Bioinformatics
The sequential and alphabetical nature of both DNA and proteins makes them a rich source of computational research. Study of these essential and foundational biomolecules provides a window into the evolutionary history of life and its chemistry, as well as the impressive structural diversity of protein folds. Our own research frequently occupies the interface between chemistry and biology, the interactions between large biological polymers and smaller molecules being fundamental to processes of life and disease alike. Since Margaret Dayhoff launched the modern science of bioinformatics in the 1960s by inventing the one-letter code as a memory saving innovation, the field has grown to be at the heart of our understanding of life and at the core of medical and biotechnological advances.
Since being fortunate enough to work with Dame Janet Thornton through the 1990s, my interest has been specifically in the structural side of bioinformatics. That is, understanding, categorising and predicting the structures of proteins, in order to understand and potentially adapt their functions, and the gain insight into natural history by inferring how molecular evolution has occurred. Protein structures, and therefore protein folding, lie at the core of structural bioinformatics. I am amongst those who have said that there are two protein folding problems:
- Simulating an actual folding pathway across the energy surface – very difficult and expensive. Limited by large number of possible conformations, folding being a rare event on MD timescale (in the real world approximately 10-3 – 1 sec).
- Protein structure prediction – where we care only about final answer and can use knowledge of sequence-structure relationships amongst known proteins. Generally less difficult, unless the protein has a very novel structure that’s not been seen before.
While the first of these remains largely open, AlphaFold has in essence solved the second, protein structure prediction; though the solution is sufficiently imperfect and incomplete to leave space for many years’ work. Bioinformatics now is responding to the seismic shock of one of the great problems being resolved, and making the most of the opportunities that this beings. At the same time, like all computational sciences, approaches to computation are being both challenged and advanced by the continuing rise of ML and AI methods.
Much of our work in these areas has centred on enzymes, their chemical functions and their evolutionary histories. In this post-AlphaFold era, we continue to seek out new research questions that can shed light on the rich and diverse repertoire of biochemistry. In this endeavour, we frequently collaborate with colleagues in Biology as well as Chemistry.
Some of our Recent Publications in Bioinformatics
Read, B., Mitchell, J. B. O. & da Silva, R. G., Allosteric activation unveils protein-mass modulation of ATP phosphoribosyltransferase product release, Communications Chemistry, 7:77 (2024) https://doi.org/10.1038/s42004-024-01165-8
Robust identification of interactions between heat-stress responsive genes in the chicken brain using Bayesian networks and augmented expression data, Videla Rodriguez, E. A., Mitchell, J. B. O. & Smith, V. A., Scientific Reports, 14: 9019 (2024) https://doi.org/10.1038/s41598-024-58679-3
Videla Rodriguez, E. A., Pértille, F., Guerrero-Bosagna, C., Mitchell, J. B. O., Jensen, P. & Smith, V. A., Practical application of a Bayesian network approach to poultry epigenetics and stress, BMC Bioinformatics, 23: 261.(2022) https://doi.org/10.1186/s12859-022-04800-0