Journal articles on the topic 'Quality assessment of protein-ligand crystal structures'
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Chakraborti, Sohini, Kaushik Hatti, and Narayanaswamy Srinivasan. "‘All That Glitters Is Not Gold’: High-Resolution Crystal Structures of Ligand-Protein Complexes Need Not Always Represent Confident Binding Poses." International Journal of Molecular Sciences 22, no. 13 (June 25, 2021): 6830. http://dx.doi.org/10.3390/ijms22136830.
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Our understanding of the structure–function relationships of biomolecules and thereby applying it to drug discovery programs are substantially dependent on the availability of the structural information of ligand–protein complexes. However, the correct interpretation of the electron density of a small molecule bound to a crystal structure of a macromolecule is not trivial. Our analysis involving quality assessment of ~0.28 million small molecule–protein binding site pairs derived from crystal structures corresponding to ~66,000 PDB entries indicates that the majority (65%) of the pairs might need little (54%) or no (11%) attention. Out of the remaining 35% of pairs that need attention, 11% of the pairs (including structures with high/moderate resolution) pose serious concerns. Unfortunately, most users of crystal structures lack the training to evaluate the quality of a crystal structure against its experimental data and, in general, rely on the resolution as a ‘gold standard’ quality metric. Our work aims to sensitize the non-crystallographers that resolution, which is a global quality metric, need not be an accurate indicator of local structural quality. In this article, we demonstrate the use of several freely available tools that quantify local structural quality and are easy to use from a non-crystallographer’s perspective. We further propose a few solutions for consideration by the scientific community to promote quality research in structural biology and applied areas.
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Jaskolski, Mariusz, Zbigniew Dauter, Ivan G. Shabalin, Miroslaw Gilski, Dariusz Brzezinski, Marcin Kowiel, Bernhard Rupp, and Alexander Wlodawer. "Crystallographic models of SARS-CoV-2 3CLpro: in-depth assessment of structure quality and validation." IUCrJ 8, no. 2 (February 9, 2021): 238–56. http://dx.doi.org/10.1107/s2052252521001159.
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The appearance at the end of 2019 of the new SARS-CoV-2 coronavirus led to an unprecedented response by the structural biology community, resulting in the rapid determination of many hundreds of structures of proteins encoded by the virus. As part of an effort to analyze and, if necessary, remediate these structures as deposited in the Protein Data Bank (PDB), this work presents a detailed analysis of 81 crystal structures of the main protease 3CLpro, an important target for the design of drugs against COVID-19. The structures of the unliganded enzyme and its complexes with a number of inhibitors were determined by multiple research groups using different experimental approaches and conditions; the resulting structures span 13 different polymorphs representing seven space groups. The structures of the enzyme itself, all determined by molecular replacement, are highly similar, with the exception of one polymorph with a different inter-domain orientation. However, a number of complexes with bound inhibitors were found to pose significant problems. Some of these could be traced to faulty definitions of geometrical restraints for ligands and to the general problem of a lack of such information in the PDB depositions. Several problems with ligand definition in the PDB itself were also noted. In several cases extensive corrections to the models were necessary to adhere to the evidence of the electron-density maps. Taken together, this analysis of a large number of structures of a single, medically important protein, all determined within less than a year using modern experimental tools, should be useful in future studies of other systems of high interest to the biomedical community.
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Shelley, Kathryn L., Thomas P. E. Dixon, Jonathan C. Brooks-Bartlett, and Elspeth F. Garman. "RABDAM: quantifying specific radiation damage in individual protein crystal structures." Journal of Applied Crystallography 51, no. 2 (March 28, 2018): 552–59. http://dx.doi.org/10.1107/s1600576718002509.
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Radiation damage remains one of the major limitations to accurate structure determination in protein crystallography (PX). Despite the use of cryo-cooling techniques, it is highly probable that a number of the structures deposited in the Protein Data Bank (PDB) have suffered substantial radiation damage as a result of the high flux densities of third generation synchrotron X-ray sources. Whereas the effects of global damage upon diffraction pattern reflection intensities are readily detectable, traditionally the (earlier onset) site-specific structural changes induced by radiation damage have proven difficult to identify within individual PX structures. More recently, however, development of theBDamagemetric has helped to address this problem.BDamageis a quantitative, per-atom metric identifies potential sites of specific damage by comparing the atomicB-factor values of atoms that occupy a similar local packing density environment in the structure. Building upon this past work, this article presents a program,RABDAM, to calculate theBDamagemetric for all selected atoms within any standard-format PDB or mmCIF file.RABDAMprovides several useful outputs to assess the extent of damage suffered by an input PX structure. This free and open-source software will allow assessment and improvement of the quality of PX structures both previously and newly deposited in the PDB.
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Das, Debanu, Matthew Duncton, Patricia Pellicena, Ashley Deacon, David Wilson, and Millie Georgiadis. "Development of a DNA damage response (DDR) therapeutics platform for oncology." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): e15036-e15036. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e15036.
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e15036 Background: Cancer cells respond to increases in DNA damage by deploying their DNA damage response (DDR) pathways. We are building a platform for the discovery and development of target-specific DDR therapeutics, including small molecule inhibitors and targeted protein degradation warheads, founded on fragment- and structure-based drug discovery. Methods: Our DDR platform, which includes hit-to-lead, lead optimization and candidate selection, starts with hit generation from a new technology that uses high-throughput protein X-ray crystallography to directly screen compound libraries. Our hit generation process produces empirical evidence of direct target engagement. The elucidation of high-quality ligand-bound 3D structures reveals the location and pose of the ligand and details of the protein-ligand interactions. Thus we can predict the structure-function consequences of the hit molecule engagement, which sets the stage for rapid assessment of synthetic tractability and intellectual property. After hit identification, we apply a multi-pronged approach in hit-to-lead conversion and lead optimization using iterative biophysical and biochemical assays, coupled with crystallography. We are applying our approach to several new targets in DDR and will present some early progress in this space. Results: Apurinic/apyrimidinic endonuclease 1 (APE1) is the major repair enzyme for abasic sites in DNA and contributes to DNA strand break processing. Many studies have associated increased APE1 levels with enhanced growth, migration, and drug resistance in human tumor cells, and with decreased patient survival. APE1 has been implicated in over 20 human cancers, including glioblastoma, making the protein an attractive target for the development of anticancer therapeutics. Despite intensive effort, there are no clinical endonuclease inhibitors of APE1. We have identified 25 diverse small molecule fragments that bind to APE1 at two distinct sites, including the endonuclease site. Pol eta (or PolH) is a DNA polymerase implicated, among other things, in the development of cisplatin resistance in a subset of ovarian cancers. In our quest to develop PolH inhibitors, we have identified 5 diverse fragments that bind to two distinct sites in the polymerase including a new potential allosteric site. Our results on APE1 and PolH represent the first known cases of crystal structures of small molecules bound to these proteins. Flap endonuclease (FEN1) is implicated in several cancers including for example ER/tamoxifen-resistant breast cancer. We are developing a targeted protein degradation approach using PROTACs (Proteolysis-Targeting Chimeras) toward the development of novel therapeutics against FEN1. Conclusions: Our results will help us develop small molecule inhibitors and targeted protein degradation against DDR targets that may be effective as single therapies or be used to make existing therapies more effective.
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Rambo, Robert, Greg Hura, Michal Hammel, and John Tainer. "X-ray scattering methods for accurate analyses of flexible complexes." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C409. http://dx.doi.org/10.1107/s2053273314095904.
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A grand challenge for structural biology is to efficiently inform macromolecular functions that involve flexible or unstructured regions and require multiple conformational states including membrane proteins and protein-nucleic acid complexes. Small angle X-ray scattering (SAXS) can probe at resolutions sufficient to distinguish conformational states, characterize flexible macromolecules, and screen in high-throughput under most solution conditions. However, methods for analyzing SAXS data and models have restricted progress. We are therefore developing SAXS methods that provide high-throughput, quantitative and superposition-independent evaluation of solution-state conformations and quantitatively define flexibility and disorder. The SIBYLS beamline provides hardware and software to integrate SAXS and crystal structure results. Our SAXS methods aim to improve crystallization and interpretation of crystal structures and NMR structure quality. We have invented a statistically robust method for assessing model-data agreements (chi-square free) akin to cross-validation. We also developed a metric and method for rapid quantitative and comprehensive assessment of molecular similarity suitable to examine functionally important conformational changes. To extend SAXS analysis to low concentrations and complex mixtures, we are developing SAXS with gold nano-crystal labels to enable examination of protein-induced DNA distortions along pathways key to the DNA repair, replication, transcription, and packaging. Collective results suggest SAXS can provide accurate shapes, assembly states, and comprehensive conformations of flexible complexes in solution that inform biology in fundamental ways.
