Molecular Interaction Fingerprint Research Articles

Unveiling the multitargeted potential of deprodone and control comparison with linezolid against hydrolase and transferase enzymes of methicillin-resistant Staphylococcus aureus

Staphylococcus aureus (S. aureus), commonly found on the skin and nose, causes minor skin conditions to life-threatening diseases, including boils or impetigo, pneumonia, and bloodstream infections. MRSA (Methicillin-Resistant S. aureus) is a strain resistant to many antibiotics and poses a significant challenge in clinical settings. Nowadays, the alternative drug Linezolid is used, and it is not clear when MRSA starts resistance to it, necessitating the need for more alternative drugs with the least chance of developing resistance. This study aims to identify a multitargeted drug candidate with better efficacy than Linezolid. We have taken three hydrolase and transferase proteins from S. aureus, performed the multitargeted docking studies with human-approved drugs, and compared them with the control drug Linezolid. The docking and MM\\GBSA scores ranging from −6.79 to −5.78 Kcal/mol and − 37.47 to 30.16 Kcal/mol, respectively, that revealed Deprodone (used for inflammatory skin disorders, bowel disease, and fatty acid metabolism disorders) can be a far better and multitargeted drug candidate than Linezolid. We extended our studies to include extensive pharmacokinetics and molecular interaction fingerprints for interaction pattern studies. Also, the DFT computations optimised the drug, and we extended our studies for MD Simulation in water for 100 ns, which showed the complexes among the identified drug with proteins are entirely stable with acceptable deviation, fluctuations and many intermolecular interactions that make them stable. We also performed the MM\\GBSA studies on MD simulation's all 1000 frames to understand the complex energy level. All the results reveal promising interactions between Deprodone and the targeted enzymes, suggesting its potential as a multitargeted therapeutic agent—however, experimental studies need to validate Deprodone against MRSA.

Mitoxantrone 2HCl’s adroit activity against cervical cancer replication and maintenance proteins: a multitargeted approach

Cervical cancer poses a significant global health challenge, ranking as the fourth most common cancer among women worldwide and resulting in approximately 300,000 deaths yearly, predominantly caused by high-risk human papillomavirus strains (HPV), mainly types 16 and 18. The scenario poses the urgent need of the hour to develop effective treatment strategies that can address the complexity of cervical cancer and multitargeted inhibitor designing that holds promise as it can simultaneously target multiple proteins and pathways involved in its progression and have the potential to enhance treatment efficacy, reduce the likelihood of drug resistance. In this study, we have performed multitargeted molecular docking of FDA-approved drugs against cervical cancer replication and maintenance proteins- Xenopus kinesin-like protein-2 (3KND), cell division cycle protein-20 (4N14), MCM2-histone complex (4UUZ) and MCM6 Minichromosome maintenance (2KLQ) with HTVS, SP and XP algorithms and have obtained the docking and MM\\GBSA score ranging from −8.492 to −5.189 Kcal/mol and −58.16 to −39.07 Kcal/mol. Further, the molecular interaction fingerprints identified ALA, THR, SER, ASN, LEU, and ILE were among the most interacted residues, leaning towards hydrophobic and polar amino acids. The pharmacokinetics and DFT of the compound have shown promising results. The complexes were simulated for 100 ns to study the stability by computing the deviation, fluctuations, and intermolecular interactions formed during the simulation. This study produced promising results, satisfying the criteria that Mitoxantrone 2HCl can be a multitargeted inhibitor against cervical cancer proteins—however, experimental validation is a must before human use. Communicated by Ramaswamy H. Sarma

Genome-wide CRISPR off-target prediction and optimization using RNA-DNA interaction fingerprints

The powerful CRISPR genome editing system is hindered by its off-target effects, and existing computational tools achieved limited performance in genome-wide off-target prediction due to the lack of deep understanding of the CRISPR molecular mechanism. In this study, we propose to incorporate molecular dynamics (MD) simulations in the computational analysis of CRISPR system, and present CRISOT, an integrated tool suite containing four related modules, i.e., CRISOT-FP, CRISOT-Score, CRISOT-Spec, CRISORT-Opti for RNA-DNA molecular interaction fingerprint generation, genome-wide CRISPR off-target prediction, sgRNA specificity evaluation and sgRNA optimization of Cas9 system respectively. Our comprehensive computational and experimental tests reveal that CRISOT outperforms existing tools with extensive in silico validations and proof-of-concept experimental validations. In addition, CRISOT shows potential in accurately predicting off-target effects of the base editors and prime editors, indicating that the derived RNA-DNA molecular interaction fingerprint captures the underlying mechanisms of RNA-DNA interaction among distinct CRISPR systems. Collectively, CRISOT provides an efficient and generalizable framework for genome-wide CRISPR off-target prediction, evaluation and sgRNA optimization for improved targeting specificity in CRISPR genome editing.

