Transforming the Language of Life

Nambiar, Ananthan; Heflin, Maeve; Liu, Simon; Maslov, Sergei; Hopkins, Mark; Ritz, Anna

doi:10.1145/3388440.3412467

Cited by 75 publications

(25 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another viable option are the recurrent and attention-based neural networks, which have enough computational power to describe relevant dependencies in protein sequences [108, 109, 110]. However, while modern neural networks have been successfully applied to annotation of protein families [111, 112], their performance in modeling short protein sequence fragments is yet too be evaluated.…”

Section: Discussionmentioning

confidence: 99%

Exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins

Wojciechowski

Tekoglu

Gąsior-Głogowska

et al. 2022

Preprint

View full text Add to dashboard Cite

NLR proteins are intracellular receptors constituting a conserved component of the innate immune system of multicellular organisms. In fungi, NLRs are characterized by high diversity of architectures and presence of amyloid signaling. Here, we explore the diverse world of effector and signaling domains of fungal NLRs using state-of-the-art bioinformatic methods including MMseqs2 for fast clustering, probabilistic context-free grammars for sequence analysis, and AlphaFold2 deep neural networks for structure prediction. In addition to substantially improving the overall annotation, especially in basidiomycetes, the study identifies novel domains and reveals the structural similarity of MLKL-related HeLo- and Goodbye-like domains forming the most abundant superfamily of fungal NLR effectors. Moreover, compared to previous studies, we found several times more amyloid motifs, including novel families, and validated aggregating and prion-forming properties of the most abundant of them in vitro and in vivo. Also, through an extensive in silico search, the NLR-associated amyloid signaling is for the first time identified in basidiomycetes. The emerging picture highlights similarities and differences in the NLR architectures and amyloid signaling in ascomycetes, basidiomycetes and other branches of life.

show abstract

Section: Discussionmentioning

confidence: 99%

Exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins

Wojciechowski

Tekoglu

Gąsior-Głogowska

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Work in [8] debuts the PRoBERTa model. The model is pre-trained to learn task-agnostic sequence representations of amino-acid sequences.…”

Section: Related Workmentioning

confidence: 99%

“…In particular, they provide an alternative framework to link protein sequence to function without relying on sequence similarity. Sequence representations learned via PLMs have been shown useful for various prediction tasks, from predicting secondary structure [4], subcellular localization [4], [5], evolutionary relationships within protein families [6], superfamily [7], and family [8] membership.…”

Section: Introductionmentioning

confidence: 99%

GOProFormer: A Multi-modal Transformer Method for Gene Ontology Protein Function Prediction

Kabir

Shehu

2022

Preprint

View full text Add to dashboard Cite

Protein Language Models (PLMs) are shown capable of learning sequence representations useful for various prediction tasks, from subcellular localization, evolutionary relationships, family membership, and more. They have yet to be demonstrated useful for protein function prediction. In particular, the problem of automatic annotation of proteins under the Gene Ontology (GO) framework remains open. This paper makes two key contributions. It debuts a novel method that leverages the transformer architecture in two ways. A sequence transformer encodes protein sequences in a task-agnostic feature space. A graph transformer learns a representation of GO terms while respecting their hierarchical relationships. The learned sequence and GO terms representations are combined and utilized for multi-label classification, with the labels corresponding to GO terms. The method is shown superior over recent representative GO prediction methods. The second major contribution in this paper is a deep investigation of different ways of constructing training and testing datasets. The paper shows that existing approaches under- or over-estimate the generalization power of a model. A novel approach is proposed to address these issues, resulting a new benchmark dataset to rigorously evaluate and compare methods and advance the state-of-the-art.

show abstract

“…For embedding protein sequences, Nambiar et al . [ 79 ] designed PRoBERTa, a neural network architecture based on RoBERTa. After pretraining on SWISS-PROT database and fine-tuning, their method performs well in protein family classification and protein interaction prediction.…”

Section: Application Of Pretraining Modelmentioning

confidence: 99%

Pretraining model for biological sequence data

Song¹,

Li²,

Lin³

et al. 2021

Briefings in Functional Genomics

View full text Add to dashboard Cite

With the development of high-throughput sequencing technology, biological sequence data reflecting life information becomes increasingly accessible. Particularly on the background of the COVID-19 pandemic, biological sequence data play an important role in detecting diseases, analyzing the mechanism and discovering specific drugs. In recent years, pretraining models that have emerged in natural language processing have attracted widespread attention in many research fields not only to decrease training cost but also to improve performance on downstream tasks. Pretraining models are used for embedding biological sequence and extracting feature from large biological sequence corpus to comprehensively understand the biological sequence data. In this survey, we provide a broad review on pretraining models for biological sequence data. Moreover, we first introduce biological sequences and corresponding datasets, including brief description and accessible link. Subsequently, we systematically summarize popular pretraining models for biological sequences based on four categories: CNN, word2vec, LSTM and Transformer. Then, we present some applications with proposed pretraining models on downstream tasks to explain the role of pretraining models. Next, we provide a novel pretraining scheme for protein sequences and a multitask benchmark for protein pretraining models. Finally, we discuss the challenges and future directions in pretraining models for biological sequences.

show abstract

Transforming the Language of Life

Cited by 75 publications

References 31 publications

Exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins

Exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins

GOProFormer: A Multi-modal Transformer Method for Gene Ontology Protein Function Prediction

Pretraining model for biological sequence data

Contact Info

Product

Resources

About