Design Of Peptide Inhibitors Research Articles

In Silico Design of Peptide Inhibitors Targeting HER2 for Lung Cancer Therapy

Background/Objectives: Human epidermal growth factor receptor 2 (HER2) is overexpressed in several malignancies, such as breast, gastric, ovarian, and lung cancers, where it promotes aggressive tumor proliferation and unfavorable prognosis. Targeting HER2 has thus emerged as a crucial therapeutic strategy, particularly for HER2-positive malignancies. The present study focusses on the design and optimization of peptide inhibitors targeting HER2, utilizing machine learning to identify and enhance peptide candidates with elevated binding affinities. The aim is to provide novel therapeutic options for malignancies linked to HER2 overexpression. Methods: This study started with the extraction and structural examination of the HER2 protein, succeeded by designing the peptide sequences derived from essential interaction residues. A machine learning technique (XGBRegressor model) was employed to predict binding affinities, identifying the top 20 peptide possibilities. The candidates underwent further screening via the FreeSASA methodology and binding free energy calculations, resulting in the selection of four primary candidates (pep-17, pep-7, pep-2, and pep-15). Density functional theory (DFT) calculations were utilized to evaluate molecular and reactivity characteristics, while molecular dynamics simulations were performed to investigate inhibitory mechanisms and selectivity effects. Advanced computational methods, such as QM/MM simulations, offered more understanding of peptide–protein interactions. Results: Among the four principal peptides, pep-7 exhibited the most elevated DFT values (−3386.93 kcal/mol) and the maximum dipole moment (10,761.58 Debye), whereas pep-17 had the lowest DFT value (−5788.49 kcal/mol) and the minimal dipole moment (2654.25 Debye). Molecular dynamics simulations indicated that pep-7 had a steady binding free energy of −12.88 kcal/mol and consistently bound inside the HER2 pocket during a 300 ns simulation. The QM/MM simulations showed that the overall total energy of the system, which combines both QM and MM contributions, remained around −79,000 ± 400 kcal/mol, suggesting that the entire protein–peptide complex was in a stable state, with pep-7 maintaining a strong, well-integrated binding. Conclusions: Pep-7 emerged as the most promising therapeutic peptide, displaying strong binding stability, favorable binding free energy, and molecular stability in HER2-overexpressing cancer models. These findings suggest pep-7 as a viable therapeutic candidate for HER2-positive cancers, offering a potential novel treatment strategy against HER2-driven malignancies.

Pred-AHCP: Robust Feature Selection-Enabled Sequence-Specific Prediction of Anti-Hepatitis C Peptides via Machine Learning.

Every year, an estimated 1.5 million people worldwide contract Hepatitis C, a significant contributor to liver problems. Although many studies have explored machine learning's potential to predict antiviral peptides, very few have addressed the problem of predicting peptides against specific viruses such as Hepatitis C. In this study, we demonstrate the application and fine-tuning of machine learning (ML) algorithms to predict peptides that are effective against Hepatitis C virus (HCV). We developed a fine-tuned and explainable ML model that harnesses the amino acid sequence of a peptide to predict its anti-hepatitis C potential. Specifically, features were computed based on sequence and physicochemical properties. The feature selection was performed using a combined strategy of mutual information and variance inflation factor. This facilitated the removal of redundant and multicollinear features, enhancing the model's generalizability in predicting anti-hepatitis C peptides (AHCPs). The model using the random forest algorithm produced the best performance with an accuracy of about 92%. The feature analysis highlights that the distributions of hydrophobicity, polarizability, coil-forming residues, frequency of glycine residues and the existence of dipeptide motifs VL, LV, and CC emerged as the key predictors for identifying AHCPs targeting different components of HCV. The developed model can be accessed through the Pred-AHCP web server, provided at http://tinyurl.com/web-Pred-AHCP. This resource facilitates the prediction and re-engineering of AHCPs for designing peptide-based therapeutics while also proposing an exploration of similar strategies for designing peptide inhibitors effective against other viruses. The developed ML model can also be used for validating peptide sequences generated using generative artificial intelligence methods for further optimization.

