Showing papers on "Docking (molecular) published in 2017"
TL;DR: Docking against homology-modeled targets also becomes possible for proteins whose structures are not known, and the druggability of the compounds and their specificity against a particular target can be calculated for further lead optimization processes.
Abstract: Molecular docking methodology explores the behavior of small molecules in the binding site of a target protein. As more protein structures are determined experimentally using X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy, molecular docking is increasingly used as a tool in drug discovery. Docking against homology-modeled targets also becomes possible for proteins whose structures are not known. With the docking strategies, the druggability of the compounds and their specificity against a particular target can be calculated for further lead optimization processes. Molecular docking programs perform a search algorithm in which the conformation of the ligand is evaluated recursively until the convergence to the minimum energy is reached. Finally, an affinity scoring function, ΔG [U total in kcal/mol], is employed to rank the candidate poses as the sum of the electrostatic and van der Waals energies. The driving forces for these specific interactions in biological systems aim toward complementarities between the shape and electrostatics of the binding site surfaces and the ligand or substrate.
817 citations
TL;DR: Tested on the cases with weakly homologous complexes of <30% sequence identity from five docking benchmarks, the HDOCK pipeline tied with template-based modeling on the protein–protein and protein–DNA benchmarks and performed better than template- based modelingon the three protein–RNA benchmarks when the top 10 predictions were considered.
Abstract: Protein-protein and protein-DNA/RNA interactions play a fundamental role in a variety of biological processes. Determining the complex structures of these interactions is valuable, in which molecular docking has played an important role. To automatically make use of the binding information from the PDB in docking, here we have presented HDOCK, a novel web server of our hybrid docking algorithm of template-based modeling and free docking, in which cases with misleading templates can be rescued by the free docking protocol. The server supports protein-protein and protein-DNA/RNA docking and accepts both sequence and structure inputs for proteins. The docking process is fast and consumes about 10-20 min for a docking run. Tested on the cases with weakly homologous complexes of <30% sequence identity from five docking benchmarks, the HDOCK pipeline tied with template-based modeling on the protein-protein and protein-DNA benchmarks and performed better than template-based modeling on the three protein-RNA benchmarks when the top 10 predictions were considered. The performance of HDOCK became better when more predictions were considered. Combining the results of HDOCK and template-based modeling by ranking first of the template-based model further improved the predictive power of the server. The HDOCK web server is available at http://hdock.phys.hust.edu.cn/.
519 citations
TL;DR: This work focuses on the mechanisms by which EVs dock and transfer their contents to cells, and highlights how these findings may provide new avenues for therapeutic intervention.
209 citations
TL;DR: Crystal structures of human AT2R bound to an AT1R-selective ligand and to an At1R/AT2R dual ligand are reported, capturing the receptor in an active-like conformation, and helix VIII was found in a non-canonical position, stabilizing the active- like state, but at the same time preventing the recruitment of G proteins or β-arrestins.
Abstract: The angiotensin II receptors AT1R and AT2R serve as key components of the renin-angiotensin-aldosterone system. AT1R has a central role in the regulation of blood pressure, but the function of AT2R is unclear and it has a variety of reported effects. To identify the mechanisms that underlie the differences in function and ligand selectivity between these receptors, here we report crystal structures of human AT2R bound to an AT2R-selective ligand and to an AT1R/AT2R dual ligand, capturing the receptor in an active-like conformation. Unexpectedly, helix VIII was found in a non-canonical position, stabilizing the active-like state, but at the same time preventing the recruitment of G proteins or β-arrestins, in agreement with the lack of signalling responses in standard cellular assays. Structure-activity relationship, docking and mutagenesis studies revealed the crucial interactions for ligand binding and selectivity. Our results thus provide insights into the structural basis of the distinct functions of the angiotensin receptors, and may guide the design of new selective ligands.
170 citations
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TL;DR: A general 3-dimensional spatial convolution operation for learning atomic-level chemical interactions directly from atomic coordinates and offers a strong foundation for future improvements in structure-based bioactivity prediction.