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Hassan, Heba H. A., Muhammad I. Ismail, Mohammed A. S. Abourehab, Frank M. Boeckler, Tamer M. Ibrahim, and Reem K. Arafa. "In Silico Targeting of Fascin Protein for Cancer Therapy: Benchmarking, Virtual Screening and Molecular Dynamics Approaches." Molecules 28, no. 3 (January 29, 2023): 1296. http://dx.doi.org/10.3390/molecules28031296.
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Fascin is an actin-bundling protein overexpressed in various invasive metastatic carcinomas through promoting cell migration and invasion. Therefore, blocking Fascin binding sites is considered a vital target for antimetastatic drugs. This inspired us to find new Fascin binding site blockers. First, we built an active compound set by collecting reported small molecules binding to Fascin’s binding site 2. Consequently, a high-quality decoys set was generated employing DEKOIS 2.0 protocol to be applied in conducting the benchmarking analysis against the selected Fascin structures. Four docking programs, MOE, AutoDock Vina, VinaXB, and PLANTS were evaluated in the benchmarking study. All tools indicated better-than-random performance reflected by their pROC-AUC values against the Fascin crystal structure (PDB: ID 6I18). Interestingly, PLANTS exhibited the best screening performance and recognized potent actives at early enrichment. Accordingly, PLANTS was utilized in the prospective virtual screening effort for repurposing FDA-approved drugs (DrugBank database) and natural products (NANPDB). Further assessment via molecular dynamics simulations for 100 ns endorsed Remdesivir (DrugBank) and NANPDB3 (NANPDB) as potential binders to Fascin binding site 2. In conclusion, this study delivers a model for implementing a customized DEKOIS 2.0 benchmark set to enhance the VS success rate against new potential targets for cancer therapies.
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Haghighi, Fatemeh, Semen Yesylevskyy, Siamak Davani, and Christophe Ramseyer. "Membrane Environment Modulates Ligand-Binding Propensity of P2Y12 Receptor." Pharmaceutics 13, no. 4 (April 9, 2021): 524. http://dx.doi.org/10.3390/pharmaceutics13040524.
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The binding of natural ligands and synthetic drugs to the P2Y12 receptor is of great interest because of its crucial role in platelets activation and the therapy of arterial thrombosis. Up to now, all computational studies of P2Y12 concentrated on the available crystal structures, while the role of intrinsic protein dynamics and the membrane environment in the functioning of P2Y12 was not clear. In this work, we performed all-atom molecular dynamics simulations of the full-length P2Y12 receptor in three different membrane environments and in two possible conformations derived from available crystal structures. The binding of ticagrelor, its two major metabolites, adenosine diphosphate (ADP) and 2-Methylthioadenosine diphosphate (2MeS-ADP) as agonist, and ethyl 6-[4-(benzylsulfonylcarbamoyl)piperidin-1-yl]-5-cyano-2-methylpyridine-3-carboxylate (AZD1283)as antagonist were assessed systematically by means of ensemble docking. It is shown that the binding of all ligands becomes systematically stronger with the increase of the membrane rigidity. Binding of all ligands to the agonist-bound-like conformations is systematically stronger in comparison to antagonist-bound-likes ones. This is dramatically opposite to the results obtained for static crystal structures. Our results show that accounting for internal protein dynamics, strongly modulated by its lipid environment, is crucial for correct assessment of the ligand binding to P2Y12.
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Moreno-Chicano, Tadeo, Ali Ebrahim, Danny Axford, Martin V. Appleby, John H. Beale, Amanda K. Chaplin, Helen M. E. Duyvesteyn, et al. "High-throughput structures of protein–ligand complexes at room temperature using serial femtosecond crystallography." IUCrJ 6, no. 6 (October 10, 2019): 1074–85. http://dx.doi.org/10.1107/s2052252519011655.
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High-throughput X-ray crystal structures of protein–ligand complexes are critical to pharmaceutical drug development. However, cryocooling of crystals and X-ray radiation damage may distort the observed ligand binding. Serial femtosecond crystallography (SFX) using X-ray free-electron lasers (XFELs) can produce radiation-damage-free room-temperature structures. Ligand-binding studies using SFX have received only modest attention, partly owing to limited beamtime availability and the large quantity of sample that is required per structure determination. Here, a high-throughput approach to determine room-temperature damage-free structures with excellent sample and time efficiency is demonstrated, allowing complexes to be characterized rapidly and without prohibitive sample requirements. This yields high-quality difference density maps allowing unambiguous ligand placement. Crucially, it is demonstrated that ligands similar in size or smaller than those used in fragment-based drug design may be clearly identified in data sets obtained from <1000 diffraction images. This efficiency in both sample and XFEL beamtime opens the door to true high-throughput screening of protein–ligand complexes using SFX.
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Ridhwan, Mohamad Jemain Mohamad, Syahrul Imran Abu Bakar, Normala Abd Latip, Nurunajah Ab Ghani, and Nor Hadiani Ismail. "A Comprehensive Analysis of Human CYP3A4 Crystal Structures as a Potential Tool for Molecular Docking-Based Site of Metabolism and Enzyme Inhibition Studies." Journal of Computational Biophysics and Chemistry 21, no. 03 (February 17, 2022): 259–85. http://dx.doi.org/10.1142/s2737416522300012.
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The notable ability of human liver cytochrome P450 3A4 (CYP3A4) to metabolize diverse xenobiotics encourages researchers to explore in-depth the mechanism of enzyme action. Numerous CYP3A4 protein crystal structures have been deposited in protein data bank (PDB) and are majorly used in molecular docking analysis. The quality of the molecular docking results depends on the three-dimensional CYP3A4 protein crystal structures from the PDB. Present review endeavors to provide a brief outline of some technical parameters of CYP3A4 PDB entries as valuable information for molecular docking research. PDB entries between 22 April 2004 and 2 June 2021 were compiled and the active sites were thoroughly observed. The present review identified 76 deposited PDB entries and described basic information that includes CYP3A4 from human genetic, Escherichia coli (E. coli) use for protein expression, crystal structure obtained from X-ray diffraction method, taxonomy ID 9606, Uniprot ID P08684, ligand–protein structure description, co-crystal ligand, protein site deposit and resolution ranges between 1.7[Formula: see text]Å and 2.95[Formula: see text]Å. The observation of protein–ligand interactions showed the various residues on the active site depending on the ligand. The residues Ala305, Ser119, Ala370, Phe304, Phe108, Phe213 and Phe215 have been found to frequently interact with ligands from CYP3A4 PDB. Literature surveys of 17 co-crystal ligands reveal multiple mechanisms that include competitive inhibition, noncompetitive inhibition, mixed-mode inhibition, mechanism-based inhibition, substrate with metabolite, inducer, or combination modes of action. This overview may help researchers choose a trustworthy CYP3A4 protein structure from the PDB database to apply the protein in molecular docking analysis for drug discovery.
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Wang, Meng-yu, Peng Li, and Pei-li Qiao. "The Virtual Screening of the Drug Protein with a Few Crystal Structures Based on the Adaboost-SVM." Computational and Mathematical Methods in Medicine 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/4809831.
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Using the theory of machine learning to assist the virtual screening (VS) has been an effective plan. However, the quality of the training set may reduce because of mixing with the wrong docking poses and it will affect the screening efficiencies. To solve this problem, we present a method using the ensemble learning to improve the support vector machine to process the generated protein-ligand interaction fingerprint (IFP). By combining multiple classifiers, ensemble learning is able to avoid the limitations of the single classifier’s performance and obtain better generalization. According to the research of virtual screening experiment with SRC and Cathepsin K as the target, the results show that the ensemble learning method can effectively reduce the error because the sample quality is not high and improve the effect of the whole virtual screening process.
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Martin, J. L. "Protein Crystallography and Examples of its Applications in Medicinal Chemistry." Current Medicinal Chemistry 3, no. 6 (December 1999): 419–36. http://dx.doi.org/10.2174/0929867303666220307175609.
