Congratulations to the first round of PeerJ Award winners!

With the PeerJ Awards program in full swing, we are eager to highlight the winners so far. Here is a brief look at their award-winning contributions and their next steps in open science.

By teaming up with a number of conferences to offer these awards, we are making it as easy as possible for organizers to reward excellence in science and signal to the wider research community that open science is better science. Learn more here and get in touch if you are a conference organizer and are looking to offer a ‘Best Contribution’ award for open science –

Sebastian Daberdaku – Best Contribution BBCC2019

Can you tell us a bit about yourself and your research interests?

I am a post-doc researcher at the Department of Information Engineering – University of Padova. I have a computer engineering background and I got my PhD in bioinformatics with a dissertation entitled “Protein contour modelling and computation for complementarity detection and docking”. My research interests are in the areas of structural bioinformatics and computational biology. More specifically, I have worked on algorithms for the computation of high-resolution voxelised protein surfaces and on descriptors that can jointly evaluate the geometrical and physico-chemical similarity/complementarity of protein surface portions for protein-protein interaction interface prediction and docking. In the computational biology area I am studying the effect of single point mutations on metabolic pathways with techniques such as Flux Balance Analysis.

Can you briefly explain the research you presented on at BBCC?

I developed a novel purely geometric algorithm for the detection of ligand-binding protein pockets and cavities which is based on the Euclidean Distance Transform (EDT). The EDT can be used to compute the Solvent-Excluded surface for any given probe sphere radius value at high resolutions and in a timely manner. The algorithm is adaptive to the specific candidate ligand: it computes two voxelised protein surfaces using two different probe sphere radii depending on the shape of the candidate ligand. The pocket regions consist of the voxels located between the two surfaces, which exhibit a certain minimum depth value from the outer surface. The distance map values computed by the EDT algorithm during the second surface computation can be used to elegantly determine the depth of each candidate pocket and to rank them accordingly. Cavities on the other hand, are identified by scanning the inside of the protein for voids. The algorithm determines and outputs the best k candidate pockets and cavities, i.e. the ones that are more likely to bind to the given ligand. The method was applied to a representative set of protein-ligand complexes and their corresponding unbound protein structures to evaluate its ligand binding site prediction capabilities, and was shown to outperform most of the previously developed purely geometric pocket and cavity search methods.

What are your next steps? How will you continue to build on this research?

The algorithm I developed is purely geometric and does not take into account any physico-chemical properties that ligand-binding pockets and cavities might have. As a next step, I plan to identify a set of physico-chemical properties to be mapped on the voxelised representation of the pocket/cavity surface which will help discriminate ligand-binding sites from the rest of the candidates. I believe that this methodology could be useful for structure-based drug design and for protein-ligand docking applications.

Maria Elena Martino – Best Talk IGC Portugal

Can you tell us a bit about yourself and your research interests?

I am a researcher at the Department of Comparative Biomedicine and Food Science of the University of Padua (Italy). I am a microbiologist and my research interests lie in understanding the processes underpinning the evolution of bacteria and their adaptation to new environments. I am currently working on deciphering the evolutionary mechanisms governing the mutualistic interaction between bacteria and their hosts, to understand how evolution shapes their constant association and reciprocal benefits.

Can you briefly explain the research you presented on at the IGC Symposium?

At the IGC Symposium I presented the results of my postdoctoral work that I conducted in the laboratory of François Leulier at the Institute of Functional Genomics of Lyon (France). I have been working on the mutualism between Drosophila melanogaster and Lactobacillus plantarum. We recently showed that, in this type of mutualism, the host diet, and not the host per se, is the driving force of the emergence of the mutualistic association between the two partners. In other words, bacterial adaptation to the host nutritional environment is the foremost step underlying the emergence of Drosophila/Lactobacillus mutualism. Once mutualistic bacteria are adapted to the host diet, they become beneficial for their animal partner.

What are your next steps? How will you continue to build on this research?

I am now starting my own lab at the University of Padua, aiming at deciphering the evolutionary mechanisms shaping the beneficial relationships between animals and bacteria. In particular, I am interested in disclosing the role of the animal host in the evolution of its mutualistic bacteria, how ecology drives the establishment and evolution of mutualisms in nature, and how bacteria affect the evolution of their animal partner. This will contribute to our understanding of animal/microbe relationships, including those occurring in humans.

Congratulations to the PeerJ Award winners and looking forward to working with more conferences to recognize excellent science in 2019.