Multiple Highly Diverse Structures Complementary to Enzyme Binding Sites: Results of Extensive Application of a de Novo Design Method Incorporating Combinatorial Growth

1994 ◽  
Vol 116 (13) ◽  
pp. 5560-5571 ◽  
Author(s):  
Regine S. Bohacek ◽  
Colin McMartin
Keyword(s):  
Binding Sites ◽  
De Novo ◽  
Design Method ◽  
De Novo Design ◽  
Enzyme Binding

scholarly journals DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of enhancers

2021 ◽  
Author(s):  
Bernardo P de Almeida ◽  
Franziska Reiter ◽  
Michaela Pagani ◽  
Alexander Stark
Keyword(s):  
Dna Sequence ◽  
Binding Sites ◽  
De Novo ◽  
De Novo Design ◽  
Flanking Sequence ◽  
Enhancer Activity ◽  
Drosophila Melanogaster S2 Cells ◽  
Enhancer Sequences ◽  
The Relationship ◽  
Deep Learning Model

Enhancer sequences control gene expression and comprise binding sites (motifs) for different transcription factors (TFs). Despite extensive genetic and computational studies, the relationship between DNA sequence and regulatory activity is poorly understood and enhancer de novo design is considered impossible. Here we built a deep learning model, DeepSTARR, to quantitatively predict the activities of thousands of developmental and housekeeping enhancers directly from DNA sequence in Drosophila melanogaster S2 cells. The model learned relevant TF motifs and higher-order syntax rules, including functionally non-equivalent instances of the same TF motif that are determined by motif-flanking sequence and inter-motif distances. We validated these rules experimentally and demonstrated their conservation in human by testing more than 40,000 wildtype and mutant Drosophila and human enhancers. Finally, we designed and functionally validated synthetic enhancers with desired activities de novo.

scholarly journals Computational approaches for de novo design and redesign of metal-binding sites on proteins

2017 ◽  
Vol 37 (2) ◽  
Author(s):  
Gunseli Bayram Akcapinar ◽  
Osman Ugur Sezerman
Keyword(s):  
Metal Ions ◽  
Metal Binding ◽  
Binding Sites ◽  
Metal Ion ◽  
De Novo ◽  
De Novo Design ◽  
Ion Binding ◽  
Chemical Transformations ◽  
Metal Ion Binding ◽  
Metal Binding Sites

Metal ions play pivotal roles in protein structure, function and stability. The functional and structural diversity of proteins in nature expanded with the incorporation of metal ions or clusters in proteins. Approximately one-third of these proteins in the databases contain metal ions. Many biological and chemical processes in nature involve metal ion-binding proteins, aka metalloproteins. Many cellular reactions that underpin life require metalloproteins. Most of the remarkable, complex chemical transformations are catalysed by metalloenzymes. Realization of the importance of metal-binding sites in a variety of cellular events led to the advancement of various computational methods for their prediction and characterization. Furthermore, as structural and functional knowledgebase about metalloproteins is expanding with advances in computational and experimental fields, the focus of the research is now shifting towards de novo design and redesign of metalloproteins to extend nature’s own diversity beyond its limits. In this review, we will focus on the computational toolbox for prediction of metal ion-binding sites, de novo metalloprotein design and redesign. We will also give examples of tailor-made artificial metalloproteins designed with the computational toolbox.

Chemical-Space-Based de Novo Design Method To Generate Drug-Like Molecules

2016 ◽  
Vol 56 (10) ◽  
pp. 1885-1893 ◽  
Author(s):  
Shunichi Takeda ◽  
Hiromasa Kaneko ◽  
Kimito Funatsu
Keyword(s):  
De Novo ◽  
Chemical Space ◽  
Design Method ◽  
De Novo Design

A novel human scFv fragment against TNF-α from de novo design method

2007 ◽  
Vol 44 (15) ◽  
pp. 3789-3796 ◽  
Author(s):  
Hong Chang ◽  
Weisong Qin ◽  
Yan Li ◽  
Jiyan Zhang ◽  
Zhou Lin ◽  
...  
Keyword(s):  
De Novo ◽  
Design Method ◽  
De Novo Design ◽  
Scfv Fragment ◽  
Tnf Α ◽  
Human Scfv
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