A Comprehensive Review on Current Advances in Peptide Drug Development and Design

pACPs-DNN: Predicting anticancer peptides using novel peptide transformation into evolutionary and structure matrix-based images with self-attention deep learning model

Globally, cancer remains a major health challenge due to its high mortality rates. Traditional experimental approaches and therapies are resource-intensive and often cause significant side effects. Anticancer peptides (ACPs) have emerged as alternative therapeutic agents owing to their selectivity, safety, and potential to mitigate drug resistance. In this paper, we propose pACPs-DNN, a novel attention mechanism-based deep learning model developed for the accurate prediction of ACPs and non-ACPs. The pACPs-DNN model transforms input peptides into image representations using residue-wise energy contact matrix (RECM), substitution Matrix Representation (SMR), and Position Specific Scoring Matrix (PSSM) embeddings, followed by local binary pattern (LBP)-based decomposition to capture enhanced structural and local semantic features. These transformations generate novel feature sets, including RECM_LBP, LBP_SMR, and LBP_PSSM. Subsequently, a two-tier feature selection approach is employed to identify a high-ranking optimal feature set, which is then used to train an attention-based deep neural network. The proposed pACPs-DNN model achieves an impressive training accuracy of 96.91 % and an AUC of 0.98. To evaluate its generalization capability, the model was validated on independent datasets, demonstrating significant improvements of 5 % and 3.5 % in accuracy over existing models on the Ind-I and Ind-II datasets, respectively. The demonstrated efficacy and robustness of pACPs-DNN highlight its potential as a valuable tool for advancing drug discovery and academic research in cancer-related therapeutic development.

Inhibiting Neuropilin-1 as a novel therapeutic approach to mitigate pulmonary fibrosis: Highlighting the potential anti-fibrotic effects of ATWLPPR peptide

Latent transforming growth factor 1 (TGF-β1) is present in substantial quantities in healthy tissues. The regulation of TGF-β1 under fibrotic conditions predominantly relies on mechanisms that activate latent TGF-β1 to its active/mature form rather than its secretion and synthesis. Consequently, one strategy for mitigating pulmonary fibrosis involves targeting activation of the master cytokine TGF-β1 from its latent form. Recent evidence suggests Neuropilin-1 (NRP-1) is an activator of TGF-β1 with significant biological functions, including acting as a co-receptor for TGF-β1 and facilitating divergent Smad signaling in favor of fibrogenesis. In the present study, we conducted an initial preclinical investigation to validate NRP-1 as a potential target in fibrosis, utilizing a specific NRP-1 inhibitor, ATWLPPR (A7R). A7R is a heptapeptide that specifically binds to NRP-1 to inhibit its activity. This study was performed using a 21-day model of bleomycin-induced pulmonary fibrosis in Swiss Albino mice. Our results demonstrated that the inhibition of NRP-1 by A7R exhibited promising anti-fibrotic activity. We found that A7R downregulated the expression of epithelial-mesenchymal transition proteins. A7R demonstrated significant potential to attenuate the expression of TGF-β1 and its downstream Smad signaling through substantial inhibition of NRP-1. Additionally, we observed a reduction in collagen deposition after A7R treatment. Notably, the anti-fibrotic effect of A7R was comparable to that of pirfenidone. In conclusion, our study demonstrated that the NRP-1 specific inhibitor A7R, exhibits considerable potential to attenuate pulmonary fibrosis, although further comprehensive investigations are required.

Peptide-Based Strategies in PLGA-Enhanced Tumor Therapy

Peptide-based therapeutics have gained attention in cancer treatment because of their good specificity, low toxicity, and ability to modulate immune responses. However, challenges such as enzymatic degradation and poor bioavailability limit their clinical application. Peptide-functionalized poly(lactic-co-glycolic acid) (PLGA) systems have emerged as a transformative platform in cancer therapy that offers unique advantages, including enhanced stability, sustained release, and precise delivery of therapeutic agents. This review highlights the synergistic integration of peptides with PLGA and addresses key challenges of peptide-based therapeutics. The application of peptide-functionalized PLGA systems encompasses a diverse range of strategies for cancer therapy. In chemotherapy, peptides disrupt critical tumor pathways, induce apoptosis, and inhibit angiogenesis, demonstrating their versatility in targeting various aspects of tumor progression. In immunotherapy, peptides act as antigens to stimulate robust immune responses or as immune checkpoint inhibitors to restore T cell activity, overcoming tumor immune evasion. These systems also harness the enhanced permeability and retention effect, facilitating preferential accumulation in tumor tissues while leveraging tumor microenvironment (TME)-responsive mechanisms, such as pH-sensitive or enzyme-triggered drug release, to achieve controlled, localized delivery. Collectively, peptide-functionalized PLGA systems represent a promising, versatile approach for precise cancer therapy that integrates innovative delivery strategies with highly specific, potent therapeutic agents.

AI-Assisted Protein-Peptide Complex Prediction in a Practical Setting

Accurate prediction of protein-peptide complex structures plays a critical role in structure-based drug design, including antibody design. Most peptide-docking benchmark studies were conducted using crystal structures of protein-peptide complexes; as such, the performance of the current peptide docking tools in the practical setting is unknown. Here, the practical setting implies there are no crystal or other experimental structures for the complex, nor for the receptor and peptide. In this work, we have developed a practical docking protocol that incorporated two famous machine learning models, AlphaFold 2 for structural prediction and ANI-2x for ab initio potential prediction, to achieve a high success rate in modeling protein-peptide complex structures. The docking protocol consists of three major stages. In the first stage, the 3D structure of the receptor is predicted by AlphaFold 2 using the monomer mode, and that of the peptide is predicted by AlphaFold 2 using the multimer mode. We found that it is essential to include the receptor information to generate a high-quality 3D structure of the peptide. In the second stage, rigid protein-peptide docking is performed using ZDOCK software. In the last stage, the top 10 docking poses are relaxed and refined by ANI-2x in conjunction with our in-house geometry optimization algorithm-conjugate gradient with backtracking line search (CG-BS). CG-BS was developed by us to more efficiently perform geometry optimization, which takes the potential and force directly from ANI-2x machine learning models. The docking protocol achieved a very encouraging performance for a set of 62 very challenging protein-peptide systems which had an overall success rate of 34% if only the top 1 docking poses were considered. This success rate increased to 45% if the top 3 docking poses were considered. It is emphasized that this encouraging protein-peptide docking performance was achieved without using any crystal or experimental structures.

Dual-Site Targeting by Peptide Inhibitors of the N-Terminal Domain of Hsp90: Mechanism and Design

Heat shock protein 90 (Hsp90) is a pivotal molecular chaperone crucial in the maturation of client proteins, positioning it as a significant target for cancer therapy. However, the design of effective Hsp90 inhibitors presents substantial challenges due to the complex interaction network and the requisite specificity of the inhibitors. This study tackles the task of designing peptide inhibitors capable of concurrently binding to both the ATP-binding pocket and the Cdc37-binding site within the N-terminal domain of Hsp90. In response to these challenges, we developed an advanced peptide screening protocol that merges machine learning with various molecular simulation techniques to boost the identification and optimization of potent inhibitors. Our integrated approach employs a convolutional neural network-based framework to predict peptide binding propensities. This predictive model is augmented by comprehensive molecular docking and dynamic simulations to assess the stability and interaction dynamics of Hsp90/peptide complexes. We successfully identified three heptapeptides that demonstrate the ability to interact with both binding sites, effectively obstructing the entrance to the ATP-binding pocket. This study elucidates the inhibitory mechanisms of these peptides, paves the way for the development of more efficacious therapeutic agents targeting Hsp90, and underscores the value of integrating machine learning techniques with molecular modeling in the peptide design process.