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The field of protein crystallography inspires and enthrals, whether it be for the beauty and symmetry of a perfectly formed protein crystal, the unlocked secrets of a novel protein fold, or the precise atomic-level detail yielded from a protein-ligand complex. Since 1958, when the first protein structure was solved, there have been tremendous advances in all aspects of protein crystallography, from protein preparation and crystallisation through to diffraction data measurement and structure refinement. These advances have significantly reduced the time required to solve protein crystal structures, while at the same time substantially improving the quality and resolution of the resulting structures. Moreover, the technological developments have induced researchers to tackle ever more complex systems, including ribosomes and intact membrane-bound proteins, with a reasonable expectation of success. In this review, the steps involved in determining a protein crystal structure are described and the impact of recent methodological advances identified. <p> Protein crystal stuctures have proved to be extraordinarily useful in medicinal chemistry research, particularly with respect to inhibitor design. The precise interaction between a drug and its receptor can be visualised at the molecular level using protein crystal structures, and this information then used to improve the complementarity and thus increase the potency and selectivity of an inhibitor. The use of protein crystal structures in receptor-based drug design is highlighted by (i) HIV protease, (ii) influenza virus neuraminidase and (iii) prostaglandin H2- synthetase. These represent, respectively, examples of protein crystal structures that (i) influenced the design of drugs currently approved for use in the treatment of HIV infection, (ii) led to the design of compounds currently in clinical trials for the treatment of influenza infection and (iii) could enable the design of highly specific non-steroidal anti-inflammatory drugs that lack the common side-effects of this drug class.</p>
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Iershov, Anton, Konstantin Odynets, Alexander Kornelyuk, and Vadim Kavsan. "Homology modeling of 3D structure of human chitinase-like protein CHI3L2." Open Life Sciences 5, no. 4 (August 1, 2010): 407–20. http://dx.doi.org/10.2478/s11535-010-0039-8.
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AbstractThe human genome encodes six proteins of family 18 glycosyl hydrolases, two active chitinases and four chitinase-like lectins (chi-lectins) lacking catalytic activity. The present article is dedicated to homology modeling of 3D structure of human chitinase 3-like 2 protein (CHI3L2), which is overexpressed in glial brain tumors, and its structural comparison with homologous chi-lectin CHI3L1. Two crystal structures of CHI3L1 in free state (Protein Data Bank codes 1HJX and 1NWR) were used as structural templates for the homology modeling by Modeller 9.7 program, and the best quality model structure was selected from the obtained model ensemble. Analysis of potential oligosaccharide-binding groove structures of CHI3L1 and CHI3L2 revealed significant differences between these two homologous proteins. 8 of 19 amino acid residues important for ligand binding are substituted in CHI3L2: Tyr34/Asp39, Trp69/Lys74, Trp71/Lys76, Trp99/Tyr104, Asn100/Leu105, Met204/Leu210, Tyr206/Phe212 and Arg263/His271. The differences between these residues could influence the structure of the ligand-binding groove and substantially change the ability of CHI3L2 to bind oligosaccharide ligands.
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Rai, Brajesh K., Vishnu Sresht, Qingyi Yang, Ray Unwalla, Meihua Tu, Alan M. Mathiowetz, and Gregory A. Bakken. "Comprehensive Assessment of Torsional Strain in Crystal Structures of Small Molecules and Protein–Ligand Complexes using ab Initio Calculations." Journal of Chemical Information and Modeling 59, no. 10 (October 2019): 4195–208. http://dx.doi.org/10.1021/acs.jcim.9b00373.
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Sadybekov, Arman A., Rebecca L. Brouillette, Egor Marin, Anastasiia V. Sadybekov, Aleksandra Luginina, Anastasiia Gusach, Alexey Mishin, et al. "Structure-Based Virtual Screening of Ultra-Large Library Yields Potent Antagonists for a Lipid GPCR." Biomolecules 10, no. 12 (December 3, 2020): 1634. http://dx.doi.org/10.3390/biom10121634.
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Cysteinyl leukotriene G protein-coupled receptors, CysLT1R and CysLT2R, regulate bronchoconstrictive and pro-inflammatory effects and play a key role in allergic disorders, cardiovascular diseases, and cancer. CysLT1R antagonists have been widely used to treat asthma disorders, while CysLT2R is a potential target against uveal melanoma. However, very few selective antagonist chemotypes for CysLT receptors are available, and the design of such ligands has proved to be challenging. To overcome this obstacle, we took advantage of recently solved crystal structures of CysLT receptors and an ultra-large Enamine REAL library, representing a chemical space of 680 M readily available compounds. Virtual ligand screening employed 4D docking models comprising crystal structures of CysLT1R and CysLT2R and their corresponding ligand-optimized models. Functional assessment of the candidate hits yielded discovery of five novel antagonist chemotypes with sub-micromolar potencies and the best Ki = 220 nM at CysLT1R. One of the hits showed inverse agonism at the L129Q constitutively active mutant of CysLT2R, with potential utility against uveal melanoma.
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Kapla, Jon, Ismael Rodríguez-Espigares, Flavio Ballante, Jana Selent, and Jens Carlsson. "Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?" PLOS Computational Biology 17, no. 5 (May 13, 2021): e1008936. http://dx.doi.org/10.1371/journal.pcbi.1008936.
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The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.
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Lieske, Julia, Maximilian Cerv, Stefan Kreida, Dana Komadina, Janine Fischer, Miriam Barthelmess, Pontus Fischer, et al. "On-chip crystallization for serial crystallography experiments and on-chip ligand-binding studies." IUCrJ 6, no. 4 (June 19, 2019): 714–28. http://dx.doi.org/10.1107/s2052252519007395.
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Efficient and reliable sample delivery has remained one of the bottlenecks for serial crystallography experiments. Compared with other methods, fixed-target sample delivery offers the advantage of significantly reduced sample consumption and shorter data collection times owing to higher hit rates. Here, a new method of on-chip crystallization is reported which allows the efficient and reproducible growth of large numbers of protein crystals directly on micro-patterned silicon chips for in-situ serial crystallography experiments. Crystals are grown by sitting-drop vapor diffusion and previously established crystallization conditions can be directly applied. By reducing the number of crystal-handling steps, the method is particularly well suited for sensitive crystal systems. Excessive mother liquor can be efficiently removed from the crystals by blotting, and no sealing of the fixed-target sample holders is required to prevent the crystals from dehydrating. As a consequence, `naked' crystals are obtained on the chip, resulting in very low background scattering levels and making the crystals highly accessible for external manipulation such as the application of ligand solutions. Serial diffraction experiments carried out at cryogenic temperatures at a synchrotron and at room temperature at an X-ray free-electron laser yielded high-quality X-ray structures of the human membrane protein aquaporin 2 and two new ligand-bound structures of thermolysin and the human kinase DRAK2. The results highlight the applicability of the method for future high-throughput on-chip screening of pharmaceutical compounds.
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Vidad, Ashley Ryan, Stephen Macaspac, and Ho Leung Ng. "Locating ligand binding sites in G-protein coupled receptors using combined information from docking and sequence conservation." PeerJ 9 (September 24, 2021): e12219. http://dx.doi.org/10.7717/peerj.12219.
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GPCRs (G-protein coupled receptors) are the largest family of drug targets and share a conserved structure. Binding sites are unknown for many important GPCR ligands due to the difficulties of GPCR recombinant expression, biochemistry, and crystallography. We describe our approach, ConDockSite, for predicting ligand binding sites in class A GPCRs using combined information from surface conservation and docking, starting from crystal structures or homology models. We demonstrate the effectiveness of ConDockSite on crystallized class A GPCRs such as the beta2 adrenergic and A2A adenosine receptors. We also demonstrate that ConDockSite successfully predicts ligand binding sites from high-quality homology models. Finally, we apply ConDockSite to predict the ligand binding sites on a structurally uncharacterized GPCR, GPER, the G-protein coupled estrogen receptor. Most of the sites predicted by ConDockSite match those found in other independent modeling studies. ConDockSite predicts that four ligands bind to a common location on GPER at a site deep in the receptor cleft. Incorporating sequence conservation information in ConDockSite overcomes errors introduced from physics-based scoring functions and homology modeling.
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Alex, Jimi M., Martin L. Rennie, Sylvain Engilberge, Gábor Lehoczki, Hajdu Dorottya, Ádám Fizil, Gyula Batta, and Peter B. Crowley. "Calixarene-mediated assembly of a small antifungal protein." IUCrJ 6, no. 2 (February 5, 2019): 238–47. http://dx.doi.org/10.1107/s2052252519000411.
