Biology subjects

Levy, R. M.

Publications and source records attributed to Levy, R. M..

7 recordsLinked to original sources

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↗

bioRxiv2025

Benchmarking Free Energy Calculations: Analysis of Single and Double Mutations Across Two Simulation Software Platforms for Two Protein Systems

Mutation analysis of single and double mutations of proteins including for the two widely studied proteins: Staphylococcus nuclease (S. nuclease) and T4 lysozyme, provide critical insights into their stability and fitness. Furthermore, such analysis facilitates a deeper understanding of the complex interplay between a proteins sequence, structural conformation, and functional output. Free Energy Perturbation (FEP) is an alchemical, all-atom molecular dynamics based computational approach that determines the free energy change ({Delta}{Delta}G) from wild type to mutant states. Two widely adopted software platforms used for this purpose are Schrodinger and GROMACS. We have compared the results of FEP simulations for mutations using these platforms, employing the OPLS4 force field implemented in Schrodinger, and the Amber99SB-ILDN force field implemented in GROMACS for this work. For the 38 single mutants of S. nuclease, the Pearson r between the experimental and the calculated free energy change ({Delta}{Delta}G) was 0.86 using Schrodinger and 0.87 using GROMACS. The reliability of the FEP method using the two software platforms with the specified force fields was further demonstrated by its performance on 24 single mutants of T4 lysozyme, yielding strong correlation between predicted and experimental{Delta}{Delta} G values, with Pearson r of 0.80 for Schrodinger and 0.85 for GROMACS. Additionally, the computed folding free energy changes for 45 double mutations in S. nuclease ([Formula]) using Schrodinger correlated well with both experimental measurements (Pearson r = 0.74) and previously reported GROMACS values (Pearson r = 0.71). Correspondingly, the nonadditivities([Formula]) of the double mutations derived using Schrodinger for these 45 double mutants of S. nuclease were also found to be in good agreement with the experimental values (Pearson r = 0.79), as well as with the previously reported GROMACS results (Pearson r = 0.61). A good correlation was also observed between computed values from Schrodinger and GROMACS, with a Pearson r of 0.71 for double mutants and 0.61 for their nonadditivities. Collectively, these findings establish the efficiency, accuracy, and reliability of Schrodinger FEP+ and GROMACS in predicting mutation induced free energy changes in S. nuclease and T4 lysozyme. The integration of FEP based computational methodologies with experimental validation provides a framework for quantifying mutation induced changes in protein thermostability, which can be used as a tool for protein engineering and design.

biophysics↗

bioRxiv2025

De Novo Hydration of Cryo-EM Reconstructions through Molecular Dynamics Simulations of the Excess Chemical Potential

Predicting the precise positions of water molecules at the protein interface remains a formidable challenge, fueling active research in this field. Here, we present a novel approach based on molecular dynamics simulations that utilizes statistical thermodynamic signatures of water at protein interfaces to improve the accuracy of water placement in cryo-EM maps, with apoferritin as a model benchmark system. The interaction energy of solvent with the protein is insufficient to distinguish between high- and lower-consensus water positions, consistent with reports of earlier work. Instead, we employ a detailed statistical thermodynamic analysis based on the excess chemical potential (WT) - a measure of the thermodynamic balance between the interaction energy of an interfacial water molecule with the protein and its free energy of interaction with all of the other solvent molecules. WT is proportional to the log ratio of the local density of water molecules at the protein interface to the bulk density. 85% of the top 100 water locations with the most favorable excess chemical potential values are observed in one or more high resolution Cryo-EM maps deposited in the PDB, and 70% of the top 200 water locations indexed by excess chemical potential are observed in the cryo-EM maps. This work paves the way for the development of a cryo-EM refinement tool that integrates molecular dynamics simulations with cryo-EM data for high-resolution modeling of water networks.

biochemistry↗

bioRxiv2025

Using AlphaFold2 to Predict the Conformations of Side Chains in Folded Proteins

AlphaFold has revolutionized protein structure prediction by accurately creating 3D structures from just the amino acid sequence. However, even with extensive research validating its overall accuracy, a key question remains: Can AlphaFold predict the conformation of individual amino acid residue side chains within a folded protein? This is important for the field of molecular modeling, particularly when predicting the effects of mutations on protein stability and ligand binding. AlphaFold generates a set of atomic coordinates not just for the mutated side chain but also for potential rearrangements across the entire protein structure. In this study we investigate the ability of ColabFold, an online implementation of AlphaFold2 (AF2), to predict the conformations of residue side chains in folded proteins. We find that over a set of 10 benchmark proteins, the side chain conformation prediction error of ColabFold is on average [~]14% for {chi}1 dihedral angles, and increases to [~]48% for {chi}3 dihedral angles. The prediction error is smaller for non-polar side chains and is somewhat improved using structural templates. ColabFold demonstrates a bias towards the most prevalent rotamer states in the protein data bank (PDB), potentially limiting its ability to capture rare side chain conformations effectively. As an application of AlphaFold to explore the structural consequences of strongly cooperative mutations on side chain rearrangements, we employ a Potts sequence-based statistical energy model to perform large scale mutational scans of two proteins ABL1 and PIM1 kinase, searching for the most strongly cooperative mutational pairs, and then use ColabFold to predict the structural signatures of this cooperativity on the interacting side chains. Our results demonstrate that integration of the sequence-based Potts model with AlphaFold into a single pipeline provides a new tool that can be used to explore the fundamental relationship between protein mutations, cooperative changes in structure, and fitness.

