FfAME:org :: Cen Chen's Publications

Cen Chen's Publications

Enzyme-assisted high throughput sequencing of an expanded genetic alphabet at single base resolution
Bang Wang, Kevin M. Bradley, Myong-Jung Kim, Roberto Laos, Cen Chen, Dietlind L. Gerloff, Luran Manfio, Zunyi Yang & Steven A. Benner
Nat. Commun. 15 (4057) , Nature (2024) https://doi.org/10.1038/s41467-024-48408-9
<Abstract>

With just four building blocks, low sequence information density, few functional groups, poor control over folding, and difficulties in forming compact folds, natural DNA and RNA have been disappointing platforms from which to evolve receptors, ligands, and catalysts. Accordingly, synthetic biology has created "artificially expanded genetic information systems" (AEGIS) to add nucleotides, functionality, and information density. With the expected improvements seen in AegisBodies and AegisZymes, the task for synthetic biologists shifts to developing for expanded DNA the same analytical tools available to natural DNA. Here we report one of these, an enzyme-assisted sequencing of expanded genetic alphabet (ESEGA) method to sequence six-letter AEGIS DNA. We show how ESEGA analyses this DNA at single base resolution, and applies it to optimized conditions for six-nucleotide PCR, assessing the fidelity of various DNA polymerases, and extending this to AEGIS components with functional groups. This supports the renewed exploitation of expanded DNA alphabets in biotechnology.

A folding motif formed with an expanded genetic alphabet
Bang Wang, James R. Rocca, Shuichi Hoshika, Cen Chen, Zunyi Yang, Reza Esmaeeli, Jianguo Wang, Xiaoshu Pan, Jianrong Lu, Kevin K. Wang, Y. Charles Cao, Weihong Tan & Steven A. Benner
Nat. Chem. , Nature (2024) https://doi.org/10.1038/s41557-024-01552-7
<Abstract>

Adding synthetic nucleotides to DNA increases the linear information density of DNA molecules. Here we report that it also can increase the diversity of their three-dimensional folds. Specifically, an additional nucleotide (dZ, with a 5-nitro-6-aminopyridone nucleobase), placed at twelve sites in a 23-nucleotides-long DNA strand, creates a fairly stable unimolecular structure (that is, the folded Z-motif, or fZ-motif) that melts at 66.5°C at pH 8.5. Spectroscopic, gel and two-dimensional NMR analyses show that the folded Z-motif is held together by six reverse skinny dZ^-:dZ base pairs, analogous to the crystal structure of the free heterocycle. Fluorescence tagging shows that the dZ^-:dZ pairs join parallel strands in a four-stranded compact down-up-down-up fold. These have two possible structures: one with intercalated dZ^-:dZ base pairs, the second without intercalation. The intercalated structure would resemble the i-motif formed by dC:dC⁺-reversed pairing at pH ≤ 6.5. This fZ-motif may therefore help DNA form compact structures needed for binding and catalysis.

2'-β-Selenium Atom on Thymidine to Control β-Form DNA Conformation and Large Crystal Formation
Cen Chen, Ziyuan Fang, and Zhen Huang
Cryst. Growth Des , ACS (2022) 22, 6, 3601-3604, DOI:10.1021/acs.cgd.2c00474
<Abstract>

