The development of ReFOLDa a new rapid iterative protein refinement protocol guided by state-of-the-art quality assessment programs for the selection of optimal refined model.
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1
University of Science, Malaysia, Malaysia
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2
School of Biological Sciences, University of Reading, United Kingdom
Background
Extraction of relevant information on structures and functions of proteins have been dependent on numerous potential computer-based protein modelling programs that are resourceful and time efficient. However, progress on refining the predicted models has been slow. Most of the computer-based refinement programs had generally failed to improve over the starting protein model consistently. Thus enhancing the accuracy of starting models continues to be one of the crucial and challenging problems in structural bioinformatics as the development and exploration of new drugs are highly dependent on model accuracy. Previously we have developed state of the art hybrid refinement protocol known as ReFOLD server (http://www.reading.ac.uk/bioinf/ReFOLD/) for protein refinement. With ReFOLD success in the recent CASP12 experiment, we aim to design another variant of ReFOLD refinement programs that can run on any platform with minimal computational power.
Methods
In this experiment we have developed another variant of ReFOLD server (ReFOLDa) that uses i3Drefine to improve the structure of the starting protein models. ReFOLDa protocol was also integrated with Method 42 that select the best predicted refined models based on the trajectory of the predicted QA scores (ModFOLD5_single) for the first seventh iterations. The performance of ReFOLDa in comparison to the ReFOLD was then tested using the targets from the CAPS12 experiment. TMscore was used to calculate the observed scores for the generated refined models and R statistical software was used to study the significance of the observed differences between ReFOLD and ReFOLDa.
Results
The paired-sample t-tests and Wilcoxon-ranked sum-tests revealed that ReFOLDa (Mean = 0.3962) protocol performed better than ReFOLD (Mean = 0.3839) for refining the targets from the refinement category and was on par with ReFOLD for refining the targets from the tertiary structure prediction category of the CASP12 experiment.
Conclusion
ReFOLDa protocol is a sufficiently rapid and computationally non-intensive strategy in the selection for the optimal refined models.
Keywords:
Refold,
ReFOLDa,
protein refinement,
drug design,
structural bioinformatics
Conference:
International Conference on Drug Discovery and Translational Medicine 2018 (ICDDTM '18)
“Seizing Opportunities and Addressing Challenges of Precision Medicine”, Putrajaya, Malaysia, 3 Dec - 5 Feb, 2019.
Presentation Type:
Oral Presentation
Topic:
Infectious diseases
Citation:
Shuid
AN and
McGuffin
LJ
(2019). The development of ReFOLDa a new rapid iterative protein refinement protocol guided by state-of-the-art quality assessment programs for the selection of optimal refined model..
Front. Pharmacol.
Conference Abstract:
International Conference on Drug Discovery and Translational Medicine 2018 (ICDDTM '18)
“Seizing Opportunities and Addressing Challenges of Precision Medicine”.
doi: 10.3389/conf.fphar.2019.63.00039
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Received:
29 Nov 2018;
Published Online:
17 Jan 2019.
*
Correspondence:
Mr. Ahmad N Shuid, University of Science, Malaysia, George Town, Malaysia, ahmadnaqibreading85@gmail.com