Peptide Degrader‐Based Targeting of METTL3/14 Improves Immunotherapy Response in Cutaneous Melanoma

AbstractMETTL3 has emerged as a promising therapeutic target in cancer treatment, although its oncogenic functions in melanoma development and potential for therapeutic targeting drug have not been fully explored. In this study, we define the oncogenic role of METTL3 in melanoma development and progression. Building on this insight, we examine our recently designed peptide inhibitor RM3, which targets the binding interface of METTL3/14 complex for disruption and subsequent ubiquitin‐mediated proteasomal degradation via the E3 ligase STUB1. RM3 treatment reduces proliferation, migration, and invasion, and induces apoptosis in melanoma cells in vitro and in vivo. Subsequent transcriptomic analysis identified changes in immuno‐related genes following RM3‐mediated suppression of METTL3/14 N6‐methyladenosine (m6A) methyltransferase activity, suggesting a potential for interaction with immunotherapy. A combination treatment of RM3 with anti‐PD‐1 antibody results in significantly higher beneficial tumor response in vivo, with a good safety profile. Collectively, these findings not only delineate the oncogenic role of METTL3 in melanoma but also showcase RM3, acting as a peptide degrader, as a novel and promising strategy for melanoma treatment.

Peptide Degrader-Based Targeting of METTL3/14 Improves Immunotherapy Response in Cutaneous Melanoma.

METTL3 has emerged as a promising therapeutic target in cancer treatment, although its oncogenic functions in melanoma development and potential for therapeutic targeting drug have not been fully explored. In this study, we define the oncogenic role of METTL3 in melanoma development and progression. Building on this insight, we examine our recently designed peptide inhibitor RM3, which targets the binding interface of METTL3/14 complex for disruption and subsequent ubiquitin-mediated proteasomal degradation via the E3 ligase STUB1. RM3 treatment reduces proliferation, migration, and invasion, and induces apoptosis in melanoma cells in vitro and in vivo. Subsequent transcriptomic analysis identified changes in immuno-related genes following RM3-mediated suppression of METTL3/14 N6-methyladenosine (m6A) methyltransferase activity, suggesting a potential for interaction with immunotherapy. A combination treatment of RM3 with anti-PD-1 antibody results in significantly higher beneficial tumor response in vivo, with a good safety profile. Collectively, these findings not only delineate the oncogenic role of METTL3 in melanoma but also showcase RM3, acting as a peptide degrader, as a novel and promising strategy for melanoma treatment.

A structure-redesigned intrinsically disordered peptide that selectively inhibits a plant transcription factor in jasmonate signaling.

Plant hormone-related transcription factors (TFs) are key regulators of plant development, responses to environmental stress such as climate changes, pathogens, and pests. These TFs often function as families that exhibit genetic redundancy in higher plants, and are affected by complex crosstalk mechanisms between different plant hormones. These properties make it difficult to analyze and control them in many cases. In this study, we introduced a chemical inhibitor to manipulate plant hormone-related TFs, focusing on the jasmonate (JA) and ethylene (ET) signaling pathways, with the key TFs MYC2/3/4 and EIN3/EIL1. This study revealed that JAZ10CMID, the binding domain of the repressor involved in the desensitization of both TFs, is an intrinsically disordered region in the absence of binding partners. Chemical inhibitors have been designed based on this interaction to selectively inhibit MYC TFs while leaving EIN3/EIL1 unaffected. This peptide inhibitor effectively disrupts MYC-mediated responses while activating EIN3-mediated responses and successfully uncouples the crosstalk between JA and ET signaling in Arabidopsis thaliana. Furthermore, the designed peptide inhibitor was also shown to selectively inhibit the activity of MpMYC, an ortholog of AtMYC in Marchantia polymorpha, demonstrating its applicability across different plant species. This underscores the potential of using peptide inhibitors for specific TFs to elucidate hormone crosstalk mechanisms in non-model plants without genetic manipulation. Such a design concept for chemical fixation of the disordered structure is expected to limit the original multiple binding partners and provide useful chemical tools in chemical biology research.

