Search Results (112)

Bioactive peptides constitute a highly diverse and therapeutically relevant molecular class, yet their systematic exploration remains challenging because of the vast size, heterogeneity, and fragmented annotation of peptide chemical space. In this context, complex networks have emerged as a complementary computational framework for organizing, analyzing, and exploiting peptide diversity. This methodological review examines the main components of graph-based peptide informatics, from graph-based data integration and curated repositories to descriptor-based representations, similarity-driven network construction, and topology-informed analysis. We describe how peptide sequences can be projected into multidimensional reference spaces using molecular descriptors, aggregation operators, and unsupervised feature selection, and how these representations support the construction of Chemical Space Networks, Half-Space Proximal Networks, and Metadata Networks. Special attention is given to topological analysis, including threshold selection, community detection, and centrality-based identification of representative peptides and scaffolds. We also review the development of Multi-query Similarity Searching Models as training-independent, topology-guided alternatives to conventional supervised predictors. Finally, we highlight the implementation of these methodologies in computational resources such as StarPepDB, StarPep Toolbox, and StarPepWeb, which illustrate the transition of peptide network science from conceptual workflows to accessible, scalable, and reproducible infrastructures. Overall, complex networks are presented as a mature and interpretable paradigm for the structured exploration, analysis, and discovery of bioactive peptides. Full article

Animal venoms represent an extraordinary, yet largely untapped, biochemical reservoir for oncological drug discovery. This review provides a comprehensive analysis of venom-derived enzyme inhibitors as emerging anticancer agents, emphasizing their chemical diversity, structure–activity relationships (SAR), molecular targets, and mechanistic pathways. Venom-derived peptides and proteins exhibit exceptional binding affinity and structural rigidity, characteristics frequently enforced by conserved disulfide networks. This specific architecture allows them to selectively modulate critical cancer-associated enzymes, including matrix metalloproteinases, phospholipases A₂, serine proteases, and kinases. Inhibiting these highly specific targets successfully disrupts tumour angiogenesis, extracellular matrix remodelling, and metastatic dissemination, while simultaneously inducing apoptosis through unique pathways such as reactive oxygen species generation. Modern computational approaches, encompassing deep learning algorithms, molecular docking, and molecular dynamics simulations, are substantially accelerating and transforming the discovery pipeline by rapidly mapping intricate peptide–receptor interactions and guiding rational drug design. Translating these potent molecules into clinical therapeutics remains heavily challenged by pharmacokinetic instability, rapid proteolytic degradation, and systemic toxicity. The integration of computationally optimized scaffolds with advanced targeted delivery platforms, such as nanocarriers and liposomal encapsulation, offers a highly viable strategy to overcome these barriers, ultimately paving the way for next-generation, venom-inspired cancer therapies. Full article

Protein–protein interactions (PPIs) represent a vast and largely underexplored landscape of therapeutic targets, yet their structural features—including large, flat, and dynamic interfaces—have historically limited their druggability. In this context, cyclic peptides have emerged as a powerful class of PPI modulators, sitting at the interface between biologics and small molecules, and thus garnering key advantages of both classes. Their conformational constraint enhances binding affinity, proteolytic stability and, in some instances, cell permeability, thus enabling access to intracellular targets. This review provides an updated overview of cyclic peptides as modulators of PPIs, focusing on both conceptual foundations and practical strategies for their discovery and optimization. The main discovery approaches include natural sources, de novo design based on secondary structure mimetics, high-throughput screening, and computational approaches. Integration of these complementary strategies is crucial to enhance success rates in the discovery of effective and developable cyclic peptides. Accordingly, the present review aims to provide a practical guide for researchers entering this rapidly growing field, outlining current opportunities, methodological advances, and remaining challenges in the development of cyclic peptide-based PPI modulators. Full article

