Biology subjects

Gifford, D. K.

Publications and source records attributed to Gifford, D. K..

At least 19 recordsLinked to original sources

bioRxiv2025

Combinatorial factorizable libraries outperform enumerated and random libraries for antibody discovery

The effective use of high-throughput functional assays to discover novel biological therapeutics requires a diverse library of desirable candidates. Here we design candidate antibody libraries using factorizable neural networks (FNNs) that permit the economic synthesis of factorizable libraries that are depleted of non-specific binders. We experimentally evaluated an FNN designed antibody CDR-H3 factorizable library that is designed to contain binders to peptide-MHC (pMHC) complexes with depletion of library sequences that exhibit non-specific pMHC binding. The factorizable library comprises 104 prefixes that were randomly combined with 104 suffixes. We find that it contains more viable candidates than designed libraries with the same DNA synthesis budget on our pMHC binding task. FNNs make it possible to efficiently computationally design combinatorial libraries, a task that is not possible with conventional methods.

immunology↗

bioRxiv2023

Machine learning model interpretations explain T cell receptor binding

T cells mediate immune responses against pathogens and cancer through T cell receptors (TCRs) that recognize foreign peptides presented on the cell surface by Major Histocompatibility Complex (MHC) proteins. TCRs carry enormous diversity and differ across individuals, and mechanisms that determine TCR-pMHC binding are poorly understood. The ability to predict TCR-pMHC interactions would accelerate development of cellular therapeutics to design TCRs that specifically bind to a target of interest. We designed a randomized library of 196 TCR CDR3{beta} sequences and experimentally evaluated their affinities for the Tax-A02 peptide-MHC target. We trained ML models that predict TCR binding to Tax-A02 from TCR sequence and used model interpretation to identify TCR sequence features associated with binding. We found these features accurately mirror the true sequence features in our experimental data.

immunology↗

bioRxiv2023

Systematic elucidation of genetic mechanisms underlying cholesterol uptake

Genetic variation contributes greatly to LDL cholesterol (LDL-C) levels and coronary artery disease risk. By combining analysis of rare coding variants from the UK Biobank and genome-scale CRISPR-Cas9 knockout and activation screening, we have substantially improved the identification of genes whose disruption alters serum LDL-C levels. We identify 21 genes in which rare coding variants significantly alter LDL-C levels at least partially through altered LDL-C uptake. We use co-essentiality-based gene module analysis to show that dysfunction of the RAB10 vesicle transport pathway leads to hypercholesterolemia in humans and mice by impairing surface LDL receptor levels. Further, we demonstrate that loss of function of OTX2 leads to robust reduction in serum LDL-C levels in mice and humans by increasing cellular LDL-C uptake. Altogether, we present an integrated approach that improves our understanding of genetic regulators of LDL-C levels and provides a roadmap for further efforts to dissect complex human disease genetics.

genetics↗

bioRxiv2022

A pan-variant mRNA-LNP T cell vaccine protects HLA transgenic mice from mortality after infection with SARS-CoV-2 Beta

Clinically licensed COVID-19 vaccines ameliorate viral infection by inducing vaccinee production of neutralizing antibodies that bind to the SARS-CoV-2 Spike protein to inhibit viral cellular entry (Walsh et al., 2020; Baden et al., 2021), however the clinical effectiveness of these vaccines is transitory as viral variants arise that escape antibody neutralization (Tregoning et al., 2021; Willett et al., 2022). Vaccines that solely rely upon a T cell response to combat viral infection could be transformational because they can be based on highly conserved short peptide epitopes that hold the potential for pan-variant immunity, but a mRNA-LNP T cell vaccine has not been shown to be sufficient for effective antiviral prophylaxis. Here we show that a mRNA-LNP vaccine based on highly conserved short peptide epitopes activates a CD8+ and CD4+ T cell response that prevents mortality in HLA-A*02:01 transgenic mice infected with the SARS-CoV-2 Beta variant of concern (B.1.351). In mice vaccinated with the T cell vaccine, 24% of the nucleated cells in lung were CD8+ T cells on day 7 post infection. This was 5.5 times more CD8+ T cell infiltration of the lungs in response to infection compared to the Pfizer-BioNTech Comirnaty(R) vaccine. Between days 2 and 7 post infection, the number of CD8+ T cells in the lung increased in mice vaccinated with the T cell vaccine and decreased in mice vaccinated with Comirnaty(R). The T cell vaccine did not produce neutralizing antibodies, and thus our results demonstrate that SARS-CoV-2 viral infection can be controlled by a T cell response alone. Our results suggest that further study is merited for pan-variant T cell vaccines, and that T cell vaccines may be relevant for individuals that cannot produce neutralizing antibodies or to help mitigate Long COVID.