Abstract: Empirical scoring functions based on either molecular force fields or cheminformatics descriptors are widely used, in conjunction with molecular docking, during the early stages of drug discovery to predict potency and binding affinity of a drug-like molecule to a given target. These models require expert-level knowledge of physical chemistry and biology to be encoded as hand-tuned parameters or features rather than allowing the underlying model to select features in a data-driven procedure. Here, we develop a general 3-dimensional spatial convolution operation for learning atomic-level chemical interactions directly from atomic coordinates and demonstrate its application to structure-based bioactivity prediction. The atomic convolutional neural network is trained to predict the experimentally determined binding affinity of a protein-ligand complex by direct calculation of the energy associated with the complex, protein, and ligand given the crystal structure of the binding pose. Non-covalent interactions present in the complex that are absent in the protein-ligand sub-structures are identified and the model learns the interaction strength associated with these features. We test our model by predicting the binding free energy of a subset of protein-ligand complexes found in the PDBBind dataset and compare with state-of-the-art cheminformatics and machine learning-based approaches. We find that all methods achieve experimental accuracy and that atomic convolutional networks either outperform or perform competitively with the cheminformatics based methods. Unlike all previous protein-ligand prediction systems, atomic convolutional networks are end-to-end and fully-differentiable. They represent a new data-driven, physics-based deep learning model paradigm that offers a strong foundation for future improvements in structure-based bioactivity prediction.
149 citations
TL;DR: Many important aspects of molecular docking in terms of its approaches, types, applications and challenges are briefly discussed in this article.
Abstract: Molecular docking is a kind of bioinformatic modelling which involves the interaction of two or more molecules to give the stable adduct. Depending upon binding properties of ligand and target, it predicts the three-dimensional structure of any complex. Molecular docking generates different possible adduct structures that are ranked and grouped together using scoring function in the software. Docking simulations predict optimized docked conformer based upon total energy of the system. In spite of all potential approaches, ligand chemistry (tautomerism and ionization), receptor flexibility (single conformation of rigid receptor) and scoring function (differentiate true binding mode) still remained the challenge. Many important aspects of molecular docking in terms of its approaches, types, applications and challenges are briefly discussed in this article.
106 citations
TL;DR: This review focuses on computational aspects of predicting the best native-like complex structure and binding affinities and provides the latest developments on protein- protein docking and binding affinity studies along with a list of available computational resources for understanding protein-protein interactions.
104 citations
TL;DR: These studies indicated that while both deacetylation and sulfation of HA individually decrease CD44 interaction, both chemical modifications are required to minimize interaction with CD44+ cells.
Abstract: CD44 is a widely-distributed type I transmembrane glycoprotein that binds hyaluronic acid (HA) in most cell types, including primary tumor cells and cancer-initiating cells and has roles in cell migration, cell-cell, and cell-matrix adhesion. HA-derived conjugates and nanoparticles that target the CD44 receptor on cells have been reported for targeted delivery of therapeutics and imaging agents. Altering crucial interactions of HA with CD44 active sites holds significant importance in modulating targeting ability of hyaluronic acid to other cancer types that do not express the CD44 receptor or minimizing the interaction with CD44+ cells that are not target cells. The approach adopted here was deacetylation of the N-acetyl group and selective sulfation on the C6-OH on the HA polymer, which form critical interactions with the CD44 active site. Major interactions identified by molecular modeling were confirmed to be hydrogen bonding of the C6-OH with Tyr109 and hydrophobic interaction of the N-acetyl group with Tyr46, 83 and Ile 92. Modified HA was synthesized and characterized and its interactions were assessed by in vitro and molecular modeling approaches. In vitro techniques included flow cytometry and fluorescence polarization, while in silico approaches included docking and binding calculations by a MM-PBSA approach. These studies indicated that while both deacetylation and sulfation of HA individually decrease CD44 interaction, both chemical modifications are required to minimize interaction with CD44+ cells. The results of this study represent the first step to effective retargeting of HA-derived NPs for imaging and drug delivery.
98 citations
TL;DR: A step-by-step guide for those interested in incorporating contemporary basic molecular docking and homology modelling into their design strategy is provided.
