scholarly journals Exploration of the close chemical space of tryptophan and tyrosine reveals importance of hydrophobicity in photo-CIDNP performances

2021 ◽  
Author(s):  
Felix Torres ◽  
Alois Renn ◽  
Roland Riek
Keyword(s):  
Magnetic Field ◽  
Nuclear Magnetic Resonance ◽  
Nuclear Polarization ◽  
Radical Pair ◽  
Chemical Space ◽  
Chemical Properties ◽  
Complex Interplay ◽  
Chemical Library ◽  
Physico Chemical ◽  
Small Chemical

Abstract. Sensitivity being one of the main hurdles of Nuclear Magnetic Resonance (NMR) can be gained by polarization techniques including Chemical Induced Dynamic Nuclear Polarization (CIDNP). Kaptein demonstrated that in CIDNP the polarization arises from the formation and the recombination of a radical pair in a magnetic field. In photo-CIDNP of interest here the radical pair is between a dye and the molecule to be polarized. The polarization obtained is thereby dependent on a complex interplay between the two molecules and their physico chemical properties. Here, we explore photo-CIDNP with a set of ten tryptophan and tyrosine analogs and observe not only signal enhancement of two orders of magnitude for 1H at 600 MHz (corresponding to 10'000 times in measurement time), but also reveal that the hydrophobicity of the molecule appears to be an important factor in the polarisation extend. Furthermore, the small chemical library established indicate the existence of many photo-CIDNP active molecules.

scholarly journals Exploration of the close chemical space of tryptophan and tyrosine reveals importance of hydrophobicity in CW-photo-CIDNP performances

2021 ◽  
Vol 2 (1) ◽  
pp. 321-329
Author(s):  
Felix Torres ◽  
Alois Renn ◽  
Roland Riek
Keyword(s):  
Magnetic Field ◽  
Nuclear Polarization ◽  
Continuous Wave ◽  
Radical Pair ◽  
Chemical Space ◽  
Spin Dynamics ◽  
Basic Mechanism ◽  
Chemical Library ◽  
Chemically Induced ◽  
Small Chemical

Abstract. Sensitivity being one of the main hurdles of nuclear magnetic resonance (NMR) can be gained by polarization techniques including chemically induced dynamic nuclear polarization (CIDNP). Kaptein demonstrated that the basic mechanism of the CIDNP arises from spin sorting based on coherent electron–electron nuclear spin dynamics during the formation and the recombination of a radical pair in a magnetic field. In photo-CIDNP of interest here the radical pair is between a dye and the molecule to be polarized. Here, we explore continuous-wave (CW) photo-CIDNP (denoted CW-photo-CIDNP) with a set of 10 tryptophan and tyrosine analogues, many of them newly identified to be photo-CIDNP active, and we observe not only signal enhancement of 2 orders of magnitude for 1H at 600 MHz (corresponding to 10 000 times in measurement time) but also reveal that polarization enhancement correlates with the hydrophobicity of the molecules. Furthermore, the small chemical library established indicates the existence of many photo-CIDNP-active molecules.

Large volume liquid state scalar Overhauser dynamic nuclear polarization at high magnetic field

2019 ◽  
Vol 21 (38) ◽  
pp. 21200-21204 ◽  
Author(s):  
Thierry Dubroca ◽  
Sungsool Wi ◽  
Johan van Tol ◽  
Lucio Frydman ◽  
Stephen Hill
Keyword(s):  
Magnetic Field ◽  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Magnetic Fields ◽  
High Magnetic Field ◽  
Dynamic Nuclear Polarization ◽  
Large Volume ◽  
Liquid State ◽  
Nuclear Polarization ◽  
High Magnetic Fields

Dynamic Nuclear Polarization (DNP) can increase the sensitivity of Nuclear Magnetic Resonance (NMR), but it is challenging in the liquid state at high magnetic fields.

Chemically Induced Dynamic Nuclear Polarization. X. On Effects of Nuclear Relaxation and the Magnetic Field Dependence

1972 ◽  
Vol 27 (8-9) ◽  
pp. 1300-1307 ◽  
Author(s):  
M. Lehnig ◽  
H Fischer
Keyword(s):  
Magnetic Field ◽  
Field Dependence ◽  
Magnetic Field Dependence ◽  
Nuclear Polarization ◽  
Radical Pair ◽  
Reaction Products ◽  
Theory Analysis ◽  
Alkyl Radicals ◽  
Chemically Induced ◽  
The Magnetic Field

Abstract The magnetic field dependence of CIDNP is presented for two reaction products of independently generated alkyl radicals. It is shown that nuclear spin relaxation of the products influences the intensity distributions within multiplets, and how this relaxation can be included in the calculation of CIDNP effects from the radical pair theory. Analysis of the experimental results supports the recent view that CIDNP is created in pairs of radicals which undergo many diffusive displacements before reencounter.

scholarly journals Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning

2020 ◽  
Author(s):  
Alice Capecchi ◽  
Jean-Louis Reymond
Keyword(s):  
Machine Learning ◽  
Natural Products ◽  
Chemical Space ◽  
Chemical Properties ◽  
Structural Diversity ◽  
Physico Chemical ◽  
Machine Learning Model ◽  
Interactive Map ◽  
Microbial Natural Products ◽  
Tree Map

