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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

An artificial intelligence accelerated virtual screening platform for drug discovery

Abstract Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel Na V 1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to Na V 1.7, all with single digit micromolar binding affinities. Screening in both cases is completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery.

Science & Technology - Other Topics↗
DOE OSTI2021

Engineered pH-Sensitive Protein G/IgG Interaction

While natural protein–protein interactions have evolved to be induced by complex stimuli, rational design of interactions that can be switched-on-demand still remain challenging in the protein design world. Here, we demonstrate that a computationally redesigned natural interface for improved binding affinity could further be mutated to adopt a pH switchable interaction. The redesigned interface of Protein G/human IgG Fc domain (referred to as PrG/hIgG), when incorporated with histidine and glutamic acid on PrG (PrG-EHHE), showed a switch in binding affinity by 50-fold when the pH was altered from mild acidic to mild basic. The wild-type (WT) interface showed a negligible switch. The overall binding affinity under mild acidic pH for PrG-EHHE outperformed the wild-type PrG (PrG-WT) interaction. Overall, we find that the new reagent PrG-EHHE can be revolutionary in IgG purification, since the standard method of using an extreme acidic pH for elution can be circumvented.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Effects of SARS-CoV-2 Main Protease Mutations at Positions L50, E166, and L167 Rendering Resistance to Covalent and Noncovalent Inhibitors

SARS-CoV-2 propagation under nirmatrelvir and ensitrelvir pressure selects for main protease (MPro) drug-resistant mutations E166V (DRM2), L50F/E166V (DRM3), E166A/L167F (DRM4), and L50F/E166A/L167F (DRM5). DRM2-DRM5 undergoes N-terminal autoprocessing to produce mature MPro with dimer dissociation constants (K dimer ) 2–3 times larger than that of the wildtype. Co-selection of L50F restores catalytic activity of DRM2 and DRM4 from ~10 to 30%, relative to that of the wild-type enzyme, without altering K dimer . Binding affinities and thermodynamic profiles that parallel the drug selection pressure, exhibiting significant decreases in affinity through entropy/enthalpy compensation, were compared with GC373. Reorganization of the active sites due to mutations observed in the inhibitor-free DRM3 and DRM4 structures as compared to MPro WT may account for the reduced binding affinities, although DRM2 and DRM3 complexes with ensitrelvir are almost identical to MPro WT -ensitrelvir. In conclusion, chemical reactivity changes of the mutant active sites due to differences in electrostatic and protein dynamics effects likely contribute to losses in binding affinities.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Crystal Structure of CYP3A4 Complexed with Fluorol Identifies the Substrate Access Channel as a High-Affinity Ligand Binding Site

Cytochrome P450 3A4 (CYP3A4) is a major human drug-metabolizing enzyme, notoriously known for its extreme substrate promiscuity, allosteric behavior, and implications in drug–drug interactions. Despite extensive investigations, the mechanism of ligand binding to CYP3A4 is not fully understood. We determined the crystal structure of CYP3A4 complexed with fluorol, a small fluorescent dye that can undergo hydroxylation. In the structure, fluorol associates to the substrate channel, well suited for the binding of planar polyaromatic molecules bearing polar groups, through which stabilizing H-bonds with the polar channel residues, such as Thr224 and Arg372, can be established. Mutagenesis, spectral, kinetic, and functional data confirmed the involvement but not strict requirement of Thr224 for the association of fluorol. Collectively, our data identify the substrate channel as a high-affinity ligand binding site and support the notion that hydrophobic ligands first dock to the nearby peripheral surface, before migrating to the channel and, subsequently, into the active site.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Comparing GGA, GGA+ U , and meta-GGA functionals for redox-dependent binding at open metal sites in metal–organic frameworks

