Machine Learning for Protein Engineering - Part 2 banner

第2回年次会議「タンパク質工学向け機械学習:パート2」

in silicoでの予測・エンジニアリング・設計は、将来の医薬品の発見、設計、最適化の方法を変えています。これらのツールはまだ開発の初期段階にあり、抗体やワクチンの発見、トレーニング、予測、開発可能性、シミュレーション、最適化で使用する場合、どのように適応させるかについては、多くのことを学ぶ必要があります。

Scientific Advisory Board:
     M. Frank Erasmus, PhD, Head, Bioinformatics, Specifica, Inc. 
     Victor Greiff, PhD, Associate Professor, Oslo University Hospital
     Maria Wendt, PhD, Global Head and Vice President, Digital and Biologics Strategy and Innovation, Sanofi

 

Recommended Short Course*
Monday, 13 November, 14:00 - 17:00
SC1: Machine Learning Tools for Protein Engineering
*Separate registration required. See short courses page for details. All short courses take place in-person only.

11月16日(木)

Registration Open and Morning Coffee07:30

PLM AND GENERATIVE MODELING FOR DE NOVO DESIGN
de novo 設計向けPLMとジェネレーティブモデリング

08:25

Chairperson's Remarks

Maria Wendt, PhD, Global Head and Vice President, Digital and Biologics Strategy and Innovation, Sanofi

08:30

Enhancing Antibody Discovery with Generative AI

Melody Shahsavarian, PhD, Digital Biologics Platform, Large Molecules Research, Sanofi

With a growing majority of its pipeline composed of biologics, there is an increasing need at Sanofi to bring more molecules to development at a faster pace. Generative AI and in silico screening methods provide opportunities to improve probability of success and decrease discovery-to-lead timelines. Combining deep repertoire mining technologies and generative ML modeling, we are building a de novo protein design platform and a more targeted drug discovery approach.

09:00

The Singular Immune Response to Dengue and Machine Learning Identification of Antibodies in High-Throughput Sequences

Enkelejda Miho, PhD, Professor, Dean, University of Applied Sciences and Arts Northwestern Switzerland

Dengue virus is a threat to global health. However, no specific therapeutics are available so far. Broadly neutralizing antibodies recognizing the various serotypes could serve as potential treatment. High-throughput adaptive immune receptor repertoire high-throughput sequencing (AIRR-seq) and bioinformatic analyses enable in-depth understanding of the B cell immune response. We investigated the dengue antibody response with these technologies and machine learning to identify rare and underrepresented broadly neutralizing antibody sequences. Dengue immunization elicited following signatures on the antibody repertoire: (i) an increase of CDR3 and germline gene diversity; (ii) a change in the architecture by eliciting power-law network distributions and CDR3 enrichment in polar amino acids; and (iii) an increase in the expression of JNK/Fos transcription factors and ribosomal proteins. Our work shows the applicability of computational methods and machine learning to AIRR-seq datasets for identification of potential neutralizing antibody candidate sequences. Further investigation of antibody expression and functional binding assays validated the obtained results.

Sponsored Presentation (Opportunity Available)09:30

Coffee Break in the Exhibit Hall with Poster Viewing10:00

10:45 KEYNOTE PRESENTATION:

Launching into the Future: Sanofi’s Biologics AI Moonshot Program - Advancing AI Strategy and Innovation for Biologics

Maria Wendt, PhD, Global Head and Vice President, Digital and Biologics Strategy and Innovation, Sanofi

Sanofi recently launched the BioAIM program to push forward on our ambition to transform biologics drug discovery. This talk will discuss the landscape of opportunities for ML and AI in all aspects of antibody generation to design and engineering of advanced modalities, our approach, examples of novel methods developed and early results.

11:15

Protein Engineering with Deep Generative Models

Ali Madani, PhD, Founder and CEO, Profluent Bio

Generative models have shown promise in capturing the distribution of natural proteins. In this talk, we'll cover some recent advances in large-scale machine learning models for functional protein design with focus on practical engineering tasks and experimental characterization.

11:45

Computational Counterselection Identifies Nonspecific Therapeutic Biologic Candidates

Stefan Ewert, PhD, Associate Director, Biologics Center, Novartis Institutes for Biomedical Research

Biologics require high specificity for targets, but current affinity-selection-based discovery methods do not guarantee this property. We present a method, computational counterselection, that identifies nonspecific candidates using machine learning models of affinity trained on high-throughput data from single-target affinity selection experiments.

12:15 Talk Title to be Announced

Anne Goupil-Lamy, PhD, Science Council Fellow at BIOVIA, BIOVIA, Dassault Systemes

Session Break12:45

Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own12:50

Dessert Break in the Exhibit Hall & Last Chance for Poster Viewing13:50

Session Break14:45

PLM AND GENERATIVE MODELING FOR DE NOVO DESIGN (CONT.)
de novo 設計向けPLMとジェネレーティブモデリング(続き)

15:00

Chairperson's Remarks

Victor Greiff, PhD, Associate Professor, Immunology, University of Oslo

15:05

Applying Deep Learning Anomaly Detection to Antibody Structures

Hiroki Shirai, PhD, Coordinator, RIKEN Center for Computational Science

While generators by deep leaning are bringing about revolutions in various fields of science, technology, and business, there are also warnings about the dangers of such generators. Generation of antibody sequences is also occurring in various generative models, but to what extent is the humanness of these models guaranteed? Evaluation of humanness has also been described as improved by deep learning, but they are all sequence-based humanness scores, and there is a common limitation in the presence of V-D-J sequence faults. To overcome this problem, the humanness should also be evaluated from the structure of the antibody. However, at present, this approach is limited to non-deep machine learning, so it is necessary to extract features from the structure in advance, which is extremely difficult to do. Deep learning is revolutionary in that it enables end-to-end prediction without prior extraction of such features. Therefore, we developed a new end-to-end method to evaluate the humanness of antibodies from 2D pixel images of antibody structures using CNN-VAE, which is a technique used to detect outliers in factory-produced products. This is expected to ensure the humanness of antibody sequences automatically generated by AI.

