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FoldAffinity: binding affinities from nDSF experiments.

ABSTRACT: Differential scanning fluorimetry (DSF) using the inherent fluorescence of proteins (nDSF) is a popular technique to evaluate thermal protein stability in different conditions (e.g. buffer, pH). In many cases, ligand binding increases thermal stability of a protein and often this can be detected as a clear shift in nDSF experiments. Here, we evaluate binding affinity quantification based on thermal shifts. We present four protein systems with different binding affinity ligands, ranging from nM to high μM. Our study suggests that binding affinities determined by isothermal analysis are in better agreement with those from established biophysical techniques (ITC and MST) compared to apparent K_ds obtained from melting temperatures. In addition, we describe a method to optionally fit the heat capacity change upon unfolding ([Formula: see text]) during the isothermal analysis. This publication includes the release of a web server for easy and accessible application of isothermal analysis to nDSF data.

SUBMITTER: Niebling S

PROVIDER: S-EPMC8099913 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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FoldAffinity: binding affinities from nDSF experiments.

Niebling Stephan S Burastero Osvaldo O Bürgi Jérôme J Günther Christian C Defelipe Lucas A LA Sander Simon S Gattkowski Ellen E Anjanappa Raghavendra R Wilmanns Matthias M Springer Sebastian S Tidow Henning H García-Alai María M

Scientific reports 20210505 1

Differential scanning fluorimetry (DSF) using the inherent fluorescence of proteins (nDSF) is a popular technique to evaluate thermal protein stability in different conditions (e.g. buffer, pH). In many cases, ligand binding increases thermal stability of a protein and often this can be detected as a clear shift in nDSF experiments. Here, we evaluate binding affinity quantification based on thermal shifts. We present four protein systems with different binding affinity ligands, ranging from nM t ...[more]

PMID: 33953265

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Project description:Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, the docking scores are not sufficiently precise to represent the protein-ligand binding affinity. Here, we developed an efficient computational method for calculating protein-ligand binding affinity, which is based on molecular mechanics generalized Born/surface area (MM-GBSA) calculations and Jarzynski identity. Jarzynski identity is an exact relation between free energy differences and the work done through non-equilibrium process, and MM-GBSA is a semimacroscopic approach to calculate the potential energy. To calculate the work distribution when a ligand is pulled out of its binding site, multiple protein-ligand conformations are randomly generated as an alternative to performing an explicit single-molecule pulling simulation. We assessed the new method, multiple random conformation/MM-GBSA (MRC-MMGBSA), by evaluating ligand-binding affinities (scores) for four target proteins, and comparing these scores with experimental data. The calculated scores were qualitatively in good agreement with the experimental binding affinities, and the optimal docking structure could be determined by ranking the scores of the multiple docking poses obtained by the molecular docking process. Furthermore, the scores showed a strong linear response to experimental binding free energies, so that the free energy difference of the ligand binding (??G) could be calculated by linear scaling of the scores. The error of calculated ??G was within ? ± 1.5 kcal.mol(-1) of the experimental values. Particularly, in the case of flexible target proteins, the MRC-MMGBSA scores were more effective in ranking ligands than those generated by the MM-GBSA method using a single protein-ligand conformation. The results suggest that, owing to its lower computational costs and greater accuracy, the MRC-MMGBSA offers efficient means to rank the ligands, in the post-docking process, according to their binding affinities, and to compare these directly with the experimental values.

Dataset Information

FoldAffinity: binding affinities from nDSF experiments.

Publications

FoldAffinity: binding affinities from nDSF experiments.

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