Protein-ligand Interaction Fingerprints Research Articles

Deciphering the Molecular Basis of Mutated Binding Site Bromodomain-Ligand Complexes: Insights from Molecular Dynamics Simulations and Decoded Interaction Fingerprint Analysis

This study aims to predict structural stability changes and to identify the molecular determinants of ligand-bromodomain complex interactions that undergo mutations in the ligand binding site (LBS) using computational chemistry methods. The stability changes of the complexes were investigated using Molecular Dynamics (MD) simulations during a 25 ns production run. The identification of molecular interaction determinants was performed by decoding the interaction fingerprint, utilizing the output of trajectory data of the MD simulations, which were converted into a series of pdb files along the time step. The system preparations were done using CHARMM-GUI. The MD simulations were carried out using the GROMACS program. Protein-ligand interaction fingerprints (PLIF) were identified using the PyPLIF-HIPPOS program. This study successfully predicted the stability of both wild-type and mutated ligand-bromodomain complex structures, where the W81A mutation led to a decrease in complex stability. The key residues and non-hydrophobic interaction types responsible for the stabilities were identified as TRP81 aromatic edge-to-face, TYR139 aromatic edge-to-face, and TYR139 aromatic face-to-face.

Caffeic Acid in Spent Coffee Grounds as a Dual Inhibitor for MMP-9 and DPP-4 Enzymes.

Type 2 diabetes mellitus and diabetic foot ulcers remain serious worldwide health problems. Caffeic acid is one of the natural products that has been experimentally proven to have diverse pharmacological properties. This study aimed to assess the inhibitory activity of caffeic acid and ethanolic extract of spent coffee grounds targeting DPP-4 and MMP-9 enzymes and evaluate the molecular interactions through 50-ns molecular dynamics simulations. This study also introduced our new version of PyPLIF HIPPOS, PyPLIF HIPPOS 0.2.0, which allowed us to identify protein-ligand interaction fingerprints and interaction hotspots resulting from molecular dynamics simulations. Our findings revealed that caffeic acid inhibited the DPP-4 and MMP-9 activity with an IC50 of 158.19 ± 11.30 µM and 88.99 ± 3.35 µM while ethanolic extract of spent coffee grounds exhibited an IC50 of 227.87 ± 23.80 µg/100 µL and 81.24 ± 6.46 µg/100 µL, respectively. Molecular dynamics simulations showed that caffeic acid interacted in the plausible allosteric sites of DPP-4 and in the active site of MMP-9. PyPLIF HIPPOS 0.2.0 identified amino acid residues interacting more than 10% throughout the simulation, which were Lys463 and Trp62 in the plausible allosteric site of DPP-4 and His226 in the active site of MMP-9.

Molecular analysis of dUTPase of Helicobacter pylori for identification of novel inhibitors using in silico studies

The human gastric pathogen Helicobacter pylori chronically affects the gastric mucosal layer of approximately half of world’s population. The emergence of resistant strains urges the need for identification of novel and selective drug against new molecular targets. A ubiquitous enzyme, Deoxyuridine 5’-triphosphate nucleotidohydrolase (dUTPase), is considered as first line of defense against uracil mis-incorporation into DNA, and essential for genome integrity. Lack of dUTPase triggers an elevated recombination frequency, DNA breaks and ultimately cell death. Hence, dUTPase can be considered as a promising target for development of novel lead inhibitor compounds in H. pylori treatment. Herein, we report the generation of three-dimensional model of the target protein using comparative modelling and its validation. To identify dUTPase inhibitors, a high throughput virtual screening approach utilizing Knowledge-based inhibitors and DrugBank database was implemented. Top ranked compounds were scrutinized based on investigations of the protein-ligand interaction fingerprints, molecular interaction maps and binding affinities and the drug potentiality. The best ligands were studied further for complex stability and intermolecular interaction profiling with respect to time under 100 ns classical molecular dynamic stimulation, establishing significant stability in dynamic states as observed from RMSD and RMSF parameters and interactions with the catalytic site residues. The binding free energy calculation computed using MM-GBSA method from the MD simulation trajectories demonstrated that our molecules possess strong binding affinity towards the Helicobacter pylori dUTPase protein. We conclude that our proposed molecules may be potential lead molecules for effective inhibition against the H. pylori dUTPase protein subject to experimental validation. Communicated by Ramaswamy H. Sarma

Exploration of Novel PDEδ Inhibitor Based on Pharmacophore and Molecular Docking against KRAS Mutant in Colorectal Cancer.

