Molecular dynamics simulations suggest stabilizing mutations in a de novo designed α/β protein

De novo design of mini-binder proteins against IL-2 receptor β chain

IL-2 regulates the immune response by interacting with different IL-2 receptor (IL-2R) subunits. High dose of IL-2 binds to IL-2Rβγc heterodimer, which induce various side effects while activating immune function. Disrupting IL-2 and IL-2R interactions can block IL-2 mediated immune response. Here, we used a computational approach to de novo design mini-binder proteins against IL-2R β chain (IL-2Rβ) to block IL-2 signaling. The hydrophobic region where IL-2 binds to IL-2Rβ was selected and the promising binding mode was broadly explored. Three mini-binders with amino acid numbers ranging from 55 to 65 were obtained and binder 1 showed the best effects in inhibiting CTLL-2 cells proliferation and STAT5 phosphorylation. Molecular dynamics simulation showed that the binding of binder 1 to IL-2Rβ was stable; the free energy of binder1/IL-2Rβ complex was lower, indicating that the affinity of binder 1 to IL-2Rβ was higher than that of IL-2. Free energy decomposition suggested that the ARG35 and ARG131 of IL-2Rβ might be the key to improve the affinity of binder. Our efforts provided new insights in developing of IL-2R blocker, offering a potential strategy for ameliorating the side effects of IL-2 treatment.

Experimental and molecular dynamics simulation studies on the physical properties of three HBc-VLP derivatives as nanoparticle protein vaccine candidates

Self-assembling virus-like particles (VLPs) are promising platforms for vaccine development. However, the unpredictability of the physical properties, such as self-assembly capability, hydrophobicity, and overall stability in engineered protein particles fused with antigens, presents substantial challenges in their downstream processing. We envision that these challenges can be addressed by combining more precise computer-aided molecular dynamics (MD) simulations with experimental studies on the modified products, with more to-date forcefield descriptions and larger models closely resembling real assemblies, realized by rapid advancement in computing technology. In this study, three chimeric designs based on the hepatitis B core (HBc) protein as model vaccine candidates were constructed to study and compare the influence of inserted epitopes as well as insertion strategy on HBc modifications. Large partial VLP models containing 17 chains for the HBc chimeric model vaccines were constructed based on the wild-type (wt) HBc assembly template. The findings from our simulation analysis have demonstrated good consistency with experimental results, pertaining to the surface hydrophobicity and overall stability of the chimeric vaccine candidates. Furthermore, the different impact of foreign antigen insertions on the HBc scaffold was investigated through simulations. It was found that separately inserting two epitopes into the HBc platform at the N-terminal and the major immunogenic regions (MIR) yields better results compared to a serial insertion at MIR in terms of protein structural stability. This study substantiates that an MD-guided design approach can facilitate vaccine development and improve its manufacturing efficiency by predicting products with extreme surface hydrophobicity or structural instability.

De novo design of drug-binding proteins with predictable binding energy and specificity

The de novo design of small molecule-binding proteins has seen exciting recent progress; however, high-affinity binding and tunable specificity typically require laborious screening and optimization after computational design. We developed a computational procedure to design a protein that recognizes a common pharmacophore in a series of poly(ADP-ribose) polymerase-1 inhibitors. One of three designed proteins bound different inhibitors with affinities ranging from <5 nM to low micromolar. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free energy calculations performed directly on the designed models were in excellent agreement with the experimentally measured affinities. We conclude that de novo design of high-affinity small molecule-binding proteins with tuned interaction energies is feasible entirely from computation.

De novo design of drug-binding proteins with predictable binding energy and specificity

The de novo design of small-molecule-binding proteins has seen exciting recent progress; however, the ability to achieve exquisite affinity for binding small molecules while tuning specificity has not yet been demonstrated directly from computation. Here, we develop a computational procedure that results in the highest affinity binders to date with predetermined relative affinities, targeting a series of PARP1 inhibitors. Two of four designed proteins bound with affinities ranging from < 5 nM to low μM, in a predictable manner. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free-energy calculations performed directly on the designed models are in excellent agreement with the experimentally measured affinities, suggesting that the de novo design of small-molecule-binding proteins with tuned interaction energies is now feasible entirely from computation. We expect these methods to open many opportunities in biomedicine, including rapid sensor development, antidote design, and drug delivery vehicles.

