SAPdb: A database of short peptides and the corresponding nanostructures formed by self-assembly

Learning the rules of peptide self-assembly through data mining with large language models

Peptides are ubiquitous and important biomolecules that self-assemble into diverse structures. Although extensive research has explored the effects of chemical composition and exterior conditions on self-assembly, a systematic study consolidating these data to uncover global rules is lacking. In this work, we curate a peptide assembly database through a combination of manual processing by human experts and large language model-assisted literature mining. As a result, we collect over 1000 experimental data entries with information about peptide sequence, experimental conditions, and corresponding self-assembly phases. Using the data, machine learning models are developed, demonstrating excellent accuracy (>80%) in assembly phase classification. Moreover, we fine-tune a GPT model for peptide literature mining with the developed dataset, which markedly outperforms the pretrained model in extracting information from academic publications. This workflow can improve efficiency when exploring potential self-assembling peptide candidates, through guiding experimental work, while also deepening our understanding of the governing mechanisms.

IAMPDB: A Knowledgebase of Manually Curated Insects-Derived Antimicrobial Peptides

Insects, a majority of animal species, rely on innate immunity and antimicrobial peptides (AMPs), which are a part of their innate immunity, to combat diverse parasites and pathogens. These peptides have applications ranging from agriculture to antimicrobial resistance (AMR). However, there is a lack of a specialized database, prompting the development of the Insect Antimicrobial Peptide Database (IAMPDB) as a pioneering comprehensive Knowledgebase dedicated to insect-derived antimicrobial peptides (IAMPs), serving as a resource for researchers and industry professionals. Curated from UniProt and associated literature(s), IAMPDB currently houses 438 curated entries of IAMPs from various insect species, spanning 10 taxonomical orders of insects. Each entry is meticulously annotated with details on peptide sequence, source organism, activities, physicochemical properties, and more. IAMPDB offers a user-friendly interface with diverse search options, interactive visualizations, and links to external databases; advanced tools, including a peptide sequence alignment toolbox and a peptide feature calculation toolbox, facilitating sequence alignment, physicochemical property calculation, and in-depth analysis. The knowledgebase is accessible online (at URL https://bblserver.org.in/iampdb/).

Advances of deep Neural Networks (DNNs) in the development of peptide drugs

Peptides are able to bind to difficult disease targets with high potency and specificity, providing great opportunities to meet unmet medical requirements. Nevertheless, the unique features of peptides, such as their small size, high structural flexibility, and scarce data availability, bring extra challenges to the design process. Firstly, this review sums up the application of peptide drugs in treating diseases. Then, the review probes into the advantages of Deep Neural Networks (DNNs) in predicting and designing peptide structures. DNNs have demonstrated remarkable capabilities in structural prediction, enabling accurate three-dimensional modeling of peptide drugs through models like AlphaFold and its successors. Finally, the review deliberates on the challenges and coping strategies of DNNs in the development of peptide drugs, along with future research directions. Future research directions focus on further improving the accuracy and efficiency of DNN-based peptide drug design, exploring novel applications of peptide drugs, and accelerating their clinical translation. With continuous advancements in technology and data accumulation, DNNs are poised to play an increasingly crucial role in the field of peptide drug development.

The anti-MRSA resource: a comprehensive archive of anti-MRSA peptides and essential oils

Methicillin-resistant Staphylococcus aureus (MRSA), a major cause of fatalities due to Antimicrobial Resistance (AMR), can act as an opportunistic pathogen despite being part of the normal human flora. MRSA infections, such as skin infections, pneumonia, sepsis, and surgical site infections, have risen significantly, with bloodstream infection cases increasing from 21% in 2016 to 35% in 2020. This surge has prompted research into alternative treatments like nanomaterials, photodynamic therapy, antimicrobial peptides (AMPs), and essential oils (EOs). AMPs and EOs have shown higher success rates compared to other alternatives, gaining significant attention for their effectiveness against MRSA. In this perspective, we have created a database for peptides and EOs that have been discovered to treat MRSA. Manual data curation was done to get related information on each of the anti-MRSA EOs and AMPs from the PubMed articles. This led to the curation of 1789 peptides (1029 unique) and 863 EOs (671 unique) that have been reported against MRSA. This was followed by database creation and the development of tools for sequence analysis and determination of physiochemical properties. This resource has been named 'The Anti-MRSA Resource' or 'TAMRSAR' which we believe will aid in future drug development efforts to combat the diseases caused by MRSA. The database is accessible on any web browser at the URL: https://bblserver.org.in/tamrsar/.

