Machine Learning Methods for the Protein Fold Recognition Problem

Stąpor, Katarzyna; Roterman-Konieczna, Irena; Fabian, Piotr

doi:10.1007/978-3-319-94030-4_5

Cited by 5 publications

(6 citation statements)

References 68 publications

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“…These can be divided according to the task they aim to solve. The first one is pairwise fold recognition (PFR), in which the fold class of the query protein is inferred by comparing with templates with known structure [16][17][18]. PFR approaches mainly include methods based on homology modeling (sequence alignments [19], profile alignments [20], and Markov random fields [21]); threading [22][23][24][25][26][27][28]; machine learning for binary classification [29][30][31]; multi-view learning [32][33][34]; and learning to rank [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Protein Language Model Embeddings for Fold Prediction

Villegas-Morcillo

Gómez

Sánchez

2022

Preprint

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The identification of the protein fold class is a challenging problem in structural biology. Recent computational methods for fold prediction leverage deep learning techniques to extract protein fold-representative embeddings mainly using evolutionary information in the form of multiple sequence alignment (MSA) as input source. In contrast, protein language models (LM) have reshaped the field thanks to their ability to learn efficient protein representations (protein-LM embeddings) from purely sequential information in a self-supervised manner. In this paper, we analyze a framework for protein fold prediction using pre-trained protein-LM embeddings as input to several fine-tuning neural network models which are supervisedly trained with fold labels. In particular, we compare the performance of six protein-LM embeddings: the LSTM-based UniRep and SeqVec, and the transformer-based ESM-1b, ESM-MSA, ProtBERT, and ProtT5; as well as three neural networks: Multi-Layer Perceptron (MLP), ResCNN-BGRU (RBG), and Light-Attention (LAT). We separately evaluated the pairwise fold recognition (PFR) and direct fold classification (DFC) tasks on well-known benchmark datasets. The results indicate that the combination of transformer-based embeddings, particularly those obtained at amino acid-level, with the RBG and LAT fine-tuning models performs remarkably well in both tasks. To further increase prediction accuracy, we propose several ensemble strategies for PFR and DFC, which provide a significant performance boost over the current state-of-the-art results. All this suggests that moving from traditional protein representations to protein-LM embeddings is a very promising approach to protein fold-related tasks.

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Section: Introductionmentioning

confidence: 99%

An Analysis of Protein Language Model Embeddings for Fold Prediction

Villegas-Morcillo

Gómez

Sánchez

2022

Preprint

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“…Many methods have been developed to assign folds to a protein coding sequence 41. These methods can be divided into three groups: sequencestructure homology recognition methods, threading methods and machine-learning-based methods.…”

Section: Introductionmentioning

confidence: 99%

“…Sequencing projects are fast at producing protein coding sequences, but only a small portion of protein coding sequences have experimentally solved 3D structures. This is due to the expensive and timeconsuming laboratory methods, such as X-ray crystallography and nuclear magnetic resonance (NMR) 41. This problem is becoming more pressing as the number of known protein coding sequences expands as a result of genome and other sequencing projects 54.…”

Section: Introductionmentioning

confidence: 99%

“…Many methods have been developed to assign folds to a protein coding sequence. 41 These methods can be divided into three groups: sequencestructure homology recognition methods, threading methods and machine-learning-based methods. Sequence-structure homology and threading methods methods align the target sequence onto known structural templates and calculate their sequence-structure compatibilities, using, for example, environment-specific substitution tables or pseudo-energy-based functions to calculate if it is possible that a template is a fold of a target sequence.…”

Section: Introductionmentioning

confidence: 99%

“…This is due to the expensive and timeconsuming laboratory methods, such as X-ray crystallography and nuclear magnetic resonance (NMR). 41 This problem is becoming more pressing as the number of known protein coding sequences expands as a result of genome and other sequencing projects. 54 Because of this, tools that can predict PS rapidly and accurately, like the one presented in this paper, are needed.…”

Section: Introductionmentioning

confidence: 99%

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Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures

Arana‐Daniel

Álvarez-Gallegos

López-Franco

et al. 2016

Evolutionary Bioinformatics

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With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.

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An analysis of protein language model embeddings for fold prediction

Villegas-Morcillo

Gómez

Sánchez

2022

Briefings in Bioinformatics

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The identification of the protein fold class is a challenging problem in structural biology. Recent computational methods for fold prediction leverage deep learning techniques to extract protein fold-representative embeddings mainly using evolutionary information in the form of multiple sequence alignment (MSA) as input source. In contrast, protein language models (LM) have reshaped the field thanks to their ability to learn efficient protein representations (protein-LM embeddings) from purely sequential information in a self-supervised manner. In this paper, we analyze a framework for protein fold prediction using pre-trained protein-LM embeddings as input to several fine-tuning neural network models, which are supervisedly trained with fold labels. In particular, we compare the performance of six protein-LM embeddings: the long short-term memory-based UniRep and SeqVec, and the transformer-based ESM-1b, ESM-MSA, ProtBERT and ProtT5; as well as three neural networks: Multi-Layer Perceptron, ResCNN-BGRU (RBG) and Light-Attention (LAT). We separately evaluated the pairwise fold recognition (PFR) and direct fold classification (DFC) tasks on well-known benchmark datasets. The results indicate that the combination of transformer-based embeddings, particularly those obtained at amino acid level, with the RBG and LAT fine-tuning models performs remarkably well in both tasks. To further increase prediction accuracy, we propose several ensemble strategies for PFR and DFC, which provide a significant performance boost over the current state-of-the-art results. All this suggests that moving from traditional protein representations to protein-LM embeddings is a very promising approach to protein fold-related tasks.

show abstract

Machine Learning Methods for the Protein Fold Recognition Problem

Cited by 5 publications

References 68 publications

An Analysis of Protein Language Model Embeddings for Fold Prediction

An Analysis of Protein Language Model Embeddings for Fold Prediction

Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures

An analysis of protein language model embeddings for fold prediction

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