Multilayered Computational Framework for Designing Peptide Inhibitors of HVEM-LIGHT Interaction.

The herpesvirus entry mediator (HVEM) and its ligand LIGHT play crucial roles in immune system regulation, including T-cell proliferation, B-cell differentiation, and immunoglobulin secretion. However, excessive T-cell activation can lead to chronic inflammation and autoimmune diseases. Thus, inhibiting the HVEM-LIGHT interaction emerges as a promising therapeutic strategy for these conditions and in preventing adverse reactions in organ transplantation. This study focused on designing peptide inhibitors, targeting the HVEM-LIGHT interaction, using molecular dynamics (MD) simulations of 65 peptides derived from HVEM. These peptides varied in length and disulfide-bond configurations, crucial for their interaction with the LIGHT trimer. By simulating 31 HVEM domain variants, including the full-length protein, we assessed conformational changes upon LIGHT binding to understand the influence of HVEM segments and disulfide bonds on the binding mechanism. Employing multitrajectory microsecond-scale, all-atom MD simulations and molecular mechanics with generalized Born and surface area (MM-GBSA) binding energy estimation, we identified promising CRD2 domain variants with high LIGHT affinity. Notably, point mutations in these variants led to a peptide with a single disulfide bond (C58-C73) and a K54E substitution, exhibiting the highest binding affinity. The importance of the CRD2 domain and Cys58-Cys73 disulfide bond for interrupting HVEM-LIGHT interaction was further supported by analyzing truncated CRD2 variants, demonstrating similar binding strengths and mechanisms. Further investigations into the binding mechanism utilized steered MD simulations at various pulling speeds and umbrella sampling to estimate the energy profile of HVEM-based inhibitors with LIGHT. These comprehensive analyses revealed key interactions and different binding mechanisms, highlighting the increased binding affinity of selected peptide variants. Experimental circular dichroism techniques confirmed the structural properties of these variants. This study not only advances our understanding of the molecular basis of HVEM-LIGHT interactions but also provides a foundation for developing novel therapeutic strategies for immune-related disorders. Furthermore, it sets a gold standard for peptide inhibitor design in drug development due to its systematic approach.

Protonation-State Dependent Modulation of Hen Egg-White Lysozyme Fibrillation under the Influence of a Short Synthetic Peptide.

Under the influence of various conditions, misfolding of soluble proteins occurs, leading to the formation of toxic insoluble amyloids. The formation and deposition of such amyloids within the body are associated with detrimental biological consequences such as the onset of several amyloid-related diseases. Previously, we established a strategy for the rational design of peptide inhibitors against amyloid formation based on the amyloidogenic-prone region of the protein. In the current study, we have designed and identified an Asp-containing rationally designed hexapeptide (SqP4) as an excellent inhibitor of hen egg-white lysozyme (HEWL) amyloid progression in vitro. First, SqP4 showed strong affinity toward the native monomeric HEWL leading to the stabilization of the native form and restriction in the unfolding process of monomeric HEWL. Second, SqP4 was found to arrest the amyloidogenic misfolded structure of HEWL in a nonfibrillar monomer-like stage. We also observed the differential effect of the protonation state of the charged amino acid (Asp) within the peptide inhibitor on the amyloid formation of HEWL and explored the reason behind the observations. The findings of this study can be implemented in future strategies for the development of potent therapeutics against other amyloid-related diseases.

