Biology subjects

Naegle, K. M.

Publications and source records attributed to Naegle, K. M..

17 recordsLinked to original sources

bioRxiv2026

ProteomeScout v4: An open-source, harmonized, ecosystem for post-translational modifications

There are many databases of post-translational modifications, but each has different protein and PTM coverage, making the current state of post-translational modifications difficult to obtain, assess, or harmonize. ProteomeScout has been a relational database that harmonized a myriad of resources to address this challenge. In this update, we wished to create a lightweight, sustainable open source resource for the community that retained the power of harmonization, protein visualization, and Python tools for interacting with the database. ProteomeScout-v4 presents an ecosystem of open-source tools for aggregating and updating proteins and post-translational modifications and presenting the web-based and code-based access tools for interacting with the data. ProteomeScout has moved from using a complex relational database to a flat-text file format. The database file update is version controlled on FigShare and all features have processes for continuous updating, including migrating PTMs to updated protein records, when external databases are updated. The open-source nature and flexibility allow for user-based control and community-based feature integration. Fast and easy analysis is possible with the API, with example analyses provided here.

systems biology↗

bioRxiv2026

KSTAR v1.2: A faster and more and accessible KSTAR for kinase activity inference

Motivation: KSTAR is an algorithm with high flexibility for inferring kinase activity from any phosphoproteomic pipeline. However, in its first instantiation (v0.1) it requires Python programming and lots of memory and computational resources. Hence, we wished to improve speed and accessibility for broader uptake by researchers. Results: Here, we provide an updated algorithm that improves speed and memory, without affecting accuracy, along with some new features for increased usability and insight. KSTAR v1.2 has also been integrated into Galaxy for programming-free activity analysis and ProteomeScout for dataset preparation and interactive plotting. Availability and implementation: KSTAR is available at https://github.com/NaegleLab/KSTAR or on Galaxy on https://usegalaxy.org/. KSTAR Network resource assets are managed on Figshare at: https://doi.org/10.6084/m9.figshare.14944305.

systems biology↗

bioRxiv2026

SpY-C: Supervised Learning of Phosphopeptide Sequence Constraints Enables Global Prediction of SH2 Domain Binding

Tyrosine kinase signaling for cell development and homeostasis in multicelluar organisms and a major biochemical contribution is by driving interactions between phosphorylated tyrosines (pY) and SH2 domain containing proteins. This assembly is so important to driving cell outcomes that a wide variety of experimental and computational approaches have been used to understand which SH2-pY interactions occur, which still remains a challenge given the immensity (more than 45,000 pY and 120 SH2 domains in the human proteome). Based on biophysical constraints suggested by comprehensive contact mapping, here, we ask whether an approach might consider first asking if pY sequences conform to the shared rules of SH2 domain recognition by developing a classification approach that combines diverse training data. A wide range of validation suggests this approach, SpY-C, can classify pY sites as having the potential, or not, to be involved in SH2 domain interactions. We find that a relatively small set of representative SH2 binders, integrated from different experimental techniques, provides good classification. We use this classifier to annotate the human phosphoproteome and individual experiments, to explore the consequences of using super-SH2 domain reagents for pY enrichment, and to analyze the effects of mutations in altering pY site function. SpY-C provides a helpful step to more rapidly annotating pY function and for possibly improving machine learning approaches focused on specific SH2-pY interactions downstream of a first pass classification approach.

systems biology↗

bioRxiv2026

Studying the effect of conserved tyrosine phosphorylation within SH2 domains

SH2 domains are phosphotyrosine-binding modules that play a critical role in cell signaling by mediating protein-protein interactions. While tyrosine phosphorylation has been shown to impact SH2 domain function in signaling, the specific effects of phosphorylation at different sites within the domain remain poorly understood. In this study, we selected two conserved regions of tyrosine phosphorylation within SH2 domains, near conserved binding interface residues, and developed approaches to evaluate the impact of those sites on ligand binding. Using a modified dot blot assay to screen phosphomimic mutations, we studied specific tyrosine residues within the PTPN11-N, LYN, and SYK-C SH2 domains, finding that the PTPN11 N-terminal site (Y63) modulates the specificity, reducing binding of physiologically relevant substrates. Our findings provide new insights into the regulatory mechanisms governing SH2 domain function and highlight the importance of site-specific phosphorylation in modulating protein-protein interactions in cell signaling pathways.

