# A Topological Selection of Folding Pathways from Native States of Knotted Proteins

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. The Knotoid Distribution Describes a Protein’s Entanglement

#### 2.2. KnotoEMD: A Topological Distance to Distinguish Geometric Features of Knotted Proteins

#### 2.3. Folding Hypotheses for Knotted Proteins

#### 2.4. Methods

## 3. Results

#### 3.1. Sequence Similarity from the Geometry of Proteins’ Native States

#### 3.2. KnotoEMD Captures Subtle Geometric Differences between Double-Loop and Single-Loop Open Trefoils

#### 3.3. Local Geometric Features Suggest Different Folding Pathways for Trefoil Proteins

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

PDB | Protein Data Bank |

2D | 2-dimensional |

3D | 3-dimensional |

1-L | Single-loop open trefoil configuration |

2-L | Double-loop open trefoil configuration |

AOTCases | N-acetylornithine transcarbamylase |

OTCases | Ornithine Carbamoyltransferase |

$C\alpha $ | alpha-carbon |

UMAP | Uniform Manifold Approximation and Projection for Dimension Reduction |

SMOG | Structure-based Models for Biomolecules |

DNA | Deoxyribonucleic acid |

RNA | Ribonucleic acid |

tRNA | Transfer RNA |

PL | Piecewise-linear |

## Appendix A. Trefoil Proteins

#### Appendix A.1. Restriction to Knot Core

#### Appendix A.2. Clustering by Sequence-Similarity

`scipy.cluster.hierarchy.linkage`[50]. We used a distance threshold of 70% dissimilarity to produce a clustering of the 517 proteins. We extracted the nine largest clusters from these clusters to study.

#### Appendix A.3. List of Non-Redundant Proteins

## Appendix B. Double-Loop and Single-Loop Open Trefoil Configurations

#### Appendix B.1. The Twelve 2-L Configurations and Local Moves between Them

- The mutual positions of the blue end and the two loops (indicated by a string of length two in R and L).
- The sign (+ or −) of the bottom crossing in each diagram.
- The (signed) number of (positive or negative) twists in the two loops (indicated by two integers a and b);

**Figure A1.**2-L configurations and local moves between them. The 12 2-L configurations differ by the number of twists in the loops and their relative positions. These configurations can be divided into groups related by local transformations that do not change the overall geometry of the curves. The 4 configurations on the left-hand side are the ones with lowest average crossing number. We will refer to them as simple 2-L configurations.

#### Appendix B.2. Generating Our Dataset of Trajectories

- Step 1: create the representative trajectories. For each of the 12 2-L configurations we created two different representative piecewise-linear (PL) curves using the software KnotPlot [51]. In the same way, we created four different 1-L PL curves representing minimal (thus, admitting a projection with only 3 crossings) geometrical embeddings of open trefoils. All the curves were drawn to be quite shallow (i.e., with most of the curve involved in the knot).
- Step 2: take different lengths of each curve. We then subdivide each trajectory in three different ways, to obtain curves of length approximately (here the length is measured as the number of segments in the PL curve) 80, 160 and 240 (this is to match the different lengths of trefoil proteins’ knotted cores). In this way, we obtain a total of six different PL curves for each 2-L configuration, and 12 curves representing a 1-L configuration, for a total of 84 curves.
- Step 3: perturb each curve. We then generate 10 different trajectories for each of the 84 curves by performing numerical perturbations. The minimal distance ${d}_{m}$ between vertices of each trajectory is determined. Each vertex is perturbed uniformly within a sphere of radius ${d}_{m}$ centred at the vertex. This step adds some randomness to a curve without breaking the geometry of the loops. The perturbation script is available in our GitHub repository [46].

## Appendix C. Computation of Knotoid Distributions and KnotoEMD

#### Appendix C.1. Knotoid Distributions

#### Appendix C.2. KnotoEMD

#### Appendix C.3. Distance Matrices

**Figure A2.**Pairwise KnotoEMD on trefoil proteins. On the left, knotoid distributions are computed from the entire chain, while on the right, from the knot core of each protein. Proteins are ordered according to sequence similarity: proteins in the same sequence similarity cluster are placed close to each other. The top noisy square represents pairwise distances between singletons (i.e., proteins that are not similar to any other protein considered). Note that pairwise distances between proteins in the same sequence similarity group are very low, showing that our distance recovers clustering by sequence similarity. This remains true when we only analyse the core, although the results are noisier in this case.

