Modeling of Protein Tertiary and Quaternary Structures Based on Evolutionary Information

2018 ◽  
pp. 301-316 ◽  
Author(s):  
Gabriel Studer ◽  
Gerardo Tauriello ◽  
Stefan Bienert ◽  
Andrew Mark Waterhouse ◽  
Martino Bertoni ◽  
...  
Keyword(s):  
Evolutionary Information ◽  
Quaternary Structures

Mother Culture, Meet Mother Nature

2018 ◽  
Author(s):  
Luc Faucher ◽  
Pierre Poirier
Keyword(s):  
Evolutionary Algorithms ◽  
Cultural Change ◽  
Evolutionary Information ◽  
Multiple Forms ◽  
Human Immune System ◽  
Units Of Selection ◽  
Pluralistic Approach ◽  
Human Immune ◽  
Plausible Theory ◽  
Different Time Scales

Research on the adaptive characteristics of the human immune system reveals that evolutionary algorithms are not strictly matters of replication. And research in genomics suggests that there is no a single source of evolutionary information that carries the same content in every environment. A plausible theory of cultural evolution must acknowledge the possibility that multiple selective algorithms are operating at different time-scales, on different units of selection, with different logical structures; but it must explain how different selective processes are interfaced to yield culturally stable phenomena. This paper advances an empirically plausible approach to memetics that recognizes a wider variety of evolutionary algorithms; and it advances a pluralistic approach to cultural change. Finally, it shows that multiple forms of processing, operating at different timescales, on different units of selection, collectively sustain the human capacity to form and use certain types of representations.

Information on evolutionary age in redundancy of complex network

2019 ◽  
Vol 33 (27) ◽  
pp. 1950331
Author(s):  
Shiguo Deng ◽  
Henggang Ren ◽  
Tongfeng Weng ◽  
Changgui Gu ◽  
Huijie Yang
Keyword(s):  
Principal Component Analysis ◽  
Complex Networks ◽  
Metabolic Network ◽  
Complex Network ◽  
Cellular Networks ◽  
Topological Structure ◽  
Quantitative Measure ◽  
Principal Component ◽  
Evolutionary Information ◽  
Local Patterns

Evolutionary processes of many complex networks in reality are dominated by duplication and divergence. This mechanism leads to redundant structures, i.e. some nodes share most of their neighbors and some local patterns are similar, called redundancy of network. An interesting reverse problem is to discover evolutionary information from the present topological structure. We propose a quantitative measure of redundancy of network from the perspective of principal component analysis. The redundancy of a community in the empirical human metabolic network is negatively and closely related with its evolutionary age, which is consistent with that for the communities in the modeling protein–protein network. This behavior can be used to find the evolutionary difference stored in cellular networks.

scholarly journals A Comprehensive Phylogenetic and Bioinformatics Survey of Lectins in the Fungal Kingdom

2021 ◽  
Vol 7 (6) ◽  
pp. 453
Author(s):  
Annie Lebreton ◽  
François Bonnardel ◽  
Yu-Cheng Dai ◽  
Anne Imberty ◽  
Francis M. Martin ◽  
...  
Keyword(s):  
Evolutionary Relationship ◽  
Large Family ◽  
Gene Families ◽  
Carbohydrate Binding ◽  
Sequence Motifs ◽  
Fungal Lectin ◽  
Quaternary Structures ◽  
High Degree ◽  
Carbohydrate Ligand ◽  
Fungal Kingdom

Fungal lectins are a large family of carbohydrate-binding proteins with no enzymatic activity. They play fundamental biological roles in the interactions of fungi with their environment and are found in many different species across the fungal kingdom. In particular, their contribution to defense against feeders has been emphasized, and when secreted, lectins may be involved in the recognition of bacteria, fungal competitors and specific host plants. Carbohydrate specificities and quaternary structures vary widely, but evidence for an evolutionary relationship within the different classes of fungal lectins is supported by a high degree of amino acid sequence identity. The UniLectin3D database contains 194 fungal lectin 3D structures, of which 129 are characterized with a carbohydrate ligand. Using the UniLectin3D lectin classification system, 109 lectin sequence motifs were defined to screen 1223 species deposited in the genomic portal MycoCosm of the Joint Genome Institute. The resulting 33,485 putative lectin sequences are organized in MycoLec, a publicly available and searchable database. These results shed light on the evolution of the lectin gene families in fungi.

scholarly journals NOD: a web server to predict New use of Old Drugs to facilitate drug repurposing

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tarun Jairaj Narwani ◽  
Narayanaswamy Srinivasan ◽  
Sohini Chakraborti
Keyword(s):  
Chemical Space ◽  
Web Server ◽  
Drug Repurposing ◽  
Cost Effective ◽  
Evolutionary Information ◽  
Cost Effective Alternative ◽  
Similarity Searches ◽  
Old Drugs ◽  
Control Study ◽  
Made In

AbstractComputational methods accelerate the drug repurposing pipelines that are a quicker and cost-effective alternative to discovering new molecules. However, there is a paucity of web servers to conduct fast, focussed, and customized investigations for identifying new uses of old drugs. We present the NOD web server, which has the mentioned characteristics. NOD uses a sensitive sequence-guided approach to identify close and distant homologs of a protein of interest. NOD then exploits this evolutionary information to suggest potential compounds from the DrugBank database that can be repurposed against the input protein. NOD also allows expansion of the chemical space of the potential candidates through similarity searches. We have validated the performance of NOD against available experimental and/or clinical reports. In 65.6% of the investigated cases in a control study, NOD is able to identify drugs more effectively than the searches made in DrugBank. NOD is freely-available at http://pauling.mbu.iisc.ac.in/NOD/NOD/.

scholarly journals Embeddings from deep learning transfer GO annotations beyond homology

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maria Littmann ◽  
Michael Heinzinger ◽  
Christian Dallago ◽  
Tobias Olenyi ◽  
Burkhard Rost
Keyword(s):  
Protein Function ◽  
Protein Sequences ◽  
Language Models ◽  
Evolutionary Information ◽  
Pairwise Sequence Identity ◽  
Intrinsically Disordered ◽  
Intrinsically Disordered Regions ◽  
Sequence Identity ◽  
Experimental Function ◽  
Go Terms

AbstractKnowing protein function is crucial to advance molecular and medical biology, yet experimental function annotations through the Gene Ontology (GO) exist for fewer than 0.5% of all known proteins. Computational methods bridge this sequence-annotation gap typically through homology-based annotation transfer by identifying sequence-similar proteins with known function or through prediction methods using evolutionary information. Here, we propose predicting GO terms through annotation transfer based on proximity of proteins in the SeqVec embedding rather than in sequence space. These embeddings originate from deep learned language models (LMs) for protein sequences (SeqVec) transferring the knowledge gained from predicting the next amino acid in 33 million protein sequences. Replicating the conditions of CAFA3, our method reaches an Fmax of 37 ± 2%, 50 ± 3%, and 57 ± 2% for BPO, MFO, and CCO, respectively. Numerically, this appears close to the top ten CAFA3 methods. When restricting the annotation transfer to proteins with < 20% pairwise sequence identity to the query, performance drops (Fmax BPO 33 ± 2%, MFO 43 ± 3%, CCO 53 ± 2%); this still outperforms naïve sequence-based transfer. Preliminary results from CAFA4 appear to confirm these findings. Overall, this new concept is likely to change the annotation of proteins, in particular for proteins from smaller families or proteins with intrinsically disordered regions.

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