The prenyl-binding protein, phosphodiesterase-δ (PDEδ), is essential for the localization of prenylated KRas to the plasma membrane for its signaling in cancer. The general objective of this work was to develop virtually new potential inhibitors of the PDEδ protein that prevent Ras enrichment at the plasma membrane. All computational molecular modeling studies were performed by Molecular Operating Environment (MOE). In this study, sixteen crystal structures of PDEδ in complex with fifteen different fragment inhibitors were used in the protein-ligand interaction fingerprints (PLIF) study to identify the chemical features responsible for the inhibition of the PDEδ protein. Based on these chemical characteristics, a pharmacophore with representative characteristics was obtained for screening the BindingDB database. Compounds that matched the pharmacophore model were filtered by the Lipinski filter. The ADMET properties of the compounds that passed the Lipinski filter were predicted by the Swiss ADME webserver and by the ProTox-II-Prediction of Toxicity of Chemicals web server. The selected compounds were subjected to a molecular docking study. In the PLIF study, it was shown that the fifteen inhibitors formed interactions with residues Met20, Trp32, Ile53, Cys56, Lys57, Arg61, Gln78, Val80, Glu88, Ile109, Ala11, Met117, Met118, Ile129, Thr131, and Tyr149 of the prenyl-binding pocket of PDEδ. Based on these chemical features, a pharmacophore with representative characteristics was composed of three bond acceptors, two hydrophobic elements, and one hydrogen bond donor. When the pharmacophore model was used in the virtual screening of the Binding DB database, 2532 compounds were selected. Then, the 2532 compounds were screened by the Lipinski rule filter. Among the 2532 compounds, two compounds met the Lipinski's rule. Subsequently, a comparison of the ADMET properties and the drug properties of the two compounds was performed. Finally, compound 2 was selected for molecular docking analysis and as a potential inhibitor against PDEδ. The hit found by the combination of structure-based pharmacophore generation, pharmacophore- based virtual screening, and molecular docking showed interaction with key amino acids in the hydrophobic pocket of PDEδ, leading to the discovery of a novel scaffold as a potential inhibitor of PDEδ.

Structure-Kinetic Relationship for Drug Design Revealed by a PLS Model with Retrosynthesis-Based Pre-Trained Molecular Representation and Molecular Dynamics Simulation.

Drug design based on kinetic properties is growing in application. Here, we applied retrosynthesis-based pre-trained molecular representation (RPM) in machine learning (ML) to train 501 inhibitors of 55 proteins and successfully predicted the dissociation rate constant (koff) values of 38 inhibitors from an independent dataset for the N-terminal domain of heat shock protein 90α (N-HSP90). Our RPM molecular representation outperforms other pre-trained molecular representations such as GEM, MPG, and general molecular descriptors from RDKit. Furthermore, we optimized the accelerated molecular dynamics to calculate the relative retention time (RT) for the 128 inhibitors of N-HSP90 and obtained the protein-ligand interaction fingerprints (IFPs) on their dissociation pathways and their influencing weights on the koff value. We observed a high correlation among the simulated, predicted, and experimental -log(koff) values. Combining ML, molecular dynamics (MD) simulation, and IFPs derived from accelerated MD helps design a drug for specific kinetic properties and selectivity profiles to the target of interest. To further validate our koff predictive ML model, we tested our model on two new N-HSP90 inhibitors, which have experimental koff values and are not in our ML training dataset. The predicted koff values are consistent with experimental data, and the mechanism of their kinetic properties can be explained by IFPs, which shed light on the nature of their selectivity against N-HSP90 protein. We believe that the ML model described here is transferable to predict koff of other proteins and will enhance the kinetics-based drug design endeavor.

Beware of Simple Methods for Structure-Based Virtual Screening: The Critical Importance of Broader Comparisons.

We discuss how data unbiasing and simple methods such as protein-ligand Interaction FingerPrint (IFP) can overestimate virtual screening performance. We also show that IFP is strongly outperformed by target-specific machine-learning scoring functions, which were not considered in a recent report concluding that simple methods were better than machine-learning scoring functions at virtual screening.

