Biology subjects

Cowen, L. J.

Publications and source records attributed to Cowen, L. J..

4 recordsLinked to original sources

bioRxiv2026

Comprehensive Mapping of Immune Nanobody Repertoires with NanoMAP

Nanobodies have recently emerged as alternatives to classical antibodies in therapeutic and diagnostic contexts from parasites to bacteria to viruses, promising improved stability and simpler manufacturing. To improve nanobody discovery efficiency, we developed an integrated experimental and computational pipeline for detailed characterization of the target binding properties of complete alpaca immune repertoires using our custom Nanobody Meta-clustering Analysis Platform (NanoMAP). We tested our pipeline on three distinct pools of targets, immunizing two alpacas with each pool and generating cDNA and phage display libraries from their immune repertoires. We then panned the phage libraries on each target. To produce more detailed binding information, we performed panning variations using subunits, natural variants, intact pathogens, and binding site competitors. Deep sequencing reads from nanobody libraries before and after each panning were pooled and analyzed with NanoMAP to identify nanobody clonal families and assess their levels of enrichment from the library in each panning, reflecting their affinities. NanoMAP outperformed standard clustering methods, producing clonal families that are coherent in sequence and function and detecting rare but high affinity families. By aggregating sequencing data within clonal families, NanoMAP produced reliable and rich data on nanobody repertoire binding phenotypes for each antigen, enhancing nanobody discovery capabilities.

immunology↗

bioRxiv2026

Fast structural search for classification of gut bacterial mucin O-glycan degrading enzymes

The Enzyme Commission (EC) numbering scheme provides a hierarchical way to classify enzymes according to their catalytic functions. While recent protein language model (PLM) based approaches like CLEAN and ProteInter have improved sequence-based EC number prediction, they struggle with fine-grained classification at the deepest hierarchical level. Structure-based approaches for grouping similar proteins using alignment tools excel at finding proteins that share overall global structure, but suffer from high false positive rates when classifying proteins that are globally structurally similar but functional differentiation depends on a localized region. This problem is particularly relevant to EC number prediction, as enzymatic function depends on its catalytic domain, which is a relatively small, specific region of the protein. We introduce Deep Enzyme Function Transfer (DEFT) that harmonizes sequence- and structure-based approaches through the key insight that PLM based annotations of the first two EC number hierarchy levels vastly reduce false positives that are likely to show in purely structure-based EC number prediction. Given an enzyme of interest, DEFT first uses a PLM based method to assign the first two levels of the enzymes EC number, and then uses a structure-based method to predict the remaining two levels of the EC number. Using benchmarking datasets, we demonstrate that DEFT achieves superior accuracy compared with current state-of-the-art tools for EC number prediction. Furthermore we show that DEFTs computational efficiency enables high-throughput, genome-wide annotations of total enzyme repertoires in organisms. We illustrate this capability by experimentally validating DEFT predicted glycoside hydrolase (GH) profiles of intestinal mucus associated bacteria. Author summaryEnzymes are ubiquitous proteins that catalyze chemical reactions of living cells. Enzymes are classified using a hierarchical numbering system called Enzyme Commission (EC) numbers that describe the chemical reactions the enzymes catalyze, from a general reaction type (e.g., breaking bonds, transferring chemical groups, etc.) to more specific aspects such as chemical bonds and substrates involved in the reaction. We present a new machine learning method for predicting EC numbers called Deep Enzyme Function Transfer (DEFT). This method improves on previous methods that use either protein sequence- or three-dimensional (3D) structure-based comparisons between enzymes of known and unknown classification. DEFT combines the strengths of both approaches by first using a protein sequence-based model to predict the general enzyme category and then using protein structure comparisons to predict the finer subcategories. We demonstrate that DEFT achieves superior accuracy compared with current state-of-the-art tools for EC number prediction. We next demonstrate how DEFTs computational efficiency enables us to perform high-throughput, genome-wide annotations of organisms enzyme repertoires. We illustrate this capability by experimentally validating DEFT predicted sugar metabolizing enzyme profiles of intestinal mucus associated bacteria.

bioinformatics↗

bioRxiv2023

Fast Approximate IsoRank for Scalable Global Alignment of Biological Networks

A well-studied approximate version of the graph matching problem is directly relevant for the study of protein-protein interaction networks. Called by the computational biology community Global Network Alignment, the two networks to be matched are derived from the protein-protein interaction (PPI) networks from organisms of two different species. If these two species evolved recently from a common ancestor, we can view the two PPI networks as a single network that evolved over time. It is the two versions of this network that we want to align using approximate graph matching. The first spectral method for the PPI global alignment problem proposed by the biological community was the IsoRank method of Singh et al. This method for global biological network alignment is still used today. However, with the advent of many more experiments, the size of the networks available to match has grown considerably, making running IsoRank unfeasible on these networks without access to state of the art computational resources. In this paper, we develop a new IsoRank approximation, which exploits the mathematical properties of IsoRanks linear system to solve the problem in quadratic time with respect to the maximum size of the two PPI networks. We further propose a computationally cheaper refinement to this initial approximation so that the updated result is even closer to the original IsoRank formulation. In experiments on synthetic and real PPI networks, we find that the results of our approximate IsoRank are not only nearly as accurate as the original IsoRank results but are also much faster, which makes the global alignment of large-scale biological networks feasible and scalable.

bioinformatics↗

bioRxiv2018

Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases

Identification of modules in molecular networks is at the core of many current analysis methods in biomedical research. However, how well different approaches identify disease-relevant modules in different types of gene and protein networks remains poorly understood. We launched the "Disease Module Identification DREAM Challenge", an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology, and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies (GWAS). Our critical assessment of 75 contributed module identification methods reveals novel top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets and correctly prioritize candidate disease genes. This community challenge establishes benchmarks, tools and guidelines for molecular network analysis to study human disease biology (https://synapse.org/modulechallenge).

bioinformatics↗