Biology subjects

Mobarec, J. C.

Publications and source records attributed to Mobarec, J. C..

3 recordsLinked to original sources

bioRxiv2026

A metagenomic thermostable monomeric meganuclease with novel specificity and unique palindromic 3-prime overhangs

As an alternative to historical enzyme isolation, metagenomic databases (e.g. MGnify) provide information on vast unculturable microbial diversity, especially from extreme environments, and constitute an enormous source of functional proteins. Conservative mining of these data by close sequence homology alone tends to identify merely different versions of known enzymes. Here we present a discovery strategy of meganucleases based on wider capture of less homologous enzymes with new function in metagenomic databases, incorporating metadata with homology, relying on cell-free expression to bypass host incompatibility and the need for purification, along with using deep sequencing for experimental assessment of substrate specificity and cleavage pattern, circumventing classical gel-based profiling. Specifically, we discovered the temperature-stable (>55{degrees}C), intron-encoded LAGLIDADG meganuclease I-MG11 that recognizes a 17 base pair sequence to generate unique 4 base pair palindromic 3'-overhangs -- the first monomeric meganuclease to produce such overhangs. Co-folding models of I-MG11 bound to DNA provide a structural context for enzyme-DNA interactions, highlighting differences from other monomeric LAGLIDADG meganucleases (e.g. I-SceI) shaped by InDels (insertion-deletions) in the DNA binding region that may cause specificity changes. Our strategy streamlines bona fide identification and annotation of meganucleases, while the unique properties of I-MG11 expand the molecular biology toolbox. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=31 SRC="FIGDIR/small/712669v1_ufig1.gif" ALT="Figure 1"> View larger version (11K): org.highwire.dtl.DTLVardef@199394aorg.highwire.dtl.DTLVardef@806490org.highwire.dtl.DTLVardef@14a533aorg.highwire.dtl.DTLVardef@9e13db_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

Topology-Driven Negative Sampling Enhances Generalizability in Protein-Protein Interaction Prediction

Unraveling the human interactome to uncover disease-specific patterns and discover drug targets hinges on accurate protein-protein interaction (PPI) predictions. However, challenges persist in machine learning (ML) models due to a scarcity of quality hard negative samples, shortcut learning, and limited generalizability to novel proteins. Here, we introduce a novel approach for strategic sampling of protein-protein non-interactions (PPNIs) by leveraging higher-order network characteristics that capture the inherent complementarity-driven mechanisms of PPIs. Next, we introduce UPNA-PPI (Unsupervised Pre-training of Node Attributes tuned for PPI), a high throughput sequence-to-function ML pipeline, integrating unsupervised pretraining in protein representation learning with topological PPNI samples, capable of efficiently screening billions of interactions. UPNA-PPI improves PPI prediction generalizability and interpretability, particularly in identifying potential binding sites locations on amino acid sequences, strengthening the prioritization of screening assays and facilitating the transferability of ML predictions across protein families and homodimers. UPNA-PPI establishes the foundation for a fundamental negative sampling methodology in graph machine learning by integrating insights from network topology.

bioinformatics↗

bioRxiv2024

Robust prediction of relative binding energies for protein-protein complex mutations using free energy perturbation calculations

Computational free energy-based methods have the potential to significantly improve throughput and decrease costs of protein design efforts. Such methods must reach a high level of reliability, accuracy, and automation to be effectively deployed in practical industrial settings in a way that impacts protein design projects. Here, we present a benchmark study for the calculation of relative changes in protein-protein binding affinity for single point mutations across a variety of systems from the literature, using free energy perturbation (FEP+) calculations. We describe a method for robust treatment of alternate protonation states for titratable amino acids, which yields improved correlation with and reduced error compared to experimental binding free energies. Following careful analysis of the largest outlier cases in our dataset, we assess limitations of the default FEP+ protocols and introduce an automated script which identifies probable outlier cases that may require additional scrutiny and calculates an empirical correction for a subset of charge-related outliers. Through a series of three additional case study systems, we discuss how protein FEP+ can be applied to real-world protein design projects, and suggest areas of further study. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/590325v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1c5e607org.highwire.dtl.DTLVardef@1810ee5org.highwire.dtl.DTLVardef@1f8f1acorg.highwire.dtl.DTLVardef@c28053_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIReliable calculation of relative binding free energy changes for most protein mutations to within [~]1 kcal/mol. C_LIO_LIAutomated Protein FEP+ Groups treatment of alternate protonation states for titratable residues. C_LIO_LIApplication of FEP+ methodology to "real-world" protein design projects. C_LI

biophysics↗