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Cui X, Zheng Z, Rahman MU, Hong X, Ji X, Li Z, Chen HF. Drude2019IDPC polarizable force field reveals structure-function relationship of insulin. Int J Biol Macromol 2024; 280:136256. [PMID: 39366599 DOI: 10.1016/j.ijbiomac.2024.136256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2024] [Revised: 09/30/2024] [Accepted: 10/01/2024] [Indexed: 10/06/2024]
Abstract
Intrinsically disordered proteins (IDPs) lack stable tertiary structures under physiological conditions, yet play key roles in biological processes and associated with human complex diseases. Their conformational characteristics and high content of charged residues make the use of polarizable force fields an advantageous for simulating IDPs. The Drude2019IDP polarizable force field, previously introduced, has demonstrated comprehensive enhancements and improvements in dipeptides, short peptides, and IDPs, achieving a balanced sampling between IDPs and structured proteins. However, the performance in simulating 5 dipeptides was found to be underestimate. Therefore, we individually performed reweighting and grid-based energy correction map (CMAP) optimization for these 5 dipeptides, resulting in the enhanced Drude2019IDPC force field. The performance of Drude2019IDPC was evaluated with 5 dipeptides, 5 disordered short peptides, and a representative IDP. The results demonstrated a marked improvement comparing with original Drude2019IDP. To further substantiate the capabilities of Drude2019IDPC, MD simulation and Markov state model (MSM) were applied to wild type and mutant for insulin, to elucidate the difference of conformational characteristics and transition path. The findings reveal that mutation can maintain the monomorphic characteristics, providing insights for engineered insulin development. These results indicate that Drude2019IDPC could be used to reveal the structure-function relationship for other proteins.
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Affiliation(s)
- Xiaochen Cui
- State Key Laboratory of Microbial metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zhuoqi Zheng
- State Key Laboratory of Microbial metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Mueed Ur Rahman
- State Key Laboratory of Microbial metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiaokun Hong
- College of Biological Science and Engineering, Fuzhou University, Fuzhou, Fujian 350116, China
| | - Xiaoyue Ji
- State Key Laboratory of Microbial metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zhengxin Li
- State Key Laboratory of Microbial metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Hai-Feng Chen
- State Key Laboratory of Microbial metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
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2
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Hasan ME, Samir A, Khalil MM, Shafaa MW. Bioinformatics approach for prediction and analysis of the Non-Structural Protein 4B (NSP4B) of the Zika virus. J Genet Eng Biotechnol 2024; 22:100336. [PMID: 38494248 PMCID: PMC10860876 DOI: 10.1016/j.jgeb.2023.100336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
BACKGROUND The Nonstructural Protein (NSP) 4B of Zika virus of 251 amino acids from (ZIKV/Human/POLG_ZIKVF) with accession number (A0A024B7W1), Induces the production of Endoplasmic Reticulum ER-derived membrane vesicles, which are the sites of viral replication. To understand the physical basis of how proteins fold in nature and to solve the challenge of protein structure prediction, Ab-initio and comparative modeling are crucial tools. RESULTS The systematic in silico technique, ThreaDom, had only predicted one domain (4 - 190) of NSP4B. I-TASSER, and Alphafold were ranked as the best servers for full-length 3-D protein structure predictions of NSP4B, where the predicted models were evaluated quantitatively using benchmarked metrics including C-score (-3.43), TM-score (0.77949), RMSD (2.73), and Z-score (1.561). The functional and protein binding motifs were realized using motif databases, secondary and surface accessibility predictions combined with Post-Translational Modification Sites (PTMs) prediction. Two highly conserved protein-binding motifs (Flavi NS4B and Bacillus papRprotein), together with three (PTMs) (Casein Kinase II, Myristyl site, and ASN-Glycosylation site) were predicted utilizing the Motif scan and Scanprosite servers. These patterns and PTMs were associated with NSP4B's role in triggering the development of the viral replication complex and its participation in the localization of NS3 and NS5 on the membrane. Only one hit from Structural Classification of Protein (SCOP) matched the protein sequence at positions 10 to 397 and was categorized six-hairpin glycosidases superfamily according to CATH (Class, Architecture, Topology, and Homology). Integrating this NSP4B information with the templates' SCOP and CATH annotations achieves it easier to attribute structure-function/evolution links to both previously known and recently discovered protein structures.
