable of inhibiting Nsp4 and possibly effective as antiviral therapeutics against PRRSV.

In Silico Prediction Methods for Site-Saturation Mutagenesis

Directed enzyme evolution has proven to be a powerful means to endow biocatalysts with novel catalytic repertoires. Apart from completely random gene mutagenesis, site-directed or site-saturation mutagenesis requires a semi-rational selection of the amino acid positions or the substituted residues, which can dramatically reduce the screening efforts in protein engineering. To this end, in silico prediction methods play a pivotal role in targeting site-saturation mutagenesis. In this chapter, we provide two distinct computational methods, (a) conformational dynamics-guided design and (b) protein-ligand interaction fingerprinting analysis, to identify specific positions for site-saturation mutagenesis toward manipulating substrate specificity/stereoselectivity of an alcohol dehydrogenase, and improving activity of a carboxylic acid reductase, respectively.

Binding thermodynamics and interaction patterns of human purine nucleoside phosphorylase-inhibitor complexes from extensive free energy calculations

Human purine nucleoside phosphorylase (hPNP) plays a significant role in the catabolism of deoxyguanosine. The trimeric protein is an important target in the treatment of T-cell cancers and autoimmune disorders. Experimental studies on the inhibition of the hPNP observe that the first ligand bound to one of three subunits effectively inhibits the protein, while the binding of more ligands to the subsequent sites shows negative cooperativities. In this work, we performed extensive end-point and alchemical free energy calculations to determine the binding thermodynamics of the trimeric protein-ligand system. 13 Immucillin inhibitors with experimental results are under calculation. Two widely accepted charge schemes for small molecules including AM1-BCC and RESP are adopted for ligands. The results of RESP are in better agreement with the experimental reference. Further investigations of the interaction networks in the protein-ligand complexes reveal that several residues play significant roles in stabilizing the complex structure. The most commonly observed ones include PHE200, GLU201, MET219, and ASN243. The conformations of the protein in different protein-ligand complexes are observed to be similar. We expect these insights to aid the development of potent drugs targeting hPNP.

Identify the interactions between phytochemicals and proteins in the complicated food matrix

Phytochemicals usually mix with food proteins in our regular diet. Unexpected interactions may lead to changes in bioaccessibility, bioactivity, and bioavailability of phytochemicals. However, our understanding of these interactions between phytochemical and food proteins is limited because of the experimental restrictions. Here, we used pulse-proteolysis to conduct the unfolding equilibrium and dose-dependent experiments on the food proteins for the first time. The interaction between epigallocatechin gallate (EGCG) and caseins was identified in the complicated food matrix, whole milk. Another food proteome, soymilk, was also optimized for identifying the binding targets of EGCG and caffeine. Among the identified interactions, the mixing of milk with coffee generates the most prominent masking effect of 46.61 ± 3.86% relative to the calculated antioxidant capacity. Our results demonstrated that pulse proteolysis is applicable for identifying the interactions between phytochemicals and proteins in the complicated food matrix.

Investigations of Lipid Binding to Acyl-CoA-Binding Proteins (ACBP) Using Isothermal Titration Calorimetry (ITC)

Isothermal titration calorimetry (ITC) is a quantitative, biophysical method to investigate intermolecular binding between biomolecules by directly measuring the heat exchange in the binding reaction. The assay is carried out in solution when the molecules interact in vitro. This allows to determine values for binding affinity (K_d), binding stoichiometry (n), as well as changes in Gibbs free energy (ΔG), entropy (ΔS), and enthalpy (ΔH). This method also addresses the kinetics of enzymatic reactions for a substrate during conversion to a product. ITC has been used to study the interactions between proteins and ligands such as those of acyl-CoA-binding proteins (ACBPs) and acyl-CoA thioesters or ACBPs with protein partners. ITC has also been used in investigating interactions between antiserum and antigen, as well as those involving RNA and DNA and other macromolecules. We describe the methods used to isolate and purify a recombinant rice ACBP (OsACBP) for ITC. To study OsACBP binding to long-chain acyl-CoA thioesters, a microcalorimeter was used at 30 °C, and the ligand (acyl-CoA thioesters or a protein partner in the first cell), was mixed with the ACBP protein solution in a second cell, for more than 40 min comprising 20 injections. Subsequently, the binding parameters including the heat-release data were analyzed and various thermodynamic parameters were calculated.

Hydrogen-Deuterium Exchange Mass Spectrometry for Probing Changes in Conformation and Dynamics of Proteins

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) is, nowadays, an increasingly important technique in studying protein conformation and dynamics. This technique possesses the advantages of low sample consumption, less limitation in protein size, and relatively simple experimental workflow. An HDX-MS experiment typically includes the steps of sample preparation, HDX reaction, quenching of HDX reaction, protease digestion, and LC-MS analysis. Although HDX-MS has been an established technique and automatic sample handling devices are commercially available nowadays, proper experimental conditions of each step are crucial for a successful HDX-MS experiment. This chapter is to provide a general guideline for each step in the HDX-MS workflow and highlight some precautions needed to be taken in order to acquire useful conformational and dynamic information.

Ligand Binding in Allosteric Flavoproteins: Part 2. Quantitative Analysis of the Redox-Dependent Interaction of the Apoptosis-Inducing Factor (AIF) with Its Protein Partner

To perform their action usually flavoproteins interact transiently with a variety of molecular partners, whose binding is reciprocally affected and often controlled by the redox state of the bound flavin cofactor. As a case study, here we describe an approach for the quantitative characterization of the redox-controlled interaction of the mammalian apoptosis inducing factor (AIF) with one of its known protein partners, namely, the mitochondrial coiled-coil-helix-coiled-coil-helix domain-containing protein 4 (CHCHD4). In particular, we report a protocol for the titration of the flavoprotein in both in its oxidized and reduced states with CHCHD4, using an implementation of the MicroScale Thermophoresis (MST) technique.

Ligand Binding in Allosteric Flavoproteins: Part 1. Quantitative Analysis of the Interaction with NAD+ of the Apoptosis Inducing Factor (AIF) Harboring FAD in the Reduced State

To perform their action, flavoproteins usually interact with a variety of low molecular weight partners, including electron transporters, yielding transient complexes whose tightness is often controlled by the redox state of the bound flavin cofactor. As a case study, here we describe the quantitative analysis of the redox-dependent interaction of the mammalian apoptosis inducing factor (AIF) with its NAD+ ligand. In particular, we report a protocol for the spectrophotometric titration of AIF in its reduced state under anaerobic conditions with NAD+, in order to determine the dissociation constant of the resulting complex.

QM Implementation in Drug Design: Does It Really Help?

Computational chemistry allows one to characterize the structure, dynamics, and energetics of protein-ligand interactions, which makes it a valuable tool in drug discovery in both academic research and pharmaceutical industry. Molecular mechanics (MM)-based approaches are widely utilized to assist the discovery of new drug candidates. However, the complexity of protein-ligand interactions challenges the accuracy and efficiency of the commonly used empirical methods. Aiming to provide better accuracy in the description of protein-ligand interactions, quantum mechanics (QM)-based approaches are becoming increasingly explored. In principle, QM calculation includes all contributions to the energy, accounting for terms usually missing in empirical force fields, and provides a greater degree of transferability. The usefulness of QM in drug design cannot be overemphasized. In this chapter, we present recent developments and applications of fragment-based QM method in studying the protein-ligand and protein-protein interactions. We critically discuss the performance of the fragment-based QM method at different ab initio levels while trying to answer a critical question: do QM-based methods really help in drug design?

Essential site scanning analysis: A new approach for detecting sites that modulate the dispersion of protein global motions

Despite the wealth of methods developed for exploring the molecular basis of allostery in biomolecular systems, there is still a need for structure-based predictive tools that can efficiently detect susceptible sites for triggering allosteric responses. Toward this goal, we introduce here an elastic network model (ENM)-based method, Essential Site Scanning Analysis (ESSA). Essential sites are here defined as residues that would significantly alter the protein's global dynamics if bound to a ligand. To mimic the crowding induced upon substrate binding, the heavy atoms of each residue are incorporated as additional network nodes into the α-carbon-based ENM, and the resulting shifts in soft mode frequencies are used as a metric for evaluating the essentiality of each residue. Results on a dataset of monomeric proteins indicate the enrichment of allosteric and orthosteric binding sites, as well as global hinge regions among essential residues, highlighting the significant role of these sites in controlling the overall structural dynamics. Further integration of ESSA with information on predicted pockets and their local hydrophobicity density enables successful predictions of allosteric pockets for both ligand-bound and -unbound structures. ESSA can be efficiently applied to large multimeric systems. Three case studies, namely (i) G-protein binding to a GPCR, (ii) heterotrimeric assembly of the Ser/Thr protein phosphatase PP2A, and (iii) allo-targeting of AMPA receptor, demonstrate the utility of ESSA for identifying essential sites and narrowing down target allosteric sites identified by druggability simulations.

Molecular Docking Using Quantum Mechanical-Based Methods

Computational methods are a powerful and consolidated tool in the early stage of the drug lead discovery process. Among these techniques, high-throughput molecular docking has proved to be extremely useful in identifying novel bioactive compounds within large chemical libraries. In the docking procedure, the predominant binding mode of each small molecule within a target binding site is assessed, and a docking score reflective of the likelihood of binding is assigned to them. These methods also shed light on how a given hit could be modified in order to improve protein-ligand interactions and are thus able to guide lead optimization. The possibility of reducing time and cost compared to experimental approaches made this technology highly appealing. Due to methodological developments and the increase of computational power, the application of quantum mechanical methods to study macromolecular systems has gained substantial attention in the last decade. A quantum mechanical description of the interactions involved in molecular association of biomolecules may lead to better accuracy compared to molecular mechanics, since there are many physical phenomena that cannot be correctly described within a classical framework, such as covalent bond formation, polarization effects, charge transfer, bond rearrangements, halogen bonding, and others, that require electrons to be explicitly accounted for. Considering the fact that quantum mechanics-based approaches in biomolecular simulation constitute an active and important field of research, we highlight in this work the recent developments of quantum mechanical-based molecular docking and high-throughput docking.

A Decoupled Automation Platform for Hydrogen/Deuterium Exchange Mass Spectrometry Experiments

Hydrogen/deuterium exchange mass spectrometry (HDX-MS) is a biophysical technique well suited to the characterization of protein dynamics and protein-ligand interactions. In order to accurately define the rate of exchange, HDX experiments require the repeated measure of deuterium incorporation into the target protein across a range of time points. Accordingly, the HDX-MS experiment is well suited to automation, and a number of automated systems for HDX-MS have been developed. The most widely utilized platforms all operate an integrated design, where robotic liquid handling is interfaced directly with a mass spectrometer. With integrated designs, the exchange samples are prepared and injected into the LC-MS following a "real-time" serial workflow. Here we describe a new HDX-MS platform that is comprised of two complementary pieces of automation that disconnect the sample preparation from the LC-MS analysis. For preparation, a plate-based automation system is used to prepare samples in parallel, followed by immediate freezing and storage. A second piece of automation has been constructed to perform the thawing and LC-MS analysis of frozen samples in a serial mode and has been optimized to maximize the duty cycle of the mass spectrometer. The decoupled configuration described here reduces experiment time, significantly improves capacity, and improves the flexibility of the platform when compared with a fully integrated system.

Applications of Differential Scanning Fluorometry and Related Technologies in Characterization of Protein-Ligand Interactions

Differential scanning fluorometry (DSF) is an efficient and high-throughput method to analyze protein stability, as well as detect ligand interactions through perturbations of the protein's melting temperature. The method monitors protein unfolding by observing the fluorescence changes of a sample, whether through an environmentally sensitive fluorophore or by intrinsic protein fluorescence, while a temperature gradient is applied. Here, we describe in detail how to develop and optimize DSF assays to identify protein-ligand interactions while exploring different buffer and additive conditions. Analysis of the data and further applications of the method are also discussed.

Predicting binding poses and affinity ranking in D3R Grand Challenge using PL-PatchSurfer2.0

Computational prediction of protein-ligand interactions is a useful approach that aids the drug discovery process. Two major tasks of computational approaches are to predict the docking pose of a compound in a known binding pocket and to rank compounds in a library according to their predicted binding affinities. There are many computational tools developed in the past decades both in academia and industry. To objectively assess the performance of existing tools, the community has held a blind assessment of computational predictions, the Drug Design Data Resource Grand Challenge. This round, Grand Challenge 4 (GC4), focused on two targets, protein beta-secretase 1 (BACE-1) and cathepsin S (CatS). We participated in GC4 in both BACE-1 and CatS challenges using our molecular surface-based virtual screening method, PL-PatchSurfer2.0. A unique feature of PL-PatchSurfer2.0 is that it uses the three-dimensional Zernike descriptor, a mathematical moment-based shape descriptor, to quantify local shape complementarity between a ligand and a receptor, which properly incorporates molecular flexibility and provides stable affinity assessment for a bound ligand-receptor complex. Since PL-PatchSurfer2.0 does not explicitly build a bound pose of a ligand, we used an external docking program, such as AutoDock Vina, to provide an ensemble of poses, which were then evaluated by PL-PatchSurfer2.0. Here, we provide an overview of our method and report the performance in GC4.

DLIGAND2: an improved knowledge-based energy function for protein-ligand interactions using the distance-scaled, finite, ideal-gas reference state

Performance of structure-based molecular docking largely depends on the accuracy of scoring functions. One important type of scoring functions are knowledge-based potentials derived from known three-dimensional structures of proteins and/or protein–ligand complex structures. This study seeks to improve a knowledge-based protein–ligand potential based on a distance-scale finite ideal-gas reference (DFIRE) state (DLIGAND) by expanding the representation of protein atoms from 13 mol2 atom types to 167 residue-specific atom types, and employing a recently updated dataset containing 12,450 monomer protein chains for training. We found that the updated version DLIGAND2 has a consistent improvement over DLIGAND in predicting binding affinities for either native complex structures or docking-generated poses. More importantly, DLIGAND2 has a 52% increase over DLIGAND in enrichment factors in top 1% predictions based on the DUD-E decoy set, and consistently improves over Autodock Vina and other statistical energy functions in all three benchmark tests. We further found that DLIGAND2 outperforms empirical and machine-learning methods compared for virtual screening on new targets that are not homologous to the DUD-E training set. Given the best performance as a parameter-free statistical potential and among the best in all performance measures, DLIGAND2 should be useful for re-assessing the poses generated by docking software, or acting as one term in other scoring functions. The program is available at https://github.com/sysu-yanglab/DLIGAND2.

Urea moiety as amide bond mimetic in peptide-like inhibitors of VEGF-A165/NRP-1 complex

NRP-1 is an important co-receptor of vascular endothelial growth factor receptor-2 (VEGFR-2). Many reports suggested that NRP-1 might also serve as a separate receptor for VEGF-A₁₆₅ causing stimulation of tumour growth and metastasis. Therefore, compounds interfering with VEGF-A₁₆₅/NRP-1 complex triggered interest in the design of new molecules, including peptides, as anti-angiogenic and anti-tumour drugs. Here, we report the synthesis, affinity and stability evaluation of the urea-peptide hybrids, based on general Lys(hArg)-AA²-AA³-Arg sequence, where hArg residue was substituted by Arg urea unit. Such substitution does not substantially affected affinity of compounds for NRP-1 but significantly increased their proteolytic stability in plasma.

An Overview of Scoring Functions Used for Protein-Ligand Interactions in Molecular Docking

Currently, molecular docking is becoming a key tool in drug discovery and molecular modeling applications. The reliability of molecular docking depends on the accuracy of the adopted scoring function, which can guide and determine the ligand poses when thousands of possible poses of ligand are generated. The scoring function can be used to determine the binding mode and site of a ligand, predict binding affinity and identify the potential drug leads for a given protein target. Despite intensive research over the years, accurate and rapid prediction of protein-ligand interactions is still a challenge in molecular docking. For this reason, this study reviews four basic types of scoring functions, physics-based, empirical, knowledge-based, and machine learning-based scoring functions, based on an up-to-date classification scheme. We not only discuss the foundations of the four types scoring functions, suitable application areas and shortcomings, but also discuss challenges and potential future study directions.

Predicting protein-ligand binding modes for CELPP and GC3: workflows and insight

Drug Design Data Resource (D3R) continues to release valuable benchmarking datasets to promote improvement and development of computational methods for new drug discovery. We have developed several methods for protein-ligand binding mode prediction during the participation in the D3R challenges. In the present study, these methods were integrated, automated, and systematically tested using the large-scale data from Continuous Evaluation of Ligand Pose Prediction (CELPP) and a subset of Grand challenge 3 (GC3). The results show that current molecular docking methods benefit from the increasing number of protein-ligand complex structures deposited in Protein Data Bank. Using an appropriate protein structure for docking significantly improves the success rate of the binding mode prediction. The results of our template-based method and docking method are compared and discussed. Our future direction include the combination of these two methods for binding mode prediction.

Surface Induced Dissociation Coupled with High Resolution Mass Spectrometry Unveils Heterogeneity of a 211 kDa Multicopper Oxidase Protein Complex

Manganese oxidation is an important biogeochemical process that is largely regulated by bacteria through enzymatic reactions. However, the detailed mechanism is poorly understood due to challenges in isolating and characterizing these unknown enzymes. A manganese oxidase, Mnx, from Bacillus sp. PL-12 has been successfully overexpressed in active form as a protein complex with a molecular mass of 211 kDa. We have recently used surface induced dissociation (SID) and ion mobility-mass spectrometry (IM-MS) to release and detect folded subcomplexes for determining subunit connectivity and quaternary structure. The data from the native mass spectrometry experiments led to a plausible structural model of this multicopper oxidase, which has been difficult to study by conventional structural biology methods. It was also revealed that each Mnx subunit binds a variable number of copper ions. Becasue of the heterogeneity of the protein and limited mass resolution, ambiguities in assigning some of the observed peaks remained as a barrier to fully understanding the role of metals and potential unknown ligands in Mnx. In this study, we performed SID in a modified Fourier transform-ion cyclotron resonance (FTICR) mass spectrometer. The high mass accuracy and resolution offered by FTICR unveiled unexpected artificial modifications on the protein that had been previously thought to be iron bound species based on lower resolution spectra. Additionally, isotopically resolved spectra of the released subcomplexes revealed the metal binding stoichiometry at different structural levels. This method holds great potential for in-depth characterization of metalloproteins and protein-ligand complexes. Graphical Abstract ᅟ.

Differential Radial Capillary Action of Ligand Assay (DRaCALA) for High-Throughput Detection of Protein-Metabolite Interactions in Bacteria

Bacteria rely on numerous nucleotide second messengers for signal transduction such as cyclic AMP, cyclic-di-GMP, and cyclic-di-AMP. Although a number of receptors responsible for known regulated phenotypes have been established, the completeness of protein receptors in any given organism remains elusive. We have developed a method called differential radial capillary action of ligand assay (DRaCALA) that allows for an unbiased, systematic high-throughput screen for the detection of ligand binding proteins encoded by a genome. DRaCALA permits interrogation of ligand binding directly to an overexpressed protein in a cell lysate and bypasses the need of protein purification. Gateway-cloning-compatible open reading frame libraries are available for a diverse range of bacterial species and permits generation of the lysates overexpressing each open reading frame. These lysates can be assessed by DRaCALA in a 96-well format to allow rapid identification of protein-ligand interactions, including previously unknown proteins. Here, we present the protocols for generating the expression library, conducting the DRaCALA screen, data analysis, and hit validation.

Virtual Ligand Screening Using PL-PatchSurfer2, a Molecular Surface-Based Protein-Ligand Docking Method

Virtual screening is a computational technique for predicting a potent binding compound for a receptor protein from a ligand library. It has been a widely used in the drug discovery field to reduce the efforts of medicinal chemists to find hit compounds by experiments.Here, we introduce our novel structure-based virtual screening program, PL-PatchSurfer, which uses molecular surface representation with the three-dimensional Zernike descriptors, which is an effective mathematical representation for identifying physicochemical complementarities between local surfaces of a target protein and a ligand. The advantage of the surface-patch description is its tolerance on a receptor and compound structure variation. PL-PatchSurfer2 achieves higher accuracy on apo form and computationally modeled receptor structures than conventional structure-based virtual screening programs. Thus, PL-PatchSurfer2 opens up an opportunity for targets that do not have their crystal structures. The program is provided as a stand-alone program at http://kiharalab.org/plps2 . We also provide files for two ligand libraries, ChEMBL and ZINC Drug-like.

Combined and Iterative Use of Computational Design and Directed Evolution for Protein-Ligand Binding Design

The advantages of computational design and directed evolution are complementary, and only through combined and iterative use of both approaches, a daunting task such as protein-ligand interaction design, can be achieved efficiently. Here, we describe a systematic strategy to combine structure-guided computational design, iterative site saturation mutagenesis, and yeast two-hybrid system (Y2H)-based phenotypic screening to engineer novel and orthogonal interactions between synthetic ligands and human estrogen receptor α (hERα) for the development of novel gene switches.

Target-specific NMR detection of protein-ligand interactions with antibody-relayed 15N-group selective STD

Fragment-based drug design has been successfully applied to challenging targets where the detection of the weak protein-ligand interactions is a key element. ¹H saturation transfer difference (STD) NMR spectroscopy is a powerful technique for this work but it requires pure homogeneous proteins as targets. Monoclonal antibody (mAb)-relayed ¹⁵N-GS STD spectroscopy has been developed to resolve the problem of protein mixtures and impure proteins. A ¹⁵N-labelled target-specific mAb is selectively irradiated and the saturation is relayed through the target to the ligand. Tests on the anti-Gal-1 mAb/Gal-1/lactose system showed that the approach is experimentally feasible in a reasonable time frame. This method allows detection and identification of binding molecules directly from a protein mixture in a multicomponent system.

Quantitative analysis of protein-ligand interactions by NMR

Protein-ligand interactions have been commonly studied through static structures of the protein-ligand complex. Recently, however, there has been increasing interest in investigating the dynamics of protein-ligand interactions both for fundamental understanding of the underlying mechanisms and for drug development. NMR is a versatile and powerful tool, especially because it provides site-specific quantitative information. NMR has widely been used to determine the dissociation constant (KD), in particular, for relatively weak interactions. The simplest NMR method is a chemical-shift titration experiment, in which the chemical-shift changes of a protein in response to ligand titration are measured. There are other quantitative NMR methods, but they mostly apply only to interactions in the fast-exchange regime. These methods derive the dissociation constant from population-averaged NMR quantities of the free and bound states of a protein or ligand. In contrast, the recent advent of new relaxation-based experiments, including R2 relaxation dispersion and ZZ-exchange, has enabled us to obtain kinetic information on protein-ligand interactions in the intermediate- and slow-exchange regimes. Based on R2 dispersion or ZZ-exchange, methods that can determine the association rate, kon, dissociation rate, koff, and KD have been developed. In these approaches, R2 dispersion or ZZ-exchange curves are measured for multiple samples with different protein and/or ligand concentration ratios, and the relaxation data are fitted to theoretical kinetic models. It is critical to choose an appropriate kinetic model, such as the two- or three-state exchange model, to derive the correct kinetic information. The R2 dispersion and ZZ-exchange methods are suitable for the analysis of protein-ligand interactions with a micromolar or sub-micromolar dissociation constant but not for very weak interactions, which are typical in very fast exchange. This contrasts with the NMR methods that are used to analyze population-averaged NMR quantities. Essentially, to apply NMR successfully, both the type of experiment and equation to fit the data must be carefully and specifically chosen for the protein-ligand interaction under analysis. In this review, we first explain the exchange regimes and kinetic models of protein-ligand interactions, and then describe the NMR methods that quantitatively analyze these specific interactions.

Pentamidine blocks the interaction between mutant S100A5 and RAGE V domain and inhibits the RAGE signaling pathway

The human S100 protein family contains small, dimeric and acidic proteins that contain two EF-hand motifs and bind calcium. When S100A5 binds calcium, its conformation changes and promotes interaction with the target protein. The extracellular domain of RAGE (Receptor of Advanced Glycation End products) contain three domains: C1, C2 and V. The RAGE V domain is the target protein of S100A5 that promotes cell survival, growth and differentiation by activating several signaling pathways. Pentamidine is an apoptotic and antiparasitic drug that is used to treat or prevent pneumonia. Here, we found that pentamidine interacts with S100A5 using HSQC titration. We elucidated the interactions of S100A5 with RAGE V domain and pentamidine using fluorescence and NMR spectroscopy. We generated two binary models-the S100A5-RAGE V domain and S100A5-Pentamidine complex-and then observed that the pentamidine and RAGE V domain share a similar binding region in mS100A5. We also used the WST-1 assay to investigate the bioactivity of S100A5, RAGE V domain and pentamidine. These results indicated that pentamidine blocks the binding between S100A5 and RAGE V domain. This finding is useful for the development of new anti-proliferation drugs.

Site specific polarization transfer from a hyperpolarized ligand of dihydrofolate reductase

Protein-ligand interaction is often characterized using polarization transfer by the intermolecular nuclear Overhauser effect (NOE). For such NOE experiments, hyperpolarization of nuclear spins presents the opportunity to increase the spin magnetization, which is transferred, by several orders of magnitude. Here, folic acid, a ligand of dihydrofolate reductase (DHFR), was hyperpolarized on (1)H spins using dissolution dynamic nuclear polarization (D-DNP). Mixing hyperpolarized ligand with protein resulted in observable increases in protein (1)H signal predominantly in the methyl group region of the spectra. Using (13)C single quantum selection in a series of one-dimensional spectra, the carbon chemical shift ranges of the corresponding methyl groups can be elucidated. Signals observed in these hyperpolarized spectra could be confirmed using 3D isotope filtered NOESY spectra, although the hyperpolarized spectra were obtained in single scans. By further correlating the signal intensities observed in the D-DNP experiments with the occurrence of short distances in the crystal structure of the protein-ligand complex, the observed methyl proton signals could be matched to the chemical shifts of six amino acids in the active site of DHFR-folic acid binary complex. These data demonstrate that (13)C chemical shift selection of protein resonances, combined with the intrinsic selectivity towards magnetization originating from the initially hyperpolarized spins, can be used for site specific characterization of protein-ligand interactions.

Protein stability, conformational change and binding mechanism of human serum albumin upon binding of embelin and its role in disease control

Here, we present the inclusive binding mode of phytochemical embelin, an anticancer drug with human serum albumin (HSA) established under physiological condition. Also, to understand the pharmacological role of embelin molecule, here, we have studied the anti-cancer activity of embelin on human cervical cancer cell line (HeLa cell line), which revealed that embelin showed dose dependent inhibition in the growth of cancer cells and also induces 26.3% of apoptosis at an IC50 value of 29μM. Further, embelin was titrated with HSA and the fluorescence emission quenching of HSA due to the formation of the HSA-embelin complex was observed. The binding constant of this complex is 5.9±.01×10(4)M(-1) and the number of bound embelin molecules is approximately 1.0. Consequently, molecular displacement and computational docking experiments show that the embelin is binding to subdomain IB to HSA. Further evidence from microTOF-Q mass spectrometry showed an increase in mass from 66,563Da to 66,857Da observed for free HSA and HSA+embelin complex, signifying that there is robust binding of embelin with HSA. In addition, the variations of HSA secondary structural elements in presence of embelin were confirmed by circular dichroism which indicates partial unfolding of protein. Furthermore, the transmission electron micrographs established that complex formation leads to aggregation of HSA plus embelin. Molecular dynamics simulations revealed that the stability of the HSA-embelin complexes and results suggests that at around 3500ps the complex reaches equilibration state which clearly contributes to the understanding of the stability of the HSA-embelin complexes.

Self-recognition behavior of a helix-loop-helix domain by a fragment scan

The inhibitors of DNA binding Id1-4 are helix-loop-helix (HLH) proteins that exert their biological function by interacting with members of the basic-HLH (bHLH) transcription-factor family. The HLH domains of the Id and bHLH proteins allow both self- and hetero-association. Due to their abnormal expression in cancer cells, the Id proteins are potential protein targets for cancer treatment. Suitable Id-protein inactivators should promote self-association and/or prevent hetero-association. In this work we evaluated the ability of the Id-protein HLH domain to recognize itself in form of short sequences extracted from the helical and loop regions. We performed a peptide scan of the Id1 HLH domain 64-106 based on three-residue overlapping octapeptides. Interaction of each octapeptide with the natively folded Id1 HLH domain was investigated by CD and fluorescence spectroscopy. The results from both techniques showed that the helix-based but not the loop-based octapeptides interacted with the Id1 HLH domain in the low-micromolar range. In contrast, a nitrotyrosine-containing analog of the Id1 HLH region, which was unable to reproduce the native-like conformation, quenched only the 2-amino-benzoyl-(Abz)-labeled loop-based octapeptides. This opposite self-recognition pattern suggests that the short helix-based and loop-based sequences should be able to distinguish different folding states of the Id1 HLH domain. This feature may be biologically relevant, as the Id proteins are predicted to behave as intrinsically disordered proteins, being in equilibrium between rapidly exchanging monomeric conformations and structurally better-defined homo-/heterodimers displaying the parallel four-helix bundle.

The effect of celastrol, a triterpene with antitumorigenic activity, on conformational and functional aspects of the human 90kDa heat shock protein Hsp90α, a chaperone implicated in the stabilization of the tumor phenotype

BACKGROUND

Hsp90 is a molecular chaperone essential for cell viability in eukaryotes that is associated with the maturation of proteins involved in important cell functions and implicated in the stabilization of the tumor phenotype of various cancers, making this chaperone a notably interesting therapeutic target. Celastrol is a plant-derived pentacyclic triterpenoid compound with potent antioxidant, anti-inflammatory and anticancer activities; however, celastrol's action mode is still elusive.

RESULTS

In this work, we investigated the effect of celastrol on the conformational and functional aspects of Hsp90α. Interestingly, celastrol appeared to target Hsp90α directly as the compound induced the oligomerization of the chaperone via the C-terminal domain as demonstrated by experiments using a deletion mutant. The nature of the oligomers was investigated by biophysical tools demonstrating that a two-fold excess of celastrol induced the formation of a decameric Hsp90α bound throughout the C-terminal domain. When bound, celastrol destabilized the C-terminal domain. Surprisingly, standard chaperone functional investigations demonstrated that neither the in vitro chaperone activity of protecting against aggregation nor the ability to bind a TPR co-chaperone, which binds to the C-terminus of Hsp90α, were affected by celastrol.

CONCLUSION

Celastrol interferes with specific biological functions of Hsp90α. Our results suggest a model in which celastrol binds directly to the C-terminal domain of Hsp90α causing oligomerization. However, the ability to protect against protein aggregation (supported by our results) and to bind to TPR co-chaperones are not affected by celastrol. Therefore celastrol may act primarily by inducing specific oligomerization that affects some, but not all, of the functions of Hsp90α.

GENERAL SIGNIFICANCE

To the best of our knowledge, this study is the first work to use multiple probes to investigate the effect that celastrol has on the stability and oligomerization of Hsp90α and on the binding of this chaperone to Tom70. This work provides a novel mechanism by which celastrol binds Hsp90α.

Force spectroscopy studies on protein-ligand interactions: a single protein mechanics perspective

Protein-ligand interactions are ubiquitous and play important roles in almost every biological process. The direct elucidation of the thermodynamic, structural and functional consequences of protein-ligand interactions is thus of critical importance to decipher the mechanism underlying these biological processes. A toolbox containing a variety of powerful techniques has been developed to quantitatively study protein-ligand interactions in vitro as well as in living systems. The development of atomic force microscopy-based single molecule force spectroscopy techniques has expanded this toolbox and made it possible to directly probe the mechanical consequence of ligand binding on proteins. Many recent experiments have revealed how ligand binding affects the mechanical stability and mechanical unfolding dynamics of proteins, and provided mechanistic understanding on these effects. The enhancement effect of mechanical stability by ligand binding has been used to help tune the mechanical stability of proteins in a rational manner and develop novel functional binding assays for protein-ligand interactions. Single molecule force spectroscopy studies have started to shed new lights on the structural and functional consequence of ligand binding on proteins that bear force under their biological settings.