Computational study of aptamer botulinum bindings for optimization and design of biosensor for detection of botulinum neurotoxin

VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY NGUYEN HAI LY COMPUTATIONAL STUDY OF APTAMER – BOTULINUM BINDINGS FOR OPTIMIZATION AND DESIGN OF BIOSENSOR FOR DETECTION OF BOTULINUM NEUROTOXIN MASTER’S THESIS VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY NGUYEN HAI LY COMPUTATIONAL STUDY OF APTAMER – BOTULINUM BINDINGS FOR OPTIMIZATION AND DESIGN OF BIOSENSOR FOR DETECTION OF BOTULINUM NEUROTOXIN MAJOR: NANOTECHNOLOGY CODE: 8440140.11QTD RESEARCH SUPERVISORS: Prof Dr NGUYEN THE TOAN Dr NGUYEN HOA MI Hanoi, 2022 COMMITMENT I have read and understood the plagiarism violations I pledge with personal honor that this research result is my own and does not violate the Regulation on prevention of plagiarism in academic and science research activities at VNU Vietnam Japan University (Issued together with Decision No 700/QD-ĐHVN dated 30/09/2021 by the Rector of Vietnam Japan University) Author of the thesis Nguyen Hai Ly ACKNOWLEDGEMENTS Throughout the writing of this thesis, I have received a great deal of support and assistance I would first like to thank my supervisor, Professor Nguyen The Toan, whose I am greatly admired by his extensive knowledge and dedicated guidance Your insightful feedback pushed me to sharpen my thinking and brought my work to a higher level The door to the Professor’s office was always open whenever I ran into a trouble spot or had a question about my research I also would like to thank my co-supervisor, Doctor Nguyen Hoa Mi, for their valuable guidance throughout my studies You provided me with the tools that I needed to choose the right direction and successfully complete my thesis Besides, I am so grateful for my friends in VNU Vietnam Japan University and VNU Key Laboratory on Multiscale Simulation of Complex Systems for sharing expertise, valuable guidance, and encouragement extended to me I am also grateful to my family, who have always encouraged me during the time of writing this thesis The total support of my parents has helped me to complete my thesis This work is financially supported by the Vietnamese Ministry of Science and Technology under the bi-lateral Vietnam-Germany grant NDT/DE/21/28 Hanoi, July 2022 Nguyen Hai Ly LIST OF CONTENTS LIST OF TABLES i LIST OF FIGURES ii LIST OF ABBREVIATIONS iii CHAPTER 1: INTRODUCTION 1.1 Introduction about botulism 1.2 Botulinum neurotoxin (BoNT) proteins 1.3 Aptamer 1.4 Dual detection biosensor of BoNT type A and type C CHAPTER 2: METHODOLOGY 2.1 Molecular dynamics (MD) simulation 2.1.1 Simulation settings 2.1.2 Protein structures 2.1.3 Primary structure of Aptamer 2.1.4 Method for building secondary structures of Aptamers .10 2.2 Docking of Molecular biomolecules 10 2.3 Interaction free energy estimation by MM-PBSA method 11 2.4 Flowchart of the computational pipeline .13 CHAPTER 3: RESULTS AND DISCUSSION 14 3.2 Docking of aptamers with BoNT type A and type C proteins 19 3.3 Molecular dynamics simulation results .23 3.3.1 Root mean square deviation and equilibrium analysis 23 3.3.2 Root mean square fluctuation and stability analysis .25 3.3.3 Hydrogen bond interactions 27 3.3.4 Binding free energy from MM/PBSA calculation 30 3.4 Discussion 31 CONCLUSION .34 REFERENCES 36 APPENDIX 41 LIST OF TABLES Table 2.1: The primary structure of BoNT type A and Type C light chain Both are 430 residues long The residues of coordinates with the zinc ion are denoted by bold letter .7 Table 2.2: The list of aptamers selected for investigation in this work, five aptamers for each BoNT type The aptamers are selected from a list of experimental aptamers known to bind well to their respective target protein.