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  • 1
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2023
    In:  Scientific Reports Vol. 13, No. 1 ( 2023-07-18)
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 13, No. 1 ( 2023-07-18)
    Abstract: Antibodies with similar amino acid sequences, especially across their complementarity-determining regions, often share properties. Finding that an antibody of interest has a similar sequence to naturally expressed antibodies in healthy or diseased repertoires is a powerful approach for the prediction of antibody properties, such as immunogenicity or antigen specificity. However, as the number of available antibody sequences is now in the billions and continuing to grow, repertoire mining for similar sequences has become increasingly computationally expensive. Existing approaches are limited by either being low-throughput, non-exhaustive, not antibody specific, or only searching against entire chain sequences. Therefore, there is a need for a specialized tool, optimized for a rapid and exhaustive search of any antibody region against all known antibodies, to better utilize the full breadth of available repertoire sequences. We introduce Known Antibody Search (KA-Search), a tool that allows for the rapid search of billions of antibody variable domains by amino acid sequence identity across either the variable domain, the complementarity-determining regions, or a user defined antibody region. We show KA-Search in operation on the $$\sim $$ ∼ 2.4 billion antibody sequences available in the OAS database. KA-Search can be used to find the most similar sequences from OAS within 30 minutes and a representative subset of 10 million sequences in less than 9 seconds. We give examples of how KA-Search can be used to obtain new insights about an antibody of interest. KA-Search is freely available at https://github.com/oxpig/kasearch .
    Type of Medium: Online Resource
    ISSN: 2045-2322
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2615211-3
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  • 2
    In: Journal of Molecular Biology, Elsevier BV, Vol. 414, No. 2 ( 2011-11), p. 289-302
    Type of Medium: Online Resource
    ISSN: 0022-2836
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2011
    detail.hit.zdb_id: 1355192-9
    SSG: 12
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  • 3
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2022
    In:  Bioinformatics Advances Vol. 2, No. 1 ( 2022-01-10)
    In: Bioinformatics Advances, Oxford University Press (OUP), Vol. 2, No. 1 ( 2022-01-10)
    Abstract: General protein language models have been shown to summarize the semantics of protein sequences into representations that are useful for state-of-the-art predictive methods. However, for antibody specific problems, such as restoring residues lost due to sequencing errors, a model trained solely on antibodies may be more powerful. Antibodies are one of the few protein types where the volume of sequence data needed for such language models is available, e.g. in the Observed Antibody Space (OAS) database. Results Here, we introduce AbLang, a language model trained on the antibody sequences in the OAS database. We demonstrate the power of AbLang by using it to restore missing residues in antibody sequence data, a key issue with B-cell receptor repertoire sequencing, e.g. over 40% of OAS sequences are missing the first 15 amino acids. AbLang restores the missing residues of antibody sequences better than using IMGT germlines or the general protein language model ESM-1b. Further, AbLang does not require knowledge of the germline of the antibody and is seven times faster than ESM-1b. Availability and implementation AbLang is a python package available at https://github.com/oxpig/AbLang. Supplementary information Supplementary data are available at Bioinformatics Advances online.
    Type of Medium: Online Resource
    ISSN: 2635-0041
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 3076075-6
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  • 4
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2015
    In:  Bioinformatics Vol. 31, No. 4 ( 2015-02-15), p. 614-615
    In: Bioinformatics, Oxford University Press (OUP), Vol. 31, No. 4 ( 2015-02-15), p. 614-615
    Abstract: Supplementary information:  Supplementary Data are available at Bioinformatics online. Contact: iainios@hotmail.com
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2015
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 5
    In: Bioinformatics, Oxford University Press (OUP), Vol. 33, No. 12 ( 2017-06-15), p. 1806-1813
    Abstract: In order to function, proteins frequently bind to one another and form 3D assemblies. Knowledge of the atomic details of these structures helps our understanding of how proteins work together, how mutations can lead to disease, and facilitates the designing of drugs which prevent or mimic the interaction. Results Atomic modeling of protein–protein interactions requires the selection of near-native structures from a set of docked poses based on their calculable properties. By considering this as an information retrieval problem, we have adapted methods developed for Internet search ranking and electoral voting into IRaPPA, a pipeline integrating biophysical properties. The approach enhances the identification of near-native structures when applied to four docking methods, resulting in a near-native appearing in the top 10 solutions for up to 50% of complexes benchmarked, and up to 70% in the top 100. Availability and Implementation IRaPPA has been implemented in the SwarmDock server (http://bmm.crick.ac.uk/∼SwarmDock/), pyDock server (http://life.bsc.es/pid/pydockrescoring/) and ZDOCK server (http://zdock.umassmed.edu/), with code available on request. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2017
    detail.hit.zdb_id: 1468345-3
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  • 6
    Online Resource
    Online Resource
    MDPI AG ; 2010
    In:  International Journal of Molecular Sciences Vol. 11, No. 10 ( 2010-09-28), p. 3623-3648
    In: International Journal of Molecular Sciences, MDPI AG, Vol. 11, No. 10 ( 2010-09-28), p. 3623-3648
    Type of Medium: Online Resource
    ISSN: 1422-0067
    Language: English
    Publisher: MDPI AG
    Publication Date: 2010
    detail.hit.zdb_id: 2019364-6
    SSG: 12
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  • 7
    In: Proteins: Structure, Function, and Bioinformatics, Wiley, Vol. 82, No. 4 ( 2014-04), p. 620-632
    Abstract: We report the first assessment of blind predictions of water positions at protein–protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community‐wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions—20 groups submitted a total of 195 models—were assessed by measuring the recall fraction of water‐mediated protein contacts. Of the 176 high‐ or medium‐quality docking models—a very good docking performance per se—only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 Å, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high‐quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein–water interactions and their role in stabilizing protein complexes. Proteins 2014; 82:620–632. © 2013 Wiley Periodicals, Inc.
