With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.

In recent years, peptides have gained significant relevance due to their therapeutic properties. The surge in peptide production and synthesis has generated vast amounts of data, enabling the creation of comprehensive databases and information repositories. Advances in sequencing techniques and artificial intelligence have further accelerated the design of tailor-made peptides. However, leveraging these techniques requires versatile and continuously updated storage systems, along with tools that facilitate peptide research and the implementation of machine learning for predictive systems. This work introduces Peptipedia v2.0, one of the most comprehensive public repositories of peptides, supporting biotechnological research by simplifying peptide study and annotation. Peptipedia v2.0 has expanded its collection by over 45% with peptide sequences that have reported biological activities. The functional biological activity tree has been revised and enhanced, incorporating new categories such as cosmetic and dermatological activities, molecular binding, and antiageing properties. Utilizing protein language models and machine learning, more than 90 binary classification models have been trained, validated, and incorporated into Peptipedia v2.0. These models exhibit average sensitivities and specificities of 0.877±0.0530 and 0.873±0.054, respectively, facilitating the annotation of more than 3.6 million peptide sequences with unknown biological activities, also registered in Peptipedia v2.0. Additionally, Peptipedia v2.0 introduces description tools based on structural and ontological properties and user-friendly machine learning tools to facilitate the application of machine learning strategies to study peptide sequences. Database URL: https://peptipedia.cl/

Idiotype vaccination for follicular lymphoma is primarily being developed as remission consolidation after chemotherapy. We investigated idiotype vaccination as primary intervention for treatment-naive indolent B-cell lymphoma and in a separate cohort as remission consolidation after chemotherapy to assess immunization-induced immune responses in relation to progression-free survival (German Clinical Trials Register, DRKS00000227). Twenty-one patients in each cohort received 6 intradermal injections of adjuvanted recombinant idiotype Fab fragment (Fab^Id); 76% of patients in both groups developed anti-idiotype antibodies and/or cellular immunity as measured by enzyme-linked immunosorbent assay and interferon-γ ELISpot. In treatment-naive patients, only cellular responses correlated with superior progression-free survival (P < .002) and durable objective remissions (P = .04). Immunization-induced T cells recognized hypermutated or complementarity-determining region 3 epitopes. After remission consolidation immunization, induction of anti-idiotype antibodies correlated with progression-free survival. Low B-cell counts after rituximab therapy predicted for failure to develop anti-idiotype antibodies. These results are similar to published trials showing an association of humoral immunity with control of residual lymphoma. In contrast, effective immunity against untreated lymphoma appears to be dependent on idiotype-specific T cells. Sustained remissions in patients with vaccination-induced cellular immunity suggest clinical benefit and warrant a randomized comparison of this vaccine with expectant management for asymptomatic follicular lymphoma.

The British Journal of Haematology publishes original research papers in clinical, laboratory and experimental haematology. The Journal also features annotations, reviews, short reports, images in haematology and Letters to the Editor.

Most cancers become more dangerous by the outgrowth of malignant subclones with additional DNA mutations that favor proliferation or survival. Using chronic lymphocytic leukemia (CLL), a disease exemplary of this process, and a model for neoplasms in general, we created transgenic mice overexpressing the enzyme, activation-induced deaminase (AID), whose normal function is to induce DNA mutations in B lymphocytes. AID allows normal B lymphocytes to develop more effective immunoglobulin (Ig)-mediated immunity, but also is able to mutate non-Ig genes, predisposing to cancer. In chronic lymphocytic leukemia (CLL), AID expression correlates with poor prognosis suggesting a role for this enzyme in disease progression. Nevertheless, direct experimental evidence identifying the specific genes that are mutated by AID and indicating that those genes are associated with disease progression is not available. To address this point, we overexpressed Aicda in a murine model of CLL (Em-TCL1). Analyses of TCL1/AID mice demonstrate a role for AID in disease kinetics, CLL-cell proliferation, and the development of cancer-related target mutations with canonical AID signatures in non-Igs genes. Notably, our mouse models can accumulate mutations in the same genes that are mutated in human cancers. Moreover, some of these mutations occur at homologous positions, leading to identical or chemically-similar amino acid substitutions as in human CLL and lymphoma.Together, these findings support a direct link between aberrant AID activity and CLL driver mutations that are then selected for their oncogenic effects, whereby AID promotes aggressiveness in CLL and other B-cell neoplasms.