[62, 63] Table 3.1: Binding free energy of the docking pose with the lowest S score, configuration and placement energy 19 Table 3.2: The binding free energy (in units of kJ/mol) of aptamer with BoNT/A or BoNT/C calculated using the MM/PBSA method The columns are, respectively, the van der Waals, electrostatic, solvation and SASA (solvent-accessible surface area) energies, and the total binding energy 30 i LIST OF FIGURES Figure 1.1: The three dimensions structure for the light chain BoNT type A (download from the Protein Data bank with code ID 3DDA) Figure 2.1: The zinc finger structure of the BoNT light chain: for type A (left) and for type C (right) The zinc ion coordinates with target substrate protein and play an important role in the catalytic function of the BoNT enzymes For BoNT type A light chain, the fourth residue comes from the human SNAP-25 protein segment Figure 2.2: Illustration for ligand-protein binding 11 Figure 2.3: Thermal dynamics cycle for calculation of ligand-protein binding energy 12 Figure 3.1: The secondary structures of aptamers built from MFold and RNAcomposer The later output is of PDB format, and is presented using PyMol viewer .18 Figure 3.2: The three-dimensional structures of the aptamer – BoNT complex with the lowest S score 22 Figure 3.3: The RMSD values of protein (column 2) and aptamer (column 3), both with respect to the protein 24 Figure 3.4: Root mean square fluctuation of aptamer in complexation with BoNT/A (violet color) and BoNT/C (cyan color) 27 Figure 3.5: The number of hydrogen bonds between aptamer and BoNT proteins as a function of time 29 ii LIST OF ABBREVIATIONS BoNT: MD: MM-PBSA: RMSD: RMSF: FDA: Ach: LC: HC: HCT: HCR: FRET: SELEX: CNT: SASA: Botulinum Neurotoxin Molecular dynamics Molecular mechanics Poisson−Boltzmann surface area Root mean square deviation Root mean square fluctuation Food and Drug Administration Acetylcholine Light chain C-terminal heavy chain Translocation domain The receptor-binding domain The fluorescence quenching effect Systematic evolution of ligands by exponential enrichment Clostridial neurotoxins Solvent accessible surface area iii CHAPTER 1: INTRODUCTION 1.1 Introduction about botulism Botulism was discovered about the 18th century as a rare but potentially fatal disease Between 1735 and 1802, in Württemberg, Germany, there has been an outbreak of botulism cases related to blood sausages causing the government to introduce safety measures; by 1820, botulism was a mandatory reportable disease [1] Potentially poisonous foods are given: sausages, vegetables, olives, cheese, canned food [1] When these foods go stale, a bacteria called Clostridium botulinum is produced The toxin of this species causes mostly mild consequences However, they can also be potentially fatal, especially when the toxin is neurotoxic in nature [2,3,4] The causes of botulism are caused by bacteria, fungi, algae, and plants and are a threat to human life Among them is the neurotoxin Botulinum produced by Clostridium, in some cases, found in open wounds, or by surgery [1] 1.2 Botulinum neurotoxin (BoNT) proteins Botulinum neurotoxin is a potent toxin, produced by gram-positive anaerobes, that weakens muscle by inhibiting neurotransmitter release at peripheral neuromuscular junctions BoNT is the most lethal poison known to man with fatal dose of only 13ng/kg in humans [5] Therefore, BoNT is considered a very dangerous biological warfare agent, declared a Class A biologic agent by the US Centers for Infectious Diseases Interestingly, these toxins also serve as therapeutic agents to treat many diseases and are approved by the Food and Drug Administration (FDA) and other regulatory agencies [6-13] Potent inhibition and simultaneous selective release of Acetylcholine (ACh) induces the development of BoNT toxicity release at the neuromuscular junction [14-20] Interestingly, this selective inhibition of ACh release also enables BoNT/A to become a highly useful therapeutic agent [21-23] In 1989, it is regarded as the treatment of choice for an increasing number of neurologic, autonomic and cosmetic conditions [23-27] BoNT in low concentrations has been approved by the Food and Drug Administration (FDA) for the treatment of wrinkles [28] When exposed to favorable temperature and environmental conditions, they will be activated and cause initial symptoms BoNTs are quite resistant to changes in body temperature allowing them to remain active for months or even years BoNT can avoid denaturation and proteolysis by stomach acid and digestive enzymes and transported through the intestinal wall into the blood with original structure BoNT-producing bacteria include the species Clostridium botulinum and some strains of C argentinense, C baratii and C butyricum [29, 30] Among them, Clostridium botulinum is an anaerobic, rod-shaped bacterium that produces and releases potent toxins that cause the symptoms of botulism BoNT are divided according to their immunological properties into different serotypes (A-G) [31] Different types will affect different objects, BoNT/D is toxic to animals, BoNT/A, B, E, and F affect humans, BoNT/C affects avians [32] However, proteins of these types are closely related and share the same mechanism of action Once in the circulation, BoNT proteins potentially have access to all peripheral cell types They bind specifically and with high affinity to active nerve synapses, internalize by endocytosis and then translocate an enzymatically active protease subunit into the target cell cytoplasm [33] Together, these activities give rise to the high potency and specificity that BoNTs show towards active neuromuscular junctions [34, 35] In the framework of the thesis, due to the focus of our experimental colleagues, we study BoNT types A and C BoNTs are toxins composed of two domains [36, 37, 38, 39, 40] The N-terminal light chain (LC) comprises the enzymatic (zinc metalloprotease) domain The C-terminal heavy chain (HC) includes two independent functional domains, the translocation domain (HCT) and the receptor-binding domain (HCR) [41] The three domains are structurally independent [42] Earlier studies showed that a Clostridium-derived endoprotease was responsible for nicking BoNT/A to generate the di-chain toxin [4346] where cleavage removed ten amino acids at the junction between the LC and the HC yielding an LC of ~438 amino acids, which is considered the active form of the LC [40] Reduction in the toxicity of several derivatives of BoNTs produced by clostridia in an unnicked form shows the importance of nicking Due to this mechanism, the light chain of BoNT is usually the target of biosensors for detection of A4 C4 C5 Figure 3.5: The number of hydrogen bonds between aptamer and BoNT proteins as a function of time The results of hydrogen bond analyses agree quite well with RMSF structural analyses The aptamer A2 once again show strong preference for BoNT/A binding with nearly 10 more hydrogen bonding compare to BoNT/C binding The aptamers A3, A4 show similar number of hydrogen bonds to both BoNT/A and BoNT/C protein The aptamer C4 and C5 seems to show slightly smaller number of hydrogen 29 bonds upon binding to BoNT/A than to BoNT/C, once again agree with docking and RMSF analyses, as agree with RMSD analyses within uncertainty Among the aptamers, aptamer A4 has the highest number of hydrogen bonds This makes them the most suitable candidate for further experimental study 3.3.4 Binding free energy from MM/PBSA calculation As one final analysis, we calculate the binding free energy of the aptamer with the protein using MM/PBSA method The dielectric constant of water is set to 80, while that of the protein environment is set to The polar and electrostatic energy is calculated using the mean-field Poisson Boltzmann