**Figure A3.**KnotoEMD of 2-L configurations, 1-L configurations and trefoil proteins cores. All the pairwise distances shown here are computed from the knotoid distributions of the knot core of each curve. Pairwise distances of our generated trajectories are shown on the left, and distance between trajectories and trefoil proteins are on the right. Note that KnotoEMD clusters each group of 2-L configurations, and the group of 1-L configurations. Similarly, distances from trajectories to proteins respect both the sequence similarity subdivision of proteins and the subdivision of trajectories.

- The simple 2-L trajectories, in the following order:
- the 60 trajectories for RR(+,0,0);
- the 60 trajectories for RL(+,0,-1);
- the 60 trajectories for LR(+,-1,0);
- the 60 trajectories for LL(+,-1,-1).

- The first group of complex 2-L trajectories, in the following order:
- the 60 trajectories for LR(-,1,2);
- the 60 trajectories for LL(-,1,1);
- the 60 trajectories for RR(-,2,2);
- the 60 trajectories for RL(-,2,1).

- The second group of complex 2-L trajectories, in the following order:
- the 60 trajectories for LR(-,0,-2);
- the 60 trajectories for RR(-,1,-2);
- the 60 trajectories for RR(-,-2,1);
- the 60 trajectories for RL(-,-2,0).

- The 120 1-L trajectories.

#### Appendix C.4. UMAP Projections

`n_neighbors`: a constraint on the size of local neighbourhood considered in the dimension reduction.`min_dist`: the minimum distance separating points in the reduced dimension space.`metric`: the metric used to compare the points of the input space (in our case rows of a large distance matrix).`n_components`: the target dimension of the low dimensional space to which we project.

`n_neighbors`controls the emphasis on local vs global structure preservation in dimension reduction, and was set to 60 representing ∼$10\%$ the number of points. The parameter

`min_dist`affects the ability for points to tightly pack in the low dimensional embedding, and was set to $0.3$ to balance fine and broad topological structure. Our plots were not highly sensitive to the choice of either the

`n_neighbors`or

`min_dist`parameter. We set

`metric = ‘precomputed’`to indicate that our input is a distance matrix rather than a sample-feature matrix. We produced both 2 and 3 dimensional plots [46].

#### Appendix C.5. The Knotted/Unknotted Homologous Pair

**Figure A4.**KnotoEMD of knotted and unknotted proteins with sequence similar to 3KZN. The deeply knotted AOTCases with PDB entry 3KZN has sequence highly similar to several unknotted proteins. The heat map shows pairwise distances for proteins in this group. The first 17 entries represent the knotted ones, while the following ones are unknotted. The pair knotted AOTCases/unknotted OTCases, whose structures are almost superimposable [4], is highlighted.

