Adaptive machine learning for protein engineering

• URL: http://arxiv.org/abs/2106.05466v1
• Date: Thu, 10 Jun 2021 02:56:35 GMT
• Title: Adaptive machine learning for protein engineering
• Authors: Brian L. Hie, Kevin K. Yang
• Abstract summary: We discuss how to use a sequence-to-function machine-learning surrogate model to select sequences for experimental measurement. First, we discuss how to select sequences through a single round of machine-learning optimization. Then, we discuss sequential optimization, where the goal is to discover optimized sequences and improve the model across multiple rounds of training, optimization, and experimental measurement.
• Score: 0.4568777157687961
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Machine-learning models that learn from data to predict how protein sequence encodes function are emerging as a useful protein engineering tool. However, when using these models to suggest new protein designs, one must deal with the vast combinatorial complexity of protein sequences. Here, we review how to use a sequence-to-function machine-learning surrogate model to select sequences for experimental measurement. First, we discuss how to select sequences through a single round of machine-learning optimization. Then, we discuss sequential optimization, where the goal is to discover optimized sequences and improve the model across multiple rounds of training, optimization, and experimental measurement.

Related papers

• SeqProFT: Applying LoRA Finetuning for Sequence-only Protein Property Predictions [8.112057136324431]
  This study employs the LoRA method to perform end-to-end fine-tuning of the ESM-2 model. A multi-head attention mechanism is integrated into the downstream network to combine sequence features with contact map information.
  arXiv  Detail & Related papers  (2024-11-18T12:40:39Z)
• Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization [44.356888079704156]
  Protein engineering is a daunting task due to the vast sequence space of any given protein. Protein engineering is typically conducted through an iterative process of adding mutations to the wild-type or lead sequences. We propose a tree search-based bandit learning method, which expands a tree starting from the initial sequence with the guidance of a bandit machine learning model.
  arXiv  Detail & Related papers  (2024-01-08T06:33:27Z)
• Uncovering mesa-optimization algorithms in Transformers [61.06055590704677]
  Some autoregressive models can learn as an input sequence is processed, without undergoing any parameter changes, and without being explicitly trained to do so. We show that standard next-token prediction error minimization gives rise to a subsidiary learning algorithm that adjusts the model as new inputs are revealed. Our findings explain in-context learning as a product of autoregressive loss minimization and inform the design of new optimization-based Transformer layers.
  arXiv  Detail & Related papers  (2023-09-11T22:42:50Z)
• Protein Sequence Design with Batch Bayesian Optimisation [0.0]
  Protein sequence design is a challenging problem in protein engineering, which aims to discover novel proteins with useful biological functions. directed evolution is a widely-used approach for protein sequence design, which mimics the evolution cycle in a laboratory environment and conducts an iterative protocol. We propose a new method based on Batch Bayesian Optimization (Batch BO), a well-established optimization method, for protein sequence design.
  arXiv  Detail & Related papers  (2023-03-18T14:53:20Z)
• Reprogramming Pretrained Language Models for Protein Sequence Representation Learning [68.75392232599654]
  We propose Representation Learning via Dictionary Learning (R2DL), an end-to-end representation learning framework. R2DL reprograms a pretrained English language model to learn the embeddings of protein sequences. Our model can attain better accuracy and significantly improve the data efficiency by up to $105$ times over the baselines set by pretrained and standard supervised methods.
  arXiv  Detail & Related papers  (2023-01-05T15:55:18Z)
• Structure-aware Protein Self-supervised Learning [50.04673179816619]
  We propose a novel structure-aware protein self-supervised learning method to capture structural information of proteins. In particular, a well-designed graph neural network (GNN) model is pretrained to preserve the protein structural information. We identify the relation between the sequential information in the protein language model and the structural information in the specially designed GNN model via a novel pseudo bi-level optimization scheme.
  arXiv  Detail & Related papers  (2022-04-06T02:18:41Z)
• EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Models [53.17320541056843]
  We propose a fully-differentiable approach for protein structure optimization, guided by a data-driven generative network. Our EBM-Fold approach can efficiently produce high-quality decoys, compared against traditional Rosetta-based structure optimization routines.
  arXiv  Detail & Related papers  (2021-05-11T03:40:29Z)
• Combination of digital signal processing and assembled predictive models facilitates the rational design of proteins [0.0]
  Predicting the effect of mutations in proteins is one of the most critical challenges in protein engineering. We use clustering, embedding, and dimensionality reduction techniques to select combinations of physicochemical properties for the encoding stage. We then select the best performing predictive models in each set of properties and create an assembled model.
  arXiv  Detail & Related papers  (2020-10-07T16:35:02Z)
• AdaLead: A simple and robust adaptive greedy search algorithm for sequence design [55.41644538483948]
  We develop an easy-to-directed, scalable, and robust evolutionary greedy algorithm (AdaLead) AdaLead is a remarkably strong benchmark that out-competes more complex state of the art approaches in a variety of biologically motivated sequence design challenges.
  arXiv  Detail & Related papers  (2020-10-05T16:40:38Z)
• Fast differentiable DNA and protein sequence optimization for molecular design [0.0]
  Machine learning models that accurately predict biological fitness from sequence are becoming a powerful tool for molecular design. Here, we build on a previously proposed straight-through approximation method to optimize through discrete sequence samples. The resulting algorithm, which we call Fast SeqPropProp, achieves up to 100-fold faster convergence compared to previous versions.
  arXiv  Detail & Related papers  (2020-05-22T17:03:55Z)
• Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning [100.83444258562263]
  We propose a novel Bayesian optimization (BO) algorithm to tackle the challenge of model selection in this setting. In order to solve the resulting multiple black-box function optimization problem jointly and efficiently, we exploit potential correlations among black-box functions. We are the first to formulate the problem of stepwise model selection (SMS) for sequence prediction, and to design and demonstrate an efficient joint-learning algorithm for this purpose.
  arXiv  Detail & Related papers  (2020-01-12T09:42:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.