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Gisbert Schneider - One of the best experts on this subject based on the ideXlab platform.
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tuning artificial intelligence on the De Novo Design of natural product inspired retinoid x receptor modulators
Communications Chemistry, 2018Co-Authors: Daniel Merk, Lukas Friedrich, Francesca Grisoni, Gisbert SchneiderAbstract:Instances of artificial intelligence equip medicinal chemistry with innovative tools for molecular Design and lead discovery. Here we Describe a Deep recurrent neural network for De Novo Design of new chemical entities that are inspired by pharmacologically active natural products. Natural product characteristics are incorporated into a Deep neural network that has been trained on synthetic low molecular weight compounds. This machine-learning moDel successfully generates readily synthesizable mimetics of the natural product templates. Synthesis and in vitro pharmacological characterization of four De Novo Designed mimetics of retinoid X receptor modulating natural products confirms isofunctional activity of two computer-generated molecules. These results positively advocate generative neural networks for natural-product-inspired drug discovery, reveal both opportunities and certain limitations of the current approach, and point to potential future Developments. Artificial intelligence approaches to medicinal chemistry are increasingly powerful but struggle to predict bioactive molecules. Here a machine learning moDel generates synthetically accessible mimetics of natural products, which are shown to be bioactive against the retinoid X receptor.
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Scaffold hopping from synthetic RXR modulators by virtual screening and De Novo Design
MedChemComm, 2018Co-Authors: Daniel Merk, Lukas Friedrich, Francesca Grisoni, Elena Gelzinyte, Gisbert SchneiderAbstract:The lack of potent subtype-selective modulators of retinoid X receptors (RXRs) has hinDered their full exploitation as promising drug targets. Using computational similarity searching, target prediction and automated De Novo Design, we iDentified novel RXR ligands exhibiting innovative molecular frameworks, pronounced receptor-subtype preference and suitable properties for hit-to-lead expansion.
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De Novo Design of Bioactive Small Molecules by Artificial Intelligence
Molecular Informatics, 2018Co-Authors: Daniel Merk, Lukas Friedrich, Francesca Grisoni, Gisbert SchneiderAbstract:Generative artificial intelligence offers a fresh view on molecular Design. We present the first-time prospective application of a Deep learning moDel for Designing new druglike compounds with Desired activities. For this purpose, we trained a recurrent neural network to capture the constitution of a large set of known bioactive compounds represented as SMILES strings. By transfer learning, this general moDel was fine-tuned on recognizing retinoid X and peroxisome proliferator-activated receptor agonists. We synthesized five top-ranking compounds Designed by the generative moDel. Four of the compounds revealed nanomolar to low-micromolar receptor modulatory activity in cell-based assays. Apparently, the computational moDel intrinsically captured relevant chemical and biological knowledge without the need for explicit rules. The results of this study advocate generative artificial intelligence for prospective De Novo molecular Design, and Demonstrate the potential of these methods for future medicinal chemistry. Abstract: Generative artificial intelligence offers a fresh view on molecular Design. We present the first-time prospective application of a Deep learning moDel for Designing new druglike compounds with Desired activities. For this purpose, we trained a recurrent neural network to capture the constitution of a large set of known bioactive compounds represented as SMILES strings. By transfer learning, this general moDel was fine-tuned on recognizing retinoid X and peroxisome proliferator-activated receptor agonists. We synthesized five top-ranking compounds Designed by the generative moDel. Four of the compounds revealed nanomolar to low-micromolar receptor modulatory activity in cell-based assays. Apparently, the computational moDel intrinsically captured relevant chemical and biological knowledge without the need for explicit rules. The results of this study advocate generative artificial intelligence for prospective De Novo molecular Design, and Demonstrate the potential of these methods for future medicinal chemistry. Computational De Novo Design aims to generate new chemical entities with Desired properties. [1] There are several such methodologies, largely differing in the process of chemical structure generation and the scoring methods employed.
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from complex natural products to simple synthetic mimetics by computational De Novo Design
Angewandte Chemie, 2016Co-Authors: Lukas Friedrich, Tiago Rodrigues, Petra Schneider, Claudia S Neuhaus, Gisbert SchneiderAbstract:We present the computational De Novo Design of synthetically accessible chemical entities that mimic the complex sesquiterpene natural product (-)-Englerin A. We synthesized lead-like probes from commercially available building blocks and profiled them for activity against a computationally predicted panel of macromolecular targets. Both the Design template (-)-Englerin A and its low-molecular weight mimetics presented nanomolar binding affinities and antagonized the transient receptor potential calcium channel TRPM8 in a cell-based assay, without showing target promiscuity or frequent-hitter properties. This proof-of-concept study outlines an expeditious solution to obtaining natural-product-inspired chemical matter with Desirable properties.
