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Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...

Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...

Conservation of Protein Domains Over Different Proteins

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Ligand Binding and Linkage

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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked. In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...

Conservation of Protein Domains

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Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Protein Folding Quality Check in the RER

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ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...

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Related Experiment Video

Updated: Jul 10, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

LoMuS: Low-Rank Adaptation with Sequence Multi-representation Improves Protein Stability Prediction.

Samuel Infante¹, Akash Singh¹, Anowarul Kabir¹

¹Bellini College of AI, Cybersecurity and Computing, University of South Florida, Florida 33620, United States.

Bioinformatics (Oxford, England)

|July 9, 2026

Summary

This summary is machine-generated.

Predicting protein folding stability is crucial for protein engineering. LoMuS, a new deep learning model, accurately predicts protein stability from primary sequences using combined physicochemical descriptors and sequence embeddings, outperforming existing methods.

Keywords:

low-rank adaptation physicochemical descriptors protein language models protein stability prediction

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Last Updated: Jul 10, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

Computational Biology
Biophysics
Machine Learning in Biology

Background:

Protein folding stability is vital for protein function, dynamics, and engineering.
Accurate prediction of protein stability is challenging, especially with limited structural data.
Existing methods struggle with data variability and sequence-only predictions.

Purpose of the Study:

Introduce LoMuS, a novel deep learning model for predicting protein stability directly from primary sequences.
Integrate physicochemical descriptors with sequence-derived embeddings for enhanced prediction accuracy.
Improve protein engineering by enabling better prediction and ranking of stability scores.

Main Methods:

Developed LoMuS, a multi-representation deep learning model.
Fused explicit physicochemical descriptors with low-rank adapted protein language model embeddings.
Evaluated model performance across diverse settings including absolute scoring, mutation landscapes, and out-of-distribution data.

Main Results:

LoMuS consistently outperforms sequence-only baselines across multiple benchmarks.
Achieved at least a 10% absolute performance gain in Spearman's rank correlation.
Ablation studies confirmed the critical contribution of both physicochemical descriptors and sequence embeddings.

Conclusions:

LoMuS advances the prediction of protein stability using a multi-representation deep learning approach.
The model's ability to leverage sequence information enhances protein engineering applications.
Open-source availability of code facilitates further research and development.