The folding of proteins is an important biological process that determines the structure, role and functionality of proteins. It is often studied by molecular dynamics (MD) simulations, in order to obtain the folding trajectory of all the atoms in the system. To date, pure MD simulations require huge computational resources and are still unable to access the timescales of folding processes that have biological relevance. In my work, I am exploiting machine learning techniques and one recent AI milestone, Deepmind’s Alphafold, in order to create an advanced algorithm able to explore the folding trajectories within short computational times. It becomes possible to extract atomistic conformations from the folding pathways, and identify folding intermediates and long-lived states. This method can be used to facilitate the identification of biologically relevant protein conformations, later to be used for pharmacological targeting or biophysical studies.

1. Free University of Bolzano - Bozen
Faculty of Engineering
Alan Ianeselli
SFSCON 2023
Machine learning-driven simulation of
protein folding atomistic trajectories

2. Protein structure
~20 amino acids
They link by peptide bonds to form a polymer
aa1 aa2
peptide bond
A protein is a polymer of tens, hundreds or
thousands of amino acids

3. Protein structure
→ Unique conformation given a specific aminoacidic sequence
= the protein folding problem
Primary structure
(aa sequence)
Secondary structure
(local structures)
Tertiary structure
(3D conformation)
Quaternary structure
(protein complexes)

4. Protein structure
All-atom
representation
Ribbon
representation

5. Protein synthesis
→ How does the newly synthesized disordered
protein achieve its final conformation?
Author: Juan Gaertner
mRNA
newly synthesized protein
(disordered)
tRNA
carried
single aa
ribosome

6. Molecular dynamics (MD)
simulations
‣ Numerically solve Newton’s equations of motion over time
for each atom of the system

7. ‣ Forces are computed from force fields
Non-bonded interactions
Coulomb
Van der Waals
Molecular dynamics (MD)
simulations

8. Bonded interactions
Bond stretching Angle bending Dihedral torsions
‣ Forces are computed from force fields
Molecular dynamics (MD)
simulations

9. Molecular dynamics (MD)
simulations
Time
Unfolded
Folded
Folding time ~ microseconds (for small proteins)
= Weeks of simulation on supercomputers

10. DE Shaw et al., 2009
Anton Supercomputer
~50 µs/day for ~100’000 atoms
Molecular dynamics (MD)
simulations

11. ~100 years of simulation!!
DE Shaw et al., 2009
Anton Supercomputer
~50 µs/day for ~100’000 atoms
For example, Lysozyme in water (~100’000 atoms)
requires SECONDS to fold
Molecular dynamics (MD)
simulations

12. ~100 years of simulation!!
‣ Conventional MD approaches are unfeasible
DE Shaw et al., 2009
Anton Supercomputer
~50 µs/day for ~100’000 atoms
For example, Lysozyme in water (~100’000 atoms)
requires SECONDS to fold
Molecular dynamics (MD)
simulations

13. Timescales of macromolecules
ps ns µs ms s m h
Classical MD simulations “Relevant” biology

14. Timescales of macromolecules
ps ns µs ms s m h
Classical MD simulations “Relevant” biology
Coarse grained and approximated models

15. Timescales of macromolecules
ps ns µs ms s m h
Classical MD simulations “Relevant” biology
Coarse grained and approximated models
→ Sometimes unrealistic or unfalsifiable

16. What I have in mind?
A smart algorithm to study protein folding trajectories

17. What I have in mind?
How do you measure if it went “forward”?
A smart algorithm to study protein folding trajectories

18. Reaction Coordinate and
Collective Variables
Folded
Unfolded
Transition
State 1 Transition
State 2
Reaction coordinate (ideal)
Free
energy
(a.u.)

19. Reaction Coordinate and
Collective Variables
Folded
Unfolded
Transition
State 1 Transition
State 2
Reaction coordinate (ideal)
Free
energy
(a.u.)
Collective variable (measurable)

20. „Best“ collective variables (CVs)
for protein folding
CVs can quantify the difference
between two conformations

21. „Best“ collective variables (CVs)
for protein folding
1. Dihedral angles deviation from the native state
Syzonenko et al,
J. Chem. Inf. Model. 2020
CVs can quantify the difference
between two conformations

22. „Best“ collective variables (CVs)
for protein folding
1. Dihedral angles deviation from the native state
2. Inter-aa contact deviation from the native state
residue #
residue
#
Syzonenko et al,
J. Chem. Inf. Model. 2020
Beccara et al,
Phys. Rev. Lett. 2015
CVs can quantify the difference
between two conformations