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Synthetic macrocycles such as calixarenes and cucurbiturils are increasingly applied as mediators of protein assembly and crystallization. The macrocycle can facilitate assembly by providing a surface on which two or more proteins bind simultaneously. This work explores the capacity of the sulfonato-calix[n]arene (sclx n ) series to effect crystallization of PAF, a small, cationic antifungal protein. Co-crystallization with sclx4, sclx6 or sclx8 led to high-resolution crystal structures. In the absence of sclx n , diffraction-quality crystals of PAF were not obtained. Interestingly, all three sclx n were bound to a similar patch on PAF. The largest and most flexible variant, sclx8, yielded a dimer of PAF. Complex formation was evident in solution via NMR and ITC experiments, showing more pronounced effects with increasing macrocycle size. In agreement with the crystal structure, the ITC data suggested that sclx8 acts as a bidentate ligand. The contributions of calixarene size/conformation to protein recognition and assembly are discussed. Finally, it is suggested that the conserved binding site for anionic calixarenes implicates this region of PAF in membrane binding, which is a prerequisite for antifungal activity.
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Zhao, Haifan, Heng Zhang, Zhun She, Zengqiang Gao, Qi Wang, Zhi Geng, and Yuhui Dong. "Exploring AlphaFold2′s Performance on Predicting Amino Acid Side-Chain Conformations and Its Utility in Crystal Structure Determination of B318L Protein." International Journal of Molecular Sciences 24, no. 3 (February 1, 2023): 2740. http://dx.doi.org/10.3390/ijms24032740.
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Recent technological breakthroughs in machine-learning-based AlphaFold2 (AF2) are pushing the prediction accuracy of protein structures to an unprecedented level that is on par with experimental structural quality. Despite its outstanding structural modeling capability, further experimental validations and performance assessments of AF2 predictions are still required, thus necessitating the development of integrative structural biology in synergy with both computational and experimental methods. Focusing on the B318L protein that plays an essential role in the African swine fever virus (ASFV) for viral replication, we experimentally demonstrate the high quality of the AF2 predicted model and its practical utility in crystal structural determination. Structural alignment implies that the AF2 model shares nearly the same atomic arrangement as the B318L crystal structure except for some flexible and disordered regions. More importantly, side-chain-based analysis at the individual residue level reveals that AF2′s performance is likely dependent on the specific amino acid type and that hydrophobic residues tend to be more accurately predicted by AF2 than hydrophilic residues. Quantitative per-residue RMSD comparisons and further molecular replacement trials suggest that AF2 has a large potential to outperform other computational modeling methods in terms of structural determination. Additionally, it is numerically confirmed that the AF2 model is accurate enough so that it may well potentially withstand experimental data quality to a large extent for structural determination. Finally, an overall structural analysis and molecular docking simulation of the B318L protein are performed. Taken together, our study not only provides new insights into AF2′s performance in predicting side-chain conformations but also sheds light upon the significance of AF2 in promoting crystal structural determination, especially when the experimental data quality of the protein crystal is poor.
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Lokwani, Deepak K., Aniket P. Sarkate, Kshipra S. Karnik, Anna Pratima G. Nikalje, and Julio A. Seijas. "Structure-Based Site of Metabolism (SOM) Prediction of Ligand for CYP3A4 Enzyme: Comparison of Glide XP and Induced Fit Docking (IFD)." Molecules 25, no. 7 (April 1, 2020): 1622. http://dx.doi.org/10.3390/molecules25071622.
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Metabolism is one of the prime reasons where most of drugs fail to accomplish their clinical trials. The enzyme CYP3A4, which belongs to the superfamily of cytochrome P450 enzymes (CYP), helps in the metabolism of a large number of drugs in the body. The enzyme CYP3A4 catalyzes oxidative chemical processes and shows a very broad range of ligand specificity. The understanding of the compound’s structure where oxidation would take place is crucial for the successful modification of molecules to avoid unwanted metabolism and to increase its bioavailability. For this reason, it is required to know the site of metabolism (SOM) of the compounds, where compounds undergo enzymatic oxidation. It can be identified by predicting the accessibility of the substrate’s atom toward oxygenated Fe atom of heme in a CYP protein. The CYP3A4 enzyme is highly flexible and can take significantly different conformations depending on the ligand with which it is being bound. To predict the accessibility of substrate atoms to the heme iron, conventional protein-rigid docking methods failed due to the high flexibility of the CYP3A4 protein. Herein, we demonstrated and compared the ability of the Glide extra precision (XP) and Induced Fit docking (IFD) tool of Schrodinger software suite to reproduce the binding mode of co-crystallized ligands into six X-ray crystallographic structures. We extend our studies toward the prediction of SOM for compounds whose experimental SOM is reported but the ligand-enzyme complex crystal structure is not available in the Protein Data Bank (PDB). The quality and accuracy of Glide XP and IFD was determined by calculating RMSD of docked ligands over the corresponding co-crystallized bound ligand and by measuring the distance between the SOM of the ligand and Fe atom of heme. It was observed that IFD reproduces the exact binding mode of available co-crystallized structures and correctly predicted the SOM of experimentally reported compounds. Our approach using IFD with multiple conformer structures of CYP3A4 will be one of the effective methods for SOM prediction.
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Janowski, Pawel A., Nigel W. Moriarty, Brian P. Kelley, David A. Case, Darrin M. York, Paul D. Adams, and Gregory L. Warren. "Improved ligand geometries in crystallographic refinement usingAFITTinPHENIX." Acta Crystallographica Section D Structural Biology 72, no. 9 (August 31, 2016): 1062–72. http://dx.doi.org/10.1107/s2059798316012225.
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Modern crystal structure refinement programs rely on geometry restraints to overcome the challenge of a low data-to-parameter ratio. While the classical Engh and Huber restraints work well for standard amino-acid residues, the chemical complexity of small-molecule ligands presents a particular challenge. Most current approaches either limit ligand restraints to those that can be readily described in the Crystallographic Information File (CIF) format, thus sacrificing chemical flexibility and energetic accuracy, or they employ protocols that substantially lengthen the refinement time, potentially hindering rapid automated refinement workflows.PHENIX–AFITTrefinement uses a full molecular-mechanics force field for user-selected small-molecule ligands during refinement, eliminating the potentially difficult problem of finding or generating high-quality geometry restraints. It is fully integrated with a standard refinement protocol and requires practically no additional steps from the user, making it ideal for high-throughput workflows.PHENIX–AFITTrefinements also handle multiple ligands in a single model, alternate conformations and covalently bound ligands. Here, the results of combiningAFITTand thePHENIXsoftware suite on a data set of 189 protein–ligand PDB structures are presented. Refinements usingPHENIX–AFITTsignificantly reduce ligand conformational energy and lead to improved geometries without detriment to the fit to the experimental data. For the data presented,PHENIX–AFITTrefinements result in more chemically accurate models for small-molecule ligands.
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Nakatani, Yoshio, Wanting Jiao, David Aragão, Yosuke Shimaki, Jessica Petri, Emily J. Parker, and Gregory M. Cook. "Crystal structure of type II NADH:quinone oxidoreductase from Caldalkalibacillus thermarum with an improved resolution of 2.15 Å." Acta Crystallographica Section F Structural Biology Communications 73, no. 10 (September 23, 2017): 541–49. http://dx.doi.org/10.1107/s2053230x17013073.
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Type II NADH:quinone oxidoreductase (NDH-2) is a respiratory enzyme found in the electron-transport chain of many species, with the exception of mammals. It is a 40–70 kDa single-subunit monotopic membrane protein that catalyses the oxidation of NADH and the reduction of quinone molecules via the cofactor FAD. NDH-2 is a promising new target for drug development given its essential role in many bacterial species and intracellular parasites. Only two bacterial NDH-2 structures have been reported and these structures are at moderate resolution (2.3–2.5 Å). In this communication, a new crystallization platform is reported that produced high-quality NDH-2 crystals that diffracted to high resolution (2.15 Å). The high-resolution NDH-2 structure was used for in silico quinone substrate-docking studies to investigate the binding poses of menadione and ubiquinone molecules. These studies revealed that a very limited number of molecular interactions occur at the quinone-binding site of NDH-2. Given that the conformation of the active site is well defined, this high-resolution structure is potentially suitable for in silico inhibitor-compound screening and ligand-docking applications.
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Chopra, Deepak, and Dhananjay Dey. "Computational approaches towards crystal engineering in molecular crystals." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C642. http://dx.doi.org/10.1107/s2053273314093577.
