Structure‐based design of combinatorial mutagenesis libraries

Abstract

The development of protein variants with improved properties (thermostability, binding affinity, catalytic activity, etc.) has greatly benefited from the application of high-throughput screens evaluating large, diverse combinatorial libraries. At the same time, since only a very limited portion of sequence space can be experimentally constructed and tested, an attractive possibility is to use computational protein design to focus libraries on a productive portion of the space. We present a general-purpose method, called "Structure-based Optimization of Combinatorial Mutagenesis" (SOCoM), which can optimize arbitrarily large combinatorial mutagenesis libraries directly based on structural energies of their constituents. SOCoM chooses both positions and substitutions, employing a combinatorial optimization framework based on library-averaged energy potentials in order to avoid explicitly modeling every variant in every possible library. In case study applications to green fluorescent protein, b-lactamase, and lipase A, SOCoM optimizes relatively small, focused libraries whose variants achieve energies comparable to or better than previous library design efforts, as well as larger libraries (previously not designable by structure-based methods) whose variants cover greater diversity while still maintaining substantially better energies than would be achieved by representative random library approaches. By allowing the creation of large-scale combinatorial libraries based on structural calculations, SOCoM promises to increase the scope of applicability of computational protein design and improve the hit rate of discovering beneficial variants. While designs presented here focus on variant stability (predicted by total energy), SOCoM can readily incorporate other structure-based assessments, such as the energy gap between alternative conformational or bound states.

Computationally optimized deimmunization libraries yield highly mutated enzymes with low immunogenicity and enhanced activity

“…EpiSweep is an integrated deimmunization software suite that is generic to the epitope score, supports both sequence-and structure-based analysis of mutational effects on function, and designs either individual variants or combinatorial libraries (52). Although structure-based library designs have been described elsewhere (11,51), the results presented here demonstrate the use of a sequence score to drive the design of a combinatorial deimmunization library and demonstrate the scaling of the approach to a massive library size. In short, the sequence score is derived from a multiple sequence alignment of P99βL homologs obtained by PSI-BLAST; it includes both one-body (conservation) and two-body (coupling) terms (15).…”

“…Although structure-based design allows stepping outside the confines of evolutionarily accepted mutations, we conjecture that such measures were not necessary here because of the richness of the sequence record, the size of the feasible design space, and the do-no-harm context of deimmunization, in which function is to be preserved rather than altered. Nonetheless, we have recently extended the combinatorial library design to support structure-based modeling in cases where that is advantageous (51).…”

EpiSweep: Computationally Driven Reengineering of Therapeutic Proteins to Reduce Immunogenicity While Maintaining Function

“…To create the next generation of computational tools for therapeutic protein deimmunization, we have integrated computational T cell epitope prediction with computational analysis of the structural and functional consequences of epitope-deleting mutations [11,18–20,13,21]. As opposed to serial application of T cell epitope predictors followed by bioinformatics-based or experimental mutation analysis, our protein design algorithms simultaneously optimize therapeutic candidates for both low immunogenicity and high-level stability and activity.…”

“…Here, we provide a step by step guide to the application of the EpiSweep suite of deimmunization algorithms, as introduced in our series of algorithmic papers [11,18,19,13,21] and prospectively applied in our series of experimental papers [12,22,14,23–25]. To assess immunogenicity, the software utilizes any pocket profile-based epitope predictor; we illustrate here with the publicly available ProPred matrices [26].…”

“… 13 The following equation [21] provides an approximate number n of sequences to generate for CLEVER model training, based on the number of positions l, choices per position m , and pairs p . Please note that more sequences results in a better-trained model.…”