Multi-state Model-Based Identification of Cryptic Allosteric Sites on Human Serotonin Transporter.

Serotonin transporter (SERT) plays a fundamental role in taking the synaptic cleft serotonin back to the presynaptic neuron. The discovery of allosteric SERT modulators represents the next-generation medication for psychiatric disorders such as depression. Here, based on the cryo-EM structures of ibogaine in complex with SERT in distinct conformations, the multiple functional structures of the transporter bound to serotonin, including outward-open (OOholo), outward-occluded (OCholo), and inward-open (IOholo and IOholo'), were carefully characterized by induced-fit docking Gaussian-accelerated molecular dynamics (IFD-GaMD) simulation and the free-energy landscape analysis. Further MM/GBSA binding free energy, per-residue contribution, and molecular interaction fingerprint calculations revealed the interaction variations of serotonin with SERT in functional structures, which confirmed the allostery of SERT during serotonin reuptake. Moreover, five unique cryptic allosteric sites, which are druggable and capable of targeting by small molecules, were identified on the characterized multistate structures. These results provide structural and energetic information for the molecular mechanism of serotonin reuptake and will provide opportunities for the development of novel therapeutics based on the identified new allosteric sites on SERT.

Infrared nanospectroscopy reveals the molecular interaction fingerprint of an aggregation inhibitor with single A\u03b242 oligomers

Significant efforts have been devoted in the last twenty years to developing compounds that can interfere with the aggregation pathways of proteins related to misfolding disorders, including Alzheimer’s and Parkinson’s diseases. However, no disease-modifying drug has become available for clinical use to date for these conditions. One of the main reasons for this failure is the incomplete knowledge of the molecular mechanisms underlying the process by which small molecules interact with protein aggregates and interfere with their aggregation pathways. Here, we leverage the single molecule morphological and chemical sensitivity of infrared nanospectroscopy to provide the first direct measurement of the structure and interaction between single Aβ42 oligomeric and fibrillar species and an aggregation inhibitor, bexarotene, which is able to prevent Aβ42 aggregation in vitro and reverses its neurotoxicity in cell and animal models of Alzheimer’s disease. Our results demonstrate that the carboxyl group of this compound interacts with Aβ42 aggregates through a single hydrogen bond. These results establish infrared nanospectroscopy as a powerful tool in structure-based drug discovery for protein misfolding diseases.

Computational characterization of the selective inhibition of human norepinephrine and serotonin transporters by an escitalopram scaffold.

Human norepinephrine and serotonin transporters (hNET and hSERT) are closely related monoamine transporters (MATs) that regulate neurotransmitter signaling in neurons and are primary targets for a wide range of therapeutic drugs used in the treatment of mood disorders. The subtle modifications of an escitalopram scaffold exhibit distinct selective inhibition profiles of hNET and hSERT. However, the structural details of escitalopram scaffold binding to hSERT and (or) hNET are poorly understood and still remain a great challenge. In this work, on the basis of more recently solved X-ray crystallographic structure of hSERT in complex with escitalopram, 3 μs long all-atom MD simulations and binding free energy calculations via MM/GB(PB)SA, thermodynamic integration (TI) and MM/3D-RISM methods were performed to reproduce experimental free energies. And both MM/GBSA and TI have a high correlation coefficient (R2 = 0.95 and 0.96, respectively) between the relative binding free energies of the calculated and experimental values. Furthermore, MM/GBSA per-residue energy decomposition, molecular interaction fingerprints and thermodynamics-structure relationship analysis were employed to investigate and characterize the selectivity of the escitalopram scaffold with three modifications (escitalopram, ligand10 and talopram) to hNET and hSERT. As a result, 4 warm spots (A73, Y151, A477 and I481) in hNET and 4 warm spots (A96, A173, T439 and L443) in hSERT were thus discovered to exert a pronounced effect on the selective inhibition of hNET and hSERT by the studied ligands. These simulation results would provide great insight into the design of inhibitors with the desired selectivity to hNET and hSERT, thus further promoting the research of more efficacious antidepressants.