Metal–organic frameworks (MOFs) with open metal sites have been widely investigated for the selective adsorption of small molecules via redox mechanisms where charge transfer can take place between the binding site and the adsorbate of interest. Quantum-chemical screening methods based on density functional theory have emerged as a promising route to accelerate the discovery of MOFs with enhanced binding affinities toward various adsorbates. However, the success of this approach is linked to the accuracy of the underlying density functional approximations (DFAs). In this work, we compare commonly used generalized gradient approximation (GGA), GGA+U, and meta-GGA exchange-correlation functionals in modeling redox-dependent binding at open metal sites in MOFs using O 2 and N 2 as representative small molecules. We find that the self-interaction error inherent to the widely used Perdew, Burke, and Ernzerhof (PBE) GGA predicts metal sites that are artificially redox-active, as evidenced by their strong binding affinities, short metal–adsorbate bond distances, and large degree of charge transfer. The incorporation of metal-specific, empirical Hubbard U corrections based on the transition metal oxide literature systematically reduces the redox activity of the open metal sites, often improving agreement with experiment. Additionally, the binding behavior shifts from strong chemisorption to weaker physisorption as a function of U. The M06-L meta-GGA typically predicts binding energies between those of PBE-D3(BJ) and PBE-D3(BJ)+U when using empirically derived U values from the transition metal oxide literature. Despite the strong sensitivity of the binding affinities toward a given DFA, the GGA, GGA+U, and meta-GGA approaches often yield the same qualitative trends and structure–property relationships.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Dynamics-Based Peptide–MHC Binding Optimization by a Convolutional Variational Autoencoder: A Use-Case Model for CASTELO

An unsolved challenge in the development of antigen-specific immunotherapies is determining the optimal antigens to target. Comprehension of antigen–major histocompatibility complex (MHC) binding is paramount toward achieving this goal. Here, we apply CASTELO, a combined machine learning-molecular dynamics (ML-MD) approach, to identify per-residue antigen binding contributions and then design novel antigens of increased MHC-II binding affinity for a type 1 diabetes-implicated system. We build upon a small-molecule lead optimization algorithm by training a convolutional variational autoencoder (CVAE) on MD trajectories of 48 different systems across four antigens and four HLA serotypes. We develop several new machine learning metrics including a structure-based anchor residue classification model as well as cluster comparison scores. ML-MD predictions agree well with experimental binding results and free energy perturbation-predicted binding affinities. Moreover, ML-MD metrics are independent of traditional MD stability metrics such as contact area and root-mean-square fluctuations (RMSF), which do not reflect binding affinity data. Finally, our work supports the role of structure-based deep learning techniques in antigen-specific immunotherapy design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Intra- and inter-molecular regulation by intrinsically-disordered regions governs PUF protein RNA binding

PUF proteins are characterized by globular RNA-binding domains. They also interact with partner proteins that modulate their RNA-binding activities. Caenorhabditis elegans PUF protein fem-3 binding factor-2 (FBF-2) partners with intrinsically disordered Lateral Signaling Target-1 (LST-1) to regulate target mRNAs in germline stem cells. Here, we report that an intrinsically disordered region (IDR) at the C-terminus of FBF-2 autoinhibits its RNA-binding affinity by increasing the off rate for RNA binding. Moreover, the FBF-2 C-terminal region interacts with its globular RNA-binding domain at the same site where LST-1 binds. This intramolecular interaction restrains an electronegative cluster of amino acid residues near the 5' end of the bound RNA to inhibit RNA binding. LST-1 binding in place of the FBF-2 C-terminus therefore releases autoinhibition and increases RNA-binding affinity. This regulatory mechanism, driven by IDRs, provides a biochemical and biophysical explanation for the interdependence of FBF-2 and LST-1 in germline stem cell self-renewal.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

PRIME: An evaluation framework for protein representation inference and generalization in viral mutation space

