A Machine Learning Method to Characterize Conformational Changes of Amino Acids in Proteins

Mollaei, Parisa; Farimani, Amir Barati

doi:10.1101/2023.04.16.536913

Cited by 2 publications

(3 citation statements)

References 57 publications

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“…We developed GPCR-BERT which investigates the high-order interactions in GPCRs. The reason we chose GPCRs is that they are an essential class of cell membrane proteins found in organisms ranging from bacteria to humans. – Currently, over one-third of U.S. Food and Drug Administration-approved medications target the receptors. ,– GPCRs exhibit remarkable functional diversity, including sensory perception, neurotransmission, immune response, cardiovascular regulation, vision, metabolism, and energy homeostasis. ,,– Although each of the receptors has its own amino acid sequence, there are a few conserved motifs among them, such as NPxxY, CWxP, and E/DRY. Each conserved motif serves as a crucial component in the GPCRs – and the type of each x can vary according to the receptor class.…”

Section: Introductionmentioning

confidence: 99%

“…The reason we chose GPCRs is that they are an essential class of cell membrane proteins found in organisms ranging from bacteria to humans. 31 – 39 Currently, over one-third of U.S. Food and Drug Administration-approved medications target the receptors. 35 , 40 – 45 GPCRs exhibit remarkable functional diversity, including sensory perception, neurotransmission, immune response, cardiovascular regulation, vision, metabolism, and energy homeostasis.…”

Section: Introductionmentioning

confidence: 99%

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GPCR-BERT: Interpreting Sequential Design of G Protein-Coupled Receptors Using Protein Language Models

Kim,

Mollaei,

Antony

et al. 2024

J. Chem. Inf. Model.

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With the rise of transformers and large language models (LLMs) in chemistry and biology, new avenues for the design and understanding of therapeutics have been opened up to the scientific community. Protein sequences can be modeled as language and can take advantage of recent advances in LLMs, specifically with the abundance of our access to the protein sequence data sets. In this letter, we developed the GPCR-BERT model for understanding the sequential design of G proteincoupled receptors (GPCRs). GPCRs are the target of over onethird of Food and Drug Administration-approved pharmaceuticals. However, there is a lack of comprehensive understanding regarding the relationship among amino acid sequence, ligand selectivity, and conformational motifs (such as NPxxY, CWxP, and E/DRY). By utilizing the pretrained protein model (Prot-Bert) and fine-tuning with prediction tasks of variations in the motifs, we were able to shed light on several relationships between residues in the binding pocket and some of the conserved motifs. To achieve this, we took advantage of attention weights and hidden states of the model that are interpreted to extract the extent of contributions of amino acids in dictating the type of masked ones. The fine-tuned models demonstrated high accuracy in predicting hidden residues within the motifs. In addition, the analysis of embedding was performed over 3D structures to elucidate the higher-order interactions within the conformations of the receptors.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GPCR-BERT: Interpreting Sequential Design of G Protein-Coupled Receptors Using Protein Language Models

Kim,

Mollaei,

Antony

et al. 2024

J. Chem. Inf. Model.

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“…Peptides are organic molecules containing amino acids, ranging from only a few amino acids to numerous units that are joined together in ordered sequences. − The length and arrangement of amino acids in a sequence govern a protein’s structural and biological properties. − Consequently, the peptide sequence determines how the peptide engages with its environment and various molecules. For example, the peptides’ therapeutic properties such as hemolysis, fouling characteristics, and solubility − are defined by sequences of amino acids.…”

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confidence: 99%

PeptideBERT: A Language Model Based on Transformers for Peptide Property Prediction

Guntuboina,

Das,

Mollaei

et al. 2023

J. Phys. Chem. Lett.

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Recent advances in language models have enabled the protein modeling community with a powerful tool that uses transformers to represent protein sequences as text. This breakthrough enables a sequence-to-property prediction for peptides without relying on explicit structural data. Inspired by the recent progress in the field of large language models, we present PeptideBERT, a protein language model specifically tailored for predicting essential peptide properties such as hemolysis, solubility, and nonfouling. The PeptideBERT utilizes the ProtBERT pretrained transformer model with 12 attention heads and 12 hidden layers. Through fine-tuning the pretrained model for the three downstream tasks, our model is state of the art (SOTA) in predicting hemolysis, which is crucial for determining a peptide's potential to induce red blood cells as well as nonfouling properties. Leveraging primarily shorter sequences and a data set with negative samples predominantly associated with insoluble peptides, our model showcases remarkable performance.

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A Machine Learning Method to Characterize Conformational Changes of Amino Acids in Proteins

Cited by 2 publications

References 57 publications

GPCR-BERT: Interpreting Sequential Design of G Protein-Coupled Receptors Using Protein Language Models

GPCR-BERT: Interpreting Sequential Design of G Protein-Coupled Receptors Using Protein Language Models

PeptideBERT: A Language Model Based on Transformers for Peptide Property Prediction

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