Protein Secondary Structure Prediction Research Articles

MFTrans: A multi-feature transformer network for protein secondary structure prediction

In the rapidly evolving field of computational biology, accurate prediction of protein secondary structures is crucial for understanding protein functions, facilitating drug discovery, and advancing disease diagnostics. In this paper, we propose MFTrans, a deep learning-based multi-feature fusion network aimed at enhancing the precision and efficiency of Protein Secondary Structure Prediction (PSSP). This model employs a Multiple Sequence Alignment (MSA) Transformer in combination with a multi-view deep learning architecture to effectively capture both global and local features of protein sequences. MFTrans integrates diverse features generated by protein sequences, including MSA, sequence information, evolutionary information, and hidden state information, using a multi-feature fusion strategy. The MSA Transformer is utilized to interleave row and column attention across the input MSA, while a Transformer encoder and decoder are introduced to enhance the extracted high-level features. A hybrid network architecture, combining a convolutional neural network with a bidirectional Gated Recurrent Unit (BiGRU) network, is used to further extract high-level features after feature fusion. In independent tests, our experimental results show that MFTrans has superior generalization ability, outperforming other state-of-the-art PSSP models by 3 % on average on public benchmarks including CASP12, CASP13, CASP14, TEST2016, TEST2018, and CB513. Case studies further highlight its advanced performance in predicting mutation sites. MFTrans contributes significantly to the protein science field, opening new avenues for drug discovery, disease diagnosis, and protein.

SERT-StructNet: Protein secondary structure prediction method based on multi-factor hybrid deep model

Protein secondary structure prediction (PSSP) is a pivotal research endeavour that plays a crucial role in the comprehensive elucidation of protein functions and properties. Current prediction methodologies are focused on deep-learning techniques, particularly focusing on multi-factor features. Diverging from existing approaches, in this study, we placed special emphasis on the effects of amino acid properties and protein secondary structure propensity scores (SSPs) on secondary structure during the meticulous selection of multi-factor features. This differential feature-selection strategy results in a distinctive and effective amalgamation of the sequence and property features. To harness these multi-factor features optimally, we introduced a hybrid deep feature extraction model. The model initially employs mechanisms such as dilated convolution (D-Conv) and a channel attention network (SENet) for local feature extraction and targeted channel enhancement. Subsequently, a combination of recurrent neural network variants (BiGRU and BiLSTM), along with a transformer module, was employed to achieve global bidirectional information consideration and feature enhancement. This approach to multi-factor feature input and multi-level feature processing enabled a comprehensive exploration of intricate associations among amino acid residues in protein sequences, yielding a Q3 accuracy of 84.9% and an Sov score of 85.1%. The overall performance surpasses that of the comparable methods. This study introduces a novel and efficient method for determining the PSSP domain, which is poised to deepen our understanding of the practical applications of protein molecular structures.

Salp-J Colony Optimization-based advanced hybrid ensemble deep predictor with LSTM for protein structure prediction

Protein structure prediction (PSP) is a key concern in computational biology, which is considered a challenging task that is vital to determine the structure and the protein function since each protein possesses a definite shape, whereas the protein secondary structure prediction (PSSP) is the foundation for three-dimensional PSP. An Advanced hybrid ensemble deep predictor is utilized for predicting the structure of a protein using Long-Short Term Memory (LSTM), in which the performance of the predictor is improved for obtaining the features through the Salp-J Colony Optimization, which is developed by integrating the features of three optimizations the exploration behavior of Ulmaris, the immune system of virus colony and the teamwork of salp for solution update that helps to predict the accurate protein structure. The proposed method achieved the value of 99.1% accuracy, 99.5% sensitivity, 98.85% specificity, and 0.9% error at the 80% of training percentage 90 using CullPDB. Similarly, in Protein Net, the attained value of accuracy is 97.27%, sensitivity is 98.13%, specificity is 97%, and error is 2.7% concerning training percentage 90%. Communicated by Ramaswamy H. Sarma

PSSP-MFFNet: A Multifeature Fusion Network for Protein Secondary Structure Prediction.

