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Next-generation systematic evolution of ligands by exponential enrichment approaches consistently combine the experimental protocol with high-throughput sequencing after each round of selection. This extension, known as HT-SELEX, results in a vast amount of raw sequencing data that requires tailored bioinformatics approaches to efficiently process and analyze these reads. Here, we present a step-by-step walkthrough of AptaSUITE, a previously published, open-source, and platform-independent software collection of peer-reviewed bioinformatics tools for HT-SELEX data, integrated into a convenient and efficient graphical user interface. We highlight AptaSUITE's main components and illustrate their utility for a variety of usage scenarios. These include, but are not limited to, computational solutions for data preprocessing such as demultiplexing and quality control, aptamer candidate identification, as well as binding motif elucidation. Taken together, AptaSUITE comprises a complete bioinformatics solution for HT-SELEX data analysis while providing real-time, intuitive, and graphical access to the aptamer information., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Cancer genomes accumulate a large number of somatic mutations resulting from a combination of stochastic errors in DNA processing, cancer-related aberrations of the DNA repair machinery, or carcinogenic exposures; each mutagenic process leaves a characteristic mutational signature. A key challenge is understanding the interactions between signatures, particularly as DNA repair deficiencies often modify the effects of other mutagens. Here, we introduce RepairSig, a computational method that explicitly models additive primary mutagenic processes; non-additive secondary processes, which interact with the primary processes; and a mutation opportunity, that is, the distribution of sites across the genome that are vulnerable to damage or preferentially repaired. We demonstrate that RepairSig accurately recapitulates experimentally identified signatures, identifies autonomous signatures of deficient DNA repair processes, and explains mismatch repair deficiency in breast cancer by de novo inference of both primary and secondary signatures from patient data. RepairSig is freely available for download at https://github.com/ncbi/RepairSig., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021. Published by Elsevier Inc.)

Background: Cancer immunotherapy with monoclonal antibodies and chimeric antigen receptor (CAR) T cell therapies can benefit from selection of new targets with high levels of tumor specificity and from early assessments of efficacy and safety to derisk potential therapies., Methods: Employing mass spectrometry, bioinformatics, immuno-mass spectrometry and CRISPR/Cas9 we identified the target of the tumor-specific SF-25 antibody. We engineered IgE and CAR T cell immunotherapies derived from the SF-25 clone and evaluated potential for cancer therapy., Results: We identified the target of the SF-25 clone as the tumor-associated antigen SLC3A2, a cell surface protein with key roles in cancer metabolism. We generated IgE monoclonal antibody, and CAR T cell immunotherapies each recognizing SLC3A2. In concordance with preclinical and, more recently, clinical findings with the first-in-class IgE antibody MOv18 (recognizing the tumor-associated antigen Folate Receptor alpha), SF-25 IgE potentiated Fc-mediated effector functions against cancer cells in vitro and restricted human tumor xenograft growth in mice engrafted with human effector cells. The antibody did not trigger basophil activation in cancer patient blood ex vivo, suggesting failure to induce type I hypersensitivity, and supporting safe therapeutic administration. SLC3A2-specific CAR T cells demonstrated cytotoxicity against tumor cells, stimulated interferon-γ and interleukin-2 production in vitro. In vivo SLC3A2-specific CAR T cells significantly increased overall survival and reduced growth of subcutaneous PC3-LN3-luciferase xenografts. No weight loss, manifestations of cytokine release syndrome or graft-versus-host disease, were detected., Conclusions: These findings identify efficacious and potentially safe tumor-targeting of SLC3A2 with novel immune-activating antibody and genetically modified cell therapies., Competing Interests: Competing interests: SNK and JS are founders and shareholders of Epsilogen Ltd., and HJB is now employed through a fund provided by Epsilogen Ltd., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Peptide macrocyclization is typically associated with the development of higher affinity and more protease stable protein ligands, and, as such, is an important tool in peptide drug discovery. Yet, within the context of a diverse library, does cyclization give inherent advantages over linear peptides? Here, we used mRNA display to create a peptide library of diverse ring sizes and topologies (monocyclic, bicyclic, and linear). Several rounds of in vitro selection against streptavidin were performed and the winning peptide sequences were analyzed for their binding affinities and overall topologies. The effect of adding a protease challenge on the enrichment of various peptides was also investigated. Taken together, the selection output yields insights about the relative abundance of binders of various topologies within a structurally diverse library.

