Recent advances in de novo computational design and redesign of intrinsically disordered proteins and intrinsically disordered protein regions

被引:2
|
作者
Saikia, Bondeepa [1 ]
Baruah, Anupaul [1 ]
机构
[1] Dibrugarh Univ, Dept Chem, Dibrugarh 786004, Assam, India
关键词
Computational protein design; Intrinsically disordered proteins; Intrinsically disordered protein regions; Potential energy landscape; SEQUENCE-ENSEMBLE RELATIONSHIPS; CONFORMATIONAL PROPENSITIES; FORCE-FIELDS; SIMULATIONS; DYNAMICS; BINDING; DETERMINANTS; FLUCTUATIONS; PREDICTION; ALGORITHM;
D O I
10.1016/j.abb.2023.109857
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
In the early 2000s, the concept of "unstructured biology"has emerged to be an important field in protein science by generating various new research directions. Many novel strategies and methods have been developed that are focused on effectively identifying/predicting intrinsically disordered proteins (IDPs) and intrinsically disordered protein regions (IDPRs), identifying their potential functions, disorder based drug design etc. Due to the range of functions of IDPs/IDPRs and their involvement in various debilitating diseases they are of contemporary interest to the scientific community. Recent researches are focused on designing/redesigning specific IDPs/IDPRs de novo. These de novo design/redesigns of IDPs/IDPRs are carried out by altering compositional biases and specific sequence patterning parameters. The main focus of these researches is to influence specific molecular functions, phase behavior, cellular phenotypes etc. In this review, we first provide the differences of natively folded and natively unfolded or IDPs with respect to their potential energy landscapes. Here, we provide current understandings on the different computational design strategies and methods that have been utilized in de novo design and redesigns of IDPs and IDPRs. Finally, we conclude the review by discussing the challenges that have been faced during the computational design/design attempts of IDPs/IDPRs.
引用
收藏
页数:10
相关论文
共 50 条
  • [31] Conformational and Spectroscopic Characterization of Intrinsically Disordered Regions in Proteins
    Sethi, Anurag
    Vu, Dung
    Gnanakaran, S.
    BIOPHYSICAL JOURNAL, 2011, 100 (03) : 13 - 13
  • [32] Computational approaches for inferring the functions of intrinsically disordered proteins
    Varadi, Mihaly
    Vranken, Wim
    Guharoy, Mainak
    Tompa, Peter
    FRONTIERS IN MOLECULAR BIOSCIENCES, 2015, 2
  • [33] Nanomechanics of intrinsically disordered muscle proteins: Computational approaches
    Forbes, Jeffrey G.
    Wang, Kuan
    BIOPHYSICAL JOURNAL, 2007, : 523A - 524A
  • [34] Intrinsically Disordered Proteins: An Overview
    Trivedi, Rakesh
    Nagarajaram, Hampapathalu Adimurthy
    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2022, 23 (22)
  • [35] Intrinsically disordered proteins and biomineralization
    Boskey, Adele L.
    Villarreal-Ramirez, Eduardo
    MATRIX BIOLOGY, 2016, 52-54 : 43 - 59
  • [36] Intrinsically Disordered Proteins in Cancer
    Meszaros, Balint
    Dosztanyi, Zsuzsanna
    Zeke, Andras
    Remenyi, Attila
    PROTEIN SCIENCE, 2018, 27 : 112 - 113
  • [37] Intrinsically disordered proteins: An update
    Dunker, A. Keith
    Yang, Jack Y.
    Oldfield, Christopher J.
    Obradovic, Zoran
    Meng, Jingwei
    Romero, Pedro
    Uversky, Vladimir N.
    PROCEEDINGS OF THE 7TH IEEE INTERNATIONAL SYMPOSIUM ON BIOINFORMATICS AND BIOENGINEERING, VOLS I AND II, 2007, : 49 - +
  • [38] Druggability of Intrinsically Disordered Proteins
    Joshi, Priyanka
    Vendruscolo, Michele
    INTRINSICALLY DISORDERED PROTEINS STUDIED BY NMR SPECTROSCOPY, 2015, 870 : 383 - 400
  • [39] Identification of Disordered Regions of Intrinsically Disordered Proteins by Multi-features Fusion
    Canzhuang, Sun
    Yonge, Feng
    CURRENT BIOINFORMATICS, 2021, 16 (09) : 1126 - 1132
  • [40] Recent Force Field Strategies for Intrinsically Disordered Proteins
    Mu, Junxi
    Liu, Hao
    Zhang, Jian
    Luo, Ray
    Chen, Hai-Feng
    JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2021, 61 (03) : 1037 - 1047