Identification of potential biological target for trypanocidal sesquiterpene lactones derivatives

Sesquiterpene lactones are natural products of the Asteraceae family that have shown trypanocidal activity against Trypanosoma cruzi, even exceeding the effectiveness of drugs used in the treatment of American trypanosomiasis. However, there is no agreement on their mechanism of action and their specificity to interact with parasite proteins. For this reason, we aimed to find biological targets that can interact with these compounds by reverse virtual screening with ligand pharmacophores and putative binding sites and the use of bioinformatic databases. Therefore, 41 possible biological targets were found, and four of them (with crystallized proteins), interfering directly or indirectly in the trypanosomatid redox system, were studied in detail. As a first approach, we focused on the study of trypanothione reductase, and protein-ligand interaction fingerprint analyses were performed to find binding site determinants that promote a possible inhibition of the enzyme. This study contributes to the understanding of one of the putative mechanisms of action of sesquiterpene lactones on one of the numerous suggested targets.Communicated by Ramaswamy H. Sarma

Thermal Titration Molecular Dynamics (TTMD): Not Your Usual Post-Docking Refinement.

Molecular docking is one of the most widely used computational approaches in the field of rational drug design, thanks to its favorable balance between the rapidity of execution and the accuracy of provided results. Although very efficient in exploring the conformational degrees of freedom available to the ligand, docking programs can sometimes suffer from inaccurate scoring and ranking of generated poses. To address this issue, several post-docking filters and refinement protocols have been proposed throughout the years, including pharmacophore models and molecular dynamics simulations. In this work, we present the first application of Thermal Titration Molecular Dynamics (TTMD), a recently developed method for the qualitative estimation of protein-ligand unbinding kinetics, to the refinement of docking results. TTMD evaluates the conservation of the native binding mode throughout a series of molecular dynamics simulations performed at progressively increasing temperatures with a scoring function based on protein-ligand interaction fingerprints. The protocol was successfully applied to retrieve the native-like binding pose among a set of decoy poses of drug-like ligands generated on four different pharmaceutically relevant biological targets, including casein kinase 1δ, casein kinase 2, pyruvate dehydrogenase kinase 2, and SARS-CoV-2 main protease.

Prediction of drug-induced mitochondrial dysfunction using succinate-cytochrome c reductase activity, QSAR and molecular docking

There is increasing evidence that links mitochondrial off-target effects with organ toxicities. For this reason, predictive strategies need to be developed to identify mitochondrial dysfunction early in the drug discovery process. In this study, as a major mechanism of mitochondrial toxicity, first, the inhibitory activity of 35 compounds against succinate-cytochrome c reductase (SCR) was investigated. This in vitro study led to the generation of consistent experimental data for a diverse range of compounds, including pharmaceutical drugs and fungicides. Next, molecular docking and protein-ligand interaction fingerprinting (PLIF) analysis were used to identify significant residues and protein-ligand interactions for the Qo site of complex III and Q site of complex II. Finally, this data was used for the development of QSAR models using a regression-based approach to highlight structural and chemical features that might be responsible for SCR inhibition. The statistically validated QSAR models from this work highlighted the importance of low aqueous solubility, low ionisation, fewer 6-membered rings and shorter hydrocarbon alkane chains in the molecular structure for increased inhibition of SCR, hence mitochondrial toxicity. PLIF analysis highlighted two key residues for inhibitory activity of the Qo site of complex III: His 161 as H-bond acceptor and Pro 270 for arene interactions. Currently, there are limited structure-activity models published in the scientific literature for the prediction of mitochondrial toxicity. We believe this study helps shed light on the chemical space for the inhibition of mitochondrial electron transport chain (ETC).

Qualitative Estimation of Protein-Ligand Complex Stability through Thermal Titration Molecular Dynamics Simulations.

The prediction of ligand efficacy has long been linked to thermodynamic properties such as the equilibrium dissociation constant, which considers both the association and the dissociation rates of a defined protein-ligand complex. In the last 15 years, there has been a paradigm shift, with an increased interest in the determination of kinetic properties such as the drug-target residence time since they better correlate with ligand efficacy compared to other parameters. In this article, we present thermal titration molecular dynamics (TTMD), an alternative computational method that combines a series of molecular dynamics simulations performed at progressively increasing temperatures with a scoring function based on protein-ligand interaction fingerprints for the qualitative estimation of protein-ligand-binding stability. The protocol has been applied to four different pharmaceutically relevant test cases, including protein kinase CK1δ, protein kinase CK2, pyruvate dehydrogenase kinase 2, and SARS-CoV-2 main protease, on a variety of ligands with different sizes, structures, and experimentally determined affinity values. In all four cases, TTMD was successfully able to distinguish between high-affinity compounds (low nanomolar range) and low-affinity ones (micromolar), proving to be a useful screening tool for the prioritization of compounds in a drug discovery campaign.

PyPLIF HIPPOS and Receptor Ensemble Docking Increase the Prediction Accuracy of the Structure-Based Virtual Screening Protocol Targeting Acetylcholinesterase.