Abstract: Elucidating details of the relationship between molecular structure and a particular biological end point is essential for successful, rational drug discovery. Molecular docking is a widely accepted tool for lead identification however, navigating the intricacies of the software can be daunting. Our objective was therefore to provide a step-by-step guide for those interested in incorporating contemporary basic molecular docking and homology modelling into their design strategy. Three molecular docking programs, AutoDock4, SwissDock and Surflex-Dock, were compared in the context of a case study where a set of steroidal and non-steroidal ligands were docked into the human androgen receptor (hAR) using both rigid and flexible target atoms. Metrics for comparison included how well each program predicted the X-ray structure orientation via root mean square deviation (rmsd), predicting known actives via ligand ranking and comparison to biological data where available. Benchmarking metrics were discussed in terms of identifying accurate and reliable results. For cases where no three dimensional structure exists, we provided a practical example for creating a homology model using Swiss-Model. Results showed an rmsd between X-ray ligands from wild-type and mutant receptors and docked poses were 4.15A and 0.83A (SwissDock), 2.69A and 8.80A (AutoDock4) and 0.39A and 0.71A (Surflex-Dock) respectively. Surflex-Dock performed consistently well in pose prediction (less than 2A) while Auto- Dock4 predicted known active non-steroidal antiandrogens most accurately. Introducing flexibility into target atoms produced the largest degree of change in ligand ranking in Surflex-Dock. We produced a viable homology model of the P2X1 purireceptor for subsequent docking analysis.
97 citations
TL;DR: The results have demonstrated that it is very important to keep a few top poses for rescoring, if the binding is non-specific or the binding mode is not well determined by the docking calculation.
Abstract: Background: A high-throughput virtual screening pipeline has been extended from single energetically minimized structure Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) rescoring to ensemble-average MM/GBSA rescoring. The correlation coefficient (R2) of calculated and experimental binding free energies for a series of antithrombin ligands has been improved from 0.36 to 0.69 when switching from the single-structure MM/GBSA rescoring to ensemble-average one. The electrostatic interactions in both solute and solvent are identified to play an important role in determining the binding free energy after the decomposition of the calculated binding free energy. The increasing negative charge of the compounds provides a more favorable electrostatic energy change but creates a higher penalty for the solvation free energy. Such a penalty is compensated by the electrostatic energy change, which results in a better binding affinity. A highly hydrophobic ligand is determined by the docking program to be a non-specific binder. Results: Our results have demonstrated that it is very important to keep a few top poses for rescoring, if the binding is non-specific or the binding mode is not well determined by the docking calculation.
96 citations
TL;DR: The computational design of proteins that bind the potent analgesic fentanyl is described, using the designs to generate plant sensors for fentanyl by coupling ligand binding to design stability and should be generally useful for detecting toxic hydrophobic compounds in the environment.
Abstract: We describe the computational design of proteins that bind the potent analgesic fentanyl. Our approach employs a fast docking algorithm to find shape complementary ligand placement in protein scaffolds, followed by design of the surrounding residues to optimize binding affinity. Co-crystal structures of the highest affinity binder reveal a highly preorganized binding site, and an overall architecture and ligand placement in close agreement with the design model. We use the designs to generate plant sensors for fentanyl by coupling ligand binding to design stability. The method should be generally useful for detecting toxic hydrophobic compounds in the environment.
TL;DR: It is feasible that dynamic docking will replace static docking approaches in the near future, leading to a major paradigm shift in in silico drug discovery, and the key achievements that have paved the way are reviewed.
Abstract: Molecular docking is the methodology of choice for studying in silico protein-ligand binding and for prioritizing compounds to discover new lead candidates. Traditional docking simulations suffer from major limitations, mostly related to the static or semi-flexible treatment of ligands and targets. They also neglect solvation and entropic effects, which strongly limits their predictive power. During the last decade, methods based on full atomistic molecular dynamics (MD) have emerged as a valid alternative for simulating macromolecular complexes. In principle, compared to traditional docking, MD allows the full exploration of drug-target recognition and binding from both the mechanistic and energetic points of view (dynamic docking). Binding and unbinding kinetic constants can also be determined. While dynamic docking is still too computationally expensive to be routinely used in fast-paced drug discovery programs, the advent of faster computing architectures and advanced simulation methodologies are changing this scenario. It is feasible that dynamic docking will replace static docking approaches in the near future, leading to a major paradigm shift in in silico drug discovery. Against this background, we review the key achievements that have paved the way for this progress.
TL;DR: This work has successfully addressed the challenge of accurate and efficient global peptide-protein docking at high-resolution with remarkable accuracy, as validated on a small but representative set of peptile-protein complex structures well resolved by X-ray crystallography.
