Machine learning-guided directed evolution for protein engineering

Machine learning (ML)-guided directed evolution is a new paradigm for biological design that enables optimization of complex functions. ML methods use data to predict how sequence maps to function without requiring a detailed model of the underlying physics or biological pathways. To demonstrate ML-guided directed evolution, we introduce the steps required to build ML sequence-function models and use them to guide engineering, making recommendations at each stage. This review covers basic concepts relevant to using ML for protein engineering as well as the current literature and applications of this new engineering paradigm. ML methods accelerate directed evolution by learning from information contained in all measured variants and using that information to select sequences that are likely to be improved. We then provide two case studies that demonstrate the ML-guided directed evolution process. We also look to future opportunities where ML will enable discovery of new protein functions and uncover the relationship between protein sequence and function.Comment: Made significant revisions to focus on aspects most relevant to applying machine learning to speed up directed evolutio

Synthetic Gene Circuits: Design with Directed Evolution

Synthetic circuits offer great promise for generating insights into nature's underlying design principles or forward engineering novel biotechnology applications. However, construction of these circuits is not straightforward. Synthetic circuits generally consist of components optimized to function in their natural context, not in the context of the synthetic circuit. Combining mathematical modeling with directed evolution offers one promising means for addressing this problem. Modeling identifies mutational targets and limits the evolutionary search space for directed evolution, which alters circuit performance without the need for detailed biophysical information. This review examines strategies for integrating modeling and directed evolution and discusses the utility and limitations of available methods

Emergent spatial structures in critical sandpiles

We introduce and study a new directed sandpile model with threshold dynamics and stochastic toppling rules. We show that particle conservation law and the directed percolation-like local evolution of avalanches lead to the formation of a spatial structure in the steady state, with the density developing a power law tail away from the top. We determine the scaling exponents characterizing the avalanche distributions in terms of the critical exponents of directed percolation in all dimensions.Comment: 4 pages, 4 Postscript figures, to appear in Phys. Rev. Let

Directed Percolation Universality in Asynchronous Evolution of Spatio-Temporal Intermittency

We present strong evidence that a coupled-map-lattice model for spatio-temporal intermittency belongs to the universality class of directed percolation when the updating rules are asynchronous, i.e. when only one randomly chosen site is evolved at each time step. In contrast, when the system is subjected to parallel updating, available numerical evidence suggests that it does not belong to this universality class and that it is not even universal. We argue that in the absence of periodic external forcing, the asynchronous rule is the more physical.Comment: 12 pages, RevTeX, includes 6 figures, submitted to Physical Review Letters; changed version includes a better physical motivation for asynchronous updates, extra references and minor change

Directed cycles and related structures in random graphs: II--Dynamic properties

We study directed random graphs (random graphs whose edges are directed) as they evolve in discrete time by the addition of nodes and edges. For two distinct evolution strategies, one that forces the graph to a condition of near acyclicity at all times and another that allows the appearance of nontrivial directed cycles, we provide analytic and simulation results related to the distributions of degrees. Within the latter strategy, in particular, we investigate the appearance and behavior of the strong components that were our subject in the first part of this study.Comment: submitted to Physica

A C35 Carotenoid Biosynthetic Pathway

Upon coexpression with Erwinia geranylgeranyldiphosphate (GGDP) synthase in Escherichia coli, C30 carotenoid synthase CrtM from Staphylococcus aureus produces novel carotenoids with the asymmetrical C35 backbone. The products of condensation of farnesyldiphosphate and GDP, C35 structures comprise 40 to 60% of total carotenoid accumulated. Carotene desaturases and carotene cyclases from C40 or C30 pathways accepted and converted the C35 substrate, thus creating a C35 carotenoid biosynthetic pathway in E. coli. Directed evolution to modulate desaturase step number, together with combinatorial expression of the desaturase variants with lycopene cyclases, allowed us to produce at least 10 compounds not previously described. This result highlights the plastic and expansible nature of carotenoid pathways and illustrates how combinatorial biosynthesis coupled with directed evolution can rapidly access diverse chemical structures

Directed percolation depinning models: Evolution equations

We present the microscopic equation for the growing interface with quenched noise for the model first presented by Buldyrev et al. [Phys. Rev. A 45, R8313 (1992)]. The evolution equation for the height, the mean height, and the roughness are reached in a simple way. The microscopic equation allows us to express these equations in two contributions: the contact and the local one. We compare this two contributions with the ones obtained for the Tang and Leschhorn model [Phys. Rev A 45, R8309 (1992)] by Braunstein et al. [Physica A 266, 308 (1999)]. Even when the microscopic mechanisms are quiet different in both model, the two contribution are qualitatively similar. An interesting result is that the diffusion contribution, in the Tang and Leschhorn model, and the contact one, in the Buldyrev model, leads to an increase of the roughness near the criticality.Comment: 10 pages and 4 figures. To be published in Phys. Rev.

Directed enzyme evolution: climbing fitness peaks one amino acid at a time

Directed evolution can generate a remarkable range of new enzyme properties. Alternate substrate specificities and reaction selectivities are readily accessible in enzymes from families that are naturally functionally diverse. Activities on new substrates can be obtained by improving variants with broadened specificities or by step-wise evolution through a sequence of more and more challenging substrates. Evolution of highly specific enzymes has been demonstrated, even with positive selection alone. It is apparent that many solutions exist for any given problem, and there are often many paths that lead uphill, one step at a time