Computational Design of Peptide Inhibitors Targeting the SARS-CoV-2 Main Protease

Computational Design of Peptide Inhibitors Targeting the SARS-CoV-2 Main Protease

In silico design of peptide inhibitors for Dengue virus to treat Dengue virus-associated infections

Dengue virus is a single positive-strand RNA virus that is composed of three structural proteins including capsid, envelope, and precursor membrane while seven non-structural proteins (NS1, NS2A, NS2B, NS3A, NS3B, NS4, and NS5). Dengue is a viral infection caused by the dengue virus (DENV). DENV infections are asymptomatic or produce only mild illness. However, DENV can occasionally cause more severe cases and even death. There is no specific treatment for dengue virus infections. Therapeutic peptides have several important advantages over proteins or antibodies: they are small in size, easy to synthesize, and have the ability to penetrate the cell membranes. They also have high activity, specificity, affinity, and less toxicity. Based on the known peptide inhibitor, the current study designs peptide inhibitors for dengue virus envelope protein using an alanine and residue scanning technique. By replacing I21 with Q21, L14 with H14, and V28 with K28, the binding affinity of the peptide inhibitors was increased. The newly designed peptide inhibitors with single residue mutation improved the binding affinity of the peptide inhibitors. The inhibitory capability of the new promising peptide inhibitors was further confirmed by the utilization of MD simulation and free binding energy calculations. The molecular dynamics simulation demonstrated that the newly engineered peptide inhibitors exhibited greater stability compared to the wild-type peptide inhibitors. According to the binding free energies MM(GB)SA of these developed peptides, the first peptide inhibitor was the most effective against the dengue virus envelope protein. All peptide derivatives had higher binding affinities for the envelope protein and have the potential to treat dengue virus-associated infections. In this study, new peptide inhibitors were developed for the dengue virus envelope protein based on the already reported peptide inhibitor.

The Development of CDC25A-Derived Phosphoseryl Peptides That Bind 14-3-3ε with High Affinities.

Overexpression of the 14-3-3ε protein is associated with suppression of apoptosis in cutaneous squamous cell carcinoma (cSCC). This antiapoptotic activity of 14-3-3ε is dependent on its binding to CDC25A; thus, inhibiting 14-3-3ε - CDC25A interaction is an attractive therapeutic approach to promote apoptosis in cSCC. In this regard, designing peptide inhibitors of 14-3-3ε - CDC25A interactions is of great interest. This work reports the rational design of peptide analogs of pS, a CDC25A-derived peptide that has been shown to inhibit 14-3-3ε-CDC25A interaction and promote apoptosis in cSCC with micromolar IC50. We designed new peptide analogs in silico by shortening the parent pS peptide from 14 to 9 amino acid residues; then, based on binding motifs of 14-3-3 proteins, we introduced modifications in the pS(174-182) peptide. We studied the binding of the peptides using conventional molecular dynamics (MD) and steered MD simulations, as well as biophysical methods. Our results showed that shortening the pS peptide from 14 to 9 amino acids reduced the affinity of the peptide. However, substituting Gln176 with either Phe or Tyr amino acids rescued the binding of the peptide. The optimized peptides obtained in this work can be candidates for inhibition of 14-3-3ε - CDC25A interactions in cSCC.

Rational Design of TDP-43 Derived α-Helical Peptide Inhibitors: An In Silico Strategy to Prevent TDP-43 Aggregation in Neurodegenerative Disorders.

