Discrete Differential Geometry of *n*-Simplices and Protein Structure Analysis ()

Naoto Morikawa

Genocript, Zama, Japan.

**DOI: **10.4236/am.2014.516237
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Genocript, Zama, Japan.

This paper proposes
a novel discrete differential geometry of *n*-simplices.
It was originally developed for protein structure analysis. Unlike previous
works, we consider connection between space-filling *n*-simplices. Using cones of an integer lattice, we introduce
tangent bundle-like structure on a collection of *n*-simplices naturally. We have applied the mathematical framework
to analysis of protein structures. In this paper, we propose a simple encoding
method which translates the conformation of a protein backbone into a 16-valued
sequence.

Share and Cite:

Morikawa, N. (2014) Discrete Differential Geometry of *n*-Simplices and Protein Structure Analysis. *Applied Mathematics*, **5**, 2458-2463. doi: 10.4236/am.2014.516237.

1. Introduction

This paper proposes a novel discrete differential geometry of n-simplices, which is originally developed for protein structure analysis [1] [2] . Discrete differential geometry is the study of discrete equivalents of the geometric notions and methods of classical differential geometry [3] [4] . It mainly deals with polygonal curves and polyhedral surfaces whose properties are analogous to continuous counterparts, where the smooth theory is established as limit of the discrete theory.

On the other hand, we consider connection between space-filling n-simplices. We define gradient of n-simplices and obtain a flow of n-simplices by piling up n-cubes diagonally. Second derivative along a trajectory is given as a binary-valued sequence for any n (>1). As a result, we could encode the shape of n-dimensional objects if we approximate them by sweeping the occupied area with a trajectory of n-simplices.

Proteins are a sequence of amino acids linked by peptide bonds and fold into a unique three-dimensional structure in nature. Protein backbone structure is usually studied via manually-curated hierarchical classification [5] [6] but there also exist studies on differential geometric approach for protein structure analysis [7] -[11] . As for discrete differential geometry of protein backbones, proteins are usually represented as a polygonal chain, where curvature and torsion are defined at each vertex [7] .

In our method, protein backbone structures are approximated by a trajectory of 3-simplices (tetrahedrons). Particularly we consider second derivative along a trajectory to encode local protein structures. Our method performs comparably with more sophisticated but more time-consuming methods which are specifically designed for protein structure analysis [12] [13] . In the following, we first describe the discrete differential geometry of n-simplices. Then, we apply the mathematical framework to analysis of protein structures and propose a simple encoding method which translates the conformation of a protein backbone into a 16-valued sequence.

2. Discrete Differential Geometry of n-Simplices

2.1. Basic Ideas

Recall that an n-simplex is an n-dimensional polytope which is the convex hull of its n + 1 vertices. As an introduction, we would consider the case of n = 2 before we give the definitions in the general case. In the case of n = 2, we obtain a flow of 2-simplices (triangles) by piling up unit cubes in the three-dimensional Euclidean space as shown in Figure 1(a).

First, cubes are pilled up in the direction of, where three upper faces of each unit cube are divided into two triangles by a diagonal line. Then, the diagonal lines on the faces of the cubes form a drawing on the surface of the “peaks and valleys” of cubes. By projecting the drawing onto a hyperplane that is perpendicular to, a flow of triangles would be obtained. For example, the grey “slant” triangles on the surface specify the closed trajectory of the grey “flat” triangles on the hyperplane in Figure 1(a).

2.2. Differential Structure

Because of convenience, we use monomials to represent coordinates of points. That is, point is denoted by monomial of n indeterminates for integer n (n > 1).

First of all, we give the definition of “slant” and “flat” n-simplices. Let’s consider n-cube in the n-dimensional Euclidean space. Note that the facets of n-cubes are -dimensional unit cubes. To obtain “slant” n-simplices, we divide each of the n facets which contain origin into -simplices along diagonal as follows.

Definition 1. For any integer n > 1, n-dimensional standard lattice is the collection of all integer points of, i.e.,

.

Definition 2. For any integer n > 1, the collection of all slant n-simplices is defined by

where is the n-th symmetric group and denotes the convex hull of n points

i.e.,

.

Figure 1. Discrete differential geometry of 2-simplices: (a) “Peaks and valleys” of cubes; (b) Tangent bundle-like structure; (c) Local trajectory; (d) Smoothness condition.

Definition 3. For any integer n > 1, the collection of all flat n-simplices is defined as the quotient of by “shift operator” on, i.e,

where.

