INDI—integrated nanobody database for immunoinformatics

Deszyński, Piotr; Młokosiewicz, Jakub; Volanakis, Adam; Jaszczyszyn, Igor; Castellana, Natalie; Bonissone, Stefano; Ganesan, Rajkumar; Krawczyk, Konrad

doi:10.1093/nar/gkab1021

Cited by 42 publications

(28 citation statements)

References 46 publications

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“…Two further databases deal with antibodies, including nanobodies - antibodies consisting of a single monomeric variable domain. INDI ( 87 ) collects sequences and structures plus associated metadata from a variety of sources and allows various modes of sequence or text search. The authors envisage the dataset being valuable for computational efforts towards nanobody design.…”

Section: New and Updated Databasesmentioning

confidence: 99%

The 2022Nucleic Acids Researchdatabase issue and the online molecular biology database collection

Rigden

Fernández

2021

Nucleic Acids Research

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The 2022 Nucleic Acids Research Database Issue contains 185 papers, including 87 papers reporting on new databases and 85 updates from resources previously published in the Issue. Thirteen additional manuscripts provide updates on databases most recently published elsewhere. Seven new databases focus specifically on COVID-19 and SARS-CoV-2, including SCoV2-MD, the first of the Issue's Breakthrough Articles. Major nucleic acid databases reporting updates include MODOMICS, JASPAR and miRTarBase. The AlphaFold Protein Structure Database, described in the second Breakthrough Article, is the stand-out in the protein section, where the Human Proteoform Atlas and GproteinDb are other notable new arrivals. Updates from DisProt, FuzDB and ELM comprehensively cover disordered proteins. Under the metabolism and signalling section Reactome, ConsensusPathDB, HMDB and CAZy are major returning resources. In microbial and viral genomes taxonomy and systematics are well covered by LPSN, TYGS and GTDB. Genomics resources include Ensembl, Ensembl Genomes and UCSC Genome Browser. Major returning pharmacology resource names include the IUPHAR/BPS guide and the Therapeutic Target Database. New plant databases include PlantGSAD for gene lists and qPTMplants for post-translational modifications. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). Our latest update to the NAR online Molecular Biology Database Collection brings the total number of entries to 1645. Following last year's major cleanup, we have updated 317 entries, listing 89 new resources and trimming 80 discontinued URLs. The current release is available at http://www.oxfordjournals.org/nar/database/c/.

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Section: New and Updated Databasesmentioning

confidence: 99%

The 2022Nucleic Acids Researchdatabase issue and the online molecular biology database collection

Rigden

Fernández

2021

Nucleic Acids Research

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“…Non-machine learning statistical methods in the therapeutic antibody sphere continue to be developed [ 123 , 140 , 141 ]. Learnings from antibodies are being transferred to their sisterly format such as nanobodies [ 18 , 135 , 141 ]. Of note, synergies between existing data sources provide novel findings, such as employing structural information to annotate large NGS datasets [ 76 , 95 , 142–144 ].…”

Section: Discussionmentioning

confidence: 99%

Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery

Wilman¹,

Wróbel²,

Bielska³

et al. 2022

Briefings in Bioinformatics

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Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody–antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.

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“…Llamanade is a dedicated open-source computational pipeline for robust V H H humanisation [ 267 ]. More recently, an integrated nanobody database for immunoinformatics (INDI) lists V H Hs from all the major public outlets of biological sequences and patents ( , last accessed date: 11 February 2022) from GenBank, next-generation sequencing repositories and structure databases and publications [ 268 ]. Even the famous SAbDab has an extension for V H H with SAbDab-nano ( , last accessed date: 12 February 2022) [ 159 ].…”

Section: Discussionmentioning

confidence: 99%

VHH Structural Modelling Approaches: A Critical Review

Vishwakarma

Vattekatte

Shinada³

et al. 2022

IJMS

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VHH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. The number of experimentally solved VHH structures has significantly improved recently, which is of great help, because it offers the ability to directly work on 3D structures to humanise or improve them. Unfortunately, most VHHs do not have 3D structures. Thus, it is essential to find alternative ways to get structural information. The methods of structure prediction from the primary amino acid sequence appear essential to bypass this limitation. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given VHH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of VHHs. A brief explanation of each methodology is supplied, and pertinent examples of their usage are provided. Finally, we present a structure prediction case study of a recently solved VHH structure. According to some recent studies and the present analysis, AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of VHH from its sequence.

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INDI—integrated nanobody database for immunoinformatics

Cited by 42 publications

References 46 publications

The 2022Nucleic Acids Researchdatabase issue and the online molecular biology database collection

The 2022Nucleic Acids Researchdatabase issue and the online molecular biology database collection

Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery

VHH Structural Modelling Approaches: A Critical Review

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