molecular biology↗

bioRxiv2026

Evaluating splicing factor and kinase network crosstalk through global phosphoproteomics

Both splicing and kinase signaling are biochemical processes that fundamentally determine and shape cell physiology. Although there has been some indication that there is an interaction between the two - splicing can alter the availability of exons encoding kinase targets and kinases can phosphorylate splicing factors - it has yet to be established the extent to which altering splicing factor expression impacts kinase signaling networks. In this work, we implemented a data-driven analysis using ENCODE RNA-sequencing data and prior work mapping post-translational modifications onto splice events to identify candidate splice factor perturbations that show extensive alterations to phosphorylation-encoding protein products. We then replicated the ENCODE knockdown experiments and performed global phosphoproteomics for two candidates, U2AF1 and SRSF3, complementing the transcription-level data. Both knockdowns showed extensive changes in phosphorylation and kinase activities, both basally and upon receptor tyrosine kinase stimulation. U2AF1 knockdown drove decreased JNK-associated cell death signaling but elevated chromosome regulation through CSNK2A1, PLK, and EIF2AK4 activity. SRSF3 knockdown, on the other hand, led to decreased cell cycle signaling through CDK and HIPK2 but increased cytoskeletal signaling through various PAKs. In addition, we found a striking enrichment of phosphorylated splicing regulators in both knockdowns that were linked to their splicing activity, such as HNRNPC, suggesting potential feedback and crosstalk between splice factors through signaling pathway activation. Importantly, comparison of differential phosphorylation measurements from this study to mRNA expression and splicing measurements from ENCODE revealed significant knockdown-dependent protein regulation, not captured by transcriptomic measurements alone, underscoring the value of phosphoproteomic profiling after splice factor perturbations. Combined, the transcriptomics and phosphoproteomics reveal deep interconnection between the two processes that are relevant to understanding cell signaling in health and disease.

systems biology↗

bioRxiv2026

Critical role of cell competition in gliomagenesis

ABSTRACTMalignant glioma is incurable. Using a mouse genetic mosaic system to generate sporadic Trp53,Nf1-null OPCs, we previously identified oligodendrocyte precursor cell (OPC) as a cell-of-origin of glioma. Here, we report that pre-malignant Trp53,Nf1-null OPCs outcompete wildtype counterparts during their expansion. Blocking competition by mutating/strengthening wildtype OPCs impeded both pre-malignant progression and malignant expansion of glioma. "In-tissue" phosphoproteomic profiling revealed an enrichment of phosphopeptides related to RNA splicing and protein translation at the peak of cell competition, suggesting that competitiveness may stem from unique protein species. Among candidates was mTORC1, whose pharmacological inhibition or genetic disruption resulted in a loss of competitiveness in our mouse model. Finally, analysis of patient biopsies and interrogating the role of individual gliomagenic mutations in OPC competition supported its relevance in human gliomas. Together, these findings identified the driving role of competitive interactions among OPCs in gliomagenesis, and suggest unconventional therapeutic strategies to target this process.

cancer biology↗

bioRxiv2025

Mechanistic modeling identifies emergent properties of tandem SH2 domain interactions

In signaling cascades, where domain-motif interactions tend to interact with relatively low affinity (allowing for reversibility), signaling proteins often encode multiple domains or motifs. This presents the possibility for avidity where multivalent binding drastically increases the interaction strength and duration. However, given the large combinatorial space, predicting and validating multivalent interactions that interact with avidity is a challenge. Here, we integrate mechanistic modeling, structure-based analysis, and experimental approaches as a framework for defining the conditions under which avidity plays a role. We explore the tandem SH2 domain family of interactions with bisphosphorylated partners as a multivalent archetype, which encompasses key secondary messengers in tyrosine kinase signaling networks. While certain multivalent interactions have been shown to be necessary in immune receptor recruitment of partners, bivalent recruitment of tandem SH2 domains more broadly is poorly understood. Theoretical modeling suggests that maximum avidity occurs with closely spaced tyrosine phosphorylation sites combined with moderate monovalent affinities - exactly around the innate range of SH2 domain affinity - or with phosphorylation sites separated by sufficiently flexible linkers. Surprisingly, despite sequence diversity, structure-based analysis showed relatively conserved three-dimensional spacing between SH2 domains across all tandem SH2 families, which we corroborate experimentally, suggesting evolutionary optimization for avidity interactions. The combination of structure-based analysis of domain spacing with available monovalent experimental data appears, along with iterative experimental refinement of biophysical parameters, can identify high affinity interactions of tandem SH2 domain recruitment to the EGFR C-terminal tail. Using these principles, we extended bivalent predictions into the full phosphoproteome space and structural parameterization of other partners of SH2 domain binding, providing resources and methods for more rapid expansion of bivalent analysis. These approaches lay the groundwork for larger utility in multivalent prediction and testing to help better understand protein interactions that drive cell signaling.

