Sequence-Based Identification of DNA Replication Proteins and DNA Replication Inhibitors using Statistical Moments and PseAAC
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Date
2019
Authors
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Publisher
UMT, Lahore
Abstract
DNA is undoubtedly important for all living beings and a DNA molecule that holds a lot of
information about heritage, also predicts if they are at risk for certain diseases. Double helix
DNA consists of two integrated branches. These strings are separated during the copy process.
Next, each strand of the original DNA molecule functions as a template and generates its
counterpart. This is a process known as semi-conservative iteration. Because of the semi-
conservative replication, the new coil is composed of both the original DNA strand and the
newly synthesized strand. Cell error correction and error-checking mechanisms ensure almost
complete commitment to DNA replication. DNA replication can also be performed in the
laboratory. DNA synthesis can be initiated from a known sequence of template DNA
molecules using DNA polymers isolated from cells and artificial DNA primers. Examples of
polymerase chain reaction, ligase chain reaction and transcription-mediated amplification, but
it can be very costly and time-consuming. Similarly, the identification of DNA replication
proteins and DNA replication inhibitor proteins is somewhat extremely crucial that requires
the reliable and comprehensive computational method that can precisely predict and
discriminate the proteins. In this study, identification of DNA replication proteins and DNA
replication inhibitors was aimed. This study is totally followed by Chou’s 5 step rule and
different types of techniques used to get efficient prediction results by using an artificial
neural network algorithm. This study comprehends the construction of novel prediction
model to serve the proposed purpose. A prediction model was developed based on the
artificial neural network by integrating the position relative features and sequence statistical
moments in PseAAC for training neural networks. 10-fold cross-validation and Leave-one-
out method was opted by validating at different levels like overall accuracy, sensitivity, and
specificity. The study results recommend that the proposed strategy may play a fundamental
part in the other existing strategies for DNA replication inhibitors and proteins prediction.
Hence the proposed prediction method can offer assistance in foreseeing the DNA replication
proteins and inhibitors in a productive and exact way. Our astonishing experimental results
demonstrated that the proposed predictor surpass the existing models that can be served as a
xi
time and cost-effective stratagem for designing novel to identify DNA replication proteins
and inhibitors.