TDP-43, an essential RNA/DNA-binding protein, is central to the pathology of neurodegenerative diseases, such as amyotrophic lateral sclerosis and frontotemporal dementia. Pathological mislocalization and aggregation of TDP-43 disrupt RNA splicing, mRNA stability, and mRNA transport, thereby impairing neuronal function and survival. The formation of amyloid-like TDP-43 filaments is largely facilitated by the destabilization of an α-helical segment within the disordered C-terminal region. In this study, we hypothesized that preventing the destabilization of the α-helical domain could potentially halt the growth of these pathological filaments. To explore this, we utilized a range of in silico techniques to design and evaluate peptide-based therapeutics that bind to pathological TDP-43 amyloid-like filament crystal structures and resist β sheet conversion. Our computational approaches, including biophysical and secondary structure property prediction, molecular docking, 3D structure prediction, and molecular dynamics simulations, were used to assess the structure, stability, and binding affinity of these peptides in relation to pathological TDP-43 filaments. The results of our in silico analyses identified a selection of promising peptides which displayed a stable α-helical structure, exhibited an increased number of intramolecular hydrogen bonds within the helical domain, and demonstrated high binding affinities for pathological TDP-43 amyloid-like filaments. Molecular dynamics simulations provided further support for the structural and thermodynamic stability of these peptides, as they exhibited lower root-mean-square deviation and more favorable free energy landscapes over 300 ns. These findings establish α-helical propensity peptides as potential lead molecules for the development of novel therapeutics against TDP-43 aggregation. This structure-based computational approach for the rational design of peptide inhibitors opens a new direction in the search for effective interventions for ALS, FTD, and other related neurodegenerative diseases. The peptides identified as the most promising candidates in this study are currently subject to further testing and validation through both in vitro and in vivo experiments.

Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations

We introduce a computational approach for the design of target-specific peptides. Our method integrates a Gated Recurrent Unit-based Variational Autoencoder with Rosetta FlexPepDock for peptide sequence generation and binding affinity assessment. Subsequently, molecular dynamics simulations are employed to narrow down the selection of peptides for experimental assays. We apply this computational strategy to design peptide inhibitors that specifically target β-catenin and NF-κB essential modulator. Among the twelve β-catenin inhibitors, six exhibit improved binding affinity compared to the parent peptide. Notably, the best C-terminal peptide binds β-catenin with an IC50 of 0.010 ± 0.06 μM, which is 15-fold better than the parent peptide. For NF-κB essential modulator, two of the four tested peptides display substantially enhanced binding compared to the parent peptide. Collectively, this study underscores the successful integration of deep learning and structure-based modeling and simulation for target specific peptide design.

Discovering peptide inhibitors of thrombin as a strategy for anticoagulation.

Unusual blood clots can cause serious health problems, such as lung embolism, stroke, and heart attack. Inhibiting thrombin activity was adopted as an effective strategy for preventing blood clots. In this study, we explored computational-based method for designing peptide inhibitors of human thrombin therapeutic peptides to prevent platelet aggregation. The random peptides and their 3-dimentional structures were generated to build a virtual peptide library. The generated peptides were docked into the binding pocket of human thrombin. The designed strong binding peptides were aligned with the native binder by comparative study, and we showed the top 5 peptide binders display strong binding affinity against human thrombin. The 5 peptides were synthesized and validated their inhibitory activity. Our result showed the 5-mer peptide AEGYA, EVVNQ, and FASRW with inhibitory activity against thrombin, range from 0.53 to 4.35 μM. In vitro anti-platelet aggregation assay was carried out, suggesting the 3 peptides can inhibit the platelet aggregation induced by thrombin. This study showed computer-aided peptide inhibitor design can be a robust method for finding potential binders for thrombin, which provided solutions for anticoagulation.

Rational design of peptide inhibitors targeting HSP90-CDC37 protein-protein interaction.

Background: Specifically blocking HSP90-CDC37 interaction is emerging as a prospective strategy for cancer therapy. Aim: Applying akinase pseudopeptide rationale to the discovery of HSP90-CDC37 protein-protein interaction (PPI) inhibitors. Methods: Pseudosubstrates were identified through sequence alignment and evaluated by biolayer interferometry assay, co-immunoprecipitation assay and antiproliferation assay. Results: TAT-DDO-59120 was identified to disrupt HSP90-CDC37 PPI through directly binding to HSP90, both extracellularly and intracellularly. In addition, the identified peptide showed ideal antiproliferative activity against thecolorectal cancer cellHCT116 (IC50=12.82μM). Conclusion: Compared with the traditional method of screening a large compound library to identify PPI inhibitors, this method is rapid and efficient with strong purpose, which provides a novel strategy for designing HSP90-CDC37 PPI inhibitors.

