Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation

Narayanan, Harini; Dingfelder, Fabian; Butté, Alessandro; Lorenzen, Nikolai; Sokolov, Michael; Arosio, Paolo

doi:10.1016/j.tips.2020.12.004

Cited by 116 publications

(105 citation statements)

References 107 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Iterating with experimental screen, our cluster-learning sampling approach is a special type of active learning in protein engineering [3]. The current active learning methods usually use supervised learning to make decisions for the next round of experiment.…”

Section: Discussionmentioning

confidence: 99%

“…The last decade has witnessed the rapid development of machine learning and deep learning algorithms for biological data [2,3,4,5,6]. Supervised models can learn relationships between sequences and fitness properties, and provide quantitative predictions on protein thermostability [7], protein folding energy [8,9], protein solubility [10], protein-ligand binding affinity [11], and protein-protein binding affinity [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

CLADE: Cluster learning-assisted directed evolution

Qiu

Wei

2021

Preprint

View full text Add to dashboard Cite

Directed evolution (DE), a strategy for protein engineering, optimizes protein properties (i.e. fitness) by expensive and time-consuming screen or selection of a large combinatorial sequence space. Machine learning-assisted directed evolution (MLDE) that screens variant properties in silico can reduce the experimental burden. However, the MLDE utilizing small experimentally labeled training data from random sampling renders low global maximal fitness hitting rates. This work introduces a cluster learning-assisted directed evolution (CLADE) framework, particularly designed for systems without high-throughput screening assays, that combines sampling through hierarchical unsupervised clustering and supervised learning to guide protein engineering. Based on general biological information, CLADE splits the genetic combinatorial space into various subspaces with heterogeneous evolutionary traits, which guides the selection of experimental sampling sets and the subsequent building up of supervised learning training sets. By virtually screening two four-site combinatorial fitness landscapes from protein G domain B1 (GB1) and PhoQ, our CLADE consistently showed near 3-fold improvement on global maximal fitness hitting rate than using randomly sampled training data. Our CLADE can be easily applied to various biological systems and customized for systems with different throughput levels to maximize its accuracy and efficiency. It promises a significant impact to protein engineering.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CLADE: Cluster learning-assisted directed evolution

Qiu

Wei

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The majority of ML studies in 3DP medicines has been applied to oral-formulations, and further research needs to explore the feasibility of ML in fabricating other delivery devices [276]. A concerted effort is being made to address the current challenges of combining AI with pharmaceutical 3DP; such as lack of AI skillset, algorithm decision-making transparency, and production of ML techniques that provide high performances even with small datasets [8,277]. These issues are universally felt across both academia and industry, irrespective of the research field, thus driving a collective impetus.…”

Section: Pharmaceutical 3d Printing's Intelligent Trajectorymentioning

confidence: 99%

Harnessing artificial intelligence for the next generation of 3D printed medicines

Elbadawi

McCoubrey

Gavins

et al. 2021

Advanced Drug Delivery Reviews

110

View full text Add to dashboard Cite

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

show abstract

“…And recently, immense efforts to utilize mAbs for the neutralization of viral agents, such as HIV, influenza, and SARS-CoV-2 (2-4) are ongoing as well. So far, however, lead times to mAb discovery and design are on average >3 years (5)(6)(7)(8). The reason for this is that current mAb development pipelines mostly rely on a combination of large screening libraries and experimental heuristics with very little to no emphasis on rule-driven discovery (9).…”

Section: Introductionmentioning

confidence: 99%

“…Recent reports suggest that ML may be able to learn the rules of efficient antibody (protein) design (6,(10)(11)(12)(13)(14)(15)(16)(17). Specifically, Amimeur and colleagues (18) trained generative adversarial networks (GANs) (19) on sequences obtained from the Observed Antibody Space (OAS) database (20) to demonstrate the capacity of deep generative networks to discover mAbs with certain developability parameters.…”

Section: Introductionmentioning

confidence: 99%

In silico proof of principle of machine learning-based antibody design at unconstrained scale

Akbar

Weber

et al. 2021

Preprint

View full text Add to dashboard Cite

Generative machine learning (ML) has been postulated to be a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences for their most critical design parameters: paratope, epitope, affinity, and developability. To address this challenge, we leveraged a lattice-based antibody-antigen binding simulation framework, which incorporates a wide range of physiological antibody binding parameters. The simulation framework enables both the computation of antibody-antigen 3D-structures as well as functions as an oracle for unrestricted prospective evaluation of the antigen specificity of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (1D) data can be used to design native-like conformational (3D) epitope-specific antibodies, matching or exceeding the training dataset in affinity and developability variety. Furthermore, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Finally, we validated that the antibody design insight gained from simulated antibody-antigen binding data is applicable to experimental real-world data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design.HighlightsA large-scale dataset of 70M [3 orders of magnitude larger than the current state of the art] synthetic antibody-antigen complexes, that reflect biological complexity, allows the prospective evaluation of antibody generative deep learningCombination of generative learning, synthetic antibody-antigen binding data, and prospective evaluation shows that deep learning driven antibody design and discovery at an unconstrained level is feasibleTransfer learning (low-N learning) coupled to generative learning shows that antibody-binding rules may be transferred across unrelated antibody-antigen complexesExperimental validation of antibody-design conclusions drawn from deep learning on synthetic antibody-antigen binding dataGraphical abstractWe leverage large synthetic ground-truth data to demonstrate the (A,B) unconstrained deep generative learning-based generation of native-like antibody sequences, (C) the prospective evaluation of conformational (3D) affinity, paratope-epitope pairs, and developability. (D) Finally, we show increased generation quality of low-N-based machine learning models via transfer learning.

show abstract

Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation

Cited by 116 publications

References 107 publications

CLADE: Cluster learning-assisted directed evolution

CLADE: Cluster learning-assisted directed evolution

Harnessing artificial intelligence for the next generation of 3D printed medicines

In silico proof of principle of machine learning-based antibody design at unconstrained scale

Contact Info

Product

Resources

About