Background Protein language models (PLMs) have revolutionized protein fitness prediction, yet their application to rapidly evolving viral pathogens is often confounded by extreme sequence homology. This homology leads to “data leakage” in standard random validation splits, yielding inflated performance metrics that fail to translate into real-world biosurveillance utility. Results We present Protein Representation Inference for Mutation Evaluation (PRIME), a framework that integrates domain-specific fine-tuning with a rigorous position-stratified validation protocol to evaluate viral threats. Using a dataset of 347,432 SARS-CoV-2 receptor binding domain (RBD) sequences, we demonstrate that while random training data split yields deceptive R 2 values (> 0.90), they fail to generalize to novel mutational sites. By benchmarking models up to 650 M parameters, we show that domain-specific fine-tuning of the ESM-C 600 M model with correctly stratified data provides an initial demonstration of predictive signal for binding affinity and expression at unseen mutational sites of binding affinity and expression on unseen sites (R 2 ~0.23), a significant advancement over base foundation models which exhibit no predictive power (R 2 <0). PRIME’s embedding-based clustering identified 3.03% of bat coronavirus sequences as candidates for further experimental prioritization based on their functional similarity to human-infective strains in embedding space, offering a perspective complementary to traditional phylogenetic methods. Conclusion PRIME establishes a new benchmark for the application of PLMs in pathogen surveillance. Our findings demonstrate that state-of-the-art models and fine-tuning, when paired with stratified validation, provide biologically meaningful insights into pathogen evolution and zoonotic risk.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Structure-guided approach to modulate small molecule binding to a promiscuous ligand-activated protein

Ligand-binding promiscuity in detoxification systems protects the body from toxicological harm but is a roadblock to drug development due to the difficulty in optimizing small molecules to both retain target potency and avoid metabolic events. Immense effort is invested in evaluating metabolism of molecules to develop safer, more effective treatments, but engineering specificity into or out of promiscuous proteins and their ligands is a challenging task. To better understand the promiscuous nature of detoxification networks, we have used X-ray crystallography to characterize a structural feature of pregnane X receptor (PXR), a nuclear receptor that is activated by diverse molecules (with different structures and sizes) to up-regulate transcription of drug metabolism genes. We found that large ligands expand PXR’s ligand-binding pocket, and the ligand-induced expansion occurs through a specific unfavorable compound-protein clash that likely contributes to reduced binding affinity. Removing the clash by compound modification resulted in more favorable binding modes with significantly enhanced binding affinity. We then engineered the unfavorable ligand-protein clash into a potent, small PXR ligand, resulting in marked reduction in PXR binding and activation. Structural analysis showed that PXR is remodeled, and the modified ligands reposition in the binding pocket to avoid clashes, but the conformational changes result in less favorable binding modes. Thus, ligand-induced binding pocket expansion increases ligand-binding potential of PXR but is an unfavorable event; therefore, drug candidates can be engineered to expand PXR’s ligand-binding pocket and reduce their safety liability due to PXR binding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Predicting receptor-ligand pairing preferences in plant-microbe interfaces via molecular dynamics and machine learning

Microbiome assembly, structure, and dynamics significantly influence plant health. Secreted microbial signaling molecules initiate and mediate symbiosis by binding to structurally compatible plant receptors. For example, lipo-chitooligosaccharides (LCOs), produced by nitrogen-fixing rhizobial bacteria and various fungi, are recognized by plant lysin motif receptor-like kinases (LysM-RLKs), which activate the common symbiotic pathway. Accurately predicting these molecular interactions could reveal complementary signatures underlying the initial stages of endosymbiosis. Despite the breakthrough in protein-ligand structure prediction with deep learning-based tools, such as AlphaFold3, the large size and highly flexible nature of signaling compounds like LCOs present major challenges for detailed structural characterization and binding-affinity prediction. Typical structure-/physics-based methods of ligand virtual screening are designed for small, drug-like molecules, often rely on high-resolution, experimentally determined structures of the protein receptors, and rarely achieve sufficient sampling to obtain converged thermodynamic quantities with large ligands. In this study, we developed a hybrid molecular dynamics/machine learning (MD/ML) approach capable of predicting binding affinity rankings with high accuracy in systems involving large, flexible ligands, despite limited experimental structural information. Using coarse initial structural models, the predictions using the MD/ML workflow achieved strong alignment with experimental trends, particularly in the top-affinity tier for four legume LysM-RLKs (LYR3) binding to LCOs and a chitooligosaccharide. Furthermore, the MD-based conformation selection protocol provided critical structural insights into substrate specificity and binding mechanisms. This study demonstrates a powerful method to screen for challenging cognate ligand-receptors and advance our understanding of the molecular basis of microbial colonization in plants.

