Structure-Based Design of Small Peptide Ligands to Inhibit Early-Stage Protein Aggregation Nucleation

Brain-targeted drug delivery by in vivo functionalized liposome with stable D-peptide ligand

Brain-targeted drug delivery poses a great challenge due to the blood-brain barrier (BBB). In a previous study, we have developed a peptide-modified stealth liposome (SP-sLip) to enhance BBB penetration via the adsorption of apolipoproteins in plasma. SP is an 11-amino acid peptide derived from 25 to 35 of the Amyloid β peptide (Aβ1-42), which is a nature ligand of apolipoproteins. Although freshly prepared SP-sLip exhibited efficient brain targeting performance, it occured self-aggregation and instability in storage. In this study, we developed a D-peptide ligand according to the reverse sequence of SP with D-amino acids, known as DSP, to improve the stability in storage. Notably, DSP exhibited a reduced tendency for self-aggregation and improved stability in comparison to the SP peptide. Furthermore, compared to SP-sLip, DSP-modified sLip (DSP-sLip) demonstrated enhanced stability (>2 weeks), prolonged blood circulation (AUC increased 44.4%), reduced liver and spleen accumulation (reduced by 2.23 times and 1.86 times) with comparable brain-targeting efficiency. Similar to SP-sLip, DSP-sLip selectively adsorbed apolipoprotein A1, E, and J in the blood to form functionalized protein corona, thus crossing BBB via apolipoprotein receptor-mediated transcytosis. These findings underscored the importance of ligand stability in the in vitro and in vivo performance of brain-targeted liposomes, therefore paving the way for the design and optimization of efficient and stable nanocarriers.

Accelerated NLRP3 inflammasome-inhibitory peptide design using a recurrent neural network model and molecular dynamics simulations

Anomalous NLRP3 inflammasome responses have been linked to multiple health issues, including but not limited to atherosclerosis, diabetes, metabolic syndrome, cardiovascular disease, and neurodegenerative disease. Thus, targeting NLRP3 and modulating its associated immune response might be a promising strategy for developing new anti-inflammatory drugs. Herein, we report a computational method for de novo peptide design for targeting NLRP3 inflammasomes. The described method leverages a long-short-term memory (LSTM) network based on a recurrent neural network (RNN) to model a valuable latent space of molecules. The resulting classifiers are utilized to guide the selection of molecules generated by the model based on circular dichroism spectra and physicochemical features derived from high-throughput molecular dynamics simulations. Of the experimentally tested sequences, 60% of the peptides showed NLRP3-mediated inhibition of IL-1β and IL-18. One peptide displayed high potency against NLRP3-mediated IL-1β inhibition. However, NLRC4 and AIM2 inflammasome-mediated IL-1β secretion was uninterrupted by this peptide, demonstrating its selectivity toward the NLRP3 inflammasome. Overall, these results indicate that deep learning and molecular dynamics can accelerate the discovery of NLRP3 inhibitors with potent and selective activity.

An efficient computational protocol for template-based design of peptides that inhibit interactions involving SARS-CoV-2 proteins

The RNA-dependent RNA polymerase (RdRp) complex of SARS-CoV-2 lies at the core of its replication and transcription processes. The interfaces between holo-RdRp subunits are highly conserved, facilitating the design of inhibitors with high affinity for the interaction interface hotspots. We, therefore, take this as a model protein complex for the application of a structural bioinformatics protocol to design peptides that inhibit RdRp complexation by preferential binding at the interface of its core subunit nonstructural protein, nsp12, with accessory factor nsp7. Here, the interaction hotspots of the nsp7-nsp12 subunit of RdRp, determined from a long molecular dynamics trajectory, are used as a template. A large library of peptide sequences constructed from multiple hotspot motifs of nsp12 is screened in-silico to determine sequences with high geometric complementarity and interaction specificity for the binding interface of nsp7 (target) in the complex. Two lead designed peptides are extensively characterized using orthogonal bioanalytical methods to determine their suitability for inhibition of RdRp complexation. Binding affinity of these peptides to accessory factor nsp7, determined using a surface plasmon resonance (SPR) assay, is slightly better than that of nsp12: dissociation constant of 133nM and 167nM, respectively, compared to 473nM for nsp12. A competitive ELISA is used to quantify inhibition of nsp7-nsp12 complexation, with one of the lead peptides giving an IC50 of 25μM . Cell penetrability and cytotoxicity are characterized using a cargo delivery assay and MTT cytotoxicity assay, respectively. Overall, this work presents a proof-of-concept of an approach for rational discovery of peptide inhibitors of SARS-CoV-2 protein-protein interactions.