Protein secondary structure prediction (PSSP) is a fundamental task in modern bioinformatics research and is particularly important for uncovering the functional mechanisms of proteins. To improve the accuracy of PSSP, various general and essential features generated from amino acid sequences are often used for predicting the secondary structure. In this paper, we propose PSSP-MFFNet, a deep learning-based multi-feature fusion network for PSSP, which incorporates a multi-view deep learning architecture with the multiple sequence alignment (MSA) Transformer to efficiently capture global and local features of protein sequences. In practice, PSSP-MFFNet adopts a multi-feature fusion strategy, integrating different features generated from protein sequences, including MSA, sequence information, evolutionary information, and hidden state information. Moreover, we employ the MSA Transformer to interleave row and column attention across the input MSA. A hybrid network architecture of convolutional neural networks and long short-term memory networks is applied to extract high-level features after feature fusion. Furthermore, we introduce a transformer encoder to enhance the extracted high-level features. Comparative experimental results on independent tests demonstrate that PSSP-MFFNet has excellent generalization ability, outperforming other state-of-the-art PSSP models by an average of 1% on public benchmarks, including CASP12, CASP13, CASP14, TEST2018, and CB513. Our method can contribute to a better understanding of the biological functions of proteins, which has significant implications for drug discovery, disease diagnosis, and protein engineering.

A novel approach for protein secondary structure prediction using encoder-decoder with attention mechanism model.

Computational biology faces many challenges like protein secondary structure prediction (PSS), prediction of solvent accessibility, etc. In this work, we addressed PSS prediction. PSS is based on sequence-structure mapping and interaction among amino acid residues. We proposed an encoder-decoder with an attention mechanism model, which considers the mapping of sequence structure and interaction among residues. The attention mechanism is used to select prominent features from amino acid residues. The proposed model is trained on CB513 and CullPDB open datasets using the Nvidia DGX system. We have tested our proposed method for Q 3 and Q 8 accuracy, segment of overlap, and Mathew correlation coefficient. We achieved 70.63 and 78.93% Q 3 and Q 8 accuracy, respectively, on the CullPDB dataset whereas 79.8 and 77.13% Q 3 and Q 8 accuracy on the CB513 dataset. We observed improvement in SOV up to 80.29 and 91.3% on CullPDB and CB513 datasets. We achieved the results using our proposed model in very few epochs, which is better than the state-of-the-art methods.

Functional Categorization and Comparative 3D Models Study of TMEM16B

TMEMs expressed nearly in every part of the body, but its expression is high in brain.TMEM16B is a transmembrane protein perform numerous physiological functions. It encodes a conserved Ca2+-activated Cl- channel (CaCC). This protein mediates the regulation of Ca2+ levels and is involved in anxiety related disorders. Its distributive pattern shows that it performs an important role in many neurological disorders via multiple signaling pathways. The stability and reactivity of this protein was assessed physio chemically and by determination of its domain. Different bioinformatics tools such as Pfam, ProtParam, and SOPMA were used respectively for protein secondary structures prediction. The drug discovery processes were dependent upon the protein modelling to large extent. This study was based upon the comparative in-silico modelling of 3-dimensional (3D) structure prediction through Robetta, I-TASSER, and AlphaFold (bioinformatics tools). The validation of these models were checked by using the SAVESv6.0 (PROCHECK) server. We got the best 3D structure by using the Robetta. The 3D structure prediction of TMEM16B was important because its function was determined by its structure and, after the structure finding, scientists would develop the drugs that could cure the neurological disorders associated with this protein. It can be concluded that this structure will help us in assembly of complete structural proteome profile. Huge data generated through experimentation will help in future machine learning that will assist in increasing the number of structural templates.