method The results are presented in Table 3.2 Table 3.2: The binding free energy (in units of kJ/mol) of aptamer with BoNT/A or BoNT/C calculated using the MM/PBSA method The columns are, respectively, the van der Waals, electrostatic, solvation and SASA (solvent-accessible surface area) energies, and the total binding energy System Van der Waal energy (kJ/mol) Electrostatic energy (kJ/mol) Polar solvation energy (kJ/mol) SASA energy (kJ/mol) Binding energy (kJ/mol) AptamerA2BoNT/A -696.044 ±115.808 -1767.422 ±660.510 1091.517 ±343.000 -38.589 ± 10.445 -1410.538 ±461.031 AptamerA2BoNT/C -284.142 ± 51.340 -4485.540 ±824.635 954.860 ±309.745 0.609 ± 5.114 -3814.213 ±634.540 AptamerA3BoNT/A -282.965 ± 60.611 -3849.611 ±1157.980 859.897 ±316.462 2.514 ± 9.132 -3270.166 ±952.635 AptamerA3BoNT/C -252.766 ± 43.085 -3932.046 ±524.024 667.090 ±218.504 5.554 ± 7.874 -3512.168 ±470.259 AptamerA4BoNT/A -500.058 ± 50.550 -12994.214 ±868.730 2027.317 ±318.206 -26.479 ± 9.034 -11493.434 ±611.874 AptamerA4BoNT/C -618.019 ±128.449 -5965.378 ±726.503 1269.030 ±286.230 -30.289 ± 12.522 -5344.655 ±592.279 AptamerC4BoNT/A -219.435 ± 46.063 -9298.381 ±669.563 1079.992 ±208.415 3.189 ± 5.640 -8434.636 ±497.586 AptamerC4BoNT/C -383.149 ± 74.249 -3942.273 ±392.341 980.408 ±234.080 -7.949 ± 6.583 -3352.963 ±469.138 AptamerC5BoNT/A -197.831 ± 50.194 -2974.148 ±539.753 322.262 ±173.624 13.769 ± 7.547 -2835.947 ±574.692 30 AptamerC5BoNT/C -292.320 ± 45.204 -8879.753 ±508.025 1045.781 ±212.791 -2.189 ± 7.162 -8128.481 ±393.428 Just like the binding free energy from the docking step, all the binding free energy calculated from MD simulation are strongly negative in all the systems, indicating that they are all able to bind the proteins However, unlike the docking results, there are large variations among the energies from MD simulation As already discussed, this is due to the relaxation of the complex from its static docking pose Unlike small drug molecules, aptamers are large ligands, with many degrees of freedom The docking cannot capture the relaxation of the flexible aptamer structure to the protein structure upon complexation One of the most important conclusions from Table 3.2 is that the aptamer A4 has the strongest binding energy to both types of BoNT Although the binding to BoNT/A is strongest (as it should be, since this is the target protein for this aptamer), its binding BoNT/C are comparable, or sometimes, even stronger than those of aptamer C4 and C5 This computational study strongly suggest that aptamer A4 could be used in biosensors to detect both of these types of Botulinum Neurotoxin For other aptamers, the binding energy is significantly stronger for their designed target protein than for the cross-target type as one expects If one were to design biosensors for specificity instead of cross-detection the C5 aptamer would be the best candidate for BoNT/C detection A4 would be a better suit to detect BoNT in general, without type differentiation function 3.4 Discussion Combining the results from docking calculation, RMSD and RMSF structural analyses, hydrogen bond and MM/PBSA energetical analyses, we can summary the main results are following: ● All the aptamers showed good binding to both types of BoNT/A and BoNT/C neurotoxin The static docking binding free energy shown very similar energy among the complexes, however, molecular simulation allows the flexible aptamer to relax to their most stable binding poses, leading to a much larger variation in the binding free energy Clearly, MD simulation is a must if one wants to properly 31 capture the interactions and specificity of aptamers to the protein The qualitative features of the complexations are reasonably reflected in the docking result This makes the molecular docking a good pre-screening from a large selection of aptamers despite many of its glaring