Can We Rely on Computational Predictions To Correctly Identify Ligand Binding Sites on Novel Protein Drug Targets? Assessment of Binding Site Prediction Methods and a Protocol for Validation of Predicted Binding Sites

Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

SiteMine: Large-scale binding site similarity searching in protein structure databases

Drug discovery and design challenges, such as drug repurposing, analyzing protein-ligand and protein-protein complexes, ligand promiscuity studies, or function prediction, can be addressed by protein binding site similarity analysis. Although numerous tools exist, they all have individual strengths and drawbacks with regard to run time, provision of structure superpositions, and applicability to diverse application domains. Here, we introduce SiteMine, an all-in-one database-driven, alignment-providing binding site similarity search tool to tackle the most pressing challenges of binding site comparison. The performance of SiteMine is evaluated on the ProSPECCTs benchmark, showing a promising performance on most of the data sets. The method performs convincingly regarding all quality criteria for reliable binding site comparison, offering a novel state-of-the-art approach for structure-based molecular design based on binding site comparisons. In a SiteMine showcase, we discuss the high structural similarity between cathepsin L and calpain 1 binding sites and give an outlook on the impact of this finding on structure-based drug design. SiteMine is available at https://uhh.de/naomi.

Exploring the composition of protein-ligand binding sites for cancerous inhibitor of PP2A (CIP2A) by inhibitor guided binding analysis: paving a new way for the Discovery of drug candidates against triple negative breast cancer (TNBC)

Triple-negative breast cancer (TNBC) is associated with high-grade invasive carcinoma leading to a 10% to 15% death rate in younger premenopausal women. Targeting cancerous inhibitors of protein phosphatase (CIP2A) has been a highly effective approach for exploring therapeutic drug candidates. Lapatinib, a dual tyrosine kinase inhibitor, has shown promising inhibition properties by inducing apoptosis in TNBC carcinogenesis in vivo. Despite knowledge of the 3D structure of CIP2A, no reports provide insight into CIP2A ligand binding sites. To this effect, we conducted in silico site identification guided by lapatinib binding. Four of the five sites identified were cross-validated, and the stem domain revealed more excellent ligand binding affinity. The binding affinity of lapatinib in these sites was further computed using the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) approach. According to MM/PBSA//200 ns MD simulations, lapatinib exhibited a higher binding affinity against CIP2A in site 2 with ΔG critical values of -37.1 kcal/mol. The steadiness and tightness of lapatinib with CIP2A inside the stem domain disclosed glutamic acid-318 as the culprit amino acid with the highest electrostatic energy. These results provide clear information on the CIP2A domain capable of ligand binding and validate lapatinib as a promising CIP2A inhibitor in TNBC carcinogenesis.

An overview of recent advances and challenges in predicting compound-protein interaction (CPI)

Compound-protein interactions (CPIs) are critical in drug discovery for identifying therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) algorithms have emerged as powerful tools for CPI prediction, offering notable advantages in cost-effectiveness and efficiency. This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models, highlighting their performance and achievements. It also offers insights into CPI prediction-related datasets and evaluation benchmarks. Lastly, the article presents a comprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.

Discovery of a novel cardiac-specific myosin modulator using artificial intelligence-based virtual screening

Direct modulation of cardiac myosin function has emerged as a therapeutic target for both heart disease and heart failure. However, the development of myosin-based therapeutics has been hampered by the lack of targeted in vitro screening assays. In this study we use Artificial Intelligence-based virtual high throughput screening (vHTS) to identify novel small molecule effectors of human β-cardiac myosin. We test the top scoring compounds from vHTS in biochemical counter-screens and identify a novel chemical scaffold called 'F10' as a cardiac-specific low-micromolar myosin inhibitor. Biochemical and biophysical characterization in both isolated proteins and muscle fibers show that F10 stabilizes both the biochemical (i.e. super-relaxed state) and structural (i.e. interacting heads motif) OFF state of cardiac myosin, and reduces force and left ventricular pressure development in isolated myofilaments and Langendorff-perfused hearts, respectively. F10 is a tunable scaffold for the further development of a novel class of myosin modulators.

Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites

The coronavirus disease 2019 (COVID-19) pandemic has spurred a wide range of approaches to control and combat the disease. However, selecting an effective antiviral drug target remains a time-consuming challenge. Computational methods offer a promising solution by efficiently reducing the number of candidates. In this study, we propose a structure- and deep learning-based approach that identifies vulnerable regions in viral proteins corresponding to drug binding sites. Our approach takes into account the protein dynamics, accessibility and mutability of the binding site and the putative mechanism of action of the drug. We applied this technique to validate drug targeting toward severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein S. Our findings reveal a conformation- and oligomer-specific glycan-free binding site proximal to the receptor binding domain. This site comprises topologically important amino acid residues. Molecular dynamics simulations of Spike in complex with candidate drug molecules bound to the potential binding sites indicate an equilibrium shifted toward the inactive conformation compared with drug-free simulations. Small molecules targeting this binding site have the potential to prevent the closed-to-open conformational transition of Spike, thereby allosterically inhibiting its interaction with human angiotensin-converting enzyme 2 receptor. Using a pseudotyped virus-based assay with a SARS-CoV-2 neutralizing antibody, we identified a set of hit compounds that exhibited inhibition at micromolar concentrations.

Computer-Aided Drug Design: An Update

Computer-aided drug design (CADD) approaches are playing an increasingly important role in understanding the fundamentals of ligand-receptor interactions and helping medicinal chemists design therapeutics. About 5 years ago, we presented a chapter devoted to an overview of CADD methods and covered typical CADD protocols including structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches that were frequently used in the antibiotic drug design process. Advances in computational hardware and algorithms and emerging CADD methods are enhancing the accuracy and ability of CADD in drug design and development. In this chapter, an update to our previous chapter is provided with a focus on new CADD approaches from our laboratory and other peers that can be employed to facilitate the development of antibiotic therapeutics.

Structure-based discovery and in vitro validation of inhibitors of chloride intracellular channel 4 protein

The use of computer-aided methods have continued to propel accelerated drug discovery across various disease models, interestingly allowing the specific inhibition of pathogenic targets. Chloride Intracellular Channel Protein 4 (CLIC4) is a novel class of intracellular ion channel highly implicated in tumor and vascular biology. It regulates cell proliferation, apoptosis and angiogenesis; and is involved in multiple pathologic signaling pathways. Absence of specific inhibitors however impedes its advancement to translational research. Here, we integrate structural bioinformatics and experimental research approaches for the discovery and validation of small-molecule inhibitors of CLIC4. High-affinity allosteric binders were identified from a library of 1615 Food and Drug Administration (FDA)-approved drugs via a high-performance computing-powered blind-docking approach, resulting in the selection of amphotericin B and rapamycin. NMR assays confirmed the binding and conformational disruptive effects of both drugs while they also reversed stress-induced membrane translocation of CLIC4 and inhibited endothelial cell migration. Structural and dynamics simulation studies further revealed that the inhibitory mechanisms of these compounds were hinged on the allosteric modulation of the catalytic glutathione (GSH)-like site loop and the extended catalytic β loop which may elicit interference with the catalytic activities of CLIC4. Structure-based insights from this study provide the basis for the selective targeting of CLIC4 to treat the associated pathologies.

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.

Study of tyramine-binding mechanism and insecticidal activity of oil extracted from Eucalyptus against Sitophilus oryzae

The rice weevil, Sitophilus oryzae (L.), is a major pest of stored grains throughout the world, which causes quantitative and qualitative losses of food commodities. Eucalyptus essential oils (EOs) possess insecticidal and repellent properties, which make them a potential option for insect control in stored grains with environmentally friendly properties. In the current study, the binding mechanism of tyramine (TA) as a control compound has been investigated by funnel metadynamics (FM) simulation toward the homology model of tyramine1 receptor (TyrR) to explore its binding mode and key residues involved in the binding mechanism. EO compounds have been extracted from the leaf and flower part of Eucalyptus camaldulensis and characterized by GC/MS, and their effectiveness has been evaluated by molecular docking and conventional molecular dynamic (CMD) simulation toward the TyrR model. The FM results suggested that Asp114 followed by Asp80, Asn91, and Asn427 are crucial residues in the binding and the functioning of TA toward TyrR in Sitophilus Oryzae. The GC/MS analysis confirmed a total of 54 and 31 constituents in leaf and flower, respectively, where most of the components (29) are common in both groups. This analysis also revealed the significant concentration of Eucalyptus and α-pinene in leaves and flower EOs. The docking followed by CMD was performed to find the most effective compound in Eucalyptus EOs. In this regard, butanoic acid, 3-methyl-, 3-methyl butyl ester (B12) and 2-Octen-1-ol, 3,7-dimethyl- (B23) from leaf and trans- β-Ocimene (G04) from flower showed the maximum dock score and binding free energy, making them the leading candidates to replace tyramine in TyrR. The MM-PB/GBSA and MD analysis proved that the B12 structure is the most effective compound in inhibition of TyrR.

