In-vitro validated methods for encoding digital data in deoxyribonucleic acid (DNA).

Deoxyribonucleic acid (DNA) is emerging as an alternative archival memory technology. Recent advancements in DNA synthesis and sequencing have both increased the capacity and decreased the cost of storing information in de novo synthesized DNA pools. In this survey, we review methods for translating digital data to and/or from DNA molecules. An emphasis is placed on methods which have been validated by storing and retrieving real-world data via in-vitro experiments.

An alternative approach to nucleic acid memory.

DNA is a compelling alternative to non-volatile information storage technologies due to its information density, stability, and energy efficiency. Previous studies have used artificially synthesized DNA to store data and automated next-generation sequencing to read it back. Here, we report digital Nucleic Acid Memory (dNAM) for applications that require a limited amount of data to have high information density, redundancy, and copy number. In dNAM, data is encoded by selecting combinations of single-stranded DNA with (1) or without (0) docking-site domains. When self-assembled with scaffold DNA, staple strands form DNA origami breadboards. Information encoded into the breadboards is read by monitoring the binding of fluorescent imager probes using DNA-PAINT super-resolution microscopy. To enhance data retention, a multi-layer error correction scheme that combines fountain and bi-level parity codes is used. As a prototype, fifteen origami encoded with 'Data is in our DNA!\n' are analyzed. Each origami encodes unique data-droplet, index, orientation, and error-correction information. The error-correction algorithms fully recover the message when individual docking sites, or entire origami, are missing. Unlike other approaches to DNA-based data storage, reading dNAM does not require sequencing. As such, it offers an additional path to explore the advantages and disadvantages of DNA as an emerging memory material.

A Coding Scheme for Nucleic Acid Memory (NAM).

The global demand for digital data is projected to be greater than the supply of semiconductor grade silicon in 2040 [1]. When combined with the need to archive information [2], nucleic acids are being explored as an alternative memory material [1-7]. According to a recent study, the information density of nucleic acid memory (NAM) is 1000 times greater than flash memory and has the ability to last for hundreds of years [1]. Presented here is an algorithm for converting digital data into unique DNA sequences for glacial storage. Biologically inspired, our coding scheme maps hexadecimal characters to sequences of three DNA nucleotides. This mapping avoids repeating sequences and start codons, which could have adverse effects. We were able to encode and decode various file types without error.

Computational exploration of a protein receptor binding space with student proposed peptide ligands.

Computational molecular docking is a fast and effective in silico method for the analysis of binding between a protein receptor model and a ligand. The visualization and manipulation of protein to ligand binding in three-dimensional space represents a powerful tool in the biochemistry curriculum to enhance student learning. The DockoMatic tutorial described herein provides a framework by which instructors can guide students through a drug screening exercise. Using receptor models derived from readily available protein crystal structures, docking programs have the ability to predict ligand binding properties, such as preferential binding orientations and binding affinities. The use of computational studies can significantly enhance complimentary wet chemical experimentation by providing insight into the important molecular interactions within the system of interest, as well as guide the design of new candidate ligands based on observed binding motifs and energetics. In this laboratory tutorial, the graphical user interface, DockoMatic, facilitates docking job submissions to the docking engine, AutoDock 4.2. The purpose of this exercise is to successfully dock a 17-amino acid peptide, α-conotoxin TxIA, to the acetylcholine binding protein from Aplysia californica-AChBP to determine the most stable binding configuration. Each student will then propose two specific amino acid substitutions of α-conotoxin TxIA to enhance peptide binding affinity, create the mutant in DockoMatic, and perform docking calculations to compare their results with the class. Students will also compare intermolecular forces, binding energy, and geometric orientation of their prepared analog to their initial α-conotoxin TxIA docking results.

GAMPMS: Genetic algorithm managed peptide mutant screening.

The prominence of endogenous peptide ligands targeted to receptors makes peptides with the desired binding activity good molecular scaffolds for drug development. Minor modifications to a peptide's primary sequence can significantly alter its binding properties with a receptor, and screening collections of peptide mutants is a useful technique for probing the receptor-ligand binding domain. Unfortunately, the combinatorial growth of such collections can limit the number of mutations which can be explored using structure-based molecular docking techniques. Genetic algorithm managed peptide mutant screening (GAMPMS) uses a genetic algorithm to conduct a heuristic search of the peptide's mutation space for peptides with optimal binding activity, significantly reducing the computational requirements of the virtual screening. The GAMPMS procedure was implemented and used to explore the binding domain of the nicotinic acetylcholine receptor (nAChR) α3ß2-isoform with a library of 64,000 α-conotoxin (α-CTx) MII peptide mutants. To assess GAMPMS's performance, it was compared with a virtual screening procedure that used AutoDock to predict the binding affinity of each of the α-CTx MII peptide mutants with the α3ß2-nAChR. The GAMPMS implementation performed AutoDock simulations for as few as 1140 of the 64,000 α-CTx MII peptide mutants and could consistently identify a set of 10 peptides with an aggregated binding energy that was at least 98% of the aggregated binding energy of the 10 top peptides from the exhaustive AutoDock screening.

