DrugScore Meets CoMFA:  Adaptation of Fields for Molecular Comparison (AFMoC) or How to Tailor Knowledge-Based Pair-Potentials to a Particular Protein

Holger Gohlke; Gerhard Klebe

doi:10.1021/jm020808p

Predicting transmembrane helix pair configurations with knowledge‐based distance‐dependent pair potentials

Proteins Structure Function and Bioinformatics ◽

10.1002/prot.21574 ◽

2008 ◽

Vol 70 (3) ◽

pp. 984-999 ◽

Cited By ~ 7

Author(s):

Christina Wendel ◽

Holger Gohlke

Keyword(s):

Transmembrane Helix ◽

Knowledge Based ◽

Pair Potentials ◽

Dependent Pair

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Random Forest Refinement of the KECSA2 Knowledge-based Scoring Function for Protein Decoy Detection

10.26434/chemrxiv.7231058.v1 ◽

2018 ◽

Author(s):

Jun Pei ◽

Zheng Zheng ◽

Kenneth M. Merz Jr.

Keyword(s):

Random Forest ◽

Scoring Function ◽

Peak Height ◽

Data Sets ◽

Atom Pair ◽

Probability Functions ◽

Knowledge Based ◽

Pair Potentials ◽

Native Proteins ◽

Combined Data

In this work, via the use of the ‘comparison’ concept, Random Forest (RF) models were successfully generated using unbalanced data sets that assign different importance factors to atom pair potentials to enhance their ability to identify native proteins from decoy proteins. Individual and combined data sets consisting of twelve decoy sets were used to test the performance of the RF models. We find that RF models increase the recognition of native structures without affecting their ability to identify the best decoy structures. We also created models using scrambled atom types, which create physically unrealistic probability functions, in order to test the ability of the RF algorithm to create useful models based on inputted scrambled probability functions. From this test we find that we are unable to create models that are of similar quality relative to the unscrambled probability functions. Next we created uniform probability functions where the peak positions as the same as the original, but each interaction has the same peak height. Using these uniform potentials we were able to recover models as good as the ones using the full potentials suggesting all that is important in these models are the experimental peak positions.

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Random Forest Refinement of the KECSA2 Knowledge-based Scoring Function for Protein Decoy Detection

10.26434/chemrxiv.7231058 ◽

2018 ◽

Author(s):

Jun Pei ◽

Zheng Zheng ◽

Kenneth M. Merz Jr.

Keyword(s):

Random Forest ◽

Scoring Function ◽

Peak Height ◽

Data Sets ◽

Atom Pair ◽

Probability Functions ◽

Knowledge Based ◽

Pair Potentials ◽

Native Proteins ◽

Combined Data

In this work, via the use of the ‘comparison’ concept, Random Forest (RF) models were successfully generated using unbalanced data sets that assign different importance factors to atom pair potentials to enhance their ability to identify native proteins from decoy proteins. Individual and combined data sets consisting of twelve decoy sets were used to test the performance of the RF models. We find that RF models increase the recognition of native structures without affecting their ability to identify the best decoy structures. We also created models using scrambled atom types, which create physically unrealistic probability functions, in order to test the ability of the RF algorithm to create useful models based on inputted scrambled probability functions. From this test we find that we are unable to create models that are of similar quality relative to the unscrambled probability functions. Next we created uniform probability functions where the peak positions as the same as the original, but each interaction has the same peak height. Using these uniform potentials we were able to recover models as good as the ones using the full potentials suggesting all that is important in these models are the experimental peak positions.

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Is the Personal Always Political?

Journal of Individual Differences ◽

10.1027/1614-0001/a000230 ◽

2017 ◽

Vol 38 (3) ◽

pp. 133-143 ◽

Cited By ~ 4

Author(s):

Danny Osborne ◽

Yannick Dufresne ◽

Gregory Eady ◽

Jennifer Lees-Marshment ◽

Cliff van der Linden

Keyword(s):

Survey Data ◽

National Survey ◽

Negative Relationship ◽

Openness To Experience ◽

Political Sophistication ◽

Knowledge Based

Abstract. Research demonstrates that the negative relationship between Openness to Experience and conservatism is heightened among the informed. We extend this literature using national survey data (Study 1; N = 13,203) and data from students (Study 2; N = 311). As predicted, education – a correlate of political sophistication – strengthened the negative relationship between Openness and conservatism (Study 1). Study 2 employed a knowledge-based measure of political sophistication to show that the Openness × Political Sophistication interaction was restricted to the Openness aspect of Openness. These studies demonstrate that knowledge helps people align their ideology with their personality, but that the Openness × Political Sophistication interaction is specific to one aspect of Openness – nuances that are overlooked in the literature.

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