DeepSELEX: inferring DNA-binding preferences from HT-SELEX data using multi-class CNNs

Aptamer-based therapy for fighting biofilm-associated infections

Biofilms are key players in the pathogenesis of most of chronic infections associated with host tissue or fluids and indwelling medical devices. These chronic infections are hard to be treated due to the increased biofilms tolerance towards antibiotics in comparison to planktonic (or free living) cells. Despite the advanced understanding of their formation and physiology, biofilms continue to be a challenge and there is no standardized therapeutic approach in clinical practice to eradicate them. Aptamers offer distinctive properties, including excellent affinity, selectivity, stability, making them valuable tools for therapeutic purposes. This review explores the flexibility and designability of aptamers as antibiofilm drugs but, importantly, as targeting tools for diverse drug and delivery systems. It highlights specific examples of application of aptamers in biofilms of diverse species according to different modes of action including inhibition of motility and adhesion, blocking of quorum sensing molecules, and dispersal of biofilm-cells to planktonic state. Moreover, it discusses the limitations and challenges that impaired an increased success of the use of aptamers on biofilm management, as well as the opportunities related to aptamers modifications that can significantly expand their applicability on the biofilm field.

ExplaiNN: interpretable and transparent neural networks for genomics

Deep learning models such as convolutional neural networks (CNNs) excel in genomic tasks but lack interpretability. We introduce ExplaiNN, which combines the expressiveness of CNNs with the interpretability of linear models. ExplaiNN can predict TF binding, chromatin accessibility, and de novo motifs, achieving performance comparable to state-of-the-art methods. Its predictions are transparent, providing global (cell state level) as well as local (individual sequence level) biological insights into the data. ExplaiNN can serve as a plug-and-play platform for pretrained models and annotated position weight matrices. ExplaiNN aims to accelerate the adoption of deep learning in genomic sequence analysis by domain experts.

DNA binding specificity of all four Saccharomyces cerevisiae forkhead transcription factors

Quantifying the nucleotide preferences of DNA binding proteins is essential to understanding how transcription factors (TFs) interact with their targets in the genome. High-throughput in vitro binding assays have been used to identify the inherent DNA binding preferences of TFs in a controlled environment isolated from confounding factors such as genome accessibility, DNA methylation, and TF binding cooperativity. Unfortunately, many of the most common approaches for measuring binding preferences are not sensitive enough for the study of moderate-to-low affinity binding sites, and are unable to detect small-scale differences between closely related homologs. The Forkhead box (FOX) family of TFs is known to play a crucial role in regulating a variety of key processes from proliferation and development to tumor suppression and aging. By using the high-sequencing depth SELEX-seq approach to study all four FOX homologs in Saccharomyces cerevisiae, we have been able to precisely quantify the contribution and importance of nucleotide positions all along an extended binding site. Essential to this process was the alignment of our SELEX-seq reads to a set of candidate core sequences determined using a recently developed tool for the alignment of enriched k-mers and a newly developed approach for the reprioritization of candidate cores.

Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease

Past studies have shown that incubation of human serum samples on high density peptide arrays followed by measurement of total antibody bound to each peptide sequence allows detection and discrimination of humoral immune responses to a variety of infectious diseases. This is true even though these arrays consist of peptides with near-random amino acid sequences that were not designed to mimic biological antigens. This "immunosignature" approach, is based on a statistical evaluation of the binding pattern for each sample but it ignores the information contained in the amino acid sequences that the antibodies are binding to. Here, similar array-based antibody profiles are instead used to train a neural network to model the sequence dependence of molecular recognition involved in the immune response of each sample. The binding profiles used resulted from incubating serum from 5 infectious disease cohorts (Hepatitis B and C, Dengue Fever, West Nile Virus and Chagas disease) and an uninfected cohort with 122,926 peptide sequences on an array. These sequences were selected quasi-randomly to represent an even but sparse sample of the entire possible combinatorial sequence space (~1012). This very sparse sampling of combinatorial sequence space was sufficient to capture a statistically accurate representation of the humoral immune response across the entire space. Processing array data using the neural network not only captures the disease-specific sequence-binding information but aggregates binding information with respect to sequence, removing sequence-independent noise and improving the accuracy of array-based classification of disease compared with the raw binding data. Because the neural network model is trained on all samples simultaneously, a highly condensed representation of the differential information between samples resides in the output layer of the model, and the column vectors from this layer can be used to represent each sample for classification or unsupervised clustering applications.

