Experimental vs. calculated HFEs of compounds from the validation set (GROMOS 54a7).

<p>Correlation is captured by the regression line, its parameters, Pearson correlation coefficient and overall RMSE, with the outlier 2-nitrophenol in red (X symbol). The same comparison for canonical amino acids is shown in the inset. Note that error bars of calculated HFEs are comparable to the size of the symbols, with the average standard error of 0.4 kJ/mol.</p

A Systematic Framework for Molecular Dynamics Simulations of Protein Post-Translational Modifications

<div><p>By directly affecting structure, dynamics and interaction networks of their targets, post-translational modifications (PTMs) of proteins play a key role in different cellular processes ranging from enzymatic activation to regulation of signal transduction to cell-cycle control. Despite the great importance of understanding how PTMs affect proteins at the atomistic level, a systematic framework for treating post-translationally modified amino acids by molecular dynamics (MD) simulations, a premier high-resolution computational biology tool, has never been developed. Here, we report and validate force field parameters (GROMOS 45a3 and 54a7) required to run and analyze MD simulations of more than 250 different types of enzymatic and non-enzymatic PTMs. The newly developed GROMOS 54a7 parameters in particular exhibit near chemical accuracy in matching experimentally measured hydration free energies (RMSE = 4.2 kJ/mol over the validation set). Using this tool, we quantitatively show that the majority of PTMs greatly alter the hydrophobicity and other physico-chemical properties of target amino acids, with the extent of change in many cases being comparable to the complete range spanned by native amino acids.</p></div

Hydrophobicity-related properties of PTMs compared to canonical amino acids.

<p>a) hydration free energies (HFEs) and b) molecular hydrophobicity potentials (MHPs). Distributions calculated of HFEs and MHPs of the canonical amino acids are captured using white boxes on the left side of both a) and b) panels. The distributions of HFE and MHP changes upon different types of PTMs are shown in colored boxes sorted according to the median of the underlying distributions. The distributions are shown using the box-and-whisker plotting method. Color code: methylation-yellow, carbonylation-blue, hydroxylation-green, phosphorylation-red, other enzymatic modifications-gray, other non-enzymatic modification-orange and all-white; c) change in surface MHP upon arginine carbonylation and cysteine oxidation, modifications with the most positive and the most negative MHP change, respectively. Note that we have not taken N-acetylglucosamine into account for the HFE and MHP analysis, since glycosylation modifications predominantly result in carbohydrate chains attached to target residues, while we provide parameters for this carbohydrate only as the first one in a typical chain.</p

Summary of the number and coverage of parameterized PTMs.

<p>a) the number of parameterized PTMs by type (outer annulus) together with the number of parameterized non-redundant compounds by type (inner circle), labeled accordingly (number of PTMs: number of compounds); b) the number of experimentally verified PTMs by type annotated in the UniProt database (total of 72,984); c) coverage of experimentally verified PTMs shown as percentages with the values and the number of covered modifications displayed (top of bars). Color code: phosphorylation-red, acetylation-blue, methylation-yellow, hydroxylation-green, other PTMs-orange, terminal PTMs-gray and all-white.</p

HFEs of the molecules in the validation set: comparison between experimental and calculated values using the GROMOS 54a7 parameter set.

<p>The outlier 2-nitrophenol described by:</p>*<p>parameters used for other nitro-containing compounds, and</p>#<p>parameters derived to match the experimental HFE.</p><p>Experimental HFEs are taken from refs. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003154#pcbi.1003154-Oostenbrink1" target="_blank">[21]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003154#pcbi.1003154-Gallicchio1" target="_blank">[48]</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003154#pcbi.1003154-Sulea1" target="_blank">[50]</a>.</p

Data_Sheet_1_Engineering the N-glycosylation pathway of Nicotiana tabacum for molecular pharming using CRISPR/Cas9.docx