AMPEC4: Naja ashei Venom-Derived Peptide as a Stimulator of Fibroblast Migration with Antibacterial Activity

The treatment of proctological conditions, including hemorrhoids, anal fissures, and perianal abscesses, is often complicated by bacterial infections, particularly those involving multidrug-resistant Escherichia coli. This study presents the synthesis, characterization, and biological evaluation of the newly designed synthetic peptide AMPEC4, inspired by cytotoxin 5 from Naja ashei snake venom. AMPEC4 demonstrated potent antimicrobial properties with MIC values of 100 and 200 µg/mL, effectively inhibiting biofilm formation (up to 84%) and eradicating the pre-formed biofilm by up to 35%. The antibacterial activity of AMPEC4 was further supported by a membrane permeabilization assay, demonstrating its capacity to disrupt bacterial membrane integrity in a dose-dependent manner. Furthermore, AMPEC4 significantly promoted fibroblast migration, a critical step in tissue regeneration, while exhibiting notable biocompatibility, as evidenced by the absence of hemolytic, cytotoxic, and genotoxic effects. By addressing both infection control and tissue regeneration, AMPEC4 represents a promising therapeutic strategy for managing chronic wounds, particularly in the challenging environment of the anorectal region. Its ability to target Escherichia coli reference and clinical strains while accelerating the wound-healing process underscores its potential for future clinical applications.

Therapeutic peptides: chemical strategies fortify peptides for enhanced disease treatment efficacy

Therapeutic peptides, as a unique form of medication composed of orderly arranged sequences of amino acids, are valued for their high affinity, specificity, low immunogenicity, and economical production costs. Currently, more than 100 peptides have already secured market approval. Over 150 are actively undergoing clinical trials, while an additional 400-600 are in the preclinical research stage. Despite this, their clinical application is limited by factors such as salt sensitivity, brief residence in the bloodstream, inadequate cellular uptake, and high structural flexibility. By employing suitable chemical methods to modify peptides, it is possible to regulate important physicochemical factors such as charge, hydrophobicity, conformation, amphiphilicity, and sequence that affect the physicochemical properties and biological activity of peptides. This can overcome the inherent deficiencies of peptides, enhance their pharmacokinetic properties and biological activity, and promote continuous progress in the field of research. A diverse array of modified peptides is currently being developed and investigated across numerous therapeutic fields. Drawing on the latest research, this review encapsulates the essential physicochemical factors and significant chemical modification strategies that influence the properties and biological activity of peptides as pharmaceuticals. It also assesses how physicochemical factors affect the application of peptide drugs in disease treatment and the effectiveness of chemical strategies in disease therapy. Concurrently, this review discusses the prospective advancements in therapeutic peptide development, with the goal of offering guidance for designing and optimizing therapeutic peptides and to delve deeper into the therapeutic potential of peptides for disease intervention.

Asymmetric Intramolecular α-Arylation of Polar Amino Acids Bearing β-Leaving Groups

The α-arylation of amino acids may be achieved by intramolecular nucleophilic aromatic substitution (SNAr) reactions of amino-acid derived enolates, but for amino acids bearing β-leaving groups, such reactions are complicated by competing E1cB elimination of the β-substituent. In this paper we report an approach to the arylation of the polar amino acids serine, cysteine, diaminopropionic acid, and allothreonine by inducing intramolecular SNAr reactions of heterocycles, which the heteroatom substituent is stereoelectronically protected from elimination by incorporating it into the ring system of N-carbamoyl oxazolidines, thiazolidines, or imidazolidines. The sequence comprises the diastereoselective formation of a heterocyclic urea followed by an intramolecular N-to-C aryl migration, yielding bicyclic hydantoins that can be further hydrolysed to afford quaternary α-aryl amino acids. The method is practical and scalable, avoids the use of transition metals or chiral auxiliaries, and provides the opportunity to access a variety of α-arylated products bearing electronically diverse benzenoid or heterocyclic substituents (35 examples).

Engineering RiPP pathways: strategies for generating complex bioactive peptides

Historically, natural products have been essential sources of therapeutic agents, many of which are currently used to manage various diseases. In recent years, ribosomally synthesized and post-translationally modified peptides (RiPPs) have garnered considerable interest in drug discovery and development due to their biosynthetic plasticity and their ability to generate diverse bioactive structural scaffolds. Unfortunately, many RiPPs have suboptimal bioavailability and proteolytic stability, significantly limiting their clinical potential. Moreover, the complexity of RiPP structures makes total synthesis extremely difficult. These drawbacks necessitate pathway engineering to create derivatives with potentially optimized physicochemical properties. Herein, we review recent efforts to surmount pathway engineering challenges and to rationally modify components of RiPP pathways for new functions to derive new bioactive analogs.

A fast and efficient virtual screening and identification strategy for helix peptide binders based on finDr webserver: A case study of bovine serum albumin (BSA)

Peptides offer unique advantages, including strong specificity, rapid action, and low side effects, making them a prominent focus in the development of new drugs and functional materials. However, the rapid and efficient screening and identification of high-affinity peptides for specific targets remains a significant challenge. In this study, we successfully screened 12-helix candidate peptides using bovine serum albumin (BSA) as the target protein, employing the computer-aided peptide virtual screening webserver finDr. Among the top five candidate peptides, we identified E4-TP2 (GVATVVARLFLL) as the peptide capable of binding BSA with high affinity constant (KD = 39.4 nM), confirmed through an in vitro molecular interaction instrument. The interaction mode of the peptide-BSA complex was analyzed using Ligplot software, revealing that the primary interactions involved hydrophobic forces and hydrogen bonds. Additionally, molecular dynamics simulations further elucidated the molecular mechanisms underlying the high-affinity peptide interactions, the results demonstrated that the complex exhibited good conformational stability and strong binding free energy (MM/PBSA: -21.075 ± 5.471 kJ/mol). In conclusion, the finDr virtual screening strategy and the molecular interaction identification method employed in this study provide a robust technical approach for the rapid and efficient acquisition of high-affinity binding peptides for target proteins of interest.

ANG-Modified Liposomes Coloaded With α-Melittin and Resveratrol Induce Apoptosis and Pyroptosis in Glioblastoma Cells by Impeding Wnt/β-Catenin Signaling

MAIN PROBLEM

Glioblastoma (GB) is one of the most prevalent and devastating types of brain cancer for which efficient treatments are currently lacking because of limitations such as antitumor efficacy, brain delivery, tumor selectivity, and drug resistance. A promising strategy to overcome these obstacles is developing anticancer agents that can be delivered to GB tissues to inhibit tumors with low toxicity to normal brain tissue.

METHODS

We developed liposomes encapsulating resveratrol (RES), a polyphenolic compound, and α-melittin (α-MEL), which is composed of melittin conjugated with an amphiphilic α-helical peptide at its N-terminus. RES-, α-MEL-, and α-MEL-RES-loaded liposomes (Lips) were modified with Angiopep-2 (ANG). The effects of the above liposomes on GB cells were assessed, and the possible mechanisms were analyzed.

RESULTS

ANG-modified α-MEL-RES-Lips treatment facilitated the passage of these agents through the blood-brain barrier (BBB), increased tumor targeting, and significantly reduced α-MEL-associated hemolysis. The combined management of α-MEL with RES impeded GB cell growth and prolonged the lifespan of GB tumor-bearing model mice. α-MEL-RES-Lips treatment triggered GB cell apoptosis and induced pyroptosis-associated protein expressions of gasdermin-D (GSDMD), gasdermin E (GSDME), cleaved caspase 3, and NLR family pyrin domain containing 3 (NLRP3), and inhibited epithelial-mesenchymal transition (EMT) by modulating the Wnt/β-catenin signaling pathway.

CONCLUSION

ANG-modified α-MEL-RES-Lips might be a potential nanosystem for GB therapy, and polyphenolic compounds combined with antimicrobial peptides may promote the induction of apoptosis, pyroptosis, and the apoptosis-pyroptosis switch in GB.

Harnessing marine antimicrobial peptides for novel therapeutics: A deep dive into ocean-derived bioactives

Marine antimicrobial peptides (AMPs) are potent bioactive compounds with broad-spectrum activity against bacteria, viruses, and fungi, offering a promising alternative to traditional antibiotics. These small, cationic, and amphiphilic peptides (3-50 amino acids) are key components of marine organisms' immune defenses, adapted to harsh oceanic environments. Discovered in the 1980s, marine AMPs have garnered interest for their unique structures and potential applications in human health. However, despite the ocean's vast biodiversity, they remain underexplored compared to land-based AMPs. This review emphasizes the therapeutic potential of marine AMPs, including their modes of action, structural variety, and applications in drug development, tissue regeneration, and cancer treatment. Moreover, it discusses their antibacterial, antiviral, antifungal, and antiparasitic properties. Additionally, the review addresses strategies to enhance the therapeutic potential of marine AMPs and the challenges associated with their development. By examining the promising future of marine AMPs, this review aims to pave the way for new approaches to combat antimicrobial resistance and develop innovative treatments for various infectious diseases. The potential of marine AMPs as the "medicine bank of the new millennium" remains vast, providing a valuable resource for future drug discovery and sustainable practices across industries.

