Article
Multidisciplinary Sciences
R. Charlotte Eccleston, David D. Pollock, Richard A. Goldstein
Summary: Epistasis and cooperativity in protein folding are both influenced by networks of energetic interactions within proteins, and their selection can affect each other. Selection for cooperativity may be crucial for predicting protein structure using epistasis measurements.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Chemistry, Multidisciplinary
Patrick Bryant, Arne Elofsson
Summary: This article describes a computational method for designing peptide binders towards specific protein interfaces. By combining multiple methods, including Foldseek, ESM-IF1, and AlphaFold2, the researchers developed a peptide binder design tool and demonstrated its ability to improve the success rate.
COMMUNICATIONS CHEMISTRY
(2023)
Article
Multidisciplinary Sciences
Noel Q. Hoffer, Krishna Neupane, Michael T. Woodside
Summary: By using optical tweezers at high resolution, the dynamics of folding were probed, revealing brief but ubiquitous pauses in the transition states. These pauses were found to be sequence-dependent and position-dependent, providing insights into the key microscopic events during folding.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Review
Endocrinology & Metabolism
Balamurugan Dhayalan, Deepak Chatterjee, Yen-Shan Chen, Michael A. Weiss
Summary: Analysis of diabetes-associated mutations in the human insulin gene has provided insights into the folding mechanisms of proinsulin, revealing the impact of mutations on pancreatic beta-cell dysfunction and insulin secretion. Studies suggest that conserved residues play a crucial role in folding efficiency and the susceptibility of proinsulin to impaired foldability can contribute to the development of diseases. This highlights the molecular links between biophysical principles and the impact on diseases such as diabetes and obesity.
FRONTIERS IN ENDOCRINOLOGY
(2021)
Article
Biochemistry & Molecular Biology
Paola Turina, Piero Fariselli, Emidio Capriotti
Summary: The study of protein folding is crucial for understanding protein function and the relationship between genetics and phenotypes. K-Pro is a new database that collects experimental kinetic data on monomeric proteins with a two-state folding mechanism. It provides a user-friendly interface for browsing and downloading relevant data.
JOURNAL OF MOLECULAR BIOLOGY
(2023)
Article
Biochemistry & Molecular Biology
Yang Yang, Zhang Chong, Mauno Vihinen
Summary: Most proteins fold into unique three-dimensional structures and their folding rates can be influenced by variations in proteins. We developed a machine-learning-based method, PON-Fold, to predict the folding rate effects of amino acid substitutions in two-state folding proteins. PON-Fold outperformed existing tools in blind tests, showing higher specificity, sensitivity, and correlation coefficient. The tool was also tested for protein domain substitutions and showed varying predictions depending on protein conformations and structures.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Review
Biochemistry & Molecular Biology
Mohammed AlQuraishi
Summary: Prediction of protein structure from sequence has made significant progress in the past two years, driven by the increasing use of neural networks in structure prediction pipelines. These neural networks have optimized the previous energy models and sampling procedures, resulting in algorithms that can now predict protein structures with a median accuracy of 2.1 angstroms.
CURRENT OPINION IN CHEMICAL BIOLOGY
(2021)
Article
Biochemical Research Methods
Liang Yu, Yujia Zheng, Lin Gao
Summary: In this study, a novel method for predicting miRNA-disease associations was proposed, achieving high accuracy in miRNA-disease association prediction and providing new insights for drug development and disease prediction.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Chemistry, Physical
Qinglu Zhong, Guohui Li
Summary: Protein structure prediction is vital for understanding new protein functions, but predicting the effects of proteins with no detectable templates remains challenging. AIMS, a universal multiscale simulation strategy, allows simulations to iteratively switch among multiple resolutions to adaptively balance AA accuracy and CG efficiency. Through AIMS, faster and more accurate predictions of protein structures can be achieved, providing special insights on folding metastable states.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2021)
Article
Biochemical Research Methods
M. Walder, E. Edelstein, M. Carroll, S. Lazarev, J. E. Fajardo, A. Fiser, R. Viswanathan
Summary: This study developed an integrated method for protein interface prediction, which combines template-free and template-based features using orthogonal structure-based properties to enhance the effectiveness of the prediction method. The integrated method outperforms individual classifiers in identifying protein binding interfaces.
BMC BIOINFORMATICS
(2022)
Article
Polymer Science
Henrik Kalmer, Federica Sbordone, Hendrik Frisch
Summary: Inspired by the folding of polypeptides into proteins, synthetic polymers have been folded into single chain nanoparticles. Surprisingly, the natural building blocks of amino acids and peptides have been underutilized in the design of synthetic folded structures. This study presents a synthetic strategy that allows for easy incorporation of functional amino acid sequences, resulting in functional folded macromolecular architectures. The catalytic activity of the folded structures was demonstrated by the hydrolysis of para-nitrophenylacetate using pentapeptides containing N-terminal cysteines.
Review
Biochemistry & Molecular Biology
Subash C. Pakhrin, Bikash Shrestha, Badri Adhikari, Dukka B. KC
Summary: The article explores the significant progress in the field of protein structure prediction due to Deep Learning (DL)-based methods, including advancements in protein contact map prediction, protein distogram prediction, protein real-valued distance prediction, and Quality Assessment/refinement steps. It also mentions recent DL-based developments in protein structure determination using Cryo-Electron (Cryo-EM) microscopy, and provides an outlook on possible future research directions for DL-based approaches in the protein structure prediction arena.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Biology
Balachandran Manavalan, Jooyoung Lee
Summary: Protein folding rate is crucial for understanding the protein folding process and designing proteins. This study presents FRTpred, a novel approach that accurately predicts the logarithmic protein folding rate constant and folding type from the provided sequence. FRTpred outperforms existing methods and can accelerate the characterization of protein data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Chemistry, Physical
Remi Casier, Jean Duhamel
Summary: A blob-based model (BBM) can predict protein folding time using long-range backbone dynamics. This departure from other methods relies solely on LRBD and the modularity of BBM. The study finds that BBM is particularly effective for moderately large proteins with locally formed motifs or those that fold in multiple steps.
JOURNAL OF PHYSICAL CHEMISTRY B
(2023)
Article
Biochemistry & Molecular Biology
Andriy Kryshtafovych, Torsten Schwede, Maya Topf, Krzysztof Fidelis, John Moult
Summary: CASP is a community experiment aimed at advancing methods for computing three-dimensional protein structure, including rigorous blind testing and evaluation by independent assessors. In the recent CASP14 experiment, deep-learning methods from one research group consistently delivered computed structures rivaling the corresponding experimental ones in accuracy. These results represent a solution to the classical protein-folding problem, at least for single proteins.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2021)