immunology↗

bioRxiv2022

A high-throughput yeast display approach to profile pathogen proteomes for MHC-II binding

T cells play a critical role in the adaptive immune response, recognizing peptide antigens presented on the cell surface by Major Histocompatibility Complex (MHC) proteins. While assessing peptides for MHC binding is an important component of probing these interactions, traditional assays for testing peptides of interest for MHC binding are limited in throughput. Here we present a yeast display-based platform for assessing the binding of tens of thousands of user-defined peptides in a high throughput manner. We apply this approach to assess a tiled library covering the SARS-CoV-2 proteome and four dengue virus serotypes for binding to human class II MHCs, including HLA-DR401, -DR402, and -DR404. This approach identifies binders missed by computational prediction, highlighting the potential for systemic computational errors given even state-of-the-art training data, and underlines design considerations for epitope identification experiments. This platform serves as a framework for examining relationships between viral conservation and MHC binding, and can be used to identify potentially high-interest peptide binders from viral proteins. These results demonstrate the utility of this approach for determining high-confidence peptide-MHC binding.

immunology↗

bioRxiv2022

Ultra high diversity factorizable libraries for efficient therapeutic discovery

The successful discovery of novel biological therapeutics by selection requires highly diverse libraries of candidate sequences that contain a high proportion of desirable candidates. Here we propose the use of computationally designed factorizable libraries made of concatenated segment libraries as a method of creating large libraries that meet an objective function at low cost. We show that factorizable libraries can be designed efficiently by representing objective functions that describe sequence optimality as an inner product of feature vectors, which we use to design an optimization method we call Stochastically Annealed Product Spaces (SAPS). We then use this approach to design diverse and efficient libraries of antibody CDR-H3 sequences with various optimized characteristics.

bioengineering↗

bioRxiv2021

seqgra: Principled Selection of Neural Network Architectures for Genomics Prediction Tasks

Sequence models based on deep neural networks have achieved state-of-the-art performance on regulatory genomics prediction tasks, such as chromatin accessibility and transcription factor binding. But despite their high accuracy, their contributions to a mechanistic understanding of the biology of regulatory elements is often hindered by the complexity of the predictive model and thus poor interpretability of its decision boundaries. To address this, we introduce seqgra, a deep learning pipeline that incorporates the rule-based simulation of biological sequence data and the training and evaluation of models, whose decision boundaries mirror the rules from the simulation process. The method can be used to (1) generate data under the assumption of a hypothesized model of genome regulation, (2) identify neural network architectures capable of recovering the rules of said model, and (3) analyze a models predictive performance as a function of training set size, noise level, and the complexity of the rules behind the simulated data.

bioinformatics↗

bioRxiv2021

spatzie: An R package for identifying significant transcription factor motif co-enrichment from enhancer-promoter interactions

Genomic interactions provide important context to our understanding of the state of the genome. One question is whether specific transcription factor interactions give rise to genome organization. We introduce spatzie, an R package and a website that implements statistical tests for significant transcription factor motif cooperativity between enhancer-promoter interactions. We conducted controlled experiments under realistic simulated data from ChIP-seq to confirm spatzie is capable of discovering co-enriched motif interactions even in noisy conditions. We then use spatzie to investigate cell type specific transcription factor cooperativity within recent human ChIA-PET enhancer-promoter interaction data. The method is available online at https://spatzie.mit.edu.

