scholarly journals ZODIAC: database-independent molecular formula annotation using Gibbs sampling reveals unknown small molecules

2019 ◽  
Author(s):  
Marcus Ludwig ◽  
Louis-Félix Nothias ◽  
Kai Dührkop ◽  
Irina Koester ◽  
Markus Fleischauer ◽  
...  
Keyword(s):  
Small Molecules ◽  
Gibbs Sampling ◽  
De Novo ◽  
Structure Identification ◽  
Algorithm Engineering ◽  
Molecular Formula ◽  
Tandem Mass Spectra ◽  
Challenging Tasks ◽  
Structure Databases ◽  
Fragmentation Tree

1AbstractThe confident high-throughput identification of small molecules remains one of the most challenging tasks in mass spectrometry-based metabolomics. SIRIUS has become a powerful tool for the interpretation of tandem mass spectra, and shows outstanding performance for identifying the molecular formula of a query compound, being the first step of structure identification. Nevertheless, the identification of both molecular formulas for large compounds above 500 Daltons and novel molecular formulas remains highly challenging. Here, we present ZODIAC, a network-based algorithm for the de novo estimation of molecular formulas. ZODIAC reranks SIRIUS’ molecular formula candidates, combining fragmentation tree computation with Bayesian statistics using Gibbs sampling. Through careful algorithm engineering, ZODIAC’s Gibbs sampling is very swift in practice. ZODIAC decreases incorrect annotations 16.2-fold on a challenging plant extract dataset with most compounds above 700 Dalton; we then show improvements on four additional, diverse datasets. Our analysis led to the discovery of compounds with novel molecular formulas such as C24H47BrNO8P which, as of today, is not present in any publicly available molecular structure databases.

scholarly journals Searching molecular structure databases with tandem mass spectra using CSI:FingerID

2015 ◽  
Vol 112 (41) ◽  
pp. 12580-12585 ◽  
Author(s):  
Kai Dührkop ◽  
Huibin Shen ◽  
Marvin Meusel ◽  
Juho Rousu ◽  
Sebastian Böcker
Keyword(s):  
Molecular Structure ◽  
Mass Spectra ◽  
Metabolite Identification ◽  
Tandem Ms ◽  
Unknown Compound ◽  
Tandem Mass Spectra ◽  
Structure Database ◽  
Fragmentation Spectrum ◽  
Structure Databases ◽  
Fragmentation Tree

Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually rely on tandem MS to identify the thousands of compounds in a biological sample. Today, the vast majority of metabolites remain unknown. We present a method for searching molecular structure databases using tandem MS data of small molecules. Our method computes a fragmentation tree that best explains the fragmentation spectrum of an unknown molecule. We use the fragmentation tree to predict the molecular structure fingerprint of the unknown compound using machine learning. This fingerprint is then used to search a molecular structure database such as PubChem. Our method is shown to improve on the competing methods for computational metabolite identification by a considerable margin.

scholarly journals De Novo Drug Design Using Artificial Intelligence Applied on SARS-CoV-2 Viral Proteins ASYNT-GAN

BioChem ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 36-48
Author(s):  
Ivan Jacobs ◽  
Manolis Maragoudakis
Keyword(s):  
Deep Learning ◽  
Small Molecules ◽  
Natural Product ◽  
De Novo ◽  
Viral Proteins ◽  
Computer Assisted ◽  
De Novo Drug Design ◽  
Learning Techniques ◽  
Latent Space ◽  
Synthetic Molecule

Computer-assisted de novo design of natural product mimetics offers a viable strategy to reduce synthetic efforts and obtain natural-product-inspired bioactive small molecules, but suffers from several limitations. Deep learning techniques can help address these shortcomings. We propose the generation of synthetic molecule structures that optimizes the binding affinity to a target. To achieve this, we leverage important advancements in deep learning. Our approach generalizes to systems beyond the source system and achieves the generation of complete structures that optimize the binding to a target unseen during training. Translating the input sub-systems into the latent space permits the ability to search for similar structures, and the sampling from the latent space for generation.

