Machine learning can guide experimental approaches for protein digestibility estimations

• URL: http://arxiv.org/abs/2211.00625v1
• Date: Tue, 1 Nov 2022 17:43:58 GMT
• Title: Machine learning can guide experimental approaches for protein digestibility estimations
• Abstract summary: We propose a machine learning approach to predict the true ileal digestibility coefficient of food items. This first AI-based model for predicting food protein digestibility has an accuracy of 90% compared to existing models.
• Score: 1.447032077887874
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Food protein digestibility and bioavailability are critical aspects in addressing human nutritional demands, particularly when seeking sustainable alternatives to animal-based proteins. In this study, we propose a machine learning approach to predict the true ileal digestibility coefficient of food items. The model makes use of a unique curated dataset that combines nutritional information from different foods with FASTA sequences of some of their protein families. We extracted the biochemical properties of the proteins and combined these properties with embeddings from a Transformer-based protein Language Model (pLM). In addition, we used SHAP to identify features that contribute most to the model prediction and provide interpretability. This first AI-based model for predicting food protein digestibility has an accuracy of 90% compared to existing experimental techniques. With this accuracy, our model can eliminate the need for lengthy in-vivo or in-vitro experiments, making the process of creating new foods faster, cheaper, and more ethical.

Related papers

• Protein Large Language Models: A Comprehensive Survey [71.65899614084853]
  Protein-specific large language models (Protein LLMs) are revolutionizing protein science by enabling more efficient protein structure prediction, function annotation, and design. This work provides the first comprehensive overview of Protein LLMs, covering their architectures, training datasets, evaluation metrics, and diverse applications.
  arXiv  Detail & Related papers  (2025-02-21T19:22:10Z)
• Multi-modal Representation Learning Enables Accurate Protein Function Prediction in Low-Data Setting [0.0]
  HOPER (HOlistic ProtEin Representation) is a novel framework designed to enhance protein function prediction (PFP) in low-data settings. Our results highlight the effectiveness of multimodal representation learning for overcoming data limitations in biological research.
  arXiv  Detail & Related papers  (2024-11-22T20:13:55Z)
• One protein is all you need [17.551862138613405]
  Generalization beyond training data remains a central challenge in machine learning for biology.<n>We show that our Protein Test-Time Training (ProteinTTT) method consistently enhances generalization across different models, their sizes, and datasets.
  arXiv  Detail & Related papers  (2024-11-04T14:23:59Z)
• Protein-Mamba: Biological Mamba Models for Protein Function Prediction [18.642511763423048]
  Protein-Mamba is a novel two-stage model that leverages both self-supervised learning and fine-tuning to improve protein function prediction. Our experiments demonstrate that Protein-Mamba achieves competitive performance, compared with a couple of state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-09-22T22:51:56Z)
• NutritionVerse-Direct: Exploring Deep Neural Networks for Multitask Nutrition Prediction from Food Images [63.314702537010355]
  Self-reporting methods are often inaccurate and suffer from substantial bias. Recent work has explored using computer vision prediction systems to predict nutritional information from food images. This paper aims to enhance the efficacy of dietary intake estimation by leveraging various neural network architectures.
  arXiv  Detail & Related papers  (2024-05-13T14:56:55Z)
• Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient Clustering [24.415612744612773]
  Proteins are essential to life's processes, underpinning evolution and diversity. Advances in sequencing technology have revealed millions of proteins, underscoring the need for sophisticated pre-trained protein models for biological analysis and AI development. Facebook's ESM2, the most advanced protein language model to date, leverages a masked prediction task for unsupervised learning, crafting amino acid representations with notable biochemical accuracy. Yet, it lacks in delivering functional protein insights, signaling an opportunity for enhancing representation quality. This study addresses this gap by incorporating protein family classification into ESM2's training, while a contextual prediction task fine-tunes local
  arXiv  Detail & Related papers  (2024-04-24T11:09:43Z)
• ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training [82.37346937497136]
  We propose a versatile cross-modal large language model (LLM) for both protein-centric and protein-language tasks. ProtLLM features a unique dynamic protein mounting mechanism, enabling it to handle complex inputs. By developing a specialized protein vocabulary, we equip the model with the capability to predict not just natural language but also proteins from a vast pool of candidates.
  arXiv  Detail & Related papers  (2024-02-28T01:29:55Z)
• Efficiently Predicting Protein Stability Changes Upon Single-point Mutation with Large Language Models [51.57843608615827]
  The ability to precisely predict protein thermostability is pivotal for various subfields and applications in biochemistry. We introduce an ESM-assisted efficient approach that integrates protein sequence and structural features to predict the thermostability changes in protein upon single-point mutations.
  arXiv  Detail & Related papers  (2023-12-07T03:25:49Z)
• Multi-level Protein Representation Learning for Blind Mutational Effect Prediction [5.207307163958806]
  This paper introduces a novel pre-training framework that cascades sequential and geometric analyzers for protein structures. It guides mutational directions toward desired traits by simulating natural selection on wild-type proteins. We assess the proposed approach using a public database and two new databases for a variety of variant effect prediction tasks.
  arXiv  Detail & Related papers  (2023-06-08T03:00:50Z)
• A Latent Diffusion Model for Protein Structure Generation [50.74232632854264]
  We propose a latent diffusion model that can reduce the complexity of protein modeling. We show that our method can effectively generate novel protein backbone structures with high designability and efficiency.
  arXiv  Detail & Related papers  (2023-05-06T19:10:19Z)
• Learning Geometrically Disentangled Representations of Protein Folding Simulations [72.03095377508856]
  This work focuses on learning a generative neural network on a structural ensemble of a drug-target protein. Model tasks involve characterizing the distinct structural fluctuations of the protein bound to various drug molecules. Results show that our geometric learning-based method enjoys both accuracy and efficiency for generating complex structural variations.
  arXiv  Detail & Related papers  (2022-05-20T19:38:00Z)
• 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)

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.