The AptaBlocks Web Interface is focused on providing graphical, intuitive, and platform-independent access to AptaBlocks, an experimentally validated algorithmic approach for the in silico design of oligonucleotide sticky bridges. The availability of AptaBlocks online to the nucleic acid research community at large makes this software a highly effective tool for accelerating the design and development of novel oligonucleotide-based drugs and other biotechnologies.

Understanding the principles of DNA binding by transcription factors (TFs) is of primary importance for studying gene regulation. Recently, several lines of evidence suggested that both DNA sequence and shape contribute to TF binding. However, the following compelling question is yet to be considered: in the absence of any sequence similarity to the binding motif, can DNA shape still increase binding probability? To address this challenge, we developed Co-SELECT, a computational approach to analyze the results of in vitro HT-SELEX experiments for TF-DNA binding. Specifically, Co-SELECT leverages the presence of motif-free sequences in late HT-SELEX rounds and their enrichment in weak binders allows Co-SELECT to detect an evidence for the role of DNA shape features in TF binding. Our approach revealed that, even in the absence of the sequence motif, TFs have propensity to bind to DNA molecules of the shape consistent with the motif specific binding. This provides the first direct evidence that shape features that accompany the preferred sequence motifs also bestow an advantage for weak, sequence non-specific binding., (Published by Oxford University Press on behalf of Nucleic Acids Research 2019.)

The identification of subpopulations of cells in single-cell experiments, and the comparison of such subpopulations across experiments are among the most frequently performed analysis of single-cell data. This important task still awaits a fully satisfying computational solution. To address this need, we introduce a computational method, single-cell subpopulations comparison (scPopCorn). Leveraging the information from all input datasets, scPopCorn performs these two tasks simultaneously by optimizing a joint objective function. The optimization involves a measure of cohesiveness of a cell population, which combined with Google's personalized PageRank approach, guides subpopulation detection, while a measure of cell-to-cell similarity is used to guide the mapping. scPopCorn not only outperforms currently used approaches but also introduces mathematical concepts that can serve as stepping stones to improve other tools., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

RNA-based therapeutics, i.e. the utilization of synthetic RNA molecules to alter cellular functions, have the potential to address targets which are currently out of scope for traditional drug design pipelines. This potential however hinges on the ability to selectively deliver and internalize therapeutic RNAs into cells of interest. Cell internalizing RNA aptamers selected against surface receptors and discriminatively expressed on target cells hold particular promise as suitable candidates for such delivery agents. Specifically, these aptamers can be combined with a therapeutic cargo and facilitate internalization of the cargo into the cell of interest. A recently proposed method to obtain such aptamer-cargo constructs employs a double-stranded "sticky bridge" where the complementary strands constituting the bridge are conjugated with the aptamer and the cargo respectively. The design of appropriate sticky bridge sequences however has proven highly challenging given the structural and functional constraints imposed on them during synthesis and administration. These include, but are not limited to, guaranteed formation and stability of the complex, non-interference with the aptamer or the cargo, as well as the prevention of spurious aggregation of the molecules during incubation. In order to address these issues, we have developed AptaBlocks - a computational method to design RNA complexes that hybridize via sticky bridges. The effectiveness of our approach has been verified computationally, and experimentally in the context of drug delivery to pancreatic cancer cells. Importantly, AptaBlocks is a general method for the assembly of nucleic acid systems that, in addition to designing of RNA-based drug delivery systems, can be used in other applications of RNA nanotechnology. AptaBlocks is available at https://github.com/wyjhxq/AptaBlocks.

