Abstract: Traditionally, the birth of new proteins in biological entities has been attributed to gene duplication and subsequent specialization. However, it has been recognized over the last decade that new genes may materialize de novo from parts of the genome that were previously non-coding. Such is the case of the common rice Oryza sativa subspecies japonica, in which a comparative genomics study that analyzed 13 related Oryza species found at least 175 de novo open reading frames with significantly similar ancestral non-coding sequences, and with an unexpectedly high rate of generation and retention of de novo genes (Zhang et al., 2019). However, the structure of these proteins and their relevance to rice biological pathways is still unknown. In this talk, I will give a brief overview of the bioinformatic approaches that we are using at Stefan Arold’s lab for predicting the structural features of these de novo sequences, with a focus on 3D structure and biophysical properties. I will explain how these techniques help us prioritize candidates from such comparative and/or meta-genomics studies for experimental testing, and how, through some curious findings, they might improve our understanding of the applications and limitations of one of the latest structure prediction algorithms.

Bio: Obtained my B.S. in Biotechnology from Tecnológico de Monterrey, Mexico, in 2017. Then completed a M.S. in Bioscience in KAUST, at the Structural Biology and Engineering lab, with Stefan Arold as my advisor. I am now pursuing a PhD in Bioscience in Stefan Arold's lab, where I am focusing in methods to model protein structures and predict the impact of mutations in the structure and function of proteins with clinical relevance, as well as applying similar methods to predict the 3D structure of de novo proteins and the catalytic fitness of new proteins of environmental and commercial interest such as oceanic PETases.

Abstract: Traditionally, the birth of new proteins in biological entities has been attributed to gene duplication and subsequent specialization. However, it has been recognized over the last decade that new genes may materialize de novo from parts of the genome that were previously non-coding. Such is the case of the common rice Oryza sativa subspecies japonica, in which a comparative genomics study that analyzed 13 related Oryza species found at least 175 de novo open reading frames with significantly similar ancestral non-coding sequences, and with an unexpectedly high rate of generation and retention of de novo genes (Zhang et al., 2019). However, the structure of these proteins and their relevance to rice biological pathways is still unknown. In this talk, I will give a brief overview of the bioinformatic approaches that we are using at Stefan Arold’s lab for predicting the structural features of these de novo sequences, with a focus on 3D structure and biophysical properties. I will explain how these techniques help us prioritize candidates from such comparative and/or meta-genomics studies for experimental testing, and how, through some curious findings, they might improve our understanding of the applications and limitations of one of the latest structure prediction algorithms.

Bio: Obtained my B.S. in Biotechnology from Tecnológico de Monterrey, Mexico, in 2017. Then completed a M.S. in Bioscience in KAUST, at the Structural Biology and Engineering lab, with Stefan Arold as my advisor. I am now pursuing a PhD in Bioscience in Stefan Arold's lab, where I am focusing in methods to model protein structures and predict the impact of mutations in the structure and function of proteins with clinical relevance, as well as applying similar methods to predict the 3D structure of de novo proteins and the catalytic fitness of new proteins of environmental and commercial interest such as oceanic PETases.