Dataset Information

Zero-shot mutation effect prediction on protein stability and function using RoseTTAFold.

ABSTRACT: Predicting the effects of mutations on protein function and stability is an outstanding challenge. Here, we assess the performance of a variant of RoseTTAFold jointly trained for sequence and structure recovery, RF_joint , for mutation effect prediction. Without any further training, we achieve comparable accuracy in predicting mutation effects for a diverse set of protein families using RF_joint to both another zero-shot model (MSA Transformer) and a model that requires specific training on a particular protein family for mutation effect prediction (DeepSequence). Thus, although the architecture of RF_joint was developed to address the protein design problem of scaffolding functional motifs, RF_joint acquired an understanding of the mutational landscapes of proteins during model training that is equivalent to that of recently developed large protein language models. The ability to simultaneously reason over protein structure and sequence could enable even more precise mutation effect predictions following supervised training on the task. These results suggest that RF_joint has a quite broad understanding of protein sequence-structure landscapes, and can be viewed as a joint model for protein sequence and structure which could be broadly useful for protein modeling.

SUBMITTER: Mansoor S

PROVIDER: S-EPMC10578109 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Zero-shot mutation effect prediction on protein stability and function using RoseTTAFold.

Mansoor Sanaa S Baek Minkyung M Juergens David D Watson Joseph L JL Baker David D

Protein science : a publication of the Protein Society 20231101 11

Predicting the effects of mutations on protein function and stability is an outstanding challenge. Here, we assess the performance of a variant of RoseTTAFold jointly trained for sequence and structure recovery, RF<sub>joint</sub> , for mutation effect prediction. Without any further training, we achieve comparable accuracy in predicting mutation effects for a diverse set of protein families using RF<sub>joint</sub> to both another zero-shot model (MSA Transformer) and a model that requires spec ...[more]

PMID: 37695922

Similar Datasets

Project description:Recently, advancements in cheminformatics such as representation learning for chemical structures, deep learning (DL) for property prediction, data-driven discovery, and optimization of chemical data handling, have led to increased demands for handling chemical simplified molecular input line entry system (SMILES) data, particularly in text analysis tasks. These advancements have driven the need to optimize components like positional encoding and positional embeddings (PEs) in transformer model to better capture the sequential and contextual information embedded in molecular representations. SMILES data represent complex relationships among atoms or elements, rendering them critical for various learning tasks within the field of cheminformatics. This study addresses the critical challenge of encoding complex relationships among atoms in SMILES strings to explore various PEs within the transformer-based framework to increase the accuracy and generalization of molecular property predictions. The success of transformer-based models, such as the bidirectional encoder representations from transformer (BERT) models, in natural language processing tasks has sparked growing interest from the domain of cheminformatics. However, the performance of these models during pretraining and fine-tuning is significantly influenced by positional information such as PEs, which help in understanding the intricate relationships within sequences. Integrating position information within transformer architectures has emerged as a promising approach. This encoding mechanism provides essential supervision for modeling dependencies among elements situated at different positions within a given sequence. In this study, we first conduct pretraining experiments using various PEs to explore diverse methodologies for incorporating positional information into the BERT model for chemical text analysis using SMILES strings. Next, for each PE, we fine-tune the best-performing BERT (masked language modeling) model on downstream tasks for molecular-property prediction. Here, we use two molecular representations, SMILES and DeepSMILES, to comprehensively assess the potential and limitations of the PEs in zero-shot learning analysis, demonstrating the model's proficiency in predicting properties of unseen molecular representations in the context of newly proposed and existing datasets.Scientific contributionThis study explores the unexplored potential of PEs using BERT model for molecular property prediction. The study involved pretraining and fine-tuning the BERT model on various datasets related to COVID-19, bioassay data, and other molecular and biological properties using SMILES and DeepSMILES representations. The study details the pretraining architecture, fine-tuning datasets, and the performance of the BERT model with different PEs. It also explores zero-shot learning analysis and the model's performance on various classification and regression tasks. In this study, newly proposed datasets from different domains were introduced during fine-tuning in addition to the existing and commonly used datasets. The study highlights the robustness of the BERT model in predicting chemical properties and its potential applications in cheminformatics and bioinformatics.

Dataset Information

Zero-shot mutation effect prediction on protein stability and function using RoseTTAFold.

Publications

Zero-shot mutation effect prediction on protein stability and function using RoseTTAFold.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets