Design and Evaluation of Peptide Binders : In silico evaluation and comparison of generative AI for de novo peptide binder design

Detta är en Uppsats för yrkesexamina på avancerad nivå från Uppsala universitet/Beräkningsbiologi och bioinformatik

Sammanfattning: Peptide binders are short proteins that bind to larger proteins. Due to peptide binders having high specificity and being cheap to synthesize, they are a prime candidate for drug design. Creating new proteins in silico can be divided into three steps: protein backbone generation, sequence design, and computational filtering. With the release of AlphaFold2 (AF2), protein structure prediction is possible with atomic accuracy, even for peptide-protein complexes. Structure predictions enables some important computational filtering, which saves time and resources before doing experimental validation. After the release of AF2 together with the advancements in generative AI, new computational methods for the first two design steps have been developed. In this report, three different methods for backbone generation and sequence design were evaluated and compared: EvoBind, RFdiffusion, and ProteinMPNN. The latter two were developed solely for protein design. However, their broad application capabilities allowed for peptide development, which was implemented in this report. In total, 5500 peptides for 55 different protein targets were designed by each method, with the purpose to evaluate the performance and identify advantages of the methods. Combining the three methods in unexplored ways allowed for additional evaluation as well as gaining deeper understanding of how the methods worked. Whilst not being one-shot design approaches, all methods used in the report showed potential of being able to design de novo peptide binders with varying degree of in silico success. The methods’ peptide design success rate ranged from 16% to 2.6%. The direct evolution approach applied with EvoBind generated most peptide binder backbones with close binding to the specified interfaces. Using the message passing neural network (MPNN) in ProteinMPNN, the sequences designed were optimized for binding affinity and resulted in sequences that were easier for AF2 to predict. The methods allow for the potential development of peptide binder therapeutics to become more cost- and time efficient, on the basis that AF2’s predictions are aligned with the expressed peptides’ bindings and structures. 

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