Tinberg, C. E. | Search bioRxiv

bioRxiv2025

Mapping the evolution of computationally designed protein binders

Computational protein design enables the generation of binders that target specific epitopes on proteins. However, current approaches often require substantial screening from which hits require further affinity maturation. Methods for experimentally improving designed proteins and exploring their sequence-affinity landscapes could therefore streamline the development of high-affinity binders and inform future design strategies. Here, we use OrthoRep, a system for continuous hypermutation in vivo, to drive the evolution of computationally designed mini protein binders ("minibinders") that target a mammalian receptor. Despite their small sizes (59-72 amino acids), we successfully affinity matured multiple minibinders through strong selection for improved binding and also sampled new regions of minibinder fitness landscapes through extensive neutral drift. One evolved minibinder variant was used to construct a combinatorially complete sequence-affinity map for its six affinity increasing mutations, which revealed nearly full additivity in their contributions to binding. Another minibinder was subjected to both deep mutational scanning and extensive evolution under weak selection, resulting in an evolutionarily diverged collection of binder sequences that revealed non-additive relationships among mutations. Our results highlight that the affinity of computationally designed binders can be rapidly increased through evolution and provide a scalable approach for the evolutionary exploration and subsequent mapping of sequence-affinity landscapes. We suggest that this work will complement protein binder design both as a reliable experimental optimization process and as a vehicle for generating new training data.

synthetic biology↗

bioRxiv2022

Towards generalizable prediction of antibody thermostability using machine learning on sequence and structure features

Over the last three decades, the appeal for monoclonal antibodies (mAbs) as therapeutics has been steadily increasing as evident with FDAs recent landmark approval of the 100th mAb. Unlike mAbs that bind to single targets, multispecific biologics (bsAbs) with their single-chain variable fragment (scFv) modules have garnered particular interest owing to the advantage of engaging distinct targets. Despite their exquisite specificity and affinity, the relatively poor thermostability of these scFv modules often hampers their development as a potential therapeutic drug. In recent years, engineering antibody sequences to enhance their stability by mutations has gained considerable momentum. As experimental methods for antibody engineering are time-intensive, laborious, and expensive, computational methods serve as a fast and inexpensive alternative to conventional routes. In this work, we show two machine learning methods - one with pre-trained language models (PTLM) capturing functional effects of sequence variation, and second, a supervised convolutional neural network (CNN) trained with Rosetta energetic features - to better classify thermostable scFv variants from sequence. Both these models are trained over temperature-specific data (TS50 measurements) derived from multiple libraries of scFv sequences. In this work, we show that a sufficiently simple CNN model trained with energetic features generalizes better than a pre-trained language model on out-of-distribution (blind) sequences (average Spearman correlation coefficient of 0.4 as opposed to 0.15). Further, we demonstrate that for an independent mAb with available thermal melting temperatures for 20 experimentally characterized thermostable mutations, these models trained on TS50 data could identify 18 residue positions and 5 identical amino-acid mutations showing remarkable generalizability. Our results suggest that such models can be broadly applicable for improving the biological characteristics of antibodies. Further, transferring such models for alternative physico-chemical properties of scFvs can have potential applications in optimizing large-scale production and delivery of mAbs or bsAbs.

bioinformatics↗