molecular biology↗

bioRxiv2024

A signaling inspired synthetic toolkit for efficient production of tyrosine phosphorylated proteins

Tyrosine phosphorylation is an important post-translational modification that regulates many biochemical signaling networks in multicellular organisms. To date, 46,000 tyrosines have been observed in human proteins, but relatively little is known about the function and regulation of most of these sites. A major challenge has been producing recombinant phosphoproteins in order to test the effects of phosphorylation. Mutagenesis to acidic amino acids often fails to replicate the size and charge of a phosphorylated tyrosine residue and synthetic amino acid incorporation has high cost with relatively low yield. Here, we demonstrate an approach, inspired by how native tyrosine kinases find targets in cells - through a secondary targeting interaction, augmenting innate catalytic specificity of a tyrosine kinase, without overriding it. We engineered complementary vector systems for multiple approaches to producing high yields of phosphoprotein products in E. coli. Here, we test phosphorylation as a function of the targeting interaction (an SH3-polyproline sequence) affinity, different reaction methods across kinases of different specificity. This system presents an inexpensive and tractable system to producing phosphoproteins and phosphopeptides and we demonstrate how it can be used for testing antibody specificity on targets of EGFR and PD-1. This methodology is a generalizable approach for enhancing the enzymatic action on a recombinant protein via the flexibility of in vitro reactions and co-expression approaches. We refer to this as SISA-KiT, for Signaling Inspired Synthetically Augmented Kinase Toolkit.

molecular biology↗

bioRxiv2024

Linguistic networks uncover grammatical constraints of protein sentences comprised by domain-based words

Protein-protein interaction networks can help identify co-regulated modules, emergent biology (such as pathways), disease-associated partners, and function through association. However, these networks are limited by the breadth of experimental data behind them, which is incomplete and uneven across the proteome. For example, the coverage of interactions driven by reversible post-translational modifications (acetylation, phosphorylation, etc.) or protein fusions in diseases are particularly difficult to establish. Protein domains, conserved structural and functional units, are a major component of what defines a proteins function and its interactions. Whereas protein interactions are sparsely understood at this time, domain identification and coverage is mature. In this work, we propose a language-based network that utilizes the domain as "words" that makes up protein "sentences" to cover an entire proteome. We first convert proteins into n-grams (a formalization of contiguous words in a sentence) and assemble n-grams into a comprehensive network. We then use information theory to reduce the complexity of the network, collapsing to a network and n-gram size that recovers the majority of the proteome complexity. Using network theoretic approaches, we explore the larger human proteome and subnetwork analysis to understand specific properties in two applications: reversible systems of post-translational modifications (PTMs) and cancer fusion genes. PTM analysis across species suggests that reversible PTM systems convergently evolved similar domain architectures - allowing higher interconnectivity between reader and writer domains, while eraser domains remained highly disconnected. Cancer fusion analysis finds that, despite the possibility that fusions may sample novel domain word combinations, creating new connections or altering the human protein network, most cancer fusion genes follow existing domain combination rules. Collectively, these results suggest that an n-gram based analysis of proteomes complements direct protein interaction approaches, but provides a more fully described network of interconnected protein function that can provide unique insights on signaling pathway analysis. We refer to this approach for converting proteomes into functional linguistic networks as Domain Architecture Network Syntax (DANSy). SignificanceHere, we develop a novel computational method that treats proteins as sentences made up of domain words. We integrate this linguistic representation with networks to uncover abstract functional relationships to more fully represent the proteome than traditional protein-protein interaction networks, which are limited by incomplete experimental data. Our findings show how this framework, which we term Domain Architecture Network Syntax (DANSy), uncovers common "grammar" governing protein functionality in the evolution of post-translational modification systems and demonstrates how cancer fusion genes maintain established principles of the natural proteome.

systems biology↗

bioRxiv2024

CoDIAC: A comprehensive approach for interaction analysis reveals novel insights into SH2 domain function and regulation