Computer-Aided Drug Design Boosts RAS Inhibitor Discovery

The Rat Sarcoma (RAS) family (NRAS, HRAS, and KRAS) is endowed with GTPase activity to regulate various signaling pathways in ubiquitous animal cells. As proto-oncogenes, RAS mutations can maintain activation, leading to the growth and proliferation of abnormal cells and the development of a variety of human cancers. For the fight against tumors, the discovery of RAS-targeted drugs is of high significance. On the one hand, the structural properties of the RAS protein make it difficult to find inhibitors specifically targeted to it. On the other hand, targeting other molecules in the RAS signaling pathway often leads to severe tissue toxicities due to the lack of disease specificity. However, computer-aided drug design (CADD) can help solve the above problems. As an interdisciplinary approach that combines computational biology with medicinal chemistry, CADD has brought a variety of advances and numerous benefits to drug design, such as the rapid identification of new targets and discovery of new drugs. Based on an overview of RAS features and the history of inhibitor discovery, this review provides insight into the application of mainstream CADD methods to RAS drug design.

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.

Improving Small-Molecule Force Field Parameters in Ligand Binding Studies

Small molecules are major players of many chemical processes in diverse fields, from material science to biology. They are made by a combination of carbon and heteroatoms typically organized in system-specific structures of different complexity. This peculiarity hampers the application of standard force field parameters and their in silico study by means of atomistic simulations. Here, we combine quantum-mechanics and atomistic free-energy calculations to achieve an improved parametrization of the ligand torsion angles with respect to the state-of-the-art force fields in the paradigmatic molecular binding system benzamidine/trypsin. Funnel-Metadynamics calculations with the new parameters greatly reproduced the high-resolution crystallographic ligand binding mode and allowed a more accurate description of the binding mechanism, when the ligand might assume specific conformations to cross energy barriers. Our study impacts on future drug design investigations considering that the vast majority of marketed drugs are small-molecules.

Insights into the binding of morin to human γD-crystallin

Crystallin aggregation in the eye lens is one of the leading causes of cataract formation. The increase in the human γD-crystallin (HγDC) aggregation propensity has been associated with the oligomerization of its partially folded and fully unfolded structure. A recent study demonstrated that the binding of flavonoid morin (MOR) to HγDC inhibits the fibrillation of this protein. In this work, we carry out an exhaustive search for the possible binding site of MOR on HγDC by combining an ensemble docking approach with the Wrap 'N' Shake protocol. In agreement with previous results, we found a potential MOR-binding site in the cleft formed between the N-terminal and C-terminal domains of HγDC. MOR preference for the cleft residues was observed even with the interface-opened intermediate conformers of HγDC. In addition, metadynamics simulations were carried out to corroborate the stabilizing activity of MOR on HγDC structure and to identify the structural regions implicated during the unfolding inhibition. Overall, this study provides relevant insights into the identification of new HγDC aggregation inhibitors.

Binding Mechanism between Acetylcholinesterase and Drugs Pazopanib and Lapatinib: Biochemical and Biophysical Studies

Tyrosine kinase inhibitors (TKIs) are antitumor compounds that prevent the phosphorylation of proteins in a biological environment. However, the multitarget performance of TKIs promotes them as possible candidates for drug repositioning. In this work, interaction and inhibition studies through spectroscopic and computational techniques to evaluate the binding effectiveness of lapatinib and pazopanib TKIs to acetylcholinesterase (AChE) are reported. The results indicated potent inhibition at the μM level. The types of inhibition were identified, with pazopanib acting through non-competitive inhibition and lapatinib through acompetitive inhibition. The fluorescence suppression studies indicate a static mechanism for lapatinib-AChE and pazopanib-AChE systems, with a binding constant in the order of 10⁵ M^-1. The obtained thermodynamic parameters reveal interactions driven by van der Waals forces and hydrogen bonds in the lapatinib-AChE system (ΔH° and ΔS° < 0). In contrast, the pazopanib-AChE system shows positive ΔH° and ΔS°, characteristic of hydrophobic interactions. The Foster resonance energy transfer study supports the fluorescence studies performed. The 3D fluorescence studies suggest changes in the microenvironment of the tryptophan and tyrosine residues of the protein in contact with lapatinib and pazopanib. The results suggest effective inhibition and moderate interaction of the drugs with AChE, making them interesting for conducting more in-depth repositioning studies as AChE inhibitors.

