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  1. Thesis ; Online: Data efficient machine learning-guided protein engineering

    Minot, Mason

    2024  

    Keywords info:eu-repo/classification/ddc/004 ; info:eu-repo/classification/ddc/570 ; info:eu-repo/classification/ddc/620 ; Data processing ; computer science ; Life sciences ; Engineering & allied operations
    Language English
    Publisher ETH Zurich
    Publishing country ch
    Document type Thesis ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Meta learning addresses noisy and under-labeled data in machine learning-guided antibody engineering.

    Minot, Mason / Reddy, Sai T

    Cell systems

    2024  Volume 15, Issue 1, Page(s) 4–18.e4

    Abstract: Machine learning-guided protein engineering is rapidly progressing; however, collecting high-quality, large datasets remains a bottleneck. Directed evolution and protein engineering studies often require extensive experimental processes to eliminate ... ...

    Abstract Machine learning-guided protein engineering is rapidly progressing; however, collecting high-quality, large datasets remains a bottleneck. Directed evolution and protein engineering studies often require extensive experimental processes to eliminate noise and label protein sequence-function data. Meta learning has proven effective in other fields in learning from noisy data via bi-level optimization given the availability of a small dataset with trusted labels. Here, we leverage meta learning approaches to overcome noisy and under-labeled data and expedite workflows in antibody engineering. We generate yeast display antibody mutagenesis libraries and screen them for target antigen binding followed by deep sequencing. We then create representative learning tasks, including learning from noisy training data, positive and unlabeled learning, and learning out of distribution properties. We demonstrate that meta learning has the potential to reduce experimental screening time and improve the robustness of machine learning models by training with noisy and under-labeled training data.
    MeSH term(s) Antibodies ; Amino Acid Sequence ; Engineering ; Machine Learning ; Mutagenesis
    Chemical Substances Antibodies
    Language English
    Publishing date 2024-01-08
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2854138-8
    ISSN 2405-4720 ; 2405-4712
    ISSN (online) 2405-4720
    ISSN 2405-4712
    DOI 10.1016/j.cels.2023.12.003
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Nucleotide augmentation for machine learning-guided protein engineering.

    Minot, Mason / Reddy, Sai T

    Bioinformatics advances

    2022  Volume 3, Issue 1, Page(s) vbac094

    Abstract: Summary: Machine learning-guided protein engineering is a rapidly advancing field. Despite major experimental and computational advances, collecting protein genotype (sequence) and phenotype (function) data remains time- and resource-intensive. As a ... ...

    Abstract Summary: Machine learning-guided protein engineering is a rapidly advancing field. Despite major experimental and computational advances, collecting protein genotype (sequence) and phenotype (function) data remains time- and resource-intensive. As a result, the quality and quantity of training data are often a limiting factor in developing machine learning models. Data augmentation techniques have been successfully applied to the fields of computer vision and natural language processing; however, there is a lack of such augmentation techniques for biological sequence data. Towards this end, we develop nucleotide augmentation (NTA), which leverages natural nucleotide codon degeneracy to augment protein sequence data via synonymous codon substitution. As a proof of concept for protein engineering, we test several online and offline augmentation implementations to train machine learning models with benchmark datasets of protein genotype and phenotype, revealing performance gains on par and surpassing benchmark models using a fraction of the training data. NTA also enables substantial improvements for classification tasks under heavy class imbalance.
    Availability and implementation: The code used in this study is publicly available at https://github.com/minotm/NTA.
    Supplementary information: Supplementary data are available at
    Language English
    Publishing date 2022-12-09
    Publishing country England
    Document type Journal Article
    ISSN 2635-0041
    ISSN (online) 2635-0041
    DOI 10.1093/bioadv/vbac094
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  4. Article ; Online: Deep mutational scanning for therapeutic antibody engineering.

    Hanning, Kyrin R / Minot, Mason / Warrender, Annmaree K / Kelton, William / Reddy, Sai T

    Trends in pharmacological sciences

    2021  Volume 43, Issue 2, Page(s) 123–135

    Abstract: The biophysical and functional properties of monoclonal antibody (mAb) drug candidates are often improved by protein engineering methods to increase the probability of clinical efficacy. One emerging method is deep mutational scanning (DMS) which ... ...