Peptide-based drugs in immunotherapy: current advances and future prospects

In immunotherapy, peptide-based medications are showing great promise as a new class of therapies that can be used to treat autoimmune diseases, cancer, and other immune-related conditions. Peptides are being created for use in immunotherapy as vaccines, immunological modulators, and adjuvants because of their capacity to precisely alter immune responses. They can imitate endogenous signals or interact with immune cells, improving the body's capacity to identify and combat malignancies or reestablishing immunological tolerance in autoimmune disorders. Notably, peptide-based treatments have demonstrated promise in promoting tumor-specific immune responses and improving the effectiveness of already available immunotherapies, such as immune checkpoint inhibitors. Notwithstanding its potential, peptide-based medications' clinical translation is fraught with difficulties, such as those pertaining to immunogenicity, bioavailability, and peptide stability. Overcoming these obstacles has been made possible by developments in peptide engineering, including pharmacokinetic optimization, receptor-binding affinity enhancement, and the creation of innovative delivery systems. The targeted distribution and effectiveness of peptide medications can be improved by using liposomes, nanoparticles, and other delivery methods, increasing their therapeutic utility. With an emphasis on recent scientific developments, mechanisms of action, and therapeutic uses, this review examines the present status of peptide-based medications in immunotherapy. We also look at the obstacles that still need to be overcome in order to get peptide-based treatments from the lab to the clinic and offer suggestions for future research initiatives. By tackling these important problems, we hope to demonstrate how peptide-based medications have the ability to revolutionize immunotherapeutic treatment approaches.

Engineering the Bioactive Profile of Medicinal Peptides by Multiarm Polyethylene Glycol Conjugation

PEGylation plays a crucial role in peptide modification and has been widely applied in the field of biomedicine, demonstrating significant potential for enhancing peptide drug performance. Herein, we synthesized melittin peptides modified with single arm, double arm, and four arm of PEG12, utilizing lysine side chains as branching points, to systematically investigate the effects of multiarm PEGylation on toxicity, hemolytic activity, stability, and membrane-disrupting ability. Our results revealed that increasing the number of PEG arms significantly reduced the cytotoxicity and hemolytic activity of melittin (with IC50 increasing approximately 20-fold) while simultaneously enhancing serum stability. These effects were attributed to the improved water solubility and altered hydrophilicity/hydrophobicity balance at the N-terminus, which modulated the interactions with cell membranes and reduced the membrane penetration capacity. Meanwhile, the steric hindrance effect that was caused by multiarm PEG modification prevented the destruction of cell membranes by melittin. The strategy of terminal PEGylation was expected to minimize systemic toxicity and in vivo degradation. Collectively, our findings highlight the critical role of the topological structure PEG in fine-tuning peptide drug performance, providing valuable insights for the design of safer and more effective peptide-based therapeutics.

Taming Inert B-H Bond with Low Energy Light: A Near-Infrared Light-Induced Approach to Facile Carborane Cluster-Amino Acid Coupling

The selective functionalization of inert B-H bonds in carborane clusters has been a formidable challenge. Recent advances have witnessed such reactions through photoredox methods utilizing ultraviolet or visible light irradiation. However, high-energy light sources often suffer from poor energy efficiency, a limited substrate scope, undesired side reactions, and low scalability. Here, we present the first successful B-H bond functionalization under low-energy near-infrared (NIR) light using a carborane-based electron donor-acceptor complex. Both photophysical investigations and theoretical modeling reveal a facile single-electron transfer from the carborane cage to the electron-deficient photocatalyst, generating a carborane cage radical under NIR light irradiation. The follow-up radical pathway enables the direct coupling of carboranes with amino acids or oligopeptides, yielding a diverse array of carborane-functionalized amino acids or oligopeptides. Beyond expanding the known chemical space of boron cluster derivatives, we further demonstrate that carborane-based amino acids with imaging and targeting capabilities could serve as promising multifunctional boron carriers for boron neutron capture therapy. Thus, the selective B-H bond functionalization of the carboranes via NIR light not only provides a straightforward and practical strategy in boron cluster synthetic chemistry but also lays the foundation for the development of next-generation boron-containing biomolecules and advanced functional materials.

Tripeptides inhibit dual targets AChE and BACE-1: a computational study

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, with amyloid-beta (Aβ) plaques and acetylcholine deficits being central pathological features. Inhibition of dual targets including acetylcholinesterase (AChE) and beta-site amyloid precursor protein cleaving enzyme 1 (BACE-1) represents a promising strategy to address cholinergic deficits and amyloid pathology. In this study, we used computational approaches to evaluate 8000 tripeptides as potential dual inhibitors of AChE and BACE-1. Machine learning models revealed the four top-lead tripeptides including WHM, HMW, WMH, and HWM. Molecular docking simulations indicated that WHM possessed the most favorable interactions through hydrogen bonds, π-π stacking, and salt bridges with key catalytic residues in both enzymes. Molecular dynamics simulations confirmed the stability of the protein-ligand complexes, with WHM exhibiting the most consistent conformations and significant disruption of catalytic residue geometries. Free energy perturbation analysis further supported WHM's superior stability across both targets. ADMET predictions suggested moderate oral absorption and limited brain penetration, consistent with the typical behavior of peptide-based compounds. Overall, WHM demonstrated the strongest potential as a dual inhibitor of AChE and BACE-1, offering a promising lead for future therapeutic development in AD.

UMPPI: Unveiling Multilevel Protein-Peptide Interaction Prediction via Language Models

Protein-peptide interactions are essential to cellular processes and disease mechanisms. Identifying protein-peptide binding residues is critical for understanding peptide function and advancing drug discovery. However, experimental methods are costly and time-intensive, while existing computational approaches often predict interactions or binding residues separately, lack effective feature integration, or rely heavily on limited high-quality structural data. To address these challenges, we propose UMPPI (Unveiling Multilevel Protein-Peptide Interaction), a multiobjective framework based on the pretrained protein language model ESM2. UMPPI simultaneously predicts binary protein-peptide interactions and binding residues on both peptides and proteins through a multiobjective optimization strategy. By integrating ESM2 to encode sequences and extract latent structural information, UMPPI bridges the gap between sequence-based and structure-based methods. Extensive experiments demonstrated that UMPPI successfully captured binary interactions between peptides and proteins and identified the binding residues on peptides and proteins. UMPPI can serve as a useful tool for protein-peptide interaction prediction and identification of critical binding residues, thereby facilitating the peptide drug discovery process.

Main methods and tools for peptide development based on protein-protein interactions (PPIs)

Protein-protein interactions (PPIs) regulate essential physiological and pathological processes. Due to their large and shallow binding surfaces, PPIs are often considered challenging drug targets for small molecules. Peptides offer a viable alternative, as they can bind these targets, acting as regulators or mimicking interaction partners. This review focuses on competitive peptides, a class of orthosteric modulators that disrupt PPI formation. We provide a concise yet comprehensive overview of recent advancements in in-silico peptide design, highlighting computational strategies that have improved the efficiency and accuracy of PPI-targeting peptides. Additionally, we examine cutting-edge experimental methods for evaluating PPI-based peptides. By exploring the interplay between computational design and experimental validation, this review presents a structured framework for developing effective peptide therapeutics targeting PPIs in various diseases.

