Rita Pizzi Department of Information Technology Università degli Studi di Milano

1. Rita PizziDepartment of Information TechnologyUniversità degli Studi di Milano

2. ARTIFICIAL NEURAL NETWORKS IDENTIFY THE DYNAMIC ORGANIZATION OF MICROTUBULES AND TUBULIN SUBJECTED TO ELECTROMAGNETIC FIELD

3. TUBULIN • Tubulin is a globular protein and the fundamental component of microtubules. • Microtubules (MTs) constitute the cytoskeleton of all the eukaryotic cells and are supposed to be involved in many key cellular functions.

4. MICROTUBULES • MTs are cylindrical polymers composed by aligned tubulin dimers, alpha and beta-tubulins, that polymerize in a helix that creates the microtubule.

5. MICROTUBULES • Their diameter is around 15 nm and their length can vary from some nm up to some centimeters • Many researchers claim they are involved in the information transmission among cells

6. MICROTUBULES • In past researches we tried to validate the hypothesis that microtubules possess anomalous properties due to their tubular structure • To this purpose we tested possible modifications of their biophysical structures with two physical measures: resonance and birefringence

7. We studied the MT and Tubulin biophysical behavior in presence of electromagnetic field, to assess if their tubular structure could make them cavity antennas, as carbon nanotubes (CNT) recently showed to be. CARBON NANOTUBE PROPERTIES

8. CNT have the same tubular structure and the same dimensions as MTs It was shown that they behave as antennas for extremely high frequencies, receiving and transmitting nanoscale waves CARBON NANOTUBES PROPERTIES

9. Our experimental approach verified the existence of a sharp mechanical resonance in MTs at a frequency of 1510 MHz. RESONANCE

10. This lack of response in tubulin and control solution can be considered a hint that MT resonance was caused by their molecular tubular structure. RESONANCE The tubulin solution and the control solution did not show any reaction.

11. Moreover we submitted MTs behavior to a birefringence test. Birefringence is an optical property of materials that arises from the interaction of light with oriented molecular components. BIREFRINGENCE

12. We submitted MT, tubulin and control solution to electric and magnetic field BIREFRINGENCE

13. We measured birefringence of polarized light crossing the MT, tubulin and control samples. We observed that MTs react to electromagnetic fields in a different way than tubulin and control. Birefringence effect is always higher in MTs than in tubulin and control, with statistical significance This suggests again that the molecular structure of MTs could be the cause of their reaction to e-m fields. BIREFRINGENCE

14. In this study we considered for comparison carbon nanotubes (CNT) and buckyballs (BB) NANOTUBES AND BUCKYBALLS MT structure is very similar to the CNT one The globular and regular structure of BB can be compared to the tubulin structure

15. In order to assess the significance of these findings we performed a dynamic simulation of the molecular structures of tubulin, MT , CNT and BB subjected to different levels of e-m fields. • We adopted the Ascalaph simulation environment. It allows simulations of large molecular structures and many parameterizations. ASCALAPH

16. ASCALAPH The tertiary structures were obtained by Protein Data Bank and NANO_D INRIA group. 1° simulation: No electric field 2° simulation: A= 2 V/cm, F= 90 Hz 3° simulation: A= 90 V/cm, F= 90 Hz The structures were immersed in water at 298.15 °K Simulation time: 7000 ps

17. ARTIFICIAL NEURAL NETWORKS • The simulation results were submitted to two different artificial neural networks. • ANN are known to be effective non-linear classifiers useful in case of complex patterns • The xyz values of the molecules after simulation (energy minimization) were chosen as input values of the ANNs • We adopted two different Artificial Neural Networks : SONNIA and ITSOM

18. SONNIA • SONNIA is an ANN environment useful in the field of drug discovery and protein prediction. Performs both supervised and unsupervised learning. • Its output are a set of colored boxes, one for each competitive neuron (in case of SOM). • Each box represents : • Occupancy: number f patterns mapped onto the same neuron, i.e. similarities in the input domain • Conflicts, i.e. neurons that refer to inputs belonging to different classes. e

19. ITSOM • ITSOM is an evolution of the Kohonen SOM. • The sequence of its winning neurons form series of numbers that repeat “nearly” periodically, i.e. constitute cahotic attractors. • Each attractor identifies the input pattern univocally • The graphical representation of the cahotic attarctor gives the idea of the dynamical organization of the pattern

20. SONNIA TUBULIN A=2V/cm F=90Hz No field Occupancy: 51 Conflict: 384 Occupancy: 55 Conflict: 251 A=90V/cm F=90Hz Occupancy: 53 Conflict: 1020

21. SONNIA MICROTUBULE A=2V/cm F=90Hz No field Occupancy: 35 Conflict: 780 Conflict: 676 Occupancy: 37 A=90V/cm F=90Hz Occupancy: 38 Conflict: 117

22. SONNIA BUCKYBALL NANOTUBE No field C: 0 O: 7 C: 0 O: 8 A=2V/cm F=90Hz C: 0 O: 6 O: 4 C: 0 A=90V/cm F=90Hz C: 0 O: 13 O: 7 C: 0

23. SONNIA • At zero field, tubulin shows a high occupancy and conflicts value. • With weak electric field tubulin maintains the same occupancy and conflicts. • With higher electric field the numberr of conflicts grows, to indicate that the structure organization decreases. • In absence of field the MT show a more restricted occupancy than tubulin, indicating a compactness in their spatial structure. • No changes with a weak electric field. • With a stronger electric field the conflicts decrease. • BB and NT show low occupancy and conflict values, due to a low number of components and to their extremely regular structure. • Although NT structure is bigger than BB, occupancy is low to indicate its strong stability.

24. ITSOM Tubulin No field Grafici in MatLab A=2V/cm F=90Hz A=90V/cm F=90Hz

25. ITSOM Microtubule No field A=2V/cm F=90Hz A=90V/cm F=90Hz

26. ITSOM Nanotube Buckyball Senza campo A=2V/cm F=90Hz A=90V/cm F=90Hz

27. The dynamical attractors generated by ITSOM reach analogous conclusions. Tubulin creates a steady attractor without field , that tends to become less structured in presence of field. MTs show the same strong organization as tubulin in absence of field, but on the contrary, their attractors tend to become more compact in presence of electric field, concentrating into a restricted configuration space (after a transient). CONCLUSIONS Buckyballs don’t change their regular behavior in presence of electric field NTs increase their spatial occupancy, but show an interesting increase of regularity in presence of field

28. The results obtained by SONNIA and ITSOM highlight the same behavior observed during the Ascalaph dynamical simulation. • In fact BBs and CNTs have an axial movement that becomes a regular pulse in presence of electric field. • Tubulin instead seems to have some internal forces that tend to oppose to a dynamical stabilization, and does not show any special behavior in presence of electric field. CONCLUSIONS

29. The MT, that without field tends to move off its starting position, after the application of field tends to return to its initial position and stabilize. • The interesting behavior of MT in presence of electric field confirms our findings about the electrical behavior of MTs. CONCLUSIONS • In spite of their structural complexity, MTs show a strong dynamic stability, that is significantly increased by the electric field. • This effect is in part present also in CNTs And confirms their known electrical properties.

30. These results confirm the experimental biophysical findings and motivate us to deepen our research on the structural properties of MTs. • This Artificial Intelligence approach can help explain the experimental evidences at a microcopic level, allowing a more correct interpretation of these findings. CONCLUSIONS

31. TUBULIN MICROTUBULE

32. CNT SIMULATION 90 V/cm field No field