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Sanz Moreta, L.

Publications and source records attributed to Sanz Moreta, L..

6 recordsLinked to original sources
bioRxiv2026

A time-calibrated phylogeny of hummingbirds supports stepwise diversification in the Andes

Hummingbirds (Trochilidae) represent the second largest avian family, with [~]356 species occupying diverse habitats across the Neotropical and Nearctic regions. Their extensive diversification includes notable adaptations to extreme environments, with nearly one-third of extant taxa belonging to the predominantly high-altitude Coquettes and Brilliants. Although recent work proposed a monophyletic Andean clade uniting these groups, consistent with rapid radiation during Andean orogeny, we find no support for this relationship. Using 2,949 nuclear loci sampled from 47 species spanning all nine major hummingbird clades, we recover a different evolutionary pattern: a stepwise sequence of diversification in which Brilliants are sister to a broader assemblage comprising Coquettes, the genus Patagona, Emeralds, Mountain Gems, and the recently diverged Bees. By assembling complete mitochondrial genomes, we additionally detect significant discordance between nuclear topologies and those derived from the mitogenome and Z chromosome. Analyses of gene-tree heterogeneity show that incomplete lineage sorting is pervasive across the phylogeny, with particularly strong impacts on branches associated with Andean diversification. Divergence-time estimation further indicates that the major Andean radiation--including Brilliants, Coquettes, Emeralds, Bees, and Mountain Gems--originated around [~]14 Ma, with the three younger clades diversifying [~]12 Ma, coinciding with both the mid-Miocene Andean uplift and the mid-Miocene Climate Transition that increased habitat heterogeneity and likely promoted rapid speciation. To support future phylogenomic efforts, we identify a reduced set of highly informative, independent protein-coding loci and present a near-complete species-level phylogeny constrained by our autosomal backbone. Our findings highlight the importance of integrating loci with distinct inheritance modes to detect and interpret phylogenetic incongruence in rapid radiations.

evolutionary biology↗
bioRxiv2023

AptaBERT: Predicting aptamer binding interactions

AO_SCPLOWBSTRACTC_SCPLOWAptamers, short single-stranded DNA or RNA, are promising as future diagnostic and therapeutic agents. Traditional selection methods, such as the Systemic Evolution of Ligands by Exponential Enrichment (SELEX), are not without limitations being both resource-intensive and prone to biases in library construction and the selection phase. Leveraging Dianoxs extensive aptamer database, we introduce a novel computational approach, AptaBERT, built upon the BERT architecture. This method utilizes self-supervised pre-training on vast amounts of data, followed by supervised fine-tuning to enhance the prediction of aptamer interactions with proteins and small molecules. AptaBERT is fine-tuned for binary classification tasks, distinguishing between positive and negative interactions with proteins and small molecules. AptaBERT achieves a ROC-AUC of 96% for protein interactions, surpassing existing models by at least 15%. For small molecule interactions, AptaBERT attains an ROC-AUC of 85%. Our findings demonstrate AptaBERTs superior predictive capability and its potential to identify novel aptamers binding to targets.

bioinformatics↗
bioRxiv2023

Dromi: Python package for parallel computation of similarity measures among vector-encoded sequences

Calculating similarities among sequences (i.e biological sequences) can be a challenging task. Here I introduce Dromi, a simple python package that can compute different similarity measurements (i.e percent identity, cosine similarity, kmer similarities) across aligned vector-encoded sequences. This is a crucial step required to perform both upstream and downstream sequence machine learning tasks such as sequence clustering [1, 2, 3], sequence analysis [4] and other pre- or post-processing demands on sequences. Additionally, this package introduces the calculation of the measure referred as positional weights. These represent the cosine similarities or residue-conservation across sequence elements (i.e amino acids in peptide sequences) in the same site (column). The program can also deal with sequences of variable length since end-padded positions are not considered for the calculations. The presented implementations are an incorporation into the arsenal of tools to measure similarity among small peptide sequences such as epitopes.