## References

- Berman, H.; Henrick, K.; Nakamura, H.; Markley, J.L. The worldwide Protein Data Bank (wwPDB): Ensuring a single, uniform archive of PDB data. Nucleic Acids Res.
**2007**, 35, D301–D303. [Google Scholar] [CrossRef] [PubMed][Green Version] - Dabrowski-Tumanski, P.; Rubach, P.; Goundaroulis, D.; Dorier, J.; Sułkowski, P.; Millett, K.C.; Rawdon, E.J.; Stasiak, A.; Sułkowska, J.I. KnotProt 2.0: A database of proteins with knots and other entangled structures. Nucleic Acids Res.
**2019**, 47, D367–D375. [Google Scholar] [CrossRef] [PubMed][Green Version] - Sułkowska, J.I.; Sułkowski, P.; Onuchic, J. Dodging the crisis of folding proteins with knots. Proc. Natl. Acad. Sci. USA
**2009**, 106, 3119–3124. [Google Scholar] [CrossRef][Green Version] - Dabrowski-Tumanski, P.; Stasiak, A.; Sulkowska, J.I. In search of functional advantages of knots in proteins. PLoS ONE
**2016**, 11, e0165986. [Google Scholar] [CrossRef] [PubMed] - Jackson, S.E. Why are there knots in proteins? Topol. Geom. Biopolym.
**2020**, 746, 129. [Google Scholar] - Jackson, S.E.; Suma, A.; Micheletti, C. How to fold intricately: Using theory and experiments to unravel the properties of knotted proteins. Curr. Opin. Struct. Biol.
**2017**, 42, 6–14. [Google Scholar] [CrossRef][Green Version] - Mallam, A.L. How does a knotted protein fold? FEBS J.
**2009**, 276, 365–375. [Google Scholar] [CrossRef] - Sułkowska, J.I.; Noel, J.K.; Onuchic, J.N. Energy landscape of knotted protein folding. Proc. Natl. Acad. Sci. USA
**2012**, 109, 17783–17788. [Google Scholar] [CrossRef][Green Version] - Li, W.; Terakawa, T.; Wang, W.; Takada, S. Energy landscape and multiroute folding of topologically complex proteins adenylate kinase and 2ouf-knot. Proc. Natl. Acad. Sci. USA
**2012**, 109, 17789–17794. [Google Scholar] [CrossRef][Green Version] - Chwastyk, M.; Cieplak, M. Cotranslational folding of deeply knotted proteins. J. Phys. Condens. Matter
**2015**, 27, 354105. [Google Scholar] [CrossRef][Green Version] - Covino, R.; Škrbić, T.; Beccara, S.A.; Faccioli, P.; Micheletti, C. The role of non-native interactions in the folding of knotted proteins: Insights from molecular dynamics simulations. Biomolecules
**2014**, 4, 1–19. [Google Scholar] [CrossRef][Green Version] - Lim, N.C.; Jackson, S.E. Mechanistic insights into the folding of knotted proteins in vitro and in vivo. J. Mol. Biol.
**2015**, 427, 248–258. [Google Scholar] [CrossRef][Green Version] - Taylor, W.R. Protein knots and fold complexity: Some new twists. Comput. Biol. Chem.
**2007**, 31, 151–162. [Google Scholar] [CrossRef] - Najafi, S.; Potestio, R. Folding of small knotted proteins: Insights from a mean field coarse-grained model. J. Chem. Phys.
**2015**, 143, 12B606_1. [Google Scholar] [CrossRef] [PubMed][Green Version] - Noel, J.K.; Sułkowska, J.I.; Onuchic, J.N. Slipknotting upon native-like loop formation in a trefoil knot protein. Proc. Natl. Acad. Sci. USA
**2010**, 107, 15403–15408. [Google Scholar] [CrossRef][Green Version] - Wang, I.; Chen, S.Y.; Hsu, S.T.D. Folding analysis of the most complex Stevedore’s protein knot. Sci. Rep.
**2016**, 6, 1–11. [Google Scholar] [CrossRef] [PubMed][Green Version] - He, C.; Li, S.; Gao, X.; Xiao, A.; Hu, C.; Hu, X.; Hu, X.; Li, H. Direct observation of the fast and robust folding of a slipknotted protein by optical tweezers. Nanoscale
**2019**, 11, 3945–3951. [Google Scholar] [CrossRef] [PubMed] - Mallam, A.L.; Jackson, S.E. Knot formation in newly translated proteins is spontaneous and accelerated by chaperonins. Nat. Chem. Biol.
