An optimization case study for solving a transport robot scheduling problem on quantum-hybrid and quantum-inspired hardware.

We present a comprehensive case study comparing the performance of D-Waves' quantum-classical hybrid framework, Fujitsu's quantum-inspired digital annealer, and Gurobi's state-of-the-art classical solver in solving a transport robot scheduling problem. This problem originates from an industrially relevant real-world scenario. We provide three different models for our problem following different design philosophies. In our benchmark, we focus on the solution quality and end-to-end runtime of different model and solver combinations. We find promising results for the digital annealer and some opportunities for the hybrid quantum annealer in direct comparison with Gurobi. Our study provides insights into the workflow for solving an application-oriented optimization problem with different strategies, and can be useful for evaluating the strengths and weaknesses of different approaches.

Surface ID: a geometry-aware system for protein molecular surface comparison.

MOTIVATION: A protein can be represented in several forms, including its 1D sequence, 3D atom coordinates, and molecular surface. A protein surface contains rich structural and chemical features directly related to the protein's function such as its ability to interact with other molecules. While many methods have been developed for comparing the similarity of proteins using the sequence and structural representations, computational methods based on molecular surface representation are limited. RESULTS: Here, we describe "Surface ID," a geometric deep learning system for high-throughput surface comparison based on geometric and chemical features. Surface ID offers a novel grouping and alignment algorithm useful for clustering proteins by function, visualization, and in silico screening of potential binding partners to a target molecule. Our method demonstrates top performance in surface similarity assessment, indicating great potential for protein functional annotation, a major need in protein engineering and therapeutic design. AVAILABILITY AND IMPLEMENTATION: Source code for the Surface ID model, trained weights, and inference script are available at https://github.com/Sanofi-Public/LMR-SurfaceID.

SICOR: Subgraph Isomorphism Comparison of RNA Secondary Structures.

RNA aptamer selection during SELEX experiments builds on secondary structural diversity. Advanced structural comparison methods can focus this diversity. We develop SICOR, which uses probabilistic subgraph isomorphisms for graph distances between RNA secondary structure graphs. SICOR outperforms other comparison methods and is applicable to many structural comparisons in experimental design.

Optimization of the experimental parameters of the ligase cycling reaction.

The ligase cycling reaction (LCR) is a scarless and efficient method to assemble plasmids from fragments of DNA. This assembly method is based on the hybridization of DNA fragments with complementary oligonucleotides, so-called bridging oligos (BOs), and an experimental procedure of thermal denaturation, annealing and ligation. In this study, we explore the effect of molecular crosstalk of BOs and various experimental parameters on the LCR by utilizing a fluorescence-based screening system. The results indicate an impact of the melting temperatures of BOs on the overall success of the LCR assembly. Secondary structure inhibitors, such as dimethyl sulfoxide and betaine, are shown to negatively impact the number of correctly assembled plasmids. Adjustments of the annealing, ligation and BO-melting temperature further improved the LCR. The optimized LCR was confirmed by validation experiments. Based on these findings, a step-by-step protocol is offered within this study to ensure a routine for high efficient LCR assemblies.

Tuning the Performance of Synthetic Riboswitches using Machine Learning.

Riboswitch development for clinical, technological, and synthetic biology applications constantly seeks to optimize regulatory behavior. Here, we present a machine learning approach to improve the regulation of a tetracycline (tc)-dependent riboswitch device composed of two individual tc aptamers. We developed a bioinformatics model that combines random forest analysis with a convolutional neural network to predict the switching behavior of such tandem riboswitches. We found that both biophysical parameters and the hydrogen bond pattern influence regulation. Our new design pipeline led to significant improvement of the tc riboswitch device with a dynamic range extension from 8.5 to 40-fold. We are confident that our novel method not only results in an excellent tc-dependent riboswitch device but further holds great promise and potential for the optimization of other riboswitches.

StreaMD: Advanced analysis of molecular dynamics using R.

Gromacs is one of the most popular molecular simulation suites currently available. In this contribution we present streaMD, the first interface between Gromacs trajectory files and the statistical language R. The amount of data created due to ever increasing computational power renders fast and efficient analysis of trajectories into a challenge. Especially as standard approaches such as root-mean square fluctuations and the like provide only limited physical insight. In our streaMD package integration of the Gromacs I/O libraries with advanced, graph-based analysis methods as the java library Stream leads to both: improved speed and analysis depth. We benchmark our results and highlight the applicability of the package by an interesting problem in RNA design, namely the interaction of tetracycline with an aptamer. © 2018 Wiley Periodicals, Inc.

Riboswitching with ciprofloxacin-development and characterization of a novel RNA regulator.