Abstract: Peptide-protein interactions contribute a significant fraction of the protein-protein interactome. Accurate modeling of these interactions is challenging due to the vast conformational space associated with interactions of highly flexible peptides with large receptor surfaces. To address this challenge we developed a fragment based high-resolution peptide-protein docking protocol. By streamlining the Rosetta fragment picker for accurate peptide fragment ensemble generation, the PIPER docking algorithm for exhaustive fragment-receptor rigid-body docking and Rosetta FlexPepDock for flexible full-atom refinement of PIPER docked models, we successfully addressed the challenge of accurate and efficient global peptide-protein docking at high-resolution with remarkable accuracy, as validated on a small but representative set of peptide-protein complex structures well resolved by X-ray crystallography. Our approach opens up the way to high-resolution modeling of many more peptide-protein interactions and to the detailed study of peptide-protein association in general. PIPER-FlexPepDock is freely available to the academic community as a server at http://piperfpd.furmanlab.cs.huji.ac.il.
TL;DR: The results suggest that simple equilibrium simulations can serve as an effective filter to exclude decoy poses that cannot be distinguished by docking scores from the computationally expensive free-energy calculations.
Abstract: Docking has become an indispensable approach in drug discovery research to predict the binding mode of a ligand. One great challenge in docking is to efficiently refine the correct pose from various putative docking poses through scoring functions. We recently examined the stability of self-docking poses under molecular dynamics (MD) simulations and showed that equilibrium MD simulations have some capability to discriminate between correct and decoy poses. Here, we have extended our previous work to cross-docking studies for practical applications. Three target proteins (thrombin, heat shock protein 90-alpha, and cyclin-dependent kinase 2) of pharmaceutical interest were selected. Three comparable poses (one correct pose and two decoys) for each ligand were then selected from the docking poses. To obtain the docking poses for the three target proteins, we used three different protocols, namely: normal docking, induced fit docking (IFD), and IFD against the homology model. Finally, five parallel MD equilib...
TL;DR: The molecular docking results illustrated that both hydrogen bonding and the hydrophobic interaction are involved as an acting force during the binding process, which may contribute to a better understanding over the enhanced physicochemical proprieties of anthocyanins due to the complexation with milk proteins.
TL;DR: Using a motif based search, structural fragments from the Protein Data Bank are retrieved that are very similar to the peptide's final, bound conformation to perform global rigid body docking of these fragments to the receptor.
Abstract: Summary We present an approach for the efficient docking of peptide motifs to their free receptor structures. Using a motif based search, we can retrieve structural fragments from the Protein Data Bank (PDB) that are very similar to the peptide's final, bound conformation. We use a Fast Fourier Transform (FFT) based docking method to quickly perform global rigid body docking of these fragments to the receptor. According to CAPRI peptide docking criteria, an acceptable conformation can often be found among the top-ranking predictions. Availability and implementation The method is available as part of the protein-protein docking server ClusPro at https://peptidock.cluspro.org/nousername.php. Contact midas@laufercenter.org or oraf@ekmd.huji.ac.il. Supplementary information Supplementary data are available at Bioinformatics online.
TL;DR: A Hybrid DOCKing protocol of template‐based and template‐free approaches, referred to as HDOCK, which made correct predictions on other CAPRI challenges such as protein–peptide binding for 6 out of 8 targets and water predictions for 2 out of 2 targets.
Abstract: Protein-protein docking is an important computational tool for predicting protein-protein interactions. With the rapid development of proteomics projects, more and more experimental binding information ranging from mutagenesis data to three-dimensional structures of protein complexes are becoming available. Therefore, how to appropriately incorporate the biological information into traditional ab initio docking has been an important issue and challenge in the field of protein-protein docking. To address these challenges, we have developed a Hybrid DOCKing protocol of template-based and template-free approaches, referred to as HDOCK. The basic procedure of HDOCK is to model the structures of individual components based on the template complex by a template-based method if a template is available; otherwise, the component structures will be modeled based on monomer proteins by regular homology modeling. Then, the complex structure of the component models is predicted by traditional protein-protein docking. With the HDOCK protocol, we have participated in the CPARI experiment for rounds 28-35. Out of the 25 CASP-CAPRI targets for oligomer modeling, our HDOCK protocol predicted correct models for 16 targets, ranking one of the top algorithms in this challenge. Our docking method also made correct predictions on other CAPRI challenges such as protein-peptide binding for 6 out of 8 targets and water predictions for 2 out of 2 targets. The advantage of our hybrid docking approach over pure template-based docking was further confirmed by a comparative evaluation on 20 CASP-CAPRI targets. Proteins 2017; 85:497-512. © 2016 Wiley Periodicals, Inc.
TL;DR: In an attempt to develop potent anti-tubulin agents against most dreadful disease cancer, a library of 28 novel triazole tethered isatin-coumarin hybrids were synthesized by click chemistry approach and three most potent hybrids (A-1, A-2 and B-1) were further tested for tubulin polymerization inhibition.