**2012**, 8, 147–153. [Google Scholar] [CrossRef] - Dabrowski-Tumanski, P.; Piejko, M.; Niewieczerzal, S.; Stasiak, A.; Sulkowska, J.I. Protein knotting by active threading of nascent polypeptide chain exiting from the ribosome exit channel. J. Phys. Chem. B
**2018**, 122, 11616–11625. [Google Scholar] [CrossRef] - Sulkowska, J.I. On folding of entangled proteins: Knots, lassos, links and θ-curves. Curr. Opin. Struct. Biol.
**2020**, 60, 131–141. [Google Scholar] [CrossRef] - Flapan, E.; He, A.; Wong, H. Topological descriptions of protein folding. Proc. Natl. Acad. Sci. USA
**2019**, 116, 9360–9369. [Google Scholar] [CrossRef][Green Version] - Bölinger, D.; Sułkowska, J.I.; Hsu, H.P.; Mirny, L.A.; Kardar, M.; Onuchic, J.N.; Virnau, P. A Stevedore’s protein knot. PLoS Comput. Biol.
**2010**, 6, e1000731. [Google Scholar] [CrossRef] [PubMed][Green Version] - Turaev, V. Knotoids. Osaka J. Math.
**2012**, 49, 195–223. [Google Scholar] - Goundaroulis, D.; Dorier, J.; Stasiak, A. Knotoids and protein structure. Topol. Geom. Biopolym.
**2020**, 746, 185. [Google Scholar] - Pele, O.; Werman, M. Fast and robust earth mover’s distances. In Proceedings of the 2009 IEEE 12th International Conference on Computer Vision, Kyoto, Japan, 27 September–4 October 2009; pp. 460–467. [Google Scholar]
- Piejko, M.; Niewieczerzal, S.; Sulkowska, J.I. The Folding of Knotted Proteins: Distinguishing the Distinct behavior of Shallow and Deep Knots. Isr. J. Chem.
**2020**, 60, 713–724. [Google Scholar] [CrossRef] - Chwastyk, M.; Cieplak, M. Multiple folding pathways of proteins with shallow knots and co-translational folding. J. Chem. Phys.
**2015**, 143, 07B611_1. [Google Scholar] [CrossRef] - Rolfsen, D. Knots and Links; American Mathematical Society: Providence, RI, USA, 2003; Volume 346. [Google Scholar]
- Taylor, W.R. A deeply knotted protein structure and how it might fold. Nature
**2000**, 406, 916–919. [Google Scholar] [CrossRef] [PubMed] - Dorier, J.; Goundaroulis, D.; Rawdon, E.J.; Stasiak, A. Open Knots. In Encyclopedia of Knot Theory; Chapman and Hall/CRC: Boca Raton, FL, USA, 2020; Chapter 84. [Google Scholar]
- Millett, K.C.; Rawdon, E.J.; Stasiak, A.; Sułkowska, J.I. Identifying knots in proteins. Biochem. Soc. Trans.
**2013**, 41, 533–537. [Google Scholar] [CrossRef] - Sułkowska, J.I.; Rawdon, E.J.; Millett, K.C.; Onuchic, J.N.; Stasiak, A. Conservation of complex knotting and slipknotting patterns in proteins. Proc. Natl. Acad. Sci. USA
**2012**, 109, E1715–E1723. [Google Scholar] [CrossRef][Green Version] - King, N.P.; Yeates, E.O.; Yeates, T.O. Identification of rare slipknots in proteins and their implications for stability and folding. J. Mol. Biol.
**2007**, 373, 153–166. [Google Scholar] [CrossRef] - Goundaroulis, D.; Gügümcü, N.; Lambropoulou, S.; Dorier, J.; Stasiak, A.; Kauffman, L. Topological models for open-knotted protein chains using the concepts of knotoids and bonded knotoids. Polymers
**2017**, 9, 444. [Google Scholar] [CrossRef][Green Version] - Dorier, J.; Goundaroulis, D.; Benedetti, F.; Stasiak, A. Knoto-ID: A tool to study the entanglement of open protein chains using the concept of knotoids. Bioinformatics
**2018**, 34, 3402–3404. [Google Scholar] [CrossRef][Green Version] - Gügümcü, N.; Kauffman, L.H. New invariants of knotoids. Eur. J. Comb.
**2017**, 65, 186–229. [Google Scholar] [CrossRef] - Barbensi, A.; Goundaroulis, D. f-distance of knotoids and protein structure. Proc. R. Soc. A
**2021**, 477, 20200898. [Google Scholar] [CrossRef] - Tubiana, L.; Orlandini, E.; Micheletti, C. Probing the entanglement and locating knots in ring polymers: A comparative study of different arc closure schemes. Prog. Theor. Phys. Suppl.
**2011**, 191, 192–204. [Google Scholar] [CrossRef] - Community, B.O. Blender—A 3D Modelling and Rendering Package; Blender Foundation, Stichting Blender Foundation: Amsterdam, The Netherlands, 2018. [Google Scholar]
- McInnes, L.; Healy, J.; Saul, N.; Großberger, L. UMAP: Uniform Manifold Approximation and Projection. J. Open Source Softw.