Biomimetic Design of Peptide Inhibitor to Block CD47/SIRPα Interactions.

CD47 on the surface of tumor cells has become a research hot spot in immunotherapy and anticancer therapy, as it can bind to SIRPα protein on the surface of macrophages, which ultimately leads to immune escape of tumor cells. In the present study, molecular interactions between CD47 and human SIRPα proteins (including variant 1, V1 and variant 2, V2) were analyzed through molecular dynamics (MD) simulation and the molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) method. Hydrophobic interactions were found as the main driving force for the binding of CD47 on SIRPα. The residues including pyroglutamate acid (Z)1, L2, E35, Y37, E97, L101, and T102 of CD47 were identified with a significant favorable contribution to the binding of CD47 on SIRPα (both V1 and V2). Based on this, a peptide inhibitor library with the sequence ZLXRTLXEXY was designed (X represents the arbitrary residue of 20 standard amino acids) and then screened using molecular docking, MD simulations, and experimental validation. Finally, a peptide ZLIRTLHEWY was determined with high affinity with SIRPα from 8000 candidates, containing 6/10 residues favorable for the binding on SIRPα V1 and 8/10 residues favorable for the binding on SIRPα V2, which was thus considered to have potential anticancer function.

Rational design peptide inhibitors of Cyclophilin D as a potential treatment for acute pancreatitis.

Cyclophilin D (CypD) is a mitochondrial matrix peptidyl prolidase that regulates the mitochondrial permeability transition pore. Inhibition of CypD was suggested as a therapeutic strategy for acute pancreatitis. Peptide inhibitors emerged as novel binding ligand for blocking receptor activity. In this study, we present our computational approach for designing peptide inhibitors of CypD. The 3-D structure of random peptides were built, and docked into the active center of CypD using Rosetta script integrated FlexPepDock module. The peptide displayed the lowest binding energy against CypD was further selected for virtual iterative mutation based on virtual mutagenesis and molecular docking. Finally, the top 5 peptides with the lowest binding energy was selected for validating their affinity against CypD using inhibitory assay. We showed 4 out of the selected 5 peptides were capable for blocking the activity of CypD, while WACLQ display the strongest affinity against CypD, which reached 0.28 mM. The binding mechanism between WACLQ and CypD was characterized using molecular dynamics simulation. Here, we proved our approach can be a robust method for screening peptide inhibitors.

Designing Cyclic-Constrained Peptides to Inhibit Human Phosphoglycerate Dehydrogenase.

Although loop epitopes at protein-protein binding interfaces often play key roles in mediating oligomer formation and interaction specificity, their binding sites are underexplored as drug targets owing to their high flexibility, relatively few hot spots, and solvent accessibility. Prior attempts to develop molecules that mimic loop epitopes to disrupt protein oligomers have had limited success. In this study, we used structure-based approaches to design and optimize cyclic-constrained peptides based on loop epitopes at the human phosphoglycerate dehydrogenase (PHGDH) dimer interface, which is an obligate homo-dimer with activity strongly dependent on the oligomeric state. The experimental validations showed that these cyclic peptides inhibit PHGDH activity by directly binding to the dimer interface and disrupting the obligate homo-oligomer formation. Our results demonstrate that loop epitope derived cyclic peptides with rationally designed affinity-enhancing substitutions can modulate obligate protein homo-oligomers, which can be used to design peptide inhibitors for other seemingly intractable oligomeric proteins.

Computer-Aided Designing Peptide Inhibitors of Human Hematopoietic Prostaglandin D2 Synthase Combined Molecular Docking and Molecular Dynamics Simulation.

