1
|
Tučs A, Ito T, Kurumida Y, Kawada S, Nakazawa H, Saito Y, Umetsu M, Tsuda K. Extensive antibody search with whole spectrum black-box optimization. Sci Rep 2024; 14:552. [PMID: 38177656 PMCID: PMC10767033 DOI: 10.1038/s41598-023-51095-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/30/2023] [Indexed: 01/06/2024] Open
Abstract
In designing functional biological sequences with machine learning, the activity predictor tends to be inaccurate due to shortage of data. Top ranked sequences are thus unlikely to contain effective ones. This paper proposes to take prediction stability into account to provide domain experts with a reasonable list of sequences to choose from. In our approach, multiple prediction models are trained by subsampling the training set and the multi-objective optimization problem, where one objective is the average activity and the other is the standard deviation, is solved. The Pareto front represents a list of sequences with the whole spectrum of activity and stability. Using this method, we designed VHH (Variable domain of Heavy chain of Heavy chain) antibodies based on the dataset obtained from deep mutational screening. To solve multi-objective optimization, we employed our sequence design software MOQA that uses quantum annealing. By applying several selection criteria to 19,778 designed sequences, five sequences were selected for wet-lab validation. One sequence, 16 mutations away from the closest training sequence, was successfully expressed and found to possess desired binding specificity. Our whole spectrum approach provides a balanced way of dealing with the prediction uncertainty, and can possibly be applied to extensive search of functional sequences.
Collapse
Affiliation(s)
- Andrejs Tučs
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| | - Tomoyuki Ito
- Department of Biomolecular Engineering, Graduate School of Engineering, Tohoku University, Sendai, Japan
| | - Yoichi Kurumida
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan
- Department of Data Science, School of Frontier Engineering, Kitasato University, Sagamihara, Japan
| | - Sakiya Kawada
- Department of Biomolecular Engineering, Graduate School of Engineering, Tohoku University, Sendai, Japan
| | - Hikaru Nakazawa
- Department of Biomolecular Engineering, Graduate School of Engineering, Tohoku University, Sendai, Japan
| | - Yutaka Saito
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan
- RIKEN Center for Advanced Intelligence Project, RIKEN, Tokyo, 103-0027, Japan
- AIST-Waseda University Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), Tokyo, Japan
- Department of Data Science, School of Frontier Engineering, Kitasato University, Sagamihara, Japan
| | - Mitsuo Umetsu
- Department of Biomolecular Engineering, Graduate School of Engineering, Tohoku University, Sendai, Japan.
- RIKEN Center for Advanced Intelligence Project, RIKEN, Tokyo, 103-0027, Japan.
| | - Koji Tsuda
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan.
- RIKEN Center for Advanced Intelligence Project, RIKEN, Tokyo, 103-0027, Japan.
- Center for Basic Research on Materials, National Institute for Materials Science (NIMS), Tsukuba, Japan.
| |
Collapse
|
2
|
Szymczak P, Szczurek E. Artificial intelligence-driven antimicrobial peptide discovery. Curr Opin Struct Biol 2023; 83:102733. [PMID: 37992451 DOI: 10.1016/j.sbi.2023.102733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 10/06/2023] [Accepted: 10/30/2023] [Indexed: 11/24/2023]
Abstract
Antimicrobial peptides (AMPs) emerge as promising agents against antimicrobial resistance, providing an alternative to conventional antibiotics. Artificial intelligence (AI) revolutionized AMP discovery through both discrimination and generation approaches. The discriminators aid in the identification of promising candidates by predicting key peptide properties such as activity and toxicity, while the generators learn the distribution of peptides and enable sampling novel AMP candidates, either de novo or as analogs of a prototype peptide. Moreover, the controlled generation of AMPs with desired properties is achieved by discriminator-guided filtering, positive-only learning, latent space sampling, as well as conditional and optimized generation. Here we review recent achievements in AI-driven AMP discovery, highlighting the most exciting directions.
Collapse
Affiliation(s)
- Paulina Szymczak
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland.
| | - Ewa Szczurek
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland.
| |
Collapse
|