Crystallization phasing and obtaining high-quality crystals are bottleneck challenges for the X-ray crystallographic analysis of nucleic acids, especially when dynamic behavior is to be inferred from crystallographic B-factors. Crystallization of DNA duplexes, normally existing in the B-form in solution, is especially challenging, as the high salt used in many crystallization processes favors their transformation to A-form DNA duplexes. To address crystallization challenges while avoiding structural perturbation, we explored atom-specific incorporation to place a selenium atom on the 2'-β (arabino) position of the 2'-deoxyribose ring. This incorporation is expected to favor the B-form of a DNA duplex during crystallization. Here, we report the first synthesis of the β-2'-MeSe-thymidine (^SeT) nucleoside, the corresponding Se-phosphoramidite, and Se-containing DNA oligonucleotides. We found that particular Se-DNAs form crystals that are surprisingly larger than we have often observed, having higher quality and giving improved diffraction resolution when compared to crystals from analogous standard oligonucleotides. Surprisingly, one duplex made from a self-complementary Se-containing oligonucleotide gave crystals 600 × 200 µm² in size; this is 10–100 times larger in volume than the corresponding crystals grown from native DNA. CD and a solved crystal structure showed that the selenium in the β-orientation did not perturb the native structure and gave diffraction data from which dynamic information could be extracted. Crystals of this size are especially important for neutron diffraction studies. Moreover, we discovered that the high-quality Se-DNA crystals diffracted to 1.15 Å. The Se-derivatized structure was virtually identical with the native structure. These discoveries suggest a simple strategy to address other crystallization challenges in nucleic acid crystallography, a strategy whose scope deserves further exploration.

Molecular Dynamics and Machine Learning in Catalysts
Wenxiang Liu, Yang Zhu, Yongqiang Wu, Cen Chen, Yang Hong, Yanan Yue, Jingchao Zhang, Bo Hou
Catalysts , MDPI (2021) 11:1129, DOI:10.3390/catal11091129
<Abstract>

Given the importance of catalysts in the chemical industry, they have been extensively investigated by experimental and numerical methods. With the development of computational algorithms and computer hardware, large-scale simulations have enabled influential studies with more atomic details reflecting microscopic mechanisms. This review provides a comprehensive summary of recent developments in molecular dynamics, including ab initio molecular dynamics and reaction force-field molecular dynamics. Recent research on both approaches to catalyst calculations is reviewed, including growth, dehydrogenation, hydrogenation, oxidation reactions, bias, and recombination of carbon materials that can guide catalyst calculations. Machine learning has attracted increasing interest in recent years, and its combination with the field of catalysts has inspired promising development approaches. Its applications in machine learning potential, catalyst design, performance prediction, structure optimization, and classification have been summarized in detail. This review hopes to shed light and perspective on ML approaches in catalysts.

Nucleic Acid Crystallization and Phase Determination Facilitated by Selenium Functionalization
X. Chen, C. Chen, J. Gan, W. Zhang, O. O. Gerlits, J. Salon, J. Caton-Williams, S. Jiang, H. Liu and Z. Huang
Acta Crystallogr. , Wiley (2017) A73, a186
<Abstract>

Selenium atom-specific functionalization can offer nucleic acids with many unique and novel properties (such as facilitated crystallization and phase determination) without significant perturbation of 3D structures of nucleic acids and their protein complexes. Nucleic acids possess not only the ability to store genetic information and participate in transcription and translation, but also the capacity to adopt well-defined 3D structures, which can be readily adjusted to meet various functional needs (such as catalysis and therapeutics). Although the importance of numerous nucleic acids in catalysis, gene expression, protein binding and therapeutics has been acknowledged by the entire scientific society, current understanding of nucleic acid-protein functions and structures is still limited, especially highresolution structures. This novel Se-atom-specific functionalization will provide important tools to investigate nucleic acid structure/folding, recognition and catalysis, to study nucleic acids and their protein interactions, to improve biochemical and biophysical properties of nucleic acids, and to explore potential nucleic acid therapeutics and diagnostics. Our presentation will focus on the most recent selenium-atom functionalization of nucleic acids and their potential applications in 3D structure-and-function studies and anticancer therapeutics in molecular medicine.

Asymmetric alkyne addition to aldehydes catalyzed by Schiff bases made from 1,1'-bi-2-naphthol and chiral benzylic amines
Cen Chen, Qingfei Huang, Sheng Zou, Lei Wang, Bao Luan, Jin Zhu, Qiwei Wang, Lin Pu
Tetrahedron Asymmetry , Elsevier (2014) 25:199-201, DOI:10.1016/j.tetasy.2013.12.013