bioinformatics↗

bioRxiv2021

Ranking Reprogramming Factors for Directed Differentiation

Transcription factor over-expression is a proven method for reprogramming cells to a desired cell type for regenerative medicine and therapeutic discovery. However, a general method for the identification of reprogramming factors to create an arbitrary cell type is an open problem. We examine the success rate of methods and data for directed differentiation by testing the ability of nine computational methods (CellNet, GarNet, EBSeq, AME, DREME, HOMER, KMAC, diffTF, and DeepAccess) to correctly discover and rank candidate factors for eight target cell types with known reprogramming solutions. We compare methods that utilize gene expression, biological networks, and chromatin accessibility data to identify eight sets of known reprogramming factors and comprehensively test parameter and pre-processing of input data to optimize performance of these methods. We find the best factor identification methods can identify an average of 50-60% of reprogramming factors within the top 10 candidates, and methods that use chromatin accessibility perform the best. Among the chromatin accessibility methods, complex methods DeepAccess and diffTF are more likely to consistently correctly rank the significance of transcription factor candidates within reprogramming protocols for differentiation. We provide evidence that AME and DeepAccess are optimal methods for transcription factor recovery and ranking which will allow for systematic prioritization of transcription factor candidates to aid in the design of novel reprogramming protocols.

systems biology↗

bioRxiv2021

Preliminary Immunogenicity of a Pan-COVID-19 T Cell Vaccine in HLA-A*02:01 Mice

New strains of SARS-CoV-2 have emerged, including B.1.351 and P.1, that demonstrate increased transmissibility and the potential of rendering current SARS-CoV-2 vaccines less effective. A concern is that existing SARS-CoV-2 spike subunit vaccines produce neutralizing antibodies to three dimensional spike epitopes that are subject to change during viral drift. Here we provide an initial report on the hypothesis that adaptive T cell based immunity may provide a path for a pan-COVID-19 vaccine that is resilient to viral drift. T cell based adaptive immunity can be based on short peptide sequences selected from the viral proteome that are less subject to drift, and can utilize multiple such epitopes to provide redundancy in the event of drift. We find that SARS-CoV-2 peptides contained in a mRNA-LNP T cell vaccine for SARS-CoV-2 are immunogenic in mice transgenic for the human HLA-A*02:01 gene. We plan to test the efficacy of this vaccine with SARS-CoV-2 B.1.351 challenge trials with HLA-A*02:01 mice.

immunology↗

bioRxiv2021

General and cell-type-specific aspects of the motor neuron maturation transcriptional program

Building a nervous system is a protracted process that starts with the specification of individual neuron types and ends with the formation of mature neural circuits. The molecular mechanisms that regulate the temporal progression of maturation in individual cell types remain poorly understood. In this work, we have mapped the gene expression and chromatin accessibility changes in mouse spinal motor neurons throughout their lifetimes. We found that both motor neuron gene expression and putative regulatory elements are dynamic during the first three weeks of postnatal life, when motor circuits are maturing. Genes that are up-regulated during this time contribute to adult motor neuron diversity and function. Almost all of the chromatin regions that gain accessibility during maturation are motor neuron specific, yet a majority of the transcription factor binding motifs enriched in these regions are shared with other mature neurons. Collectively, these findings suggest that a core transcriptional program operates in a context-dependent manner to access cell-type-specific cis-regulatory systems associated with maturation genes. Discovery of general principles governing neuronal maturation might inform methods for transcriptional reprogramming of neuronal age and for improved modelling of age-related neurodegenerative diseases.

developmental biology↗

bioRxiv2021

Discovering differential genome sequence activity with interpretable and efficient deep learning

