Biology subjects

Florez Prada, A.

Publications and source records attributed to Florez Prada, A..

3 recordsLinked to original sources

bioRxiv2026

mock-fastq-generator: A synthetic FASTQ generator

Validating bioinformatics pipelines and benchmarking sequence processing algorithms requires reliable test datasets. Existing read simulation tools rely on reference genomes and empirical error profiles, lacking fine-grained control over specific targeted DNA constructs and controlled error injection. mock-fastq-generator is an open-source software suite available both as an installable PyPI Python package and a standalone, client-side web application. It constructs synthetic FASTQ files by combining template constructs with customizable adapter sequences, selectable quality decay functions (Gaussian, Exponential, Sigmoidal), NovaSeq 3-state quality binning, and context-dependent error penalties. The software allows developers to benchmark sequence trimmers, test alignment sensitivity, and execute automated quality control pipelines in test suites without using proprietary or empirical biological data.

bioinformatics↗

bioRxiv2026

Affinity-enhanced peptides delivered by mRNA lipid nanoparticles inhibit influenza A virus replication by disrupting PA-PB1 interaction

Seasonal influenza causes up to 650,000 deaths annually and remains a persistent pandemic threat due to zoonotic strains crossing the species barrier. Current antivirals, which target neuraminidase or the viral polymerase, have limited efficacy and rapidly select for resistant variants, highlighting the urgent need for new therapeutic strategies. The heterotrimeric influenza polymerase (FluPol), comprising PA, PB1, and PB2 subunits, is essential for viral replication and harbors virus-specific protein-protein interfaces that are potential drug targets. We focused on the highly conserved PA-PB1 interface, where the N-terminal peptide of PB1 binds the PA C-terminal domain with high affinity. Disrupting this interaction abrogates polymerase function, halting viral replication. Using phage display, we identified PB1-derived peptides with enhanced affinity for PA and characterized their binding via biophysical methods and X-ray crystallography. Lead peptides efficiently disrupted the PB1-PA interaction and inhibited polymerase activity in cell-based assays. To address peptide delivery challenges, we expressed these inhibitors intracellularly from synthetic mRNA formulated in lipid nanoparticles, achieving robust inhibition of viral replication in cultured cells. This work establishes intracellularly expressed peptide inhibitors as a viable antiviral strategy and provides a generalizable framework for targeting essential protein-protein interactions of influenza and other RNA viruses.

microbiology↗

bioRxiv2026

Calculation of sequence space coverage in a mutagenesis library

Directed evolution requires screening of large mutagenesis libraries, but accurate calculation of library sizes needed to discover functional variants remains challenging. Existing models provide baseline estimates, yet current computational approaches for finding the best variants scale poorly with library complexity. Here, we introduce a scalable algorithmic framework to compute exact discovery probabilities in saturation mutagenesis libraries with no requirement for explicit sequence enumeration. By aggregating variants into a composition log-sum distribution and applying log-space convolution across randomisation blocks, it is possible to extend this to massive sequence spaces and mixed codon schemes. By inverting these calculations, absolute mathematical ceilings for experimental design are established. Ultimately, this framework provides a rapid, quantitative tool to balance the statistical coverage-diversity trade-off within the limitations of laboratory screening. Finally, this is implemented as an open-source web application (SSCC) that allows researchers to construct heterogeneous library designs and compute required sampling depths, coverage probabilities, and absolute randomisation limits.

bioinformatics↗