Design of a flexible component gathering algorithm for converting cell-based models to graph representations for use in evolutionary search.

BACKGROUND: The ability of science to produce experimental data has outpaced the ability to effectively visualize and integrate the data into a conceptual framework that can further higher order understanding. Multidimensional and shape-based observational data of regenerative biology presents a particularly daunting challenge in this regard. Large amounts of data are available in regenerative biology, but little progress has been made in understanding how organisms such as planaria robustly achieve and maintain body form. An example of this kind of data can be found in a new repository (PlanformDB) that encodes descriptions of planaria experiments and morphological outcomes using a graph formalism. RESULTS: We are developing a model discovery framework that uses a cell-based modeling platform combined with evolutionary search to automatically search for and identify plausible mechanisms for the biological behavior described in PlanformDB. To automate the evolutionary search we developed a way to compare the output of the modeling platform to the morphological descriptions stored in PlanformDB. We used a flexible connected component algorithm to create a graph representation of the virtual worm from the robust, cell-based simulation data. These graphs can then be validated and compared with target data from PlanformDB using the well-known graph-edit distance calculation, which provides a quantitative metric of similarity between graphs. The graph edit distance calculation was integrated into a fitness function that was able to guide automated searches for unbiased models of planarian regeneration. We present a cell-based model of planarian that can regenerate anatomical regions following bisection of the organism, and show that the automated model discovery framework is capable of searching for and finding models of planarian regeneration that match experimental data stored in PlanformDB. CONCLUSION: The work presented here, including our algorithm for converting cell-based models into graphs for comparison with data stored in an external data repository, has made feasible the automated development, training, and validation of computational models using morphology-based data. This work is part of an ongoing project to automate the search process, which will greatly expand our ability to identify, consider, and test biological mechanisms in the field of regenerative biology.

Homology modeling and molecular docking for the science curriculum.

DockoMatic 2.0 is a powerful open source software program (downloadable from sourceforge.net) that allows users to utilize a readily accessible computational tool to explore biomolecules and their interactions. This manuscript describes a practical tutorial for use in the undergraduate curriculum that introduces students to macromolecular structure creation, ligand binding calculations, and visualization of docking results. A student procedure is provided that illustrates the use of DockoMatic to create a homology model for the amino propeptide region (223 amino acids with two disulfide bonds) of collagen α1 (XI), followed by molecular docking of the commercial drug Arixtra(®) to the homology model of α1 (XI), and finally, analysis of the results of the docking experiment. The activities and Supporting Information described are intended to educate students in the use of computational tools to create and investigate homology models for other systems of interest and to train students to perform and analyze molecular docking studies. The tutorial also serves as a foundation for investigators seeking to explore the viability of using computational biochemistry to study their receptor-ligand binding motifs. © 2013 by The International Union of Biochemistry and Molecular Biology, 42(2):179-182, 2014.

Safeguarding forensic DNA reference samples with nullomer barcodes.

Unintended transfer of biological material containing DNA is a concern to all laboratories conducting PCR analysis. While forensic laboratories have protocols in place to reduce the possibility of contaminating casework samples, there is no way to detect when a reference sample is mislabeled as evidence, or contaminates a forensic sample. Thus there is public concern regarding the safeguarding of DNA submitted to crime labs. We demonstrate a method of introducing an internal amplification control to reference samples, in the form of a nullomer barcode which is based upon sequences absent or rare from publically accessible DNA databases. The detection of this barcode would indicate that the source of analyzed DNA was from a reference sample provided by an individual, and not from an evidence sample. We demonstrate that the nullomers can be added directly to collection devices (FTA paper) to allow tagging during the process of sample collection. We show that such nullomer oligonucleotides can be added to existing forensic typing and quantification kits, without affecting genotyping or quantification results. Finally, we show that even when diluted a million-fold and spilled on a knife, the nullomer tags can be clearly detected. These tags support the National Research Council of the National Academy recommendation that "Quality control procedures should be designed to identify mistakes, fraud, and bias" in forensic science (National Academy of Sciences, 2009).

Accessible high-throughput virtual screening molecular docking software for students and educators.

