New Knowledge-Based Scoring Function with Inclusion of Backbone Conformational Entropies from Protein Structures

2018 ◽  
Vol 58 (3) ◽  
pp. 724-732 ◽  
Author(s):  
Xinxiang Wang ◽  
Di Zhang ◽  
Sheng-You Huang
Keyword(s):  
Protein Structures ◽  
Scoring Function ◽  
Knowledge Based ◽  
New Knowledge

scholarly journals PB-kPRED: knowledge-based prediction of protein backbone conformation using a structural alphabet

2017 ◽  
Author(s):  
Iyanar Vetrivel ◽  
Swapnil Mahajan ◽  
Manoj Tyagi ◽  
Lionel Hoffmann ◽  
Yves-Henri Sanejouand ◽  
...  
Keyword(s):  
Protein Structures ◽  
Scoring Function ◽  
Evolutionary Information ◽  
Structural Alphabet ◽  
Local Structures ◽  
Knowledge Based ◽  
Scanning Strategies ◽  
Accuracy Of Prediction ◽  
The Impact ◽  
Protein Blocks

AbstractLibraries of structural prototypes that abstract protein local structures are known as structural alphabets and have proven to be very useful in various aspects of protein structure analyses and predictions. One such library, Protein Blocks (PBs), is composed of 16 standard 5-residues long structural prototypes. This form of analyzing proteins involves drafting its structure as a string of PBs. Thus, predicting the local structure of a protein in terms of protein blocks is a step towards the objective of predicting its 3-D structure. Here a new approach, kPred, is proposed towards this aim that is independent of the evolutionary information available. It involves (i) organizing the structural knowledge in the form of a database of pentapeptide fragments extracted from all protein structures in the PDB and (ii) apply a purely knowledge-based algorithm, not relying on secondary structure predictions or sequence alignment profiles, to scan this database and predict most probable backbone conformations for the protein local structures.Based on the strategy used for scanning the database, the method was able to achieve efficient mean Q16 accuracies between 40.8% and 66.3% for a non-redundant subset of the PDB filtered at 30% sequence identity cut-off. The impact of these scanning strategies on the prediction was evaluated and is discussed. A scoring function that gives a good estimate of the accuracy of prediction was further developed. This score estimates very well the accuracy of the algorithm (R2 of 0.82). An online version of the tool is provided freely for non-commercial usage at http://www.bo-protscience.fr/kpred/.

Geocartographic thinking in modern science

2020 ◽  
Vol 961 (7) ◽  
pp. 27-36
Author(s):  
A.K. Cherkashin
Keyword(s):  
Differential Geometry ◽  
Fiber Bundle ◽  
Modern Science ◽  
The State ◽  
Theory Of Knowledge ◽  
Knowledge Map ◽  
Knowledge Based ◽  
Geographical Environment ◽  
New Knowledge ◽  
Thematic Layer

The purpose of the study is to show how the features of geocartographic way of thinking are manifested in the meta-theory of knowledge based on mathematical formalisms. General cartographic concepts and regularities are considered in the view of metatheoretic analysis using cognitive procedures of fiber bundle from differential geometry. On levels of metainformation generalization, the geocartographic metatheoretic approach to the study of reality is higher than the system-theoretical one. It regulates the type of equations, models, and methods of each intertheory expressed in its own system terms. There is a balance between the state of any system and its geographical environment; therefore the observed phenomena are only explained theoretically in a metatheoretic projection on the corresponding system-thematic layer of the knowledge map. Metatheoretic research enables passing from the systematization of already known patterns to the formation of new knowledge through the scientific stratification of reality. General methods of metatheoretic analysis are mathematically distinguished

A knowledge-based halogen bonding scoring function for predicting protein-ligand interactions

2013 ◽  
Vol 19 (11) ◽  
pp. 5015-5030 ◽  
Author(s):  
Yingtao Liu ◽  
Zhijian Xu ◽  
Zhuo Yang ◽  
Kaixian Chen ◽  
Weiliang Zhu
Keyword(s):  
Scoring Function ◽  
Halogen Bonding ◽  
Knowledge Based ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

Pattern Matching Synthesis As an Automated Approach to Mechanism Design

1989 ◽  
Author(s):  
D. A. Hoeltzel ◽  
W.-H. Chieng
Keyword(s):  
Neural Network ◽  
Mechanism Design ◽  
Pattern Matching ◽  
Network Models ◽  
Hopfield Neural Network ◽  
Neural Network Models ◽  
Knowledge Based ◽  
Synthesis Of Mechanisms ◽  
New Knowledge ◽  
Programming Techniques

Abstract A new knowledge-based approach for the synthesis of mechanisms, referred to as Pattern Matching Synthesis, has been developed based on committee machine and Hopfield neural network models of pattern matching applied to coupler curves. Computational tests performed on a dimensionally parameterized four bar mechanism have yielded 15 distinct coupler curve groups (patterns) from a total of 356 generated coupler curves. This innovative approach represents a first step toward the automation of mapping structure-to-function in mechanism design based on the application of artificial intelligence programming techniques.

Towards a new knowledge-based framework for integrated quality control planning

2021 ◽  
Vol 15 (5) ◽  
pp. 583
Author(s):  
Fadwa Oukhay ◽  
Ahmed Badreddine ◽  
Taieb Ben Romdhane
Keyword(s):  
Quality Control ◽  
Knowledge Based ◽  
New Knowledge
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