RNA molecules play important and diverse regulatory roles in the cell. Inspired by this natural versatility, RNA devices are increasingly important for many synthetic biology applications, e.g. optimizing engineered metabolic pathways, gene therapeutics or building up complex logical units. A major advantage of RNA is the possibility of de novo design of RNA-based sensing domains via an in vitro selection process (SELEX). Here, we describe development of a novel ciprofloxacin-responsive riboswitch by in vitro selection and next-generation sequencing-guided cellular screening. The riboswitch recognizes the small molecule drug ciprofloxacin with a KD in the low nanomolar range and adopts a pseudoknot fold stabilized by ligand binding. It efficiently interferes with gene expression both in lower and higher eukaryotes. By controlling an auxotrophy marker and a resistance gene, respectively, we demonstrate efficient, scalable and programmable control of cellular survival in yeast. The applied strategy for the development of the ciprofloxacin riboswitch is easily transferrable to any small molecule target of choice and will thus broaden the spectrum of RNA regulators considerably.

Improvement in the Thermostability of a ß-Amino Acid Converting ω-Transaminase by Using FoldX.

ω-Transaminases (ω-TAs) are important biocatalysts for the synthesis of active, chiral pharmaceutical ingredients containing amino groups, such as ß-amino acids, which are important in peptidomimetics and as building blocks for drugs. However, the application of ω-TAs is limited by the availability and stability of enzymes with high conversion rates. One strategy for the synthesis and optical resolution of ß-phenylalanine and other important aromatic ß-amino acids is biotransformation by utilizing an ω-transaminase from Variovorax paradoxus. We designed variants of this ω-TA to gain higher process stability on the basis of predictions calculated by using the FoldX software. We herein report the first thermostabilization of a nonthermostable S-selective ω-TA by FoldX-guided site-directed mutagenesis. The melting point (Tm ) of our best-performing mutant was increased to 59.3 °C, an increase of 4.0 °C relative to the Tm value of the wild-type enzyme, whereas the mutant fully retained its specific activity.

StreAM-[Formula: see text]: algorithms for analyzing coarse grained RNA dynamics based on Markov models of connectivity-graphs.

BACKGROUND: In this work, we present a new coarse grained representation of RNA dynamics. It is based on adjacency matrices and their interactions patterns obtained from molecular dynamics simulations. RNA molecules are well-suited for this representation due to their composition which is mainly modular and assessable by the secondary structure alone. These interactions can be represented as adjacency matrices of k nucleotides. Based on those, we define transitions between states as changes in the adjacency matrices which form Markovian dynamics. The intense computational demand for deriving the transition probability matrices prompted us to develop StreAM-[Formula: see text], a stream-based algorithm for generating such Markov models of k-vertex adjacency matrices representing the RNA. RESULTS: We benchmark StreAM-[Formula: see text] (a) for random and RNA unit sphere dynamic graphs (b) for the robustness of our method against different parameters. Moreover, we address a riboswitch design problem by applying StreAM-[Formula: see text] on six long term molecular dynamics simulation of a synthetic tetracycline dependent riboswitch (500 ns) in combination with five different antibiotics. CONCLUSIONS: The proposed algorithm performs well on large simulated as well as real world dynamic graphs. Additionally, StreAM-[Formula: see text] provides insights into nucleotide based RNA dynamics in comparison to conventional metrics like the root-mean square fluctuation. In the light of experimental data our results show important design opportunities for the riboswitch.

Cleavage Product Accumulation Decreases the Activity of Cutinase during PET Hydrolysis.

The Fusarium solani cutinase (FsC) is a promising candidate for the enzymatic degradation of the synthetic polyester polyethylene terephthalate (PET) but still suffers from a lack of activity. Using atomic MD simulations with different concentrations of cleavage product ethylene glycol (EG), we show influences of EG on the dynamic of FsC. We observed accumulation of EG in the active site region reducing the local flexibility of FsC. Furthermore, we used a coarse-grained mechanical model to investigate whether substrate binding in the active site causes an induced fit. We observed this supposed induced fit or "breath-like" movement during substrate binding indicating that the active site has to be flexible for substrate conversion. This guides rational design: mutants with an increased flexibility near the active site should be considered to compensate the solvent-mediated reduction in activity.

Quantum coupled mutation finder: predicting functionally or structurally important sites in proteins using quantum Jensen-Shannon divergence and CUDA programming.