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The investigation of a large number of crystal structures has resulted in the development of the area of crystal engineering, which involves the study of intermolecular interactions in crystalline solids [1]. It is now of importance to understand the nature and energetics associated with different interactions [2] which influence the crystal packing. In this regard, different computational approaches (utilizing PIXEL and TURBOMOLE) have been developed which aid in the understanding of intra- and intermolecular interactions (for example, hydrogen and halogen bonding) in molecular crystals. This approach has been successfully applied in different classes of molecules [3]. These approaches can be combined with topological analysis of the electron density using the quantum theory of atoms in molecules (QTAIM) (in absence of high quality crystals for experimental electron density studies). In order to validate the above-mentioned methodology, we have performed a comprehensive analysis of a series of synthesized fluoro-derivatives of N'-phenylbenzimidamide to gain quantitative insights into different interactions which accompany crystal formation. The packing of the molecules has contributions from strong N-H...N, weak N-H...π [Fig 1], C-H...N, C-H...F, and C-H...π intermolecular interactions along with π-π stacking. In addition to that, ubiquitous H...H contacts are also present in the solid state. This methodology can be extended to include cocrystals, polymorphs (including solvates) and protein-ligand interactions at the active site.
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Lim, Victoria T., David F. Hahn, Gary Tresadern, Christopher I. Bayly, and David L. Mobley. "Benchmark assessment of molecular geometries and energies from small molecule force fields." F1000Research 9 (December 3, 2020): 1390. http://dx.doi.org/10.12688/f1000research.27141.1.
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Background: Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction. The quality of these studies relies on the quality of the force fields used to represent the systems. Methods: Focusing on small molecules of fewer than 50 heavy atoms, our aim in this work is to compare nine force fields: GAFF, GAFF2, MMFF94, MMFF94S, OPLS3e, SMIRNOFF99Frosst, and the Open Force Field Parsley, versions 1.0, 1.1, and 1.2. On a dataset comprising 22,675 molecular structures of 3,271 molecules, we analyzed force field-optimized geometries and conformer energies compared to reference quantum mechanical (QM) data. Results: We show that while OPLS3e performs best, the latest Open Force Field Parsley release is approaching a comparable level of accuracy in reproducing QM geometries and energetics for this set of molecules. Meanwhile, the performance of established force fields such as MMFF94S and GAFF2 is generally somewhat worse. We also find that the series of recent Open Force Field versions provide significant increases in accuracy. Conclusions: This study provides an extensive test of the performance of different molecular mechanics force fields on a diverse molecule set, and highlights two (OPLS3e and OpenFF 1.2) that perform better than the others tested on the present comparison. Our molecule set and results are available for other researchers to use in testing.
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Goldsmith, Michael-Rock, Christopher M. Grulke, Daniel T. Chang, Thomas R. Transue, Stephen B. Little, James R. Rabinowitz, and Rogelio Tornero-Velez. "DockScreen: A Database of In Silico Biomolecular Interactions to Support Computational Toxicology." Dataset Papers in Science 2014 (November 11, 2014): 1–5. http://dx.doi.org/10.1155/2014/421693.
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We have developed DockScreen, a database of in silico biomolecular interactions designed to enable rational molecular toxicological insight within a computational toxicology framework. This database is composed of chemical/target (receptor and enzyme) binding scores calculated by molecular docking of more than 1000 chemicals into 150 protein targets and contains nearly 135 thousand unique ligand/target binding scores. Obtaining this dataset was achieved using eHiTS (Simbiosys Inc.), a fragment-based molecular docking approach with an exhaustive search algorithm, on a heterogeneous distributed high-performance computing framework. The chemical landscape covered in DockScreen comprises selected environmental and therapeutic chemicals. The target landscape covered in DockScreen was selected based on the availability of high-quality crystal structures that covered the assay space of phase I ToxCast in vitro assays. This in silico data provides continuous information that establishes a means for quantitatively comparing, on a structural biophysical basis, a chemical’s profile of biomolecular interactions. The combined minimum-score chemical/target matrix is provided.
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Wallace, B. A. "Protein characterisation by synchrotron radiation circular dichroism spectroscopy." Quarterly Reviews of Biophysics 42, no. 4 (November 2009): 317–70. http://dx.doi.org/10.1017/s003358351000003x.
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AbstractCircular dichroism (CD) spectroscopy is a well-established technique for the study of proteins. Synchrotron radiation circular dichroism (SRCD) spectroscopy extends the utility of conventional CD spectroscopy (i.e. using laboratory-based instruments) because the high light flux from a synchrotron enables collection of data to lower wavelengths, detection of spectra with higher signal-to-noise levels and measurements in the presence of strongly absorbing non-chiral components such as salts, buffers, lipids and detergents. This review describes developments in instrumentation, methodologies and bioinformatics that have enabled new applications of the SRCD technique for the study of proteins. It includes examples of the use of SRCD spectroscopy for providing static and dynamic structural information on molecules, including determinations of secondary structures of intact proteins and domains, assessment of protein stability, detection of conformational changes associated with ligand and drug binding, monitoring of environmental effects, examination of the processes of protein folding and membrane insertion, comparisons of mutant and modified proteins, identification of intermolecular interactions and complex formation, determination of the dispositions of proteins in membranes, identification of natively disordered proteins and their binding partners and examination of the carbohydrate components of glycoproteins. It also discusses how SRCD can be used in conjunction with macromolecular crystallography and other biophysical techniques to provide a more complete picture of protein structures and functions, including how proteins interact with other macromolecules and ligands. This review also includes a discussion of potential new applications in structural and functional genomics using SRCD spectroscopy and future instrumentation and bioinformatics developments that will enable such studies. Finally, the appendix describes a number of computational/bioinformatics resources for secondary structure analyses that take advantage of the improved data quality available from SRCD. In summary, this review discusses how SRCD can be used for a wide range of structural and functional studies of proteins.
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Zeng, Zhen-Fang, Qiu-Ping Huang, Jie-Hui Cai, Guang-Jin Zheng, Qiu-Chan Huang, Zi-Lu Liu, Zi-Lu Chen, and You-Huan Wei. "Synthesis, Characterization, DNA/HSA Interactions, and Anticancer Activity of Two Novel Copper(II) Complexes with 4-Chloro-3-Nitrobenzoic Acid Ligand." Molecules 26, no. 13 (July 1, 2021): 4028. http://dx.doi.org/10.3390/molecules26134028.
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The purpose of this study was to identify new metal-based anticancer drugs; to this end, we synthesized two new copper(II) complexes, namely [Cu(ncba)4(phen)] (1) and [Cu(ncba)4(bpy)] (2), comprised 4-chloro-3-nitrobenzoic acid as the main ligand. The single-crystal XRD approach was employed to determine the copper(II) complex structures. Binding between these complexes and calf thymus DNA (CT-DNA) and human serum albumin (HSA) was explored by electronic absorption, fluorescence spectroscopy, and viscometry. Both complexes intercalatively bound CT-DNA and statically and spontaneously quenched DNA/HSA fluorescence. A CCK-8 assay revealed that complex 1 and complex 2 had substantial antiproliferative influences against human cancer cell lines. Moreover, complex 1 had greater antitumor efficacy than the positive control cisplatin. Flow cytometry assessment of the cell cycle demonstrated that these complexes arrested the HepG2 cell cycle and caused the accumulation of G0/G1-phase cells. The mechanism of cell death was elucidated by flow cytometry-based apoptosis assays. Western blotting revealed that both copper(II) complexes induced apoptosis by regulating the expression of the Bcl-2(Bcl-2, B cell lymphoma 2) protein family.
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Du, Hongyan, Junbo Gao, Gaoqi Weng, Junjie Ding, Xin Chai, Jinping Pang, Yu Kang, Dan Li, Dongsheng Cao, and Tingjun Hou. "CovalentInDB: a comprehensive database facilitating the discovery of covalent inhibitors." Nucleic Acids Research 49, no. D1 (October 17, 2020): D1122—D1129. http://dx.doi.org/10.1093/nar/gkaa876.
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Abstract Inhibitors that form covalent bonds with their targets have traditionally been considered highly adventurous due to their potential off-target effects and toxicity concerns. However, with the clinical validation and approval of many covalent inhibitors during the past decade, design and discovery of novel covalent inhibitors have attracted increasing attention. A large amount of scattered experimental data for covalent inhibitors have been reported, but a resource by integrating the experimental information for covalent inhibitor discovery is still lacking. In this study, we presented Covalent Inhibitor Database (CovalentInDB), the largest online database that provides the structural information and experimental data for covalent inhibitors. CovalentInDB contains 4511 covalent inhibitors (including 68 approved drugs) with 57 different reactive warheads for 280 protein targets. The crystal structures of some of the proteins bound with a covalent inhibitor are provided to visualize the protein–ligand interactions around the binding site. Each covalent inhibitor is annotated with the structure, warhead, experimental bioactivity, physicochemical properties, etc. Moreover, CovalentInDB provides the covalent reaction mechanism and the corresponding experimental verification methods for each inhibitor towards its target. High-quality datasets are downloadable for users to evaluate and develop computational methods for covalent drug design. CovalentInDB is freely accessible at http://cadd.zju.edu.cn/cidb/.
