Connecting people, proteins and technology through network analysis

By Phillip Ndagurwa

 

The notion of ‘six degrees of separation’ is a fundamental network analysis concept in social sciences, in which persons who are quite removed from one another are linked by a chain of, at most, six shared acquaintances. Therefore, we find ourselves using the phrase "Such a small world" to express our astonishment when we discover that we share a friend with another person. Beyond that, would it not be even more astonishing to consider amino acids as social groups of people with different roles and interactions? What's in store if proteins mutate or a few amino acids change? 

On 02 August 2022, Professor Özlem Taştan Bishop, Director of the Research Unit in Bioinformatics (RUBi), presented a lecture titled "Six degrees of computational drug design: from social sciences to human health", which explored the historical development and basics of network analysis and its application to computational drug design. This lecture followed her conferral of the 2020 Vice Chancellor's Distinguished Senior Research Award, which recognises an established staff member of indisputable academic status, engaged in research and more general scholarly activity.

"Today, I will take you on a bridge from social sciences to biological sciences," Professor Taştan Bishop said before guiding attendees on a journey into the world of proteins as drug targets and the interactions of amino acids that make a protein.

Professor Taştan Bishop, who was born and grew up in Turkey, is a multidisciplinary scientist with a background in physics, mathematics, genetics, biochemistry and structural biology/bioinformatics.

She followed her passion for science in different countries, cultures and education systems before settling at Rhodes University in 2009. During her time at the University, Professor Taştan Bishop has frequently appeared on the Annual Research Report's Top 30 Researchers List.

Her research into residue networks dates back to late 2012, when the genetic and environmental basis for human diseases was of particular interest, and necessitated the development of bioinformatics capacity in Africa. Over the years, she adopted a different way of looking at mutations and a more dynamic approach to protein analysis. As her methodology evolved, she introduced new metrics and combined metric analysis concepts.

"Network analysis is a part of graph theory in mathematics, and the beauty of it is its universal language," said Professor Taştan Bishop.

During her lecture, the Professor expanded on the applications of Dynamic Residue Networks, which include: 

1. Mutations and genetic disease
2. Allostery, which involves the identification of allosteric hot spot residues and allosteric modulator design
3. Drug resistance mechanisms
4. Understanding mutation effects for future drug design

Under drug resistance mechanisms, she highlighted the importance of gaining insight into the underlying molecular processes of mutations' impacts on protein therapeutic targets when researching medications. Such understanding may help in circumventing the drug resistance problem. For example, her collaborative work on HIV Protease Inhibitors addresses the research question, "Is there a common resistance mechanism against all protease inhibitors?" Here, the outcome after residue analysis and statistical testing was indeed an observation of a collective and common mechanism.

The presentation also highlighted Professor Taştan Bishop's recent COVID-19 research project. Brilliant minds came together to demonstrate the importance of considering mutations in drug/inhibitor identification and how mutations change the interactions and communication pathways between the virus's spike protein and the viral receptor, i.e. the human angiotensin-converting enzyme, which is required for SARS-CoV-2 infection and spread. 

As a scientist, in the short term, Professor Taştan Bishop said she would like to impact drug discovery by establishing further approaches and tools to circumvent mutation-related effects in the context of drug resistance and drug toxicity and efficacy. In the long term, she hopes to be known for Afrocentric computational drug discovery research, specifically aimed at improving scientific competencies, which can lead to better healthcare and stimulate economic growth on the African continent.