Biology subjects

Guo, A. B.

Publications and source records attributed to Guo, A. B..

3 recordsLinked to original sources

bioRxiv2026

A Hybrid Physics-Deep Learning Framework for Combinatorial De Novo Design of Small-Molecule Binding Proteins

Engineering small-molecule binding proteins de novo remains a significant challenge as even advanced generative models struggle to model the atom-level details of protein-ligand interactions with sufficient accuracy. Higher experimental success rates have resulted from methods that explicitly scaffold predefined binding interactions into helical bundles. Here we introduce a scaffolding strategy that generalizes to alpha-beta architectures. By screening thousands of combinatorially assembled protein-ligand interactions against diverse de novo backbones with finely varied pocket geometries, the protocol allows for high-fidelity accommodation of target interaction geometries. Our protocol then integrates physics-based and deep learning methods for optimization of interfacial interactions and sequence-structure compatibility, considerably improving in silico design metrics. Applying this method to two chemically similar steroids achieved a notable experimental success rate (4/26 designs bind their targets), and NMR structures of two designs are in good agreement with design models. Our generalizable, atomically precise approach offers a robust framework for small-molecule binder design, effectively eliminating the need for high-throughput screening.

bioengineering↗

bioRxiv2025

A combinatorial mutational map of active non-native protein kinases by deep learning guided sequence design

Mapping protein sequence-function landscapes has either been limited to small steps (only few mutations) or to sequences similar to those already explored by evolution to maintain activity. Here, we overcome both limitations by applying deep-learning guided redesign to a natural protein tyrosine kinase to generate novel, functional sequences with highly combinatorial mutations. Using cell-free assays, we measure the activities and concentrations of 537 redesigned sequences, which differ from the wild-type by an average of 37 mutations while retaining activity in 85% of variants. These sequences sample 436 unique mutations at 76 different positions throughout the kinase domain. A simple regression model identifies key sequence determinants of function and predicts the function of unseen sequences. Our approach demonstrates how integrating deep-learning guided redesign, functional measurement at scale, and interpretable computational modelling enables functional exploration of highly combinatorial and sparse sequence-function landscapes at mutational scales not possible before.

biochemistry↗

bioRxiv2024

Deep learning guided design of dynamic proteins

Deep learning has greatly advanced design of highly stable static protein structures, but the controlled conformational dynamics that are hallmarks of natural switch-like signaling proteins have remained inaccessible to de novo design. Here, we describe a general deep-learning-guided approach for de novo design of dynamic changes between intra-domain geometries of proteins, similar to switch mechanisms prevalent in nature, with atom-level precision. We solve 4 structures validating the designed conformations, show microsecond transitions between them, and demonstrate that the conformational landscape can be modulated by orthosteric ligands and allosteric mutations. Physics-based simulations are in remarkable agreement with deep-learning predictions and experimental data, reveal distinct state-dependent residue interaction networks, and predict mutations that tune the designed conformational landscape. Our approach demonstrates that new modes of motion can now be realized through de novo design and provides a framework for constructing biology-inspired, tunable and controllable protein signaling behavior de novo.

bioengineering↗