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De Novo Design at the edge of chaos
Journal of Medicinal Chemistry, 2016Co-Authors: Petra Schneider, Gisbert SchneiderAbstract:Computational medicinal chemistry offers viable strategies for finding, characterizing, and optimizing innovative pharmacologically active compounds. Technological advances in both computer hardware and software as well as biological chemistry have enabled a renaissance of computer-assisted “De Novo” Design of molecules with Desired pharmacological properties. Here, we present our current perspective on the concept of automated molecule generation by highlighting chemocentric methods that may capture druglike chemical space, consiDer ligand promiscuity for hit and lead finding, and proviDe fresh iDeas for the rational Design of customized screening of compound libraries.
William F Degrado - One of the best experts on this subject based on the ideXlab platform.
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De Novo Design of tetranuclear transition metal clusters stabilized by hydrogen bonDed networks in helical bundles
Journal of the American Chemical Society, 2018Co-Authors: Shaoqing Zhang, William F Degrado, Marco Chino, Youzhi Tang, Xiaozhen Hu, Angela LombardiAbstract:De Novo Design proviDes an attractive approach to test the mechanism by which metalloproteins Define the geometry and reactivity of their metal ion cofactors. While there has been consiDerable progress in Designing proteins that bind transition metal ions including iron–sulfur clusters, the Design of tetranuclear clusters with oxygen-rich environments has not been accomplished. Here, we Describe the Design of tetranuclear clusters, consisting of four Zn2+ and four carboxylate oxygens situated at the vertices of a distorted cube-like structure. The tetra-Zn2+ clusters are bound at a buried site within a four-helix bundle, with each helix donating a single carboxylate (Glu or Asp) and imidazole (His) ligand, as well as second- and third-shell ligands. Overall, the Designed site consists of four Zn2+ and 16 polar siDe chains in a fully connected hydrogen-bonDed network. The Designed proteins have apolar cores at the top and bottom of the bundle, which drive the assembly of the liganding residues near the cen...
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De Novo Design and molecular assembly of a transmembrane diporphyrin binding protein complex
Journal of the American Chemical Society, 2010Co-Authors: Ivan V. Korendovych, William F Degrado, Alessandro Senes, James D Lear, Michael J Therien, Kent J Blasie, Ann F WalkerAbstract:The De Novo Design of membrane proteins remains difficult Despite recent advances in unDerstanding the factors that drive membrane protein folding and association. We have Designed a membrane protein PRIME (PoRphyrins In MEmbrane) that positions two non-natural iron diphenylporphyrins (FeIIIDPP’s) sufficiently close to proviDe a multicentered pathway for transmembrane electron transfer. Computational methods previously used for the Design of multiporphyrin water-soluble helical proteins were extenDed to this membrane target. Four helices were arranged in a D2-symmetrical bundle to bind two Fe(II/III) diphenylporphyrins in a bis-His geometry further stabilized by second-shell hydrogen bonds. UV−vis absorbance, CD spectroscopy, analytical ultracentrifugation, redox potentiometry, and EPR Demonstrate that PRIME binds the cofactor with high affinity and specificity in the expected geometry.
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De Novo Design of a redox active minimal rubredoxin mimic
Journal of the American Chemical Society, 2005Co-Authors: Vikas Nanda, Hidetoshi Kono, Michael M Rosenblatt, Artur Osyczka, Zelleka Getahun, Leslie P Dutton, And Jeffery G Saven, William F DegradoAbstract:Metal-binding sites in metalloproteins frequently occur at the interfaces of elements of secondary structure, which has enabled the retrostructural analysis of natural proteins and the De Novo Design of helical bundles that bind metal ion cofactors. However, the Design of metalloproteins containing β-structure is less well Developed, Despite the frequent occurrence of β-conformations in natural metalloproteins. Here, we Describe the Design and construction of a β-protein, RM1, that forms a stable, redox-active 4-Cys thiolate Fe(II/III) site analogous to the active site of rubredoxin. The protein folds into a β-structure in the presence and absence of metal ions and binds Fe(II/III) to form a redox-active site that is stable to repeated cycles of oxidation and reduction, even in an aerobic environment.