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Mandal, Suraj Kumar, and Shankar Prasad Kanaujia. "Structural and thermodynamic insights into a novel Mg2+–citrate-binding protein from the ABC transporter superfamily." Acta Crystallographica Section D Structural Biology 77, no. 12 (November 11, 2021): 1516–34. http://dx.doi.org/10.1107/s2059798321010457.
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More than one third of proteins require metal ions to accomplish their functions, making them obligatory for the growth and survival of microorganisms in varying environmental niches. In prokaryotes, besides their involvement in various cellular and physiological processes, metal ions stimulate the uptake of citrate molecules. Citrate is a source of carbon and energy and is reported to be transported by secondary transporters. In Gram-positive bacteria, citrate molecules are transported in complex with divalent metal ions, whereas in Gram-negative bacteria they are translocated by Na+/citrate symporters. In this study, the presence of a novel divalent-metal-ion-complexed citrate-uptake system that belongs to the primary active ABC transporter superfamily is reported. For uptake, the metal-ion-complexed citrate molecules are sequestered by substrate-binding proteins (SBPs) and transferred to transmembrane domains for their transport. This study reports crystal structures of an Mg2+–citrate-binding protein (MctA) from the Gram-negative thermophilic bacterium Thermus thermophilus HB8 in both apo and holo forms in the resolution range 1.63–2.50 Å. Despite binding various divalent metal ions, MctA possesses the coordination geometry to bind its physiological metal ion, Mg2+. The results also suggest an extended subclassification of cluster D SBPs, which are known to bind and transport divalent-metal-ion-complexed citrate molecules. Comparative assessment of the open and closed conformations of the wild-type and mutant MctA proteins suggests a gating mechanism of ligand entry following an `asymmetric domain movement' of the N-terminal domain for substrate binding.
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Oo, Adrian, Pouya Hassandarvish, Sek Peng Chin, Vannajan Sanghiran Lee, Sazaly Abu Bakar, and Keivan Zandi. "In silico study on anti-Chikungunya virus activity of hesperetin." PeerJ 4 (October 26, 2016): e2602. http://dx.doi.org/10.7717/peerj.2602.
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BackgroundThe re-emerging,Aedes spp.transmitted Chikungunya virus (CHIKV) has recently caused large outbreaks in a wide geographical distribution of the world including countries in Europe and America. Though fatalities associated with this self-remitting disease were rarely reported, quality of patients’ lives have been severely diminished by polyarthralgia recurrence. Neither effective antiviral treatment nor vaccines are available for CHIKV. Our previous in vitro screening showed that hesperetin, a bioflavonoid exhibits inhibitory effect on the virus intracellular replication. Here, we present a study using the computational approach to identify possible target proteins for future mechanistic studies of hesperetin.Methods3D structures of CHIKV nsP2 (3TRK) and nsP3 (3GPG) were retrieved from Protein Data Bank (PDB), whereas nsP1, nsP4 and cellular factor SPK2 were modeled using Iterative Threading Assembly Refinement (I-TASSER) server based on respective amino acids sequence. We performed molecular docking on hesperetin against all four CHIKV non-structural proteins and SPK2. Proteins preparation and subsequent molecular docking were performed using Discovery Studio 2.5 and AutoDock Vina 1.5.6. The Lipinski’s values of the ligand were computed and compared with the available data from PubChem. Two non-structural proteins with crystal structures 3GPG and 3TRK in complexed with hesperetin, demonstrated favorable free energy of binding from the docking study, were further explored using molecular dynamics (MD) simulations.ResultsWe observed that hesperetin interacts with different types of proteins involving hydrogen bonds, pi-pi effects, pi-cation bonding and pi-sigma interactions with varying binding energies. Among all five tested proteins, our compound has the highest binding affinity with 3GPG at −8.5 kcal/mol. The ligand used in this study also matches the Lipinski’s rule of five in addition to exhibiting closely similar properties with that of in PubChem. The docking simulation was performed to obtain a first guess of the binding structure of hesperetin complex and subsequently analysed by MD simulations to assess the reliability of the docking results. Root mean square deviation (RMSD) of the simulated systems from MD simulations indicated that the hesperetin complex remains stable within the simulation timescale.DiscussionThe ligand’s tendencies of binding to the important proteins for CHIKV replication were consistent with our previous in vitro screening which showed its efficacy in blocking the virus intracellular replication. NsP3 serves as the highest potential target protein for the compound’s inhibitory effect, while it is interesting to highlight the possibility of interrupting CHIKV replication via interaction with host cellular factor. By complying the Lipinski’s rule of five, hesperetin exhibits drug-like properties which projects its potential as a therapeutic option for CHIKV infection.
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Delfosse, Vanessa, Marina Grimaldi, Jean-Luc Pons, Vincent Cavaillès, Gilles Labesse, Patrick Balaguer, and William Bourguet. "Structural and functional profiling of estrogen receptors environmental ligands." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C1399. http://dx.doi.org/10.1107/s2053273314086008.
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Endocrine-disrupting chemicals (EDCs) are exogenous substances that interfere with the function of hormonal systems and cause deleterious effects on humans and wildlife. Many EDCs are man-made chemicals produced by industry and released into the environment. Some naturally occurring EDCs can also be found in plants or fungi. Epidemiological studies suggest a link between the exposure to these chemicals and the development of diseases like cancers, reproduction defects, or metabolic disorders. Endocrine disruption has raised considerable concern in recent years so that several countries have developed risk assessment programs aimed at evaluating the toxic potential of more than 100,000 chemicals. In this context, we have been using a battery of structural, biophysical and cell-based approaches to investigate the mechanisms by which a variety of environmental pollutants, including bisphenols [1], pesticides, phthalates, benzophenones, parabens, myco- and phytoestrogens or alkylphenols, bind to and modulate the activity of the estrogen receptors ERα and ERβ, two nuclear hormone receptors (NRs) that are primary targets of environmental contaminants. Crystallographic analysis reveals that these structurally and chemically diverse compounds bind to ERs via diverse sets of protein–ligand interactions reflecting their differential activities, binding affinities and specificities. A detailed analysis of the various binding/activation mechanisms will be presented. Based on these structural data, we are developing a protocol for in silico evaluation of the interaction between pollutants and ERs or other members of the NR family. The server which utilises crystal structures to model any NR/xenobiotics complexes and estimate binding affinities will also be presented. Overall, this study provides a wealth of tools and information that could be used for the development of safer chemicals devoid of NR-mediated activity and more generally for environmental risk assessment.
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Rana, Rabia Mukhtar, Shailima Rampogu, Noman Bin Abid, Amir Zeb, Shraddha Parate, Gihwan Lee, Sanghwa Yoon, Yumi Kim, Donghwan Kim, and Keun Woo Lee. "In Silico Study Identified Methotrexate Analog as Potential Inhibitor of Drug Resistant Human Dihydrofolate Reductase for Cancer Therapeutics." Molecules 25, no. 15 (July 31, 2020): 3510. http://dx.doi.org/10.3390/molecules25153510.