**2018**, 3, 861. [Google Scholar] [CrossRef] - Abraham, M.J.; Murtola, T.; Schulz, R.; Páll, S.; Smith, J.C.; Hess, B.; Lindahl, E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX
**2015**, 1, 19–25. [Google Scholar] [CrossRef][Green Version] - Clementi, C.; Nymeyer, H.; Onuchic, J.N. Topological and energetic factors: What determines the structural details of the transition state ensemble and “en-route” intermediates for protein folding? An investigation for small globular proteins. J. Mol. Biol.
**2000**, 298, 937–953. [Google Scholar] [CrossRef] [PubMed] - Noel, J.K.; Levi, M.; Raghunathan, M.; Lammert, H.; Hayes, R.L.; Onuchic, J.N.; Whitford, P.C. SMOG 2: A versatile software package for generating structure-based models. PLoS Comput. Biol.
**2016**, 12, e1004794. [Google Scholar] [CrossRef] [PubMed][Green Version] - Noel, J.K.; Whitford, P.C.; Onuchic, J.N. The shadow map: A general contact definition for capturing the dynamics of biomolecular folding and function. J. Phys. Chem. B
**2012**, 116, 8692–8702. [Google Scholar] [CrossRef] [PubMed][Green Version] - Kufareva, I.; Abagyan, R. Methods of protein structure comparison. In Homology Modeling; Springer: Berlin/Heidelberg, Germany, 2011; pp. 231–257. [Google Scholar]
- Yerolemou, N.; Vipond, O.; Goundaroulis, D. KnotoEMD for Proteins. 2021. Available online: https://github.com/nyerolemou/proteins-knotoEMD (accessed on 1 June 2021).
- Rabbani, G.; Ahmad, E.; Khan, M.V.; Ashraf, M.T.; Bhat, R.; Khan, R.H. Impact of structural stability of cold adapted Candida antarctica lipase B (CaLB): In relation to pH, chemical and thermal denaturation. RSC Adv.
**2015**, 5, 20115–20131. [Google Scholar] [CrossRef] - Zhao, Y.; Dabrowski-Tumanski, P.; Niewieczerzal, S.; Sulkowska, J.I. The exclusive effects of chaperonin on the behavior of proteins with 5
_{2}knot. PLoS Comput. Biol.**2018**, 14, e1005970. [Google Scholar] [CrossRef] [PubMed][Green Version] - Rose, Y.; Duarte, J.M.; Lowe, R.; Segura, J.; Bi, C.; Bhikadiya, C.; Chen, L.; Rose, A.S.; Bittrich, S.; Burley, S.K.; et al. RCSB Protein Data Bank: Architectural Advances Towards Integrated Searching and Efficient Access to Macromolecular Structure Data from the PDB Archive. J. Mol. Biol.
**2020**, 166704. [Google Scholar] [CrossRef] - Virtanen, P.; Gommers, R.; Oliphant, T.E.; Haberland, M.; Reddy, T.; Cournapeau, D.; Burovski, E.; Peterson, P.; Weckesser, W.; Bright, J.; et al. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nat. Methods
**2020**, 17, 261–272. [Google Scholar] [CrossRef] [PubMed][Green Version] - Scharein, R.G.; Booth, K.S. Interactive knot theory with KnotPlot. In Multimedia Tools for Communicating Mathematics; Springer: Berlin/Heidelberg, Germany, 2002; pp. 277–290. [Google Scholar]
- Pele, O.; Werman, M. A linear time histogram metric for improved sift matching. In Computer Vision—ECCV 2008; Springer: Berlin/Heidelberg, Germany, 2008; pp. 495–508. [Google Scholar]
- Goundaroulis, D.; Dorier, J.; Stasiak, A. A systematic classification of knotoids on the plane and on the sphere. arXiv
**2019**, arXiv:1902.07277. [Google Scholar]

