A quantitative systems pharmacology workflow toward optimal design and biomarker stratification of atopic dermatitis clinical trials.

BACKGROUND: The development of atopic dermatitis (AD) drugs is challenged by many disease phenotypes and trial design options, which are hard to explore experimentally. OBJECTIVE: We aimed to optimize AD trial design using simulations. METHODS: We constructed a quantitative systems pharmacology model of AD and standard of care (SoC) treatments and generated a phenotypically diverse virtual population whose parameter distribution was derived from known relationships between AD biomarkers and disease severity and calibrated using disease severity evolution under SoC regimens. RESULTS: We applied this workflow to the immunomodulator OM-85, currently being investigated for its potential use in AD, and calibrated the investigational treatment model with the efficacy profile of an existing trial (thereby enriching it with plausible marker levels and dynamics). We assessed the sensitivity of trial outcomes to trial protocol and found that for this particular example the choice of end point is more important than the choice of dosing regimen and patient selection by model-based responder enrichment could increase the expected effect size. A global sensitivity analysis revealed that only a limited subset of baseline biomarkers is needed to predict the drug response of the full virtual population. CONCLUSIONS: This AD quantitative systems pharmacology workflow built around knowledge of marker-severity relationships as well as SoC efficacy can be tailored to specific development cases to optimize several trial protocol parameters and biomarker stratification and therefore has promise to become a powerful model-informed AD drug development and personalized medicine tool.

Decomposing protein-DNA binding and recognition using simplified protein models.

We analyze the role of different physicochemical factors in protein/DNA binding and recognition by comparing the results from all-atom molecular dynamics simulations with simulations using simplified protein models. These models enable us to separate the role of specific amino acid side chains, formal amino acid charges and hydrogen bonding from the effects of the low-dielectric volume occupied by the protein. Comparisons are made on the basis of the conformation of DNA after protein binding, the ionic distribution around the complex and the sequence specificity. The results for four transcription factors, binding in either the minor or major grooves of DNA, show that the protein volume and formal charges, with one exception, play a predominant role in binding. Adding hydrogen bonding and a very small number of key amino acid side chains at the all-atom level yields results in DNA conformations and sequence recognition close to those seen in the reference all-atom simulations.

Protein-DNA interfaces: a molecular dynamics analysis of time-dependent recognition processes for three transcription factors.

We have studied the dynamics of three transcription factor-DNA complexes using all-atom, microsecond-scale MD simulations. In each case, the salt bridges and hydrogen bond interactions formed at the protein-DNA interface are found to be dynamic, with lifetimes typically in the range of tens to hundreds of picoseconds, although some interactions, notably those involving specific binding to DNA bases, can be a hundred times longer lived. Depending on the complex studied, this dynamics may or may not lead to the existence of distinct conformational substates. Using a sequence threading technique, it has been possible to determine whether DNA sequence recognition is sensitive or not to such conformational changes, and, in one case, to show that recognition appears to be locally dependent on protein-mediated cation distributions.

Implication of Terminal Residues at Protein-Protein and Protein-DNA Interfaces.

Terminal residues of protein chains are charged and more flexible than other residues since they are constrained only on one side. Do they play a particular role in protein-protein and protein-DNA interfaces? To answer this question, we considered large sets of non-redundant protein-protein and protein-DNA complexes and analyzed the status of terminal residues and their involvement in interfaces. In protein-protein complexes, we found that more than half of terminal residues (62%) are either modified by attachment of a tag peptide (10%) or have missing coordinates in the analyzed structures (52%). Terminal residues are almost exclusively located at the surface of proteins (94%). Contrary to charged residues, they are not over or under-represented in protein-protein interfaces, but strongly prefer the peripheral region of interfaces when present at the interface (83% of terminal residues). The almost exclusive location of terminal residues at the surface of the proteins or in the rim regions of interfaces explains that experimental methods relying on tail hybridization can be successfully applied without disrupting the complexes under study. Concerning conformational rearrangement in protein-protein complexes, despite their expected flexibility, terminal residues adopt similar locations between the free and bound forms of the docking benchmark. In protein-DNA complexes, N-terminal residues are twice more frequent than C-terminal residues at interfaces. Both N-terminal and C-terminal residues are under-represented in interfaces, in contrast to positively charged residues, which are strongly favored. When located in protein-DNA interfaces, terminal residues prefer the periphery. N-terminal and C-terminal residues thus have particular properties with regard to interfaces, which cannot be reduced to their charged nature.

Dynamics and recognition within a protein-DNA complex: a molecular dynamics study of the SKN-1/DNA interaction.

Molecular dynamics simulations of the Caenorhabditis elegans transcription factor SKN-1 bound to its cognate DNA site show that the protein-DNA interface undergoes significant dynamics on the microsecond timescale. A detailed analysis of the simulation shows that movements of two key arginine side chains between the major groove and the backbone of DNA generate distinct conformational sub-states that each recognize only part of the consensus binding sequence of SKN-1, while the experimentally observed binding specificity results from a time-averaged view of the dynamic recognition occurring within this complex.