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Drug resistance is a core issue in cancer chemotherapy. A known folate antagonist, methotrexate (MTX) inhibits human dihydrofolate reductase (hDHFR), the enzyme responsible for the catalysis of 7,8-dihydrofolate reduction to 5,6,7,8-tetrahydrofolate, in biosynthesis and cell proliferation. Structural change in the DHFR enzyme is a significant cause of resistance and the subsequent loss of MTX. In the current study, wild type hDHFR and double mutant (engineered variant) F31R/Q35E (PDB ID: 3EIG) were subject to computational study. Structure-based pharmacophore modeling was carried out for wild type (WT) and mutant (MT) (variant F31R/Q35E) hDHFR structures by generating ten models for each. Two pharmacophore models, WT-pharma and MT-pharma, were selected for further computations, and showed excellent ROC curve quality. Additionally, the selected pharmacophore models were validated by the Guner-Henry decoy test method, which yielded high goodness of fit for WT-hDHFR and MT-hDHFR. Using a SMILES string of MTX in ZINC15 with the selections of ‘clean’, in vitro and in vivo options, 32 MTX-analogs were obtained. Eight analogs were filtered out due to their drug-like properties by applying absorption, distribution, metabolism, excretion, and toxicity (ADMET) assessment tests and Lipinski’s Rule of five. WT-pharma and MT-pharma were further employed as a 3D query in virtual screening with drug-like MTX analogs. Subsequently, seven screening hits along with a reference compound (MTX) were subjected to molecular docking in the active site of WT- and MT-hDHFR. Through a clustering analysis and examination of protein-ligand interactions, one compound was found with a ChemPLP fitness score greater than that of MTX (reference compound). Finally, a simulation of molecular dynamics (MD) identified an MTX analog which exhibited strong affinity for WT- and MT-hDHFR, with stable RMSD, hydrogen bonds (H-bonds) in the binding site and the lowest MM/PBSA binding free energy. In conclusion, we report on an MTX analog which is capable of inhibiting hDHFR in wild type form, as well as in cases where the enzyme acquires resistance to drugs during chemotherapy treatment.
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Miyaguchi, Ikuko, Miwa Sato, Akiko Kashima, Hiroyuki Nakagawa, Yuichi Kokabu, Biao Ma, Shigeyuki Matsumoto, Atsushi Tokuhisa, Masateru Ohta, and Mitsunori Ikeguchi. "Machine learning to estimate the local quality of protein crystal structures." Scientific Reports 11, no. 1 (December 2021). http://dx.doi.org/10.1038/s41598-021-02948-y.
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AbstractLow-resolution electron density maps can pose a major obstacle in the determination and use of protein structures. Herein, we describe a novel method, called quality assessment based on an electron density map (QAEmap), which evaluates local protein structures determined by X-ray crystallography and could be applied to correct structural errors using low-resolution maps. QAEmap uses a three-dimensional deep convolutional neural network with electron density maps and their corresponding coordinates as input and predicts the correlation between the local structure and putative high-resolution experimental electron density map. This correlation could be used as a metric to modify the structure. Further, we propose that this method may be applied to evaluate ligand binding, which can be difficult to determine at low resolution.
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Clark, Jordan J., Zachary J. Orban, and Heather A. Carlson. "Predicting binding sites from unbound versus bound protein structures." Scientific Reports 10, no. 1 (September 28, 2020). http://dx.doi.org/10.1038/s41598-020-72906-7.
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Abstract We present the application of seven binding-site prediction algorithms to a meticulously curated dataset of ligand-bound and ligand-free crystal structures for 304 unique protein sequences (2528 crystal structures). We probe the influence of starting protein structures on the results of binding-site prediction, so the dataset contains a minimum of two ligand-bound and two ligand-free structures for each protein. We use this dataset in a brief survey of five geometry-based, one energy-based, and one machine-learning-based methods: Surfnet, Ghecom, LIGSITEcsc, Fpocket, Depth, AutoSite, and Kalasanty. Distributions of the F scores and Matthew’s correlation coefficients for ligand-bound versus ligand-free structure performance show no statistically significant difference in structure type versus performance for most methods. Only Fpocket showed a statistically significant but low magnitude enhancement in performance for holo structures. Lastly, we found that most methods will succeed on some crystal structures and fail on others within the same protein family, despite all structures being relatively high-quality structures with low structural variation. We expected better consistency across varying protein conformations of the same sequence. Interestingly, the success or failure of a given structure cannot be predicted by quality metrics such as resolution, Cruickshank Diffraction Precision index, or unresolved residues. Cryptic sites were also examined.
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Alzyoud, Lara, Richard A. Bryce, Mohammad Al Sorkhy, Noor Atatreh, and Mohammad A. Ghattas. "Structure-based assessment and druggability classification of protein–protein interaction sites." Scientific Reports 12, no. 1 (May 13, 2022). http://dx.doi.org/10.1038/s41598-022-12105-8.
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AbstractThe featureless interface formed by protein–protein interactions (PPIs) is notorious for being considered a difficult and poorly druggable target. However, recent advances have shown PPIs to be druggable, with the discovery of potent inhibitors and stabilizers, some of which are currently being clinically tested and approved for medical use. In this study, we assess the druggability of 12 commonly targeted PPIs using the computational tool, SiteMap. After evaluating 320 crystal structures, we find that the PPI binding sites have a wide range of druggability scores. This can be attributed to the unique structural and physiochemical features that influence their ligand binding and concomitantly, their druggability predictions. We then use these features to propose a specific classification system suitable for assessing PPI targets based on their druggability scores and measured binding-affinity. Interestingly, this system was able to distinguish between different PPIs and correctly categorize them into four classes (i.e. very druggable, druggable, moderately druggable, and difficult). We also studied the effects of protein flexibility on the computed druggability scores and found that protein conformational changes accompanying ligand binding in ligand-bound structures result in higher protein druggability scores due to more favorable structural features. Finally, the drug-likeness of many published PPI inhibitors was studied where it was found that the vast majority of the 221 ligands considered here, including orally tested/marketed drugs, violate the currently acceptable limits of compound size and hydrophobicity parameters. This outcome, combined with the lack of correlation observed between druggability and drug-likeness, reinforces the need to redefine drug-likeness for PPI drugs. This work proposes a PPI-specific classification scheme that will assist researchers in assessing the druggability and identifying inhibitors of the PPI interface.
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Veit-Acosta, Martina, and Walter Filgueira de Azevedo Junior. "The Impact of CrystallographicData for the Development of Machine Learning Models to Predict Protein-Ligand Binding Affinity." Current Medicinal Chemistry 28 (February 10, 2021). http://dx.doi.org/10.2174/0929867328666210210121320.
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Background: One of the main challenges in the early stages of drug discovery is the computational assessment of protein-ligand binding affinity. Machine learning techniques can contribute to predicting this type of interaction. We may apply these techniques following two approaches. First, using the experimental structures for which affinity data is available. Second, using protein-ligand docking simulations. Objective: In this review, we describe recently published machine learning models based on crystal structures for which binding affinity and thermodynamic data are available. Method: We used experimental structures available at the protein data bank and binding affinity and thermodynamic data accessed at BindingDB, Binding MOAD, and PDBbind. We reviewed machine learning models to predict binding created using open source programs such as SAnDReS and Taba. Results: Analysis of machine learning models trained against datasets composed of crystal structure complexes indicated the high predictive performance of these models compared with classical scoring functions. Conclusion: The rapid increase in the number of crystal structures of protein-ligand complexes created a favorable scenario for developing machine learning models to predict binding affinity. These models rely on experimental data from two sources, the structural and the affinity data. The combination of experimental data generates computational models that outperform classical scoring functions.
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Henkel, Alessandra, Marina Galchenkova, Julia Maracke, Oleksandr Yefanov, Bjarne Klopprogge, Johanna Hakanpää, Jeroen R. Mesters, Henry N. Chapman, and Dominik Oberthuer. "JINXED: just in time crystallization for easy structure determination of biological macromolecules." IUCrJ 10, no. 3 (March 9, 2023). http://dx.doi.org/10.1107/s2052252523001653.
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Macromolecular crystallography is a well established method in the field of structural biology and has led to the majority of known protein structures to date. After focusing on static structures, the method is now under development towards the investigation of protein dynamics through time-resolved methods. These experiments often require multiple handling steps of the sensitive protein crystals, e.g. for ligand-soaking and cryo-protection. These handling steps can cause significant crystal damage, and hence reduce data quality. Furthermore, in time-resolved experiments based on serial crystallography, which use micrometre-sized crystals for short diffusion times of ligands, certain crystal morphologies with small solvent channels can prevent sufficient ligand diffusion. Described here is a method that combines protein crystallization and data collection in a novel one-step process. Corresponding experiments were successfully performed as a proof-of-principle using hen egg-white lysozyme and crystallization times of only a few seconds. This method, called JINXED (Just IN time Crystallization for Easy structure Determination), promises high-quality data due to the avoidance of crystal handling and has the potential to enable time-resolved experiments with crystals containing small solvent channels by adding potential ligands to the crystallization buffer, simulating traditional co-crystallization approaches.
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García-Nafría, Javier, Yang Lee, Xiaochen Bai, Byron Carpenter, and Christopher G. Tate. "Cryo-EM structure of the adenosine A2A receptor coupled to an engineered heterotrimeric G protein." eLife 7 (May 4, 2018). http://dx.doi.org/10.7554/elife.35946.