TL;DR: The docking results suggest that TP forms stable complex with HSA (Kb ∼ 104) and its primary binding site is located in subdomain IIA (Sudlow Site I), which indicated that the hydrophobic interactions play a main role in the binding of TP to human serum albumin.
TL;DR: In this article, the authors used a Lineweaver-burk plot to explore the ACEI kinetics of Qula casein derived from yak milk and identified ACEI peptides using LC-MS/MS.
TL;DR: The following friendly user tutorial targets both undergraduate and graduate students, allowing them to understand docking as a computational tool for structure-based drug design.
Abstract: Computational docking and scoring techniques have revolutionized structural bioinformatics by providing unprecedented insights on key aspects of ligand-receptor interaction Docking is used for optimizing known drugs and for identifying novel binders by predicting their binding mode and affinity AutoDock and AutoDockTools are free of charge techniques that have been extensively cited in the literature as essential tools in structure-based drug design Moreover, these methods are fast enough to permit virtual screening of ligand libraries containing tens of thousands of compounds However using Autodock requires some knowledge in programming which creates a limitation for biologists and makes them prone for commercial applications Here, we selected a relevant target involved in the progression of Alzheimer disease and provided a fully reproducible docking protocol This example will show how docking techniques would be an important asset to identify new BACE1 inhibitors The following friendly user tutorial targets both undergraduate and graduate students, allowing them to understand docking as a computational tool for structure-based drug design
TL;DR: The synthesis of benzimidazole linked oxadiazole derivatives designed as potential EGFR and erbB2 receptor inhibitors with anticancer and apoptotic activity were studied and compounds 7a and 7n showed normal architecture of myofibrils in cardiomyopathy study whereas only compound 7a showed nearly equal biochemical parameters when compared to control.
TL;DR: Inhibition of angiogenesis through inhibition of vascular endothelial growth factor receptor 2 (VEGFR-2) has been applied in cancer therapy because of its important role in promoting cancer growth and metastasis and structure-activity relationship was inferred for future optimization.
TL;DR: The present results indicate that the screened compound 114 may act as a parent structure for designing potent derivatives against AbMurF in specific and MurF of other bacterial pathogens in general.
Abstract: MurF ligase catalyzes the final cytoplasmic step of bacterial peptidoglycan biosynthesis and, as such, is a validated target for therapeutic intervention. Herein, we performed molecular docking to identify putative inhibitors of Acinetobacter baumannii MurF (AbMurF). Based on comparative docking analysis, compound 114 (ethyl pyridine substituted 3-cyanothiophene) was predicted to potentially be the most active ligand. Computational pharmacokinetic characterization of drug-likeness of the compound showed it to fulfil all the parameters of Muegge and the MDDR rule. A molecular dynamic simulation of 114 indicated the complex to be stable on the basis of an average root mean square deviation (RMSD) value of 2.09 A for the ligand. The stability of the complex was further supported by root mean square fluctuation (RMSF), beta factor and radius of gyration values. Analyzing the complex using radial distribution function (RDF) and a novel analytical tool termed the axial frequency distribution (AFD) illustrated that after simulation the ligand is positioned in close vicinity of the protein active site where Thr42 and Asp43 participate in hydrogen bonding and stabilization of the complex. Binding free energy calculations based on the Poisson-Boltzmann or Generalized–Born Surface Area Continuum Solvation (MM(PB/GB)SA) method indicated the van der Waals contribution to the overall binding energy of the complex to be dominant along with electrostatic contributions involving the hot spot amino acids from the protein active site. The present results indicate that the screened compound 114 may act as a parent structure for designing potent derivatives against AbMurF in specific and MurF of other bacterial pathogens in general.
TL;DR: Novel tris-indole-oxadiazole hybrid analogs (1-21), structurally characterized by various spectroscopic techniques such as 1H NMR, EI-MS, and 13C NMR are synthesized, showing that almost all compounds in this series indicate the drug aptness.
TL;DR: The results suggest that the A-syn's interaction with v-SNARE through its C-terminal tail and its concurrent interaction with PS in trans through its amphipathic N-Terminal domain facilitate SNARE complex formation, whereby A- syn aids SNARE-dependent vesicle docking.