Peptide Multifunctionalization via Modular Construction of Trans-AB2C Porphyrin on Resin

Peptide multifunctionalization is a crucial technique to develop peptide-based agents for various purposes. Porphyrin-peptide conjugates are a class of popular multifunctional peptides renowned for their multifunctional and multimodal properties. However, the tedious synthetic works for porphyrin building blocks are involved in most previous studies. In this work, a modular solid-phase synthetic approach is reported to construct trans-AB2C porphyrin on peptide chains without presynthesized porphyrin building blocks. The products from this approach, which inherit both functionalities from the porphyrins and the modules employed for constructing porphyrins, show potential in biomedical and biomaterial applications. Furthermore, by extending this synthetic approach, the first example of "resin-to-resin" reaction is reported to link two peptides together along the construction of porphyrin motifs to give porphyrin-peptide conjugates with two different peptide chains.

Peptide classification landscape: An in-depth systematic literature review on peptide types, databases, datasets, predictors architectures and performance

Peptides are gaining significant attention in diverse fields such as the pharmaceutical market has seen a steady rise in peptide-based therapeutics over the past six decades. Peptides have been utilized in the development of distinct applications including inhibitors of SARS-COV-2 and treatments for conditions like cancer and diabetes. Distinct types of peptides possess unique characteristics, and development of peptide-specific applications require the discrimination of one peptide type from others. To the best of our knowledge, approximately 230 Artificial Intelligence (AI) driven applications have been developed for 22 distinct types of peptides, yet there remains significant room for development of new predictors. A Comprehensive review addresses the critical gap by providing a consolidated platform for the development of AI-driven peptide classification applications. This paper offers several key contributions, including presenting the biological foundations of 22 unique peptide types and categorizes them into four main classes: Regulatory, Therapeutic, Nutritional, and Delivery Peptides. It offers an in-depth overview of 47 databases that have been used to develop peptide classification benchmark datasets. It summarizes details of 288 benchmark datasets that are used in development of diverse types AI-driven peptide classification applications. It provides a detailed summary of 197 sequence representation learning methods and 94 classifiers that have been used to develop 230 distinct AI-driven peptide classification applications. Across 22 distinct types peptide classification tasks related to 288 benchmark datasets, it demonstrates performance values of 230 AI-driven peptide classification applications. It summarizes experimental settings and various evaluation measures that have been employed to assess the performance of AI-driven peptide classification applications. The primary focus of this manuscript is to consolidate scattered information into a single comprehensive platform. This resource will greatly assist researchers who are interested in developing new AI-driven peptide classification applications.

Targeting Hepatic Stellate Cells for the Prevention and Treatment of Liver Cirrhosis and Hepatocellular Carcinoma: Strategies and Clinical Translation

Hepatic stellate cells (HSC) are the major source of myofibroblasts (MFB) in fibrosis and cancer- associated fibroblasts (CAF) in both primary and metastatic liver cancer. Over the past few decades, there has been significant progress in understanding the cellular and molecular mechanisms by which liver fibrosis and HCC occur, as well as the key roles of HSC in their pathogenesis. HSC-targeted approaches using specific surface markers and receptors may enable the selective delivery of drugs, oligonucleotides, and therapeutic peptides that exert optimized anti-fibrotic and anti-HCC effects. Recent advances in omics, particularly single-cell sequencing and spatial transcriptomics, hold promise for identifying new HSC targets for diagnosing and treating liver fibrosis/cirrhosis and liver cancer.

Protein-Peptide Docking with ESMFold Language Model

Designing peptide therapeutics requires precise peptide docking, which remains a challenge. We assessed the ESMFold language model, originally designed for protein structure prediction, for its effectiveness in protein-peptide docking. Various docking strategies, including polyglycine linkers and sampling-enhancing modifications, were explored. The number of acceptable-quality models among top-ranking results is comparable to traditional methods and generally lower than AlphaFold-Multimer or Alphafold 3, though ESMFold surpasses it in some cases. The combination of result quality and computational efficiency underscores ESMFold's potential value as a component in a consensus approach for high-throughput peptide design.

Virally mediated expression of a biologically active peptide to restrain the nuclear functions of ERK1/2 attenuates learning extinction but not acquisition

Peptide drug technologies offer powerful approaches to develop potent and selective lead molecules for therapeutic and research applications. However, new and optimized delivery approaches are necessary to overcome current pitfalls including fast degradation in cells and tissue. Extracellular signal-regulated kinases 1/2 (ERK1/2) exemplifies proteins that play crucial and varied roles within distinct cellular compartments. Here, we established an innovative method, based on viral vectors, which utilizes the endogenous biogenesis of neurotrophins to deliver and express a biologically active peptide to attenuate specifically ERK1/2 nuclear functions in specific brain area of the adult forebrain. In contrast to our hypothesis, nuclear functions of ERK1/2 in the forebrain are fundamental for the extinction of associative-aversive memories, but not for acquisition, nor for retrieval of these memories. Our research demonstrates the feasibility and applicability of viral vectors to deliver a peptide of interest to manipulate specific molecular processes and/or protein interactions in specific tissue.

A Novel D-peptide modulates DCLK1 Gelsolin interactions, reducing PDAC tumor growth

What drives inflammation-associated tumorigenesis and progression in pancreatic ductal adenocarcinoma (PDAC)? Doublecortin-like kinase 1 (DCLK1) is a central driver of inflammation-associated tumorigenesis, with elevated expression linked to worse clinical outcomes. Isoform 4, which lacks microtubule-binding domains but contains a unique extracellular domain (ECD), plays a pivotal role in tumor progression. We identified novel D-peptides that selectively target this ECD, significantly suppressing PDAC cell proliferation in vitro and tumor growth in xenograft models without inducing cell death. In silico modeling and binding assays revealed DCLK1 isoform 4 interacts with pro-tumorigenic proteins like plasma gelsolin (pGSN), with D-peptides modulating these interactions. These findings underscore DCLK1's non-kinase functions as a therapeutic target and highlight novel avenues for developing precision treatments aimed at halting cancer progression and improving patient outcomes.

Drug delivery strategy of hemostatic drugs for intracerebral hemorrhage

Intracerebral hemorrhage (ICH) is associated with high rates of mortality and disability, underscoring an urgent need for effective therapeutic interventions. The clinical prognosis of ICH remains limited, primarily due to the absence of targeted, precise therapeutic options. Advances in novel drug delivery platforms, including nanotechnology, gel-based systems, and exosome-mediated therapies, have shown potential in enhancing ICH management. This review delves into the pathophysiological mechanisms of ICH and provides a thorough analysis of existing treatment strategies, with an emphasis on innovative drug delivery approaches designed to address critical pathological pathways. We assess the benefits and limitations of these therapies, offering insights into future directions in ICH research and highlighting the transformative potential of next-generation drug delivery systems in improving patient outcomes.

Tau degradation in Alzheimer's disease: Mechanisms and therapeutic opportunities

In Alzheimer's disease (AD), tau undergoes abnormal post-translational modifications and aggregations. Impaired intracellular degradation pathways further exacerbate the accumulation of pathological tau. A new strategy - targeted protein degradation - recently emerged as a modality in drug discovery where bifunctional molecules bring the target protein close to the degradation machinery to promote clearance. Since 2016, this strategy has been applied to tau pathologies and attracted broad interest in academia and the pharmaceutical industry. However, a systematic review of recent studies on tau degradation mechanisms is lacking. Here we review tau degradation mechanisms (the ubiquitin-proteasome system and the autophagy-lysosome pathway), their dysfunction in AD, and tau-targeted degraders, such as proteolysis-targeting chimeras and autophagy-targeting chimeras. We emphasize the need for a continuous exploration of tau degradation mechanisms and provide a future perspective for developing tau-targeted degraders, encouraging researchers to work on new treatment options for AD patients. HIGHLIGHTS: Post-translational modifications, aggregation, and mutations affect tau degradation. A vicious circle exists between impaired degradation pathways and tau pathologies. Ubiquitin plays an important role in complex degradation pathways. Tau-targeted degraders provide promising strategies for novel AD treatment.