Human hematopoietic prostaglandin D2 synthase (HPGDS) is involved in the production of prostaglandin D2, which participates in various physiological processes, including inflammation, allergic reactions, and sleep regulation. Inhibitors of HPGDS have been investigated as potential anti-inflammatory agents. For the investigation of potent HPGDS inhibitors, we carried out a computational modeling study combining molecular docking and molecular dynamics simulation for selecting and virtual confirming the designed binders. We selected the structure of HPGDS (PDB ID: 2CVD) carrying its native inhibitor compound HQL as our research target. The random 5-mer peptide library was created by building the 3-D structure of random peptides using Rosetta Buildpeptide and performing conformational optimization. Molecular docking was carried out by accommodating the peptides into the location of their native binder and then conducting docking using FlexPepDock. The two peptides RMYYY and VMYMI, which display the lowest binding energy against HPGDS, were selected to perform a comparative study. The interaction of RMYYY and VMYMI against HPGDS was further confirmed using molecular dynamics simulation and aligned with its native binder, HQL. We show the selected binders to have stronger binding energy and more frequent interactions against HPGDS than HQL. In addition, we analyzed the solubility, hydrophobicity, charge, and bioactivity of the generated peptides, and we show that the selected strong binder may be further used as therapeutic drugs.

In silico mutagenesis-based designing of oncogenic SHP2 peptide to inhibit cancer progression

Cancer is among the top causes of death, accounting for an estimated 9.6 million deaths in 2018, it appeared that approximately 500,000 people die from cancer in the United States alone annually. The SHP2 plays a major role in regulation of cell growth, proliferation, and differentiation, and functional upregulation of this enzyme is linked to oncogenesis and developmental disorders. SHP2 activity has been linked to several cancer types for which no drugs are currently available. In our study, we aimed to design peptide inhibitors against the SHP2 mutant. The crystal structure of the human Src SH2-PQpYEEIPI peptide mutant was downloaded from the protein databank. We generated several peptides from the native wild peptide using an in silico mutagenesis method, which showed that changes (P302W, Y304F, E306Q, and Q303A) might boost the peptide's affinity for binding to SHP2. Furthermore, the dynamical stability and binding affinities of the mutated peptide were confirmed using Molecular dynamics simulation and Molecular Mechanics with Generalized Born and Surface Area Solvation free energy calculations. The proposed substitution greatly enhanced the binding affinity at the residue level, according to a study that decomposed energy into its component residues. Our proposed peptide may prevent the spread of cancer by inhibiting SHP2, according to our detailed analyses of binding affinities.

Cyclic di-peptide in situ inhibited protein-aggregation

An increasing number of neurodegenerative diseases seem to be associated with protein misfolding that often leads to misfolded protein aggregates with a β-sheet conformation and accumulation in the brain which directly contributes to or modulates the associated pathology. Protein aggregation diseases like Huntington’s disease results from the deposition of aggregated huntingtin proteins within the nucleus, transmissible prion encephalopathies occur due to extracellular deposition of pathogenic prion proteins whereas Alzheimer’s disease from the accumulation of both extracellular β-amyloid and intracellular hyperphosphorylated tau protein aggregates. In the generalized purpose, we have taken the core sequence of amyloid-β (responsible for its aggregation) as the aggregating peptide (AP). Among the various emerging therapeutic approaches against aggregation-related degenerative diseases such as diminishing the monomeric precursor protein, inhibiting aggregation, or blocking aggregation-induced cellular toxicity pathways, we focussed on the strategy based on the inhibition of protein aggregation using rationally designed peptide inhibitors comprising both the recognition and β-breaking component in the sequence. The “O → N acyl migration” concept was used to form cyclic peptide in situ for the generation of a bent unit which may act as disruption moiety for the inhibition process. The kinetics of aggregation was characterized by various biophysical tools (ThT-assay, TEM, CD, and FTIR). Results implied that the designed inhibitor peptides (IP) might be valuable to inhibit all the related aggregated peptides.