Biology subjects

Espada, R.

Publications and source records attributed to Espada, R..

3 recordsLinked to original sources

bioRxiv2021

Directed Evolution of Enzymes based on in vitro Programmable Self-Replication

High-throughput directed evolution, implemented in well-controlled in vitro conditions, provides a powerful route for enzyme engineering. Most existing technologies are based on activity screening and require the sequential observation and sorting of each individual variant. By contrast, approaches based on autonomous feedback loops, linking phenotype to genotype replication, enable autonomous selection without screening. However, these approaches are only possible in vivo, or applicable to very specific activities, such as polymerases or ligases. Here, we leverage synthetic molecular networks to create a programmable in vitro feedback loop linking a target enzymatic activity to gene amplification. After encapsulation and lysis of up to 107 transformed variants, the genes present in each droplet are amplified according to the activity of the encoded enzyme, resulting in the autonomous enrichment of interesting sequences. Applied to a nicking enzyme with thermal or kinetic selection pressures, this method reveals detailed mutational landscapes and provides improved variants.

bioengineering↗

bioRxiv2020

Accurate detection of single nucleotide polymorphisms using nanopore sequencing

Nanopore sequencing is a powerful single molecule DNA sequencing technology which offers high throughput and long sequence reads. Nevertheless, its high native error rate limits the direct detection of point mutations in individual reads of amplicon libraries, as these mutations are difficult to distinguish from the sequencing noise. In this work, we developed SINGLe (SNPs In Nanopore reads of Gene Libraries), a computational method to reduce the noise in nanopore reads of amplicons containing point variations. Our approach uses the fact that all reads are very similar to a wild type sequence, for which we experimentally characterize the position-specific systematic sequencing error pattern. We then use this information to reweight the confidence given to nucleotides that do not match the wild type in individual variant reads. We tested this method in a set of variants of KlenTaq, where the true mutation rate was well below the sequencing noise. SINGLe improves between 4 and 9 fold the signal to noise ratio, in comparison to the data returned by the basecaller guppy. Downstream, this approach improves variants clustering and consensus calling. SINGLe is simple to implement and requires only a few thousands reads of the wild type sequence of interest, which can be easily obtained by multiplexing in a single minION run. It does not require any modification in the experimental protocol, it does not imply a large loss of sequencing throughput, and it can be incorporated downstream of standard basecalling.

bioinformatics↗

bioRxiv2019

Size and structure of the sequence space of repeat proteins

The coding space of protein sequences is shaped by evolutionary constraints set by requirements of function and stability. We show that the coding space of a given protein family --the total number of sequences in that family-- can be estimated using models of maximum entropy trained on multiple sequence alignments of naturally occuring amino acid sequences. We analyzed and calculated the size of three abundant repeat proteins families, whose members are large proteins made of many repetitions of conserved portions of [~]30 amino acids. While amino acid conservation at each position of the alignment explains most of the reduction of diversity relative to completely random sequences, we found that correlations between amino acid usage at different positions significantly impact that diversity. We quantified the impact of different types of correlations, functional and evolutionary, on sequence diversity. Analysis of the detailed structure of the coding space of the families revealed a rugged landscape, with many local energy minima of varying sizes with a hierarchical structure, reminiscent of fustrated energy landscapes of spin glass in physics. This clustered structure indicates a multiplicity of subtypes within each family, and suggests new strategies for protein design.

biophysics↗