PET Imaging of Solid Tumors with a G-Quadruplex-Targeting 18F-Labeled Peptide Probe

Positron emission tomography (PET) is a common imaging technique and can provide accurate information about the size, shape, and location of tumors. Recent evidence has shown that G-quadruplex structures (G4s) are identified in human oncogenes, and these special structures are recognized as diagnostic cancer markers and drug targets for anticancer therapies. Although a number of techniques for in vivo imaging of G4s have been developed, achieving sufficient sensitivity and selectivity in vivo remains challenging. Herein, we have engineered and developed a radiolabeled peptide probe [18F]AlF-NOTA-RHAU18 targeting mitochondrial DNA G4s for in vivo PET imaging. The results of the study indicate that this probe is able to visualize and detect solid tumors in living homozygous mice. In addition, the distribution of the probe in cancer cells was investigated using FITC-RHAU18. This work may offer new insights into the development of cancer diagnostic tools by targeting in vivo G4s.

Computational Modeling of Pharmaceuticals with an Emphasis on Crossing the Blood-Brain Barrier

The discovery and development of new pharmaceutical drugs is a costly, time-consuming, and highly manual process, with significant challenges in ensuring drug bioavailability at target sites. Computational techniques are highly employed in drug design, particularly to predict the pharmacokinetic properties of molecules. One major kinetic challenge in central nervous system drug development is the permeation through the blood-brain barrier (BBB). Several different computational techniques are used to evaluate both BBB permeability and target delivery. Methods such as quantitative structure-activity relationships, machine learning models, molecular dynamics simulations, end-point free energy calculations, or transporter models have pros and cons for drug development, all contributing to a better understanding of a specific characteristic. Additionally, the design (assisted or not by computers) of prodrug and nanoparticle-based drug delivery systems can enhance BBB permeability by leveraging enzymatic activation and transporter-mediated uptake. Neuroactive peptide computational development is also a relevant field in drug design, since biopharmaceuticals are on the edge of drug discovery. By integrating these computational and formulation-based strategies, researchers can enhance the rational design of BBB-permeable drugs while minimizing off-target effects. This review is valuable for understanding BBB selectivity principles and the latest in silico and nanotechnological approaches for improving CNS drug delivery.

Development and applications of enzymatic peptide and protein ligation

Chemical synthesis of complex peptides and proteins continues to play increasingly important roles in industry and academia, where strategies for covalent ligation of two or more peptide fragments to produce longer peptides and proteins in convergent manners have become critical. In recent decades, efficient and site-selective ligation strategies mediated by exploiting the biocatalytic capacity of nature's diverse toolkit (i.e., enzymes) have been widely recognized as a powerful extension of existing chemical strategies. In this review, we present a chronological overview of the development of proteases, transpeptidases, transglutaminases, and ubiquitin ligases. We survey the different properties between the ligation reactions of various enzymes, including the selectivity and efficiency of the reaction, the ligation "scar" left in the product, the type of amide bond formed (natural or isopeptide), the synthetic availability of the reactants, and whether the enzymes are orthogonal to another. This review also describes how the inherent specificity of these enzymes can be exploited for peptide and protein ligation.

Reactivity of amino acids and short peptide sequences: identifying bioactive compounds via DFT calculations

Bioactive peptides are short amino acid sequences that play important roles in various physiological processes, including antioxidant and protective effects. These compounds can be obtained through protein hydrolysis and have a wide range of potential applications in a variety of areas. However, despite the potential of these compounds, more in-depth knowledge is still necessary to better understand details regarding their chemical reactivity and electronic properties. In this study, we used molecular modeling techniques to investigate the electronic structure of isolated amino acids (AA) and short peptide sequences. Details on the relative alignments between the frontier electronic levels, local chemical reactivity and donor-acceptor properties of the 20 primary amino acids and some di- and tripeptides were evaluated in the framework of the density functional theory (DFT). Our results suggest that the electronic properties of isolated amino acids can be used to interpret the reactivity of short sequences. We found that aromatic and charged amino acids, as well as Methionine, play a key role in determining the local reactivity of peptides, in agreement with experimental data. Our analyses also allowed us to identify the influence of the relative position of AA and terminations on the local reactivity of the sequences, which can guide experimental studies and help to propose/evaluate possible mechanisms of action. In summary, our data indicate that the position of active sites of polypeptides can be predicted from short sequences, providing a promising strategy for the synthesis and bioprospection of new optimized compounds.

Fingerprint Profile Analysis of Eupolyphaga steleophaga Polypeptide Based on UHPLC-MS and Its Application

Background and Objectives: As a medicinal and food homologous substance, Eupolyphaga steleophaga is renowned for its potential health benefits, including anti-tumor effects, immune system support, and anti-inflammatory properties. Eupolyphaga steleophaga polypeptides have demonstrated significant biological activity, including the regulation of coagulation and lipid metabolism. However, the peptide composition of Eupolyphaga steleophaga requires further clarification to facilitate quality control improvements and a deeper investigation into its pharmacological effects. Therefore, this study aimed to simulate the digestive absorption process of Eupolyphaga steleophaga following oral administration and identify its enzymatic components to enhance quality control. Methods: The digestive absorption process was simulated using artificial gastric fluid and pepsin. A fingerprinting method based on ultrahigh-performance liquid chromatography-mass spectrometry (UHPLC-MS)(Acquire UPLC-Synapt G2-Si HDMS, Waters Corporation, Milford, MA, USA) was developed to identify 63 enzymatic components. The enzymolysis polypeptide fingerprint detection method was used to analyze 10 batches of Eupolyphaga steleophaga sourced from Harbin No. 4 Traditional Chinese Medicine Factory. Chromatographic collection was performed using an ACQUITY UPLC BHE C18 column. Gradient elution was carried out using a mixture of 0.1% formic acid with acetonitrile and 0.1% formic acid with water, with an average flow rate of 0.3 mL/min, a column temperature of 40 °C, and an injection volume of 2 μL. The mass spectrometry (MS) conditions were set as follows: the ion source was operated in positive electrospray ionization (ESI+) mode, with a capillary voltage of 2.8 kV and a sampling cone voltage of 40 V. The ion-source temperature was maintained at 110 °C, while the desolvation temperature was set to 400 °C. The cone gas flow rate was 50 L/h, and the desolvation gas flow rate was 800 L/h. The range for the collection of mass-to-charge ratios (m/z) was between 50 and 1200. Results: The UHPLC-MS method demonstrated high accuracy, repeatability, and stability, successfully identifying 63 enzymatic components of Eupolyphaga steleophaga. Furthermore, polypeptide markers for 63 selected components were identified in all 10 batches of Eupolyphaga steleophaga medicinal materials. This approach was validated by including numerical values such as retention times and peak areas, confirming its reliability for quality control enhancement. Conclusions: This novel UHPLC-MS approach serves as a powerful tool for advancing quality control strategies in veterinary medicine, particularly for animal-derived medicines. It lays a solid foundation for subsequent pharmacological studies of Eupolyphaga steleophaga polypeptides, offering a more reliable means to explore their biological activities and therapeutic potential.

Target-specific peptides for BK virus agnoprotein identified through phage display screening: advancing antiviral therapeutics

BK virus is implicated in polyomavirus-associated nephropathy (PVAN) and hemorrhagic cystitis, particularly in kidney transplant recipients, affecting the functionality of the transplanted kidney and posing a risk of graft loss. Despite these challenges, specific antiviral drugs targeting BK virus remain elusive. Agnoprotein, a small, positively charged protein encoded by the BK virus late gene, functions in the assembly, maturation, and release of the virus. Consequently, agnoprotein emerges as a promising target for potential anti-BK virus drugs. Utilizing phage display technology, we conducted screening to identify specific binding peptides against the agnoprotein. The primary objective of screening binding peptides is to utilize them to disrupt the virus's life cycle, impeding its replication and transmission, thereby achieving antiviral effects. In the current experimental study, a combination of phage 7 peptide libraries and 12 peptide libraries was employed for screening purposes. Following four rounds of screening, seven positive phages demonstrating the ability to bind Agnoprotein were successfully isolated. Following ELISA validation, it was observed that the optical density (OD) values for Agnoprotein binding of the seven positive clones significantly exceeded three times the value of the negative control (NC). Subsequent analysis identified one 7-peptide and six 12-peptides within the binding peptides. Moreover, OD values of dodecapeptide phage clones bound to agnoprotein were generally higher than those of heptapeptide phage clones.In conclusion, our study demonstrates the successful identification of specific binding peptides against agnoprotein, a crucial component in the BK virus life cycle.