bioinformatics↗
bioRxiv2019

A Probabilistic Programming Approach to Protein Structure Superposition

Optimal superposition of protein structures is crucial for understanding their structure, function, dynamics and evolution. We investigate the use of probabilistic programming to superimpose protein structures guided by a Bayesian model. Our model THESEUS-PP is based on the THESEUS model, a probabilistic model of protein superposition based on rotation, translation and perturbation of an underlying, latent mean structure. The model was implemented in the deep probabilistic programming language Pyro. Unlike conventional methods that minimize the sum of the squared distances, THESEUS takes into account correlated atom positions and heteroscedasticity (i.e., atom positions can feature different variances). THESEUS performs maximum likelihood estimation using iterative expectation-maximization. In contrast, THESEUS-PP allows automated maximum a-posteriori (MAP) estimation using suitable priors over rotation, translation, variances and latent mean structure. The results indicate that probabilistic programming is a powerful new paradigm for the formulation of Bayesian probabilistic models concerning biomolecular structure. Specifically, we envision the use of the THESEUS-PP model as a suitable error model or likelihood in Bayesian protein structure prediction using deep probabilistic programming.

bioinformatics↗
bioRxiv2019

Link Your Sites (LYS) Scripts: Automated search of protein structures and mapping of sites under positive selection detected by PAML

The visualization of the molecular context of an amino acid mutation in a protein structure is crucial for the assessment of its functional impact and to understand its evolutionary implications. Currently, searches for fast evolving amino acid positions using codon substitution models like those implemented in PAML [1] are done in almost complete proteomes, generating large numbers of candidate proteins that require individual structural analyses. Here we present two python wrapper scripts as the package Link Your Sites (LYS). The first one i) mines the RCSB database [10] using the blast alignment tool to find the best matching homologous sequences, ii) fetches their domain positions by using Prosites [3,8,9], iii) parses the output of PAML extracting the positional information of fast-evolving sites and transform them into the coordinate system of the protein structure, iv) outputs a file per gene with the positions correlations to its homologous sequence. The second script uses the output of the first one to generate the proteins graphical assessment. LYS can therefore generate figures to be used in publication highlighting the positively selected sites mapped on regions that are known to have functional relevance and/or be used to reduce the number of targets that will be further analyzed by providing a list of those for which structural information can be retrieved. MotivationAutomatizing the search for protein structures to assess the functional impact of sites found to be under positive selection by codeml, implemented in PAML [1]. Building publication-quality figures highlighting the sites on a protein structure model that are within and outside functional domains. reduces the workload associated with selecting proteins for which a functional assessment of the impact of mutations can be done using a protein structure. This is especially relevant when analyzing almost complete proteomes which is the case of large comparative genomic studies. SoftwareLYS scripts are executed in the command line. They automatically search for homologous proteins at the RSCB database [10], determine the functional domain locations and correlate the positions pointed by the M8 model [1], and output a data frame that can be used as the input by PyMOL [7] to generate a visualization of the results. AvailabilityLYS is easy to install and implement and they are available at https://github.com/LysSanzMoreta/LYSAutomaticSearch

bioinformatics↗
bioRxiv2018

Link Your Sites (LYS.py): Coupling your PAML codeml results and homologous protein structures in PyMOL

The visualization of the molecular context of an amino acid mutation in a protein structure is crucial for the assessment of its functional impact and to understand its evolutionary implications. Currently, searches for fast evolving amino acid positions using codon substitution models like those implemented in PAML (Z. Yang, 2000) are done in almost complete proteomes, generating large numbers of candidate proteins that require individual structural analyses. Here I present a python wrapper script that integrates the output of PAML with the PyMOL visualization tool to automate the generation of protein structure models where positively selected sites are mapped along with the location of putative functional domains.

bioinformatics↗