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Cotet, T.-S.

Publications and source records attributed to Cotet, T.-S..

5 recordsLinked to original sources

Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder Competition

In this report, we summarize and analyze the 2024 Adaptyv protein design competition. Participants used computational and Machine Learning (ML) methods of their choice to design proteins that bind the Epidermal Growth Factor Receptor (EGFR), a key drug target involved in cell growth, differentiation, and cancer development. Over 1,800 designs were submitted across two rounds. Of these, 601 proteins were selected and characterized for expression and binding affinity to EGFR, with competitors both optimizing existing binders (KD = 1.21 nM) and creating de novo binders (KD = 82 nM). All selected designs were experimentally validated using Adaptyvs automated Bio-Layer Interferometry (BLI) pipeline. This competition illustrates the potential of crowdsourcing to drive creativity and innovation in protein design. However, it also exposed key challenges, such as the lack of standardized benchmarks, experimental design targets, and robust computational metrics for method comparison. We anticipate that future competitions will address these gaps and further motivate progress in computational protein design.

bioengineering↗

Delineating inter- and intra-antibody repertoire evolution with AntibodyForests

MotivationThe rapid advancements in immune repertoire sequencing, powered by single-cell technologies and artificial intelligence, have created unprecedented opportunities to study B cell evolution at a novel scale and resolution. However, fully leveraging these data requires specialized software capable of performing inter- and intra-repertoire analyses to unravel the complex dynamics of B cell repertoire evolution during immune responses. ResultsHere, we present AntibodyForests, software to infer B cell lineages, quantify inter- and intra-antibody repertoire evolution, and analyze somatic hypermutation using protein language models and protein structure. Availability and implementationThis R package is available on CRAN (1) and Github at https://github.com/alexyermanos/AntibodyForests, a vignette is available at https://cran.case.edu/web/packages/AntibodyForests/vignettes/AntibodyForests_vignette.html

bioinformatics↗

AI.zymes - A modular platform for evolutionary enzyme design

The ability to create new-to-nature enzymes would substantially advance bioengineering, medicine, and the chemical industry. Despite recent breakthroughs in protein design and structure prediction, designing biocatalysts with activities rivaling those of natural enzymes remains challenging. Here, we present AI.zymes, a modular platform integrating cutting-edge protein engineering algorithms within an evolutionary framework. By combining programs such as Rosetta, ESMFold, ProteinMPNN, and FieldTools in iterative rounds of design and selection, AI.zymes optimizes a broad range of catalytically relevant properties, such as transition state affinity and protein stability. Notably, AI.zymes can also improve properties that are not targeted by the employed design algorithms. For instance, AI.zymes enhanced electrostatic catalysis by iteratively selecting variants with stronger catalytic electric fields. AI.zymes was benchmarked by improving the promiscuous Kemp eliminase activity of ketosteroid isomerase, yielding a 7.7-fold increase in activity after zero-shot experimental validation of only 7 variants. Due to its modularity, AI.zymes can readily incorporate emerging design algorithms, paving the way for a unifying framework for enzyme design. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/633707v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@f2ac2eorg.highwire.dtl.DTLVardef@61b2aorg.highwire.dtl.DTLVardef@e176edorg.highwire.dtl.DTLVardef@46d692_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

ePlatypus: an ecosystem for computational analysis of immunogenomics data

The maturation of systems immunology methodologies requires novel and transparent computational frameworks capable of integrating diverse data modalities in a reproducible manner. Here, we present the ePlatypus computational immunology ecosystem for immunogenomics data analysis, with a focus on adaptive immune repertoires and single-cell sequencing. ePlatypus is a web-based platform and provides programming tutorials and an integrative database that elucidates selection patterns of adaptive immunity. Furthermore, the ecosystem links novel and established bioinformatics pipelines relevant for single-cell immune repertoires and other aspects of computational immunology such as predicting ligand-receptor interactions, structural modeling, simulations, machine learning, graph theory, pseudotime, spatial transcriptomics and phylogenetics. The ePlatypus ecosystem helps extract deeper insight in computational immunology and immunogenomics and promote open science. Accessibilityhttps://alexyermanos.github.io/Platypus/index.html

immunology↗

Persistent virus-specific and clonally expanded antibody secreting cells respond to induced self antigen in the CNS

B cells contribute to the pathogenesis of both cellular- and humoral-mediated central nervous system (CNS) inflammatory diseases through a variety of mechanisms. In such conditions, B cells may enter the CNS parenchyma and contribute to local tissue destruction. It remains unexplored, however, how infection and autoimmunity drive transcriptional phenotypes, repertoire features, and antibody functionality. Here, we profiled B cells from the CNS of murine models of intracranial (i.c.) viral infections and autoimmunity. We identified a population of clonally expanded, antibody secreting cells (ASCs) that had undergone class-switch recombination and extensive somatic hypermutation following i.c. infection with attenuated lymphocytic choriomeningitis virus (rLCMV). Recombinant expression and characterisation of these antibodies revealed specificity to viral antigens (LCMV glycoprotein GP), correlating with ASC persistence in the brain weeks after resolved infection. Furthermore, these virus-specific ASCs upregulated proliferation and expansion programs in response to the conditional and transient induction of the LCMV GP as a neo-self antigen by astrocytes. This class-switched, clonally expanded, and mutated population persisted and was even more pronounced when peripheral B cells were depleted prior to autoantigen induction in the CNS. In contrast, the most expanded B cell clones in mice with persistent expression of LCMV GP in the CNS did not exhibit neo-self antigen specificity, potentially a consequence of local tolerance induction. Finally, a comparable population of clonally expanded, class-switched, proliferating ASCs was detected in the cerebrospinal fluid of multiple sclerosis patients. Taken together, our findings support the existence of B cells that populate the CNS and are capable of responding to locally encountered autoantigens. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

immunology↗