Definition 4. Tangent bundle-like structure is defined on as the quotient of by, i.e.,

For example, triangle specifies element of over element of (Figure 1(b)).

Definition 5. Gradient of is defined as a monomial of degree n − 1, i.e.,

.

For simplicity, we occasionally denote by, where. That is.

Note that we could identify with by one-to-one correspondence

.

A gradient over a flat n-simplex specifies a local trajectory at the flat n-simplex as follows.

Definition 6. The local trajectory specified by, where, is a collection of three adjacent flat n-simplices

where and.

For example, specifies local trajectory at (Figure 1(c)). We would obtain a flow on by patching these local trajectories together.

To define the “second derivative” along a trajectory, we would impose a kind of “smoothness condition” on local trajectories.

Definition 7. (Smoothness condition). Let be a section of on

where. Suppose that. Then, we impose the following conditions on the local trajectory:

Remark 8. For any two consecutive n-simplices on a trajectory, there exist and

s.t. and. Monomial is uniquely determined by and is included in both and for any section of on. That is, corresponds to the contact surface between two consecutive slant n-simplices.

As an example, let’s consider the case of n = 2 shown in Figure 1(d), where the gradient at current triangle is. Then, the gradient at next triangle could assume either or. Otherwise, we couldn’t connect the two consecutive slant triangles over the trajectory “smoothly” as shown in the figure.

2.3. Tangent Cone and Section of TB_{n}

Now we give the definition of the “peaks and valleys” of n-simplices (Figure 1(a)).

Definition 9. For, tangent cone of is defined as follows:

.

Definition 10. For tangent cone , boundary surfaces is defined by

where and, for,

.

Then, specifies a unique slant n-simplex over each and we obtain a section of on.

Definition 11. is the section of on induced by tangent cone, i.e., for,

.

Note that tangent cone induces a section of on by. Patching the local trajectories specified by together, we would obtain a flow on. As an example, let’s consider the “peaks and valleys” shown in Figure 1(a), which is induced by.

Let’s start from triangle (grey) and move downward (Figure 2): and

. specifies local trajectory at.

Since we move downward, next triangle is and we obtain. Then,

specifies local trajectory at and next traingle is. Continuing the process, we obtain a closed trajectory of length 10.

Finally, we consider variation of gradient, i.e., “second derivative”, along a trajectory. Thanks for the smoothness condition, variation of gradient along a trajectory could be specified as a binary valued sequence.

Definition 12. Let be a trajectory induced by for tangent cone. Then, “second derivative” of along is defined as a {U, D}-valued function:

where and.

Then, we could encode the conformation of a trajectory by the second derivative along the trajectory. As an example, let’s consider the trajectory of Figure 2 again. First, set any initial value:. Then, since the first two triangles and have the same gradient,. The value of the second derivative is D until, where it is changed to U because the gradient of is different from that of. Continuing the process, we obtain a binary sequence of length 10, DDDUDUUUDU, which describes the shape swept by the trajectory of triangles.

3. Encoding of Protein Backbone Structure

In the case of n = 3, we obtain a flow of 3-simplices (tetrahedrons), which is used for protein structure analysis. In this section we propose a simple encoding method which translates the conformation of a protein backbone into a sequence of letters from a 16-letter alphabet (called D^{2} codes), using the second derivative along trajectories of tetrahedrons.

First, we consider all the fragments of five amino-acids occurred in a protein. Each fragment is approximated by a tetrahedron sequence of length five, where we permit translation and rotation during the process to absorb irregularity inherent in actual protein structures.

Next, we compute the second derivative along the tetrahedron sequences to obtain binary-valued sequences of length five. We assign the binary-valued sequences, which are denoted as a base-32 number, to the center amino-acid of the corresponding fragment. For example, DDDUD is denoted by “2”, DUDDU is denoted by “9”, DUDUD is denoted by “A”, and so on.

Figure 2. Closed trajectory of 2-simplices induced by.

Figure 3. D^{2}-encoding of a protein (transferase 1RKL).

Then, we obtain a one-dimensional representation of protein backbone structure by arranging the base-32 numbers in the order the corresponding amino-acids appear in the protein. See [1] for detailed description of the algorithm.

Figure 3 shows an example of D^{2}-encoding of a protein. As you see, our method captures successfully not only recurring structural features of the protein (strand, turn, caps, helix), but also distortions (such as kink) as well.

