EVQLV reposted this
In the bustling world of scientific breakthroughs, it’s easy for foundational work to get overshadowed. Yet, it’s these under-recognized studies that often pave the way for major advancements. One such example is the 2021 paper (preprinted in 2019) by Rahmad Akbar and Victor Greiff which, in retrospect, is a cornerstone of the current explosion of language model applications in predicting antibody interactions. This paper isn’t just another academic finding; it’s a seismic shift from structure to language for predicting antibody-antigen binding, a critical factor in machine-learning powered antibody design. Read the paper here: https://lnkd.in/e36jjrrJ The crux of this paper lies in its revelation of a universal, compact, and immunity-specific motif vocabulary of paratope-epitope interactions. The introduction of a novel vocabulary of less than 104 motifs, distinct in their structure and function, marks a groundbreaking step in the application of language models to the predictability of antibody-antigen interactions. As someone deeply immersed in this field, I see this research as the starting point of truly remarkable innovation. It exemplified the beginnings of more sophisticated machine learning approaches in immunological research, which are currently exploding in number. Of course, the journey from paper to patient is long and winding, requiring further exploration and validation, especially against the backdrop of diverse antigenic structures and immune responses. In a world where cutting-edge becomes mainstream overnight, it’s crucial to acknowledge and celebrate the foundational work that often goes unnoticed. This paper is one such gem, highly influential yet widely under-recognized. It’s a testament to how foundational science, often obscured by the allure of new discoveries, remains the bedrock of revolutionary applications. As we continue to leverage language models for predicting antibody interactions, let’s not forget the roots that ground us in our quest for knowledge and innovation. Cheers to the additional authors, Geir Kjetil Sandve, Philippe Robert, Milena Pavlović, Jeliazko Jeliazkov, Igor Snapkow, Andrei Slabodkin, Cédric R. Weber, Lonneke Scheffer, Enkelejda Miho, Fridtjof Lund-Johansen, Yana Safonova #Antibodies #Science #DataScience #Immunology #MachineLearning #Innovation #FoundationalResearch