Date and Time:
December 12, 2020, 11 am – 1 pm SGT
Speaker:
Dr. Ashar Malik, Bioinformatics Institute, A*STAR, Singapore
Abstract:
The primary step in the process of characterising a novel protein sequence is by comparison with those already characterised. Similarity based functional characterisation and determination of an evolutionary origin can become a non-trivial problem for significantly diverged proteins. Protein structure, on the other hand, is considered to be conserved over longer evolutionary timescales. An evolutionary signal lost from the sequence may therefore still be retained within the conserved structure. Current times are seeing an exponential growth in protein structural data, presenting a unique opportunity to explore deep evolutionary questions. However a poor understanding of protein structural evolution prevents usage of classical sequence-based phylogenetic methods. Empirical distance-based methods have been employed, however the lack of a method to gauge the robustness of evolutionary relationships inferred limits their traction. A novel molecular dynamics-based bootstrap approach is presented which allows for the quantitative assessment of evolutionary relationships inferred from protein structural phylogenies. The novel ability to associate a measure of robustness with inferences allows protein structure-based phylogenies to uncover answers to deep evolutionary questions. In this talk sequence and structure-based phylogenetic methods will be looked at and (time permitting) a demonstration of structure-based phylogenetic analysis will be carried out.
About Speaker:
Dr. Ashar Malik completed his PhD in Computational Biochemistry from Massey University in 2018 where he developed the method to recover evolutionary information from protein structures. Currently, he is a postdoctoral research fellow at the Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR) in Singapore. His current work focuses on developing tools for the annotation of variations in the human genome.