biophysics↗

bioRxiv2024

Using phage display for rational engineering of a higher affinity humanized 3-phosphohistidine-specific antibody

Histidine phosphorylation (pHis) is a non-canonical post-translational modification (PTM) that is historically understudied due to a lack of robust reagents that are required for its investigation, such as high affinity pHis-specific antibodies. Engineering pHis-specific antibodies is very challenging due to the labile nature of the phosphoramidate (P-N) bond and the stringent requirements for selective recognition of the two isoforms, 1-phosphohistidine (1-pHis) and 3-phosphohistidine (3-pHis). Here, we present a strategy for in vitro engineering of antibodies for detection of native 3-pHis targets. Specifically, we humanized the rabbit SC44-8 anti-3-pTza (a stable 3-pHis mimetic) mAb into a scaffold (herein referred to as hSC44) that was suitable for phage display. We then constructed six unique Fab phage-displayed libraries using the hSC44 scaffold and selected high affinity 3-pHis binders. Our selection strategy was carefully designed to enrich antibodies that bound 3-pHis with high affinity and had specificity for 3-pHis versus 3-pTza. hSC44.20N32FL, the best engineered antibody, has an [~]10-fold higher affinity for 3-pHis than the parental hSC44. Eleven new Fab structures, including the first reported antibody-pHis peptide structures were solved by X-ray crystallography. Structural and quantum mechanical calculations provided molecular insights into 3-pHis and 3-pTza discrimination by different hSC44 variants and their affinity increase obtained through in vitro engineering. Furthermore, we demonstrate the utility of these newly developed high-affinity 3-pHis-specific antibodies for recognition of pHis proteins in mammalian cells by immunoblotting and immunofluorescence staining. Overall, our work describes a general method for engineering PTM-specific antibodies and provides a set of novel antibodies for further investigations of the role of 3-pHis in cell biology. Significance StatementHistidine phosphorylation is an elusive PTM whose role in mammalian cell biology is largely unknown due to the lack of robust tools and methods for its analysis. Here we report the development of antibodies with unprecedented affinity and specificity towards 3-pHis and present the first crystal structures of a pHis peptide in complex with an antibody. Finally, we show how these antibodies can be used in standard molecular biology workflows to investigate pHis-dependent biology.

synthetic biology↗

bioRxiv2022

Mechanisms of HIV-1 Integrase Resistance to Dolutegravir and Potent Inhibition of Drug Resistant Variants

HIV-1 infection depends on the integration of viral DNA into host chromatin. Integration is mediated by the viral enzyme integrase and is blocked by integrase strand transfer inhibitors (INSTIs), first-line antiretroviral therapeutics widely used in the clinic. Resistance to even the best INSTIs is a problem and the mechanisms of resistance are poorly understood. Here, we analyze combinations of the mutations E138K, G140A/S, and Q148H/K/R, which confer resistance to INSTIs. The investigational drug 4d more effectively inhibited the mutants compared with the approved drug Dolutegravir (DTG). We present 11 new cryo-EM structures of drug resistant HIV-1 intasomes bound to DTG or 4d, with better than 3 [A] resolution. These structures, complemented with free energy simulations, virology, and enzymology, explain the mechanisms of DTG resistance involving E138K+G140A/S+Q148H/K/R and show why 4d maintains potency better than DTG. These data establish a foundation for further development of INSTIs that potently inhibit resistant forms in integrase.

biophysics↗

bioRxiv2022

Evolutionary Divergence in the Conformational Landscapes of Tyrosine vs Serine/Threonine Kinases

Inactive conformations of protein kinase catalytic domains where the DFG motif has a "DFG-out" orientation and the activation loop is folded present a druggable binding pocket that is targeted by FDA-approved "type-II inhibitors" in the treatment of cancers. Tyrosine Kinases (TKs) typically show strong binding affinity with a wide spectrum of type-II inhibitors while Serine/Threonine Kinases (STKs) usually bind more weakly which we suggest here is due to differences in the folded to extended conformational equilibrium of the activation loop between TKs vs. STKs. To investigate this, we use sequence covariation analysis with a Potts Hamiltonian statistical energy model to guide absolute binding free-energy molecular dynamics simulations of 74 protein-ligand complexes. Using the calculated binding free energies together with experimental values, we estimated free-energy costs for the large-scale ([~]17-20[A]) conformational change of the activation loop by an indirect approach, circumventing the very challenging problem of simulating the conformational change directly. We also used the Potts statistical potential to thread large sequence ensembles over active and inactive kinase states. The structure-based and sequence-based analyses are consistent; together they suggest TKs evolved to have free-energy penalties for the classical "folded activation loop" DFG-out conformation relative to the active conformation that is, on average, 4-6 kcal/mol smaller than the corresponding values for STKs. Potts statistical energy analysis suggests a molecular basis for this observation, wherein the activation loops of TKs are more weakly "anchored" against the catalytic loop motif in the active conformation, and form more stable substrate-mimicking interactions in the inactive conformation. These results provide insights into the molecular basis for the divergent functional properties of TKs and STKs, and pharmacological implications for the target selectivity of type-II inhibitors.

biophysics↗