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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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Nucleic Acid Structure01:25

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The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
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Related Experiment Video

Updated: Aug 24, 2025

Self-Assembly of Gamma-Modified Peptide Nucleic Acids into Complex Nanostructures in Organic Solvent Mixtures
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Machine Learning Guides Peptide Nucleic Acid Flow Synthesis and Sequence Design.

Chengxi Li1,2,3, Genwei Zhang1, Somesh Mohapatra4

  • 1Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|October 21, 2022
PubMed
Summary

Machine learning accurately predicts peptide nucleic acid (PNA) synthesis efficiency, streamlining the design of PNA antisense therapies for various diseases. This computational tool enhances PNA sequence selection for improved drug development.

Keywords:
automated synthesisdrug designmachine learningpeptide nucleic acidyield prediction

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Medicinal Chemistry

Background:

  • Peptide nucleic acids (PNAs) show promise as antisense therapies for genetic, acquired, and viral diseases.
  • Selecting optimal PNA sequences from large genomic datasets for synthesis is a significant challenge.
  • Automated synthesis and computational methods are needed to accelerate PNA-based therapeutic development.

Purpose of the Study:

  • To develop and validate a machine learning (ML) model for predicting PNA synthesis efficiency.
  • To guide the rational design of synthetically accessible antisense PNA sequences.
  • To demonstrate the broad applicability of ML in PNA sequence design for diverse therapeutic targets.

Main Methods:

  • Collected training data from automated fluorenylmethyloxycarbonyl (Fmoc) deprotection reactions on a PNA synthesizer.
  • Developed and optimized a machine learning model to predict PNA synthesis efficiency.
  • Validated ML predictions against experimental high-performance liquid chromatography (HPLC) crude purity data.

Main Results:

  • Achieved 93% prediction accuracy and a 0.97 Pearson's correlation coefficient with the ML model.
  • Demonstrated a strong correlation (R² = 0.95) between predicted synthesis scores and experimental HPLC crude purities.
  • Successfully designed synthetically accessible antisense PNA sequences for targets including human dystrophin, SARS-CoV-2, HIV, cardiovascular diseases, type II diabetes, and cancers.

Conclusions:

  • Machine learning offers an accurate method for predicting PNA synthesis quality.
  • ML serves as a valuable computational tool to inform and optimize PNA sequence design for therapeutic applications.
  • This approach accelerates the identification and development of PNA-based antisense therapies.