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De Novo Design of catalytic proteins
Proceedings of the National Academy of Sciences of the United States of America, 2004Co-Authors: Jack H Kaplan, William F DegradoAbstract:The De Novo Design of catalytic proteins proviDes a stringent test of our unDerstanding of enzyme function, while simultaneously laying the groundwork for the Design of novel catalysts. Here we Describe the Design of an O(2)-DepenDent phenol oxidase whose structure, sequence, and activity are Designed from first principles. The protein catalyzes the two-electron oxidation of 4-aminophenol (k(cat)/K(M) = 1,500 M(-1).min(-1)) to the corresponding quinone monoimine by using a diiron cofactor. The catalytic efficiency is sensitive to changes of the size of a methyl group in the protein, illustrating the specificity of the Design.
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De Novo Design of biomimetic antimicrobial polymers
Proceedings of the National Academy of Sciences of the United States of America, 2002Co-Authors: Bin Chen, Robert J Doerksen, Justin Kaplan, Patrick J Carroll, Michael L Klein, William F DegradoAbstract:The Design of polymers and oligomers that mimic the complex structures and remarkable biological properties of proteins is an important enDeavor with both fundamental and practical implications. Recently, a number of nonnatural peptiDes with Designed sequences have been elaborated to proviDe biologically active structures; in particular, facially amphiphilic peptiDes built from β-amino acids have been shown to mimic both the structures as well as the biological function of natural antimicrobial peptiDes such as magainins and cecropins. However, these natural peptiDes as well as their β-peptiDe analogues are expensive to prepare and difficult to produce on a large scale, limiting their potential use to certain pharmaceutical applications. We therefore have Designed a series of facially amphiphilic arylamiDe polymers that capture the physical and biological properties of this class of antimicrobial peptiDes, but are easy to prepare from inexpensive monomers. The Design process was aiDed by molecular calculations with Density functional theory-computed torsional potentials. This new class of amphiphilic polymers may be applied in situations where inexpensive antimicrobial agents are required.
Uli Fechner - One of the best experts on this subject based on the ideXlab platform.
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The concept of template-based De Novo Design from drug-Derived molecular fragments and its application to TAR RNA
Journal of Computer-Aided Molecular Design, 2008Co-Authors: Andreas Schüller, Uli Fechner, Yusuf Tanrikulu, Marcel Suhartono, Sven Breitung, Ute Scheffer, Michael W. Göbel, Gisbert SchneiderAbstract:Principles of fragment-based molecular Design are presented and discussed in the context of De Novo drug Design. The unDerlying iDea is to dissect known drug molecules in fragments by straightforward pseudo - retro -synthesis. The resulting building blocks are then used for automated assembly of new molecules. A particular question has been whether this approach is actually able to perform scaffold-hopping. A prospective case study illustrates the usefulness of fragment-based De Novo Design for finding new scaffolds. We were able to iDentify a novel ligand disrupting the interaction between the Tat peptiDe and TAR RNA, which is part of the human immunoDeficiency virus (HIV-1) mRNA. Using a single template structure (acetylpromazine) as reference molecule and a topological pharmacophore Descriptor (CATS), new chemotypes were automatically generated by our De Novo Design software Flux. Flux features an evolutionary algorithm for fragment-based compound assembly and optimization. Pharmacophore superimposition and docking into the target RNA suggest perfect matching between the template molecule and the Designed compound. Chemical synthesis was straightforward, and bioactivity of the Designed molecule was confirmed in a FRET assay. This study Demonstrates the practicability of De Novo Design to generating RNA ligands containing novel molecular scaffolds.
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Flux (1): A virtual synthesis scheme for fragment-based De Novo Design
Journal of Chemical Information and Modeling, 2006Co-Authors: Uli Fechner, Gisbert SchneiderAbstract:It is Demonstrated that the fragmentation of druglike molecules by applying simplistic pseudo-retrosynthesis results in a stock of chemically meaningful building blocks for De Novo molecule generation. A stochastic search algorithm in conjunction with ligand-based similarity scoring (Flux: fragment-based ligand builDer reaxions) facilitated the generation of new molecules using a single known reference compound as a template. This molecule assembly method is applicable in the absence of receptor-structure information. In a case study, we used imantinib (Gleevec) and a Factor Xa inhibitor as the reference structures. The algorithm succeeDed in reDesigning the templates from scratch and suggested several alternative molecular structures. The resulting Designed molecules were chemically reasonable and contained essential substructure motifs. A comparison of molecular Descriptors suggests that holographic Descriptors might be advantageous over binary fingerprints for ligand-based De Novo Design.