In this article, the upgrading process of the structure-based virtual screening (SBVS) protocol targeting acetylcholinesterase (AChE) previously published in 2017 is presented. The upgraded version of PyPLIF called PyPLIF HIPPOS and the receptor ensemble docking (RED) method using AutoDock Vina were employed to calculate the ensemble protein–ligand interaction fingerprints (ensPLIF) in a retrospective SBVS campaign targeting AChE. A machine learning technique called recursive partitioning and regression trees (RPART) was then used to optimize the prediction accuracy of the protocol by using the ensPLIF values as the descriptors. The best protocol resulting from this research outperformed the previously published SBVS protocol targeting AChE.

Harnessing Protein-Ligand Interaction Fingerprints to Predict New Scaffolds of RIPK1 Inhibitors.

Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20–25% inhibition of RIPK1’s kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.

Harnessing systematic protein–ligand interaction fingerprints for drug discovery

Determining protein-ligand interaction characteristics and mechanisms is crucial to the drug discovery process. Here, we review recent progress and successful applications of a systematic protein-ligand interaction fingerprint (IFP) approach for investigating proteome-wide protein-ligand interactions for drug development. Specifically, we review the use of this IFP approach for revealing polypharmacology across the kinome, predicting promising targets from which to design allosteric inhibitors and covalent kinase inhibitors, uncovering the binding mechanisms of drugs of interest, and demonstrating resistant mechanisms of specific drugs. Together, we demonstrate that the IFP strategy is efficient and practical for drug design research for protein kinases as targets and is extensible to other protein families.

In Silico Prediction of Plasmodium falciparum Cytoadherence Inhibitors That Disrupt Interaction between gC1qR-DBLβ12 Complex.

Malaria causes about half a million deaths per year, mainly in children below 5 years of age. Cytoadherence of Plasmodium falciparum infected erythrocytes in brain and placenta has been linked to severe malaria and malarial related deaths. Cytoadherence is mediated by binding of human receptor gC1qR to the DBLβ12 domain of a P. falciparum erythrocyte membrane protein family 1 (PfEMP1) protein. In the present work, molecular dynamic simulation was extensively studied for the gC1qR-DBLβ12 complex. The stabilized protein complex was used to study the protein–protein interface interactions and mapping of interactive amino acid residues as hotspot were performed. Prediction of inhibitors were performed by using virtual protein–protein inhibitor database Timbal screening of about 15,000 compounds. In silico mutagenesis studies, binding profile and protein ligand interaction fingerprinting were used to strengthen the screening of the potential inhibitors of gC1qR-DBLβ12 interface. Six compounds were selected and were further subjected to the MAIP analysis and ADMET studies. From these six compounds, the compounds 3, 5, and 6 were found to outperform on all screening criteria from the rest selected compounds. These compounds may provide novel drugs to treat and manage severe falciparum malaria. Additionally. the identified hotspots can be used in future for designing novel interventions for disruption of interface interactions, such as through peptides or vaccines. Futher in vitro and in vivo studies are required for the confirmation of these compounds as potential inhibitors of gC1qR-DBLβ12 interaction.

AA-Score: a New Scoring Function Based on Amino Acid-Specific Interaction for Molecular Docking.

The protein-ligand scoring function plays an important role in computer-aided drug discovery and is heavily used in virtual screening and lead optimization. In this study, we developed a new empirical protein-ligand scoring function with amino acid-specific interaction components for hydrogen bond, van der Waals, and electrostatic interactions. In addition, hydrophobic, π-stacking, π-cation, and metal-ligand interactions are also included in the new scoring function. To better evaluate the performance of the AA-Score, we generated several new test sets for evaluation of scoring, ranking, and docking performances, respectively. Extensive tests show that AA-Score performs well on scoring, docking, and ranking as compared to other widely used traditional scoring functions. The performance improvement of AA-Score benefits from the decomposition of individual interaction into amino acid-specific types. To facilitate applications, we developed an easy-to-use tool to analyze protein-ligand interaction fingerprint and predict binding affinity using the AA-Score. The source code and associated running examples can be found at https://github.com/xundrug/AA-Score-Tool.