Lipo-chitooligosaccharides↗
DOE OSTI2024

Adsorption of terbium (III) on DGA and LN resins: Thermodynamics, isotherms, and kinetics

Two commercially available extraction chromatography (EXC) resins containing N,N,N’,N’-tetra-n-octyldiglycolamide (DGA Resin, Normal, 50 – 100 μm) and Bis(2-ethylhexyl) phosphate (LN Resin, 100 – 150 μm) were used as adsorbents to study fundamental adsorption properties such as thermodynamic values, equilibrium isotherms, and kinetic uptake models for terbium(III) adsorption. Weight distribution ratios (D w ) for terbium on DGA and LN resins were measured using a [ 160 Tb]Tb 3+ radiometric tracer in nitric acid as a function of acidity, temperature, initial analyte concentration, and equilibrium time. The D w values showed increasing binding affinity for DGA resin at high nitric acid concentrations and decreasing binding affinity for LN resins. Thermodynamic studies for DGA and LN resins revealed that the Gibbs free energy (ΔG) increased consistently with temperature. To model equilibrium data, increasingly higher parameter equilibrium isotherm models (Henry (1) < Langmuir, Freundlich (2) < Redlich-Peterson (3) < Fritz-Schluender (4)) were compared on their root mean squared errors (RMSE) and adjusted determination coefficients to determine the most applicable model. In all cases, the empirical four-parameter Fritz-Schluender isotherm demonstrated a superior fit. Similar comparisons for reaction-based kinetic models (Pseudo-first-order < Pseudo-second-order < Pseudo-n-order) revealed that the higher-order PNO model yielded a superior fit of kinetic data for both resins. Furthermore, in some cases, adsorption isotherms and kinetic models could also be modeled by a lower-order model with minimal change in error parameters. Weber-Morris plots revealed that two linear sections are observed for each resin, where the first linear segment is attributed to fast (film diffusion) adsorption of terbium, followed by slower intraparticle diffusion of terbium through the pores as the rate-limiting step. Based on the Weber-Morris plot, both film and intraparticle diffusion are involved in controlling the kinetic rate of adsorption for DGA and LN resins.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

A Route to Design Novel Functional Peptides by Applying a Denoising Diffusional Model to mRNA Display Libraries

In vitro directed evolution techniques, such as mRNA display, enable peptide ligand discovery and optimization. However, physical libraries that rely on a genetic code can only search a small fraction of sequence space due to inherent biases in the genetic code and experimental limitations. To address this challenge, denoising diffusion implicit models (DDIMs) are applied to generate novel peptide ligands against B‐cell lymphoma extra‐large (Bcl‐x L ), a key cancer target. Starting with high‐throughput sequencing data from previous selections, a DDIM is trained to produce novel sequences with high affinity binding. Experimental validation confirms that most generated sequences are functionally equivalent to the original library members for Bcl‐x L binding and demonstrated comparable binding kinetics and affinity relative to the wildtype and nearest original neighbors. Importantly, this approach generated rare sequences not easily accessible via mutation and directed evolution. These results indicate that DDIMs can complement and expand directed evolution data, efficiently exploring underrepresented regions of sequence space. This approach provides a broadly applicable framework for accelerating ligand discovery and optimizing molecular properties across diverse targets.

