Conformational Stability Study of a Therapeutic Peptide Plectasin Using Molecular Dynamics Simulations in Combination with NMR

Machine learning and molecular simulation-based protocols to identify novel potential inhibitors for reverse transcriptase against HIV infections

Acquired immunodeficiency syndrome (AIDS) is a potentially fatal condition affecting the human immune system, which is attributed to the human immunodeficiency virus (HIV). The suppression of reverse transcriptase activity is a promising and feasible strategy for the therapeutic management of AIDS. In this study, we employed machine learning algorithms, such as support vector machines (SVM), k-nearest neighbor (k-NN), random forest (RF), and Gaussian naive base (GNB), which are fast and effective tools commonly used in drug design. For model training, we initially obtained a dataset of 5,159 compounds from BindingDB. The models were assessed using tenfold cross-validation to ensure their accuracy and reliability. Among these compounds, 1,645 compounds were labeled as active, having an IC50 below 0.49 µM, while 3,514 compounds were labeled "inactive against reverse transcriptase. Random forest achieved 86% accuracy on the train and test set among the different machine learning algorithms. Random forest model was then applied to an external ZINC dataset. Subsequently, only three hits-ZINC1359750464, ZINC1435357562, and ZINC1545719422-were selected based on the Lipinski Rule, docking score, and good interaction. The stability of these molecules was further evaluated by deploying molecular dynamics simulation and MM/GBSA, which were found to be -38.6013 ± 0.1103 kcal/mol for the Zidovudine/RT complex, -59.1761 ± 2.2926 kcal/mol for the ZINC1359750464/RT complex, -47.6292 ± 2.4206 kcal/mol for the ZINC1435357562/RT complex, and -50.7334 ± 2.5713 kcal/mol for the ZINC1545719422/RT complex.Communicated by Ramaswamy H. Sarma.

Machine Learning-Based Virtual Screening and Molecular Simulation Approaches Identified Novel Potential Inhibitors for Cancer Therapy

Cyclin-dependent kinase 2 (CDK2) is a promising target for cancer treatment, developing new effective CDK2 inhibitors is of great significance in anticancer therapy. The involvement of CDK2 in tumorigenesis has been debated, but recent evidence suggests that specifically inhibiting CDK2 could be beneficial in treating certain tumors. This approach remains attractive in the development of anticancer drugs. Several small-molecule inhibitors targeting CDK2 have reached clinical trials, but a selective inhibitor for CDK2 is yet to be discovered. In this study, we conducted machine learning-based drug designing to search for a drug candidate for CDK2. Machine learning models, including k-NN, SVM, RF, and GNB, were created to detect active and inactive inhibitors for a CDK2 drug target. The models were assessed using 10-fold cross-validation to ensure their accuracy and reliability. These methods are highly suitable for classifying compounds as either active or inactive through the virtual screening of extensive compound libraries. Subsequently, machine learning techniques were employed to analyze the test dataset obtained from the zinc database. A total of 25 compounds with 98% accuracy were predicted as active against CDK2. These compounds were docked into CDK2's active site. Finally, three compounds were selected based on good docking score, and, along with a reference compound, underwent MD simulation. The Gaussian naïve Bayes model yielded superior results compared to other models. The top three hits exhibited enhanced stability and compactness compared to the reference compound. In conclusion, our study provides valuable insights for identifying and refining lead compounds as CDK2 inhibitors.

Site Mutation Improves the Expression and Antimicrobial Properties of Fungal Defense

Although antimicrobial peptides (AMPs) have highly desirable intrinsic characteristics in their commercial product development as new antimicrobials, the limitations of AMPs from experimental to scale development include the low oral bioavailability, and high production costs due to inadequate in vitro/in vivo gene expression- and low scale. Plectasin has good bactericidal activity against Staphylococcus and Streptococcus, and the selective bactericidal activity greatly reduces the damage to the micro-ecosystem when applied in vivo. However, its expression level was relatively low (748.63 mg/L). In view of these situations, this study will optimize and modify the structure of Plectasin, hoping to obtain candidates with high expression, no/low toxicity, and maintain desirable antibacterial activity. Through sequence alignment, Plectasin was used as a template to introduce the degenerate bases, and the screening library was constructed. After three different levels of screening, the candidate sequence PN7 was obtained, and its total protein yield in the supernatant was 5.53 g/L, with the highest value so far for the variants or constructs from the same ancestor source. PN7 had strong activity against several species of Gram-positive bacteria (MIC value range 1~16 μg/mL). It was relatively stable in various conditions in vitro; in addition, the peptide showed no toxicity to mice for 1 week after intraperitoneal injection. Meanwhile, PN7 kills Staphylococcus aureus ATCC 43300 with a mode of a quicker (>99% S. aureus was killed within 2 h, whereas vancomycin at 2× MIC was 8 h.) and longer PAE period. The findings indicate that PN7 may be a novel promising antimicrobial agent, and this study also provides a model or an example for the design, modification, or reconstruction of novel AMPs and their derivatives.