Function-specific virtual screening for GPCR ligands using a combined scoring method

The ability of scoring functions to correctly select and rank docking poses of small molecules in protein binding sites is highly target dependent, which presents a challenge for structure-based drug discovery. Here we describe a virtual screening method that combines an energy-based docking scoring function with a molecular interaction fingerprint (IFP) to identify new ligands based on G protein-coupled receptor (GPCR) crystal structures. The consensus scoring method is prospectively evaluated by: 1) the discovery of chemically novel, fragment-like, high affinity histamine H1 receptor (H1R) antagonists/inverse agonists, 2) the selective structure-based identification of ß2-adrenoceptor (ß2R) agonists, and 3) the experimental validation and comparison of the combined and individual scoring approaches. Systematic retrospective virtual screening simulations allowed the definition of scoring cut-offs for the identification of H1R and ß2R ligands and the selection of an optimal ß-adrenoceptor crystal structure for the discrimination between ß2R agonists and antagonists. The consensus approach resulted in the experimental validation of 53% of the ß2R and 73% of the H1R virtual screening hits with up to nanomolar affinities and potencies. The selective identification of ß2R agonists shows the possibilities of structure-based prediction of GPCR ligand function by integrating protein-ligand binding mode information.

Mechanistic Studies on the Stereoselectivity of the Serotonin 5-HT1A Receptor.

G‐protein‐coupled receptors (GPCRs) are involved in a wide range of physiological processes, and they have attracted considerable attention as important targets for developing new medicines. A central and largely unresolved question in drug discovery, which is especially relevant to GPCRs, concerns ligand selectivity: Why do certain molecules act as activators (agonists) whereas others, with nearly identical structures, act as blockers (antagonists) of GPCRs? To address this question, we employed all‐atom, long‐timescale molecular dynamics simulations to investigate how two diastereomers (epimers) of dihydrofuroaporphine bind to the serotonin 5‐HT1A receptor and exert opposite effects. By using molecular interaction fingerprints, we discovered that the agonist could mobilize nearby amino acid residues to act as molecular switches for the formation of a continuous water channel. In contrast, the antagonist epimer remained firmly stabilized in the binding pocket.

Mechanistic Studies on the Stereoselectivity of the Serotonin 5‐HT1A Receptor

AbstractG‐protein‐coupled receptors (GPCRs) are involved in a wide range of physiological processes, and they have attracted considerable attention as important targets for developing new medicines. A central and largely unresolved question in drug discovery, which is especially relevant to GPCRs, concerns ligand selectivity: Why do certain molecules act as activators (agonists) whereas others, with nearly identical structures, act as blockers (antagonists) of GPCRs? To address this question, we employed all‐atom, long‐timescale molecular dynamics simulations to investigate how two diastereomers (epimers) of dihydrofuroaporphine bind to the serotonin 5‐HT1A receptor and exert opposite effects. By using molecular interaction fingerprints, we discovered that the agonist could mobilize nearby amino acid residues to act as molecular switches for the formation of a continuous water channel. In contrast, the antagonist epimer remained firmly stabilized in the binding pocket.

KLIFS: a structural kinase-ligand interaction database.

Protein kinases play a crucial role in cell signaling and are important drug targets in several therapeutic areas. The KLIFS database contains detailed structural kinase-ligand interaction information derived from all (>2900) structures of catalytic domains of human and mouse protein kinases deposited in the Protein Data Bank in order to provide insights into the structural determinants of kinase-ligand binding and selectivity. The kinase structures have been processed in a consistent manner by systematically analyzing the structural features and molecular interaction fingerprints (IFPs) of a predefined set of 85 binding site residues with bound ligands. KLIFS has been completely rebuilt and extended (>65% more structures) since its first release as a data set, including: novel automated annotation methods for (i) the assessment of ligand-targeted subpockets and the analysis of (ii) DFG and (iii) αC-helix conformations; improved and automated protocols for (iv) the generation of sequence/structure alignments, (v) the curation of ligand atom and bond typing for accurate IFP analysis and (vi) weekly database updates. KLIFS is now accessible via a website (http://klifs.vu-compmedchem.nl) that provides a comprehensive visual presentation of different types of chemical, biological and structural chemogenomics data, and allows the user to easily access, compare, search and download the data.

Fragment library screening reveals remarkable similarities between the G protein-coupled receptor histamine H₄ and the ion channel serotonin 5-HT₃A.

A fragment library was screened against the G protein-coupled histamine H(4) receptor (H(4)R) and the ligand-gated ion channel serotonin 5-HT(3A) (5-HT(3A)R). Interestingly, significant overlap was found between H(4)R and 5-HT(3A)R hit sets. The data indicates that dual active H(4)R and 5 HT(3A)R fragments have a higher complexity than the selective compounds which has important implications for chemical genomics approaches. The results of our fragment-based library screening study illustrate similarities in ligand recognition between H(4)R and 5-HT(3A)R and have important consequences for selectivity profiling in ongoing drug discovery efforts on H(4)R and 5-HT(3A)R. The affinity profiles of our fragment screening studies furthermore match the chemical properties of the H(4)R and 5-HT(3A)R binding sites and can be used to define molecular interaction fingerprints to guide the in silico prediction of protein-ligand interactions and structure.