Ensemble model for accuracy prediction of protein secondary structure

<span>Predicting a protein’s secondary structure is crucial for understanding the working of proteins. Despite advancements over the years, the top predictors have achieved only 80% Q8 accuracy when sequence profile information is the sole input. An ensemble approach is proposed using convolutional neural network (CNN) and a classifier known as support vector machine (SVM) on both the partial and the whole CullPDB datasets. The protein secondary structure (PSS) has a complex hierarchical structure, as well as the ability to take into account the reliance between neighbouring labels. A detailed experiment yielding high levels of Q8 accuracy with scores of 97.91%, 85.13%, and 78.02% using 20%, 80%, and 100% respectively of the protein residues on the new predicted dataset CullPDB6133 which is better than the accuracies predicted by similar models. The proposed methodology highlights the use of CNN as a general framework, for efficiently predicting eight-state (Q8) accuracy of secondary protein structures with a low time and space complexity.</span>

Protein Secondary Structure Prediction and Perceptions of Complexities using Deep Neural Network

The Protein molecule is known as the large biological molecule in a living organism. The protein performs several works like transporting molecules, catalysing metabolic reaction, responding to stimuli etc in a human body. Protein Structure analysis and prediction is very much essential to make any research about the same protein molecule. The basic intention of protein structure prediction (PSP) is to predict the three dimensional structure that generate by the amino acid sequence. The very peculiar matter is only twenty amino acid found in a living body where as approximately one lakh protein molecules can be framed from the same amino acid compositions in different percentages.  The three dimensional structure framed by the amino acid compositions generally changes its shape and size due to the effect of external agents or medicines that comes in contact with these protein molecules. The basic intention behind the prediction of structure of the protein is to design new drugs or medicines. From the structures the medicine researchers working for the development of medicines may easily detect the changes in the living body or the requirement of drugs or medicines. The detection of the structure and the prediction of perfect structure is always a challenging task. The protein structure is basically a three dimensional structure in its secondary transformation. The structure may be in the form of ? Helix, ? sheets or loop etc. In this paper the identification of the secondary structures and the percentages of ? Helix, ? sheets or loop structures are being predicted and the probable complexities that may occur during the prediction is discussed. Deep neural network is a deep structured learning process is an application of the broader family machine learning. Deep learning architectures has a number application in various fields like medical science, bioinformatics, medical image analysis etc. A novel method is being proposed in this research article for the detection, correction and removal of various complexities during prediction using deep neural network. This technique will be helpful for different researchers working in the field for drug design and medicine research.

Effective Local and Secondary Protein Structure Prediction by Combining a Neural Network-Based Approach with Extensive Feature Design and Selection without Reliance on Evolutionary Information.

Protein structure prediction continues to pose multiple challenges despite outstanding progress that is largely attributable to the use of novel machine learning techniques. One of the widely used representations of local 3D structure-protein blocks (PBs)-can be treated in a similar way to secondary structure classes. Here, we present a new approach for predicting local conformation in terms of PB classes solely from amino acid sequences. We apply the RMSD metric to ensure unambiguous future 3D protein structure recovery. The selection of statistically assessed features is a key component of the proposed method. We suggest that ML input features should be created from the statistically significant predictors that are derived from the amino acids' physicochemical properties and the resolved structures' statistics. The statistical significance of the suggested features was assessed using a stepwise regression analysis that permitted the evaluation of the contribution and statistical significance of each predictor. We used the set of 380 statistically significant predictors as a learning model for the regression neural network that was trained using the PISCES30 dataset. When using the same dataset and metrics for benchmarking, our method outperformed all other methods reported in the literature for the CB513 nonredundant dataset (for the PBs, Q16 = 81.01%, and for the DSSP, Q3 = 85.99% and Q8 = 79.35%).

Naive Prediction of Protein Backbone Phi and Psi Dihedral Angles Using Deep Learning.

Protein structure prediction represents a significant challenge in the field of bioinformatics, with the prediction of protein structures using backbone dihedral angles recently achieving significant progress due to the rise of deep neural network research. However, there is a trend in protein structure prediction research to employ increasingly complex neural networks and contributions from multiple models. This study, on the other hand, explores how a single model transparently behaves using sequence data only and what can be expected from the predicted angles. To this end, the current paper presents data acquisition, deep learning model definition, and training toward the final protein backbone angle prediction. The method applies a simple fully connected neural network (FCNN) model that takes only the primary structure of the protein with a sliding window of size 21 as input to predict protein backbone ϕ and ψ dihedral angles. Despite its simplicity, the model shows surprising accuracy for the ϕ angle prediction and somewhat lower accuracy for the ψ angle prediction. Moreover, this study demonstrates that protein secondary structure prediction is also possible with simple neural networks that take in only the protein amino-acid residue sequence, but more complex models are required for higher accuracies.

