Protein engineering in the computational age: An open source framework for exploring mutational landscapes in silico

• Published in: Engineering biology 2023-12, Vol.7 (1-4), p.29-38
• Main Authors: Bamezai, Shirin, Maresca di Serracapriola, Giovanni, Morris, Freya, Hildebrandt, Rasmus, Amil, Marc Augustine Sojerido, Xin, Haoran, Montogomery‐Johnson, Jay, Bi, Youzi, Ding, Yuancheng, Gibbs, Fontaine, Serafini, Alessandro, Klug, Emese, Srinath, Anvita, Wang, Tairan, Maresca di Serracapriola, Allegra, Almarei, Mohamed, Astorga, Joaquin Caro, Ledesma‐Amaro, Rodrigo
• Format: Article
• Language: English
• Subjects: Accessibility; Accuracy; Amino acids; biochemical engineering; Bioinformatics; Biology; Computer applications; Deep learning; Design; E coli; Engineering; Engineering education; Engineers; Genetic engineering; Ligands; Mutagenesis; Original Research; Protein engineering; Proteins; Software; synthetic biology
• ISSN: 2398-6182; 2398-6182
• DOI: 10.1049/enb2.12028

The field of protein engineering has seen tremendous expansion in the last decade, with researchers developing novel proteins with specialised functionalities for a range of uses, from drug discovery to industrial biotechnology. The emergence of computational tools and high‐throughput screening technology has substantially sped up the process of protein engineering. However, much of the expertise required to engage in such projects is still concentrated in the hands of a few specialised individuals, including computational biologists and structural biochemists. The international Genetically Engineered Machine (iGEM) competition represents a platform for undergraduate students to innovate in synthetic biology. Yet, due to their complexity, arduous protein engineering projects are hindered by the resources available and strict timelines of the competition. The authors highlight how the 2022 iGEM Team, ‘Sporadicate’, set out to develop InFinity 1.0, a computational framework for increased accessibility to effective protein engineering, hoping to increase awareness and accessibility to novel in silico tools. Protein engineering has seen a large growth over the past decade fuelled by innovation in AI and machine learning tools. However, these often remain inaccessible to the average researcher. The authors present their experience as the ‘Sporadicate iGEM Team’ in the development of an in silico framework to increase awareness and accessibility to novel in silico tools.