A bird's-eye view of the biological mechanism and machine learning prediction approaches for cell-penetrating peptides

Cell-penetrating peptides (CPPs) are highly effective at passing through eukaryotic membranes with various cargo molecules, like drugs, proteins, nucleic acids, and nanoparticles, without causing significant harm. Creating drug delivery systems with CPP is associated with cancer, genetic disorders, and diabetes due to their unique chemical properties. Wet lab experiments in drug discovery methodologies are time-consuming and expensive. Machine learning (ML) techniques can enhance and accelerate the drug discovery process with accurate and intricate data quality. ML classifiers, such as support vector machine (SVM), random forest (RF), gradient-boosted decision trees (GBDT), and different types of artificial neural networks (ANN), are commonly used for CPP prediction with cross-validation performance evaluation. Functional CPP prediction is improved by using these ML strategies by using CPP datasets produced by high-throughput sequencing and computational methods. This review focuses on several ML-based CPP prediction tools. We discussed the CPP mechanism to understand the basic functioning of CPPs through cells. A comparative analysis of diverse CPP prediction methods was conducted based on their algorithms, dataset size, feature encoding, software utilities, assessment metrics, and prediction scores. The performance of the CPP prediction was evaluated based on accuracy, sensitivity, specificity, and Matthews correlation coefficient (MCC) on independent datasets. In conclusion, this review will encourage the use of ML algorithms for finding effective CPPs, which will have a positive impact on future research on drug delivery and therapeutics.

Insight into Protein Engineering: From In silico Modelling to In vitro Synthesis

Protein engineering alters the polypeptide chain to obtain a novel protein with improved functional properties. This field constantly evolves with advanced in silico tools and techniques to design novel proteins and peptides. Rational incorporating mutations, unnatural amino acids, and post-translational modifications increases the applications of engineered proteins and peptides. It aids in developing drugs with maximum efficacy and minimum side effects. Currently, the engineering of peptides is gaining attention due to their high stability, binding specificity, less immunogenic, and reduced toxicity properties. Engineered peptides are potent candidates for drug development due to their high specificity and low cost of production compared with other biologics, including proteins and antibodies. Therefore, understanding the current perception of designing and engineering peptides with the help of currently available in silico tools is crucial. This review extensively studies various in silico tools available for protein engineering in the prospect of designing peptides as therapeutics, followed by in vitro aspects. Moreover, a discussion on the chemical synthesis and purification of peptides, a case study, and challenges are also incorporated.

TPepPro: a deep learning model for predicting peptide-protein interactions

MOTIVATION

Peptides and their derivatives hold potential as therapeutic agents. The rising interest in developing peptide drugs is evidenced by increasing approval rates by the FDA of USA. To identify the most potential peptides, study on peptide-protein interactions (PepPIs) presents a very important approach but poses considerable technical challenges. In experimental aspects, the transient nature of PepPIs and the high flexibility of peptides contribute to elevated costs and inefficiency. Traditional docking and molecular dynamics simulation methods require substantial computational resources, and the predictive accuracy of their results remain unsatisfactory.

RESULTS

To address this gap, we proposed TPepPro, a Transformer-based model for PepPI prediction. We trained TPepPro on a dataset of 19,187 pairs of peptide-protein complexes with both sequential and structural features. TPepPro utilizes a strategy that combines local protein sequence feature extraction with global protein structure feature extraction. Moreover, TPepPro optimizes the architecture of structural featuring neural network in BN-ReLU arrangement, which notably reduced the amount of computing resources required for PepPIs prediction. According to comparison analysis, the accuracy reached 0.855 in TPepPro, achieving an 8.1% improvement compared to the second-best model TAGPPI. TPepPro achieved an AUC of 0.922, surpassing the second-best model TAGPPI with 0.844. Moreover, the newly developed TPepPro identify certain PepPIs that can be validated according to previous experimental evidence, thus indicating the efficiency of TPepPro to detect high potential PepPIs that would be helpful for amino acid drug applications.

AVAILABILITY AND IMPLEMENTATION

The source code of TPepPro is available at https://github.com/wanglabhku/TPepPro.

Target-Specific De Novo Peptide Binder Design with DiffPepBuilder

Despite the exciting progress in target-specific de novo protein binder design, peptide binder design remains challenging due to the flexibility of peptide structures and the scarcity of protein-peptide complex structure data. In this study, we curated a large synthetic data set, referred to as PepPC-F, from the abundant protein-protein interface data and developed DiffPepBuilder, a de novo target-specific peptide binder generation method that utilizes an SE(3)-equivariant diffusion model trained on PepPC-F to codesign peptide sequences and structures. DiffPepBuilder also introduces disulfide bonds to stabilize the generated peptide structures. We tested DiffPepBuilder on 30 experimentally verified strong peptide binders with available protein-peptide complex structures. DiffPepBuilder was able to effectively recall the native structures and sequences of the peptide ligands and to generate novel peptide binders with improved binding free energy. We subsequently conducted de novo generation case studies on three targets. In both the regeneration test and case studies, DiffPepBuilder outperformed AfDesign and RFdiffusion coupled with ProteinMPNN, in terms of sequence and structure recall, interface quality, and structural diversity. Molecular dynamics simulations confirmed that the introduction of disulfide bonds enhanced the structural rigidity and binding performance of the generated peptides. As a general peptide binder de novo design tool, DiffPepBuilder can be used to design peptide binders for given protein targets with three-dimensional and binding site information.

Secretoglobin 3A2 peptides have therapeutic potential for allergic airway inflammation

Three isoforms of secretoglobin (SCGB) 3A2, namely type A, B, and C, are endogenously produced through alternative splicing. SCGB3A2 type A, the correctly spliced major type, begins to be expressed from embryonic day 11.5 in mice and shows various physiological activities such as promoting lung maturation and bronchial branching, anti-inflammatory effects, and ameliorating induced pulmonary fibrosis. To investigate the potential of SCGB3A2 peptides as a therapeutic to treat respiratory diseases, in this study, serially overlapping nine peptides were synthesized to cover the entire type C isoform, and five and one peptides covering the C-terminal region of type A and B, respectively. To evaluate their biological activities, each peptide was subjected to cell proliferation and apoptosis analyses in vitro using mouse lung fibroblast-derived MLg cells, bronchial branching rate using ex vivo mouse fetal lung organ cultures, and in vivo allergic airway inflammation mouse model. Among type A and C peptides, those corresponding to the C-terminal region of the SCGB3A2 sequence exhibited its unique biological activities of promoting cell proliferation and bronchial branching, and/or inhibiting apoptosis. The type B peptide did not show any proliferative effect while inhibited apoptosis. In a mouse model of allergic airway inflammation, lung inflammation was improved by the administration of most of the C-terminal region-derived type A and type C peptides. The results suggest that the bioactivity resides towards the C-terminal region of SCGB3A2 sequence, and the peptides covering this region could be used as a therapeutic in treating lung inflammation.

MNSFβ promotes LPS-induced TNFα expression by increasing the localization of RC3H1 to stress granules, and the interfering peptide HEPN2 reduces TNFα production by disrupting the MNSFβ-RC3H1 interaction in macrophages

Abnormally elevated tumor necrosis factor-α (TNFα) levels at the maternal-fetal interface can lead to adverse pregnancy outcomes, including recurrent miscarriage (RM), but the mechanism underlying upregulated TNFα expression is not fully understood. We previously reported that the interaction between monoclonal nonspecific suppressor factor-β (MNSFβ) and RC3H1 upregulates TNFα expression, but the precise mechanisms are unknown. In this study, we found that MNSFβ stimulated the LPS-induced TNFα expression by inactivating the promoting effect of RC3H1 on TNFα mRNA degradation rather than directly inhibiting the expression of RC3H1 in THP1-Mϕs. Mechanistically, the 81-326 aa region of the RC3H1 protein binds to the 101-133 aa region of the MNSFβ protein, and MNSFβ facilitated stress granules (SGs) formation and the translocation of RC3H1 to SGs by interacting with RC3H1 and fragile X mental retardation 1 (FMR1) in response to LPS-induced stress. The SGs-localization of RC3H1 reduced its inhibitory effect on TNFα expression in LPS-treated THP1-Mϕs. The designed HEPN2 peptide effectively reduced the LPS-induced expression of TNFα in THP1-Mϕs by interfering with the MNSFβ-RC3H1 interaction. Treatment with the HEPN2 peptide significantly improved adverse pregnancy outcomes, including early pregnancy loss (EPL) and lower fetal weight (LFW), which are induced by LPS in mice. These data indicated that MNSFβ promoted TNFα expression at least partially by increasing the localization of RC3H1 to SGs under inflammatory stimulation and that the HEPN2 peptide improved the adverse pregnancy outcomes induced by LPS in mice, suggesting that MNSFβ is a potential pharmacological target for adverse pregnancy outcomes caused by abnormally increased inflammation at early pregnancy.

