Literature DB >> 31417196

Advances in protein structure prediction and design.

Brian Kuhlman1,2, Philip Bradley3,4.   

Abstract

The prediction of protein three-dimensional structure from amino acid sequence has been a grand challenge problem in computational biophysics for decades, owing to its intrinsic scientific interest and also to the many potential applications for robust protein structure prediction algorithms, from genome interpretation to protein function prediction. More recently, the inverse problem - designing an amino acid sequence that will fold into a specified three-dimensional structure - has attracted growing attention as a potential route to the rational engineering of proteins with functions useful in biotechnology and medicine. Methods for the prediction and design of protein structures have advanced dramatically in the past decade. Increases in computing power and the rapid growth in protein sequence and structure databases have fuelled the development of new data-intensive and computationally demanding approaches for structure prediction. New algorithms for designing protein folds and protein-protein interfaces have been used to engineer novel high-order assemblies and to design from scratch fluorescent proteins with novel or enhanced properties, as well as signalling proteins with therapeutic potential. In this Review, we describe current approaches for protein structure prediction and design and highlight a selection of the successful applications they have enabled.

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Year:  2019        PMID: 31417196      PMCID: PMC7032036          DOI: 10.1038/s41580-019-0163-x

Source DB:  PubMed          Journal:  Nat Rev Mol Cell Biol        ISSN: 1471-0072            Impact factor:   94.444


  94 in total

1.  Sequence-Based Prediction of Transmembrane Protein Crystallization Propensity.

Authors:  Qizhi Zhu; Lihua Wang; Ruyu Dai; Wei Zhang; Wending Tang; Yannan Bin; Zeliang Wang; Junfeng Xia
Journal:  Interdiscip Sci       Date:  2021-06-18       Impact factor: 2.233

2.  Fold recognition by scoring protein maps using the congruence coefficient.

Authors:  Pietro Di Lena; Pierre Baldi
Journal:  Bioinformatics       Date:  2021-05-01       Impact factor: 6.937

3.  A defined structural unit enables de novo design of small-molecule-binding proteins.

Authors:  Nicholas F Polizzi; William F DeGrado
Journal:  Science       Date:  2020-09-04       Impact factor: 47.728

Review 4.  Protein Engineering for Improving and Diversifying Natural Product Biosynthesis.

Authors:  Chenyi Li; Ruihua Zhang; Jian Wang; Lauren Marie Wilson; Yajun Yan
Journal:  Trends Biotechnol       Date:  2020-01-15       Impact factor: 19.536

Review 5.  Experimentally-driven protein structure modeling.

Authors:  Nikolay V Dokholyan
Journal:  J Proteomics       Date:  2020-04-05       Impact factor: 4.044

6.  A Unifying Framework for Understanding Biological Structures and Functions Across Levels of Biological Organization.

Authors:  M A Herman; B R Aiello; J D DeLong; H Garcia-Ruiz; A L González; W Hwang; C McBeth; E A Stojković; M A Trakselis; N Yakoby
Journal:  Integr Comp Biol       Date:  2022-02-05       Impact factor: 3.326

7.  Biomolecular QM/MM Simulations: What Are Some of the "Burning Issues"?

Authors:  Qiang Cui; Tanmoy Pal; Luke Xie
Journal:  J Phys Chem B       Date:  2021-01-06       Impact factor: 2.991

8.  Instrumentation and experimental procedures for robust collection of X-ray diffraction data from protein crystals across physiological temperatures.

Authors:  Tzanko Doukov; Daniel Herschlag; Filip Yabukarski
Journal:  J Appl Crystallogr       Date:  2020-11-05       Impact factor: 3.304

9.  Perturbing the energy landscape for improved packing during computational protein design.

Authors:  Jack B Maguire; Hugh K Haddox; Devin Strickland; Samer F Halabiya; Brian Coventry; Jermel R Griffin; Surya V S R K Pulavarti; Matthew Cummins; David F Thieker; Eric Klavins; Thomas Szyperski; Frank DiMaio; David Baker; Brian Kuhlman
Journal:  Proteins       Date:  2020-12-11

Review 10.  Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction.

Authors:  Donghyuk Suh; Jai Woo Lee; Sun Choi; Yoonji Lee
Journal:  Int J Mol Sci       Date:  2021-06-02       Impact factor: 5.923

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