Classification of the HCN isomerization reaction dynamics in Ar buffer gas via machine learning.

The effect of the presence of Ar on the isomerization reaction HCN ⇄ CNH is investigated via machine learning. After the potential energy surface function is developed based on the CCSD(T)/aug-cc-pVQZ level ab initio calculations, classical trajectory simulations are performed. Subsequently, with the aim of extracting insights into the reaction dynamics, the obtained reactivity, that is, whether the reaction occurs or not under a given initial condition, is learned as a function of the initial positions and momenta of all the atoms in the system. The prediction accuracy of the trained model is greater than 95%, indicating that machine learning captures the features of the phase space that affect reactivity. Machine learning models are shown to successfully reproduce reactivity boundaries without any prior knowledge of classical reaction dynamics theory. Subsequent analyses reveal that the Ar atom affects the reaction by displacing the effective saddle point. When the Ar atom is positioned close to the N atom (resp. the C atom), the saddle point shifts to the CNH (HCN) region, which disfavors the forward (backward) reaction. The results imply that analyses aided by machine learning are promising tools for enhancing the understanding of reaction dynamics.

Real-Time Search-Assisted Multiplexed Quantitative Proteomics Reveals System-Wide Translational Regulation of Non-Canonical Short Open Reading Frames.

Abnormal expression of histone deacetylases (HDACs) is reported to be associated with angiogenesis, metastasis and chemotherapy resistance regarding cancer in a wide range of previous studies. Suberoylanilide hydroxamic acid (SAHA) is well known to function as a pan-inhibitor for HDACs and recognized as one of the therapeutic drug candidates to epigenetically coordinate cancer cell fate regulation on a genomic scale. Here, we established a Real-Time Search (RTS)-assisted mass spectrometric platform for system-wide quantification of translated products encoded by non-canonical short open reading frames (ORFs) as well as already annotated protein coding sequences (CDSs) on the human transciptome and applied this methodology to quantitative proteomic analyses of suberoylanilide hydroxamic acid (SAHA)-treated human HeLa cells to evaluate proteome-wide regulation in response to drug perturbation. Very intriguingly, our RTS-based in-depth proteomic analysis enabled us to identify approximately 5000 novel peptides from the ribosome profiling-based short ORFs encoded in the diversified regions on presumed 'non-coding' nucleotide sequences of mRNAs as well as lncRNAs and nonsense mediated decay (NMD) transcripts. Furthermore, TMT-based multiplex large-scale quantification of the whole proteome changes upon differential SAHA treatment unveiled dose-dependent selective translational regulation of a limited fraction of the non-canonical short ORFs in addition to key cell cycle/proliferation-related molecules such as UBE2C, CENPF and PRC1. Our study provided the first system-wide landscape of drug-perturbed translational modulation on both canonical and non-canonical proteome dynamics in human cancer cells.

Genome sequence determination and metagenomic characterization of a Dehalococcoides mixed culture grown on cis-1,2-dichloroethene.

A Dehalococcoides-containing bacterial consortium that performed dechlorination of 0.20 mM cis-1,2-dichloroethene to ethene in 14 days was obtained from the sediment mud of the lotus field. To obtain detailed information of the consortium, the metagenome was analyzed using the short-read next-generation sequencer SOLiD 3. Matching the obtained sequence tags with the reference genome sequences indicated that the Dehalococcoides sp. in the consortium was highly homologous to Dehalococcoides mccartyi CBDB1 and BAV1. Sequence comparison with the reference sequence constructed from 16S rRNA gene sequences in a public database showed the presence of Sedimentibacter, Sulfurospirillum, Clostridium, Desulfovibrio, Parabacteroides, Alistipes, Eubacterium, Peptostreptococcus and Proteocatella in addition to Dehalococcoides sp. After further enrichment, the members of the consortium were narrowed down to almost three species. Finally, the full-length circular genome sequence of the Dehalococcoides sp. in the consortium, D. mccartyi IBARAKI, was determined by analyzing the metagenome with the single-molecule DNA sequencer PacBio RS. The accuracy of the sequence was confirmed by matching it to the tag sequences obtained by SOLiD 3. The genome is 1,451,062 nt and the number of CDS is 1566, which includes 3 rRNA genes and 47 tRNA genes. There exist twenty-eight RDase genes that are accompanied by the genes for anchor proteins. The genome exhibits significant sequence identity with other Dehalococcoides spp. throughout the genome, but there exists significant difference in the distribution RDase genes. The combination of a short-read next-generation DNA sequencer and a long-read single-molecule DNA sequencer gives detailed information of a bacterial consortium.