Qi, Pearl [Mork Family Department of Chemical Engi↗
DOE OSTI2020

In Silico Insights into Protein–Protein Interaction Disruptive Mutations in the PCSK9-LDLR Complex

Gain-of-function mutations in PCSK9 (proprotein convertase subtilisin/kexin type 9) lead to reduced uptake of LDL (low density lipoprotein) cholesterol and, therefore, increased plasma LDL levels. However, the mechanism by which these mutants reduce LDL reuptake is not fully understood. Here, we have used molecular dynamics simulations, MM/PBSA (Molecular Mechanics/Poisson–Boltzmann Surface Area) binding affinity calculations, and residue interaction networks, to investigate the protein–protein interaction (PPI) disruptive effects of two of PCSK9's gain-of-function mutations, Ser127Arg and Asp374Tyr on the PCSK9 and LDL receptor complex. In addition to these PPI disruptive mutants, a third, non-interface mutation (Arg496Trp) is included as a positive control. Our results indicate that Ser127Arg and Asp374Tyr confer significantly improved binding affinity, as well as different binding modes, when compared to the wild-type. These PPI disruptive mutations lie between the EGF(A) (epidermal growth factor precursor homology domain A) of the LDL receptor and the catalytic domain of PCSK9 (Asp374Tyr) and between the prodomain of PCSK9 and the β-propeller of the LDL receptor (Ser127Arg). The interactions involved in these two interfaces result in an LDL receptor that is sterically inhibited from entering its closed conformation. This could potentially implicate the prodomain as a target for small molecule inhibitors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Stable binding to phosphatidylserine-containing membranes requires conserved arginine residues in tandem C domains of blood coagulation factor VIII

At sites of vascular damage, factor VIII (fVIII) is proteolytically activated by thrombin and binds to activated platelet surfaces with activated factor IX (fIXa) to form the intrinsic “tenase” complex. Previous structural and mutational studies of fVIII have identified the C1 and C2 domains in binding to negatively charged membrane surfaces through β-hairpin loops with solvent-exposed hydrophobic residues and a ring of positively charged basic residues. Several hemophilia A-associated mutations within the C domains are suggested to disrupt lipid binding, preventing formation of the intrinsic tenase complex. In this study, we devised a novel platform for generating recombinant C1, C2, and C1C2 domain constructs and performed mutagenesis of several charged residues proximal to the putative membrane binding region of each C domain. Binding measurements between phosphatidylserine (PS)-containing lipid membrane surfaces and fVIII C domains demonstrated an ionic strength dependence on membrane binding affinity. Mutations to basic residues adjacent to the surface-exposed hydrophobic regions of C1 and C2 differentially disrupted membrane binding, with abrogation of binding occurring for mutations to conserved arginine residues in the C1 (R2163) and C2 (R2320) domains. Lastly, we determined the X-ray crystal structure of the porcine fVIII C2 domain bound to o-phospho-L-serine, the polar headgroup of PS, which binds to a basic cleft and makes charge-charge contact with R2320. We conclude that basic clefts in the fVIII C domains bind to PS-containing membranes through conserved arginine residues via a C domain modularity, where each C domain possesses modest electrostatic-dependent affinity and tandem C domains are required for high affinity binding.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Designing 1D multiheme peptide amphiphile assemblies reminiscent of natural systems

We discuss protein assemblies that bind and organize ordered arrays of cofactors yield function structures. Multiheme assemblies found in nature yield electronically conductivity 1D nanoscale fibers and are employed in anaerobic respiration. To understand the fundamental characteristics of these organized arrays, the design of peptide amphiphiles that assemble into 1D nanostructures and yield metalloporphyrin binding sites is presented. One challenge with this class of peptide amphiphiles is identifying the correct sequence composition for high affinity binding with high heme density. Here, the peptide c16-AH(Kx)n-CO 2 H is explored to identify the impact of sequence length (n) and amino acid identity (x = L, I, or F) on binding affinity and midpoint potential. When n = 2, the peptide assembly yields the greatest affinity. The resulting nanoscale assemblies yield ordered arrays of the redox active molecule heme and have potential utility in the development of supramolecular bioelectronic materials useful in sensing as well as the development of enzymatic materials.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Mobile barrier mechanisms for Na + -coupled symport in an MFS sugar transporter