Aggregating amyloid resources: A comprehensive review of databases on amyloid-like aggregation

Protein aggregation is responsible for several degenerative conditions in humans, and it is also a bottleneck in industrial protein production and storage of biotherapeutics. Bioinformatics tools have been developed to predict and redesign protein solubility more efficiently by understanding the underlying principles behind aggregation. As more experimental data become available, dedicated resources for storing, indexing, classifying and consolidating experimental results have emerged. These resources vary in focus, including aggregation-prone regions, 3D patches or protein stretches capable of forming amyloid fibrils. Some of these resources also consider the experimental conditions that cause protein aggregation and how they affect the process. This review article explores how protein aggregation databases have evolved and surveys state-of-the-art resources. We highlight their applications, complementarity and existing limitations. Moreover, we showcase the existing symbiosis between amyloid-related databases and predictive tools. To increase the usefulness of our review, we supplement it with a comprehensive list of present and past amyloid databases: https://biogenies.info/amyloid-database-list/.

AVR/I/SSAPDB: a comprehensive & specialised knowledgebase of antimicrobial peptides to combat VRSA, VISA, and VSSA

The rise of multi-drug resistant (MDR) bacteria, especially strains of Staphylococcus aureus like Vancomycin-resistant S. aureus (VRSA), Vancomycin-intermediate S. aureus (VISA), and Vancomycin-susceptible S. aureus (VSSA), poses a severe threat to global health. This situation underscores the urgent need for novel antimicrobial agents to combat these resistant strains effectively. Here, we are introducing the Anti-Vancomycin-Resistant/Intermediate/Susceptible Staphylococcus aureus Peptide Database (AVR/I/SSAPDB), a manually curated comprehensive and specialised knowledgebase dedicated to antimicrobial peptides (AMPs) that target VRSA, VISA, and VSSA with clinical and non-clinical significance. Our database sources data from PubMed, cataloging 491 experimentally validated AMPs with detailed annotations on peptides, activity, and cross-references to external databases like PubMed, UniProt, PDB, and DrugBank. AVR/I/SSAPDB offers a user-friendly interface with simple to advanced and list-based search capabilities, enabling researchers to explore AMPs against VRSA, VISA, and VSSA. We are hoping that this resource will be helpful to the scientific community in developing targeted peptide-based therapeutics, providing a crucial tool for combating VRSA, VISA, and VSSA, and addressing a major public health concern. AVR/I/SSAPDB is freely accessible via any web-browser at URL: https://bblserver.org.in/avrissa/ .

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

Developing anti-microbial peptide database version 1 to provide comprehensive and exhaustive resource of manually curated AMPs

Anti-Microbial Peptide Database version 1 (AMPDB v1) is a meticulously curated resource that aims to address the limitations of existing databases in the field of antimicrobial research. We have utilized the latest technology and put our best efforts into adding all relevant tools to cater to the needs of our users. AMPDB v1 is a derived database, built upon information gathered from the available resources and boasts a significant size of 59,122 entries which are classified into 88 classes. All the information in this resource was curated manually. Sequence alignment and protein feature calculation tools were integrated into the database in the form of web applications, to make them easy to use, quick, and responsive in real-time. We have included multiple types of browsing and searching options to enhance the user experience, from simple text search to a completely customizable advanced search page with intuitive options that let the user combine multiple options together to make a powerful search query. The database is accessible by a web browser at https://bblserver.org.in/ampdb/ .