    Type of Medium: Online Resource
    ISSN: 0887-3585 , 1097-0134
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2014
    detail.hit.zdb_id: 1475032-6
    SSG: 12
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  • 8
    Online Resource
    Online Resource
    Wiley ; 2015
    In:  Proteins: Structure, Function, and Bioinformatics Vol. 83, No. 4 ( 2015-04), p. 640-650
    In: Proteins: Structure, Function, and Bioinformatics, Wiley, Vol. 83, No. 4 ( 2015-04), p. 640-650
    Abstract: Mutations at protein–protein recognition sites alter binding strength by altering the chemical nature of the interacting surfaces. We present a simple surface energy model, parameterized with empirical values, yielding mean energies of −48 cal mol −1  Å −2 for interactions between hydrophobic surfaces, −51 to −80 cal mol −1  Å −2 for surfaces of complementary charge, and 66–83 cal mol −1  Å −2 for electrostatically repelling surfaces, relative to the aqueous phase. This places the mean energy of hydrophobic surface burial at −24 cal mol −1  Å −2 . Despite neglecting configurational entropy and intramolecular changes, the model correlates with empirical binding free energies of a functionally diverse set of rigid‐body interactions ( r  = 0.66). When used to rerank docking poses, it can place near‐native solutions in the top 10 for 37% of the complexes evaluated, and 82% in the top 100. The method shows that hydrophobic burial is the driving force for protein association, accounting for 50–95% of the cohesive energy. The model is available open‐source from http://life.bsc.es/pid/web/surface_energy/ and via the CCharpPPI web server http://life.bsc.es/pid/ccharppi/ . Proteins 2015; 83:640–650. © 2015 Wiley Periodicals, Inc.
    Type of Medium: Online Resource
    ISSN: 0887-3585 , 1097-0134
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2015
    detail.hit.zdb_id: 1475032-6
    SSG: 12
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  • 9
    In: Proteins: Structure, Function, and Bioinformatics, Wiley, Vol. 84, No. S1 ( 2016-09), p. 323-348
    Abstract: We present the results for CAPRI Round 30, the first joint CASP‐CAPRI experiment, which brought together experts from the protein structure prediction and protein–protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact‐sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology‐built subunit models and the smaller pair‐wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323–348. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.
    Type of Medium: Online Resource
    ISSN: 0887-3585 , 1097-0134
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2016
    detail.hit.zdb_id: 1475032-6
    SSG: 12
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  • 10
    In: Proteins: Structure, Function, and Bioinformatics, Wiley, Vol. 85, No. 3 ( 2017-03), p. 487-496
    Abstract: The sixth CAPRI edition included new modeling challenges, such as the prediction of protein–peptide complexes, and the modeling of homo‐oligomers and domain–domain interactions as part of the first joint CASP–CAPRI experiment. Other non‐standard targets included the prediction of interfacial water positions and the modeling of the interactions between proteins and nucleic acids. We have participated in all proposed targets of this CAPRI edition both as predictors and as scorers, with new protocols to efficiently use our docking and scoring scheme pyDock in a large variety of scenarios. In addition, we have participated for the first time in the servers section, with our recently developed webserver, pyDockWeb. Excluding the CASP–CAPRI cases, we submitted acceptable models (or better) for 7 out of the 18 evaluated targets as predictors, 4 out of the 11 targets as scorers, and 6 out of the 18 targets as servers. The overall success rates were below those in past CAPRI editions. This shows the challenging nature of this last edition, with many difficult targets for which no participant submitted a single acceptable model. Interestingly, we submitted acceptable models for 83% of the evaluated protein–peptide targets. As for the 25 cases of the CASP–CAPRI experiment, in which we used a larger variety of modeling techniques (template‐based, symmetry restraints, literature information, etc.), we submitted acceptable models for 56% of the targets. In summary, this CAPRI edition showed that pyDock scheme can be efficiently adapted to the increasing variety of problems that the protein interactions field is currently facing. Proteins 2017; 85:487–496. © 2016 Wiley Periodicals, Inc.
    Type of Medium: Online Resource
    ISSN: 0887-3585 , 1097-0134
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2017
    detail.hit.zdb_id: 1475032-6
    SSG: 12
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