Biology subjects

Colwell, L. J.

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

5 recordsLinked to original sources

bioRxiv2024

ProtEx: A Retrieval-Augmented Approach for Protein Function Prediction

Mapping a protein sequence to its underlying biological function is a critical problem of increasing importance in biology. In this work, we propose ProtEx, a retrieval-augmented approach for protein function prediction that leverages exemplars from a database to improve accuracy and robustness and enable generalization to unseen classes. Our approach relies on a novel multi-sequence pretraining task, and a fine-tuning strategy that effectively conditions predictions on retrieved exemplars. Our method achieves state-of-the-art results across multiple datasets and settings for predicting Enzyme Commission (EC) numbers, Gene Ontology (GO) terms, and Pfam families. Our ablations and analysis highlight the impact of conditioning predictions on exemplar sequences, especially for classes and sequences less well represented in the training data.

bioinformatics↗

bioRxiv2024

Engineering of highly active and diverse nuclease enzymes by combining machine learning and ultra-high-throughput screening

Optimizing enzymes to function in novel chemical environments is a central goal of synthetic biology, but optimization is often hindered by a rugged, expansive protein search space and costly experiments. In this work, we present TeleProt, an ML framework that blends evolutionary and experimental data to design diverse protein variant libraries, and employ it to improve the catalytic activity of a nuclease enzyme that degrades biofilms that accumulate on chronic wounds. After multiple rounds of high-throughput experiments using both TeleProt and standard directed evolution (DE) approaches in parallel, we find that our approach found a significantly better top-performing enzyme variant than DE, had a better hit rate at finding diverse, high-activity variants, and was even able to design a high-performance initial library using no prior experimental data. We have released a dataset of 55K nuclease variants, one of the most extensive genotype-phenotype enzyme activity landscapes to date, to drive further progress in ML-guided design.

bioinformatics↗

bioRxiv2019

Critiquing Protein Family Classification Models Using Sufficient Input Subsets

In many application domains, neural networks are highly accurate and have been deployed at large scale. However, users often do not have good tools for understanding how these models arrive at their predictions. This has hindered adoption in fields such as the life and medical sciences, where researchers require that models base their decisions on underlying biological phenomena rather than peculiarities of the dataset introduced. In response, we propose a set of methods for critiquing deep learning models and demonstrate their application for protein family classification, a task for which high-accuracy models have considerable potential impact. Our methods extend the sufficient input subsets technique, which we use to identify subsets of features (SIS) in each protein sequence that are alone sufficient for classification. Our suite of tools analyzes these subsets to shed light on the decision-making criteria employed by models trained on this task. These tools expose that while deep models may perform classification for biologically-relevant reasons, their behavior varies considerably across choice of network architecture and parameter initialization. While the techniques that we develop are specific to the protein sequence classification task, the approach taken generalizes to a broad set of scientific contexts in which model interpretability is essential.

bioinformatics↗

bioRxiv2019

Using Deep Learning to Annotate the Protein Universe

Understanding the relationship between amino acid sequence and protein function is a long-standing problem in molecular biology with far-reaching scientific implications. Despite six decades of progress, state-of-the-art techniques cannot annotate 1/3 of microbial protein sequences, hampering our ability to exploit sequences collected from diverse organisms. In this paper, we explore an alternative methodology based on deep learning that learns the relationship between unaligned amino acid sequences and their functional annotations across all 17929 families of the Pfam database. Using the Pfam seed sequences we establish rigorous benchmark assessments that use both random and clustered data splits to control for potentially confounding sequence similarities between train and test sequences. Using Pfam full, we report convolutional networks that are significantly more accurate and computationally efficient than BLASTp, while learning sequence features such as structural disorder and transmembrane helices. Our model co-locates sequences from unseen families in embedding space, allowing sequences from novel families to be accurately annotated. These results suggest deep learning models will be a core component of future protein function prediction tools.

bioinformatics↗

bioRxiv2018

RNA structure prediction including pseudoknots through direct enumeration of states

The accurate prediction of RNA secondary structure from primary sequence has had enormous impact on research from the past forty years. While many algorithms are available to make these predictions, the inclusion of non-nested loops, termed pseudoknots, still poses challenges. Here, we describe a new method to compute the entire free energy landscape of secondary structures of RNA resulting from a primary RNA sequence, by combining a polymer physics model for the entropy of pseudoknots with exhaustive enumeration of the set of possible structures. Our polymer physics model can address arbitrarily complex pseudoknots and has only two free loop entropy parameters that correspond to concrete physical quantities, over an order of magnitude fewer than even the sparsest state-of-the-art algorithms. Our model outperforms previously published methods in predicting pseudoknots, while performing on par with current methods in the prediction of non-pseudoknotted structures. For RNA sequences of ~ 45 nucleotides, or ~ 90 with minimal heuristics, the complet-e enumeration of possible secondary structures can be accomplished quickly despite the NP-complete nature of the problem.

biophysics↗