De novo design of diverse small molecule binders and sensors using Shape Complementary Pseudocycles

Abstract

A general method for designing proteins to bind and sense any small molecule of interest would be widely useful. Due to the small number of atoms to interact with, binding to small molecules with high affinity requires highly shape complementary pockets, and transducing binding events into signals is challenging. Here we describe an integrated deep learning and energy based approach for designing high shape complementarity binders to small molecules that are poised for downstream sensing applications. We employ deep learning generated psuedocycles with repeating structural units surrounding central pockets; depending on the geometry of the structural unit and repeat number, these pockets span wide ranges of sizes and shapes. For a small molecule target of interest, we extensively sample high shape complementarity pseudocycles to generate large numbers of customized potential binding pockets; the ligand binding poses and the interacting interfaces are then optimized for high affinity binding. We computationally design binders to four diverse molecules, including for the first time polar flexible molecules such as methotrexate and thyroxine, which are expressed at high levels and have nanomolar affinities straight out of the computer. Co-crystal structures are nearly identical to the design models. Taking advantage of the modular repeating structure of pseudocycles and central location of the binding pockets, we constructed low noise nanopore sensors and chemically induced dimerization systems by splitting the binders into domains which assemble into the original pseudocycle pocket upon target molecule addition.One Sentence SummaryWe use a pseuodocycle-based shape complementarity optimizing approach to design nanomolar binders to diverse ligands, including the flexible and polar methotrexate and thyroxine, that can be directly converted into ligand-gated nanopores and chemically induced dimerization systems.

Peptide sequencing via reverse translation of peptides into DNA

“…(63) Alternatively, new binders could be generated using emerging computational design methods. (64,65) In summary, our work lays the groundwork for sequencing peptides by converting their sequence to DNA. With the expansion of the antibody arsenal and further refinement and streamlining of our chemical process, we believe that our method will be capable of highthroughput de novo protein sequencing of both unmodified and PTM-bearing proteins with singleamino acid resolution and single-molecule sensitivity, opening up exciting new opportunities for single-molecule proteomic analysis.…”

“…( 63 ) Alternatively, new binders could be generated using emerging computational design methods. ( 64, 65 )…”

Scaffold-Lab: Critical Evaluation and Ranking of Protein Backbone Generation Methods in A Unified Framework

“…Designing extremely short and large proteins holds particularly utility in specific contexts, such as the former conductive for some peptide drugs 75 or mini-binders 76,77 and the latter for large multi-domain proteins 78 or symmetrical protein assemblies 79,80 . While a number of peptide 81 and binder design 82 methods have been proposed to address these needs, the design of large proteins remains an enduring challenge. Moreover, methods focused on generating protein complexes or dynamic backbones, which are key to the design of enzymes or allosteric proteins, are still in a nascent stage.…”

Sculpting conducting nanopore size and shape through de novo protein design

“…The design principles presented here provide a solution to the long-standing problem of engineering quiet monomeric pores ( 17 , 18 , 45 , 50 ) that has limited the use of monomeric integral TMBs such as OmpG as sensors by fusing analyte-recognition motifs ( 51 , 52 ) or biotin-bound ( 53 , 54 ) antibodies in the solvent-exposed loops ( 7 ). As illustrated in the accompanying manuscript ( 55 ), the designed nanopores can be converted into ligand-gated channels with considerably lower noise and more comprehensible signal analysis than previously engineered channels.…”