Family-Wide Biophysical Analysis of Dpr-DIP Interactions

Cosmanescu, Filip; Katsamba, Phinikoula S.; Sergeeva, Alina P.; Ahlsén, Göran; Patel, Shalin; Brewer, Joshua; Tan, Liming; Xu, Shansheng; Xiao, Qi; Zipursky, S Lawrence; Shapiro, Lawrence

doi:10.1016/j.bpj.2017.11.865

Cited by 1 publication

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“…In total we collected binding data for 9 systems. Binding data for 6 systems were obtained from the SKEMPI 2.0 database [41], along with 3 systems from our own previous work [42,43,39], including several previously unpublished measurements (Supplemental Figure 1). To increase the reliability of the experimental values, we limited the dataset to include only measurements made by isothermal calorimetry (ITC) or surface plasmon resonance (SPR).…”

Section: Benchmark Dataset Curationmentioning

confidence: 99%

“…Experimental methods for the DIP/Dpr system For the 5EO9 and 6WNA systems, Drosophila melanogaster defective proboscis extension response (Dpr) and Dpr-interacting protein (DIP) proteins were produced as previously described [42]. Briefly, DNA sequences encoding DIP and Dpr extracellular regions were amplified by PCR and sub-cloned into the mammalian expression vector VRC-8400, modified with a N-terminal BiP signal peptide (BiP: MKLSLVAAMLLLLSAARA) and a C-terminal hexa-histidine tag.…”

Section: Supplemental Informationmentioning

confidence: 99%

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Robust prediction of relative binding energies for protein-protein complex mutations using free energy perturbation calculations

Sampson,

Cannon,

Duan

et al. 2024

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Computational free energy-based methods have the potential to significantly improve throughput and decrease costs of protein design efforts. Such methods must reach a high level of reliability, accuracy, and automation to be effectively deployed in practical industrial settings in a way that impacts protein design projects. Here, we present a benchmark study for the calculation of relative changes in protein-protein binding affinity for single point mutations across a variety of systems from the literature, using free energy perturbation (FEP+) calculations. We describe a method for robust treatment of alternate protonation states for titratable amino acids, which yields improved correlation with and reduced error compared to experimental binding free energies. Following careful analysis of the largest outlier cases in our dataset, we assess limitations of the default FEP+ protocols and introduce an automated script which identifies probable outlier cases that may require additional scrutiny and calculates an empirical correction for a subset of charge-related outliers. Through a series of three additional case study systems, we discuss how protein FEP+ can be applied to real-world protein design projects, and suggest areas of further study.Graphical AbstractHighlightsReliable calculation of relative binding free energy changes for most protein mutations to within ∼1 kcal/mol.Automated Protein FEP+ Groups treatment of alternate protonation states for titratable residues.Application of FEP+ methodology to “real-world” protein design projects.

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