Small open reading frames (sORFs) encode micropeptides, which are a promising yet largely untapped resource for creating peptide design templates. Owing to their concise nature and functional efficiency, micropeptides often rely on essential structural elements and brief linear motifs, such as domains for membrane interaction, targeting sequences, and sites for protein–protein interactions, to fulfill their biological functions. This inherent simplicity makes them particularly suitable for a bottom-up design approach aimed at identifying, extracting, and systematically refining functional motifs to develop novel bioactive peptides. This review addresses the critical question of how micropeptides, particularly those involved in tumor regulation, can be explored as emerging therapeutic targets, functional templates for peptide design, and potential future therapeutic agents, by synthesizing current understanding of their mechanisms, functional significance in cancer, and the computational and design strategies for their clinical translation. We examined the current methods for analyzing the sequence and structural characteristics that underpin their functional activity and investigated how these attributes can be leveraged for drug discovery and design. Finally, we underscore the primary challenges and future prospects in converting sORF-encoded micropeptides into clinically relevant molecules with the aim of broadening the current scope of the druggable proteome. Full article

The majority of disease-associated proteins are considered “undruggable” due to the absence of well-defined binding pockets, the presence of extended interaction surfaces, and intrinsic structural disorder, which collectively limit the effectiveness of conventional small molecules and biologics. Representative examples include KRAS, p53, and c-MYC. Peptide therapeutics, particularly macrocyclic peptides, occupy a unique chemical space capable of targeting such recalcitrant protein–protein interactions (PPIs) where small molecules often fail. However, traditional peptide discovery, which relies heavily on high-throughput screening, is labor-intensive and frequently yields candidates with suboptimal pharmacological properties. The integration of artificial intelligence has begun to transform peptide discovery from a largely empirical process into a rational and design-driven paradigm. Modern deep learning approaches, including diffusion-based generative models, enable the de novo design of peptide binders with high affinity and structural precision, even for disordered or previously intractable targets. In this perspective, we highlight key structural and biological challenges associated with undruggable proteins and consider how peptide-based modalities are beginning to overcome these longstanding barriers. We further explore how advances in artificial intelligence and computational modeling may reshape the rational design of next-generation peptide therapeutics and propose an integrated experimental–computational framework to facilitate the development of clinically actionable candidates. Full article

Background/Objectives: Cyclotides are plant-derived macrocyclic peptides distinguished by their head-to-tail cyclized backbone and cystine knot motif, which confer remarkable stability against thermal, enzymatic, and chemical degradation. These features, combined with a compact and rigid structure, position cyclotides as promising scaffolds for future antibacterial agents in response to the escalating threat of multidrug-resistant (MDR) pathogens and the stagnation of conventional antibiotic discovery pipelines. This review summarizes the structural features, antibacterial mechanisms, bioengineering strategies, and translational potential of cyclotides against MDR infections. Methods: A narrative review of the literature was conducted using recent original research articles and reviews on cyclotide structure, antibacterial activity, bioengineering, computational modeling, and pharmaceutical applications. Results: Cyclotides exhibit potent antimicrobial activity, primarily through membrane disruption mediated by amphipathic surfaces and affinity for anionic bacterial membranes. Some variants also demonstrate anti-virulence and antibiofilm properties, broadening their therapeutic relevance for difficult-to-treat infections. Bioengineering approaches, including epitope grafting and rational design, have improved selectivity and potency while reducing cytotoxicity. Advances in computational modeling, molecular dynamics, and artificial intelligence have accelerated the prediction and optimization of antimicrobial activity, toxicity, and pharmacokinetic properties. Conclusions: Innovations in synthesis, including recombinant expression and enzymatic ligation, are helping overcome translational barriers related to cost and scalability. Although challenges remain in oral bioavailability and systemic delivery, strategies such as lipidation and scaffold modification support the development of cyclotide-based therapeutics as adaptable platforms for peptide drug discovery. Full article