With properties such as stability to long-term storage and amenability to repetitive use, nucleic acid aptamers are compatible with many sensing/transducing platforms intended for use in remote locations. Sensors with these properties are important for quickly identifying ebolavirus outbreaks, which frequently start in locations that lack sophisticated equipment. Soluble glycoprotein (sGP), an excellent biomarker for ebolaviruses, is produced from the same gene as the ebolavirus glycoprotein GP1,2 that decorates the surface of the viral particle and is secreted in abundance into the blood stream even during the early stages of infection. Here, we report the selection and properties of a 2'fluoro pyrimidine (2'FY)-modified RNA aptamer, 39SGP1A, that specifically binds sGP. We demonstrate by computational and biochemical analysis that the recognition motif of 39SGP1A is a novel polypyrimidine-rich sequence. Replacement of -F by -OH in the 2' position of the ribose resulted in complete loss of affinity for sGP. The protein motif to which the aptamer binds requires an intact sGP dimer and binds to an epitope conserved between Ebola virus (EBOV) and Sudan virus (SUDV) sGP, the most divergent Ebolavirus species. This identifies 39SGP1A as an excellent option for integration on a sensor platform to detect ebolavirus infections.

Highly constrained peptides such as the knotted peptide natural products are promising medicinal agents because of their impressive biostability and potent activity. Yet, libraries of highly constrained peptides are challenging to prepare. Here, we present a method which utilizes two robust, orthogonal chemical steps to create highly constrained bicyclic peptide libraries. This technology was optimized to be compatible with in vitro selections by mRNA display. We performed side-by-side monocyclic and bicyclic selections against a model protein (streptavidin). Both selections resulted in peptides with mid-nanomolar affinity, and the bicyclic selection yielded a peptide with remarkable protease resistance.

Aptamers, short and synthetic RNA/DNA molecules binding distinct targets with high affinity and specificity, are identified via Systematic Evolution of Ligands by Exponential Enrichment (SELEX), an in vitro procedure that, starting from a pool of random ssDNA/RNA sequences, selects sequences by amplifying target-affine species through a series of selection cycles. This versatile protocol has recently been combined with high throughput sequencing, allowing arbitrary stages of the selection to be sequenced and analyzed in silico. As a prerequisite, these data require extensive preprocessing by means of quality controls, error correction and demultiplexing, all while taking into account the specific design of aptamers. Existing solutions addressing this task are currently present only as integrated components in larger pipelines, limiting their applicability in independent software solutions. Here we present AptaPLEX, a standalone and platform independent demultiplexer specifically designed for HT-SELEX data. Given the multiplexed data from one or multiple HT-SELEX experiments, AptaPLEX extracts and restores aptamers into the original selection cycles by identifying the barcode and primer regions in each read. AptaPLEX is capable of fuzzy matching for both the barcode and primers, and automatically corrects mismatches between forward and reverse reads for paired-end data. Our software provides a rich set of additional features and can easily be integrated into existing analysis automation pipelines on multiple platforms ranging from desktop machines to cloud based solutions., (Copyright © 2016. Published by Elsevier Inc.)

Aptamers, short RNA or DNA molecules that bind distinct targets with high affinity and specificity, can be identified using high-throughput systematic evolution of ligands by exponential enrichment (HT-SELEX), but scalable analytic tools for understanding sequence-function relationships from diverse HT-SELEX data are not available. Here we present AptaTRACE, a computational approach that leverages the experimental design of the HT-SELEX protocol, RNA secondary structure, and the potential presence of many secondary motifs to identify sequence-structure motifs that show a signature of selection. We apply AptaTRACE to identify nine motifs in C-C chemokine receptor type 7 targeted by aptamers in an in vitro cell-SELEX experiment. We experimentally validate two aptamers whose binding required both sequence and structural features. AptaTRACE can identify low-abundance motifs, and we show through simulations that, because of this, it could lower HT-SELEX cost and time by reducing the number of selection cycles required., (Published by Elsevier Inc.)

