Insights from engineering the Affibody-Fc interaction with a computational-experimental method

Nosrati, Masoumeh; Solbak, Sara Marie Øie; Nordesjö, Olle; Nissbeck, Mikael; Dourado, Daniel F. A. R.; Andersson, Ken G.; Housaindokht, Mohammad Reza; Löfblom, John; Virtanen, Anders; Danielson, U. Helena; Flores, Samuel Coulbourn

doi:10.1093/protein/gzx023

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…As an illustration of this, recent benchmarking on a large blind subset of the recently released SKEMPI 2.0 (487 mutations in 56 complexes), which none of the predictors had previously seen, showed that iSEE, FoldX, mCSM, and BindProfX did not perform as well as on the training sets they originally used with PCC values all below 0.4. Even so, we have seen some promising contributions of such predictors to practical problems, for example, References .…”

Section: Discussionmentioning

confidence: 99%

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

Geng

Roel‐Touris

et al. 2019

WIREs Comput Mol Sci

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Predicting the structure and thermodynamics of protein–protein interactions (PPIs) are key to a proper understanding and modulation of their function. Since experimental methods might not be able to catch up with the fast growth of genomic data, computational alternatives are therefore required. We present here a review dealing with various aspects of predicting binding affinity changes upon mutations (ΔΔG). We focus on predictors that consider three‐dimensional structure information to estimate the impact of mutations on the binding affinity of a protein–protein complex, excluding the rigorous free energy perturbation methods. Training and evaluation, ΔΔG databases, data selection, and existing ΔΔG predictors are specially emphasized. We also establish the parallel with scoring functions used in docking since those share many similar PPI features with ΔΔG predictors. The field has seen a common evolution of ΔΔG predictors and scoring functions over time, transforming from purely energetic functions to statistical energy‐based and further to machine learning‐based functions. As machine learning has come to age, limitations in terms of quantity, quality and variety of the available data become the bottlenecks for the future development of these computational methods. This can be alleviated by building infrastructures for data generation, collection and sharing. Further developments can be catalyzed by conducting community‐wide blind challenges for method assessment. This article is categorized under: Structure and Mechanism > Molecular Structures Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Interactions

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Section: Discussionmentioning

confidence: 99%

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

Geng

Roel‐Touris

et al. 2019

WIREs Comput Mol Sci

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show abstract

“…Many mass-increasing mutations were selected by our method, while only the mass-decreasing mutations yielded near-WT affinities, thus FoldX appears not to get this balance right in the context of helices in the PPI core (Levy, 2010). In prior work we found near-WT affinities even with increases in molecular mass (Nosrati et al, 2017), but these were on the PPI rim (Levy, 2010), where there is more space available. Thus in future application of this method for that goal, it may be beneficial to explicitly select mass-or volume-decreasing substitutions for any positions in the core and on secondary-structural elements.…”

Section: Discussionmentioning

confidence: 80%

Amino acid substitutions in human growth hormone affect coiled-coil content and receptor binding

Rajkovic¹,

Kanchugal²,

Abdurakhmanov

et al. 2021

Preprint

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The interaction between human Growth Hormone (hGH) and hGH Receptor (hGHR) has great relevance to human diseases such as acromegaly and cancer. HGH has been extensively engineered by other workers to improve binding and other properties. We used a computational screen to select substitutions at single hGH positions within the hGHR-binding site. We find that, while many successfully slow down dissociation of the hGH-hGHR complex once bound, they also slow down the association of hGH to hGHR. We are particularly interested in E174 which belongs to the hGH zinc-binding triad, and which spans coiled-coil helices and obeys the coiled-coil heptad pattern. Surprisingly, substituting E174 with A leads to substantial increase in an experimental measure of coiled-coil content. E174A is known to increase affinity of hGH against hGHR; here we show that this is simply because the off-rate is slowed down more than the on-rate, in line with what has been found for other affinity-improving mutations. For E174Y (and mutations at other sites) the slowdown in on-rate was greater, leading to decreased affinity. The results point to a link between coiled-coiling, zinc binding, and hGHR-binding affinity in hGH, and also suggest rules for choosing affinity-increasing substitutions.

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“…Many mass-increasing mutations were selected by our method, while only the mass-decreasing mutations yielded near-WT affinities, thus FoldX appears not to get this balance right in the context of helices in the PPI core [ 23 ]. In prior work we found near-WT affinities even with increases in molecular mass [ 24 ], but these were on the PPI rim [ 23 ], where there is more space available. Thus in future application of this method for that goal, one may consider preferring mass- or volume-decreasing substitutions for any positions in the core and on secondary-structural elements.…”

Section: Discussionmentioning

confidence: 98%

Amino acid substitutions in human growth hormone affect secondary structure and receptor binding

et al. 2023

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The interaction between human Growth Hormone (hGH) and hGH Receptor (hGHR) has basic relevance to cancer and growth disorders, and hGH is the scaffold for Pegvisomant, an anti-acromegaly therapeutic. For the latter reason, hGH has been extensively engineered by early workers to improve binding and other properties. We are particularly interested in E174 which belongs to the hGH zinc-binding triad; the substitution E174A is known to significantly increase binding, but to now no explanation has been offered. We generated this and several computationally-selected single-residue substitutions at the hGHR-binding site of hGH. We find that, while many successfully slow down dissociation of the hGH-hGHR complex once bound, they also slow down the association of hGH to hGHR. The E174A substitution induces a change in the Circular Dichroism spectrum that suggests the appearance of coiled-coiling. Here we show that E174A increases affinity of hGH against hGHR because the off-rate is slowed down more than the on-rate. For E174Y (and certain mutations at other sites) the slowdown in on-rate was greater than that of the off-rate, leading to decreased affinity. The results point to a link between structure, zinc binding, and hGHR-binding affinity in hGH.

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Insights from engineering the Affibody-Fc interaction with a computational-experimental method

Cited by 9 publications

References 28 publications

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

Amino acid substitutions in human growth hormone affect coiled-coil content and receptor binding

Amino acid substitutions in human growth hormone affect secondary structure and receptor binding

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