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Friesner, R. A.

Publications and source records attributed to Friesner, R. A..

5 recordsLinked to original sources
bioRxiv2026

A functional investigation of antibody Fc-FcRn variant binding guided by *in silico* free energy perturbation methods

Accurate calculation of energy changes upon mutation is a key requirement for the effective use of computational methods in protein design. In this study, we applied free energy perturbation (FEP) calculations to predict the effects of mutations on the binding free energy between the immunoglobulin subtype G (IgG) antibody fragment-crystallizable (Fc) region and the neonatal Fc receptor (FcRn), an interaction that is primarily responsible for antibody half-life. We assembled an extensive experimental dataset of Fc-FcRn binding affinities for wild-type (wt) and mutant complexes, including values from literature and from newly measured results. Starting from a crystal structure of the M252Y/S254T/T256E ("YTE") Fc variant bound to FcRn, we prepared all-atom models of human IgG1-subtype wt and YTE variant Fc-FcRn complexes, adding explicit hydrogens and assigning protonation states for key ionizable residues. Initial results using standard FEP protocols to compute relative binding free energies were promising but exhibited multiple outliers. By accounting for coupling effects for FEP mutations near key histidine residues, we improved the results for several outliers, suggesting such coupling as an important approach for pH-sensitive systems. Further, upon determining new crystal structures of four Fc variants at multiple pH values, we observed subtle conformational changes in unbound Fc; by accounting for these conformational changes in FEP calculations, we additionally improved agreement with experiment. The detailed structural and energetic analyses of the Fc-FcRn system we present here thus provide an accurate energy-calculation framework to enable rational in silico design of novel Fc variants. SignificanceThe ability to determine changes in binding affinity upon mutation is critical to structure-based protein design. In this study, we demonstrate a successful computational approach using free energy perturbation (FEP) calculations on the antibody Fc-FcRn complex, a medically relevant system with implications for both therapeutic and prophylactic antibody use. Our successful calculation of accurate binding energies across a wide range of cases speaks to the power of the FEP methodology in navigating the free energy landscapes of dynamic molecular complexes. Furthermore, we show that accurate Fc-FcRn affinity calculations required careful consideration of conformational flexibility between bound and unbound states, contributing to our functional understanding of a system that will be important for future rational antibody-design efforts. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/721095v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@f754d8org.highwire.dtl.DTLVardef@1e366beorg.highwire.dtl.DTLVardef@6e67caorg.highwire.dtl.DTLVardef@602a14_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗
bioRxiv2026

Results of a large scale study of the binding of 50 type IIinhibitors to 348 kinases: The role of protein reorganization

Kinase family proteins constitute the second largest protein class targeted in drug development efforts, most prominently to treat cancer, but also several other diseases associated with kinase dysfunction. In this work we focus on type II kinase inhibitors which bind to the "classical" inactive conformation of the protein kinase catalytic domain where the DFG motif has a "DFG-out" orientation and the activation loop is folded. Many Tyrosine kinases (TKs) exhibit strong binding affinity with a wide spectrum of type II inhibitors while serine/threonine kinases (STKs) often bind more weakly. Recent work suggests this difference is largely due to differences in the folded to extended conformational equilibrium of the activation loop between TKs vs. STKs. The binding affinity of a type II inhibitor to its kinase target can be decomposed into a sum of two contributions: (1) the free energy cost to reorganize the protein from the active to inactive state, and (2) the binding affinity of the type II inhibitor to the inactive kinase conformation. In previous work we used a Potts statistical energy potential based on sequence co-variation to thread sequences over ensembles of active and inactive kinase structures. The threading function was used to estimate the free energy cost to reorganize kinases from the active to classical inactive conformation, and we showed that this estimator is consistent with the results of molecular dynamics free energy simulations for a small set of STKs and TKs. In the current study, we analyze the results of a large-scale study of the binding affinities of 50 type II inhibitors to 348 kinases, of which the results for 16 of the 50 type II inhibitors were reported in an earlier study (the "Davis dataset"); the binding data for the remaining 34 type II inhibitors to the panel of 348 kinases were recently obtained (the "Schrodinger dataset"). We use the Potts statistical energy model to investigate the contribution of protein reorganization to the selectivity of the large kinase panel against the set of 50 type II inhibitors, and find that protein reorganization makes a significant contribution to the selectivity. The AUC of the receiver-operator characteristic curve is [~]0.8. We report the results of an internal "blind test", that shows how Potts threading energies can provide more accurate estimates of kinase selectivity than corresponding predictions using experimental results of small sample size. We discuss why two STK phylogenetic kinase families, STE and CMGC, appear to contain many outliers, and how to improve the ability to predict kinase selectivity with a more complete analysis of the kinase conformational landscape. We compare the performance of Potts threading for predicting binding properties of the large set of (50) Type II inhibitors to 348 kinases, with those of a sequence-based purely machine learning model, DeepDTAGen, a publicly available machine learning model that was trained on the complete Davis dataset, including both Type I and Type II kinase inhibitors. We observe that DeepDTAGen performs well on binding predictions for the 16 type II inhibitors in the Davis dataset, but performs poorly on binding predictions for the 34 type II inhibitors against 348 kinases in the Schrodinger dataset.