While many 3D structures of cation-coupled transporters have been determined, the mechanistic details governing the obligatory coupling and functional regulations still remain elusive. The bacterial melibiose transporter (MelB) is a prototype of major facilitator superfamily transporters. With a conformation-selective nanobody, we determined a low-sugar affinity inward-facing Na + -bound cryoEM structure. The available outward-facing sugar-bound structures showed that the N- and C-terminal residues of the inner barrier contribute to the sugar selectivity. The inward-open conformation shows that the sugar selectivity pocket is also broken when the inner barrier is broken. Isothermal titration calorimetry measurements revealed that this inward-facing conformation trapped by this nanobody exhibited a greatly decreased sugar-binding affinity, suggesting the mechanisms for substrate intracellular release and accumulation. While the inner/outer barrier shift directly regulates the sugar-binding affinity, it has little or no effect on the cation binding, which is supported by molecular dynamics simulations. Furthermore, the hydron/deuterium exchange mass spectrometry analyses allowed us to identify dynamic regions; some regions are involved in the functionally important inner barrier-specific salt-bridge network, which indicates their critical roles in the barrier switching mechanisms for transport. These complementary results provided structural and dynamic insights into the mobile barrier mechanism for cation-coupled symport.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Guiding the design of oxidation-resistant Fe-based single atom alloy catalysts with insights from configurational space

The high activity and selectivity of Fe-based heterogeneous catalysts toward a variety of reactions that require the breaking of strong bonds are offset in large part by their considerable instability with respect to oxidative deactivation. While it has been shown that the stability of Fe catalysts is considerably enhanced by alloying them with precious metals (even at the single-atom limit), rational design criteria for choosing such secondary metals are still missing. Since oxidative deactivation occurs due to the strong binding of oxygen to Fe and reduction by adsorbed hydrogen mitigates the deactivation, we propose here to use the binding affinity of oxygen and hydrogen adatoms as the basis for rational design. As it would also be beneficial to use cheaper secondary metals, we have scanned over a large subset of 3d–5d mid-to late transition metal single atoms and computationally determined their effect on the oxygen and hydrogen adlayer binding as a function of chemical potential and adsorbate coverage. We further determine the underlying chemical origins that are responsible for these effects and connect them to experimentally tunable quantities. Our results reveal a reliable periodic trend wherein oxygen binding is weakened greatest as one moves right and down the periodic table. Hydrogen binding shows the same trend only at high (but relevant) coverages and otherwise tends to have its binding slightly increased in all systems. Trends with secondary metal coverage are also uncovered and connected to experimentally tunable parameters.

Hensley, Alyssa↗
DOE OSTI2022

Large-scale application of free energy perturbation calculations for antibody design

Abstract Alchemical free energy perturbation (FEP) is a rigorous and powerful technique to calculate the free energy difference between distinct chemical systems. Here we report our implementation of automated large-scale FEP calculations, using the Amber software package, to facilitate antibody design and evaluation. In combination with Hamiltonian replica exchange, our FEP simulations aim to predict the effect of mutations on both the binding affinity and the structural stability. Importantly, we incorporate multiple strategies to faithfully estimate the statistical uncertainties in the FEP results. As a case study, we apply our protocols to systematically evaluate variants of the m396 antibody for their conformational stability and their binding affinity to the spike proteins of SARS-CoV-1 and SARS-CoV-2. By properly adjusting relevant parameters, the particle collapse problems in the FEP simulations are avoided. Furthermore, large statistical errors in a small fraction of the FEP calculations are effectively reduced by extending the sampling, such that acceptable statistical uncertainties are achieved for the vast majority of the cases with a modest total computational cost. Finally, our predicted conformational stability for the m396 variants is qualitatively consistent with the experimentally measured melting temperatures. Our work thus demonstrates the applicability of FEP in computational antibody design.

59 BASIC BIOLOGICAL SCIENCES↗