    Abstract The biophysical and functional properties of monoclonal antibody (mAb) drug candidates are often improved by protein engineering methods to increase the probability of clinical efficacy. One emerging method is deep mutational scanning (DMS) which combines the power of exhaustive protein mutagenesis and functional screening with deep sequencing and bioinformatics. The application of DMS has yielded significant improvements to the affinity, specificity, and stability of several preclinical antibodies alongside novel applications such as introducing multi-specific binding properties. DMS has also been applied directly on target antigens to precisely map antibody-binding epitopes and notably to profile the mutational escape potential of viral targets (e.g., SARS-CoV-2 variants). Finally, DMS combined with machine learning is enabling advances in the computational screening and engineering of therapeutic antibodies.
    MeSH term(s) Antibodies, Viral ; COVID-19 ; Humans ; SARS-CoV-2 ; Spike Glycoprotein, Coronavirus
    Chemical Substances Antibodies, Viral ; Spike Glycoprotein, Coronavirus
    Language English
    Publishing date 2021-12-09
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 282846-7
    ISSN 1873-3735 ; 0165-6147
    ISSN (online) 1873-3735
    ISSN 0165-6147
    DOI 10.1016/j.tips.2021.11.010
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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
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  5. Article ; Online: Deploying synthetic coevolution and machine learning to engineer protein-protein interactions.

    Yang, Aerin / Jude, Kevin M / Lai, Ben / Minot, Mason / Kocyla, Anna M / Glassman, Caleb R / Nishimiya, Daisuke / Kim, Yoon Seok / Reddy, Sai T / Khan, Aly A / Garcia, K Christopher

    Science (New York, N.Y.)

    2023  Volume 381, Issue 6656, Page(s) eadh1720

    Abstract: Fine-tuning of protein-protein interactions occurs naturally through coevolution, but this process is difficult to recapitulate in the laboratory. We describe a platform for synthetic protein-protein coevolution that can isolate matched pairs of ... ...

    Abstract Fine-tuning of protein-protein interactions occurs naturally through coevolution, but this process is difficult to recapitulate in the laboratory. We describe a platform for synthetic protein-protein coevolution that can isolate matched pairs of interacting muteins from complex libraries. This large dataset of coevolved complexes drove a systems-level analysis of molecular recognition between Z domain-affibody pairs spanning a wide range of structures, affinities, cross-reactivities, and orthogonalities, and captured a broad spectrum of coevolutionary networks. Furthermore, we harnessed pretrained protein language models to expand, in silico, the amino acid diversity of our coevolution screen, predicting remodeled interfaces beyond the reach of the experimental library. The integration of these approaches provides a means of simulating protein coevolution and generating protein complexes with diverse molecular recognition properties for biotechnology and synthetic biology.
    MeSH term(s) Amino Acids/chemistry ; Machine Learning ; Proteins/chemistry ; Directed Molecular Evolution/methods ; Protein Interaction Domains and Motifs ; Datasets as Topic ; Staphylococcal Protein A/chemistry
    Chemical Substances Amino Acids ; Proteins ; Staphylococcal Protein A
    Language English
    Publishing date 2023-07-28
    Publishing country United States
    Document type Journal Article
    ZDB-ID 128410-1
    ISSN 1095-9203 ; 0036-8075
    ISSN (online) 1095-9203
    ISSN 0036-8075
    DOI 10.1126/science.adh1720
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 27: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)
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  6. Article ; Online: Synthetic coevolution reveals adaptive mutational trajectories of neutralizing antibodies and SARS-CoV-2

    Ehling, Roy A. / Minot, Mason / Overath, Max D. / Sheward, Daniel J. / Han, Jiami / Gao, Beichen / Taft, Joseph M. / Pertseva, Margarita / Weber, Cédric R. / Frei, Lester / Bikias, Thomas / Murrell, Ben / Reddy, Sai T.

    bioRxiv

    Abstract: The Covid-19 pandemic showcases a coevolutionary race between the human immune system and SARS-CoV-2, mirroring the Red Queen hypothesis of evolutionary biology. The immune system generates neutralizing antibodies targeting the SARS-CoV-2 spike protein9s ...

    Abstract The Covid-19 pandemic showcases a coevolutionary race between the human immune system and SARS-CoV-2, mirroring the Red Queen hypothesis of evolutionary biology. The immune system generates neutralizing antibodies targeting the SARS-CoV-2 spike protein9s receptor binding domain (RBD), crucial for host cell invasion, while the virus evolves to evade antibody recognition. Here, we establish a synthetic coevolution system combining high-throughput screening of antibody and RBD variant libraries with protein mutagenesis, surface display, and deep sequencing. Additionally, we train a protein language machine learning model that predicts antibody escape to RBD variants. Synthetic coevolution reveals antagonistic and compensatory mutational trajectories of neutralizing antibodies and SARS-CoV-2 variants, enhancing the understanding of this evolutionary conflict.
    Keywords covid19
    Language English
    Publishing date 2024-04-01
    Publisher Cold Spring Harbor Laboratory
    Document type Article ; Online
    DOI 10.1101/2024.03.28.587189
    Database COVID19

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  7. Article ; Online: Reduced order modeling and analysis of the human complement system.