A Comparison of Mutual Information, Linear Models and Deep Learning Networks for Protein Secondary Structure Prediction

Background: Over the last several decades, predicting protein structures from amino acid sequences has been a core task in bioinformatics. Nowadays, the most successful methods employ multiple sequence alignments and can predict the structure with excellent performance. These predictions take advantage of all the amino acids at a given position and their frequencies. However, the effect of single amino acid substitutions in a specific protein tends to be hidden by the alignment profile. For this reason, single-sequence-based predictions attract interest even after accurate multiple-alignment methods have become available: the use of single sequences ensures that the effects of substitution are not confounded by homologous sequences. Objective: This work aims at understanding how the single-sequence secondary structure prediction of a residue is influenced by the surrounding ones. We aim at understanding how different prediction methods use single-sequence information to predict the structure. Methods: We compare mutual information, the coefficients of two linear models, and three deep learning networks. For the deep learning algorithms, we use the DeepLIFT analysis to assess the effect of each residue at each position in the prediction. Result: Mutual information and linear models quantify direct effects, whereas DeepLIFT applied on deep learning networks quantifies both direct and indirect effects. Conclusion: Our analysis shows how different network architectures use the information of single protein sequences and highlights their differences with respect to linear models. In particular, the deep learning implementations take into account context and single position information differently, with the best results obtained using the BERT architecture.

Mapping HDX-MS Data to Protein Conformations through Training Ensemble-Based Models.

An original approach that adopts machine learning inference to predict protein structural information using hydrogen-deuterium exchange mass spectrometry (HDX-MS) is described. The method exploits an in-house optimization program that increases the resolution of HDX-MS data from peptides to amino acids. A system is trained using Gradient Tree Boosting as a type of machine learning ensemble technique to assign a protein secondary structure. Using limited training data we generate a discriminative model that uses optimized HDX-MS data to predict protein secondary structure with an accuracy of 75%. This research could form the basis for new methods exploiting artificial intelligence to model protein conformations by HDX-MS.

Comparative Functional Genomics Studies for Understanding the Hypothetical Proteins in Mycobacterium Tuberculosis Variant Microti 12

