A Nigerian researcher, Dr. Gideon Gyebi, has emphasized that emerging computational technologies can accelerate the discovery of new antibiotics and help combat the growing global challenge of antimicrobial resistance (AMR).
Dr. Gyebi, a specialist in Computational and Systems Biology, shared insights from his recent research during a presentation in Abuja. His study, titled “Computational profiling of terpenoids for putative dual-target leads against Staphylococcus aureus Penicillin-Binding Protein 2a and Beta-Lactamase,” explores how artificial intelligence (AI), machine learning, and molecular modelling can speed up drug discovery.
The research, presented at the Durban University of Technology in South Africa, focuses on Staphylococcus aureus (S. aureus) — a major cause of hospital-acquired infections and a key symbol of antimicrobial resistance.
According to Gyebi, the rise of Methicillin-Resistant Staphylococcus aureus (MRSA) has created a serious global health problem by limiting the effectiveness of commonly used antibiotics.
“By focusing on S. aureus, this study directly addresses an urgent need for innovative strategies to combat antimicrobial resistance,” he explained. “Computational biology is changing the way we approach medicine. By simulating how potential drugs interact with bacterial proteins, we can guide experiments more intelligently and make discoveries faster.”
He highlighted that computational approaches offer speed, cost-effectiveness, and precision that are difficult to achieve through traditional laboratory methods alone. Thousands of compounds can be virtually screened within hours, whereas lab testing the same number of compounds could take months or even years.
Computational modelling, he added, enables researchers to observe how drug molecules interact with bacterial proteins at the molecular level — something that is challenging to replicate in the lab. Promising compounds can then be prioritized for experimental validation, saving both time and resources.
“These tools don’t replace laboratory experiments; they complement them by providing a roadmap that makes lab work more targeted and efficient,” Gyebi said. “Emerging computational approaches increase our chances of overcoming antibiotic resistance by accelerating discovery and reducing trial-and-error.”
The study specifically examined terpenoids, natural compounds that have the potential to block two critical bacterial defence systems simultaneously — Penicillin-Binding Protein 2a (PBP2a) and Beta-lactamase.
He explained that Beta-lactamase is an enzyme that breaks down beta-lactam antibiotics before they can work, while PBP2a is a modified protein in MRSA that has low affinity for most beta-lactam drugs. This “double defence” mechanism makes S. aureus especially difficult to treat.
“Even if one mechanism is bypassed, the other still protects the bacterium,” Gyebi noted. “A dual-target strategy could restore the effectiveness of common antibiotics that S. aureus has developed resistance to.”
By simultaneously targeting both Beta-lactamase and PBP2a, antibiotics stand a far better chance of effectively treating MRSA infections.
The World Health Organization (WHO) has identified antimicrobial resistance as one of the top ten global public health threats. If left unchecked, it is projected to cause up to 10 million deaths annually by 2050.
With more than 70 publications in Scopus and Web of Science and over a thousand citations, Dr. Gyebi is among Africa’s rising scientists leveraging technology to address urgent health challenges. He believes that integrating computational biology, AI, and biotechnology could revolutionize antibiotic development and offer faster solutions to the threat of drug-resistant infections.