Advances of Antimicrobial Peptide-Based Biomaterials for the Treatment of Bacterial Infections

Owing to the increase in multidrug-resistant bacterial isolates in hospitals globally and the lack of truly effective antimicrobial agents, antibiotic resistant bacterial infections have increased substantially. There is thus an urgent need to develop new antimicrobial drugs and their related formulations. In recent years, natural antimicrobial peptides (AMPs), AMP optimization, self-assembled AMPs, AMP hydrogels, and biomaterial-assisted delivery of AMPs have shown great potential in the treatment of bacterial infections. In this review, it is focused on the development prospects and shortcomings of various AMP-based biomaterials for treating animal model infections, such as abdominal, skin, and eye infections. It is hoped that this review will inspire further innovations in the design of AMP-based biomaterials for the treatment of bacterial infections and accelerate their commercialization.

Dynamics of Human Serum Transferrin in Varying Physicochemical Conditions Explored by Using Molecular Dynamics Simulations

Conformational stability of human serum transferrin (Tf) at varying pH values and salt and excipient concentrations were investigated using molecular dynamics (MD) simulations, and the results are compared with previously published small-angle X-ray scattering (SAXS) experiments. SAXS study showed that at pH 5, Tf is predominantly present in a partially open (PO) form, and the factions of PO differ based on the physicochemical condition and drift toward the closed form (HO) as the pH increases. Tf is a bilobal glycoprotein that is composed of homologous halves termed the N- and C-lobes. The current study shows that the protonation of Y188 and K206 at pH 5 is the primary conformational drive into PO, which shifts toward the closed (HO) conformer as the pH increases. Furthermore, at pH 6.5, PO is unfavorable due to negative charge-charge repulsion at the N/C-lobe interface linker region causing increased hinge distance when compared to HO, which has favorable attractive electrostatic interactions in this region. Subsequently, the effect of salt concentration was studied at 70 and 140 mM NaCl. At 70 mM NaCl and pH 5, chloride ions bind strongly in the N-lobe iron-binding site, whereas these interactions are weak at pH 6.5. With increasing salt concentration at pH 5, the regions surrounding the N-lobe iron-binding site are saturated, and as a consequence, sodium and chloride ions accumulate into the bulk. Additionally, protein-excipient interactions were investigated. At pH 5, the excipients interact in similar loop regions, E89-T93, and D416-D420, located in the N- and C-lobes of the HO conformer, respectively. It is anticipated that interactions of additives in these two loop regions cause conformational changes that lead to iron-coordinating residues in the N-lobe to drift away from iron and thus drive HO to PO conversion. Furthermore, at pH 6.5 and 140 mM histidine, these interactions are negligible leading to the stabilization of HO.

pH- and concentration-dependent supramolecular assembly of a fungal defensin plectasin variant into helical non-amyloid fibrils

Self-assembly and fibril formation play important roles in protein behaviour. Amyloid fibril formation is well-studied due to its role in neurodegenerative diseases and characterized by refolding of the protein into predominantly β-sheet form. However, much less is known about the assembly of proteins into other types of supramolecular structures. Using cryo-electron microscopy at a resolution of 1.97 Å, we show that a triple-mutant of the anti-microbial peptide plectasin, PPI42, assembles into helical non-amyloid fibrils. The in vitro anti-microbial activity was determined and shown to be enhanced compared to the wildtype. Plectasin contains a cysteine-stabilised α-helix-β-sheet structure, which remains intact upon fibril formation. Two protofilaments form a right-handed protein fibril. The fibril formation is reversible and follows sigmoidal kinetics with a pH- and concentration dependent equilibrium between soluble monomer and protein fibril. This high-resolution structure reveals that α/β proteins can natively assemble into fibrils.