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Affiliation(s)
- Mohamed E Hasan
- Bioinformatics Department, Genetic Engineering and Biotechnology Research Institute, University of Sadat City, Sadat City 32897, Egypt.
| | - Aya Samir
- Physics Department, Medical Biophysics Division, Faculty of Science, Helwan University, Cairo, Egypt
| | - Magdy M Khalil
- Physics Department, Medical Biophysics Division, Faculty of Science, Helwan University, Cairo, Egypt; School of Biotechnology, Badr University in Cairo, Egypt
| | - Medhat W Shafaa
- Physics Department, Medical Biophysics Division, Faculty of Science, Helwan University, Cairo, Egypt
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3
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Onyango OH. In Silico Models for Anti-COVID-19 Drug Discovery: A Systematic Review. Adv Pharmacol Pharm Sci 2023; 2023:4562974. [PMID: 37362912 PMCID: PMC10287514 DOI: 10.1155/2023/4562974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 05/25/2023] [Accepted: 06/10/2023] [Indexed: 06/28/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) is a severe worldwide pandemic. Due to the emergence of various SARS-CoV-2 variants and the presence of only one Food and Drug Administration (FDA) approved anti-COVID-19 drug (remdesivir), the disease remains a mindboggling global public health problem. Developing anti-COVID-19 drug candidates that are effective against SARS-CoV-2 and its various variants is a pressing need that should be satisfied. This systematic review assesses the existing literature that used in silico models during the discovery procedure of anti-COVID-19 drugs. Cochrane Library, Science Direct, Google Scholar, and PubMed were used to conduct a literature search to find the relevant articles utilizing the search terms "In silico model," "COVID-19," "Anti-COVID-19 drug," "Drug discovery," "Computational drug designing," and "Computer-aided drug design." Studies published in English between 2019 and December 2022 were included in the systematic review. From the 1120 articles retrieved from the databases and reference lists, only 33 were included in the review after the removal of duplicates, screening, and eligibility assessment. Most of the articles are studies that use SARS-CoV-2 proteins as drug targets. Both ligand-based and structure-based methods were utilized to obtain lead anti-COVID-19 drug candidates. Sixteen articles also assessed absorption, distribution, metabolism, excretion, toxicity (ADMET), and drug-likeness properties. Confirmation of the inhibitory ability of the candidate leads by in vivo or in vitro assays was reported in only five articles. Virtual screening, molecular docking (MD), and molecular dynamics simulation (MDS) emerged as the most commonly utilized in silico models for anti-COVID-19 drug discovery.
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Affiliation(s)
- Okello Harrison Onyango
- Department of Biological Sciences, Molecular Biology, Computational Biology and Bioinformatics Section, School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P.O. BOX 190, 50100 Kakamega, Kenya
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4
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Gogoi CR, Rahman A, Saikia B, Baruah A. Protein Dihedral Angle Prediction: The State of the Art. ChemistrySelect 2023. [DOI: 10.1002/slct.202203427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
| | - Aziza Rahman
- Department of Chemistry Dibrugarh University Dibrugarh Assam India
| | - Bondeepa Saikia
- Department of Chemistry Dibrugarh University Dibrugarh Assam India
| | - Anupaul Baruah
- Department of Chemistry Dibrugarh University Dibrugarh Assam India
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5
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Cui X, Liu H, Chen HF. Polarizable Force Field of Intrinsically Disordered Proteins with CMAP and Reweighting Optimization. J Chem Inf Model 2022; 62:4970-4982. [PMID: 36178373 DOI: 10.1021/acs.jcim.2c00835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Intrinsically disordered proteins (IDPs) are highly structurally heterogeneous without a specific tertiary structure under physiology conditions and play key roles in the development of human diseases. Due to the characteristics of diverse conformations, as one of the important methods, molecular dynamics simulation can complement information for experimental methods. Because of the enrichment for charged amino acids for IDPs, polarizable force fields should be a good choice for the simulation of IDPs. However, current polarizable force fields are limited in sampling conformer features of IDPs. Therefore, a polarizable force field was released and named Drude2019IDP based on Drude2019 with reweighting and grid-based potential energy correction map optimization. In order to evaluate the performance of Drude2019IDP, 16 dipeptides, 18 short peptides, 3 representative IDPs, and 5 structural proteins were simulated. The results show that the NMR observables driven by Drude2019IDP are in better agreement with the experiment data than those by Drude2019 on short peptides and IDPs. Drude2019IDP can sample more diverse conformations than Drude2019. Furthermore, the performances of the two force fields are similar to the sample ordered proteins. These results confirm that the developed Drude2019IDP can improve the reproduction of conformers for intrinsically disordered proteins and can be used to gain insight into the paradigm of sequence-disorder for IDPs.
