Variational Autoencoding Molecular Graphs with Denoising Diffusion Probabilistic Model

In data-driven drug discovery, designing molecular descriptors is a very important task. Deep generative models such as variational autoencoders (VAEs) offer a potential solution by designing descriptors as probabilistic latent vectors derived from molecular structures. These models can be trained on large datasets, which have only molecular structures, and applied to transfer learning. Nevertheless, the approximate posterior distribution of the latent vectors of the usual VAE assumes a simple multivariate Gaussian distribution with zero covariance, which may limit the performance of representing the latent features. To overcome this limitation, we propose a novel molecular deep generative model that incorporates a hierarchical structure into the probabilistic latent vectors. We achieve this by a denoising diffusion probabilistic model (DDPM). We demonstrate that our model can design effective molecular latent vectors for molecular property prediction from some experiments by small datasets on physical properties and activity. The results highlight the superior prediction performance and robustness of our model compared to existing approaches.Comment: 2 pages. Short paper submitted to IEEE CIBCB 202

Pre-training of Molecular GNNs via Conditional Boltzmann Generator

Learning representations of molecular structures using deep learning is a fundamental problem in molecular property prediction tasks. Molecules inherently exist in the real world as three-dimensional structures; furthermore, they are not static but in continuous motion in the 3D Euclidean space, forming a potential energy surface. Therefore, it is desirable to generate multiple conformations in advance and extract molecular representations using a 4D-QSAR model that incorporates multiple conformations. However, this approach is impractical for drug and material discovery tasks because of the computational cost of obtaining multiple conformations. To address this issue, we propose a pre-training method for molecular GNNs using an existing dataset of molecular conformations to generate a latent vector universal to multiple conformations from a 2D molecular graph. Our method, called Boltzmann GNN, is formulated by maximizing the conditional marginal likelihood of a conditional generative model for conformations generation. We show that our model has a better prediction performance for molecular properties than existing pre-training methods using molecular graphs and three-dimensional molecular structures.Comment: 4 page

Experimental optimization of probe length to increase the sequence specificity of high-density oligonucleotide microarrays

<p>Abstract</p> <p>Background</p> <p>High-density oligonucleotide arrays are widely used for analysis of genome-wide expression and genetic variation. Affymetrix GeneChips – common high-density oligonucleotide arrays – contain perfect match (PM) and mismatch (MM) probes generated by changing a single nucleotide of the PMs, to estimate cross-hybridization. However, a fraction of MM probes exhibit larger signal intensities than PMs, when the difference in the amount of target specific hybridization between PM and MM probes is smaller than the variance in the amount of cross-hybridization. Thus, pairs of PM and MM probes with greater specificity for single nucleotide mismatches are desirable for accurate analysis.</p> <p>Results</p> <p>To investigate the specificity for single nucleotide mismatches, we designed a custom array with probes of different length (14- to 25-mer) tethered to the surface of the array and all possible single nucleotide mismatches, and hybridized artificially synthesized 25-mer oligodeoxyribonucleotides as targets in bulk solution to avoid the effects of cross-hybridization. The results indicated the finite availability of target molecules as the probe length increases. Due to this effect, the sequence specificity of the longer probes decreases, and this was also confirmed even under the usual background conditions for transcriptome analysis.</p> <p>Conclusion</p> <p>Our study suggests that the optimal probe length for specificity is 19–21-mer. This conclusion will assist in improvement of microarray design for both transcriptome analysis and mutation screening.</p

Comparison of Sequence Reads Obtained from Three Next-Generation Sequencing Platforms

Next-generation sequencing technologies enable the rapid cost-effective production of sequence data. To evaluate the performance of these sequencing technologies, investigation of the quality of sequence reads obtained from these methods is important. In this study, we analyzed the quality of sequence reads and SNP detection performance using three commercially available next-generation sequencers, i.e., Roche Genome Sequencer FLX System (FLX), Illumina Genome Analyzer (GA), and Applied Biosystems SOLiD system (SOLiD). A common genomic DNA sample obtained from Escherichia coli strain DH1 was applied to these sequencers. The obtained sequence reads were aligned to the complete genome sequence of E. coli DH1, to evaluate the accuracy and sequence bias of these sequence methods. We found that the fraction of “junk” data, which could not be aligned to the reference genome, was largest in the data set of SOLiD, in which about half of reads could not be aligned. Among data sets after alignment to the reference, sequence accuracy was poorest in GA data sets, suggesting relatively low fidelity of the elongation reaction in the GA method. Furthermore, by aligning the sequence reads to the E. coli strain W3110, we screened sequence differences between two E. coli strains using data sets of three different next-generation platforms. The results revealed that the detected sequence differences were similar among these three methods, while the sequence coverage required for the detection was significantly small in the FLX data set. These results provided valuable information on the quality of short sequence reads and the performance of SNP detection in three next-generation sequencing platforms

