Delıbaş, Ayşe Bahar

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Profile URL
https://hdl.handle.net/20.500.12469/6966
Name Variants
Delıbaş, Ayşe Bahar
A.,Delıbaş
A. B. Delıbaş
Ayşe Bahar, Delıbaş
Delibas, Ayse Bahar
A.,Delibas
A. B. Delibas
Ayse Bahar, Delibas
Akbal-Delibas, Bahar
Job Title
Dr. Öğr. Üyesi
Email Address
Bahar.delı[email protected]
Main Affiliation
Computer Engineering
Status
Former Staff
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WoS Researcher ID

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Scholarly Output

3

Articles

2

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22/175

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

12

Scopus Citation Count

15

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1

Scopus h-index

1

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0

Projects

0

WoS Citations per Publication

4.00

Scopus Citations per Publication

5.00

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1

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0

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JournalCount
Journal of Bioinformatics and Computational Biology1
Journal of Computational Biology1
Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics1
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Now showing 1 - 3 of 3
  • Thumbnail Image
    Conference Object
    Ranking Protein-Protein Binding Using Evolutionary Information and Machine Learning
    (Association for Computing Machinery, 2017) Farhoodi, Roshanak; Akbal-Delibas, Bahar; Haspel, Nurit
    Discriminating native-like complexes from false-positives with high accuracy is one of the biggest challenges in protein-protein docking. The relationship between various favorable intermolecular interactions (e.g. Van derWaals electrostatic desolvation forces etc.) and the similarity of a conformation to its native structure is commonly agreed though the precise nature of this relationship is not known very well. Existing protein-protein docking methods typically formulate this relationship as a weighted sum of selected terms and tune their weights by introducing a training set with which they evaluate and rank candidate complexes. Despite improvements in recent docking methods they are still producing a large number of false positives which often leads to incorrect prediction of complex binding. Using machine learning we implemented an approach that not only ranks candidate complexes relative to each other but also predicts how similar each candidate is to the native conformation. We built a Support Vector Regressor (SVR) using physico-chemical features and evolutionary conservation. We trained and tested the model on extensive datasets of complexes generated by three state-of-the-art docking methods. The set of docked complexes was generated from 79 different protein-protein complexes in both the rigid and medium categories of the Protein-Protein Docking Benchmark v.5. We were able to generally outperform the built-in scoring functions of the docking programs we used to generate the complexes attesting to the potential of our approach in predicting the correct binding of protein-protein complexes.
  • Thumbnail Image
    Article
    Machine Learning Approaches for Predicting Protein Complex Similarity
    (Mary Ann Liebert Inc Publ, 2017) Farhoodi, Roshanak; Akbal-Delibas, Bahar; Haspel, Nurit
    Discriminating native-like structures from false positives with high accuracy is one of the biggest challenges in protein-protein docking. While there is an agreement on the existence of a relationship between various favorable intermolecular interactions (e.g. Van der Waals electrostatic and desolvation forces) and the similarity of a conformation to its native structure the precise nature of this relationship is not known. Existing protein-protein docking methods typically formulate this relationship as a weighted sum of selected terms and calibrate their weights by using a training set to evaluate and rank candidate complexes. Despite improvements in the predictive power of recent docking methods producing a large number of false positives by even state-of-the-art methods often leads to failure in predicting the correct binding of many complexes. With the aid of machine learning methods we tested several approaches that not only rank candidate structures relative to each other but also predict how similar each candidate is to the native conformation. We trained a two-layer neural network a multilayer neural network and a network of Restricted Boltzmann Machines against extensive data sets of unbound complexes generated by RosettaDock and PyDock. We validated these methods with a set of refinement candidate structures. We were able to predict the root mean squared deviations (RMSDs) of protein complexes with a very small often less than 1.5 angstrom error margin when trained with structures that have RMSD values of up to 7 angstrom. In our most recent experiments with the protein samples having RMSD values up to 27 angstrom the average prediction error was still relatively small attesting to the potential of our approach in predicting the correct binding of protein-protein complexes.
  • Thumbnail Image
    Article
    Citation - WoS: 12
    Citation - Scopus: 15
    Accurate Refinement of Docked Protein Complexes Using Evolutionary Information and Deep Learning
    (Imperıal College Press, 2016) Akbal-Delibas, Bahar; Farhoodi, Roshanak; Pomplun, Marc; Haspel, Nurit
    One of the major challenges for protein docking methods is to accurately discriminate native-like structures from false positives. Docking methods are often inaccurate and the results have to be refined and re-ranked to obtain native-like complexes and remove outliers. In a previous work we introduced AccuRefiner a machine learning based tool for refining protein-protein complexes. Given a docked complex the refinement tool produces a small set of refined versions of the input complex with lower root-mean-square-deviation (RMSD) of atomic positions with respect to the native structure. The method employs a unique ranking tool that accurately predicts the RMSD of docked complexes with respect to the native structure. In this work we use a deep learning network with a similar set of features and five layers. We show that a properly trained deep learning network can accurately predict the RMSD of a docked complex with 1.40 angstrom error margin on average by approximating the complex relationship between a wide set of scoring function terms and the RMSD of a docked structure. The network was trained on 35000 unbound docking complexes generated by RosettaDock. We tested our method on 25 different putative docked complexes produced also by RosettaDock for five proteins that were not included in the training data. The results demonstrate that the high accuracy of the ranking tool enables AccuRefiner to consistently choose the refinement candidates with lower RMSD values compared to the coarsely docked input structures.