Language models enable zero-shot prediction of the effects of mutations on protein function
Modeling the effect of sequence variation on function is a fundamental problem for understanding and designing proteins. Since evolution encodes information about function into patterns in protein sequences, unsupervised models of variant effects can be learned from sequence data. The approach to date has been to fit a model to a family of related sequences. The conventional setting is limited, since a new model must be trained for each prediction task. We show that using only zero-shot inference, without any supervision from experimental data or additional training, protein language models capture the functional effects of sequence variation, performing at state-of-the-art.
Code (1)
Similar Papers 제목 키워드 기반
Exploring zero-shot structure-based protein fitness prediction
The ability to make zero-shot predictions about the fitness consequences of protein sequence changes with pre-trained machine learning models enables many practical applications. Such models can be applied for downstream…
PredictionProtein Structure PredictionLearning to Describe for Predicting Zero-shot Drug-Drug Interactions
Adverse drug-drug interactions~(DDIs) can compromise the effectiveness of concurrent drug administration, posing a significant challenge in healthcare. As the development of new drugs continues, the potential for unknown…
Language ModelingLanguage ModellingPredictionReinforcement Learning (RL)Prompt to be Consistent is Better than Self-Consistent? Few-Shot and Zero-Shot Fact Verification with Pre-trained Language Models
Few-shot or zero-shot fact verification only relies on a few or no labeled training examples. In this paper, we propose a novel method called ProToCo, to \underline{Pro}mpt pre-trained language models (PLMs) \underline{T…
Fact VerificationIn-Context Learningparameter-efficient fine-tuningWhen is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer
While recent work on multilingual language models has demonstrated their capacity for cross-lingual zero-shot transfer on downstream tasks, there is a lack of consensus in the community as to what shared properties betwe…
Cross-Lingual TransferAnalyzing the Effects of Reasoning Types on Cross-Lingual Transfer Performance
Multilingual language models achieve impressive zero-shot accuracies in many languages in complex tasks such as Natural Language Inference (NLI). Examples in NLI (and equivalent complex tasks) often pertain to various ty…
Cross-Lingual TransferNatural Language Inference