Preliminary modelling as the first stage of targeted organic synthesis

The review aims to present a classification and applicability analysis of methods for preliminary molecular modelling for targeted organic, catalytic and biocatalytic synthesis. The following three main approaches are considered as a primary classification of the methods: modelling of the target – ligand coordination without structural information on both the target and the resulting complex; calculations based on experimentally obtained structural information about the target; and dynamic simulation of the target – ligand complex and the reaction mechanism with calculation of the free energy of the reaction. The review is meant for synthetic chemists to be used as a guide for building an algorithm for preliminary modelling and synthesis of structures with specified properties.

The bibliography includes 353 references.

Spatiotemporal identification of druggable binding sites using deep learning

Identification of novel protein binding sites expands druggable genome and opens new opportunities for drug discovery. Generally, presence or absence of a binding site depends on the three-dimensional conformation of a protein, making binding site identification resemble the object detection problem in computer vision. Here we introduce a computational approach for the large-scale detection of protein binding sites, that considers protein conformations as 3D-images, binding sites as objects on these images to detect, and conformational ensembles of proteins as 3D-videos to analyze. BiteNet is suitable for spatiotemporal detection of hard-to-spot allosteric binding sites, as we showed for conformation-specific binding site of the epidermal growth factor receptor, oligomer-specific binding site of the ion channel, and binding site in G protein-coupled receptor. BiteNet outperforms state-of-the-art methods both in terms of accuracy and speed, taking about 1.5 minutes to analyze 1000 conformations of a protein with ~2000 atoms.

Kozlovskii and Popov present BiteNet, a new computational method utilizing deep learning principles for rapid detection of binding sites. BiteNet considers proteins as 3D images, enabling rapid detection of allosteric sites from either static protein structures or its dynamic ensembles.

Peptides and pseudopeptide ligands: a powerful toolbox for the affinity purification of current and next-generation biotherapeutics

Following the consolidation of therapeutic proteins in the fight against cancer, autoimmune, and neurodegenerative diseases, recent advancements in biochemistry and biotechnology have introduced a host of next-generation biotherapeutics, such as CRISPR-Cas nucleases, stem and car-T cells, and viral vectors for gene therapy. With these drugs entering the clinical pipeline, a new challenge lies ahead: how to manufacture large quantities of high-purity biotherapeutics that meet the growing demand by clinics and biotech companies worldwide. The protein ligands employed by the industry are inadequate to confront this challenge: while featuring high binding affinity and selectivity, these ligands require laborious engineering and expensive manufacturing, are prone to biochemical degradation, and pose safety concerns related to their bacterial origin. Peptides and pseudopeptides make excellent candidates to form a new cohort of ligands for the purification of next-generation biotherapeutics. Peptide-based ligands feature excellent target biorecognition, low or no toxicity and immunogenicity, and can be manufactured affordably at large scale. This work presents a comprehensive and systematic review of the literature on peptide-based ligands and their use in the affinity purification of established and upcoming biological drugs. A comparative analysis is first presented on peptide engineering principles, the development of ligands targeting different biomolecular targets, and the promises and challenges connected to the industrial implementation of peptide ligands. The reviewed literature is organized in (i) conventional (α-)peptides targeting antibodies and other therapeutic proteins, gene therapy products, and therapeutic cells; (ii) cyclic peptides and pseudo-peptides for protein purification and capture of viral and bacterial pathogens; and (iii) the forefront of peptide mimetics, such as β-/γ-peptides, peptoids, foldamers, and stimuli-responsive peptides for advanced processing of biologics.

