Biology subjects

Dickson, A. M.

Publications and source records attributed to Dickson, A. M..

4 recordsLinked to original sources

bioRxiv2025

Semi-supervised Retrieval of Functional Residues Through the Integration of Protein Language Models and Gene Ontology Data

MotivationExperimental studies of protein function often focus on mechanistic descriptions, characterizing how specific sites and residues contribute to activity. Abstractions such as domains and active sites enable quantitative descriptions of how protein features act biologically. Thanks to the abundance of high-quality sequence and function data, machine learning has achieved great success in directly predicting protein function. However, translating functional characterizations into mechanistic ones on the level of the domains, binding sites, or motifs remains challenging. This represents a semi-supervised problem: sequences and global functional labels are available, but local annotations must be inferred. ResultsWe investigate the unsupervised discovery of functionally active protein regions by integrating protein sequence models with functional information. We first formalize the residue-level functional annotation problem by constructing unified evaluation datasets linking Gene Ontology functions to annotated residues. Eight datasets are assembled, spanning levels of specificity from single active-site residues to domains covering up to 60% of a protein. We then introduce a new class of function-conditioned generative models that more accurately predict functionally important residues than existing approaches, including interpretability methods and PSSM entropy estimation, across multiple benchmark datasets. Availabilitygithub.com/mofradlab/go_interp Contactmofrad@berkeley.edu

bioinformatics↗

bioRxiv2024

MetaBiome: A Multiscale Model Integrating Agent-Based Modeling and Metabolic Networks Reveals Spatial Regulation in Mucosal Microbial Communities

Mucosal microbial communities (MMCs) are complex ecosystems near the mucosal layers of the gut, essential for maintaining health and modulating disease states. Despite advances in high-throughput omics technologies, current methodologies struggle to capture the dynamic metabolic interactions and spatiotemporal variations within MMCs. In this work, we present MetaBiome, a multiscale model integrating agent-based modeling (ABM), finite volume methods, and constraint-based models to explore the metabolic interactions within these communities. Integrating ABM allows for the detailed representation of individual microbial agents, each governed by rules that dictate cell growth, division, and interactions with their surroundings. Through a layered approach--encompassing environmental conditions, agent information, and metabolic pathways--we simulated different communities to showcase the potential of the model. Using our in-silico platform, we explored the dynamics and spatiotemporal patterns of MMCs in the proximal small intestine and the cecum, simulating the physiological conditions of the two gut regions. Our findings revealed how specific microbes adapt their metabolic processes based on substrate availability and local environmental conditions, shedding light on spatial metabolite regulation and informing targeted therapies for localized gut diseases. MetaBiome provides a detailed representation of microbial agents and their interactions, surpassing the limitations of traditional grid-based systems. This work marks a significant advancement in microbial ecology as it offers new insights into predicting and analyzing microbial communities. ImportanceOur study presents a novel multiscale model that combines agent-based modeling, finite volume methods, and genome-scale metabolic models to simulate the complex dynamics of mucosal microbial communities in the gut. This integrated approach allows us to capture spatial and temporal variations in microbial interactions and metabolism that are difficult to study experimentally. Key findings from our model include: O_LIPrediction of metabolic cross-feeding and spatial organization in multi-species communities C_LIO_LIInsights into how oxygen gradients and nutrient availability shape community composition in different gut regions C_LIO_LIIdentification of spatially-regulated metabolic pathways and enzymes in E. coli C_LI We believe this work represents a significant advance in computational modeling of microbial communities and provides new insights into the spatial regulation of gut microbiome metabolism. The multiscale modeling approach we have developed could be broadly applicable for studying other complex microbial ecosystems.

microbiology↗

bioRxiv2023

Fine-tuning Protein Embeddings for GeneralizableAnnotation Propagation

A central goal of bioinformatics research is to understand proteins on a functional level, typically by extrapolating from experimental results with the protein sequence information. One strategy is to assume that proteins with similar sequences will also share function. This has the benefit of being interpretable; it gives a very clear idea of why a protein might have a particular function by comparing with the most similar reference example. However, direct machine learning classifiers now outperform pure sequence similarity methods in raw prediction ability. A hybrid method is to use pre-trained language models to create protein embeddings, and then indirectly predict protein function using their relative similarity. We find that fine-tuning an auxiliary objective on protein function indirectly improves these hy-brid methods, to the point that they are in some cases better than direct classifiers. Our empirical results demonstrate that interpretable protein comparison models can be developed using fine-tuning techniques, without cost, or even with some benefit, to overall performance. K-nearest neighbors (KNN) embedding-based models also offer free generalization to previously unknown classes, while continuing to outperform only pre-trained models, further demonstrating the potential of fine-tuned embeddings outside of direct classification. Significance StatementFor the purposes of biological understanding, or engineering, it is particularly important that protein annotation is done through interpretable means, such as comparison to existing proteins with known properties. Our work builds upon previous efforts to do so by embedding proteins into high-dimensional vectors with pre-trained protein language models. We demonstrate that further training of these models on protein function classification drastically improves their performance, while allowing for exactly the same similarity based classifications on both known and novel potential functions.

bioinformatics↗

bioRxiv2022

GO Bench: Shared-hub for Universal Benchmarking of Machine Learning-Based Protein Functional Annotations

MotivationGene annotation is the problem of mapping proteins to their functions represented as Gene Ontology terms, typically inferred based on the primary sequences. Gene annotation is a multi-label multi-class classification problem, which has generated growing interest for its uses in the characterization of millions of proteins with unknown functions. However, there is no standard GO dataset used for benchmarking the newly developed new machine learning models within the bioinformatics community. Thus, the significance of improvements for these models remains unclear. SummaryThe Gene Benchmarking database is the first effort to provide an easy-to-use and configurable hub for the learning and evaluation of gene annotation models. It provides easy access to pre-specified datasets and takes the non-trivial steps of preprocessing and filtering all data according to custom presets using a web interface. The GO bench web application can also be used to evaluate and display any trained model on leaderboards for annotation tasks. Availability and ImplementationThe GO Benchmarking dataset is freely available at llp.berkeley.edu/GO_bench/dataset_form, and code is available at http://github.com/amdson/GO_pipeline. Contactamdickson@berkeley.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