Discovering sequence features that differentially direct cells to alternate fates is key to understanding both cellular development and the consequences of disease related mutations. We introduce Expected Pattern Effect and Differential Expected Pattern Effect, two black-box methods that can interpret genome regulatory sequences for cell type-specific or condition specific patterns. We show that these methods identify relevant transcription factor motifs and spacings that are predictive of cell state-specific chromatin accessibility. Finally, we integrate these methods into framework that is readily accessible to non-experts and available for download as a binary or installed via PyPI or bioconda at https://cgs.csail.mit.edu/deepaccess-package/. Author SummaryWithin the genome are the instructions to build all the cell types that make up the human body. However, understanding these instructions and how and when these instructions go wrong in cancer or genetically inherited disease is an open problem. Deep neural networks provide powerful models to learn the relationship between DNA sequence and functional consequence across many different cell types, such as whether a particular stretch of DNA is accessible and genes in that region can be expressed or is inaccessible and therefore genes are inactive. Despite these advances, a major setback in deep learning is that it is challenging to understand what patterns of DNA sequences a deep learning model has learned to associate with a particular genomic function, whether these patterns are significant, and how to determine whether these patterns are specific to a particular cell type or are general "housekeeping" patterns that function across many cell types. We introduce Expected Pattern Effect and Differential Expected Pattern Effect, two methods which allow us to evaluate the significance of particular patterns of DNA sequence features on models trained to predict function across multiple cell types, and apply this to problems of transcription factor binding and DNA accessibility across multiple cell types.

bioinformatics↗

bioRxiv2020

Extracting and Interpreting the Effects of Higher Order Sequence Features on Peptide MHC Binding

Understanding the factors contributing to peptide MHC (pMHC) affinity is critical for the study of immune responses and the development of novel therapeutics. Developments in yeast display platforms have enabled the collection of pMHC binding data for vast libraries of peptides. However, methods for interpreting this data are still at an early stage. In this work we propose an approach for extracting peptide sequence features that affect pMHC binding from such datasets. In the process we develop the theoretical framework for fitting and interpreting these features. We demonstrate that these features accurately capture the kinetics underlying pMHC binding, and can be used to predict pMHC binding well enough to rival the current state of the art. We then analyze the extracted factors and show that they correlate with our current structural understanding of MHC molecules. Finally, we discuss the implication these factors have on the complexity of peptide engineering.

immunology↗

bioRxiv2020

High resolution discovery of regulatory DNA with synthetic wild-type and ablated genome constructs

Genomic enhancer elements play a role in modulating gene expression by interacting with effector proteins known as transcription factors. We present a new computational method, "STARR-scan", which identifies transcription factor motif appearances in synthetic DNA constructs that are correlated with enhancer activity. Using a tiled STARR-seq assay, we demonstrate that STARR-scan can suggest the biological function of transcription factors previously suspected to activate the APOBEC-3B gene. Using the same methodology, STARR-scan can suggest transcription factors with previously unknown biological activity which may regulate the APOBEC-3B gene. These novel factors may have biological significance in our understanding of cancer biology.

genomics↗

bioRxiv2020

Generative modeling of single-cell population time series for inferring cell differentiation landscapes

Existing computational methods that use single-cell RNA-sequencing for cell fate prediction either summarize observations of cell states and their couplings without modeling the underlying differentiation process, or are limited in their capacity to model complex differentiation landscapes. Thus, contemporary methods cannot predict how cells evolve stochastically and in physical time from an arbitrary starting expression state, nor can they model the cell fate consequences of gene expression perturbations. We introduce PRESCIENT (Potential eneRgy undErlying Single Cell gradIENTs), a generative modeling framework that learns an underlying differentiation landscape from single-cell time-series gene expression data. Our generative model framework provides insight into the process of differentiation and can simulate differentiation trajectories for arbitrary gene expression progenitor states. We validate our method on a recently published experimental lineage tracing dataset that provides observed trajectories. We show that this model is able to predict the fate biases of progenitor cells in neutrophil/macrophage lineages when accounting for cell proliferation, improving upon the best-performing existing method. We also show how a model can predict trajectories for cells not found in the models training set, including cells in which genes or sets of genes have been perturbed. PRESCIENT is able to accommodate complex perturbations of multiple genes, at different time points and from different starting cell populations. PRESCIENT models are able to recover the expected effects of known modulators of cell fate in hematopoiesis and pancreatic {beta} cell differentiation.

