Optimal Design of Stochastic DNA Synthesis Protocols based on Generative Sequence Models

Abstract

Generative probabilistic models of biological sequences have widespread existing and potential applications in analyzing, predicting and designing proteins, RNA and genomes. To test the predictions of such a model experimentally, the standard approach is to draw samples, and then synthesize each sample individually in the laboratory. However, often orders of magnitude more sequences can be experimentally assayed than can affordably be synthesized individually. In this article, we propose instead to use stochastic synthesis methods, such as mixed nucleotides or trimers. We describe a black-box algorithm for optimizing stochastic synthesis protocols to produce approximate samples from any target generative model. We establish theoretical bounds on the method’s performance, and validate it in simulation using held-out sequence-to-function predictors trained on real experimental data. We show that using optimized stochastic synthesis protocols in place of individual synthesis can increase the number of hits in protein engineering efforts by orders of magnitude, e.g. from zero to a thousand.

Optimal trade-off control in machine learning-based library design, with application to adeno-associated virus (AAV) for gene therapy

“…We have focused our experiments on these constrained libraries because they are currently more cost-effective, and thus most widely used. Indeed, Weinstein et al [34] showed that for a fixed cost, the use of a constrained library construction can yield orders of magnitude more promising leads in protein engineering than an unconstrained (individual synthesis) approach. As the cost of individual synthesis declines, it will become increasingly useful to use our design approach to specify unconstrained libraries that are both diverse and fit.…”

Efficient evolution of human antibodies from general protein language models and sequence information alone

“…The efficient manifold hypothesis has direct, practical applications for those trying to evolve proteins in the laboratory. Evolution guided by a language model can be used as a drop-in replacement for current evolutionary tools based on randomization; for example, combinatorial libraries [50], [51] can recombine language-model-guided mutations alongside or instead of rationally chosen mutations [33]. By leveraging increasingly efficient technologies for nucleic acid printing [42], language-model-guided evolution could also directly replace mutagenesis strategies based on, for example, an error-prone polymerase.…”

Efficient algorithms for designing maximally sized orthogonal DNA sequence libraries

“…Since then, graph representations of sequences have become widespread as descriptive tools in bioinformatics, used to reconstruct naturally occurring biological sequences. In modern molecular biology and bioengineering, where the design of synthetic biological systems is fundamentally intertwined with the characterization of natural biological systems, there is growing interest in sequence representations amenable to design tasks (16, 17). However, outside of highly specialized applications (18, 19), graph representations of sequences are far less commonly used in design contexts.…”

“…We have implemented 197 the SeqWalk algorithm and additional filtering tools in a pip 198 distributed Python package (seqwalk , source code avail-199 able at github.com/storyetfall/seqwalk, docu-200 mented at seqwalk.readthedocs.io). Additionally, 201 In modern molecular biology and bioengineering, where the design of synthetic biological systems is fundamentally intertwined with the characterization of natural biological systems, there is growing interest in sequence representations amenable to design tasks (16,17). However, outside of highly specialized applications (18,19), graph representations of sequences are far less commonly used in design contexts.…”