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Affiliation(s)
- Xiaochen Cui
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai200240, China
| | - Hao Liu
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai200240, China
| | - Hai-Feng Chen
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai200240, China.,Shanghai Center for Bioinformation Technology, Shanghai200235, China
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6
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The accuracy of protein structures in solution determined by AlphaFold and NMR. Structure 2022; 30:925-933.e2. [DOI: 10.1016/j.str.2022.04.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/18/2022] [Accepted: 04/13/2022] [Indexed: 02/05/2023]
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7
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Cui X, Liu H, Rehman AU, Chen HF. Extensive evaluation of environment-specific force field for ordered and disordered proteins. Phys Chem Chem Phys 2021; 23:12127-12136. [PMID: 34032235 DOI: 10.1039/d1cp01385h] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Intrinsically disordered proteins (IDPs) have no fixed tertiary structure under physiological conditions and are associated with many human diseases. Because IDPs have the characteristic of possessing diverse conformations, current experimental methods cannot capture all the conformations of IDPs. However, molecular dynamics simulation can sample these atomistically diverse conformations as a valuable complement to experimental data. To accurately describe the properties of IDPs, the environment-specific precise force field (ESFF1) was successfully released to reproduce the conformer character of ordered and disordered proteins. Here, three typical IDPs and thirteen folded proteins were used to further evaluate the performance of this force field. The results indicate that the NMR observables of ESFF1 better approach experimental data than do those of ff14SB for IDPs. The sampling conformations by ESFF1 are more diverse than those of ff14SB. For folded proteins, these force fields have comparable performances for reproducing conformers. Therefore, ESFF1 can be used to reveal the model of sequence-disorder-function for IDPs.
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Affiliation(s)
- Xiaochen Cui
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Hao Liu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Ashfaq Ur Rehman
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Hai-Feng Chen
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. and Shanghai Center for Bioinformation Technology, Shanghai, 200235, China
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8
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Evaluation of protein secondary structure from FTIR spectra improved after partial deuteration. EUROPEAN BIOPHYSICS JOURNAL: EBJ 2021; 50:613-628. [PMID: 33534058 PMCID: PMC8189984 DOI: 10.1007/s00249-021-01502-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 01/08/2021] [Accepted: 01/13/2021] [Indexed: 11/11/2022]
Abstract
FTIR spectroscopy has become a major tool to determine protein secondary structure. One of the identified obstacle for reaching better predictions is the strong overlap of bands assigned to different secondary structures. Yet, while for instance disordered structures and α-helical structures absorb almost at the same wavenumber, the absorbance bands are differentially shifted upon deuteration, in part because exchange is much faster for disordered structures. We recorded the FTIR spectra of 85 proteins at different stages of hydrogen/deuterium exchange process using protein microarrays and infrared imaging for high throughput measurements. Several methods were used to relate spectral shape to secondary structure content. While in absolute terms, β-sheet is always better predicted than α-helix content, results consistently indicate an improvement of secondary structure predictions essentially for the α-helix and the category called “Others” (grouping random, turns, bends, etc.) after 15 min of exchange. On the contrary, the β-sheet fraction is better predicted in non-deuterated conditions. Using partial least square regression, the error of prediction for the α-helix content is reduced after 15-min deuteration. Further deuteration degrades the prediction. Error on the prediction for the “Others” structures also decreases after 15-min deuteration. Cross-validation or a single 25-protein test set result in the same overall conclusions.
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Caparotta M, Bustos DM, Masone D. Order–disorder skewness in alpha-synuclein: a key mechanism to recognize membrane curvature. Phys Chem Chem Phys 2020; 22:5255-5263. [DOI: 10.1039/c9cp04951g] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Currently, membrane curvature is understood as an active mechanism to control cells spatial organization and activity.