Abstract: Misfolded α-synuclein/A-syn is widely recognized as primal cause of neurodegenerative diseases including Parkinson9s and dementia with Lewy bodies. The normal cellular function of A-syn has however been elusive. There is evidence that A-syn plays a variety of roles in the exocytotic pathway in the neuron, but the underlying molecular mechanisms are unclear. A-syn has been known to interact with negatively charged phospholipids and with vesicle SNARE protein VAMP2 as well. Using single vesicle-vesicle docking/fusion assays, we find that A-syn promotes SNARE-dependent vesicles docking significantly at 2.5 µM. When phosphatidylserine (PS) is removed from t-SNARE-bearing vesicles the docking enhancement by A-syn disappears and A-syn instead acts as an inhibitor for docking. In contrast, subtraction of PS from the v-SNARE-carrying vesicles enhances vesicle docking even further. Moreover, when we truncate the C-terminal 45 residues of A-syn that participates in interacting with VAMP2 the promotion of vesicle docking is abrogated. Thus, the results suggest that the A-syn9s interaction with v-SNARE through its C-terminal tail and its concurrent interaction with PS
in trans through its amphipathic N-terminal domain facilitate SNARE complex formation, whereby A-syn aids SNARE-dependent vesicle docking.
TL;DR: A summary of how computer modeling has been used in the development of covalent-modifier drugs is presented, showing how docking methods, which are used to rank binding poses of large sets of inhibitors, have been augmented to support the formation of protein-ligand bonds.
TL;DR: This study characterized the interaction of rice glutelin (RG) and gallic acid (GA) was characterized by spectroscopic and molecular docking techniques and provided insights into micro-environmental and conformational changes of RG.
TL;DR: The important features of the multi-target therapeutics discoveries using the computational approach are reviewed, highlighting the SAR, QSAR, docking and pharmacophore methods to discover interactions between drug-target that could be leveraged for curative benefits.
Abstract: Multi-target drugs against particular multiple targets get better protection, resistance profiles and curative influence by cooperative rules of a key beneficial target with resistance behavior and compensatory elements. Computational techniques can assist us in the efforts to design novel drugs (ligands) with a preferred bioactivity outline and alternative bioactive molecules at an early stage. A number of in silico methods have been explored extensively in order to facilitate the investigation of individual target agents and to propose a selective drug. A different, progressively more significant field which is used to predict the bioactivity of chemical compounds is the data mining method. Some of the previously mentioned methods have been investigated for multi-target drug design (MTDD) to find drug leads interact simultaneously with multiple targets. Several cheminformatics methods and structure-based approaches try to extract information from units working cooperatively in a biomolecular system to fulfill their task. To dominate the difficulties of the experimental specification of ligand-target structures, rational methods, namely molecular docking, SAR and QSAR are vital substitutes to obtain knowledge for each structure in atomic insight. These procedures are logically successful for the prediction of binding affinity and have shown promising potential in facilitating MTDD. Here, we review some of the important features of the multi-target therapeutics discoveries using the computational approach, highlighting the SAR, QSAR, docking and pharmacophore methods to discover interactions between drug-target that could be leveraged for curative benefits. A summary of each, followed by examples of its applications in drug design has been provided. Computational efficiency of each method has been represented according to its main strengths and limitations.
TL;DR: The [1,2,3]triazolo[4,5-d]pyrimidine scaffold can be used as the template for designing new LSD1 inhibitors and docking studies indicated that the hydrogen interaction between the nitrogen atom in the pyridine ring and Met332 could be responsible for the improved activity of 2-thiopyridine series.
Abstract: Lysine specific demethylase 1 (LSD1) plays a pivotal role in regulating the lysine methylation. The aberrant overexpression of LSD1 has been reported to be involved in the progression of certain human malignant tumors. Abrogation of LSD1 with RNAi or small molecule inhibitors may lead to the inhibition of cancer proliferation and migration. Herein, a series of [1,2,3]triazolo[4,5-d]pyrimidine derivatives were synthesized and evaluated for their LSD1 inhibitory effects. The structure–activity relationship studies (SARs) were conducted by exploring three regions of this scaffold, leading to the discovery of compound 27 as potent LSD1 inhibitor (IC50 = 0.564 μM). Compound 27 was identified as a reversible LSD1 inhibitor and showed certain selectivity to LSD1 over monoamine oxidase A/B (MAO-A/B). When MGC-803 cells were treated with compound 27, the activity of LSD1 can be significantly inhibited, and the cell migration ability was also suppressed. Docking studies indicated that the hydrogen interaction betwe...