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How proteins fold remains a central unsolved problem in biology. While the idea of a folding code embedded in the amino acid sequence was introduced more than 6 decades ago, this code remains undefined. While we now have powerful predictive…

生物大分子 · 定量生物学 2025-11-04 Carlos Bustamante , Christian Kaiser , Erik Lindahl , Robert Sosa , Giovanni Volpe

One of the most puzzling and unsolved challenges in molecular biology is understanding how proteins fold. Despite having advanced predictive tools that can accurately estimate the native structures of proteins, we still lack a comprehensive…

生物大分子 · 定量生物学 2026-01-13 Jorge Vila

In spite of decades of research, much remains to be discovered about folding: the detailed structure of the initial (unfolded) state, vestigial folding instructions remaining only in the unfolded state, the interaction of the molecule with…

生物物理 · 物理学 2018-11-26 Walter A. Simmons

The prediction of the three-dimensional native structure of proteins from the knowledge of their amino acid sequence, known as the protein folding problem, is one of the most important yet unsolved issues of modern science. Since the…

生物物理 · 物理学 2008-11-24 Pablo Echenique

Protein folding is one of the age-old biological problems that refers to the mechanism of understanding and predicting how a protein's linear sequence of amino acids folds into its specific three dimensional structure.This structure is…

Understanding the principles of protein folding is a cornerstone of computational biology, with implications for drug design, bioengineering, and the understanding of fundamental biological processes. Lattice protein folding models offer a…

无序系统与神经网络 · 物理学 2025-08-08 Shoummo Ahsan Khandoker , Estelle M. Inack , Mohamed Hibat-Allah

Many aspects of the study of protein folding and dynamics have been affected by the recent advances in machine learning. Methods for the prediction of protein structures from their sequences are now heavily based on machine learning tools.…

生物物理 · 物理学 2019-11-25 Frank Noé , Gianni De Fabritiis , Cecilia Clementi

Understanding how monomeric proteins fold under in vitro conditions is crucial to describing their functions in the cellular context. Significant advances both in theory and experiments have resulted in a conceptual framework for describing…

软凝聚态物质 · 物理学 2010-07-20 D. Thirumalai , Edward P. O'Brien , Greg Morrison , Changbong Hyeon

Proteins are the basic building blocks of life. They usually perform functions by folding to a particular structure. Understanding the folding process could help the researchers to understand the functions of proteins and could also help to…

计算工程、金融与科学 · 计算机科学 2015-10-21 Jianzhu Ma

As protein folding is a NP-complete problem, artificial intelligence tools like neural networks and genetic algorithms are used to attempt to predict the 3D shape of an amino acids sequence. Underlying these attempts, it is supposed that…

生物大分子 · 定量生物学 2015-11-03 Jacques M. Bahi , Nathalie M. -L. Cote , Christophe Guyeux

While all the information required for the folding of a protein is contained in its amino acid sequence, one has not yet learnt how to extract this information so as to predict the detailed, biological active, three-dimensional structure of…

凝聚态物理 · 物理学 2007-05-23 R. A. Broglia , G. Tiana

This paper presents a method of reconstruction a primary structure of a protein that folds into a given geometrical shape. This method predicts the primary structure of a protein and restores its linear sequence of amino acids in the…

定量方法 · 定量生物学 2017-01-04 Andrii Riazanov , Mikhail Karasikov , Sergei Grudinin

Simulations of biological macromolecules play an important role in understanding the physical basis of a number of complex processes such as protein folding. Even with increasing computational power and evolution of specialized…

分布式、并行与集群计算 · 计算机科学 2019-09-18 Hyungro Lee , Heng Ma , Matteo Turilli , Debsindhu Bhowmik , Shantenu Jha , Arvind Ramanathan

We propose a general method for predicting potentially good folders from a given number of amino acid sequences. Our approach is based on the calculation of the rate of convergence of each amino acid chain towards the native structure using…

生物物理 · 物理学 2013-02-07 Dmitry K. Gridnev , Pedro Ojeda-May , Martin E. Garcia

Natural protein sequences somehow encode the structural forms that these molecules adopt. Recent developments in structure-prediction are agnostic to the mechanisms by which proteins fold and represent them as static objects. However, the…

生物大分子 · 定量生物学 2025-05-26 Ezequiel A. Galpern , Federico Caamaño , Diego U. Ferreiro

While all the information required for the folding of a protein is contained in its amino acid sequence, one has not yet learned how to extract this information to predict the three--dimensional, biologically active, native conformation of…

生物大分子 · 定量生物学 2009-11-10 R. A. Broglia , G. Tiana

A geometric analysis of protein folding, which complements many of the models in the literature, is presented. We examine the process from unfolded strand to the point where the strand becomes self-interacting. A central question is how it…

数学物理 · 物理学 2008-09-13 Walter A. Simmons , Joel L. Weiner

The intricate three-dimensional geometries of protein tertiary structures underlie protein function and emerge through a folding process from one-dimensional chains of amino acids. The exact spatial sequence and configuration of amino…

生物大分子 · 定量生物学 2021-02-24 Nora Molkenthin , Steffen Mühle , Antonia S J S Mey , Marc Timme

The protein folding problem must ultimately be solved on all length scales from the atomic up through a hierarchy of complicated structures. By analyzing the stability of the folding process using physics and mathematics, this paper shows…

生物物理 · 物理学 2015-05-28 Walter Simmons , Joel L. Weiner

This review is a tutorial for scientists interested in the problem of protein structure prediction, particularly those interested in using coarse-grained molecular dynamics models that are optimized using lessons learned from the energy…

生物大分子 · 定量生物学 2014-01-06 N. P. Schafer , B. L. Kim , W. Zheng , P. G. Wolynes
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