4. Discussion

In this paper, we first describe the discrete differential geometry of n-simplices. Then, we apply the mathematical framework to analysis of protein structures and propose a simple encoding method which translates the conformation of a protein backbone into a 16-valued sequence.

Unlike previous works, our version of discrete differential geometry studies connection between space-filling n-simplices. Considering cones of an integer lattice, we have introduced tangent bundle-like structure on n-simplices naturally. On notable consequence is the smoothness condition, i.e., restriction on variation of gradient along a trajectory. In particular, we could encode the shape of n-dimensional objects if we approximate them by sweeping the occupied area with a trajectory of n-simplices.

As for protein structure analysis, since we do not use clustering analysis to encode local structures, our approach not only provides a intuitively understandable description of protein structures, but also covers wide varieties of distortions. Our method performs comparably with more sophisticated but more time-consuming methods which are specifically designed for protein structure analysis. In SHREC’10 Protein Model Classification we achieved results comparable to more sophisticated methods, using the length of the longest common subsequence as the measure of structural similarity [12] . At homology level of CATH95 data set, our method performs best among all the individual classifiers considered in [13] .

Conflicts of Interest

The authors declare no conflicts of interest.

[1] | Morikawa, N. (2007) Discrete Differential Geometry of Tetrahedrons and Encoding of Local Protein Structure. arXiv: math.CO/0710.4596. |

[2] | Morikawa, N. (2011) A Novel Method for Identification of Local Conformational Changes in Proteins. arXiv: q-bio. BM/1110.6250. |

[3] | Bobenko, A.I. and Suris, Yu.B. (2008) Discrete Differential Geometry. Integrable Structure. Graduate Studies in Mathematics, 98, 404 p. arXiv:math/0504358. |

[4] |
Meyer, M., Desbrun, M., Schroder, P. and Barr, A.H. (2003) Discrete Differential-Geometry Operators for Triangulated 2-Manifolds. In: Hege, H.-C. and Polthier, K., Eds., Visualization and Mathematics III, Springer-Verlag, Berlin, 35-58. http://dx.doi.org/10.1007/978-3-662-05105-4_2 |

[5] |
Sillitoe, I., et al. (2013) New Functional Families (FunFams) in CATH to Improve the Mapping of Conserved Functional Sites to 3D Structures. Nucleic Acids Research, 41, D490-D498. http://dx.doi.org/10.1093/nar/gks1211 |

[6] |
Murzin, A.G., Brenner, S.E., Hubbard, T. and Chothia, C. (1995) SCOP: A Structural Classification of Proteins Database for the Investigation of Sequences and Structures. Journal of Molecular Biology, 247, 536-540.
http://dx.doi.org/10.1016/S0022-2836(05)80134-2 |

[7] |
Rackovsky, S. and Scheraga, H.A. (1978) Differential Geometry and Polymer Conformation. 1. Comparison of Protein Conformations. Macromolecules, 11, 1168-1174. http://dx.doi.org/10.1021/ma60066a020 |

[8] |
Louie, A.H. and Somorjai, R.L. (1982) Differential Geometry of Proteins: A Structural and Dynamical Representation of Patterns. Journal of Theoretical Biology, 98, 189-209. http://dx.doi.org/10.1016/0022-5193(82)90258-2 |

[9] |
Montalvao, R.W., Smith, R.E., Lovell, S.C. and Blundell, T.L. (2005) CHORAL: A Differential Geometry Approach to the Prediction of the Cores of Protein Structures. Bioinformatics, 21, 3719-3725.
http://dx.doi.org/10.1093/bioinformatics/bti595 |

[10] |
Gorielyn, A., Hausrath, A. and Neukirch, S. (2008) The Differential Geometry of Proteins and Its Applications to Structure Determination. Biophysical Reviews and Letters, 3, 77-101. http://dx.doi.org/10.1142/S1793048008000629 |

[11] | Hu, S., Lundgren, M. and Niemi, A.J. (2011) The Discrete Frenet Frame, Inflection Point Solitons and Curve Visualization with Applications to Folded Proteins. arXiv:q-bio.BM/1102.5658. |

[12] | Mavridis, L., et al. (2010) SHREC’10 Track: Protein Model Classification. Proceedings of Eurographics Workshop on 3D Object Retrieval, 117-124. |

[13] |
Boujenfa, K. and Limam, M. (2012) Consensus Decision for Protein Structure Classification. Journal of Intelligent Learning Systems and Applications, 4, 216-222. http://dx.doi.org/10.4236/jilsa.2012.43022 |

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