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computer based De Novo Design of drug like molecules
Nature Reviews Drug Discovery, 2005Co-Authors: Gisbert Schneider, Uli FechnerAbstract:Ever since the first automated De Novo Design techniques were conceived only 15 years ago, the computer-based Design of hit and lead structure candidates has emerged as a complementary approach to high-throughput screening. Although many challenges remain, De Novo Design supports drug discovery projects by generating novel pharmaceutically active agents with Desired properties in a cost- and time-efficient manner. In this review, we outline the various Design concepts and highlight current Developments in computer-based De Novo Design.
Gustav Oberdorfer - One of the best experts on this subject based on the ideXlab platform.
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De Novo Design of a non-local β-sheet protein with high stability and accuracy
Nature Structural & Molecular Biology, 2018Co-Authors: Enrique Marcos, Tamuka M. Chidyausiku, Andrew C. Mcshan, Thomas Evangelidis, Santrupti Nerli, Lauren Carter, Lucas G. Nivón, Gustav Oberdorfer, Audrey Davis, Konstantinos TripsianesAbstract:β-sheet proteins carry out critical functions in biology, and hence are attractive scaffolds for computational protein Design. Despite this potential, De Novo Design of all-β-sheet proteins from first principles lags far behind the Design of all-α or mixed-αβ domains owing to their non-local nature and the tenDency of exposed β-strand edges to aggregate. Through study of loops connecting unpaired β-strands (β-arches), we have iDentified a series of structural relationships between loop geometry, siDe chain directionality and β-strand length that arise from hydrogen bonding and packing constraints on regular β-sheet structures. We use these rules to De Novo Design jellyroll structures with double-stranDed β-helices formed by eight antiparallel β-strands. The nuclear magnetic resonance structure of a hyperthermostable Design closely matched the computational moDel, Demonstrating accurate control over the β-sheet structure and loop geometry. Our results open the door to the Design of a broad range of non-local β-sheet protein structures. Baker, Marcos and colleagues analyze β-arches (loops connecting unpaired β-strands) and Derive rules used for De Novo Design of a hyperthermostable jellyroll structure, with eight antiparallel β-strands forming double-stranDed β-helices.
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De Novo Design of a non-local beta-sheet protein with high stability and accuracy
Nature Structural & Molecular Biology, 2018Co-Authors: Enrique Marcos, Tamuka M. Chidyausiku, Andrew C. Mcshan, Thomas Evangelidis, Santrupti Nerli, Lauren Carter, Lucas G. Nivón, Gustav Oberdorfer, Audrey Davis, Konstantinos TripsianesAbstract:β-sheet proteins carry out critical functions in biology, and hence are attractive scaffolds for computational protein Design. Despite this potential, De Novo Design of all-β-sheet proteins from first principles lags far behind the Design of all-α or mixed-αβ domains owing to their non-local nature and the tenDency of exposed β-strand edges to aggregate. Through study of loops connecting unpaired β-strands (β-arches), we have iDentified a series of structural relationships between loop geometry, siDe chain directionality and β-strand length that arise from hydrogen bonding and packing constraints on regular β-sheet structures. We use these rules to De Novo Design jellyroll structures with double-stranDed β-helices formed by eight antiparallel β-strands. The nuclear magnetic resonance structure of a hyperthermostable Design closely matched the computational moDel, Demonstrating accurate control over the β-sheet structure and loop geometry. Our results open the door to the Design of a broad range of non-local β-sheet protein structures. Baker, Marcos and colleagues analyze β-arches (loops connecting unpaired β-strands) and Derive rules used for De Novo Design of a hyperthermostable jellyroll structure, with eight antiparallel β-strands forming double-stranDed β-helices.
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De Novo Design of a non local beta sheet protein with high stability and accuracy
Nature Structural & Molecular Biology, 2018Co-Authors: Enrique Marcos, Tamuka M. Chidyausiku, Andrew C. Mcshan, Thomas Evangelidis, Santrupti Nerli, Lauren Carter, Lucas G. Nivón, Audrey Davis, Gustav OberdorferAbstract:β-sheet proteins carry out critical functions in biology, and hence are attractive scaffolds for computational protein Design. Despite this potential, De Novo Design of all-β-sheet proteins from first principles lags far behind the Design of all-α or mixed-αβ domains owing to their non-local nature and the tenDency of exposed β-strand edges to aggregate. Through study of loops connecting unpaired β-strands (β-arches), we have iDentified a series of structural relationships between loop geometry, siDe chain directionality and β-strand length that arise from hydrogen bonding and packing constraints on regular β-sheet structures. We use these rules to De Novo Design jellyroll structures with double-stranDed β-helices formed by eight antiparallel β-strands. The nuclear magnetic resonance structure of a hyperthermostable Design closely matched the computational moDel, Demonstrating accurate control over the β-sheet structure and loop geometry. Our results open the door to the Design of a broad range of non-local β-sheet protein structures.