**Figure 1.**Knotoid Distribution and Forbidden Moves. (

**A**) An open curve in 3D space with three different planar projections. Each projection defines a knotoid diagram, and different projections might yield non-equivalent knotoid diagrams. By considering all of the possible planar projections, we can associate a distribution of knotoids to the initial open curve. (

**B**) The distribution of knotoids of the curve in panel (

**A**) is visualised by colouring each point of a 2-sphere surrounding the curve according to the topological type of the corresponding projection. This picture is obtained using the software Knoto-ID. (

**C**) The distribution of knotoids of the curve in panel (

**A**). A minimal diagram is shown for each knotoid type. The knotoid distribution of each curve analysed in this work was computed using Knoto-ID. In practice, 5000 projections are computed for each curve, and this is known to be sufficient to properly approximate the continuous distribution. (

**D**) Knotoid diagrams related by forbidden moves. Any knotoid diagram can be untangled by a finite sequence of forbidden moves.

**Figure 2.**Folding Pathways for Trefoil Proteins. (

**A**) A schematic representation of the single-loop folding theories. According to these theories, the formation of a twisted loop (native or not) is followed by either threading one terminus through the loop (top) or flipping the loop over the terminus (bottom). (

**B**) A schematic representation of the double-loop folding theory. Two loops are formed and then approached by one terminus. This is followed by a combination of threading and flipping, in either order. (

**C**) Simple 2-L configurations and moves between them. Two diagrams in the same row are related by a flipping of the green arc, while diagrams in a column are related by a twist of the red loop. Note that performing any of these moves is possible by fixing the majority of the curve and changing only one small portion. The eight other 2-L configurations are divided similarly into two groups of four. More details can be found in the Appendix B. (

**D**) An example of a generated 2-L trajectory, rendered using Blender [39]. (

**E**) An example of a generated 1-L trajectory, rendered using Blender [39].

**Figure 3.**KnotoEMD clusters trefoil proteins by sequence similarity. (

**A**) A dendrogram obtained from single-linkage hierarchical clustering of all trefoil proteins based on sequence similarity scores from PDB. (

**B**) A UMAP projection of the space of trefoil proteins with KnotoEMD distances, when considering the entire protein backbone. Non-redundant proteins are indicated with thicker boundaries. We highlight the groups corresponding to proteins with sequences similar to 6RQQ, 2ZNC, 1J85, 4QEF, 1FUG, 6QQW, 3KZK, 1UAK, and 2V3J, as these are the nine largest in our subdivision. Note that 2ZNC and 4QEF are all carbonic anhydrases with very similar structure. Similarly, 1UAK and 6QQW are both tRNA-methyltransferases. All of the deeply knotted proteins are contained in the cluster on the left, and this cluster contains no shallow knotted trefoil proteins. Depth is a dominant factor in determining the knotoid distribution, explaining why KnotoEMD separates proteins with a very deep knot from everything else. Finer structure in the space of proteins as detected by KnotoEMD can be seen in 3D-plots available in our GitHub repository [46].

**Figure 4.**Local geometric features suggest different folding pathways for trefoil proteins. (

**A**) 2D UMAP projection of the pairwise KnotoEMD between 1-L trajectories, 2-L trajectories and non-redundant trefoil proteins. Recall that 2-L configurations can be divided into three groups, in which configurations differ by small local transformations. 2-L configurations LL(-,1,1), LR(-,1,2), RL(-,2,1), RR(-,2,2) and RR(-,1,-2), LR(-,0,-2), RL(-,-2,0), and RR(-,-2,1) form the two complex groups and LL(+,-1,-1), LR(+,-1,0), RR(+,0,0), and RL(+,0,-1) the simple one. The clustering by KnotoEMD respects this subdivision almost perfectly. Similarly, 1-L configurations are clustered together, with the exception of a bottom-left group, which is separated due to oversimplification. The proteins appear distributed among the simple 2-L and the 1-L clusters. The small isolated clusters contains 1-L configurations and deeply knotted proteins whose core is oversimplified due to perturbation or noise in knot core reduction. (

**B**) The knot cores of a shallow protein in the 2-L cluster (a human carbonic anhydrase, PDB entry 4QEF), a deeply knotted protein in the 1-L cluster (a tRNA-n1g37 methyltransferase, PDB entry 6QQW) and a shallow protein in the 1-L cluster (a S-adenosylmethionine synthetase, PDB entry 1FUG).

**Figure 5.**The scheme of the folding pathway of a carbonic anhydrase (PDB code 4QEF). Folding starts from the unfolded conformation (

**left panel**). Next, one of the loops is formed (

**middle-left panel**) followed by the formation of the second loop (

**middle right panel**). Finally, both of the tails are structured, and the order of the tail attachment differs between simulations. The placement of the tails leads to the native structure (

**right panel**).

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**MDPI and ACS Style**

Barbensi, A.; Yerolemou, N.; Vipond, O.; Mahler, B.I.; Dabrowski-Tumanski, P.; Goundaroulis, D. A Topological Selection of Folding Pathways from Native States of Knotted Proteins. *Symmetry* **2021**, *13*, 1670.
https://doi.org/10.3390/sym13091670

**AMA Style**

Barbensi A, Yerolemou N, Vipond O, Mahler BI, Dabrowski-Tumanski P, Goundaroulis D. A Topological Selection of Folding Pathways from Native States of Knotted Proteins. *Symmetry*. 2021; 13(9):1670.
https://doi.org/10.3390/sym13091670

**Chicago/Turabian Style**

Barbensi, Agnese, Naya Yerolemou, Oliver Vipond, Barbara I. Mahler, Pawel Dabrowski-Tumanski, and Dimos Goundaroulis. 2021. "A Topological Selection of Folding Pathways from Native States of Knotted Proteins" *Symmetry* 13, no. 9: 1670.
https://doi.org/10.3390/sym13091670