We survey low cost high-throughput virtual screening (HTVS) computer programs for instructors who wish to demonstrate molecular docking in their courses. Since HTVS programs are a useful adjunct to the time consuming and expensive wet bench experiments necessary to discover new drug therapies, the topic of molecular docking is core to the instruction of biochemistry and molecular biology. The availability of HTVS programs coupled with decreasing costs and advances in computer hardware have made computational approaches to drug discovery possible at institutional and non-profit budgets. This paper focuses on HTVS programs with graphical user interfaces (GUIs) that use either DOCK or AutoDock for the prediction of DockoMatic, PyRx, DockingServer, and MOLA since their utility has been proven by the research community, they are free or affordable, and the programs operate on a range of computer platforms.

DockoMatic: automated peptide analog creation for high throughput virtual screening.

The purpose of this manuscript is threefold: (1) to describe an update to DockoMatic that allows the user to generate cyclic peptide analog structure files based on protein database (pdb) files, (2) to test the accuracy of the peptide analog structure generation utility, and (3) to evaluate the high throughput capacity of DockoMatic. The DockoMatic graphical user interface interfaces with the software program Treepack to create user defined peptide analogs. To validate this approach, DockoMatic produced cyclic peptide analogs were tested for three-dimensional structure consistency and binding affinity against four experimentally determined peptide structure files available in the Research Collaboratory for Structural Bioinformatics database. The peptides used to evaluate this new functionality were alpha-conotoxins ImI, PnIA, and their published analogs. Peptide analogs were generated by DockoMatic and tested for their ability to bind to X-ray crystal structure models of the acetylcholine binding protein originating from Aplysia californica. The results, consisting of more than 300 simulations, demonstrate that DockoMatic predicts the binding energy of peptide structures to within 3.5 kcal mol(-1), and the orientation of bound ligand compares to within 1.8 Å root mean square deviation for ligand structures as compared to experimental data. Evaluation of high throughput virtual screening capacity demonstrated that Dockomatic can collect, evaluate, and summarize the output of 10,000 AutoDock jobs in less than 2 hours of computational time, while 100,000 jobs requires approximately 15 hours and 1,000,000 jobs is estimated to take up to a week.

Dockomatic - automated ligand creation and docking.

BACKGROUND: The application of computational modeling to rationally design drugs and characterize macro biomolecular receptors has proven increasingly useful due to the accessibility of computing clusters and clouds. AutoDock is a well-known and powerful software program used to model ligand to receptor binding interactions. In its current version, AutoDock requires significant amounts of user time to setup and run jobs, and collect results. This paper presents DockoMatic, a user friendly Graphical User Interface (GUI) application that eases and automates the creation and management of AutoDock jobs for high throughput screening of ligand to receptor interactions. RESULTS: DockoMatic allows the user to invoke and manage AutoDock jobs on a single computer or cluster, including jobs for evaluating secondary ligand interactions. It also automates the process of collecting, summarizing, and viewing results. In addition, DockoMatic automates creation of peptide ligand .pdb files from strings of single-letter amino acid abbreviations. CONCLUSIONS: DockoMatic significantly reduces the complexity of managing multiple AutoDock jobs by facilitating ligand and AutoDock job creation and management.

Shape homeostasis in virtual embryos.

We have constructed a computational platform suitable for examining emergence of shape homeostasis in simple three-dimensional cellular systems. An embryo phenotype results from a developmental process starting with a single cell and its genome. When coupled to an evolutionary search, this platform can evolve embryos with particular stable shapes and high capacity for self-repair, even though repair is not genetically encoded or part of the fitness criteria. With respect to the genome, embryo shape and self-repair are emergent properties that arise from complex interactions among cells and cellular components via signaling and gene regulatory networks, during development or during repair. This report analyzes these networks and the underlying mechanisms that control embryo growth, organization, stability, and robustness to injury.

Absent sequences: nullomers and primes.

We describe a new publicly available algorithm for identifying absent sequences, and demonstrate its use by listing the smallest oligomers not found in the human genome (human "nullomers"), and those not found in any reported genome or GenBank sequence ("primes"). These absent sequences define the maximum set of potentially lethal oligomers. They also provide a rational basis for choosing artificial DNA sequences for molecular barcodes, show promise for species identification and environmental characterization based on absence, and identify potential targets for therapeutic intervention and suicide markers.

A biologically derived approach to tissue modeling.

Our approach to tissue modeling incorporates biologically derived primitives into a computational engine (CellSim) coupled with a genetic search algorithm. By expanding an evolved synthetic genome CellSim is capable of developing a virtual tissue with higher order properties. Using primitives based on cell signaling, gene networks, cell division, growth, and death, we have encoded a 64-cell cube-shaped tissue with emergent capacity to repair itself when up to 60% of its cells are destroyed. Other tissue shapes such as sheets of cells also repair themselves. Capacity for self-repair is an emergent property derived from, but not specified by, the rule sets used to generate these virtual tissues.