## Section: New Results

### Protein Structure

Participants : Rumen Andonov, Douglas Goncalves, Dominique Lavenier, Mathilde Le Boudic-Jamin, Antonio Mucherino.

**The molecular distance geometry problem**

The distance geometry is the problem of finding an embedding of a simple weighted undirected graph $G=(V,E,d)$ in a given dimension $K>0$. Its most interesting application arises in biology, where the conformation of molecules such as proteins can be identified by embedding a graph (representing the molecular structure and some distance information) in dimension 3. Since some years, we are working on the discretization of the distance geometry. This year, the research developed in 4 main directions, that will be briefly detailed in the following paragraphs.

The majority of the work was performed on the so-called *discretization orders*, which are particular orders for the atoms of a molecule that allow for satisfying the discretization assumptions, i.e. they allow to discretize the search domain of the problem. Finding discretization orders is therefore an important pre-processing step for the solution of distance geometry problems. In fact, not only the identification of an atomic order allowing for the discretization is important, but also the identification of orders that are able to optimize some objectives that make the solution to the problem easier to perform. In this context, with both international and local partners, we worked on discretization orders that can be identified automatically in polynomial time [13] , we worked on suitable orders for the protein side chains [10] , and we studied some objectives to be optimized in discretization orders [38] .

The algorithm that we mostly employ for the solution of distance geometry problems that can be discretized is the Branch & Prune (BP) algorithm. It recursively constructs the discretized search domain (a tree) and verifies the feasibility of the computed atomic positions. When all available distances are exact, all candidate positions for a given atom can be enumerated. This is however not possible in presence of interval distances, because a continuous subset of positions can actually be computed for the corresponding atoms. The focus of the work in [22] is on a new scheme for an adaptive generation of a discrete subset of candidate positions from this continuous subset. The generated candidate positions do not only satisfy the distances employed in the discretization process, but also additional distances that might be available (the so-called pruning distances).

Since the BP algorithm can loose in performance when dealing with large molecules containing several interval distances, we worked this year on a variation of the algorithm named BetaMDGP [29] . This is a work in collaboration with Korean researchers. The BetaMDGP algorithm is based on the concept of beta-complex, which is a geometric construct extracted from the quasi-triangulation derived from the Voronoi diagram of atoms.

From the theoretical side, we worked on two main directions. First, we proved that, in discretizable distance geometry problems where all available distances are exact, the total number of solutions is always a power of two. This is related to the fact that the discrete search space contains several symmetries [18] . Secondly, we tried to summarize in [37] the current issues for efficiently solving real-life instances of the distance geometry.

Finally, the work we performed during the last years, including another important results from other colleagues currently working on this topic, was summarized in an extensive survey on the discretization of the distance geometry [17] .

**Distance measure between Protein structure**

We propose here a new distance measure for comparing two protein structures based on their contact map representations (CMO). This novel measure (max-CMO metric), satisfies all properties of a metric on the space of protein representations. Having a metric in that space allows to avoid pairwise comparisons on the entire database and thus to significantly accelerate exploring the protein space compared to non metric spaces. We show on a gold-standard classification benchmark sets that our exact k-nearest neighbor scheme classifies up to 95% and 99% of queries correctly. Our k-NN classification thus provides a promising approach for the automatic classification of protein structures based on contact map overlap. [26] , [30]

**Local similarity of protein structure**

Finding similarities between protein structures is a main goal in molecular biology. Most of the existing tools preserve order and only find single alignments even when multiple similar regions exist. We propose a new seed-based approach that discovers multiple pairs of similar regions. Its computational complexity is polynomial and it comes with a quality guarantee that the returned alignments have both Root Mean Squared Deviations (coordinate-based as well as internal-distances based) lower than a given threshold, if such exists. We do not require the alignments to be order preserving, which makes our algorithm suitable for detecting similar domains when comparing multi-domain proteins. And because the search space for non-sequential alignments is much larger than for sequential ones, the computational burden is addressed by using both a coarse-grain level parallelism and a fine-grain level parallelism. [33]