Multi-stage structure-based virtual screening approach towards identification of potential SARS-CoV-2 NSP13 helicase inhibitors

On account of its crucial role in the virus life cycle, SARS-COV-2 NSP13 helicase enzyme was exploited as a promising target to identify a novel potential inhibitor using multi-stage structure-based drug discovery approaches. Firstly, a 3D pharmacophore was generated based on the collected data from a protein-ligand interaction fingerprint (PLIF) study using key interactions between co-crystallised fragments and the NSP13 helicase active site. The ZINC database was screened through the generated 3D-pharmacophore retrieving 13 potential hits. All the retrieved hits exceeded the benchmark score of the co-crystallised fragments at the molecular docking step and the best five-hit compounds were selected for further analysis. Finally, a combination between molecular dynamics simulations and MM-PBSA based binding free energy calculations was conducted on the best hit (compound FWM-1) bound to NSP13 helicase enzyme, which identified FWM-1 as a potential potent NSP13 helicase inhibitor with binding free energy equals −328.6 ± 9.2 kcal/mol.

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.

Encoding mu-opioid receptor biased agonism with interaction fingerprints.

Opioids are potent painkillers,however, their therapeutic use requires close medical monitoring to diminish the risk of severe adverse effects. The G-protein biased agonists of the μ-opioid receptor (MOR) have shown safer therapeutic profiles than non-biased ligands. In this work, we performed extensive all-atom molecular dynamics simulations of two markedly biased ligands and a balanced reference molecule. From those simulations, we identified a protein-ligand interaction fingerprint that characterizes biased ligands. Then, we built and virtually screened a database containing 68,740 ligands with proven or potential GPCR agonistic activity. Exemplary molecules that fulfill the interacting pattern for biased agonism are showcased, illustrating the usefulness of this work for the search of biased MOR ligands and how this contributes to the understanding of MOR biased signaling.

Structure-Based Prediction of hERG-Related Cardiotoxicity: A Benchmark Study.

Drug-induced blockade of the human ether-à-go-go-related gene (hERG) channel is today considered the main cause of cardiotoxicity in postmarketing surveillance. Hence, several ligand-based approaches were developed in the last years and are currently employed in the early stages of a drug discovery process for in silico cardiac safety assessment of drug candidates. Herein, we present the first structure-based classifiers able to discern hERG binders from nonbinders. LASSO regularized support vector machines were applied to integrate docking scores and protein–ligand interaction fingerprints. A total of 396 models were trained and validated based on: (i) high-quality experimental bioactivity information returned by 8337 curated compounds extracted from ChEMBL (version 25) and (ii) structural predictor data. Molecular docking simulations were performed using GLIDE and GOLD software programs and four different hERG structural models, namely, the recently published structures obtained by cryoelectron microscopy (PDB codes: 5VA1 and 7CN1) and two published homology models selected for comparison. Interestingly, some classifiers return performances comparable to ligand-based models in terms of area under the ROC curve (AUCMAX = 0.86 ± 0.01) and negative predictive values (NPVMAX = 0.81 ± 0.01), thus putting forward the herein proposed computational workflow as a valuable tool for predicting hERG-related cardiotoxicity without the limitations of ligand-based models, typically affected by low interpretability and a limited applicability domain. From a methodological point of view, our study represents the first example of a successful integration of docking scores and protein–ligand interaction fingerprints (IFs) through a support vector machine (SVM) LASSO regularized strategy. Finally, the study highlights the importance of using hERG structural models accounting for ligand-induced fit effects and allowed us to select the best-performing protein conformation (made available in the Supporting Information, SI) to be employed for a reliable structure-based prediction of hERG-related cardiotoxicity.

Structure-Activity Relationship Analysis of Cocrystallized Gliptin-like Pyrrolidine, Trifluorophenyl, and Pyrimidine-2,4-Dione Dipeptidyl Peptidase-4 Inhibitors.

Approved and potent reported dipeptidyl peptidase-4 (DPP-4) inhibitors with gliptin-like structures are classified here according to their structures and mechanisms of the inhibition in three groups: (i) those with pyrrolidine or analogs as P1 fragment with α-aminoacyl linker, (ii) structures with trifluorophenyl moiety or analogs as P1 fragment with β-aminobutanoyl linker, and (iii) DPP-4 inhibitors with pyrimidine-2,4-dione or analogs as P1' fragment. The structure-activity relationship analysis was performed for those whose cocrystallized structures with the enzyme were published. While inhibitors with pyrrolidine and trifluorophenyl moiety or analogs as P1 fragment bind in a similar way in S1, S2 and S2 extensive domains of the enzyme, the binding mode of pyrimidine-2,4-dione derivatives/analogs differs with additional interactions in S1' and S2' pockets. Three general schemes of fragmented gliptins and gliptin-like structures with the enzyme and protein-ligand interaction fingerprints were made, which might be useful in the creation of DPP-4 inhibitor's design strategies.