Javascript must be enabled to continue!
ISSEC: inferring contacts among protein secondary structure elements using deep object detection
View through CrossRef
Abstract
Background
The formation of contacts among protein secondary structure elements (SSEs) is an important step in protein folding as it determines topology of protein tertiary structure; hence, inferring inter-SSE contacts is crucial to protein structure prediction. One of the existing strategies infers inter-SSE contacts directly from the predicted possibilities of inter-residue contacts without any preprocessing, and thus suffers from the excessive noises existing in the predicted inter-residue contacts. Another strategy defines SSEs based on protein secondary structure prediction first, and then judges whether each candidate SSE pair could form contact or not. However, it is difficult to accurately determine boundary of SSEs due to the errors in secondary structure prediction. The incorrectly-deduced SSEs definitely hinder subsequent prediction of the contacts among them.
Results
We here report an accurate approach to infer the inter-SSE contacts (thus called as ISSEC) using the deep object detection technique. The design of ISSEC is based on the observation that, in the inter-residue contact map, the contacting SSEs usually form rectangle regions with characteristic patterns. Therefore, ISSEC infers inter-SSE contacts through detecting such rectangle regions. Unlike the existing approach directly using the predicted probabilities of inter-residue contact, ISSEC applies the deep convolution technique to extract high-level features from the inter-residue contacts. More importantly, ISSEC does not rely on the pre-defined SSEs. Instead, ISSEC enumerates multiple candidate rectangle regions in the predicted inter-residue contact map, and for each region, ISSEC calculates a confidence score to measure whether it has characteristic patterns or not. ISSEC employs greedy strategy to select non-overlapping regions with high confidence score, and finally infers inter-SSE contacts according to these regions.
Conclusions
Comprehensive experimental results suggested that ISSEC outperformed the state-of-the-art approaches in predicting inter-SSE contacts. We further demonstrated the successful applications of ISSEC to improve prediction of both inter-residue contacts and tertiary structure as well.
Springer Science and Business Media LLC
Title: ISSEC: inferring contacts among protein secondary structure elements using deep object detection
Description:
Abstract
Background
The formation of contacts among protein secondary structure elements (SSEs) is an important step in protein folding as it determines topology of protein tertiary structure; hence, inferring inter-SSE contacts is crucial to protein structure prediction.
One of the existing strategies infers inter-SSE contacts directly from the predicted possibilities of inter-residue contacts without any preprocessing, and thus suffers from the excessive noises existing in the predicted inter-residue contacts.
Another strategy defines SSEs based on protein secondary structure prediction first, and then judges whether each candidate SSE pair could form contact or not.
However, it is difficult to accurately determine boundary of SSEs due to the errors in secondary structure prediction.
The incorrectly-deduced SSEs definitely hinder subsequent prediction of the contacts among them.
Results
We here report an accurate approach to infer the inter-SSE contacts (thus called as ISSEC) using the deep object detection technique.
The design of ISSEC is based on the observation that, in the inter-residue contact map, the contacting SSEs usually form rectangle regions with characteristic patterns.
Therefore, ISSEC infers inter-SSE contacts through detecting such rectangle regions.
Unlike the existing approach directly using the predicted probabilities of inter-residue contact, ISSEC applies the deep convolution technique to extract high-level features from the inter-residue contacts.
More importantly, ISSEC does not rely on the pre-defined SSEs.
Instead, ISSEC enumerates multiple candidate rectangle regions in the predicted inter-residue contact map, and for each region, ISSEC calculates a confidence score to measure whether it has characteristic patterns or not.
ISSEC employs greedy strategy to select non-overlapping regions with high confidence score, and finally infers inter-SSE contacts according to these regions.
Conclusions
Comprehensive experimental results suggested that ISSEC outperformed the state-of-the-art approaches in predicting inter-SSE contacts.
We further demonstrated the successful applications of ISSEC to improve prediction of both inter-residue contacts and tertiary structure as well.
Related Results
Depth-aware salient object segmentation
Depth-aware salient object segmentation
Object segmentation is an important task which is widely employed in many computer vision applications such as object detection, tracking, recognition, and ret...
Tuberculosis yield among contacts of non-pulmonary bacteriologically confirmed index TB patients in the urban setting of central Uganda
Tuberculosis yield among contacts of non-pulmonary bacteriologically confirmed index TB patients in the urban setting of central Uganda
Background
The World Health Organization (WHO) recommends systematic and active investigation of TB contacts. However, lower priority is given to contact investigation among other ...
Endothelial Protein C Receptor
Endothelial Protein C Receptor
IntroductionThe protein C anticoagulant pathway plays a critical role in the negative regulation of the blood clotting response. The pathway is triggered by thrombin, which allows ...
Deep learning for small object detection in images
Deep learning for small object detection in images
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] With the rapid development of deep learning in computer vision, especially deep convolutional neural network...
Protein—protein crystal‐packing contacts
Protein—protein crystal‐packing contacts
AbstractProtein‐protein contacts in monomeric protein crystal structures have been analyzed and compared to the physiological protein‐protein contacts in oligomerization. A number ...
Correlated mutations distinguish misfolded and properly folded proteins
Correlated mutations distinguish misfolded and properly folded proteins
Knowledge about the three dimensional structure of proteins is crucial in order to learn about their behavior, stability, or role as a target in drug design. Unfortunately, traditi...
Detection of acne by deep learning object detection
Detection of acne by deep learning object detection
Abstract
Importance
State-of-the art performance is achieved with a deep learning object detection model for acne detection. Th...
The presence of a booster phenomenon among contacts of active pulmonary tuberculosis cases: a retrospective cohort
The presence of a booster phenomenon among contacts of active pulmonary tuberculosis cases: a retrospective cohort
Abstract
Background
Assuming a higher risk of latent tuberculosis (TB) infection in the population of Rio de Janeiro, Brazil, in October of 1998 ...