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Affiliation(s)
- Marcelo Caparotta
- Facultad de Ciencias Exactas y Naturales
- Universidad Nacional de Cuyo (UNCuyo)
- Mendoza
- Argentina
| | - Diego M. Bustos
- Facultad de Ciencias Exactas y Naturales
- Universidad Nacional de Cuyo (UNCuyo)
- Mendoza
- Argentina
- Instituto de Histología y Embriología de Mendoza (IHEM) – Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)
| | - Diego Masone
- Instituto de Histología y Embriología de Mendoza (IHEM) – Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)
- Universidad Nacional de Cuyo (UNCuyo)
- Mendoza
- Argentina
- Facultad de Ingeniería
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10
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Hura GL, Hodge CD, Rosenberg D, Guzenko D, Duarte JM, Monastyrskyy B, Grudinin S, Kryshtafovych A, Tainer JA, Fidelis K, Tsutakawa SE. Small angle X-ray scattering-assisted protein structure prediction in CASP13 and emergence of solution structure differences. Proteins 2019; 87:1298-1314. [PMID: 31589784 DOI: 10.1002/prot.25827] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 09/27/2019] [Accepted: 09/27/2019] [Indexed: 12/14/2022]
Abstract
Small angle X-ray scattering (SAXS) measures comprehensive distance information on a protein's structure, which can constrain and guide computational structure prediction algorithms. Here, we evaluate structure predictions of 11 monomeric and oligomeric proteins for which SAXS data were collected and provided to predictors in the 13th round of the Critical Assessment of protein Structure Prediction (CASP13). The category for SAXS-assisted predictions made gains in certain areas for CASP13 compared to CASP12. Improvements included higher quality data with size exclusion chromatography-SAXS (SEC-SAXS) and better selection of targets and communication of results by CASP organizers. In several cases, we can track improvements in model accuracy with use of SAXS data. For hard multimeric targets where regular folding algorithms were unsuccessful, SAXS data helped predictors to build models better resembling the global shape of the target. For most models, however, no significant improvement in model accuracy at the domain level was registered from use of SAXS data, when rigorously comparing SAXS-assisted models to the best regular server predictions. To promote future progress in this category, we identify successes, challenges, and opportunities for improved strategies in prediction, assessment, and communication of SAXS data to predictors. An important observation is that, for many targets, SAXS data were inconsistent with crystal structures, suggesting that these proteins adopt different conformation(s) in solution. This CASP13 result, if representative of PDB structures and future CASP targets, may have substantive implications for the structure training databases used for machine learning, CASP, and use of prediction models for biology.
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Affiliation(s)
- Greg L Hura
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California.,Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, California
| | - Curtis D Hodge
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California
| | - Daniel Rosenberg
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California
| | - Dmytro Guzenko
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, California
| | - Jose M Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, California
| | - Bohdan Monastyrskyy
- Protein Structure Prediction Center, Genome and Biomedical Sciences Facilities, University of California, Davis, California
| | - Sergei Grudinin
- Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000, Grenoble, France
| | - Andriy Kryshtafovych
- Protein Structure Prediction Center, Genome and Biomedical Sciences Facilities, University of California, Davis, California
| | - John A Tainer
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California.,Department of Molecular and Cellular Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas
| | - Krzysztof Fidelis
- Protein Structure Prediction Center, Genome and Biomedical Sciences Facilities, University of California, Davis, California
| | - Susan E Tsutakawa
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California
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11
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Niggenaber J, Hardick J, Lategahn J, Rauh D. Structure Defines Function: Clinically Relevant Mutations in ErbB Kinases. J Med Chem 2019; 63:40-51. [DOI: 10.1021/acs.jmedchem.9b00964] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Janina Niggenaber
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Strasse 4a, 44227 Dortmund, (Germany)
- Drug Discovery Hub Dortmund (DDHD) am Zentrum für Integrierte Wirkstoffforschung (ZIW), TU Dortmund University, 44227 Dortmund (Germany)
| | - Julia Hardick
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Strasse 4a, 44227 Dortmund, (Germany)
- Drug Discovery Hub Dortmund (DDHD) am Zentrum für Integrierte Wirkstoffforschung (ZIW), TU Dortmund University, 44227 Dortmund (Germany)
| | - Jonas Lategahn
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Strasse 4a, 44227 Dortmund, (Germany)
- Drug Discovery Hub Dortmund (DDHD) am Zentrum für Integrierte Wirkstoffforschung (ZIW), TU Dortmund University, 44227 Dortmund (Germany)
| | - Daniel Rauh
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Strasse 4a, 44227 Dortmund, (Germany)
- Drug Discovery Hub Dortmund (DDHD) am Zentrum für Integrierte Wirkstoffforschung (ZIW), TU Dortmund University, 44227 Dortmund (Germany)
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