Learning to design protein-protein interactions with enhanced
generalization

D 2024

Learning to design protein-protein interactions with enhanced generalization

BUSHUIEV, Anton; Roman BUSHUIEV; Petr KOUBA; Anatolii FILKIN; Marketa GABRIELOVA et al.

Základní údaje

Originální název

Learning to design protein-protein interactions with enhanced generalization

Autoři

BUSHUIEV, Anton; Roman BUSHUIEV; Petr KOUBA; Anatolii FILKIN; Marketa GABRIELOVA; Michal GABRIEL; Jiri SEDLAR; Tomas PLUSKAL4; Jiří DAMBORSKÝ; Stanislav MAZURENKO a Josef SIVIC

Vydání

12th International Conference on Learning Representations 2024, 26 s. 2024

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10608 Biochemistry and molecular biology

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Označené pro přenos do RIV

Organizační jednotka

Přírodovědecká fakulta

Klíčová slova anglicky

protein-protein interactions; protein design; generalization; self-supervised learning; equivariant 3D representations

Štítky

14314010, 143180

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 24. 4. 2024 10:22, Mgr. Marie Novosadová Šípková, DiS.

Anotace

V originále

Discovering mutations enhancing protein-protein interactions (PPIs) is critical for advancing biomedical research and developing improved therapeutics. While machine learning approaches have substantially advanced the field, they often struggle to generalize beyond training data in practical scenarios. The contributions of this work are three-fold. First, we construct PPIRef, the largest and non-redundant dataset of 3D protein-protein interactions, enabling effective large-scale learning. Second, we leverage the PPIRef dataset to pre-train PPIformer, a new SE(3)-equivariant model generalizing across diverse protein-binder variants. We fine-tune PPIformer to predict effects of mutations on protein-protein interactions via a thermodynamically motivated adjustment of the pre-training loss function. Finally, we demonstrate the enhanced generalization of our new PPIformer approach by outperforming other state-of-the-art methods on new, non-leaking splits of standard labeled PPI mutational data and independent case studies optimizing a human antibody against SARS-CoV-2 and increasing the thrombolytic activity of staphylokinase.

Návaznosti

EF17_043/0009632, projekt VaV

Název: CETOCOEN Excellence

LM2023055, projekt VaV

Název: Česká národní infrastruktura pro biologická data

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, ELIXIR-CZ: Česká národní infrastruktura pro biologická data

LM2023069, projekt VaV

Název: Výzkumná infrastruktura RECETOX

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Výzkumná infrastruktura RECETOX

857560, interní kód MU
(Kód CEP: EF17_043/0009632)

Název: CETOCOEN Excellence (Akronym: CETOCOEN Excellence)

Investor: Evropská unie, CETOCOEN Excellence, Spreading excellence and widening participation

90254, velká výzkumná infrastruktura

Název: e-INFRA CZ II

Citovat

BUSHUIEV, Anton; Roman BUSHUIEV; Petr KOUBA; Anatolii FILKIN; Marketa GABRIELOVA; Michal GABRIEL; Jiri SEDLAR; Tomas PLUSKAL4; Jiří DAMBORSKÝ; Stanislav MAZURENKO a Josef SIVIC. Learning to design protein-protein interactions with enhanced generalization. Online. In 12th International Conference on Learning Representations 2024. 2024, 26 s.

@inproceedings{2396557,
   author = {Bushuiev, Anton and Bushuiev, Roman and Kouba, Petr and Filkin, Anatolii and Gabrielova, Marketa and Gabriel, Michal and Sedlar, Jiri and Pluskal4, Tomas and Damborský, Jiří and Mazurenko, Stanislav and Sivic, Josef},
   booktitle = {12th International Conference on Learning Representations 2024},
   keywords = {protein-protein interactions; protein design; generalization; self-supervised learning; equivariant 3D representations},
   howpublished = {elektronická verze "online"},
   language = {eng},
   title = {Learning to design protein-protein interactions with enhanced generalization},
   url = {https://openreview.net/forum?id=xcMmebCT7s},
   year = {2024}
}

TY  - CONF
ID  - 2396557
AU  - Bushuiev, Anton - Bushuiev, Roman - Kouba, Petr - Filkin, Anatolii - Gabrielova, Marketa - Gabriel, Michal - Sedlar, Jiri - Pluskal4, Tomas - Damborský, Jiří - Mazurenko, Stanislav - Sivic, Josef
PY  - 2024
TI  - Learning to design protein-protein interactions with enhanced generalization
KW  - protein-protein interactions
KW  - protein design
KW  - generalization
KW  - self-supervised learning
KW  - equivariant 3D representations
UR  - https://openreview.net/forum?id=xcMmebCT7s
N2  - Discovering mutations enhancing protein-protein interactions (PPIs) is critical for advancing biomedical research and developing improved therapeutics. While machine learning approaches have substantially advanced the field, they often struggle to generalize beyond training data in practical scenarios. The contributions of this work are three-fold. First, we construct PPIRef, the largest and non-redundant dataset of 3D protein-protein interactions, enabling effective large-scale learning. Second, we leverage the PPIRef dataset to pre-train PPIformer, a new SE(3)-equivariant model generalizing across diverse protein-binder variants. We fine-tune PPIformer to predict effects of mutations on protein-protein interactions via a thermodynamically motivated adjustment of the pre-training loss function. Finally, we demonstrate the enhanced generalization of our new PPIformer approach by outperforming other state-of-the-art methods on new, non-leaking splits of standard labeled PPI mutational data and independent case studies optimizing a human antibody against SARS-CoV-2 and increasing the thrombolytic activity of staphylokinase.
ER  -

BUSHUIEV, Anton; Roman BUSHUIEV; Petr KOUBA; Anatolii FILKIN; Marketa GABRIELOVA; Michal GABRIEL; Jiri SEDLAR; Tomas PLUSKAL4; Jiří DAMBORSKÝ; Stanislav MAZURENKO a Josef SIVIC. Learning to design protein-protein interactions with enhanced generalization. Online. In \textit{12th International Conference on Learning Representations 2024}. 2024, 26 s.

Přehled o publikaci