Evaluating the Properties of Ginger Protease-Degraded Collagen Hydrolysate and Identifying the Cleavage Site of Ginger Protease by Using an Integrated Strategy and LC-MS Technology

(1) Methods: An integrated strategy, including in vitro study (degree of hydrolysis (DH) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity) and in vivo study (absorption after oral administration in rats), was developed to evaluate the properties of the fish skin gelatin hydrolysates prepared using different proteases (pepsin, alkaline protease, bromelain, and ginger protease). Meanwhile, in order to identify the hydrolysis site of ginger protease, the peptides in the ginger protease-degraded collagen hydrolysate (GDCH) were comprehensively characterized by liquid chromatography/tandem mass spectrometry (LC-MS) method. (2) Results: The GDCH exhibited the highest DH (20.37%) and DPPH radical scavenging activity (77.73%), and in vivo experiments showed that the GDCH was more efficiently absorbed by the gastrointestinal tract. Further oral administration experiments revealed that GDCH was not entirely degraded to free amino acids and can be partially absorbed as dipeptides and tripeptides in intact forms, including Pro-Hyp, Gly-Pro-Hyp, and X-Hyp-Gly tripeptides. LC-MS results determined the unique substrate specificity of ginger protease recognizing Pro and Hyp at the P2 position based on the amino acids at the P2 position from the three types of tripeptides (Gly-Pro-Y, X-Hyp-Gly, and Z-Pro-Gly) and 136 identified peptides (>4 amino acids). Interestingly, it suggested that ginger protease can also recognize Ala in the P2 position. (3) Conclusions: This study comprehensively evaluated the properties of GDCH by combining in vitro and in vivo strategies, and is the first to identify the cleavage site of ginger protease by LC-MS technique. It provides support for the follow-up study on the commercial applications of ginger protease and bioactivities of the hydrolysate produced by ginger protease.

A GSH/CB Dual-Controlled Self-Assembled Nanomedicine for High-Efficacy Doxorubicin-Resistant Breast Cancer Therapy

Chemoresistance is a major therapeutic obstacle in the treatment of breast cancer. Therefore, how to overcome chemoresistance is a problem to be solved. Here, a glutathione (GSH)/cathepsin B (CB) dual-controlled nanomedicine formed by cyclic disulfide-bridged peptide (cyclic-1a) as a potent anticancer agent is reported. Under the sequential treatment of GSH and CB, cyclic-1a can efficiently self-assemble into nanofibers. In vitro studies show that cyclic-1a promotes the apoptosis of MCF-7/DOX cells by inducing the cleavages of caspase-3 and PARP. In vivo studies confirm that cyclic-1a significantly inhibits the progression of MCF-7/DOX cells-derived xenograft in nude mice, with no obvious adverse reactions. This study provides a paradigm of GSH/CB dual-controlled nanomedicine for high-efficacy and low-toxic DOX-resistant breast cancer therapy.

In silico tools and databases for designing cancer immunotherapy

Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.

Biomaterial-Interrelated Bacterial Sweeper: Simplified Self-Assembled Octapeptides with Double-Layered Trp Zipper Induces Membrane Destabilization and Bacterial Apoptosis-Like Death

Treatment of microbial-associated infections continues to be hampered by impaired antibacterial efficiency and the variability in nanomedicines. Herein, an octapeptide library with a double-layered zipper, constructed via a systematic arrangement, simplifying the sequence and optimizing the structure (diverse motifs including surfactant-like, central-bola, and end-bola), is assessed in terms of biological efficiency and self-assembly properties. The results indicate that peptides with double-layered Trp zipper exhibit significant antimicrobial activity. Extracellularly, affinity interactions between micelles and bacteria induce the lateral flow of the membrane and electric potential perturbation. Intracellularly, lead molecules cause apoptosis-like death, as indicated by excessive accumulation of reactive oxygen species, generation of a DNA ladder, and upregulation of mazEF expression. Among them, RW-1 performs the best in vivo and in vitro. The intersecting combination of Trp zipper and surfactants possesses overwhelming superiority with respect to bacterial sweepers (therapeutic index [TI] = 52.89), nanostructures (micelles), and bacterial damage compared to RW-2 (central-bola) and RW-3 (end-bola). These findings confirm that the combination of double-layered Trp zipper and surfactants has potential for application as a combined motif for combating microbial infection and connects the vast gap between antimicrobial peptides and self-assembly, such as Jacob's ladder.