Leaving no stone unturned: Allosteric targeting of SARS-CoV-2 spike protein at putative druggable sites disrupts human angiotensin-converting enzyme interactions at the receptor binding domain

The systematic entry of SARS-CoV-2 into host cells, as mediated by its Spike (S) protein, is highly essential for pathogenicity in humans. Hence, targeting the viral entry mechanisms remains a major strategy for COVID-19 treatment. Although recent efforts have focused on the direct inhibition of S-protein receptor-binding domain (RBD) interactions with human angiotensin-converting enzyme 2 (hACE2), allosteric targeting remains an unexplored possibility. Therefore, in this study, for the first time, we employed an integrative meta-analytical approach to investigate the allosteric inhibitory mechanisms of SARS-CoV-2 S-protein and its association with hACE2. Findings revealed two druggable sites (Sites 1 and 2) located at the N-terminal domain (NTD) and S2 regions of the protein. Two high-affinity binders; ZINC3939013 (Fosaprepitant - Site 1) and ZINC27990463 (Lomitapide - Site 2) were discovered via site-directed high-throughput screening against a library of ~1500 FDA approved drugs. Interestingly, we observed that allosteric binding of both compounds perturbed the prefusion S-protein conformations, which in turn, resulted in unprecedented hACE2 displacement from the RBD. Estimated ΔG binds for both compounds were highly favorable due to high-affinity interactions at the target sites. In addition, Site 1 residues; R190, H207, K206 and K187, I101, R102, I119, F192, L226, V126 and W104 were identified for their crucial involvement in the binding and stability of ZINC3939013. Likewise, energy contributions of Q957, N953, Q954, L303, Y313, Q314, L858, V952, N953, and A956 corroborated their importance to ZINC27990463 binding at the predicted Site 2. We believe these findings would pave way for the structure-based discovery of allosteric SARS-CoV-2 S-protein inhibitors for COVID-19 treatment.

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.

Ligand binding free-energy calculations with funnel metadynamics

The accurate resolution of the binding mechanism of a ligand to its molecular target is fundamental to develop a successful drug design campaign. Free-energy calculations, which provide the energy value of the ligand-protein binding complex, are essential for resolving the binding mode of the ligand. The accuracy of free-energy calculation methods is counteracted by their poor user-friendliness, which hampers their broad application. Here we present the Funnel-Metadynamics Advanced Protocol (FMAP), which is a flexible and user-friendly graphical user interface (GUI)-based protocol to perform funnel metadynamics, a binding free-energy method that employs a funnel-shape restraint potential to reveal the ligand binding mode and accurately calculate the absolute ligand-protein binding free energy. FMAP guides the user through all phases of the free-energy calculation process, from preparation of the input files, to production simulation, to analysis of the results. FMAP delivers the ligand binding mode and the absolute protein-ligand binding free energy as outputs. Alternative binding modes and the role of waters are also elucidated, providing a detailed description of the ligand binding mechanism. The entire protocol on the paradigmatic system benzamidine-trypsin, composed of ~105 k atoms, took ~2.8 d using the Cray XC50 piz Daint cluster at the Swiss National Supercomputing Centre.

Druggability and drug-likeness concepts in drug design: are biomodelling and predictive tools having their say?

The drug discovery process typically involves target identification and design of suitable drug molecules against these targets. Despite decades of experimental investigations in the drug discovery domain, about 96% overall failure rate has been recorded in drug development due to the "undruggability" of various identified disease targets, in addition to other challenges. Likewise, the high attrition rate of drug candidates in the drug discovery process has also become an enormous challenge for the pharmaceutical industry. To alleviate this negative outlook, new trends in drug discovery have emerged. By drifting away from experimental research methods, computational tools and big data are becoming valuable in the prediction of biological target druggability and the drug-likeness of potential therapeutic agents. These tools have proven to be useful in saving time and reducing research costs. As with any emerging technique, however, controversial opinions have been presented regarding the validation of predictive computational tools. To address the challenges associated with these varying opinions, this review attempts to highlight the principles of druggability and drug-likeness and their recent advancements in the drug discovery field. Herein, we present the different computational tools and their reliability of predictive analysis in the drug discovery domain. We believe that this report would serve as a comprehensive guide towards computational-oriented drug discovery research. Graphical abstract Highlights of methods for assessing the druggability of biological targets.