biophysics↗
bioRxiv2024

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

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 Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/590325v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1c5e607org.highwire.dtl.DTLVardef@1810ee5org.highwire.dtl.DTLVardef@1f8f1acorg.highwire.dtl.DTLVardef@c28053_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIReliable calculation of relative binding free energy changes for most protein mutations to within [~]1 kcal/mol. C_LIO_LIAutomated Protein FEP+ Groups treatment of alternate protonation states for titratable residues. C_LIO_LIApplication of FEP+ methodology to "real-world" protein design projects. C_LI

biophysics↗
bioRxiv2022

Free energy perturbation calculations of mutation effects on SARS-CoV-2 RBD::ACE2 binding affinity

The strength of binding between human angiotensin converting enzyme 2 (ACE2) and the receptor binding domain (RBD) of viral spike protein plays a role in the transmissibility of the SARS-CoV-2 virus. In this study we focus on a subset of RBD mutations that have been frequently observed in infected individuals and probe binding affinity changes to ACE2 using surface plasmon resonance (SPR) measurements and free energy perturbation (FEP) calculations. Our SPR results are largely in accord with previous studies but discrepancies do arise due to differences in experimental methods and to protocol differences even when a single method is used. Overall, we find that FEP performance is superior to that of other computational approaches examined as determined by agreement with experiment and, in particular, by its ability to identify stabilizing mutations. Moreover, the calculations successfully predict the observed cooperative stabilization of binding by the Q498R N501Y double mutant present in Omicron variants and offer a physical explanation for the underlying mechanism. Overall, our results suggest that despite the significant computational cost, FEP calculations may offer an effective strategy to understand the effects of interfacial mutations on protein-protein binding affinities and in practical applications such as the optimization of neutralizing antibodies.

biophysics↗
bioRxiv2020

A pH-dependent switch mediates conformational masking of SARS-CoV-2 spike

The SARS-CoV-2 spike employs mobile receptor-binding domains (RBDs) to engage the ACE2 receptor and to facilitate virus entry. Antibodies can engage RBD but some, such as CR3022, fail to inhibit entry despite nanomolar spike affinity. Here we show the SARS-CoV-2 spike to have low unfolding enthalpy at serological pH and up to 10-times more unfolding enthalpy at endosomal pH, where we observe significantly reduced CR3022 affinity. Cryo-EM structures -at serological and endosomal pH- delineated spike recognition of up to three ACE2 molecules, revealing RBD to freely adopt the up conformation. In the absence of ACE2, single-RBD-up conformations dominated at pH 5.5, resolving into a locked all-down conformation at lower pH. Notably, a pH-dependent refolding region (residues 824-858) at the spike-interdomain interface displayed dramatic structural rearrangements and mediated RBD positioning and spike shedding of antibodies like CR3022. An endosomal mechanism involving spike-conformational change can thus facilitate immune evasion from RBD- up-recognizing antibody. HighlightsO_LIReveal spike at serological pH to have only ~10% the unfolding enthalpy of a typical globular protein, explaining how antibodies like CR3022 can bind with avidity C_LIO_LIDefine an endosomal mechanism whereby spike binds ACE2, but sheds CR3022, enabling immune evasion from potentially neutralizing antibody C_LIO_LIDetermine cryo-EM structures of the SARS-CoV-2 spike along its endosomal entry pathway-at pH 5.5, 4.5, and 4.0, and in complexes with ACE2 receptor at pH 7.4 and 5.5 C_LIO_LIShow spike to exclusively adopt an all RBD-down conformation at the low pH of the late endosome-early lysosome C_LIO_LIReveal structural basis by which a switch domain mediates RBD position in response to pH C_LI

immunology↗