Protein domains are conserved structural and functional units that serve as building blocks of proteins. Through evolutionary expansion, domain families are represented by multiple members in diverse configurations with other domains, evolving new specificities for their interacting partners. Here, we develop a structure-based interface analysis to comprehensively map domain interfaces from experimental and predicted structures, including interfaces with macromolecules and intraprotein interfaces. We hypothesized that comprehensive contact mapping of domains could yield new insights into domain selectivity, conservation of domain-domain interfaces across proteins, and identify conserved post-translational modifications (PTMs), relative to interaction interfaces, allowing for the inference of specific effects due to PTMs or mutations. We applied this approach to the human SH2 domain family, a modular unit central to phosphotyrosine-mediated signaling, identifying a novel approach to understanding binding selectivity and evidence of coordinated regulation of SH2 domain binding interfaces by tyrosine and serine/threonine phosphorylation and acetylation. These findings suggest multiple signaling systems can regulate protein activity and SH2 domain interactions in a coordinated manner. We provide the extensive features of the human SH2 domain family and this modular approach as an open source Python package for COmprehensive Domain Interface Analysis of Contacts (CoDIAC).

systems biology↗

bioRxiv2024

A systematic analysis of the effects of splicing on the diversity of post-translational modifications in protein isoforms

Post-translational modifications (PTMs) and splicing are important regulatory processes for controlling protein function and activity. Despite examples of interplay between alternative splicing and cell signaling in literature, there have been few detailed analyses of the impacts of alternative splicing on PTMs, partly due to difficulties in extracting PTM information from splicing measurements. We developed a computational pipeline, PTM Projection Onto Splice Events (PTM-POSE), to identify "prospective" PTM sites in alternative isoforms and splice events recorded in databases using only the genomic coordinates of a splice event or isoform of interest. Importantly, PTM-POSE integrates various PTM-specific databases and tools to allow for deeper analysis of the individual and global impact of spliced PTMs on isoform function, protein interactions, and regulation by enzymes like kinases. Using PTM-POSE, we performed a systematic analysis of PTM diversification across isoforms annotated in the Ensembl database. We found that 32% of PTMs are excluded from at least one Ensembl isoform, with palmitoylation being most likely to be excluded (49%) and glycosylation and crotonylation exhibiting the highest constitutive rates (75% and 94%, respectively). Further, approximately 2% of prospective PTM sites exhibited altered regulatory sequences surrounding the modification site, suggesting that regulatory or binding interactions might be different in these proteoforms. When comparing splicing of phosphorylation sites to measured phosphorylation abundance in KRAS-expressing lung cells, differential inclusion of phosphorylation sites correlated with phosphorylation levels, particularly for larger changes in inclusion (> 20%). To better understand how splicing diversification of PTMs may alter protein function and regulatory networks in specific biological contexts, we applied PTM-POSE to exon utilization measurements from TCGASpliceSeq of prostate tumor samples from The Cancer Genome Atlas (TCGA) and identified 1,489 PTMs impacted by ESRP1-correlated splicing, a splicing factor associated with worsened prognosis. We identified protein interaction and regulatory networks that may be rewired as a result of differential inclusion of PTM sites in ribosomal and cytoskeletal proteins. We also found instances in which ESRP1-mediated splicing impacted PTMs by altering flanking residues surrounding specific phosphorylation sites that may be targets of 14-3-3 proteins and SH2 domains. In addition, SGK1 signaling was found to be influenced by ESRP1 expression through increased inclusion of SGK1 substrates in ESRP1-expressing patients. Based on validation in a separate prostate cancer cohort from the Chinese Prostate Cancer Genome and EpiGenome Atlas (CPGEA), this correlated with increased phosphorylation of SGK1 substrates, particularly when SGK1 was predicted to be active. From this work, we highlighted the extensive splicing-control of PTM sites across the transcriptome and the novel information that can be gained through inclusion of PTMs in the analysis of alternative splicing. Importantly, we have provided a publicly available python package (PTM-POSE: https://github.com/NaegleLab/PTM-POSE) and all associated data for use by the broader scientific community to allow for continued exploration of the relationship between splicing and PTMs.