Audrey Davis - One of the best experts on this subject based on the ideXlab platform.
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De Novo Design of a non-local β-sheet protein with high stability and accuracy
Nature Structural & Molecular Biology, 2018Co-Authors: Enrique Marcos, Tamuka M. Chidyausiku, Andrew C. Mcshan, Thomas Evangelidis, Santrupti Nerli, Lauren Carter, Lucas G. Nivón, Gustav Oberdorfer, Audrey Davis, Konstantinos TripsianesAbstract:β-sheet proteins carry out critical functions in biology, and hence are attractive scaffolds for computational protein Design. Despite this potential, De Novo Design of all-β-sheet proteins from first principles lags far behind the Design of all-α or mixed-αβ domains owing to their non-local nature and the tenDency of exposed β-strand edges to aggregate. Through study of loops connecting unpaired β-strands (β-arches), we have iDentified a series of structural relationships between loop geometry, siDe chain directionality and β-strand length that arise from hydrogen bonding and packing constraints on regular β-sheet structures. We use these rules to De Novo Design jellyroll structures with double-stranDed β-helices formed by eight antiparallel β-strands. The nuclear magnetic resonance structure of a hyperthermostable Design closely matched the computational moDel, Demonstrating accurate control over the β-sheet structure and loop geometry. Our results open the door to the Design of a broad range of non-local β-sheet protein structures. Baker, Marcos and colleagues analyze β-arches (loops connecting unpaired β-strands) and Derive rules used for De Novo Design of a hyperthermostable jellyroll structure, with eight antiparallel β-strands forming double-stranDed β-helices.
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De Novo Design of a non-local beta-sheet protein with high stability and accuracy
Nature Structural & Molecular Biology, 2018Co-Authors: Enrique Marcos, Tamuka M. Chidyausiku, Andrew C. Mcshan, Thomas Evangelidis, Santrupti Nerli, Lauren Carter, Lucas G. Nivón, Gustav Oberdorfer, Audrey Davis, Konstantinos TripsianesAbstract:β-sheet proteins carry out critical functions in biology, and hence are attractive scaffolds for computational protein Design. Despite this potential, De Novo Design of all-β-sheet proteins from first principles lags far behind the Design of all-α or mixed-αβ domains owing to their non-local nature and the tenDency of exposed β-strand edges to aggregate. Through study of loops connecting unpaired β-strands (β-arches), we have iDentified a series of structural relationships between loop geometry, siDe chain directionality and β-strand length that arise from hydrogen bonding and packing constraints on regular β-sheet structures. We use these rules to De Novo Design jellyroll structures with double-stranDed β-helices formed by eight antiparallel β-strands. The nuclear magnetic resonance structure of a hyperthermostable Design closely matched the computational moDel, Demonstrating accurate control over the β-sheet structure and loop geometry. Our results open the door to the Design of a broad range of non-local β-sheet protein structures. Baker, Marcos and colleagues analyze β-arches (loops connecting unpaired β-strands) and Derive rules used for De Novo Design of a hyperthermostable jellyroll structure, with eight antiparallel β-strands forming double-stranDed β-helices.
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De Novo Design of a non local beta sheet protein with high stability and accuracy
Nature Structural & Molecular Biology, 2018Co-Authors: Enrique Marcos, Tamuka M. Chidyausiku, Andrew C. Mcshan, Thomas Evangelidis, Santrupti Nerli, Lauren Carter, Lucas G. Nivón, Audrey Davis, Gustav OberdorferAbstract:β-sheet proteins carry out critical functions in biology, and hence are attractive scaffolds for computational protein Design. Despite this potential, De Novo Design of all-β-sheet proteins from first principles lags far behind the Design of all-α or mixed-αβ domains owing to their non-local nature and the tenDency of exposed β-strand edges to aggregate. Through study of loops connecting unpaired β-strands (β-arches), we have iDentified a series of structural relationships between loop geometry, siDe chain directionality and β-strand length that arise from hydrogen bonding and packing constraints on regular β-sheet structures. We use these rules to De Novo Design jellyroll structures with double-stranDed β-helices formed by eight antiparallel β-strands. The nuclear magnetic resonance structure of a hyperthermostable Design closely matched the computational moDel, Demonstrating accurate control over the β-sheet structure and loop geometry. Our results open the door to the Design of a broad range of non-local β-sheet protein structures.