limitations ● Both proteins are very stable with very small RMSD value with respect to itself, and with small RMSF of the amino acid residues RMSD values are half of typical value for isolated proteins in water solution, stressing the enhanced stability or compactness of protein upon aptamer complexation This also strengthens the argument that the optimal docking pose can serve as a good starting configuration for molecular dynamics simulation ● Aptamer A4 seems to be the best candidate for use in biosensor for detection of both types of Botulinum neurotoxin Although original designed for BoNT/A selectivity, it shows very similar binding property to BoNT/C in every analysis that we perform, be it structural or energetical analyses ● Aptamer A2 shows similar binding affinity to both BoNT/A and BoNT/C in docking result However, in simulation, their binding energies and stabilities are very different for different proteins Furthermore, all energy values are quite smaller than those other aptamers, with the complex A2-BoNT/C not even reaching equilibrium within our simulation time This suggests the docking results are quite unreliable for this aptamer Only with the help of MD simulation results can we discard this aptamer from further experimental investigation ● Across different analyses, aptamer A3 also show similar binding affinity to both types of BoNT, like that of aptamer A4 However, their number of hydrogen bonds and binding energy to the protein is much weaker than that of aptamer A4 Therefore, we refrain from recommending this aptamer for further experimental investigation ● Aptamer C4 and C5 seems to prefer their target BoNT/C than the cross target BoNT/A although the difference is not as clearly cut as the preference of A2 to binding to BoNT/A They (especially aptamer C5) are good candidates for experimental investigation of BoNT/C detection For cross-type detection, we still 32 suggest aptamer A4, especially that A4 is a stock aptamer that we can purchase, offering an economical choice for the biosensor design ● The MM/PBSA calculation for the C5 aptamer seems to arriveat a strange result from docking Table 3.2 seems to suggest that this aptamer prefers binding to BoNT/A while they are actually designed for BoNT/C In Table 3.2, it prefers binding to BoNT/C as it should be This once again highlights the inefficiency of molecular docking method for large, flexible ligands such as aptamers ● The fact that aptamer A4 binds also very well to BoNT/C might have to with the fact that it shares very similar secondary structure with aptamer C5 as already shown in Figure 3.1 Both contains two hairpins and four short double helices segments In the future, we plan to look at this similarity in more details 33 CONCLUSION The computational work done in this thesis is a preliminary step in the ultimate goal of better experimental design of biosensor for detection of Botulinum Neurotoxin type A and type C, which are the objects of our experimental colleagues, Dr LUU Manh Quynh, at the Center for Materials Sciences, the Faculty of Physics, VNU University of Science For this goal, we have selected candidate aptamers for each type of BoNT proteins By investigation using molecular docking and molecular dynamics simulation of their complexation with their respective target protein, as well as, cross binding to the other type, we hope to provide some guidance to experimental aptamer selection and/or synthesis, saving time, labor and material costs Aptamer is a large protein ligand, with many degrees of freedom, the standard docking method using static structures cannot capture the non-negligible structural relaxation of the protein-aptamer ligand complex upon binding One clearly needs molecular dynamics simulation for a serious investigation of the binding affinity Nevertheless, our results show that