<p>Microbial natural products (NPs) are an important source of drugs. However, their structural diversity remains poorly understood. Here we used our recently reported MinHashed Atom Pair fingerprint with diameter of four bonds (MAP4), a fingerprint suitable for molecules across very different sizes, to analyze the Natural Products Atlas (NPAtlas), a database of 25,523 NPs of bacterial or fungal origin downloaded from <a href="https://www.npatlas.org/joomla/">https://www.npatlas.org/joomla/</a>. To visualize NPAtlas by MAP4 similarity, we used the dimensionality reduction method tree map (TMAP) (<a href="http://tmap.gdb.tools/">http://tmap.gdb.tools</a>). The resulting interactive map (<a href="https://tm.gdb.tools/map4/npatlas_map_tmap/">https://tm.gdb.tools/map4/npatlas_map_tmap/</a>) organizes molecules by physico-chemical properties and compound families such as peptides, glycosides, polyphenols or terpenoids. Remarkably, the map separates bacterial and fungal NPs from one another, revealing that these two compound families are intrinsically different despite of their related biosynthetic pathways. We used these differences to train a machine learning model capable of distinguishing between NPs of bacterial or fungal origin. </p>

scholarly journals Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning

Biomolecules ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1385
Author(s):  
Alice Capecchi ◽  
Jean-Louis Reymond
Keyword(s):  
Machine Learning ◽  
Natural Products ◽  
Chemical Space ◽  
Chemical Properties ◽  
Structural Diversity ◽  
Physico Chemical ◽  
Machine Learning Model ◽  
Interactive Map ◽  
Microbial Natural Products ◽  
Tree Map

Microbial natural products (NPs) are an important source of drugs, however, their structural diversity remains poorly understood. Here we used our recently reported MinHashed Atom Pair fingerprint with diameter of four bonds (MAP4), a fingerprint suitable for molecules across very different sizes, to analyze the Natural Products Atlas (NPAtlas), a database of 25,523 NPs of bacterial or fungal origin. To visualize NPAtlas by MAP4 similarity, we used the dimensionality reduction method tree map (TMAP). The resulting interactive map organizes molecules by physico-chemical properties and compound families such as peptides and glycosides. Remarkably, the map separates bacterial and fungal NPs from one another, revealing that these two compound families are intrinsically different despite their related biosynthetic pathways. We used these differences to train a machine learning model capable of distinguishing between NPs of bacterial or fungal origin.

scholarly journals Refining physico-chemical rules for herbicides using an antimalarial library

2020 ◽  
Author(s):  
Kirill V. Sukhoverkov ◽  
Maxime G. Corral ◽  
Julie Leroux ◽  
Joel Haywood ◽  
Philipp Johnen ◽  
...  
Keyword(s):  
Evolutionary Relationship ◽  
Chemical Properties ◽  
Recent Analysis ◽  
Chemical Library ◽  
Liver Stage ◽  
Physico Chemical ◽  
Formal Charge ◽  
Proton Donors ◽  
Malaria Box ◽  
Blood Stage Malaria

ABSTRACTSuccessful herbicides, like drugs, have physico-chemical properties that usually fall within certain limits. A recent analysis of 334 herbicides showed similar properties to the ‘rule of five’ for human orally-delivered drugs, but herbicides diverged from this for proton donors, partition coefficients and molecular weight. To refine rules for herbicides, we exploited the close evolutionary relationship between P. falciparum and plants by screening the Malaria Box, a 400-compound library composed of novel chemical scaffolds with activity against blood stage malaria parasite Plasmodium falciparum. A high proportion (52 of 400) were herbicidal to Arabidopsis thaliana on agar plates. Thirty-nine of these 52 herbicidal compounds were tested on soil and 16 compounds were herbicidal. These data were used to predict whether a herbicidal hit found on agar will work on soil-grown plants. The physico-chemical parameters were weighted to logP and formal charge and used to generate weighted scores to a large chemical library of liver-stage effective antimalarial leads. Of the six top-scoring compounds, one had a potency comparable to commercial herbicides. This novel compound MMV1206386 had no close structural matches among commercial herbicides. Physiological profiling suggested that MMV1206386 has a new mode of action and overall demonstrates how weighted rules can help during herbicide discovery programs.

scholarly journals Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning

2020 ◽  
Author(s):  
Alice Capecchi ◽  
Jean-Louis Reymond
Keyword(s):  
Machine Learning ◽  
Natural Products ◽  
Chemical Space ◽  
Chemical Properties ◽  
Structural Diversity ◽  
Physico Chemical ◽  
Machine Learning Model ◽  
Interactive Map ◽  
Microbial Natural Products ◽  
Tree Map

<p>Microbial natural products (NPs) are an important source of drugs. However, their structural diversity remains poorly understood. Here we used our recently reported MinHashed Atom Pair fingerprint with diameter of four bonds (MAP4), a fingerprint suitable for molecules across very different sizes, to analyze the Natural Products Atlas (NPAtlas), a database of 25,523 NPs of bacterial or fungal origin downloaded from <a href="https://www.npatlas.org/joomla/">https://www.npatlas.org/joomla/</a>. To visualize NPAtlas by MAP4 similarity, we used the dimensionality reduction method tree map (TMAP) (<a href="http://tmap.gdb.tools/">http://tmap.gdb.tools</a>). The resulting interactive map (<a href="https://tm.gdb.tools/map4/npatlas_map_tmap/">https://tm.gdb.tools/map4/npatlas_map_tmap/</a>) organizes molecules by physico-chemical properties and compound families such as peptides, glycosides, polyphenols or terpenoids. Remarkably, the map separates bacterial and fungal NPs from one another, revealing that these two compound families are intrinsically different despite of their related biosynthetic pathways. We used these differences to train a machine learning model capable of distinguishing between NPs of bacterial or fungal origin. </p>

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