Peptide therapeutics have emerged as a versatile class of biomolecules bridging the gap between small-molecule drugs and large biologics. Advantages of such molecules include high target specificity, potent bioactivity and reduced off-target toxicity. Despite these, broader clinical translation remains constrained by inherent limitations like poor metabolic stability, rapid renal clearance, limited membrane permeability and scalable synthesis. This review aims to systematically integrate advances in peptide science across natural discovery, synthetic methodologies, structural engineering, and translational delivery systems, while identifying critical research gaps hindering clinical adoption. We highlight diverse natural sources of bioactive peptides, including plant- (lunasin), animal- (Val-Pro-Pro (VPP) and Ile-Pro-Pro (IPP)), microbial- (nisin and cyclosporine), marine- (dolastatins) and venom-derived (chlorotoxin and ω-conotoxin MVIIA (ziconotide)) agents. Advances in solid-phase peptide synthesis (SPPS), green chemistry, and catalytic strategies are discussed alongside emerging in silico approaches, including artificial intelligence-driven sequence design and molecular modeling. Structural modifications such as cyclization, hydrocarbon stapling, PEGylation, and lipidation are critically evaluated for their role in enhancing pharmacokinetic and pharmacodynamic properties. Furthermore, nanoformulation strategies, including self-assembling peptides and cell-penetrating systems, are examined for their potential to overcome biological barriers. Importantly, this review identifies key unresolved challenges, including the lack of predictive models for peptide delivery systems, safety concerns associated with long-term modifications, and limited in vivo validation of naturally derived peptides. Addressing these gaps through integrated computational and experimental approaches will be essential for advancing next-generation peptide therapeutics. Collectively, this work provides a comprehensive framework for the rational design and translation of peptide-based precision medicines. Full article

Background: Neglected diseases caused by protozoan parasites remain a major public health burden, particularly in low- and middle-income countries. Among the chemical motifs explored in antiparasitic drug discovery, guanidine-containing compounds have attracted considerable attention due to their strong cationic character, high capacity for hydrogen bonding, and versatility in interacting with biological targets. Methodology: This review summarizes advances reported in the last decade regarding guanidine derivatives with activity against pathogens associated with Chagas disease, human African trypanosomiasis, Leishmaniasis, tuberculosis, toxoplasmosis, dengue and schistosomiasis. Results: Evidence gathered from synthetic, natural, and drug-repurposing studies indicates that the guanidine, guanidine-containing and guanidine-related compounds contribute to modulating biological activity by changing electrostatic interactions, hydrogen-bonding networks, and physicochemical properties, with enzymes, nucleic acids, and membrane-associated targets essential for parasite survival. Across the analyzed studies, several emerging structure–activity relationship trends were identified, including the contribution of polycationic or dicationic architectures, the influence of halogenated or lipophilic substituents, and the dependence of biological activity on the complete molecular framework, including heterocyclic systems, macrocycles, peptide conjugates, hybrid scaffolds, and repurposed drugs. In addition to direct antiparasitic effects, certain guanidine-containing and guanidine-related compounds demonstrate immunomodulatory or host-protective properties, expanding the therapeutic relevance of this class. Despite promising in vitro results, protonation trapping, efflux pump susceptibility, and pharmacokinetic limitations such as poor oral absorption, high polarity, plasma protein binding and limited membrane permeability remain significant challenges for clinical translation. Nonetheless, the integration of medicinal chemistry, computational modeling, and biological screening continues to accelerate the identification of optimized scaffolds. Conclusions: Overall, guanidine-based compounds constitute a promising scaffold for the development of new therapeutic strategies targeting neglected parasitic diseases, and further structural optimization may enable the emergence of candidates with improved efficacy, selectivity, and drug-like properties. Full article