Oligonucleotide aptamers represent a novel platform for creating ligands with desired specificity, and they offer many potentially significant advantages over monoclonal antibodies in terms of feasibility, cost, and clinical applicability. However, the isolation of high-affinity aptamer ligands from random oligonucleotide pools has been challenging. Although high-throughput sequencing (HTS) promises to significantly facilitate systematic evolution of ligands by exponential enrichment (SELEX) analysis, the enormous datasets generated in the process pose new challenges for identifying those rare, high-affinity aptamers present in a given pool. We show that emulsion PCR preserves library diversity, preventing the loss of rare high-affinity aptamers that are difficult to amplify. We also demonstrate the importance of using reference targets to eliminate binding candidates with reduced specificity. Using a combination of bioinformatics and functional analyses, we show that the rate of amplification is more predictive than prevalence with respect to binding affinity and that the mutational landscape within a cluster of related aptamers can guide the identification of high-affinity aptamer ligands. Finally, we demonstrate the power of this selection process for identifying cross-species aptamers that can bind human receptors and cross-react with their murine orthologs., (© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.)

High-Throughput (HT) SELEX combines SELEX (Systematic Evolution of Ligands by EXponential Enrichment), a method for aptamer discovery, with massively parallel sequencing technologies. This emerging technology provides data for a global analysis of the selection process and for simultaneous discovery of a large number of candidates but currently lacks dedicated computational approaches for their analysis. To close this gap, we developed novel in-silico methods to analyze HT-SELEX data and utilized them to study the emergence of polymerase errors during HT-SELEX. Rather than considering these errors as a nuisance, we demonstrated their utility for guiding aptamer discovery. Our approach builds on two main advancements in aptamer analysis: AptaMut-a novel technique allowing for the identification of polymerase errors conferring an improved binding affinity relative to the 'parent' sequence and AptaCluster-an aptamer clustering algorithm which is to our best knowledge, the only currently available tool capable of efficiently clustering entire aptamer pools. We applied these methods to an HT-SELEX experiment developing aptamers against Interleukin 10 receptor alpha chain (IL-10RA) and experimentally confirmed our predictions thus validating our computational methods., (Published by Oxford University Press on behalf of Nucleic Acids Research 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Systematic Evolution of Ligands by EXponential Enrichment (SELEX) is a well established experimental procedure to identify aptamers - synthetic single-stranded (ribo)nucleic molecules that bind to a given molecular target. Recently, new sequencing technologies have revolutionized the SELEX protocol by allowing for deep sequencing of the selection pools after each cycle. The emergence of High Throughput SELEX (HT-SELEX) has opened the field to new computational opportunities and challenges that are yet to be addressed. To aid the analysis of the results of HT-SELEX and to advance the understanding of the selection process itself, we developed AptaCluster. This algorithm allows for an efficient clustering of whole HT-SELEX aptamer pools; a task that could not be accomplished with traditional clustering algorithms due to the enormous size of such datasets. We performed HT-SELEX with Interleukin 10 receptor alpha chain (IL-10RA) as the target molecule and used AptaCluster to analyze the resulting sequences. AptaCluster allowed for the first survey of the relationships between sequences in different selection rounds and revealed previously not appreciated properties of the SELEX protocol. As the first tool of this kind, AptaCluster enables novel ways to analyze and to optimize the HT-SELEX procedure. Our AptaCluster algorithm is available as a very fast multiprocessor implementation upon request.

Motivation: Systematic Evolution of Ligands by EXponential Enrichment (SELEX) represents a state-of-the-art technology to isolate single-stranded (ribo)nucleic acid fragments, named aptamers, which bind to a molecule (or molecules) of interest via specific structural regions induced by their sequence-dependent fold. This powerful method has applications in designing protein inhibitors, molecular detection systems, therapeutic drugs and antibody replacement among others. However, full understanding and consequently optimal utilization of the process has lagged behind its wide application due to the lack of dedicated computational approaches. At the same time, the combination of SELEX with novel sequencing technologies is beginning to provide the data that will allow the examination of a variety of properties of the selection process., Results: To close this gap we developed, Aptamotif, a computational method for the identification of sequence-structure motifs in SELEX-derived aptamers. To increase the chances of identifying functional motifs, Aptamotif uses an ensemble-based approach. We validated the method using two published aptamer datasets containing experimentally determined motifs of increasing complexity. We were able to recreate the author's findings to a high degree, thus proving the capability of our approach to identify binding motifs in SELEX data. Additionally, using our new experimental dataset, we illustrate the application of Aptamotif to elucidate several properties of the selection process.