Selective Structure-Based Virtual Screening for Full and Partial Agonists of the β2 Adrenergic Receptor

The recently solved high-resolution X-ray structure of the beta2 adrenergic receptor has been challenged for its ability to discriminate inverse agonists/antagonists from partial/full agonists. Whereas the X-ray structure of the ground state receptor was unsuitable to distinguish true ligands with different functional effects, modifying this structure to reflect early conformational events in receptor activation led to a receptor model able to selectively retrieve full and partial agonists by structure-based virtual screening. The use of a topological scoring function based on molecular interaction fingerprints was shown to be mandatory to properly rank docking poses and achieve acceptable enrichments for partial and full agonists only.

Assessment of Scaffold Hopping Efficiency by Use of Molecular Interaction Fingerprints

A novel scoring algorithm based on molecular interaction fingerprints (IFPs) was comparatively evaluated in its scaffold hopping efficiency against four virtual screening standards (GlideXP, Gold, ROCS, and a Bayesian classifier). Decoy databases for the two targets under examination, adenosine deaminase and retinoid X receptor alpha, were obtained from the Directory of Useful Decoys and were further enriched with approximately 5% of active ligands. Structure and ligand-based methods were used to generate the ligand poses, and a Tanimoto metric was chosen for the calculation of the similarity interaction fingerprint between the reference ligand and the screening database. Database enrichments were found to strongly depend on the pose generator algorithm. In spite of these dependencies, enrichments using molecular IFPs were comparable to those obtained with GlideXP, Gold, ROCS, and the Bayesian classifier. More interestingly, the molecular IFP scoring algorithm outperformed these methods at scaffold hopping enrichment, regardless of the pose generator algorithm.

Ranking Targets in Structure-Based Virtual Screening of Three-Dimensional Protein Libraries: Methods and Problems

Structure-based virtual screening is a promising tool to identify putative targets for a specific ligand. Instead of docking multiple ligands into a single protein cavity, a single ligand is docked in a collection of binding sites. In inverse screening, hits are in fact targets which have been prioritized within the pool of best ranked proteins. The target rate depends on specificity and promiscuity in protein-ligand interactions and, to a considerable extent, on the effectiveness of the scoring function, which still is the Achilles' heel of molecular docking. In the present retrospective study, virtual screening of the sc-PDB target library by GOLD docking was carried out for four compounds (biotin, 4-hydroxy-tamoxifen, 6-hydroxy-1,6-dihydropurine ribonucleoside, and methotrexate) of known sc-PDB targets and, several ranking protocols based on GOLD fitness score and topological molecular interaction fingerprint (IFP) comparison were evaluated. For the four investigated ligands, the fusion of GOLD fitness and two IFP scores allowed the recovery of most targets, including the rare proteins which are not readily suitable for statistical analysis, while significantly filtering out most false positive entries. The current survey suggests that selecting a small number of targets (<20) for experimental evaluation is achievable with a pure structure-based approach.

Optimizing Fragment and Scaffold Docking by Use of Molecular Interaction Fingerprints

Protein-ligand interaction fingerprints have been used to postprocess docking poses of three ligand data sets: a set of 40 low-molecular-weight compounds from the Protein Data Bank, a collection of 40 scaffolds from pharmaceutically relevant protein ligands, and a database of 19 scaffolds extracted from true cdk2 inhibitors seeded in 2230 scaffold decoys. Four popular docking tools (FlexX, Glide, Gold, and Surflex) were used to generate poses for ligands of the three data sets. In all cases, scoring by the similarity of interaction fingerprints to a given reference was statistically superior to conventional scoring functions in posing low-molecular-weight fragments, predicting protein-bound scaffold coordinates according to the known binding mode of related ligands, and screening a scaffold library to enrich a hit list in true cdk2-targeted scaffolds.

A Comprehensive Docking Study on the Selectivity of Binding of Aromatic Compounds to Proteins.

Generally, computer-aided drug design is focused on screening of ligand molecules for a single protein target. The screening of several proteins for a ligand is a relatively new application of molecular docking. In the present study, complexes from the Brookhaven Protein Databank were used to investigate a docking approach of protein screening. Automated molecular docking calculations were applied to reproduce 44 protein−aromatic ligand complexes (31 different proteins and 39 different ligand molecules) of the databank. All ligands were docked to all different protein targets in altogether 12 090 docking runs. Based on the results of the extensive docking simulations, two relative measures, the molecular interaction fingerprint (MIF) and the molecular affinity fingerprint (MAF), were introduced to describe the selectivity of aromatic ligands to different proteins. MIF and MAF patterns are in agreement with fragment and similarity considerations. Limitations and future extension of our approach are discussed.