BACKGROUND: The identification of functionally or structurally important non-conserved residue sites in protein MSAs is an important challenge for understanding the structural basis and molecular mechanism of protein functions. Despite the rich literature on compensatory mutations as well as sequence conservation analysis for the detection of those important residues, previous methods often rely on classical information-theoretic measures. However, these measures usually do not take into account dis/similarities of amino acids which are likely to be crucial for those residues. In this study, we present a new method, the Quantum Coupled Mutation Finder (QCMF) that incorporates significant dis/similar amino acid pair signals in the prediction of functionally or structurally important sites. RESULTS: The result of this study is twofold. First, using the essential sites of two human proteins, namely epidermal growth factor receptor (EGFR) and glucokinase (GCK), we tested the QCMF-method. The QCMF includes two metrics based on quantum Jensen-Shannon divergence to measure both sequence conservation and compensatory mutations. We found that the QCMF reaches an improved performance in identifying essential sites from MSAs of both proteins with a significantly higher Matthews correlation coefficient (MCC) value in comparison to previous methods. Second, using a data set of 153 proteins, we made a pairwise comparison between QCMF and three conventional methods. This comparison study strongly suggests that QCMF complements the conventional methods for the identification of correlated mutations in MSAs. CONCLUSIONS: QCMF utilizes the notion of entanglement, which is a major resource of quantum information, to model significant dissimilar and similar amino acid pair signals in the detection of functionally or structurally important sites. Our results suggest that on the one hand QCMF significantly outperforms the previous method, which mainly focuses on dissimilar amino acid signals, to detect essential sites in proteins. On the other hand, it is complementary to the existing methods for the identification of correlated mutations. The method of QCMF is computationally intensive. To ensure a feasible computation time of the QCMF's algorithm, we leveraged Compute Unified Device Architecture (CUDA).The QCMF server is freely accessible at http://qcmf.informatik.uni-goettingen.de/.

Toward the blood-borne miRNome of human diseases.

In a multicenter study, we determined the expression profiles of 863 microRNAs by array analysis of 454 blood samples from human individuals with different cancers or noncancer diseases, and validated this 'miRNome' by quantitative real-time PCR. We detected consistently deregulated profiles for all tested diseases; pathway analysis confirmed disease association of the respective microRNAs. We observed significant correlations (P = 0.004) between the genomic location of disease-associated genetic variants and deregulated microRNAs.

High-throughput miRNA profiling of human melanoma blood samples.

BACKGROUND: MicroRNA (miRNA) signatures are not only found in cancer tissue but also in blood of cancer patients. Specifically, miRNA detection in blood offers the prospect of a non-invasive analysis tool. METHODS: Using a microarray based approach we screened almost 900 human miRNAs to detect miRNAs that are deregulated in their expression in blood cells of melanoma patients. We analyzed 55 blood samples, including 20 samples of healthy individuals, 24 samples of melanoma patients as test set, and 11 samples of melanoma patients as independent validation set. RESULTS: A hypothesis test based approach detected 51 differentially regulated miRNAs, including 21 miRNAs that were downregulated in blood cells of melanoma patients and 30 miRNAs that were upregulated in blood cells of melanoma patients as compared to blood cells of healthy controls. The tets set and the independent validation set of the melanoma samples showed a high correlation of fold changes (0.81). Applying hierarchical clustering and principal component analysis we found that blood samples of melanoma patients and healthy individuals can be well differentiated from each other based on miRNA expression analysis. Using a subset of 16 significant deregulated miRNAs, we were able to reach a classification accuracy of 97.4%, a specificity of 95% and a sensitivity of 98.9% by supervised analysis. MiRNA microarray data were validated by qRT-PCR. CONCLUSIONS: Our study provides strong evidence for miRNA expression signatures of blood cells as useful biomarkers for melanoma.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry: a new tool in diagnostic investigation of nail disorders?

The incidence and prevalence of onychomycosis are rising worldwide. Common diagnostic techniques often lack sensitivity or specificity. Differentiation between non-infectious nail disorders is frequently not possible. The aim of this study was to establish a better diagnostic routine procedure based on modern mass spectrometric peptide analysis techniques. One hundred and fifty-five nail samples from 145 patients with clinically suspected onychomycosis (n = 96, 62%) and without onychomycosis [e.g. nail psoriasis or nail dystrophy resulting from eczema (n = 59, 38%)] were investigated using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) peptide mass fingerprinting in comparison with standard techniques. We demonstrated that MALDI-TOF MS represents a precise, robust and fast tool in diagnostic investigation of nail disorders, which is superior to common standard methods.

Proteolytic peptide patterns as indicators for fungal infections and nonfungal affections of human nails measured by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.

The discrimination of onychomycoses from endogenous diseases showing macroscopically similar symptoms is difficult. Long-lasting but ineffective antifungal therapies using systemic medicaments with often severe adverse reactions may be the consequence. We introduce a novel mass spectrometric method for the discrimination of fungal infections and nonfungal affections. Horn samples from patients infected by Trichophyton rubrum, from patients with psoriasis affecting nails, and from healthy persons were investigated. Onychomycoses are basically associated with proteolytic attacks of the virulent fungi-secreting proteases partly hydrolyzing the horn material. Endogenous diseases lack these proteolytic activities, conserving intact structural proteins. Tryptical digestion of horn material produced cleavage peptides detectable by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. Mass spectra of horn material infected by T. rubrum were clearly different from those originating from healthy test persons and from patients with psoriasis. Two methods were successfully applied to quantify the differences between groups of samples. One is based on the Euclidean match factor, and the other is based on the identification of specific peptide peaks occurring exclusively within one group of persons. The Euclidean match factor distributions and the occurrence of specific peptide peaks allowed a clear differentiation of T. rubrum infections from psoriasis patients and healthy test persons. No differences were found between healthy test persons and psoriasis patients. The method is rapid and does not require any cultivation.