23. „Best“ collective variables (CVs)
for protein folding
1. Dihedral angles deviation from the native state
2. Inter-aa contact deviation from the native state
3. Geometrical difference from the native state
residue #
residue
#
Syzonenko et al,
J. Chem. Inf. Model. 2020
Beccara et al,
Phys. Rev. Lett. 2015
CVs can quantify the difference
between two conformations

24. „Best“ collective variables (CVs)
for protein folding
1. Dihedral angles deviation from the native state
2. Inter-aa contact deviation from the native state
3. Geometrical difference from the native state
4. Deviation from Google’s Deepmind AlphaFold tensor
residue #
residue
#
Syzonenko et al,
J. Chem. Inf. Model. 2020
Beccara et al,
Phys. Rev. Lett. 2015
CVs can quantify the difference
between two conformations

25. Alphafold
4. Deviation from Google’s Deepmind
AlphaFold tensor
Google’s Deepmind Alphafold:
Latest AI milestone in the protein folding field
Training set: 170k protein structures
Able to predict more than 200 million of
structures
Unprecedented accuracy
→ able to predict the final conformation of any
aminoacidic sequence

26. 4. Deviation from Google’s Deepmind
AlphaFold tensor
Google’s Deepmind Alphafold:
-Input = aa sequence (text string)
...
-Sequence alignment (database comparison)
-Prediction of distance and angle between aa pairs
...
-Output = 3D protein structure
Alphafold

27. 4. Deviation from Google’s Deepmind AlphaFold tensor
One of the outputs of AlphaFold is the so-called distogram:
Tensor of distance bins x aa x aa
→ probability over distance between pairs of aa
example below: aa at position 5 (Asparagine) vs aa at position 19 (Aspartic acid)
→ machine-learned 170k protein
conformations
→ corresponds to a quasi-
chemical potential
Alphafold

28. Identify the optimal CV
Training set:
Very long folding trajectories obtained by the most powerful supercomputer (Anton)
→ 200µs of trajectories (Villin and Fip35 proteins)
Anton

29. UNFOLDED
FOLDED
Training set:
Very long folding trajectories obtained by the most powerful supercomputer (Anton)
→ 200µs of trajectories (Villin and Fip35 proteins)
Anton
Identify the optimal CV

30. Training set:
Very long folding trajectories obtained by the most powerful supercomputer (Anton)
→ 200µs of trajectories (Villin and Fip35 proteins)
Training set:
396k rows
4 features
Machine learning (PCA) Optimal CV identified
Principal Component 1
Principal
Component
2
Identify the optimal CV

31. Run MD folding algorithm
Towards the native state = along the optimal CV
NOTE: trajectories are unbiased

32. Results
Obtained the folding trajectories of 4 small proteins
My simulation time = 1 day per trajectory on a weak laptop
(Anton supercomputer would need weeks)
Brute force MD
~weeks
VS
Smart algorithm
~hours

33. Results
Fip35
Frame 87
Frame 30
Frame 2
Frame 0
Fip35 (35 aa, 562 atoms): 16 trajectories, 1 folded

34. Results
Beta Hairpin
Frame 0 Frame 5 Frame 20
Beta Hairpin (16 aa, 247 atoms): 3 trajectories, 1 folded
Frame 27

35. Results
TrpCage
Frame 0 Frame 15
Frame 40
Frame 74
TrpCage (20 aa, 304 atoms): 12 trajs, 4 folded

36. Results
Villin
Frame 45
Frame 0 Frame 10 Frame 25
Villin (35 aa, 583 atoms): 16 trajs, 4 “almost” folded

37. Results
Villin
Frame 45
Frame 0 Frame 10 Frame 25
Villin (35 aa, 583 atoms): 16 trajs, 4 “almost” folded
→ High energy barrier for the last helix?

38. Future works
Kmiecik et al,
Chem. Rev. 2016,
Coarse graining Explicit solvent

39. Method applications
Conformational
transitions and
point mutations
Misfolding
Identify intermediate
conformations

40. Thanks for the attention!
Prof. Pietro Faccioli
Master‘s supervisor
University of Milan
Prof. Emiliano Biasini
Collaborator
University of Trento
Prof. Diego Calvanese
Smart Data Factory
University of Bolzano