Marine organisms have proven to be excellent sources of bioactive natural products with potential therapeutic applications. To date, seventeen marine-derived molecules are on the market for the treatment of human diseases, mainly cancer. While multiple bioactivities of marine compounds have been consecutively reported, peptides represent promising candidates for these applications. This review focuses on peptides from marine organisms living in extreme marine environments, such as the deep ocean, polar regions, and tropical ecosystems. These are particularly promising for further bioprospecting, since their distinctive conditions have driven the evolution of unique biomolecules, as well as unique stability profile that can improve efficacy, shelf life, and performance under a wide range of industrial conditions. Ziconotide (Prialt), a neurotoxic peptide derived from the venom of a marine snail (Conus sp.) found at depths greater than 1000 m, is already commercially available for the treatment of severe pain. Recent technologies and computational tools are speeding up the discovery of new peptides and enzymes (very few from extreme environments). Overall, the review reports about eight peptides with anticancer properties from deep environments, nine, two and seven from polar habitats with antioxidants, anti-inflammatory and anticancer properties, respectively, and approximately ninety peptides from tropical waters (five antioxidant, thirty-five anti-inflammatory and fifty-four anticancer peptides). However, future studies in extreme environments will need to develop and apply sampling technologies, cultivation systems, as well as methods to assess efficacy, side effects and mechanisms of action, in vitro and in vivo. Full article

Influenza A virus remains a major cause of respiratory disease worldwide and poses a persistent challenge to vaccine development due to its rapid genetic evolution and antigenic variability. T-cell-based immunity has therefore gained increasing importance, as it can provide broader and more durable protection by targeting conserved viral regions. Accurate identification of T-cell epitopes (TCEs) is a fundamental requirement for epitope-based vaccine design and immunological research. Although numerous computational methods have been proposed, many existing approaches rely on handcrafted physicochemical features, which offer limited ability to capture contextual sequence dependencies. In this study, a transformer-based viroinformatics framework is proposed for the binary prediction of TCEs from Influenza A virus peptide sequences. The framework employs a pretrained Evolutionary Scale Modeling-2 (ESM-2) protein language model (PLM) to generate rich, contextualized embeddings directly from raw amino acid sequences, eliminating the need for manual feature engineering. These embeddings are processed using a lightweight attention-based transformer classifier to learn epitope-specific sequence patterns. The model achieves strong and stable predictive performance, attaining an accuracy of approximately 97% and an AUC close to 0.99 under stratified cross-validation. Ablation analysis further confirms that protein language model representations and self-attention contribute substantially to performance gains over classical machine learning baselines. To enhance practical reliability, Monte Carlo dropout is incorporated during inference to provide uncertainty-aware predictions, enabling differentiation between high-confidence and ambiguous peptide candidates. In addition, attention-based interpretability is used to identify residue-level contributions to model decisions, offering biologically meaningful insights into epitope recognition. Overall, this study demonstrates that PLMs combined with Transformer architectures provide an effective, interpretable, and a promising computational framework for Influenza A TCE discovery and vaccine research. Full article

Antimicrobial resistance (AMR) poses an escalating global health crisis driven by multidrug-resistant ESKAPE pathogens and emerging fungal threats such as Candida auris (C. auris). In response to this urgent need for new therapeutic strategies, antimicrobial peptides (AMPs) represent a mechanistically distinct alternative to conventional antibiotics due to their membrane-targeting mechanisms and a reduced propensity for resistance development; however, clinical translation has been hindered by toxicity, instability and manufacturing constraints. Recent advances in artificial intelligence (AI) are reshaping AMP discovery and optimization. Machine learning (ML), deep learning (DL) and transformer-based protein language models now enable improved prediction of antimicrobial activity, selectivity, protease stability and host toxicity. Generative approaches, including variational autoencoders, diffusion models and reinforcement learning, facilitate de novo multi-objective peptide design and pathogen-directed optimization against resistant bacteria and multidrug-resistant fungal pathogens. Integrated design–test–learn pipelines are accelerating iterative peptide engineering by tightly coupling computational prediction with experimental validation. Clinically used peptide-derived antibiotics such as polymyxins and daptomycin demonstrate the therapeutic feasibility of peptide-based antimicrobials, while investigational peptides, including pexiganan, illustrate ongoing translational progress. Although no fully AI-designed AMP has yet achieved regulatory approval, the accelerating convergence of computational modeling and experimental validation suggests a rapidly evolving translational landscape. Advancing scalable, surveillance-informed AI frameworks that integrate resistance data, predictive safety modeling and delivery optimization will be essential to accelerate the clinical translation of next-generation, multi-objective AMPs against high-risk resistant pathogens. Full article