Molecular pharming in plants offers exciting possibilities to address global access to modern biologics. However, differences in the N-glycosylation pathway including the presence of β(1,2)-xylose and core α(1,3)-fucose can affect activity, potency and immunogenicity of plant-derived proteins. Successful glycoengineering approaches toward human-like structures with no changes in plant phenotype, growth, or recombinant protein expression levels have been reported for Arabidopsis thaliana and Nicotiana benthamiana. Such engineering of N-glycosylation would also be desirable for Nicotiana tabacum, which remains the crop of choice for recombinant protein pharmaceuticals required at massive scale and for manufacturing technology transfer to less developed countries. Here, we generated N. tabacum cv. SR-1 β(1,2)-xylosyltransferase (XylT) and α(1,3)-fucosyltransferase (FucT) knockout lines using CRISPR/Cas9 multiplex genome editing, targeting three conserved regions of the four FucT and two XylT genes. These two enzymes are responsible for generating non-human N-glycan structures. We confirmed full functional knockout of transformants by immunoblotting of total soluble protein by antibodies recognizing β(1,2)-xylose and core α(1,3)-fucose, mass spectrometry analysis of recombinantly produced VRC01, a broadly neutralizing anti-HIV-1 hIgG1 antibody, and Sanger sequencing of targeted regions of the putative transformants. These data represent an important step toward establishing Nicotiana tabacum as a biologics platform for Global Health.</p

Ligand Desolvation Steers On-Rate and Impacts Drug Residence Time of Heat Shock Protein 90 (Hsp90) Inhibitors

Residence time and more recently the association rate constant <i>k</i>_on are increasingly acknowledged as important parameters for in vivo efficacy and safety of drugs. However, their broader consideration in drug development is limited by a lack of knowledge of how to optimize these parameters. In this study on a set of 176 heat shock protein 90 inhibitors, structure–kinetic relationships, X-ray crystallography, and molecular dynamics simulations were combined to retrieve a concrete scheme of how to rationally slow down on-rates. We discovered that an increased ligand desolvation barrier by introducing polar substituents resulted in a significant <i>k</i>_on decrease. The slowdown was accomplished by introducing polar moieties to those parts of the ligand that point toward a hydrophobic cavity. We validated this scheme by increasing polarity of three Hsp90 inhibitors and observed a 9-, 13-, and 45-fold slowdown of on-rates and a 9-fold prolongation in residence time. This prolongation was driven by transition state destabilization rather than ground state stabilization

Ligand Desolvation Steers On-Rate and Impacts Drug Residence Time of Heat Shock Protein 90 (Hsp90) Inhibitors

Residence time and more recently the association rate constant <i>k</i>_on are increasingly acknowledged as important parameters for in vivo efficacy and safety of drugs. However, their broader consideration in drug development is limited by a lack of knowledge of how to optimize these parameters. In this study on a set of 176 heat shock protein 90 inhibitors, structure–kinetic relationships, X-ray crystallography, and molecular dynamics simulations were combined to retrieve a concrete scheme of how to rationally slow down on-rates. We discovered that an increased ligand desolvation barrier by introducing polar substituents resulted in a significant <i>k</i>_on decrease. The slowdown was accomplished by introducing polar moieties to those parts of the ligand that point toward a hydrophobic cavity. We validated this scheme by increasing polarity of three Hsp90 inhibitors and observed a 9-, 13-, and 45-fold slowdown of on-rates and a 9-fold prolongation in residence time. This prolongation was driven by transition state destabilization rather than ground state stabilization

Ligand Desolvation Steers On-Rate and Impacts Drug Residence Time of Heat Shock Protein 90 (Hsp90) Inhibitors

Residence time and more recently the association rate constant <i>k</i>_on are increasingly acknowledged as important parameters for in vivo efficacy and safety of drugs. However, their broader consideration in drug development is limited by a lack of knowledge of how to optimize these parameters. In this study on a set of 176 heat shock protein 90 inhibitors, structure–kinetic relationships, X-ray crystallography, and molecular dynamics simulations were combined to retrieve a concrete scheme of how to rationally slow down on-rates. We discovered that an increased ligand desolvation barrier by introducing polar substituents resulted in a significant <i>k</i>_on decrease. The slowdown was accomplished by introducing polar moieties to those parts of the ligand that point toward a hydrophobic cavity. We validated this scheme by increasing polarity of three Hsp90 inhibitors and observed a 9-, 13-, and 45-fold slowdown of on-rates and a 9-fold prolongation in residence time. This prolongation was driven by transition state destabilization rather than ground state stabilization