SYNBIP 2.0: epitopes mapping, sequence expansion and scaffolds discovery for synthetic binding protein innovation

Nucleic Acids Research, Год журнала: 2024, Номер 53(D1), С. D595 - D603

Опубликована: Окт. 16, 2024

Abstract Synthetic binding proteins (SBPs) represent a pivotal class of artificially engineered proteins, meticulously crafted to exhibit targeted properties and specific functions. Here, the SYNBIP database, comprehensive resource for SBPs, has been significantly updated. These enhancements include (i) featuring 3D structures 899 SBP–target complexes illustrate epitopes (ii) using SBPs in monomer or complex forms with target their sequence space expanded five times 12 025 by integrating structure-based protein generation framework property prediction tool, (iii) offering detailed information on 78 473 newly identified SBP-like scaffolds from RCSB Protein Data Bank, an additional 16 401 555 ones AlphaFold Structure Database, (iv) database is regularly updated, incorporating 153 new SBPs. Furthermore, structural models all have enhanced through application AlphaFold2, clinical statuses concurrently refreshed. Additionally, design methods employed each SBP are now prominently featured database. In sum, 2.0 designed provide researchers essential data, facilitating innovation research, diagnosis therapy. freely accessible at https://idrblab.org/synbip/.

Язык: Английский

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Computational modeling approaches and regulatory pathways for drug combinations

Drug Discovery Today, Год журнала: 2025, Номер unknown, С. 104345 - 104345

Опубликована: Март 1, 2025

Язык: Английский

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The 2025 Nucleic Acids Research database issue and the online molecular biology database collection

Nucleic Acids Research, Год журнала: 2024, Номер 53(D1), С. D1 - D9

Опубликована: Дек. 10, 2024

The 2025 Nucleic Acids Research database issue contains 185 papers spanning biology and related areas. Seventy three new databases are covered, while resources previously described in the account for 101 update articles. Databases most recently published elsewhere a further 11 papers. acid include EXPRESSO multi-omics of 3D genome structure (this issue's chosen Breakthrough Resource Article) NAIRDB Fourier transform infrared data. New protein predictions human isoforms at ASpdb viral proteins BFVD. UniProt, Pfam InterPro have all provided updates: metabolism signalling covered by descriptions STRING, KEGG CAZy, updated microbe-oriented Enterobase, VFDB PHI-base. Biomedical research is supported, among others, ClinVar, PubChem DrugMAP. Genomics-related Ensembl, UCSC Genome Browser dbSNP. plant cover Solanaceae (SolR) Asteraceae (AMIR) families an from NCBI Taxonomy also features. Database Issue freely available on website (https://academic.oup.com/nar). At NAR online Molecular Biology Collection (http://www.oxfordjournals.org/nar/database/c/), 932 entries been reviewed last year, 74 added 226 discontinued URLs eliminated bringing current total to 2236 databases.

Язык: Английский

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Molecular characterization and biomarker discovery in gastric cancer progression through transcriptome meta-analysis

Computers in Biology and Medicine, Год журнала: 2024, Номер 183, С. 109276 - 109276

Опубликована: Окт. 23, 2024

Язык: Английский

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Multimodal Fusion-Based Lightweight Model for Enhanced Generalization in Drug–Target Interaction Prediction

Journal of Chemical Information and Modeling, Год журнала: 2024, Номер unknown

Опубликована: Дек. 3, 2024

Predicting drug-target interactions (DTIs) with precision is a crucial challenge in the quest for efficient and cost-effective drug discovery. Existing DTI prediction models often require significant computational resources because of intricate exceptionally lengthy protein target sequences. This study introduces MMF-DTI, lightweight model that uses multimodal fusion, to improve generalizability predictions without sacrificing efficiency. The MMF-DTI combines four distinct modalities: molecular sequence, properties, function description. approach noteworthy it first use natural language-based descriptions predicting DTIs. effectiveness has been confirmed through benchmark data sets, demonstrating its comparable performance terms generalizability, especially scenarios limited information about or target. Remarkably, accomplishes this using only half parameters 17% VRAM compared previous state-of-the-art models. allows even constrained environments. combination efficiency highlights potential fusion improving overall applicability models, providing promising opportunities future discovery endeavors.

Язык: Английский

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AI comes to the Nobel Prize and drug discovery

Journal of Pharmaceutical Analysis, Год журнала: 2024, Номер 14(11), С. 101160 - 101160

Опубликована: Ноя. 1, 2024

Язык: Английский

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