Unraveling the hemolytic toxicity tapestry of peptides using chemical space complex networks

Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hemotoxicity tapestry of peptides. CSNs are powerful tools for visualizing and analyzing the relationships between peptides based on their physicochemical properties and structural features. We constructed CSNs from the StarPepDB database, encompassing 2,004 hemolytic peptides, and explored the impact of seven different (dis)similarity measures on network topology and cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing the motif discovery and enrichment process. We identified 12 consensus hemolytic motifs, whose amino acid composition unveiled a high abundance of lysine, leucine, and valine residues, whereas aspartic acid, methionine, histidine, asparagine, and glutamine were depleted. Additionally, physicochemical properties were used to characterize clusters/communities of hemolytic peptides. To predict hemolytic activity directly from peptide sequences, we constructed multi-query similarity searching models, which outperformed cutting-edge machine learning-based models, demonstrating robust hemotoxicity prediction capabilities. Overall, this novel in silico approach uses complex network science as its central strategy to develop robust model classifiers, characterize the chemical space, and discover new motifs from hemolytic peptides. This will help to enhance the design/selection of peptides with potential therapeutic activity and low toxicity.

Preclinical evaluation of a new technetium-99m labeled neurotensin analogue for NTSR1 targeted radionuclide imaging

Neurotensin is a regulatory peptide that can act as a growth factor on different types of normal and cancerous cells. Binding of Neurotensin to relevant receptors leads to cell proliferation, survival, migration and invasion by changing intracellular enzyme activity. Therefore, the design of a neurotensin-based radiopeptide plays an important role in targeted imaging or therapy of neurotensin receptor-positive tumors. A [Lys8]-neurotensin (7-13) peptide was synthesized and attached to HYNIC as a chelator via a linker. The labeling procedure was carried out at 100 °C for 10 min using 99mTc as a radionuclide and EDDA/tricine as coligands. Stability of the labeled peptide in human serum was determined using RTLC and HPLC methods. The receptor binding internalization was studied using HT-29 colon carcinoma cells, and tissue biodistribution was evaluated in mice bearing CT-26 tumors. The [99mTc]Tc-Tricine/EDDA/HYNIC-GABA-[Lys8]-neurotensin (7-13) peptide demonstrated a labeling yield of over 98 %, a specific activity of 37.00 GBq/µmol, high stability in human serum, a nanomolar range of Kd, and a tumor uptake of 0.36 ± 0.15 % ID/g at 1-h post-injection. These results suggest that the labeled peptide is a suitable imaging agent for neurotensin receptor-positive tumors.

Accurate and transferable drug-target interaction prediction with DrugLAMP

MOTIVATION

Accurate prediction of drug-target interactions (DTIs), especially for novel targets or drugs, is crucial for accelerating drug discovery. Recent advances in pretrained language models (PLMs) and multi-modal learning present new opportunities to enhance DTI prediction by leveraging vast unlabeled molecular data and integrating complementary information from multiple modalities.

RESULTS

We introduce DrugLAMP (PLM-assisted multi-modal prediction), a PLM-based multi-modal framework for accurate and transferable DTI prediction. DrugLAMP integrates molecular graph and protein sequence features extracted by PLMs and traditional feature extractors. We introduce two novel multi-modal fusion modules: (i) pocket-guided co-attention (PGCA), which uses protein pocket information to guide the attention mechanism on drug features, and (ii) paired multi-modal attention (PMMA), which enables effective cross-modal interactions between drug and protein features. These modules work together to enhance the model's ability to capture complex drug-protein interactions. Moreover, the contrastive compound-protein pre-training (2C2P) module enhances the model's generalization to real-world scenarios by aligning features across modalities and conditions. Comprehensive experiments demonstrate DrugLAMP's state-of-the-art performance on both standard benchmarks and challenging settings simulating real-world drug discovery, where test drugs/targets are unseen during training. Visualizations of attention maps and application to predict cryptic pockets and drug side effects further showcase DrugLAMP's strong interpretability and generalizability. Ablation studies confirm the contributions of the proposed modules.

AVAILABILITY AND IMPLEMENTATION

Source code and datasets are freely available at https://github.com/Lzcstan/DrugLAMP. All data originate from public sources.

Unnatural Amino Acids: Strategies, Designs, and Applications in Medicinal Chemistry and Drug Discovery

Peptides can operate as therapeutic agents that sit within a privileged space between small molecules and larger biologics. Despite examples of their potential to regulate receptors and modulate disease pathways, the development of peptides with drug-like properties remains a challenge. In the quest to optimize physicochemical parameters and improve target selectivity, unnatural amino acids (UAAs) have emerged as critical tools in peptide- and peptidomimetic-based drugs. The utility of UAAs is illustrated by clinically approved drugs such as methyldopa, baclofen, and gabapentin in addition to small drug molecules, for example, bortezomib and sitagliptin. In this Perspective, we outline the strategy and deployment of UAAs in FDA-approved drugs and their targets. We further describe the modulation of the physicochemical properties in peptides using UAAs. Finally, we elucidate how these improved pharmacological parameters and the role played by UAAs impact the progress of analogs in preclinical stages with an emphasis on the role played by UAAs.

Predicting the Solubility of Amino Acids and Peptides with the SAFT-γ Mie Approach: Neutral and Charged Models

Modeling approaches that can be used to predict accurately the solubility of amino acids and peptides are of interest for the design of new pharmaceutical processes and in the development of new peptide-based therapeutics. We investigate the capability of the SAFT-γ Mie group-contribution approach to predict the aqueous and alcohol solubility of glycine, alananine, valine, leucine, and serine and of di- and tripeptides containing these amino acids. New SAFT-γ Mie group interactions are characterized using experimental thermodynamic and phase-equilibrium data of compounds and mixtures that contain groups relevant to the amino acids and peptides, but no solubility data (except for the case of glycine). Once all the group interaction parameters are developed, predictive solid-liquid solubility calculations are carried out. Neutral and charged models are considered to account explicitly for the zwitterionic nature of the molecules in aqueous solution, and the solubility of the solution is presented as a function of pH. A detailed discussion of the molecular models and Helmholtz free-energy expressions used to represent the ionic and zwitterionic forms of the amino acids, together with their speciation in solution is also provided. Overall, very good agreement with available data is shown, with an absolute average deviation (AAD) in mole fraction of 0.0038 over 283 solubility data points for the amino acids studied and an AAD in mole fraction of 0.02128 over 141 peptide-solubility points when the systems are studied at their isoelectric point and neutral models are used. The solubility as a function of pH for a range of temperatures is also predicted accurately when charged models are incorporated. These results confirm the predictive accuracy of the SAFT-γ Mie method and pave the way for future studies involving larger peptides.

Recent Advances in Peptide Drug Discovery: Novel Strategies and Targeted Protein Degradation

Recent technological advancements, including computer-assisted drug discovery, gene-editing techniques, and high-throughput screening approaches, have greatly expanded the palette of methods for the discovery of peptides available to researchers. These emerging strategies, driven by recent advances in bioinformatics and multi-omics, have significantly improved the efficiency of peptide drug discovery when compared with traditional in vitro and in vivo methods, cutting costs and improving their reliability. An added benefit of peptide-based drugs is the ability to precisely target protein-protein interactions, which are normally a particularly challenging aspect of drug discovery. Another recent breakthrough in this field is targeted protein degradation through proteolysis-targeting chimeras. These revolutionary compounds represent a noteworthy advancement over traditional small-molecule inhibitors due to their unique mechanism of action, which allows for the degradation of specific proteins with unprecedented specificity. The inclusion of a peptide as a protein-of-interest-targeting moiety allows for improved versatility and the possibility of targeting otherwise undruggable proteins. In this review, we discuss various novel wet-lab and computational multi-omic methods for peptide drug discovery, provide an overview of therapeutic agents discovered through these cutting-edge techniques, and discuss the potential for the therapeutic delivery of peptide-based drugs.

