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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.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Conservation of Protein Domains

Conservation of Protein Domains

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.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...

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...

Multi-species Conserved Sequences

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...

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Updated: Jun 6, 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

SynFit: Synergistic Contrastive Learning for Multi-Objective Protein Fitness Prediction and Optimization.

Tony Tu¹, Wei Huang², Ziang Li¹

¹School of Computational Science and Engineering, Georgia Institute of Technology.

Biorxiv : the Preprint Server for Biology

|June 5, 2026

Summary

This summary is machine-generated.

SynFit enables simultaneous optimization of multiple protein functions, outperforming existing methods. This protein engineering framework accelerates the design of improved enzymes for biocatalysis.

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

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Published on: July 14, 2015

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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:

Protein Engineering
Computational Biology
Biocatalysis

Background:

Proteins possess complex structural and biochemical properties.
Mutations can alter these properties, creating fitness landscapes for multiple objectives.
Simultaneously optimizing multiple protein properties (e.g., activity, stability, selectivity) is a key challenge.

Purpose of the Study:

To develop a multi-objective learning framework for protein fitness prediction and engineering.
To address the limitations of current methods that treat protein properties independently.
To enable the design of proteins with simultaneously optimized multiple functional properties.

Main Methods:

Introduced SynFit, a framework integrating protein language models and experimental fitness data.
Employed a synergistic contrastive learning strategy for shared and property-specific sequence representations.
Validated SynFit on a large-scale multi-fitness deep mutational scanning benchmark.

Main Results:

SynFit outperformed state-of-the-art supervised models in predicting multi-objective protein fitness.
The framework accurately identified variants balancing competing functional constraints.
Applied SynFit to design novel cytochrome c variants for enantioselective borylation with improved activity and selectivity.

Conclusions:

SynFit provides a general framework for multidimensional protein fitness prediction.
The approach enables efficient multi-objective optimization in protein engineering, especially for biocatalysis.
SynFit facilitates the design of novel enzymes with enhanced and multiple desired functions.