Combining the GOR V algorithm with evolutionary information for protein secondary structure prediction from amino acid sequence

Protein secondary structure prediction (SSP) has a variety of applications; however, there has been relatively limited improvement in accuracy for years. With a vision of moving forward all related fields, we aimed to make a fundamental advance in SSP. There have been many admirable efforts made to improve the machine learning algorithm for SSP. This work thus took a step back by manipulating the input features. A secondary structure element-based position-specific scoring matrix (SSE-PSSM) is proposed, based on which a new set of machine learning features can be established. The feasibility of this new PSSM was evaluated by rigid independent tests with training and testing datasets sharing <25% sequence identities. In all experiments, the proposed PSSM outperformed the traditional amino acid PSSM. This new PSSM can be easily combined with the amino acid PSSM, and the improvement in accuracy was remarkable. Preliminary tests made by combining the SSE-PSSM and well-known SSP methods showed 2.0% and 5.2% average improvements in three- and eight-state SSP accuracies, respectively. If this PSSM can be integrated into state-of-the-art SSP methods, the overall accuracy of SSP may break the current restriction and eventually bring benefit to all research and applications where secondary structure prediction plays a vital role during development. To facilitate the application and integration of the SSE-PSSM with modern SSP methods, we have established a web server and standalone programs for generating SSE-PSSM available at http://10.life.nctu.edu.tw/SSE-PSSM.

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Weave amino acid sequences for protein secondary structure prediction

10.1145/882082.882100 ◽

2003 ◽

Cited By ~ 6

Author(s):

Xiaochun Yang ◽

Bin Wang

Keyword(s):

Amino Acid ◽

Secondary Structure ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Protein Secondary Structure ◽

Amino Acid Sequences ◽

Protein Secondary Structure Prediction

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Protein Secondary Structure Prediction using Recurrent Neural Networks

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e6137.018520 ◽

2020 ◽

Vol 8 (5) ◽

pp. 660-663

Keyword(s):

Neural Networks ◽

Amino Acid ◽

Secondary Structure ◽

Amino Acid Sequence ◽

Recurrent Neural Networks ◽

Protein Sequence ◽

Structure Prediction ◽

Short Term Memory ◽

Secondary Structure Prediction ◽

Protein Secondary Structure

In bioinformatics the prediction of the secondary structure of the protein from its primary amino acid sequence is very difficult, which has a huge impact on the field of science and medicine. The hardest part is how to learn the most effective and correct protein features to improve prediction. Here, we carry out a deep learning model to enhance structure prediction. The core achievement of this paper is a group of recurrent neural networks (RNNs) that can manage high-level relational features from a pair of input protein sequence and target protein sequences. This paper contrasts the different type of recurrent network in recurrent neural networks (RNNs). In addition, the emphasis is on more advanced systems which incorporate a gating utility is called long short term memory (LSTM) unit and the newly added gated recurrent unit (GRU). This recurrent units has been calculated on the basis of predicting protein secondary structure using an amino acid sequence. The dataset has been taken from a publicly available database server (RCSB), and this study shows that advanced recurrent units LSTM is better than GRU for a long protein sequence.

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Evolution of Amino Acid Properties in the Context of Protein Secondary Structure Prediction

2021 IEEE Congress on Evolutionary Computation (CEC) ◽

10.1109/cec45853.2021.9504939 ◽

2021 ◽

Author(s):

Jose Santos ◽

Hector Rivas

Keyword(s):

Amino Acid ◽

Secondary Structure ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Protein Secondary Structure ◽

Protein Secondary Structure Prediction ◽

Acid Properties ◽

Amino Acid Properties

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Localnet: A Simple Recurrent Neural Network Model for Protein Secondary Structure Prediction Using Local Amino Acid Sequences Only

10.21203/rs.3.rs-139322/v1 ◽

2021 ◽

Author(s):

Shutong Yang ◽

Yuhong Wang ◽

Kennie Cruz-Gutierrez ◽

Fangling Wu ◽

Chuan-Fan Ding

Keyword(s):

Protein Structure ◽

Amino Acid ◽

Secondary Structure ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Protein Secondary Structure ◽

Amino Acid Sequences ◽

Evolutionary Information ◽

Structure Modeling ◽

Protein Structure Modeling

Abstract BackgroundProtein secondary structure prediction (PSSP) is important for protein structure modeling and design. Over the past a few years, deep learning models have shown promising results for PSSP. However, the current good performers for PSSP often require evolutionary information such as multiple sequence alignments and even real protein structures (templates), entire protein sequences, and amino acid property profiles. ResultsIn this study, we used a fixed-size window of adjacent residues and only amino acid sequences, without any evolutionary information, as inputs, and developed a very simple, yet accurate RNN model: LocalNet. The accuracy for three states of secondary structures is as high as 85.15%, indicating that the local amino acid sequence itself contains enough information for PSSP, a well-known classical view. By comparing to other predictors, we also achieve an state-of-art accuracy on dataset of CASP11, CASP12 and CASP13.ConclusionThe well-trained models are expected to have good applications in protein structure modeling and protein design. This model can be downloaded from https://github.com/lake-chao/protein-secondary-structure-prediction.

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