Charge modifications of graphene oxide enhance the inhibitory effect on insulin amyloid fibrillation based on electrostatic interactions

Graphene oxide (GO) is a biocompatible nanomaterial that has an inhibitory effect on insulin amyloid fibrillation. In order to enhance the inhibitory effect of GO and explore the rules of electrostatic interactions on the inhibitory effect, carboxyl group, PEI and PEG were coupled to the GO nanoplatelet surface to prepare inhibitors of different surface electrical properties. The effects of surface electrical properties of inhibitors on insulin fibrillation were investigated. The results showed that GO, carboxyl group modified GO (GO-COOH), PEI modified GO (GO-PEI), and PEG modified GO (GO-PEG) inhibited insulin fibrillation in a dose-dependent manner. Compared with GO, positive charge-modified GO-PEI and negative charge-modified GO-COOH enhanced the inhibitory effect, while uncharged polymer-modified GO-PEG weakened the inhibitory effect. The inhibitory effect of the inhibitors increased with the increase of surface charge density. The difference in inhibitory effect between GO-PEI and GO-COOH was due to the different electrostatic interactions between inhibitors and insulin, and the different inhibition mechanisms. In addition, inhibitors mainly interact with insulin during the nucleation phase to hinder insulin fibrillation. The charge modifications of graphene oxide enhanced the inhibitory effect on insulin fibrillation based on electrostatic interactions, which will provide new thoughts for the development of anti-amyloid fibrillation drugs.

Multi-Level High-Throughput Screening for Discovery of Ligands That Inhibit Insulin Aggregation

We have developed a multi-level virtual screening protocol to identify lead molecules from the FDA inactives database that can inhibit insulin aggregation. The method is based on the presence of structural and interaction specificity in non-native aggregation pathway protein-protein interactions. Some key challenges specific to the present problem, when compared with native protein association, include structural heterogeneity of the protein species involved, multiple association pathways, and relatively higher probability of conformational rearrangement of the association complex. In this multi-step method, the inactives database was first screened using the dominant pharmacophore features of previously identified molecules shown to significantly inhibit insulin aggregation nucleation by binding to its aggregation-prone conformers. We then performed ensemble docking of several low-energy ligand conformations on these aggregation-prone conformers followed by molecular dynamics simulations and binding affinity calculations on a subset of docked complexes to identify a final set of five potential lead molecules to inhibit insulin aggregation nucleation. Their effect on aggregation inhibition was extensively investigated by incubating insulin under aggregation-prone aqueous buffer conditions (low pH, high temperature). Aggregation kinetics were characterized using size exclusion chromatography and Thioflavin T fluorescence assay, and the secondary structure was determined using circular dichroism spectroscopy. Riboflavin provided the best aggregation inhibition, with 85% native monomer retention after 48 h incubation under aggregation-prone conditions, whereas the no-ligand formulation showed complete monomer loss after 36 h. Further, insulin incubated with two of the screened inactives (aspartame, riboflavin) had the characteristic α-helical dip in CD spectra, while the no-ligand formulation showed a change to β-sheet rich conformations.