Here the authors report the cryo-EM structure of a triple-mutant of the anti-microbial peptide plectasin, PPI42, assembling in a pH- and concentration dependent manner into helical non-amyloid fibrils. The fibrils formation is reversible, and follows a sigmoidal kinetics. The fibrils adopt a right-handed helical superstructure composed by two protofilaments, stabilized by an outer hydrophobic ring and an inner hydrophobic centre. These findings reveal that α/β proteins can natively assemble into fibrils.

Small angle X-ray scattering and molecular dynamic simulations provide molecular insight for stability of recombinant human transferrin

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Combination of SAXS and MD simulations can explain the molecular background for the stability studies.

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Destabilizing excipients in general induce opening of the N-lobe by binding to specific residues in the protein.

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The fully open conformation of transferrin is not seen without aggregation.

Transferrin is an attractive candidate for drug delivery due to its ability to cross the blood brain barrier. However, in order to be able to use it for therapeutic purposes, it is important to investigate how its stability depends on different formulation conditions. Combining high-throughput thermal and chemical denaturation studies with small angle X-ray scattering (SAXS) and molecular dynamics (MD) simulations, it was possible to connect the stability of transferrin with its conformational changes. Lowering pH induces opening of the transferrin N-lobe, which results in a negative effect on the stability. Presence of NaCl or arginine at low pH enhances the opening and has a negative impact on the overall protein stability.

Statement of Significance

Protein-based therapeutics have become an essential part of medical treatment. They are highly specific, have high affinity and fewer off-target effects. However, stabilization of proteins is critical, time-consuming, and expensive, and it is not yet possible to predict the behavior of proteins under different conditions. The current work is focused on a molecular understanding of the stability of human serum transferrin; a protein which is abundant in blood serum, may pass the blood brain barrier and therefore with high potential in drug delivery. Combination of high throughput unfolding techniques and structural studies, using small angle X-ray scattering and molecular dynamic simulations, allows us to understand the behavior of transferrin on a molecular level.

Albumin-neprilysin fusion protein: understanding stability using small angle X-ray scattering and molecular dynamic simulations

Fusion technology is widely used in protein-drug development to increase activity, stability, and bioavailability of protein therapeutics. Fusion proteins, like any other type of biopharmaceuticals, need to remain stable during production and storage. Due to the high complexity and additional intramolecular interactions, it is not possible to predict the behavior of fusion proteins based on the behavior the individual proteins. Therefore, understanding the stability of fusion proteins on the molecular level is crucial for the development of biopharmaceuticals. The current study on the albumin-neprilysin (HSA-NEP) fusion protein uses a combination of thermal and chemical unfolding with small angle X-ray scattering and molecular dynamics simulations to show a correlation between decreasing stability and increasing repulsive interactions, which is unusual for most biopharmaceuticals. It is also seen that HSA-NEP is not fully flexible: it is present in both compact and extended conformations. Additionally, the volume fraction of each conformation changes with pH. Finally, the presence of NaCl and arginine increases stability at pH 6.5, but decreases stability at pH 5.0.

Advancing Therapeutic Protein Discovery and Development through Comprehensive Computational and Biophysical Characterization

Therapeutic protein candidates should exhibit favorable properties that render them suitable to become drugs. Nevertheless, there are no well-established guidelines for the efficient selection of proteinaceous molecules with desired features during early stage development. Such guidelines can emerge only from a large body of published research that employs orthogonal techniques to characterize therapeutic proteins in different formulations. In this work, we share a study on a diverse group of proteins, including their primary sequences, purity data, and computational and biophysical characterization at different pH and ionic strength. We report weak linear correlations between many of the biophysical parameters. We suggest that a stability comparison of diverse therapeutic protein candidates should be based on a computational and biophysical characterization in multiple formulation conditions, as the latter can largely determine whether a protein is above or below a certain stability threshold. We use the presented data set to calculate several stability risk scores obtained with an increasing level of analytical effort and show how they correlate with protein aggregation during storage. Our work highlights the importance of developing combined risk scores that can be used for early stage developability assessment. We suggest that such scores can have high prediction accuracy only when they are based on protein stability characterization in different solution conditions.