systems biology↗

bioRxiv2023

Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts

Fibroblasts are essential regulators of extracellular matrix deposition following cardiac injury. These cells exhibit highly plastic responses in phenotype during fibrosis in response to environmental stimuli. Here, we test whether and how candidate anti-fibrotic drugs differentially regulate measures of cardiac fibroblast phenotype, which may help identify treatments for cardiac fibrosis. We conducted a high content microscopy screen of human cardiac fibroblasts treated with 13 clinically relevant drugs in the context of TGF{beta} and/or IL-1{beta}, measuring phenotype across 137 single-cell features. We used the phenotypic data from our high content imaging to train a logic-based mechanistic machine learning model (LogiMML) for fibroblast signaling. The model predicted how pirfenidone and Src inhibitor WH-4-023 reduce actin filament assembly and actin-myosin stress fiber formation, respectively. Validating the LogiMML model prediction that PI3K partially mediates the effects of Src inhibition, we found that PI3K inhibition reduces actin-myosin stress fiber formation and procollagen I production in human cardiac fibroblasts. In this study, we establish a modeling approach combining the strengths of logic-based network models and regularized regression models, apply this approach to predict mechanisms that mediate the differential effects of drugs on fibroblasts, revealing Src inhibition acting via PI3K as a potential therapy for cardiac fibrosis. SignificanceCardiac fibrosis is a dysregulation of the normal wound healing response, resulting in excessive scarring and cardiac dysfunction. As cardiac fibroblasts primarily regulate this process, we explored how candidate anti-fibrotic drugs alter the fibroblast phenotype. We identify a set of 137 phenotypic features that change in response to drug treatments. Using a new computational modeling approach termed logic-based mechanistic machine learning, we predict how pirfenidone and Src inhibition affect the regulation of the phenotypic features actin filament assembly and actin-myosin stress fiber formation. We also show that inhibition of PI3K reduces actin-myosin stress fiber formation and procollagen I production in human cardiac fibroblasts, supporting a role for PI3K as a mechanism by which Src inhibition may suppress fibrosis.

systems biology↗

bioRxiv2021

KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data

Kinase inhibitors are one of the largest classes of FDA-approved drugs and are major targets in oncology. Although kinase inhibitors have played an important role in improving cancer outcomes, major challenges still exist, including the development of resistance and failure to respond to treatments. Improvements for tumor profiling of kinase activity would be an important step in improving treatment outcomes and identifying effective kinase targets. Here, we present a graph- and statistics-based algorithm, called KSTAR, which harnesses the phosphoproteomic profiling of human cells and tissues by predicting kinase activity profiles from the observed phosphorylation of kinase substrates. The algorithm is based on the hypothesis that the more active a kinase is, the more of its substrates will be observed in a phosphoproteomic experiment. This method is error- and bias-aware in its approach, overcoming challenges presented by the variability of phosphoproteomic pipelines, limited information about kinase-substrate relationships, and limitations of global kinase-substrate predictions, such as training set bias and high overlap between predicted kinase networks. We demonstrate that the predicted kinase activities: 1) reproduce physiologically-relevant expectations and generates novel hypotheses within cell-specific experiments, 2) improve the ability to compare phosphoproteomic samples on the same tissues from different labs, and 3) identify tissue-specific kinase profiles. Global benchmarking and comparison to other algorithms demonstrates that KSTAR is particularly superior for predicting tyrosine kinase activities and, given its focus on utilizing more of the available phosphoproteomic data, significantly less sensitive to study bias. Finally, we apply the approach to complex human tissue biopsies in breast cancer, where we find that KSTAR activity predictions complement current clinical standards for identifying HER2-status - KSTAR can identify clinical false positives, patients who will fail to respond to inhibitor therapy, and clinically defined HER2-negative patients that might benefit from HER2-targeted therapy. KSTAR will be useful for both basic biological understanding of signaling networks and for improving clinical outcomes through improved clinical trial design, identification of new and/or combination therapies, and for identifying the failure to respond to targeted kinase therapies.

