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As suggested by case study results, Pocket v.2 can yield consistent pharmacophore models for proteins with minor conformational changes upon binding of different ligands, indicating the robustness of the approach.
Complementary to the field of structural genomics, protein structure prediction seeks to develop efficient ways to provide plausible models for proteins whose structures have not yet been determined experimentally.
Recently, Bradley et al demonstrated an exciting achievement by building several high-resolution models for proteins of < 90 residues [ 13].
For all datasets, ProtTest selected LG + I + G or variations of it (LG is an evolutionary models for proteins developed by [ 26]).
Ovchinnikov et al. then used this method to make models for proteins belonging to 58 different protein families with currently unknown structures.
Evolutionary substitution models for proteins were selected by ProtTest 2.4 [ 134] and for DNA by jModelTest 0.1.1 [ 135] according to the Akaike Information Criterion.
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From the point of view of the so-called "personalized medicine", bioinformatics studies of reversible phosphorylation in proteins will allow the generation of models for protein-protein interaction at the atomic level taking into account each particular protein sequence.
Data mining and machine learning algorithms thus have the potential to deliver predictive models for protein crystallization.
To easily generate reduced point charge models for protein structures, libraries of amino acid templates are built.
Here we clarify the specification of the transition state by working with two types of toy models for protein association.
In this paper a computer tool that supports the calibration of chromatography models for protein purification is presented.
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