docking pose can serve well as an initial starting configuration One important information of our study is that the protein (either BoNT/A or BoNT/C) deviates very little from its starting configuration during MD simulation, as low as 12Å deviation The aptamers don’t disrupt the protein, but rather enhance its stability In turn, this allows aptamers to be an excellent candidate for biosensor for detection All the aptamers show good binding affinity to both type of aptamers, but they usually have higher preference for binding to the designed target protein than the cross type: aptamer designed for type A prefer binding to BoNT/A, and vice versa But among them, aptamer A4 seems to have good binding to BoNT/A and BoNT/C with the highest amount of hydrogen bond formation, as well as binding free energy to both types This result would suggest the experimentalist to use this aptamer in biosensor for cross detection Although if for detection of BoNT/C only, the C5 aptamer seems to be a better candidate than the A4 aptamer Interestingly the C5 aptamer has decently similarly secondary structures as that of A4 aptamer (two hair-pins, four short helical segments) This might explain why A4 is also binds BoNT/C quite well 34 In the future, we plan to continue more elaborate investigation of aptamer A4 and C5, the fact that they share many similarities in secondary structures, both binds well to BoNT/C and the A4 also binds well to BoNT/A as its preferred target Especially, more detail on the binding sites between the aptamers and the proteins, its stability will be investigated This might provide more clues to understand the molecular mechanism of binding, assist in further optimization and design of new aptamers for these Botulinum neurotoxin 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C Detection Biosensors, 10(8), 98 https://doi.org/10.3390/bios10080098 [63] Chang, T W., Blank, M., Janardhanan, P., Singh, B R., Mello, C., Blind, M., & Cai, S (2010) In vitro selection of RNA aptamers that inhibit the activity of type A botulinum neurotoxin Biochemical and biophysical research communications, 396(4), 854–860 https://doi.org/10.1016/j.bbrc.2010.05.006 [64] Zuker M Mfold web server for nucleic acid folding and hybridization prediction (2003) Nucleic Acids Res 31(13):3406-15 https://doi.org/10.1093/nar/gkg595 [65] https://www.molsis.co.jp/wp-content/themes/molsis/pdf/moe_eng.pdf [66] Nguyễn Thế Tồn, Nguyễn Họa Mi (2021) Giáo trình Vật lý sinh học protein, NXB Đại học Quốc Gia Hà Nội, ISBN: 9786043247800 40 APPENDIX Typical output from RNAcomposer: Task identifier: Task description: Sequence: 99977f50-3d68-4d48-9ced-bd8aa1deed9b example1 GCUCCUAGAAAGGCGCGGGCCGAGGUACCAAGGCAGCGUGUGGAGC Secondary structure: ((((( .(((( ((( ))).)))) ))))) 2022-06-18 16:19:55 Task launched 2022-06-18 16:19:55 STEP: Input data validation 2022-06-18 16:19:55 Secondary structure provided by the user 2022-06-18 16:19:55 Input data validation completed (0:0:0:3) 2022-06-18 16:20:05 Configuration of filters for structural elements repository: Generate A-RNA-based double helices: Off Generate A-RNA-based single strands: Off Use X-ray determined structures only: On Set resolution threshold: 3,000 2022-06-18 16:20:05 Restraints configuration: No atom distance restraints introduced by the user No torsion angle restraints introduced by the user 2022-06-18 16:20:05 No structural elements introduced by the user 2022-06-18 16:20:05 STEP 1: Secondary structure fragmentation 2022-06-18 16:20:05 Secondary elements obtained: Stem D1 GCUCC ((((( 42 46 GGAGC ))))) Stem D2 13 16 GCGC (((( 36 39 GCGU )))) Stem D3 19 21 GCC ((( 32 34 GGC ))) Loop L1 13 CUAGAAAGG ( .