STRUCTURE, DOCKING, AND DYNAMICS FUNDAMENTALS
構造、ドッキング、ダイナミクスの基礎

15:35

Unconstrained Generation of Synthetic Antibody-Antigen Structures to Guide Machine Learning Methodology for Antibody Specificity Prediction

Rahmad Akbar, PhD, Researcher, Computational Systems Immunology, University of Oslo

Antibody structures inform and improve machine learning predictions. We devise a method for the parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody-antigen-binding structures. Our method provides ground-truth access to conformational paratope, epitope, and affinity. We showcase the utility of synthetic datasets to benchmark the real-world relevance of machine learning models for antibody binding prediction.

16:05

Third-Generation Approaches of Antibody Discovery and Optimisation

Pietro Sormanni, PhD, Group Leader, Royal Society University Research Fellow, Chemistry of Health, Yusuf Hamied Department of Chemistry, University of Cambridge

Current technologies for antibody discovery and optimization have been widely successful, but are still subject to limitations. Established screening procedures are laborious, and targeting predetermined epitopes and optimizing multiple biophysical traits simultaneously remains a challenge. In this presentation, I will discuss emerging computational antibody design methods, which enable the targeted design of antibodies for predetermined epitopes and the prediction and modulation of their developability potential through the co-optimization of multiple biophysical properties. Overall, it is increasingly possible to complement well-established in vivo (first-generation) and in vitro (second-generation) methods of antibody discovery with in silico (third-generation) approaches, with time- and cost-saving benefits. These approaches are becoming sufficiently mature to be highly competitive for some applications, thus offering novel opportunities to streamline antibody development.

16:35 Accelerating Antibody Development: Advancing Discovery through Integrated Bioinformatics and Machine Learning

Jannick Bendtsen, CEO, PipeBio

Early-stage antibody discovery requires efficient and comprehensive approaches to identify promising candidates with optimal developability characteristics. This presentation explores how next-generation sequencing (NGS) analysis and machine learning can be applied to optimize antibody developability. We explore a practical implementation of analysis pipelines using PipeBio Bioinformatics Platform and illustrate the benefits of applying such analysis tools through case studies, showing their efficacy in expediting early-stage antibody discovery.

Sponsored Presentation (Opportunity Available)16:50

NOVEL/ALTERNATIVE ML-ENABLED SCREENING TECHNOLOGIES FOR HIGHER POS
高評価を実現する新規/代替のML対応スクリーニング技術

17:05

Chairperson's Remarks

M. Frank Erasmus, PhD, Head, Bioinformatics, Specifica, Inc.

17:10

scifAI: An Explainable Machine Learning Framework Applied to Functional Characterization of Therapeutic Antibodies

Fabian Schmich, PhD, Senior Data Scientist, pRED Informatics, Roche Diagnostics Deutschland GmbH

scifAI is a comprehensive, open-source explainable machine learning framework for the analysis of imaging flow cytometry data. In this presentation, I will focus on alterations to the immunological synapse, analyzing class frequency- and morphological changes of the cell, as well as showcasing the prediction of T cell cytokine production under stimulation with different antibodies, linking morphological features with function and thus demonstrating the potential to significantly impact antibody design.

17:40

Low-Data Interpretable Deep Learning Prediction of Antibody Viscosity Using a Biophysically Meaningful Representation

Brajesh K. Rai, PhD, Senior Director, Machine Learning Computational Sciences, Pfizer Inc.

Deep learning has led to substantial advances across many disciplines. However, many scientific problems of practical interest lack sufficiently large datasets amenable to deep learning. Prediction of antibody viscosity is one such problem where these methods have not yet been explored due to the relative scarcity of relevant training data. We will describe how we have overcome this limitation using a biophysically meaningful representation to develop generalizable deep learning models.

18:10

Integrating Single-Cell Immune Repertoire Sequencing, Machine Learning, and Biophysical Properties of Antibodies

Alexander Yermanos, PhD, Lecturer, Systems & Synthetic Immunology, ETH Zurich

Immune repertoires represent a diverse collection of B and T cell receptors which interact with a seemingly infinite number of molecular structures. Recent advancements in deep sequencing and microfluidics allow high-throughput recovery of paired heavy and light chain sequences, thereby linking computational features of immune repertoires to biophysical properties of antibodies at an unprecedented resolution. I will explore the intersection of repertoires, ML, and biophysical features like antigen-specificity, affinity, and epitope.


Close of PEGS Europe Summit18:40


* 不測の事態により、事前の予告なしにプログラムが変更される場合があります。

Choose your language
English



Premier Sponsors

Asimov Inc

Evitria

Immatics-Biotechnologies

Integral-Molecular_NEW

LONZA

Phenomex Logo