A multimorphic mutation in IRF4 causes human autosomal dominant combined immunodeficiency

Interferon regulatory factor 4 (IRF4) is a transcription factor (TF) and key regulator of immune cell development and function. We report a recurrent heterozygous mutation in IRF4, p.T95R, causing an autosomal dominant combined immunodeficiency (CID) in seven patients from six unrelated families. The patients exhibited profound susceptibility to opportunistic infections, notably Pneumocystis jirovecii, and presented with agammaglobulinemia. Patients' B cells showed impaired maturation, decreased immunoglobulin isotype switching, and defective plasma cell differentiation, whereas their T cells contained reduced TH17 and TFH populations and exhibited decreased cytokine production. A knock-in mouse model of heterozygous T95R showed a severe defect in antibody production both at the steady state and after immunization with different types of antigens, consistent with the CID observed in these patients. The IRF4T95R variant maps to the TF's DNA binding domain, alters its canonical DNA binding specificities, and results in a simultaneous multimorphic combination of loss, gain, and new functions for IRF4. IRF4T95R behaved as a gain-of-function hypermorph by binding to DNA with higher affinity than IRF4WT. Despite this increased affinity for DNA, the transcriptional activity on IRF4 canonical genes was reduced, showcasing a hypomorphic activity of IRF4T95R. Simultaneously, IRF4T95R functions as a neomorph by binding to noncanonical DNA sites to alter the gene expression profile, including the transcription of genes exclusively induced by IRF4T95R but not by IRF4WT. This previously undescribed multimorphic IRF4 pathophysiology disrupts normal lymphocyte biology, causing human disease.

Prediction of protein-ligand binding affinity from sequencing data with interpretable machine learning

Protein-ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing. However, these assays do not provide the biophysical parameters that most rigorously quantify molecular interactions. Here we describe a flexible machine learning method, called ProBound, that accurately defines sequence recognition in terms of equilibrium binding constants or kinetic rates. This is achieved using a multi-layered maximum-likelihood framework that models both the molecular interactions and the data generation process. We show that ProBound quantifies transcription factor (TF) behavior with models that predict binding affinity over a range exceeding that of previous resources; captures the impact of DNA modifications and conformational flexibility of multi-TF complexes; and infers specificity directly from in vivo data such as ChIP-seq without peak calling. When coupled with an assay called KD-seq, it determines the absolute affinity of protein-ligand interactions. We also apply ProBound to profile the kinetics of kinase-substrate interactions. ProBound opens new avenues for decoding biological networks and rationally engineering protein-ligand interactions.

DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors

We present a physics-based machine learning approach to predict in vitro transcription factor binding affinities from structural and mechanical DNA properties directly derived from atomistic molecular dynamics simulations. The method is able to predict affinities obtained with techniques as different as uPBM, gcPBM and HT-SELEX with an excellent performance, much better than existing algorithms. Due to its nature, the method can be extended to epigenetic variants, mismatches, mutations, or any non-coding nucleobases. When complemented with chromatin structure information, our in vitro trained method provides also good estimates of in vivo binding sites in yeast.

G4detector: Convolutional Neural Network to Predict DNA G-Quadruplexes

G-quadruplexes (G4s) are nucleic acid secondary structures that form within guanine-rich DNA or RNA sequences. G4 formation can affect chromatin architecture and gene regulation, and has been associated with genomic instability, genetic diseases, and cancer progression. The experimental data produced by the G4-seq experiment provides unprecedented details on G4 formation in the genome. Still, running the experimental protocol on a whole genome is an expensive and time-consuming process. Thus, it is highly desirable to have a computational method to predict G4 formation in new DNA sequences or whole genomes. Here, we present G4detector, a new method based on a convolutional neural network to predict G4s from DNA sequences. On top of the sequence information, we improved prediction accuracy by the addition of RNA secondary structure information. To train and test G4detector, we compiled novel high-throughput benchmarks over multiple species genomes measured by the G4-seq protocol. We show that G4detector outperforms extant methods for the same task on all benchmark datasets, can detect G4s genome-wide with high accuracy, and is able to extrapolate human-trained measurements to various non-human species. The code and benchmarks are publicly available on github.com/OrensteinLab/G4detector.

Translation of aptamers toward clinical diagnosis and commercialization

The dominance of antibodies in diagnostics has gradually changed following the discovery of aptamers in the early 1990s. Aptamers offer inherent advantages over traditional antibodies, including higher specificity, higher affinity, smaller size, greater stability, ease of manufacture, and low immunogenicity, rendering them the best candidates for point-of-care testing (POCT). In the past 20 years, the research community and pharmaceutical companies have made great efforts to promote the development of aptamer technology. Macugen® (pegaptanib) was the first aptamer drug approved by the US Food and Drug Administration (FDA), and various aptamer-based diagnostics show great promise in preclinical research and clinical trials. In this review, we introduce recent literature, ongoing clinical trials, commercial reagents of aptamer-based diagnostics, discuss the FDA regulatory mechanisms, and highlight the prospects and challenges in translating these studies into viable clinical diagnostic tools.

Systematic Evolution of Ligands by Exponential Enrichment Technologies and Aptamer-Based Applications: Recent Progress and Challenges in Precision Medicine of Infectious Diseases

Infectious diseases are considered as a pressing challenge to global public health. Accurate and rapid diagnostics tools for early recognition of the pathogen, as well as individualized precision therapy are essential for controlling the spread of infectious diseases. Aptamers, which were screened by systematic evolution of ligands by exponential enrichment (SELEX), can bind to targets with high affinity and specificity so that have exciting potential in both diagnosis and treatment of infectious diseases. In this review, we provide a comprehensive overview of the latest development of SELEX technology and focus on the applications of aptamer-based technologies in infectious diseases, such as targeted drug-delivery, treatments and biosensors for diagnosing. The challenges and the future development in this field of clinical application will also be discussed.