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008			231016s2023 sz | o |||| 0|eng d
020			_a9783031367731
024	7		_a10.1007/978-3-031-36773-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRA644 .C67 _b2023 EB
100	1		_aZhang, Jiapu _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _994226
245	1	0	_aOptimization-based Molecular Dynamics Studies of SARS-CoV-2 Molecular Structures : _bResearch on COVID- 19 _cby Jiapu Zhang
250			_a1st ed. 2023
264		1	_aCham _bSpringer Nature Switzerland _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aSpringer Series in Biophysics _x1868-2561 _v23
505	0		_a1 Papain-Like cysteine protease (PLpro) -- 2 3C-Like protease (3CLpro) -- 3 RNA-dependent RNA polymerase (RdRp) -- 4 RNA-helicase -- 5 RNA-helicase binding with [RdRp, NSP7, NSP8a, NSP8b, pRNA, tRNA, ADP-Mg2+, ATP-Mg2+] -- 6 RNA-helicase binding with [ADP-Mg2+, ATP-Mg2+, and RNA] -- 7 Spike (S) glycoprotein -- 8 Spike (S) glycoprotein D614G mutant -- 9 Spike (S) glycoprotein N501Y mutant -- 10 Spike (S) glycoprotein N165A-and-N234A mutant -- 11 SARS (SARS-CoV-1) -- 12 MERS-coronavirus (MERS) -- 13 Human-ACE2, human-L6, human-L6R, human-nAChRs -- 14 PLpro binding with 12 compounds -- 15 3CLpro binding with N3/Lopinavir/Ritonavir -- 16 3CLpro dimer binding with 7 HIV-inhibitors and Others -- 17 Spike RBDs binding with 50 drugs -- 18 Human ACE2 ectodomain binding with 78 drugs -- 19 Spike-and-ACE2 binding with 100 drugs -- 20 Envelope protein (E-protein) -- 21 Membrane glycoprotein (M-protein) -- 22 Nucleocapsid phosphoprotein (N-protein) -- 23 SARS-CoV-2 RNA genome -- 24 NSP7, NSP8, NSP9, NSP10, NSP16, NSP14 -- 25 NSP15 -- 26 Other mutants -- 27 Vaccines and Drugs -- 28 Pandemic Mathematical Models, Epidemiology and Virus Origins -- A Mathematical Optimization Algorithms and Free Energy Calculations -- References -- Index.
520			_aCOVID-19 has brought us extensive research databases in the fields of biophysics, biology, and bioinformatics. To extract valuable structural bioinformatic information of SARS-CoV-2 structural and nonstructural proteins, it is necessary to work with large-scale datasets of molecular dynamics (MD) trajectories that need to be optimized. This monograph serves as a comprehensive guide to optimization-based MD studies of the molecular structures of SARS-CoV-2 proteins and RNA. The book begins by performing local optimization, taking into account the three-body movement and optimizing the noncovalent bonds of each molecular structure. The optimized structures reach a transition state that offers the best stability and lowest energy. The optimization process utilizes a hybrid strategy that combines mathematical optimization with various local search algorithms. This approach significantly reduces data volume while eliminating irrelevant bioinformatics data. To gain a thorough understanding of molecular stability and the mechanism of action, it is essential to consider not only static NMR, X-ray, or cryo-EM structures but also dynamic information obtained through MD or Quantum Mechanics/Molecular Mechanics (QM/MM) simulations. These simulations capture the internal motions and dynamic processes of molecules. Furthermore, for each protein, the structural bioinformatics obtained from the optimized structure is validated by analyzing large-scale MD trajectory databases, which are openly and freely available online. The analysis includes key structural bioinformatics aspects such as salt bridge electrostatic interactions, hydrogen bonds, van der Waals interactions, and hydrophobic interactions specific to each SARS-CoV-2 molecular structure. The book also delves into discussions on drugs, vaccines, and the origins of the virus. Additionally, pandemic mathematical models, including those incorporating time delays, are explored. This book is particularly valuable for professionals working in practical computing roles within computational biochemistry, computational biophysics, optimization and molecular dynamics, structural bioinformatics, biological mathematics, and related fields. It serves as an accessible introduction to these disciplines and is also an excellent teaching resource for students.
988			_aSpringer_BiomedLife_2023
650		7	_2embne _9683668 _aCOVID-19
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-36773-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2024 _dz _eb _zSI