Induction of neuron-like tubes and liposome networks by cooperative effect of gangliosides and phospholipids

AbstractAlthough there is a rather large abundance of gangliosides in neurons, their functional role is still unclear. We focused on a physicochemical role of gangliosides in the formation of tubular structures, such as axons or dendrites in neurons. When a ganglioside, GM3, was added to cell-size liposomes that consisted of dioleoylphosphatidyl-choline, tubular structures were induced and liposome networks connected by the tubes were observed by differential interference microscopy and fluorescence microscopy. The potential for various gangliosides to induce tubes was dependent on the structures of their hydrophilic head group. With a large excess of gangliosides, the tubes are destabilized and small fragments, or micelles, are generated. The phenomenon was suggested by physical model calculation. Gangliosides may play a role as building material in neural unique tubular structures

Phenotypic convergence in bacterial adaptive evolution to ethanol stress

Stability of ethanol tolerance. Strain F at the end point (2,500 h) and at 576 h was cultivated for 200 generations absent ethanol stress. After the cultivation, ethanol tolerance was evaluated by measuring specific growth rates in 5 % ethanol stress (red bars). The growth rates under ethanol stress were similar to those before the non-stress cultivation (blue bars) and were significantly higher than that of the parent strain. (PDF 976 kb

Microscopic Characterization of the L10-FePt Nanoparticles Synthesized by the SiO2-Nanoreactor Method

We investigated magnetic properties of the L10-FePt nanoparticles synthesized by the SiO2-nanoreactor method by means of Moessbauer spectroscopy from the microscopic point of view. Almost all of the nanoparticles were revealed to have nearly the same Moessbauer hyperfine parameters as those of the bulk L10-FePt alloy, indicating that they have well-defined L10 structure equivalent to the bulk state in spite of their small size of 6.5 nm.Comment: 13 pages, 4 figure

Effects of chemical ischemia on purine nucleotides, free radical generation, lipids peroxidation and intracellular calcium levels in C 2C12 myotube derived from mouse myocytes

To elucidate the mechanisms of ischemia-mediated myopathy using in vitro model, changes of purine nucleotides, membrane lipid peroxidation(TBARS), intracellular calcium ([Ca2+]i)levels, generation of free radicals, and deoxyribonucleic acid (DNA) fragmentation were examined in mouse-derived C2C12 myotubes under the condition with an inhibition of glycolytic and oxidative metabolism as the ischemic condition. In purine nucleotides, intracellular adenosine triphosphate (ATP) and guanosine triphosphate (GTP) concentrations rapidly and significantly decreased after the treatment with ischemia. No remarkable differences were observed in other purine nucleotides, with the exception of inosine monophosphate (IMP) and extracellular hypoxanthine levels, both of which increased significantly during the ischemia. The lactate dehydrogenase activity in culture supernatant of C2C12 myotubes increased significantly from 2 to 4 hr after the ischemia. On the generation of free radicals, no spectrum was detected in supernatants throughout the observation period, whereas supernatant TBARS concentration increased rapidly and significantly after the ischemia. The relative intensity of [Ca2+]i significantly increased after the ischemia. On the fragmented deoxyribonucleic acid(DNA), no TUNEL positive cells was detected in C2C12 myotubes after 1 hr of the ischemia, however the positive cell percentage subsequently increased. From these results, it was suggested that the ischemic condition induced changes of membrane permeability and increase of [Ca2+]i, both of which lead to cell membrane damage, although a free radical generation was not detected. The ischemic condition also induced the release of substrate hypoxanthine for free radical generation and might initiate the apoptotic pathway in C2C12 myotubes.Facultad de Ciencias Veterinaria