Rationalizing diverse binding mechanisms to the same protein fold: insights for ligand recognition and biosensor design.

The engineering of novel protein-ligand binding interactions, particularly for complex drug-like molecules, is an unsolved problem which could enable many practical applications of protein biosensors. In this work, we analyzed two engineer ed biosensors, derived from the plant hormone sensor PYR1, to recognize either the agrochemical mandipropamid or the synthetic cannabinoid WIN55,212-2. Using a combination of quantitative deep mutational scanning experiments and molecular dynamics simulations, we demonstrated that mutations at common positions can promote protein-ligand shape complementarity and revealed prominent differences in the electrostatic networks needed to complement diverse ligands. MD simulations indicate that both PYR1 protein-ligand complexes bind a single conformer of their target ligand that is close to the lowest free energy conformer. Computational design using a fixed conformer and rigid body orientation led to new WIN55,212-2 sensors with nanomolar limits of detection. This work reveals mechanisms by which the versatile PYR1 biosensor scaffold can bind diverse ligands. This work also provides computational methods to sample realistic ligand conformers and rigid body alignments that simplify the computational design of biosensors for novel ligands of interest.

Rationalizing Diverse Binding Mechanisms to the Same Protein Fold: Insights for Ligand Recognition and Biosensor Design.

The engineering of novel protein-ligand binding interactions, particularly for complex drug-like molecules, is an unsolved problem, which could enable many practical applications of protein biosensors. In this work, we analyzed two engineered biosensors, derived from the plant hormone sensor PYR1, to recognize either the agrochemical mandipropamid or the synthetic cannabinoid WIN55,212-2. Using a combination of quantitative deep mutational scanning experiments and molecular dynamics simulations, we demonstrated that mutations at common positions can promote protein-ligand shape complementarity and revealed prominent differences in the electrostatic networks needed to complement diverse ligands. MD simulations indicate that both PYR1 protein-ligand complexes bind a single conformer of their target ligand that is close to the lowest free-energy conformer. Computational design using a fixed conformer and rigid body orientation led to new WIN55,212-2 sensors with nanomolar limits of detection. This work reveals mechanisms by which the versatile PYR1 biosensor scaffold can bind diverse ligands. This work also provides computational methods to sample realistic ligand conformers and rigid body alignments that simplify the computational design of biosensors for novel ligands of interest.

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries.

Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of nine different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries.

Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of ten different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.

Common framework mutations impact antibody interfacial dynamics and flexibility.

Introduction: With the flood of engineered antibodies, there is a heightened need to elucidate the structural features of antibodies that contribute to specificity, stability, and breadth. While antibody flexibility and interface angle have begun to be explored, design rules have yet to emerge, as their impact on the metrics above remains unclear. Furthermore, the purpose of framework mutations in mature antibodies is highly convoluted. Methods: To this end, a case study utilizing molecular dynamics simulations was undertaken to determine the impact framework mutations have on the VH-VL interface. We further sought to elucidate the governing mechanisms by which changes in the VH-VL interface angle impact structural elements of mature antibodies by looking at root mean squared deviations, root mean squared fluctuations, and solvent accessible surface area. Results and discussion: Overall, our results suggest framework mutations can significantly shift the distribution of VH-VL interface angles, which leads to local changes in antibody flexibility through local changes in the solvent accessible surface area. The data presented herein highlights the need to reject the dogma of static antibody crystal structures and exemplifies the dynamic nature of these proteins in solution. Findings from this work further demonstrate the importance of framework mutations on antibody structure and lay the foundation for establishing design principles to create antibodies with increased specificity, stability, and breadth.

Regulatory Approved Monoclonal Antibodies Contain Framework Mutations Predicted From Human Antibody Repertoires.

Monoclonal antibodies (mAbs) are an important class of therapeutics used to treat cancer, inflammation, and infectious diseases. Identifying highly developable mAb sequences in silico could greatly reduce the time and cost required for therapeutic mAb development. Here, we present position-specific scoring matrices (PSSMs) for antibody framework mutations developed using baseline human antibody repertoire sequences. Our analysis shows that human antibody repertoire-based PSSMs are consistent across individuals and demonstrate high correlations between related germlines. We show that mutations in existing therapeutic antibodies can be accurately predicted solely from baseline human antibody sequence data. We find that mAbs developed using humanized mice had more human-like FR mutations than mAbs originally developed by hybridoma technology. A quantitative assessment of entire framework regions of therapeutic antibodies revealed that there may be potential for improving the properties of existing therapeutic antibodies by incorporating additional mutations of high frequency in baseline human antibody repertoires. In addition, high frequency mutations in baseline human antibody repertoires were predicted in silico to reduce immunogenicity in therapeutic mAbs due to the removal of T cell epitopes. Several therapeutic mAbs were identified to have common, universally high-scoring framework mutations, and molecular dynamics simulations revealed the mechanistic basis for the evolutionary selection of these mutations. Our results suggest that baseline human antibody repertoires may be useful as predictive tools to guide mAb development in the future.