    Sagar, Adithya / Dai, Wei / Minot, Mason / LeCover, Rachel / Varner, Jeffrey D

    PloS one

    2017  Volume 12, Issue 11, Page(s) e0187373

    Abstract: Complement is an important pathway in innate immunity, inflammation, and many disease processes. However, despite its importance, there are few validated mathematical models of complement activation. In this study, we developed an ensemble of ... ...

    Abstract Complement is an important pathway in innate immunity, inflammation, and many disease processes. However, despite its importance, there are few validated mathematical models of complement activation. In this study, we developed an ensemble of experimentally validated reduced order complement models. We combined ordinary differential equations with logical rules to produce a compact yet predictive model of complement activation. The model, which described the lectin and alternative pathways, was an order of magnitude smaller than comparable models in the literature. We estimated an ensemble of model parameters from in vitro dynamic measurements of the C3a and C5a complement proteins. Subsequently, we validated the model on unseen C3a and C5a measurements not used for model training. Despite its small size, the model was surprisingly predictive. Global sensitivity and robustness analysis suggested complement was robust to any single therapeutic intervention. Only the simultaneous knockdown of both C3 and C5 consistently reduced C3a and C5a formation from all pathways. Taken together, we developed a validated mathematical model of complement activation that was computationally inexpensive, and could easily be incorporated into pre-existing or new pharmacokinetic models of immune system function. The model described experimental data, and predicted the need for multiple points of therapeutic intervention to fully disrupt complement activation.
    MeSH term(s) Complement Activation/genetics ; Complement C3/genetics ; Complement C3/immunology ; Complement C3a/genetics ; Complement C3a/immunology ; Complement C5/genetics ; Complement C5/immunology ; Complement C5a/genetics ; Complement C5a/immunology ; Gene Knockdown Techniques ; Humans ; Immunity, Innate ; Inflammation/drug therapy ; Inflammation/immunology ; Lectins/immunology ; Lectins/pharmacokinetics ; Lectins/therapeutic use ; Models, Theoretical ; Pharmacokinetics
    Chemical Substances Complement C3 ; Complement C5 ; Lectins ; Complement C3a (80295-42-7) ; Complement C5a (80295-54-1)
    Language English
    Publishing date 2017-11-20
    Publishing country United States
    Document type Journal Article
    ISSN 1932-6203
    ISSN (online) 1932-6203
    DOI 10.1371/journal.pone.0187373
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  8. Article ; Online: JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language.

    Bassen, David M / Vilkhovoy, Michael / Minot, Mason / Butcher, Jonathan T / Varner, Jeffrey D

    BMC systems biology

    2017  Volume 11, Issue 1, Page(s) 10

    Abstract: Background: Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using parameter or model families ... ...

    Abstract Background: Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using parameter or model families instead of single best-fit parameters or fixed model structures. Parameter ensembles can be selected based upon simulation error, along with other criteria such as diversity or steady-state performance. Simulations using parameter ensembles can estimate confidence intervals on model variables, and robustly constrain model predictions, despite having many poorly constrained parameters.
    Results: In this software note, we present a multiobjective based technique to estimate parameter or models ensembles, the Pareto Optimal Ensemble Technique in the Julia programming language (JuPOETs). JuPOETs integrates simulated annealing with Pareto optimality to estimate ensembles on or near the optimal tradeoff surface between competing training objectives. We demonstrate JuPOETs on a suite of multiobjective problems, including test functions with parameter bounds and system constraints as well as for the identification of a proof-of-concept biochemical model with four conflicting training objectives. JuPOETs identified optimal or near optimal solutions approximately six-fold faster than a corresponding implementation in Octave for the suite of test functions. For the proof-of-concept biochemical model, JuPOETs produced an ensemble of parameters that gave both the mean of the training data for conflicting data sets, while simultaneously estimating parameter sets that performed well on each of the individual objective functions.
    Conclusions: JuPOETs is a promising approach for the estimation of parameter and model ensembles using multiobjective optimization. JuPOETs can be adapted to solve many problem types, including mixed binary and continuous variable types, bilevel optimization problems and constrained problems without altering the base algorithm. JuPOETs is open source, available under an MIT license, and can be installed using the Julia package manager from the JuPOETs GitHub repository.
    MeSH term(s) Models, Biological ; Programming Languages ; Uncertainty
    Language English
    Publishing date 2017-01-25
    Publishing country England
    Document type Journal Article
    ISSN 1752-0509
    ISSN (online) 1752-0509
    DOI 10.1186/s12918-016-0380-2
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