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02:04Protein-protein Interfaces
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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02:26Conservation of Protein Domains Over Different Proteins
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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.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
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01:49Conserved Binding Sites
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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...
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01:58Mechanical Protein Function
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01:58Mechanical Protein Functions
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Proteins perform many mechanical functions in a cell. These proteins can be classified into two general categories- proteins that generate mechanical forces and proteins that are subjected to mechanical forces. Proteins providing mechanical support to the structure of the cell, such as keratin, are subjected to mechanical force, whereas proteins involved in cell movement and transport of molecules across cell membranes, such as an ion pump, are examples of generating mechanical force.
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02:54Evolutionary Relationships through Genome Comparisons
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Machine learning for evolutionary-based and physics-inspired protein design: Current and future synergies.
Cyril Malbranke1, David Bikard2, Simona Cocco3
1Laboratory of Physics of the Ecole Normale Supérieure, PSL Research, CNRS UMR 8023, Sorbonne Université, Université de Paris, Paris, France; Institut Pasteur, Université Paris Cité, CNRS UMR 6047, Synthetic Biology, 75015 Paris, France.
Current Opinion in Structural Biology
|March 22, 2023
Summary
Computational protein design creates new proteins using data-driven methods. This review covers evolutionary and physics-based approaches, highlighting their strengths and potential for combined strategies in protein engineering.
Area of Science:
- Biochemistry
- Computational Biology
- Protein Engineering
Background:
- Computational protein design accelerates the discovery of novel proteins with specific structures and functions.
- Recent advancements utilize data-driven methodologies, broadly categorized into evolutionary-based and physics-inspired approaches.
Purpose of the Study:
- To review recent progress in computational protein design.
- To discuss the strengths and weaknesses of evolutionary-based and physics-inspired methods.
- To identify opportunities for synergistic approaches in protein design.
Main Methods:
- Evolutionary-based methods infer sequence features from related proteins (e.g., conserved or coevolving positions) to generate new designs.
- Physics-inspired methods use machine learning surrogates to estimate biochemical properties (e.g., free energy, binding affinity) for optimization.
- The review synthesizes findings from recent studies in both categories.
Main Results:
- Both evolutionary-based and physics-inspired approaches have shown success in generating novel protein designs.
- Each methodology possesses distinct strengths and limitations.
- Synergistic approaches combining both tracks offer promising avenues for future research.
Conclusions:
- Computational protein design is a rapidly advancing field with significant potential.
- Understanding the interplay between evolutionary and physics-based methods is crucial for future innovation.
- Integrating these approaches can lead to more effective and efficient protein design strategies.