systems biology↗

bioRxiv2020

KinPred: A unified approach and resource for human kinase-substrate predictions

Tyrosine and serine/threonine kinases are essential regulators of cell processes and are important targets for human therapies. Unfortunately, very little is known about specific kinase-substrate relationships, making it difficult to infer meaning from dysregulated phosphoproteomic datasets or for researchers to identify possible kinases that regulate specific or novel phosphorylation sites. The last two decades have seen an explosion in algorithms to extrapolate from what little is known into the larger unknown - predicting kinase relationships with site-specific substrates using a variety of approaches that include the sequence-specificity of kinase catalytic domains and various other factors, such as evolutionary relationships, coexpression, and protein-protein interaction networks. Unfortunately, a number of limitations prevent researchers from easily harnessing these resources, such as loss of resource accessibility, limited information in publishing that results in a poor mapping to a human reference, and not being updated to match the growth of the human phosphoproteome. Here, we propose a methodological framework for publishing predictions in a unified way, which entails ensuring predictions have been run on a current reference proteome, mapping the same substrates and kinases across resources to a common reference, filtering for the human phosphoproteome, and providing methods for updating the resource easily in the future. We applied this framework on three currently available resources, published in the last decade, which provide kinase-specific predictions in the human proteome. Using the unified datasets, we then explore the role of study bias, the emergent network properties of these predictive algorithms, and comparisons within and between predictive algorithms. The combination of the code for unification and analysis, as well as the unified predictions are available under the resource we named KinPred. We believe this resource will be useful for a wide range of applications and establishes best practices for long-term usability and sustainability for new and existing predictive algorithms.

molecular biology↗

bioRxiv2020

New analysis pipeline for high-throughput domain-peptide affinity experiments improves SH2 interactiondata

Protein domain interactions with short linear peptides, such as Src homology 2 (SH2) domain interactions with phosphotyrosine-containing peptide motifs (pTyr), are ubiquitous and important to many biochemical processes of the cell. The desire to map and quantify these interactions has resulted in the development of high-throughput (HTP) quantitative measurement techniques, such as microarray or fluorescence polarization assays. For example, in the last 15 years, experiments have progressed from measuring single interactions to covering 500,000 of the 5.5 million possible SH2-pTyr interactions in the human proteome. However, high variability in affinity measurements and disagreements about positive interactions between published datasets led us to re-evaluate the analysis methods and raw data of published SH2-pTyr HTP experiments. We identified several opportunities for improving the identification of positive and negative interactions, and the accuracy of affinity measurements. We implemented model fitting techniques that are more statistically appropriate for the non-linear SH2-pTyr interaction data. We developed a novel method to account for protein concentration errors due to impurities and degradation, as well as addressing protein inactivity and aggregation. Our revised analysis increases reported affinity accuracy, reduces the false negative rate, and results in an increase in useful data due to the addition of reliable true negative results. We demonstrate improvement in classification of binding vs non-binding when using machine learning techniques, suggesting improved coherence in the reanalyzed datasets. We present revised SH2-pTyr affinity results, and propose a new analysis pipeline for future HTP measurements of domain-peptide interactions.

systems biology↗

bioRxiv2019

ProteoClade: a taxonomic toolkit for multi-species and metaproteomic analysis

We present ProteoClade, a Python toolkit that performs taxa-specific peptide assignment, protein inference, and quantitation for multi-species proteomics experiments. ProteoClade scales to hundreds of millions of protein sequences, requires minimal computational resources, and is open source, multi-platform, and accessible to non-programmers. We demonstrate its utility for processing quantitative proteomic data derived from patient-derived xenografts and its speed and scalability enable a novel de novo proteomic workflow for complex microbiota samples.

bioinformatics↗

bioRxiv2017

Accuracy through Subsampling of Protein EvolutioN: Analyzing and reconstructing protein divergence using an ensemble approach

Mapping the history of ancestor divergence can yield critical insight into protein function. However, reconstruction of this evolutionary history is subject to a great deal of uncertainty, as selection and alignment of extant descendants to represent each ancestor dramatically affects phylogenetic inference of all ancestral nodes. Although historically problematic and somewhat ignored, here we capitalize on that variability by subsampling descendants of each ancestor, generating an ensemble of reconstructions, and integrating over the resulting topological diversity to improve the accuracy of ancestor divergence reconstruction, above the level of subsampling. First, we demonstrate that reproducibility of an all-sequence, single-alignment reconstruction, measured by comparing topologies inferred from 90% subsamples, can be used to assess the likely accuracy of single-alignment reconstructions. We believe this relationship results from modulation of the likelihood landscape around the true topology induced by alignments of resampled sequences. Second, we hypothesize that topological features observed consistently across an ensemble of subsampled reconstructions best reflect true evolutionary history, despite the inaccuracy of each individual reconstruction due to the failure by substitution models to infer the correct sequence of mutations that produced the input sequences. We present a scoring function that evaluates topologies on the basis of their consistency with features extracted from an ensemble, and a reconstruction algorithm (ASPEN) that produces and ranks topologies according to that function. We test our methodology on 600 simulated protein families and the LacI transcription factor family, and find that ASPEN topologies are significantly more accurate than single-alignment reconstructions.

molecular biology↗