Protein secondary structure prediction: A survey of the state of the art

Jiang, Qian; Jin, Xin; Lee, Shin-Jye; Yao, Shaowen

doi:10.1016/j.jmgm.2017.07.015

Cited by 83 publications

(54 citation statements)

References 99 publications

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“…For instance, sequencebased secondary structure predictors available in the early 90s with an estimated accuracy between 60% and 65% (Qian and Sejnowski, 1988) were rapidly replaced by a new generation of profile-based predictors with an estimated accuracy between 70% and 76% (Rost and Sander, 1993;Jones, 1999). Since then, predictors have kept improving thanks notably to more sophisticated prediction methods and larger databases (Yang et al, 2016;Jiang et al, 2017) but still belong to the same generation of profile-based predictors. The˜10% gain in accuracy initially observed is still visible nowadays as recently showed in Heffernan et al, 2018 andTorrisi et al, 2019.…”

Section: Three Decades Of Profile-based Predictorsmentioning

confidence: 99%

Protein Profiles: Biases and Protocols

Urban¹,

Torrisi

Magnan³

et al. 2020

Preprint

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The use of evolutionary profiles to predict protein secondary structure, as well as other protein structural features, has been standard practice since the 1990s. Using profiles in the input of such predictors, in place or in addition to the sequence itself, leads to significantly more accurate predictors. While profiles can enhance structural signals, their role remains somewhat surprising as proteins do not use profiles when folding in vivo. Furthermore, the same sequence-based redundancy reduction protocols initially derived to train and evaluate sequence-based predictors, have been applied to train and evaluate profile-based predictors. This can lead to unfair comparisons since profile may facilitate the bleeding of information between training and test sets. Here we use the extensively studied problem of secondary structure prediction to better evaluate the role of profiles and show that: (1) high levels of profile similarity between training and test proteins are observed when using standard sequence-based redundancy protocols; (2) the gain in accuracy for profilebased predictors, over sequence-based predictors, strongly relies on these high levels of profile similarity between training and test proteins; and (3) the overall accuracy of a profile-based predictor on a given protein dataset provides a biased measure when trying to estimate the actual accuracy of the predictor, or when comparing it to other predictors. We show, however, that this bias can be avoided by implementing a new protocol (EVALpro) which evaluates the accuracy of profile-based predictors as a function of the profile similarity between training and test proteins. Such a protocol not only allows for a fair comparison of the predictors on equally hard or easy examples, but also completely removes the need for selecting arbitrary similarity cutoffs when selecting test proteins. The EVALpro program is available for download from the SCRATCH suite (http://scratch.proteomics.ics.uci.edu).

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Section: Three Decades Of Profile-based Predictorsmentioning

confidence: 99%

Protein Profiles: Biases and Protocols

Urban¹,

Torrisi

Magnan³

et al. 2020

Preprint

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“…These features classify similar protein sequences to reduce the execution time of PROBCONS tool. Some of the features related to protein secondary structure (PSS) prediction [18,19]. In order to complete the set of biological features, the classification of an amino acid (AA) is represented [20,21].…”

confidence: 99%

Enhanced PROBCONS for Multiple Sequence Alignment in Cloud Computing

Mohamed¹,

Mousa²,

Keshk³

2019

IJITCS

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Multiple protein sequence alignment (MPSA) intend to realize the similarity between multiple protein sequences and increasing accuracy. MPSA turns into a critical bottleneck for large scale protein sequence data sets. It is vital for existing MPSA tools to be kept running in a parallelized design. Joining MPSA tools with cloud computing will improve the speed and accuracy in case of large scale data sets. PROBCONS is probabilistic consistency for progressive MPSA based on hidden Markov models. PROBCONS is an MPSA tool that achieves the maximum expected accuracy, but it has a time-consuming problem. In this paper firstly, the proposed approach is to cluster the large multiple protein sequences into structurally similar protein sequences. This classification is done based on secondary structure, LCS, and amino acids features. Then PROBCONS MPSA tool will be performed in parallel to clusters. The last step is to merge the final PROBCONS of clusters. The proposed algorithm is in the Amazon Elastic Cloud (EC2). The proposed algorithm achieved the highest alignment accuracy. Feature classification understands protein sequence, structure and function, and all these features affect accuracy strongly and reduce the running time of searching to produce the final alignment result.

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“…Here, we are concerned with the prediction of the local conformation of the backbone of a protein, namely its organization into helices, strands, and coils (that is, loosely structured regions). As discussed in recent reviews of secondary structure prediction, this problem is important in its own right, as it impacts many areas of structural bioinformatics and functional genomics 25 , 28 . Linus Pauling can be considered the father of this field, as he correctly predicted the presence of helices and strands as stable substructures in proteins 72 , 73 .…”

Section: Secondary Structure Prediction: Significant Improvements Fromentioning

confidence: 99%

“…Those methods were introduced in the 1990s. Since then, we have seen the development of more and more sophisticated machine-learning methods with more involved neural networks, such as the recent use of deep neural networks 81 and deep convolutional neural fields 82 for secondary structure prediction (for a comprehensive review of the different types of networks and machine-learning techniques that have been applied to solve the secondary structure prediction problem, see 28 ). This recently led Yang et al .…”

Section: Secondary Structure Prediction: Significant Improvements Fromentioning

confidence: 99%

Combined approaches from physics, statistics, and computer science for ab initio protein structure prediction: ex unitate vires (unity is strength)?

Delarue

Koehl

2018

F1000Res

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Connecting the dots among the amino acid sequence of a protein, its structure, and its function remains a central theme in molecular biology, as it would have many applications in the treatment of illnesses related to misfolding or protein instability. As a result of high-throughput sequencing methods, biologists currently live in a protein sequence-rich world. However, our knowledge of protein structure based on experimental data remains comparatively limited. As a consequence, protein structure prediction has established itself as a very active field of research to fill in this gap. This field, once thought to be reserved for theoretical biophysicists, is constantly reinventing itself, borrowing ideas informed by an ever-increasing assembly of scientific domains, from biology, chemistry, (statistical) physics, mathematics, computer science, statistics, bioinformatics, and more recently data sciences. We review the recent progress arising from this integration of knowledge, from the development of specific computer architecture to allow for longer timescales in physics-based simulations of protein folding to the recent advances in predicting contacts in proteins based on detection of coevolution using very large data sets of aligned protein sequences.

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Protein secondary structure prediction: A survey of the state of the art

Cited by 83 publications

References 99 publications

Protein Profiles: Biases and Protocols

Protein Profiles: Biases and Protocols

Enhanced PROBCONS for Multiple Sequence Alignment in Cloud Computing

Combined approaches from physics, statistics, and computer science for ab initio protein structure prediction: ex unitate vires (unity is strength)?

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