Protein Redesign and Engineering Using Machine Learning

Basit, Zhuha; Akram, Hira; Iqbal, Mudassar; Muhammad, Gulzar; Aslam, Muhammad Shahbaz; Gul, Iram; Jamil, Muhammad; Tahir, Mudassir Hussain

doi:10.1002/9781394167258.ch9

Cited by 2 publications

(1 citation statement)

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“…1,2 Various theoretical studies, which include simulations, statistical comparison and machine learning, have been performed on available protein structures to understand the mechanism of protein stability and to propose a guideline for enhancing it. [3][4][5] Beside there are experimental works that try to engineer thermostable enzymes incorporating the knowledge from preceding theoretical end experimental studies. 6,7 Even after rigorous research over several decades, researchers are unable to concur with any universal adaptation mechanism of thermostability, rather, it has appeared to be an interplay of several molecular features, encoded underneath the 3D structures.…”

Section: Introductionmentioning

confidence: 99%

Revisiting structural organization of proteins at high temperature from a network perspective

Hait,

Kundu

2024

Computational Biology and Chemistry

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Section: Introductionmentioning

confidence: 99%

Revisiting structural organization of proteins at high temperature from a network perspective

Hait,

Kundu

2024

Computational Biology and Chemistry

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Revisiting structural organization of proteins at high temperature from network perspective

Hait

Kundu

2023

Preprint

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Interactions between distantly placed amino acids in the primary chain (long-range) play a very crucial role in the formation and stabilization of the tertiary structure of a protein, while interactions between closely placed amino acids in the primary chain (short-range) mostly stabilize the secondary structures. Every protein needs to maintain marginal stability in order to perform its physiological functions in its native environment. The requirements for this stability in mesophilic and thermophilic proteins are different. Thermophilic proteins need to form more interactions as well as more stable interactions to survive in the extreme environment, they live in. Here, we aim to find out how the interacting amino acids in three-dimensional space are positioned in the primary chains in thermophilic and mesophilic. How does this arrangement help thermophiles to maintain their structural integrity at high temperatures? Working on a dataset of 1560 orthologous pairs we perceive that thermophiles are not only enriched with long-range interactions, they feature bigger connected clusters and higher network densities compared to their mesophilic orthologs, at higher interaction strengths between the amino acids. Moreover, we have observed the enrichment of different types of interactions at different secondary structural regions.

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Protein Redesign and Engineering Using Machine Learning

Cited by 2 publications

References 146 publications

Revisiting structural organization of proteins at high temperature from a network perspective

Revisiting structural organization of proteins at high temperature from a network perspective

Revisiting structural organization of proteins at high temperature from network perspective

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