scholarly journals New 63 knot and other knots in human proteome from AlphaFold predictions

2022 ◽  
Author(s):  
Agata Paulina Perlinska ◽  
Wanda Helena Niemyska ◽  
Bartosz Ambrozy Gren ◽  
Pawel Rubach ◽  
Joanna Ida Sulkowska

AlphaFold is a new, highly accurate machine learning protein structure prediction method that outperforms other methods. Recently this method was used to predict the structure of 98.5% of human proteins. We analyze here the structure of these AlphaFold-predicted human proteins for the presence of knots. We found that the human proteome contains 65 robustly knotted proteins, including the most complex type of a knot yet reported in proteins. That knot type, denoted 63 in mathematical notation, would necessitate a more complex folding path than any knotted proteins characterized to date. In some cases AlphaFold structure predictions are not highly accurate, which either makes their topology hard to verify or results in topological artifacts. Other structures that we found, which are knotted, potentially knotted, and structures with artifacts (knots) we deposited in a database available at: https://knotprot.cent.uw.edu.pl/alphafold.

RSC Advances ◽  
2017 ◽  
Vol 7 (63) ◽  
pp. 39869-39876 ◽  
Author(s):  
Pengyue Gao ◽  
Sheng Wang ◽  
Jian Lv ◽  
Yanchao Wang ◽  
Yanming Ma

A swarm-intelligence-based protein structure prediction method holds promise for narrowing the sequence-structure gap of proteins.


Nature ◽  
2021 ◽  
Author(s):  
Kathryn Tunyasuvunakool ◽  
Jonas Adler ◽  
Zachary Wu ◽  
Tim Green ◽  
Michal Zielinski ◽  
...  

2017 ◽  
Vol 5 (42) ◽  
pp. 22146-22155 ◽  
Author(s):  
Fazel Shojaei ◽  
Jae Ryang Hahn ◽  
Hong Seok Kang

Based on a sophisticated crystal structure prediction method, we propose two-dimensional (2D) GeP2in the tetragonal (T) phase never observed for other group IV–V compounds.


2015 ◽  
Vol 60 (27) ◽  
pp. 2580-2587 ◽  
Author(s):  
YanMing MA ◽  
Jian L ◽  
YanChao WANG

Author(s):  
Raghunath Satpathy

Proteins play a vital molecular role in all living organisms. Experimentally, it is difficult to predict the protein structure, however alternatively theoretical prediction method holds good for it. The 3D structure prediction of proteins is very much important in biology and this leads to the discovery of different useful drugs, enzymes, and currently this is considered as an important research domain. The prediction of proteins is related to identification of its tertiary structure. From the computational point of view, different models (protein representations) have been developed along with certain efficient optimization methods to predict the protein structure. The bio-inspired computation is used mostly for optimization process during solving protein structure. These algorithms now a days has received great interests and attention in the literature. This chapter aim basically for discussing the key features of recently developed five different types of bio-inspired computational algorithms, applied in protein structure prediction problems.


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