Tricarboxylic acid cycle dehydrogenases inhibition by naringenin: experimental and molecular modelling evidence

The study of polyphenols’ effects on health has been gaining attention lately. In addition to reacting with important enzymes, altering the cell metabolism, these substances can present either positive or negative metabolic alterations depending on their consumption levels. Naringenin, a citrus flavonoid, already presents diverse metabolic effects. The objective of this work was to evaluate the effect of maternal naringenin supplementation during pregnancy on the tricarboxylic acid cycle activity in offspring’s cerebellum. Adult female Wistar rats were divided into two groups: (1) vehicle (1 ml/kg by oral administration (p.o.)) or (2) naringenin (50 mg/kg p.o.). The offspring were euthanised at 7th day of life, and the cerebellum was dissected to analyse citrate synthase, isocitrate dehydrogenase (IDH), α-ketoglutarate dehydrogenase (α-KGDH) and malate dehydrogenase (MDH) activities. Molecular docking used SwissDock web server and FORECASTER Suite, and the proposed binding pose image was created on UCSF Chimera. Data were analysed by Student’s t test. Naringenin supplementation during pregnancy significantly inhibited IDH, α-KGDH and MDH activities in offspring’s cerebellum. A similar reduction was observed in vitro, using purified α-KGDH and MDH, subjected to pre-incubation with naringenin. Docking simulations demonstrated that naringenin possibly interacts with dehydrogenases in the substrate and cofactor binding sites, inhibiting their function. Naringenin administration during pregnancy may affect cerebellar development and must be evaluated with caution by pregnant women and their physicians.

Identification and characterization of fragment binding sites for allosteric ligand design using the site identification by ligand competitive saturation hotspots approach (SILCS-Hotspots)

BACKGROUND

Fragment-based ligand design is used for the development of novel ligands that target macromolecules, most notably proteins. Central to its success is the identification of fragment binding sites that are spatially adjacent such that fragments occupying those sites may be linked to create drug-like ligands. Current experimental and computational approaches that address this problem typically identify only a limited number of sites as well as use a limited number of fragment types.

METHODS

The site-identification by ligand competitive saturation (SILCS) approach is extended to the identification of fragment bindings sites, with the method termed SILCS-Hotspots. The approach involves precomputation of the SILCS FragMaps following which the identification of Hotspots, performed by identifying of all possible fragment binding sites on the full 3D structure of the protein followed by spatial clustering.

RESULTS

The SILCS-Hotspots approach identifies a large number of sites on the target protein, including many sites not accessible in experimental structures due to low binding affinities and binding sites on the protein interior. The identified sites are shown to recapitulate the location of known drug-like molecules in both allosteric and orthosteric binding sites on seven proteins including the androgen receptor, the CDK2 and Erk5 kinases, PTP1B phosphatase and three GPCRs; the β2-adrenergic, GPR40 fatty-acid binding and M2-muscarinic receptors. Analysis indicates the importance of considering all possible fragment binding sites, and not just those accessible to experimental methods, when identifying novel binding sites and performing ligand design versus just considering the most favorable sites. The approach is shown to identify a larger number of known binding sites of drug-like molecules versus the commonly used FTMap and Fpocket methods.

GENERAL SIGNIFICANCE

The present results indicate the potential utility of the SILCS-Hotspots approach for fragment-based rational design of ligands, including allosteric modulators.

Exploring the Lapse in Druggability: Sequence Analysis, Structural Dynamics and Binding Site Characterization of K-RasG12C Variant, a Feasible Oncotherapeutics Target

BACKGROUND

The difficulty in druggability of K-Ras variant has presented a challenge in the treatment of cancer diseases associated with its dysfunctionality. Despite the identification of different binding sites, limited information exists in the literature about their characteristics. Therefore, identification, crossvalidation and characterization of its druggable sites would aid the design of chemical compounds that will arrest its dysfunctionality related oncogenesis.

OBJECTIVE

This study entails the identification, cross-validation and characterization of K-Ras G12C variant's binding sites for potential druggability, coupled with the elucidation of alterations in 3D conformations and dynamics.

METHOD

Molecular dynamics simulation was carried out on the inactive, the active and the hyperactive K-RasG12Cvariant using the amber software package. The SiteMap software was employed in identifying and characterizing the druggable binding sites while the validation of the binding sites was carried out with the SiteHound and MetaPocket servers.

