ColGen: An end-to-end deep learning model to predict thermal stability of de novo collagen sequences

Yu, Chi‐Hua; Khare, Eesha; Narayan, Om; Parker, Rachael N.; Kaplan, David L.; Buehler, Markus J.

doi:10.1016/j.jmbbm.2021.104921

Cited by 18 publications

(24 citation statements)

References 79 publications

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“…Using the transformer models, we demonstrate the effect of various mutations on the T m values as reported in ref . We start with a model (GPO) 10 sequence and determine how mutations in either the G, P, or O position along the 10 triplets of the peptide affect the resulting T m values (Figure A, B).…”

Section: Resultsmentioning

confidence: 90%

“…The above small and large transformer models are trained with a collagen data set used in our previous paper, and discussed below in the Materials and Methods section. Briefly, 633 sequences of collagen mimetic peptides and their corresponding T m values are used as a training and testing set for the transformer models.…”

Section: Resultsmentioning

confidence: 99%

“…Machine learning (ML) has emerged as a technique to uncover the underlying physicochemical behavior of biomolecules without specific fundamental chemical or physical laws as inputs. − ML approaches, where the unknown scientific relationships are discovered through neural network models, often enable much faster and higher throughput computation of properties compared to traditional methods such as molecular dynamics, forming an alternative approach to conventional modeling or experimental analyses. Recently, our laboratories demonstrated the use of a deep learning method, using natural language processing (NLP) where computers understand the contexts of human language, on predicting the mechanical stability of collagen sequences and guessing the secondary structure of proteins. , Our earlier NLP model ColGen learned the behavior of a collagen data set consisting of 633 sequences and predicted the resulting melting temperatures ( T m ), a metric used to quantify the mechanical stability of collagen sequences …”

Section: Introductionmentioning

confidence: 99%

“…Recently, our laboratories demonstrated the use of a deep learning method, using natural language processing (NLP) where computers understand the contexts of human language, on predicting the mechanical stability of collagen sequences and guessing the secondary structure of proteins. 41,42 Our earlier NLP model ColGen learned the behavior of a collagen data set consisting of 633 sequences and predicted the resulting melting temperatures (T m ), a metric used to quantify the mechanical stability of collagen sequences. 41 In this earlier work, we predicted the T m values of a large range of collagen sequences to understand how mutations affect stability in a high-throughput manner (R 2 of 0.67 for the test set).…”

Section: Introductionmentioning

confidence: 99%

“…41,42 Our earlier NLP model ColGen learned the behavior of a collagen data set consisting of 633 sequences and predicted the resulting melting temperatures (T m ), a metric used to quantify the mechanical stability of collagen sequences. 41 In this earlier work, we predicted the T m values of a large range of collagen sequences to understand how mutations affect stability in a high-throughput manner (R 2 of 0.67 for the test set). The key constituent of ColGen is a combination of convolutional layers with long short-term memory (LSTM), creating an artificial recurrent neural network.…”

Section: Introductionmentioning

confidence: 99%

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CollagenTransformer: End-to-End Transformer Model to Predict Thermal Stability of Collagen Triple Helices Using an NLP Approach

Khare

González‐Obeso

Kaplan

et al. 2022

ACS Biomater. Sci. Eng.

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Collagen is one of the most important structural proteins in biology, and its structural hierarchy plays a crucial role in many mechanically important biomaterials. Here, we demonstrate how transformer models can be used to predict, directly from the primary amino acid sequence, the thermal stability of collagen triple helices, measured via the melting temperature T m. We report two distinct transformer architectures to compare performance. First, we train a small transformer model from scratch, using our collagen data set featuring only 633 sequence-to-T m pairings. Second, we use a large pretrained transformer model, ProtBERT, and fine-tune it for a particular downstream task by utilizing sequence-to-T m pairings, using a deep convolutional network to translate natural language processing BERT embeddings into required features. Both the small transformer model and the fine-tuned ProtBERT model have similar R 2 values of test data (R 2 = 0.84 vs 0.79, respectively), but the ProtBERT is a much larger pretrained model that may not always be applicable for other biological or biomaterials questions. Specifically, we show that the small transformer model requires only 0.026% of the number of parameters compared to the much larger model but reaches almost the same accuracy for the test set. We compare the performance of both models against 71 newly published sequences for which T m has been obtained as a validation set and find reasonable agreement, with ProtBERT outperforming the small transformer model. The results presented here are, to our best knowledge, the first demonstration of the use of transformer models for relatively small data sets and for the prediction of specific biophysical properties of interest. We anticipate that the work presented here serves as a starting point for transformer models to be applied to other biophysical problems.

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Section: Resultsmentioning

confidence: 90%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CollagenTransformer: End-to-End Transformer Model to Predict Thermal Stability of Collagen Triple Helices Using an NLP Approach

Khare

González‐Obeso

Kaplan

et al. 2022

ACS Biomater. Sci. Eng.

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Predicting Collagen Triple Helix Stability through Additive Effects of Terminal Residues and Caps

et al. 2022

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Collagen model peptides (CMPs) consisting of proline-(2S,4R)-hydroxyproline-glycine (POG) repeats have provided a breadth of knowledge of the triple helical structure of collagen, the most abundant protein in mammals. Predictive tools for triple helix stability have, however, lagged behind since the effect of CMPs with different frames ([POG] n , [OGP] n , or [GPO] n ) and capped or uncapped termini have so far been underestimated. Here, we elucidated the impact of the frame, terminal functional group and its charge on the stability of collagen triple helices. Combined experimental and theoretical studies with frame-shifted, capped and uncapped CMPs revealed that electrostatic interactions, strand preorganization, interstrand H-bonding, and steric repulsion at the termini contribute to triple helix stability. We show that these individual contributions are additive and allow for the prediction of the melting temperatures of CMP trimers.

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Predicting Collagen Triple Helix Stability through Additive Effects of Terminal Residues and Caps

et al. 2022

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ColGen: An end-to-end deep learning model to predict thermal stability of de novo collagen sequences

Cited by 18 publications

References 79 publications

CollagenTransformer: End-to-End Transformer Model to Predict Thermal Stability of Collagen Triple Helices Using an NLP Approach

CollagenTransformer: End-to-End Transformer Model to Predict Thermal Stability of Collagen Triple Helices Using an NLP Approach

Predicting Collagen Triple Helix Stability through Additive Effects of Terminal Residues and Caps

Predicting Collagen Triple Helix Stability through Additive Effects of Terminal Residues and Caps

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