Algae-derived proteins and peptides have gained increasing interest as sustainable bioresources with valuable nutritional and functional properties. This review aims to synthesize current knowledge on their characteristics and applications while highlighting the emerging role of computational tools in peptide research. Key findings show that algae provide diverse proteins and bioactive peptides with advantageous amino acid profiles and notable antioxidant, antihypertensive, antidiabetic, anti-inflammatory, and skin-protective activities. Their applications span food formulation, pharmaceuticals, and cosmetics, although large-scale utilization remains constrained by production, stability, and bioavailability challenges. Computational strategies, including virtual enzymatic hydrolysis, machine-learning prediction, QSAR modeling, molecular docking, molecular dynamics, and toxicity/allergenicity assessment, offer promising avenues for efficient peptide discovery, though their use in algae is still limited. Overall, this review underscores the potential of algae-derived proteins and peptides as multifunctional ingredients and emphasizes the need to integrate in silico pipelines with improved processing and delivery systems to accelerate future translational applications. Full article

Background: The outbreak and spreading of antimicrobial resistance (AMR) in a very short time has made most of the old-fashioned antibiotics ineffective, and thus new therapeutic substances have to be developed. The traditional methods of antibiotics discovery are defined by long periods of time, high levels of expenditure, and high rates of failure, which contributes to the necessity of new approaches. Artificial intelligence (AI) has become a disruptive technology that can be used to accelerate and optimize various steps of antibiotic discovery, such as target detection and virtual screening, new molecular design, and early-stage testing. Methods: This review provides an in-depth discussion of the role of AI methodologies in the form of machine learning, deep learning, natural language processing, and generative models in the discovery of small-molecule antibiotics and antimicrobial peptides (AMPs). The major areas that are discussed include virtual screening, pharmacokinetics optimization, resistance mechanism prediction, and AMPs design, which is accompanied by relevant case studies, including the AI-based discovery of Abaucin. Results: The article highlights how AI can be used in a synergistic relationship with synthetic biology, nanotechnology, and multi-omics data as a core component in the next generation of antimicrobial approaches, such as personalized therapy and predictive stewardship. The existing issues, i.e., the lack of data, bias in algorithms, and the translational divide between research and clinical use, are addressed, as well as suggested measures of responsible, collaborative, and ethical AI use. Conclusions: The combination of computational innovation with experimentation validation, AI-driven antibiotic discovery paves the way for a potent and scalable approach in addressing the rising threat of AMR. Full article

Antimicrobial resistance represents one of the most critical global health challenges of the 21st century, urgently demanding innovative strategies for antimicrobial discovery. Traditional antibiotic development pipelines are slow, costly, and increasingly ineffective against multidrug-resistant pathogens. In this context, recent advances in artificial intelligence have emerged as transformative tools capable of accelerating antimicrobial discovery and expanding accessible chemical and biological space. This comprehensive review critically synthesizes recent progress in AI-driven approaches applied to the discovery and design of both small-molecule antibiotics and antimicrobial peptides. We examine how machine learning, deep learning, and generative models are being leveraged for virtual screening, activity prediction, mechanism-informed prioritization, and de novo antimicrobial design. Particular emphasis is placed on graph-based neural networks, attention-based and transformer architectures, and generative frameworks such as variational autoencoders and large language model-based generators. Across these approaches, AI has enabled the identification of structurally novel compounds, facilitated narrow-spectrum antimicrobial strategies, and improved interpretability in peptide prediction. However, significant challenges remain, including data scarcity and imbalance, limited experimental validation, and barriers to clinical translation. By integrating methodological advances with a critical analysis of the current limitations, this review highlights emerging trends and outlines future directions aimed at bridging the gap between in silico discovery and real-world therapeutic development. Full article