RESULTS

Four druggable binding sites were identified, validated and characterized based on physicochemical attributes such as size, volume, degree of enclosure or exposure, degree of contact, hydrophobic/hydrophilic character, hydrophobic/hydrophilic balance and hydrogen-bonding features. Conformational studies also revealed that the K-Ras variant exhibited notable structural instability, increased flexibility and a strongly anticorrelated movement compared to the inactive and active wildtype forms.

CONCLUSION

The attributes of the characterized druggable sites will be useful in designing site-specific K-Ras inhibitors for the treatment of K-Ras variant associated cancer diseases.

Identification of new allosteric sites and modulators of AChE through computational and experimental tools

Allosteric sites on proteins are targeted for designing more selective inhibitors of enzyme activity and to discover new functions. Acetylcholinesterase (AChE), which is most widely known for the hydrolysis of the neurotransmitter acetylcholine, has a peripheral allosteric subsite responsible for amyloidosis in Alzheimer's disease through interaction with amyloid β-peptide. However, AChE plays other non-hydrolytic functions. Here, we identify and characterise using computational tools two new allosteric sites in AChE, which have allowed us to identify allosteric inhibitors by virtual screening guided by structure-based and fragment hotspot strategies. The identified compounds were also screened for in vitro inhibition of AChE and three were observed to be active. Further experimental (kinetic) and computational (molecular dynamics) studies have been performed to verify the allosteric activity. These new compounds may be valuable pharmacological tools in the study of non-cholinergic functions of AChE.

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.

Computational simulations assessment of mutations impact on streptokinase (SK) from a group G streptococci with enhanced activity - insights into the functional roles of structural dynamics flexibility of SK and stabilization of SK-μplasmin catalytic complex

Streptokinase (SK), a plasminogen activator (PA) that converts inactive plasminogen (Pg) to plasmin (Pm), is a protein secreted by groups A, C, and G streptococci (GAS, GCS, and GGS, respectively), with high sequence divergence and functional heterogeneity. While roles of some residual changes in altered SK functionality are shown, the underlying structural mechanisms are less known. Herein, using computational approaches, we analyzed the conformational basis for the increased activity of SK from a GGS (SKG132) isolate with four natural residual substitutions (Ile33Phe, Arg45Gln, Asn228Lys, Phe287Ile) compared to the standard GCS (SKC). Using the crystal structure of SK.Pm catalytic complex as main template SKC.μPm catalytic complex was modeled through homology modeling process and validated by several online validation servers. Subsequently, SKG132.μPm structure was constructed by altering the corresponding residual substitutions. Results of three independent MD simulations showed increased RMSF values for SKG132.μPm, indicating the enhanced structural flexibility compared to SKC.μPm, specially in 170 and 250 loops and three regions: R1 (149-161), R2 (182-215) and R3 (224-229). In parallel, the average number of Hydrogen bonds in 170 loop, R2 and R3 (especially for Asn228Lys) of SKG132 compared to that of the SKC was decreased. Accordingly, residue interaction networks (RINs) analyses indicated that Asn228Lys might induce more level of structural flexibility by generation of free Lys256, while Phe287Ile and Ile33Phe enhanced the stabilization of the SKG132.μPm catalytic complex. These results denoted the potential role of the optimal dynamic state and stabilized catalytic complex for increased PA potencies of SK as a thrombolytic drug.

Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis

Human G-protein coupled receptors (hGPCRs) constitute a large and highly pharmaceutically relevant membrane receptor superfamily. About half of the hGPCRs' family members are chemosensory receptors, involved in bitter taste and olfaction, along with a variety of other physiological processes. Hence these receptors constitute promising targets for pharmaceutical intervention. Molecular modeling has been so far the most important tool to get insights on agonist binding and receptor activation. Here we investigate both aspects by bioinformatics-based predictions across all bitter taste and odorant receptors for which site-directed mutagenesis data are available. First, we observe that state-of-the-art homology modeling combined with previously used docking procedures turned out to reproduce only a limited fraction of ligand/receptor interactions inferred by experiments. This is most probably caused by the low sequence identity with available structural templates, which limits the accuracy of the protein model and in particular of the side-chains' orientations. Methods which transcend the limited sampling of the conformational space of docking may improve the predictions. As an example corroborating this, we review here multi-scale simulations from our lab and show that, for the three complexes studied so far, they significantly enhance the predictive power of the computational approach. Second, our bioinformatics analysis provides support to previous claims that several residues, including those at positions 1.50, 2.50, and 7.52, are involved in receptor activation.