Full textAbstract:
The adenosine A2A receptor (A2AR) is a prototypical G protein-coupled receptor (GPCR) that couples to the heterotrimeric G protein GS. Here, we determine the structure by electron cryo-microscopy (cryo-EM) of A2AR at pH 7.5 bound to the small molecule agonist NECA and coupled to an engineered heterotrimeric G protein, which contains mini-GS, the βγ subunits and nanobody Nb35. Most regions of the complex have a resolution of ~3.8 Å or better. Comparison with the 3.4 Å resolution crystal structure shows that the receptor and mini-GS are virtually identical and that the density of the side chains and ligand are of comparable quality. However, the cryo-EM density map also indicates regions that are flexible in comparison to the crystal structures, which unexpectedly includes regions in the ligand binding pocket. In addition, an interaction between intracellular loop 1 of the receptor and the β subunit of the G protein was observed.
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Meller, Artur, Saulo De Oliveira, Aram Davtyan, Tigran Abramyan, Gregory R. Bowman, and Henry van den Bedem. "Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors." Frontiers in Molecular Biosciences 10 (April 18, 2023). http://dx.doi.org/10.3389/fmolb.2023.1171143.
Full textAbstract:
Virtual screening is a widely used tool for drug discovery, but its predictive power can vary dramatically depending on how much structural data is available. In the best case, crystal structures of a ligand-bound protein can help find more potent ligands. However, virtual screens tend to be less predictive when only ligand-free crystal structures are available, and even less predictive if a homology model or other predicted structure must be used. Here, we explore the possibility that this situation can be improved by better accounting for protein dynamics, as simulations started from a single structure have a reasonable chance of sampling nearby structures that are more compatible with ligand binding. As a specific example, we consider the cancer drug target PPM1D/Wip1 phosphatase, a protein that lacks crystal structures. High-throughput screens have led to the discovery of several allosteric inhibitors of PPM1D, but their binding mode remains unknown. To enable further drug discovery efforts, we assessed the predictive power of an AlphaFold-predicted structure of PPM1D and a Markov state model (MSM) built from molecular dynamics simulations initiated from that structure. Our simulations reveal a cryptic pocket at the interface between two important structural elements, the flap and hinge regions. Using deep learning to predict the pose quality of each docked compound for the active site and cryptic pocket suggests that the inhibitors strongly prefer binding to the cryptic pocket, consistent with their allosteric effect. The predicted affinities for the dynamically uncovered cryptic pocket also recapitulate the relative potencies of the compounds (τb = 0.70) better than the predicted affinities for the static AlphaFold-predicted structure (τb = 0.42). Taken together, these results suggest that targeting the cryptic pocket is a good strategy for drugging PPM1D and, more generally, that conformations selected from simulation can improve virtual screening when limited structural data is available.
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Stachowski, Timothy R., and Marcus Fischer. "FLEXR: automated multi-conformer model building using electron-density map sampling." Acta Crystallographica Section D Structural Biology 79, no. 5 (April 18, 2023). http://dx.doi.org/10.1107/s2059798323002498.
Full textAbstract:
Protein conformational dynamics that may inform biology often lie dormant in high-resolution electron-density maps. While an estimated ∼18% of side chains in high-resolution models contain alternative conformations, these are underrepresented in current PDB models due to difficulties in manually detecting, building and inspecting alternative conformers. To overcome this challenge, we developed an automated multi-conformer modeling program, FLEXR. Using Ringer-based electron-density sampling, FLEXR builds explicit multi-conformer models for refinement. Thereby, it bridges the gap of detecting hidden alternate states in electron-density maps and including them in structural models for refinement, inspection and deposition. Using a series of high-quality crystal structures (0.8–1.85 Å resolution), we show that the multi-conformer models produced by FLEXR uncover new insights that are missing in models built either manually or using current tools. Specifically, FLEXR models revealed hidden side chains and backbone conformations in ligand-binding sites that may redefine protein–ligand binding mechanisms. Ultimately, the tool facilitates crystallographers with opportunities to include explicit multi-conformer states in their high-resolution crystallographic models. One key advantage is that such models may better reflect interesting higher energy features in electron-density maps that are rarely consulted by the community at large, which can then be productively used for ligand discovery downstream. FLEXR is open source and publicly available on GitHub at https://github.com/TheFischerLab/FLEXR.
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Sacco, Michael D., Kyle G. Kroeck, M. Trent Kemp, Xiujun Zhang, Logan D. Andrews, and Yu Chen. "Influence of the α-Methoxy Group on the Reaction of Temocillin with Pseudomonas aeruginosa PBP3 and CTX-M-14 β-Lactamase." Antimicrobial Agents and Chemotherapy 64, no. 1 (November 4, 2019). http://dx.doi.org/10.1128/aac.01473-19.
Full textAbstract:
ABSTRACT The prevalence of multidrug-resistant Pseudomonas aeruginosa has led to the reexamination of older “forgotten” drugs, such as temocillin, for their ability to combat resistant microbes. Temocillin is the 6-α-methoxy analogue of ticarcillin, a carboxypenicillin with well-characterized antipseudomonal properties. The α-methoxy modification confers resistance to serine β-lactamases, yet temocillin is ineffective against P. aeruginosa growth. The origins of temocillin’s inferior antibacterial properties against P. aeruginosa have remained relatively unexplored. Here, we analyze the reaction kinetics, protein stability, and binding conformations of temocillin and ticarcillin with penicillin-binding protein 3 (PBP3), an essential PBP in P. aeruginosa. We show that the 6-α-methoxy group perturbs the stability of the PBP3 acyl-enzyme, which manifests in an elevated off-rate constant (koff) in biochemical assays comparing temocillin with ticarcillin. Complex crystal structures with PBP3 reveal similar binding modes of the two drugs but with important differences. Most notably, the 6-α-methoxy group disrupts a high-quality hydrogen bond with a conserved residue important for ligand binding while also being inserted into a crowded active site, possibly destabilizing the active site and enabling water molecule from bulk solvent to access and cleave the acyl-enzyme bond. This hypothesis is supported by the observation that the acyl-enzyme complex of temocillin has reduced thermal stability compared with ticarcillin. Furthermore, we explore temocillin’s mechanism of β-lactamase inhibition with a high-resolution complex structure of CTX-M-14 class A serine β-lactamase. The results suggest that the α-methoxy group prevents hydrolysis by locking the compound into an unexpected conformation that impedes access of the catalytic water to the acyl-enzyme adduct.
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AWofisayo, Oladoja. "IN SILICO ANTIMALARIAL TARGET SELECTION CONSERVED IN FOUR PLASMODIUM SPECIES." Universal Journal of Pharmaceutical Research, November 15, 2020. http://dx.doi.org/10.22270/ujpr.v5i5.483.
Full textAbstract:
Objectives: The need for new antimalarials drugs and drug targets is pertinent due to the emergence of drug resistant strains of the parasites. Improper target selection has resulted in therapeutic failure. The genomic/post genomic era has made possible the deciphering of the 3D crystal structures of proteins and DNA which are drug targets and are deposited in the protein data bank.
Methods: Novel antimalarial targets obtained from evolutionary conserved short sequence motifs are utilised and are essential in transcription processes in the parasite. The motifs TGCATGCA, GTGCAC and GTGCGTGC were curated from experimental work, validated and analysed via phylogenomics genomics and comparative genomics. PlasmoDB blastn was applied to determine their similarity in Plasmodium vivax, knowlesi, Ovale and yoeli. The complete genome of Plasmodium falciparum vivax, knowlesi, Ovale and yoeli was downloaded from the plasmoDB and their positions determined.
Results: The targets are essential, conserved in rodent and mammalian species via phylogenomics with percentage identity and similarity greater than 80%, have no similar genes in the same genome and also found to be selective in the parasites vis-à-vis the Homo sapiens via comparative genomics with 0% identity and similarity in the human genome.
Conclusion: The targets reveal at the molecular and biochemical level, the vulnerable regions in the parasite while safe in human hence their choices in subsequent rationale drug discovery and design protocols.
Peer Review History:
Received: 18 July 2020; Revised: 1 October; Accepted: 12 October, Available online: 15 November 2020
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Received file
Average Peer review marks at initial stage: 5.5/10
Average Peer review marks at publication stage: 7.0/10
Reviewer(s) detail:
Dr. Tamer ELHABIBI, ERU University, Egypt, tamer_elhabibi@yahoo.com
Dr. Soroush Sardari, Biotech Pasteur Institute of Iran, Tehran, Iran, ssardari@hotmail.com
Comments of reviewer(s):
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