Computational study for suppression of CD25/IL-2 interaction

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Moein Dehbashi ◽  
Zohreh Hojati ◽  
Majid Motovali-bashi ◽  
Mazdak Ganjalikhani-Hakemi ◽  
Akihiro Shimosaka ◽  
...  
Keyword(s):  
Small Molecules ◽  
Cancer Progression ◽  
Immune Escape ◽  
De Novo ◽  
Computational Study ◽  
Treg Cells ◽  
Homology Search ◽  
Small Interfering Rnas

AbstractCancer recurrence presents a huge challenge in cancer patient management. Immune escape is a key mechanism of cancer progression and metastatic dissemination. CD25 is expressed in regulatory T (Treg) cells including tumor-infiltrating Treg cells (TI-Tregs). These cells specially activate and reinforce immune escape mechanism of cancers. The suppression of CD25/IL-2 interaction would be useful against Treg cells activation and ultimately immune escape of cancer. Here, software, web servers and databases were used, at which in silico designed small interfering RNAs (siRNAs), de novo designed peptides and virtual screened small molecules against CD25 were introduced for the prospect of eliminating cancer immune escape and obtaining successful treatment. We obtained siRNAs with low off-target effects. Further, small molecules based on the binding homology search in ligand and receptor similarity were introduced. Finally, the critical amino acids on CD25 were targeted by a de novo designed peptide with disulfide bond. Hence we introduced computational-based antagonists to lay a foundation for further in vitro and in vivo studies.

scholarly journals Fine-tuning of a generative neural network for designing multi-target compounds

2021 ◽  
Author(s):  
Thomas Blaschke ◽  
Jürgen Bajorath
Keyword(s):  
Neural Network ◽  
Small Molecules ◽  
De Novo ◽  
Pharmaceutical Research ◽  
Generative Models ◽  
Fine Tuning ◽  
Data Sets ◽  
Single Target ◽  
Fine Tune ◽  
Target Activity

AbstractExploring the origin of multi-target activity of small molecules and designing new multi-target compounds are highly topical issues in pharmaceutical research. We have investigated the ability of a generative neural network to create multi-target compounds. Data sets of experimentally confirmed multi-target, single-target, and consistently inactive compounds were extracted from public screening data considering positive and negative assay results. These data sets were used to fine-tune the REINVENT generative model via transfer learning to systematically recognize multi-target compounds, distinguish them from single-target or inactive compounds, and construct new multi-target compounds. During fine-tuning, the model showed a clear tendency to increasingly generate multi-target compounds and structural analogs. Our findings indicate that generative models can be adopted for de novo multi-target compound design.

scholarly journals De Novo Design of Bioactive Small Molecules by Artificial Intelligence

2018 ◽  
Vol 37 (1-2) ◽  
pp. 1700153 ◽  
Author(s):  
Daniel Merk ◽  
Lukas Friedrich ◽  
Francesca Grisoni ◽  
Gisbert Schneider
Keyword(s):  
Artificial Intelligence ◽  
Small Molecules ◽  
De Novo ◽  
De Novo Design

scholarly journals A defined structural unit enables de novo design of small-molecule–binding proteins

Science ◽  
2020 ◽  
Vol 369 (6508) ◽  
pp. 1227-1233 ◽  
Author(s):  
Nicholas F. Polizzi ◽  
William F. DeGrado
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Small Molecules ◽  
Structural Unit ◽  
De Novo ◽  
Computational Design ◽  
De Novo Design ◽  
X Ray ◽  
X Ray Crystallography ◽  
Chemical Groups

The de novo design of proteins that bind highly functionalized small molecules represents a great challenge. To enable computational design of binders, we developed a unit of protein structure—a van der Mer (vdM)—that maps the backbone of each amino acid to statistically preferred positions of interacting chemical groups. Using vdMs, we designed six de novo proteins to bind the drug apixaban; two bound with low and submicromolar affinity. X-ray crystallography and mutagenesis confirmed a structure with a precisely designed cavity that forms favorable interactions in the drug–protein complex. vdMs may enable design of functional proteins for applications in sensing, medicine, and catalysis.

scholarly journals De Novo Sequencing of Peptides from High-Resolution Bottom-Up Tandem Mass Spectra using Top-Down Intended Methods

PROTEOMICS ◽  
2017 ◽  
Vol 17 (23-24) ◽  
pp. 1600321 ◽  
Author(s):  
Kira Vyatkina ◽  
Lennard J. M. Dekker ◽  
Si Wu ◽  
Martijn M. VanDuijn ◽  
Xiaowen Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Mass Spectra ◽  
De Novo ◽  
De Novo Sequencing ◽  
Tandem Mass ◽  
Top Down ◽  
Bottom Up ◽  
Tandem Mass Spectra
Sign in / Sign up

Export Citation Format

Share Document