Discovery of Highly Bioactive Peptides through Hierarchical Structural Information and Molecular Dynamics Simulations

Peptide drugs play an essential role in modern therapeutics, but the computational design of these molecules is hindered by several challenges. Traditional methods like molecular docking and molecular dynamics (MD) simulation, as well as recent deep learning approaches, often face limitations related to computational resource demands, complex binding affinity assessments, extensive data requirements, and poor model interpretability. Here, we introduce PepHiRe, an innovative methodology that utilizes the hierarchical structural information in peptide sequences and employs a novel strategy called Ladderpath, rooted in algorithmic information theory, to rapidly generate and enhance the efficiency and clarity of novel peptide design. We applied PepHiRe to develop BH3-like peptide inhibitors targeting myeloid cell leukemia-1, a protein associated with various cancers. By analyzing just eight known bioactive BH3 peptide sequences, PepHiRe effectively derived a hierarchy of subsequences used to create new BH3-like peptides. These peptides underwent screening through MD simulations, leading to the selection of five candidates for synthesis and subsequent in vitro testing. Experimental results demonstrated that these five peptides possess high inhibitory activity, with IC50 values ranging from 28.13 ± 7.93 to 167.42 ± 22.15 nM. Our study explores a white-box model driven technique and a structured screening pipeline for identifying and generating novel peptides with potential bioactivity.

Tau Protein and Tauopathies: Exploring Tau Protein-Protein and Microtubule Interactions, Cross-Interactions and Therapeutic Strategies

Tau, a microtubule-associated protein (MAP), is essential to maintaining neuronal stability and function in the healthy brain. However, aberrant modifications and pathological aggregations of Tau are implicated in various neurodegenerative disorders, collectively known as tauopathies. The most common Tauopathy is Alzheimer's Disease (AD) counting nowadays more than 60 million patients worldwide. This comprehensive review delves into the multifaceted realm of Tau protein, puzzling out its intricate involvement in both physiological and pathological roles. Emphasis is put on Tau Protein-Protein Interactions (PPIs), depicting its interaction with tubulin, microtubules and its cross-interaction with other proteins such as Aβ1-42, α-synuclein, and the chaperone machinery. In the realm of therapeutic strategies, an overview of diverse possibilities is presented with their relative clinical progresses. The focus is mostly addressed to Tau protein aggregation inhibitors including recent small molecules, short peptides and peptidomimetics with specific focus on compounds that showed a double anti aggregative activity on both Tau protein and Aβ amyloid peptide. This review amalgamates current knowledge on Tau protein and evolving therapeutic strategies, providing a comprehensive resource for researchers seeking to deepen their understanding of the Tau protein and for scientists involved in the development of new peptide-based anti-aggregative Tau compounds.

Analytical considerations for characterization of generic peptide product: A regulatory insight

The Peptide therapeutics market was evaluated to be around USD 45.67 BN in 2023 and is projected to witness massive growth at a CAGR of around 5.63 % from 2024 to 2032 (USD 80.4 BN). Generic peptides are expected to reach USD 27.1 billion by 2032 after the patent monopoly of the pioneer peptides expires, and generic peptides become accessible. The generic manufacturers are venturing into peptide-based therapeutics for the aforementioned reasons. There is an abundance of material accessible regarding the characterization of peptides, which can be quite confusing for researchers. The FDA believes that an ANDA applicant may now demonstrate that the active component in a proposed generic synthetic peptide drug product is the "same" as the active ingredient in a peptide of rDNA origin that has previously been approved. To ensure the efficacy, safety, and quality of peptide therapies during development, regulatory bodies demand comprehensive characterization utilizing several orthogonal methodologies. This article elaborates the peptide characterization by segmenting into different segments as per the critical quality attribute from identification of the peptide to the physicochemical property of the peptide therapeutics which will be required to demonstrate the sameness with reference product based on the size of the peptide chain and molecular weight of the peptides. Article insights briefly on each individual technique and the orthogonal techniques for each test were explained. The impurities requirements in the generic peptides as per the regulatory requirement were also discussed.

Engineering peptide drug therapeutics through chemical conjugation and implication in clinics

The development of peptide drugs has made tremendous progress in the past few decades because of the advancements in modification chemistry and analytical technologies. The novel-designed peptide drugs have been modified through various biochemical methods with improved diagnostic, therapeutic, and drug-delivery strategies. Researchers found it a helping hand to overcome the inherent limitations of peptides and bring continued advancements in their applications. Furthermore, the emergence of peptide-drug conjugates (PDCs)-utilizes target-oriented peptide moieties as a vehicle for cytotoxic payloads via conjugation with cleavable chemical agents, resulting in the key foundation of the new era of targeted peptide drugs. This review summarizes the various classifications of peptide drugs, suitable chemical modification strategies to improve the ADME (adsorption, distribution, metabolism, and excretion) features of peptide drugs, and recent (2015-early 2024) progress/achievements in peptide-based drug delivery systems as well as their fruitful implication in preclinical and clinical studies. Furthermore, we also summarized the brief description of other types of PDCs, including peptide-MOF conjugates and peptide-UCNP conjugates. The principal aim is to provide scattered and diversified knowledge in one place and to help researchers understand the pinching knots in the science of PDC development and progress toward a bright future of novel peptide drugs.

Therapeutic peptides in the treatment of digestive inflammation: Current advances and future prospects

Digestive inflammation is a widespread global issue that significantly impacts quality of life. Recent advances have highlighted the unique potential of therapeutic peptides for treating this condition, owing to their specific bioactivity and high specificity. By specifically targeting key proteins involved in the pathological process and modulating biomolecular functions, therapeutic peptides offer a novel and promising approach to managing digestive inflammation. This review explores the development history, pharmacological characteristics, clinical applications, and regulatory mechanisms of therapeutic peptides in treating digestive inflammation. Additionally, the review addresses pharmacokinetics and quality control methods of therapeutic peptides, focusing on challenges such as low bioavailability, poor stability, and difficulties in delivery. The role of modern biotechnologies and nanotechnologies in overcoming these challenges is also examined. Finally, future directions for therapeutic peptides and their potential impact on clinical applications are discussed, with emphasis placed on their significant role in advancing medical and therapeutic practices.

Protein-peptide binding residue prediction based on protein language models and cross-attention mechanism

Accurate identifications of protein-peptide binding residues are essential for protein-peptide interactions and advancing drug discovery. To address this problem, extensive research efforts have been made to design more discriminative feature representations. However, extracting these explicit features usually depend on third-party tools, resulting in low computational efficacy and suffering from low predictive performance. In this study, we design an end-to-end deep learning-based method, E2EPep, for protein-peptide binding residue prediction using protein sequence only. E2EPep first employs and fine-tunes two state-of-the-art pre-trained protein language models that can extract two different high-latent feature representations from protein sequences relevant for protein structures and functions. A novel feature fusion module is then designed in E2EPep to fuse and optimize the above two feature representations of binding residues. In addition, we have also design E2EPep+, which integrates E2EPep and PepBCL models, to improve the prediction performance. Experimental results on two independent testing data sets demonstrate that E2EPep and E2EPep + could achieve the average AUC values of 0.846 and 0.842 while achieving an average Matthew's correlation coefficient value that is significantly higher than that of existing most of sequence-based methods and comparable to that of the state-of-the-art structure-based predictors. Detailed data analysis shows that the primary strength of E2EPep lies in the effectiveness of feature representation using cross-attention mechanism to fuse the embeddings generated by two fine-tuned protein language models. The standalone package of E2EPep and E2EPep + can be obtained at https://github.com/ckx259/E2EPep.git for academic use only.