View in EDS

Energy-driven innovations in computational de novo protein engineering.

Saved in:
Bibliographic Details
Title: Energy-driven innovations in computational de novo protein engineering.
Authors: Kırboğa, Kevser Kübra1,2 (AUTHOR) kubra.kirboga@bilecik.edu.tr, Küçüksille, Ecir Uğur3 (AUTHOR)
Source: Progress in Biophysics & Molecular Biology. Mar2026, Vol. 199, p176-196. 21p.
Subjects: Protein engineering, Molecular dynamics, Statistical thermodynamics, Ab initio quantum chemistry methods, Protein stability, Molecular models, Protein folding
Abstract: Energy models play a crucial role in the advancement of computational de novo protein engineering, enabling the design of novel proteins with tailored functionalities. Proteins serve as the foundation of biochemical processes, making their precise engineering essential for applications in biotechnology, medicine, and synthetic biology. Unlike traditional approaches that focus on modifying existing proteins, de novo engineering introduces entirely new constructs, a paradigm shift driven by energy-based strategies that guide protein folding, stability, and functionality through comprehensive simulations of energy landscapes. Computational techniques such as molecular dynamics (MD), thermodynamic integration, and Monte Carlo sampling are fundamental in evaluating designed proteins' stability and dynamic behavior. Widely used tools such as CHARMM, Amber, and Rosetta leverage advanced energy functions to optimize protein structures, facilitating accurate predictions of folding pathways and binding affinities. Additionally, the integration of machine learning (ML) and deep learning (DL) has significantly improved the speed and precision of energy-based modeling, enhancing the design and optimization process. This review systematically analyzes recent studies, provides quantitative benchmarking of major computational platforms, and presents a decision framework for method selection based on accuracy-cost-throughput trade-offs. By integrating classical force fields, quantum mechanical (QM) approaches, and AI-driven predictions with experimental validation, this work outlines a roadmap for advancing therapeutic and industrial protein design through synergistic physics-based and data-driven strategies. [ABSTRACT FROM AUTHOR]
Copyright of Progress in Biophysics & Molecular Biology is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:Energy models play a crucial role in the advancement of computational de novo protein engineering, enabling the design of novel proteins with tailored functionalities. Proteins serve as the foundation of biochemical processes, making their precise engineering essential for applications in biotechnology, medicine, and synthetic biology. Unlike traditional approaches that focus on modifying existing proteins, de novo engineering introduces entirely new constructs, a paradigm shift driven by energy-based strategies that guide protein folding, stability, and functionality through comprehensive simulations of energy landscapes. Computational techniques such as molecular dynamics (MD), thermodynamic integration, and Monte Carlo sampling are fundamental in evaluating designed proteins' stability and dynamic behavior. Widely used tools such as CHARMM, Amber, and Rosetta leverage advanced energy functions to optimize protein structures, facilitating accurate predictions of folding pathways and binding affinities. Additionally, the integration of machine learning (ML) and deep learning (DL) has significantly improved the speed and precision of energy-based modeling, enhancing the design and optimization process. This review systematically analyzes recent studies, provides quantitative benchmarking of major computational platforms, and presents a decision framework for method selection based on accuracy-cost-throughput trade-offs. By integrating classical force fields, quantum mechanical (QM) approaches, and AI-driven predictions with experimental validation, this work outlines a roadmap for advancing therapeutic and industrial protein design through synergistic physics-based and data-driven strategies. [ABSTRACT FROM AUTHOR]
ISSN:00796107
DOI:10.1016/j.pbiomolbio.2026.01.005