In silico evolution of autoinhibitory domains for a PD-L1 antagonist using deep learning models

There has been considerable progress in the development of computational methods for designing protein-protein interactions, but engineering high-affinity binders without extensive screening and maturation remains challenging. Here, we test a protein design pipeline that uses iterative rounds of deep learning (DL)-based structure prediction (AlphaFold2) and sequence optimization (ProteinMPNN) to design autoinhibitory domains (AiDs) for a PD-L1 antagonist. With the goal of creating an anticancer agent that is inactive until reaching the tumor environment, we sought to create autoinhibited (or masked) forms of the PD-L1 antagonist that can be unmasked by tumor-enriched proteases. Twenty-three de novo designed AiDs, varying in length and topology, were fused to the antagonist with a protease-sensitive linker, and binding to PD-L1 was measured with and without protease treatment. Nine of the fusion proteins demonstrated conditional binding to PD-L1, and the top-performing AiDs were selected for further characterization as single-domain proteins. Without any experimental affinity maturation, four of the AiDs bind to the PD-L1 antagonist with equilibrium dissociation constants (KDs) below 150 nM, with the lowest KD equal to 0.9 nM. Our study demonstrates that DL-based protein modeling can be used to rapidly generate high-affinity protein binders.

Characterizing Protonation-Coupled Conformational Ensembles in RNA via pH-Differential Mutational Profiling with DMS Probing

RNA molecules undergo conformational transitions in response to cellular and environmental stimuli. Site-specific protonation, a fundamental chemical property, can alter the conformational landscape of RNA to regulate their functions. However, characterizing protonation-coupled RNA conformational ensembles on a large scale remains challenging. Here, we present pH-differential mutational profiling (PD-MaP) with dimethyl sulfate probing for high-throughput detection of protonation-coupled conformational ensembles in RNA. We demonstrated this approach on microRNA-21 precursor (pre-miR-21) and recapitulated a previously discovered A+-G-coupled conformational ensemble. Additionally, we identified a secondary protonation event involving an A+-C mismatch. We validated the occurrence of both protonation-coupled ensembles in pre-miR-21 using NMR relaxation dispersion spectroscopy. Furthermore, the application of PD-MaP on a library of well-annotated human primary microRNAs uncovered widespread protonation-coupled conformational ensembles, suggesting their potentially broad functions in biology.

In silico evolution of protein binders with deep learning models for structure prediction and sequence design

There has been considerable progress in the development of computational methods for designing protein-protein interactions, but engineering high-affinity binders without extensive screening and maturation remains challenging. Here, we test a protein design pipeline that uses iterative rounds of deep learning (DL)-based structure prediction (AlphaFold2) and sequence optimization (ProteinMPNN) to design autoinhibitory domains (AiDs) for a PD-L1 antagonist. Inspired by recent advances in therapeutic design, we sought to create autoinhibited (or masked) forms of the antagonist that can be conditionally activated by proteases. Twenty-three de novo designed AiDs, varying in length and topology, were fused to the antagonist with a protease sensitive linker, and binding to PD-L1 was tested with and without protease treatment. Nine of the fusion proteins demonstrated conditional binding to PD-L1 and the top performing AiDs were selected for further characterization as single domain proteins. Without any experimental affinity maturation, four of the AiDs bind to the PD-L1 antagonist with equilibrium dissociation constants (KDs) below 150 nM, with the lowest KD equal to 0.9 nM. Our study demonstrates that DL-based protein modeling can be used to rapidly generate high affinity protein binders.

Allosteric inhibition of TEM-1 β lactamase: microsecond molecular dynamics simulations provide mechanistic insights

β-lactam antibiotics target DD-transpeptidases, enzymes that perform the last step of bacterial cell-wall synthesis. To block the antimicrobial activity of these antibiotics, bacteria have evolved lactamases that render them inert. Among these, TEM-1, a class A lactamase, has been extensively studied. In 2004, Horn et al. described a novel allosteric TEM-1 inhibitor, FTA, that binds distant from the TEM-1 orthosteric (penicillin-binding) pocket. TEM-1 has subsequently become a model for the study of allostery. In the present work, we perform molecular dynamics simulations of FTA-bound and FTA-absent TEM-1, totaling ~3 μS, that provide new insight into TEM-1 inhibition. In one of the simulations, bound FTA assumed a conformation different than that observed crystallographically. We provide evidence that the alternate pose is physiologically plausible and describe how it impacts our understanding of TEM-1 allostery. This article is protected by copyright. All rights reserved.