systems biology↗

bioRxiv2020

Machine learning optimization of peptides for presentation by class II MHCs

T cells play a critical role in normal immune responses to pathogens and cancer and can be targeted to MHC-presented antigens via interventions such as peptide vaccines. Here, we present a machine learning method to optimize the presentation of peptides by class II MHCs by modifying the peptides anchor residues. Our method first learns a model of peptide affinity for a class II MHC using an ensemble of deep residual networks, and then uses the model to propose anchor residue changes to improve peptide affinity. We use a high throughput yeast display assay to show that anchor residue optimization successfully improved peptide binding.

immunology↗

bioRxiv2020

Predicted Cellular Immunity Population Coverage Gaps for SARS-CoV-2 Subunit Vaccines and their Augmentation by Compact Joint Sets

Subunit vaccines induce immunity to a pathogen by presenting a component of the pathogen and thus inherently limit the representation of pathogen peptides for cellular immunity based memory. We find that SARS-CoV-2 subunit peptides may not be robustly displayed by the Major Histocompatibility Complex (MHC) molecules in certain individuals. We introduce an augmentation strategy for subunit vaccines that adds a small number of SARS-CoV-2 peptides to a vaccine to improve the population coverage of pathogen peptide display. Our population coverage estimates integrate clinical data on peptide immunogenicity in convalescent COVID-19 patients and machine learning predictions. We evaluate the population coverage of 9 different subunits of SARS-CoV-2, including 5 functional domains and 4 full proteins, and augment each of them to fill a predicted coverage gap.

immunology↗

bioRxiv2020

Robust computational design and evaluation of peptide vaccines for cellular immunity with application to SARS-CoV-2

We present a combinatorial machine learning method to evaluate and optimize peptide vaccine formulations, and we find for SARS-CoV-2 that it provides superior predicted display of viral epitopes by MHC class I and MHC class II molecules over populations when compared to other candidate vaccines. Our method is robust to idiosyncratic errors in the prediction of MHC peptide display and considers target population HLA haplotype frequencies during optimization. To minimize clinical development time our methods validate vaccines with multiple peptide presentation algorithms to increase the probability that a vaccine will be effective. We optimize an objective function that is based on the presentation likelihood of a diverse set of vaccine peptides conditioned on a target population HLA haplotype distribution and expected epitope drift. We produce separate peptide formulations for MHC class I loci (HLA-A, HLA-B, and HLA-C) and class II loci (HLA-DP, HLA-DQ, and HLA-DR) to permit signal sequence based cell compartment targeting using nucleic acid based vaccine platforms. Our SARS-CoV-2 MHC class I vaccine formulations provide 93.21% predicted population coverage with at least five vaccine peptide-HLA hits on average in an individual ([≥] 1 peptide 99.91%) with all vaccine peptides perfectly conserved across 4,690 geographically sampled SARS-CoV-2 genomes. Our MHC class II vaccine formulations provide 90.17% predicted coverage with at least five vaccine peptide-HLA hits on average in an individual with all peptides having observed mutation probability [≤] 0.001. We evaluate 29 previously published peptide vaccine designs with our evaluation tool with the requirement of having at least five vaccine peptide-HLA hits per individual, and they have a predicted maximum of 58.51% MHC class I coverage and 71.65% MHC class II coverage given haplotype based analysis. We provide an open source implementation of our design methods (OptiVax), vaccine evaluation tool (EvalVax), as well as the data used in our design efforts.

immunology↗