( 39 42 UGUG ) ) Loop L2 16 19 CGGG ( ( 34 36 CAG ).) Loop L3 21 32 CGAGGUACCAAG ( ) 2022-06-18 16:20:05 Secondary structure fragmentation completed (0:0:0:2) 2022-06-18 16:20:05 STEP 2: 3D structure elements preparation 2022-06-18 16:20:05 3D structure elements preparation completed (0:0:1:305) 2022-06-18 16:20:05 3D structure elements ready for composition 41 2022-06-18 16:20:05 2022-06-18 16:20:05 STEP 3: 3D structure elements rigid body transformation 3D structure elements rigid body transformation completed (0:0:0:0) 2022-06-18 16:20:05 MODEL 2022-06-18 16:20:05 3D structure elements selected to compose model 1: Stem D1 GCUCC ((((( 42 46 GGAGC ))))) based on 3DW4 (1) 0.97[A] A GCUCC ((((( A 22 26 GGAGU ))))) with homology 90.00% Stem D2 13 16 GCGC (((( 36 39 GCGU )))) based on 4E6B (1) 1.47[A] A 12 GCGC (((( B 13 16 GCGC )))) with homology 87.50% Stem D3 19 21 GCC ((( 32 34 GGC ))) based on 3UMY (1) 1.90[A] A 23 25 GCC ((( B 55 57 GGC ))) with homology 100.00% Loop L1 13 CUAGAAAGG ( .( 39 42 UGUG ) ) based on 1VQO (1) 2.20[A] 1520 1528 CAGUGAAAG ( .( 1661 1664 CGAG ) ) with homology 46.15% Loop L2 16 19 CGGG ( ( 34 36 CAG ).) based on 1VQO (1) 2.20[A] 2268 2271 CGGG ( ( 2121 2123 CAG ).) with homology 100.00% Loop L3 21 32 CGAGGUACCAAG ( ) based on 3V22 (1) 3.00[A] A 940 951 CGAAGCAACGCG ( ) with homology 58.33% 2022-06-18 16:20:05 STEP 4: 3D structure elements merging 2022-06-18 16:20:05 3D structure elements merging completed (0:0:0:176) 2022-06-18 16:20:05 STEP 5: Initial 3D structure minimization in torsion angle space 2022-06-18 16:20:05 Initial 3D structure minimization in torsion angle space completed (0:0:0:33) 2022-06-18 16:20:05 STEP 6: Composed 3D structure minimization in atomic Cartesian coordinates space 2022-06-18 16:20:05 - cycle= 100 stepsize= 0.0001 | Etotal =-740.004 grad(E)=9.899 E(BOND)=25.705 E(ANGL)=279.404 | | E(IMPR)=47.185 E(VDW )=-674.711 E(ELEC)=-445.308 E(CDIH)=0.000 | | E(NOE )=0.046 E(PLAN)=27.675 | cycle= 200 stepsize= 0.0001 | Etotal =-896.391 grad(E)=4.376 E(BOND)=15.124 E(ANGL)=202.070 | | E(IMPR)=27.581 E(VDW )=-695.516 E(ELEC)=-463.931 E(CDIH)=0.000 | | E(NOE )=0.447 E(PLAN)=17.834 | 42 - cycle= 300 stepsize= 0.0001 | Etotal =-971.438 grad(E)=3.043 E(BOND)=11.614 E(ANGL)=164.258 | | E(IMPR)=22.174 E(VDW )=-709.989 E(ELEC)=-474.563 E(CDIH)=0.000 | | E(NOE )=1.004 E(PLAN)=14.064 | cycle= 400 stepsize= 0.0001 | Etotal =-999.921 grad(E)=2.935 E(BOND)=11.195 E(ANGL)=156.511 | | E(IMPR)=21.852 E(VDW )=-716.428 E(ELEC)=-484.515 E(CDIH)=0.000 | | E(NOE )=0.512 E(PLAN)=10.950 | cycle= 500 stepsize= 0.0001 | Etotal =-1021.671 grad(E)=1.118 E(BOND)=9.891 E(ANGL)=149.929 | | E(IMPR)=21.025 E(VDW )=-718.409 E(ELEC)=-493.257 E(CDIH)=0.000 | | E(NOE )=0.901 E(PLAN)=8.248 | cycle= 600 stepsize= 0.0001 | Etotal =-1038.863 grad(E)=2.162 E(BOND)=10.024 E(ANGL)=149.399 | | E(IMPR)=21.225 E(VDW )=-724.267 E(ELEC)=-503.711 E(CDIH)=0.000 | | E(NOE )=1.272 E(PLAN)=7.195 | cycle= 700 stepsize= 0.0001 | Etotal =-1053.198 grad(E)=1.857 E(BOND)=9.669 E(ANGL)=146.649 | | E(IMPR)=21.100 E(VDW )=-727.158 E(ELEC)=-511.240 E(CDIH)=0.000 | | E(NOE )=0.877 E(PLAN)=6.905 | cycle= 800 stepsize= 0.0001 | Etotal =-1066.084 grad(E)=0.960 E(BOND)=9.251 E(ANGL)=142.055 | | E(IMPR)=20.854 E(VDW )=-728.097 E(ELEC)=-517.671 E(CDIH)=0.000 | | E(NOE )=1.084 E(PLAN)=6.441 | cycle= 900 stepsize= 0.0001 | Etotal =-1081.053 grad(E)=1.467 E(BOND)=9.188 E(ANGL)=139.598 | | E(IMPR)=21.291 E(VDW )=-731.229 E(ELEC)=-526.217 E(CDIH)=0.000 | | E(NOE )=1.015 E(PLAN)=5.301 | cycle= 1000 stepsize= 0.0001 -| Etotal =-1093.746 grad(E)=1.122 E(BOND)=8.906 E(ANGL)=136.396 | | E(IMPR)=21.152 E(VDW )=-735.472 E(ELEC)=-530.697 E(CDIH)=0.000 | | E(NOE )=0.744 E(PLAN)=5.227 | 2022-06-18 16:20:05 Composed 3D structure minimization in atomic Cartesian coordinates space completed (0:0:7:534) 2022-06-18 16:20:05 Composed 3D structure refinement completed(0:0:7:569) 2022-06-18 16:20:05 MODEL successfully composed 2022-06-18 16:20:05 MODEL composition time: 0:0:8:39 2022-06-18 16:20:05 Task completed 43