High throughput virtual screening (HTVS) of peptide library: Technological advancement in ligand discovery

High-throughput virtual screening (HTVS) is a leading biopharmaceutical technology that employs computational algorithms to uncover biologically active compounds from large-scale collections of chemical compound libraries. In addition, this method often leverages the precedence of screening focused libraries for assessing their binding affinities and improving physicochemical properties. Usually, developing a drug sometimes takes ages, and lessons are learnt from FDA-approved drugs. This screening strategy saves resources and time compared to laboratory testing in certain stages of drug discovery. Yet in-silico investigations remain challenging in some cases of drug discovery. For the last few decades, peptide-based drug discoveries have received remarkable momentum for several advantages over small molecules. Therefore, developing a high-fidelity HTVS platform for chemically versatile peptide libraries is highly desired. This review summarises the modern and frequently appreciated HTVS strategies for peptide libraries from 2011 to 2021. In addition, we focus on the software used for preparing peptide libraries, their screening techniques and shortcomings. An index of various HTVS methods reported here should assist researchers in identifying tools that could be beneficial for their peptide library screening projects.

Multiscale Model for Quantitative Prediction of Insulin Aggregation Nucleation Kinetics

We combined kinetic, thermodynamic, and structural information from single-molecule (protein folding) and two-molecule (association) explicit-solvent simulations for determination of kinetic parameters in protein aggregation nucleation with insulin as the model protein. A structural bioinformatics approach was developed to account for heterogeneity of aggregation-prone species, with the transition complex theory found applicable in modeling association kinetics involving non-native species. Specifically, the kinetic pathway for formation of aggregation-prone oligomeric species was found to contain a structurally specific dominant binding mode, making the kinetic process similar to native protein association. The kinetic parameters thus obtained were used in a population balance model, and accurate predictions for aggregation nucleation time varying over 2 orders of magnitude with changes in either insulin concentration or an aggregation-inhibitor ligand concentration were obtained, while an empirical parameter set was not found to be transferable for prediction of ligand effects. Further, this physically determined kinetic parameter set provided several mechanistic insights, such as identification of the rate-limiting step in aggregation nucleation and a quantitative explanation for the switch from Arrhenius to non-Arrhenius aggregation kinetics around the melting temperature of insulin.

Size-based Degradation of Therapeutic Proteins - Mechanisms, Modelling and Control

Protein therapeutics are in great demand due to their effectiveness towards hard-to-treat diseases. Despite their high demand, these bio-therapeutics are very susceptible to degradation via aggregation, fragmentation, oxidation, and reduction, all of which are very likely to affect the quality and efficacy of the product. Mechanisms and modelling of these degradation (aggregation and fragmentation) pathways is critical for gaining a deeper understanding of stability of these products. This review aims to provide a summary of major developments that have occurred towards unravelling the mechanisms of size-based protein degradation (particularly aggregation and fragmentation), modelling of these size-based degradation pathways, and their control. Major caveats that remain in our understanding and control of size-based protein degradation have also been presented and discussed.

DPL: a comprehensive database on sequences, structures, sources and functions of peptide ligands

DPL (http://www.peptide-ligand.cn/) is a comprehensive database of peptide ligand (DPL). DPL1.0 holds 1044 peptide ligand entries and provides references for the study of the polypeptide platform. The data were collected from PubMed-NCBI, PDB, APD3, CAMPR3, etc. The lengths of the base sequences are varied from 3 to78. DPL database has 923 linear peptides and 88 cyclic peptides. The functions of peptides collected by DPL are very wide. It includes 540 entries of antiviral peptides (including SARS-CoV-2), 55 entries of signal peptides, 48 entries of protease inhibitors, 45 entries of anti-hypertension, 37 entries of anticancer peptides, etc. There are 270 different kinds of peptide targets. All peptides in DPL have clear binding targets. Most of the peptides and receptors have 3D structures experimentally verified or predicted by CYCLOPS, I-TASSER and SWISS-MODEL. With the rapid development of the COVID-2019 epidemic, this database also collects the research progress of peptides against coronavirus. In conclusion, DPL is a unique resource, which allows users easily to explore the targets, different structures as well as properties of peptides.