The stability and dynamics of computationally designed proteins

Protein stability, dynamics and function are intricately linked. Accordingly, protein designers leverage dynamics in their designs and gain insight to their successes and failures by analyzing their proteins' dynamics. Molecular dynamics (MD) simulations are a powerful computational tool for quantifying both local and global protein dynamics. This review highlights studies where MD simulations were applied to characterize the stability and dynamics of designed proteins and where dynamics were incorporated into computational protein design. First, we discuss the structural basis underlying the extreme stability and thermostability frequently observed in computationally designed proteins. Next, we discuss examples of designed proteins, where dynamics were not explicitly accounted for in the design process, whose coordinated motions or active site dynamics, as observed by MD simulation, enhanced or detracted from their function. Many protein functions depend on sizeable or subtle conformational changes, so we finally discuss the computational design of proteins to perform a specific function that requires consideration of motion by multi-state design.

SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation

Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. These methods have been evaluated for symmetric consistency by testing with hypothetical reverse mutations. In this work, we propose transitive data augmentation, evaluating transitive consistency with our new S^transitive data set, and a new machine learning based method, the first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that predicts small relative protein stability changes for missense mutations that do not significantly alter the structure. It estimates a residue's contributions toward protein stability (ΔG) in its local structural environment, and the difference between independently predicted contributions of the reference and mutant residues is reported as ΔΔG. We show that this self-consistent machine learning architecture is immune to many common biases in data sets, relies less on data than existing methods, is robust to overfitting, and can explain a substantial portion of the variance in experimental data.

KEAP1 Cancer Mutants: A Large-Scale Molecular Dynamics Study of Protein Stability

We have performed 280 μs of unbiased molecular dynamics (MD) simulations to investigate the effects of 12 different cancer mutations on Kelch-like ECH-associated protein 1 (KEAP1) (G333C, G350S, G364C, G379D, R413L, R415G, A427V, G430C, R470C, R470H, R470S and G476R), one of the frequently mutated proteins in lung cancer. The aim was to provide structural insight into the effects of these mutants, including a new class of ANCHOR (additionally NRF2-complexed hypomorph) mutant variants. Our work provides additional insight into the structural dynamics of mutants that could not be analyzed experimentally, painting a more complete picture of their mutagenic effects. Notably, blade-wise analysis of the Kelch domain points to stability as a possible target of cancer in KEAP1. Interestingly, structural analysis of the R470C ANCHOR mutant, the most prevalent missense mutation in KEAP1, revealed no significant change in structural stability or NRF2 binding site dynamics, possibly indicating an covalent modification as this mutant's mode of action.

Stability of Engineered Ferritin Nanovaccines Investigated by Combined Molecular Simulation and Experiments

Human ferritin is regarded as an attractive and promising vaccine platform because of its uniform structure, good plasticity, and desirable thermal and chemical stabilities. Besides, it is biocompatible and presumed safe when used as a vaccine carrier. However, there is a lack of knowledge of how different antigen insertion sites on the ferritin nanocage impact the resulting protein stability and performance. To address this question, we selected Epstein-Barr nuclear antigen 1 as a model epitope and fused it at the DNA level with different insertion sites, namely, the N- and C-termini of ferritin, to engineer proteins E1F1 and F1E1, respectively. Protein properties including hydrophobicity and thermal, pH, and chemical stability were investigated both by molecular dynamics (MD) simulation and by experiments. Both methods demonstrate that the insertion site plays an important role in protein properties. The C-terminus insertion (F1E1) leads to a less hydrophobic surface and more tolerance to the external influence of high temperature, pH, and high concentration of chemical denaturants compared to N-terminus insertion (E1F1). Simulated protein hydrophobicity and thermal stability by MD were in high accordance with experimental results. Thus, MD simulation can be used as a valuable tool to engineer nanovaccine candidates, cutting down costs by reducing the experimental effort and accelerating vaccine design.