Background: The Mycobacterium tuberculosis complex (MTBC) bacteria include the slowly growing, host-associated bacteria Mycobacterium tuberculosis, Mycobacterium Bovis, Mycobacterium microti, Mycobacterium africanum, Mycobacterium pinnipedii. Aim: Comparative Functional Genomics Studies for understanding the Hypothetical Proteins in Mycobacterium tuberculosis variant microti 12. Objective: A computational genomics study was performed to understand the 247 hypothetical protein genes. Functional annotation of virtual proteins was performed on different servers to maximize confidence level. Methods: Sequence Retrieval. The whole genome sequences for the Mycobacterium tuberculosis micro variant 12 were retrieved from the KEGG database ( http://www.genome.jp/kegg/) and were used for screening 247 hypothetical proteins (Fig. 1 ). Functional Annotation and Sub-cellular localization. The Mycobacterium tuberculosis micro variant 12 hypothetical proteins were screened and sorted out from the genome and were individually analyzed for the presence of conserved functional domains by using computational biology tools like CDD-BLAST ( https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) ;Pfam ( http://pfam.xfam.org/ncbiseq/398365647); The subcellular localization of hypothetical proteins was determined by CELLO2GO ( http://cello.life.nctu.edu.tw). These web tools can search the defined conserved domains in the sequences available in the online servers or databases and assist in the classification of proteins in the appropriate families. Protein Structure Prediction. The in-silico structure predictions of the hypothetical protein sequences showing functional properties were carried out by using the PS2 Protein Structure Prediction Server ( http://www.ps2.life.nctu.edu.tw/). The online server helps to generate the 3D structures of the hypothetical proteins. The server accepts the sequences in FASTA format as a query to generate resultant proteins 3D structures. The structure determination is completely based on the conserved template regions detected during functional annotations. Protein-protein interaction through String database: The interaction of each hypothetical protein analyzed for functional characteristics was subjected to a protein-protein interaction server for the prediction of a possible functional role in interaction amongst the available known proteins ( https://string-db.org/). This information can help us to further validated the functional role of such hypothetical proteins and their possible role in the Mycobacterium Tuberculosis micro variant. Protein secondary structure prediction through JPred4: The secondary structure prediction of all the hypothetical proteins was determined through JPred4 ( http://www.compbio.dundee.ac.uk/jpred4/index.html) and served to identify the available secondary structures in the unknown hypothetical protein sequences. These further help us to understand the available templates in the uncharacterized protein sequences for the prediction of novel functions associated with these proteins. The predictions were further characterized by the Phyre2 server for structural modeling and prediction of templates based on comparative analysis based on conserved domains. Protein modeling, prediction, and analysis through Phyre2. The hypothetical proteins which were identified to have functional properties were further characterized by the Phyre2 server ( http://www.sbg.bio.ic.ac.uk/phyre2) for structural modeling and prediction of templates based on comparative analysis based on conserved domains. Results: A computational genomics study was performed to understand the 247 hypothetical protein genes Functional annotation of virtual proteins, and was performed on different servers to maximize confidence level. The functional prediction was performed by CDD-Blast and Pfam. The gene sequences of proteins have probably been successfully functionally annotated, characterized, and their subcellular localization and 3-D structural predictions have been predicted computationally. Online automated bioinformatics tools such as CDD-Blast, Pfam, CELLO2GO and PS2-Server were used for the structural and functional characterization of screened hypothetical proteins. The structure, function, and subcellular localization of a hypothetical protein from Mycobacterium tuberculosis variant microti 12 have been obtained and presented (Fig. 2 ). Also, the three-dimensional structure generated after using the template with the highest score was displayed as the template ID in the structure column of the respective hypothetical protein. However, as systems biology denies hypothetical protein functions, the structures of such proteins can be tested through biological processes and experiments, making them suitable for understanding their role in the life cycle, pathogenesis, and drug development. We can further explore these predictive possibilities in pharmaceuticals, and other clinically relevant studies. This study by HP helped find structure-function relationships in Mycobacterium tuberculosis variant microti 12 using a variety of bioinformatics tools. The string database made predictions about protein-protein interactions and the template helped us predict a hypothetical protein structure and even helped us find its 3D protein structure. Protein profiling can be performed on structures retrieved from these servers. This is useful for proteomics studies, including protein-protein interactions, protein expression of specific hypothetical proteins, and post-translational modifications of protein-coding genes. Further understanding of these hypothetical proteins can help us to know more about the Mycobacterium tuberculosis complex (MTBC) and may assist in Drugs and inhibitors against different pathogens within this complex. Conclusion: The all-inclusive bioinformatic study has helped to functionally elucidate 247 hypothetical proteins, which have resulted and made it easier to understand many functional proteins available in the Mycobacterium tuberculosis micro variant 12. The subcellular localization of the 247 sorted hypothetical proteins was also carried & which further helped us understand the localization of identified enzymes or proteins. We have successfully characterized the 247 unknown proteins of hypothetical protein sequences from Mycobacterium tuberculosis micro variant 12 to validate their structure and functions of the gene products. These predicted functions and three-dimensional structures may lead to establishing their role in the life cycle of the bacterium. This computationally generated data can also be further used for developing new protocols for new vaccines against Mycobacterium tuberculosis micro variant 12 that are essential for preventing infection, diseases, and transmission. This complete result of Hypothetical Protein is needed for further studies of the whole genomic of the Mycobacterium Tuberculosis micro variant 12 for their function interpretation which further help in the understanding of its functions as well as structure. Moreover, this interpretation would help us to study the evolution of Mycobacterium Tuberculosis micro variant 12 which further helps in the process of discovering the drugs to inhibit the causes of diseases.

In silico Analysis of Two Novel Variants in the Pyruvate Carboxylase (PC) Gene Associated with the Severe Form of PC Deficiency.

Inborne errors of metabolism are a common cause of neonatal death. This study evaluated the acute early-onset metabolic derangement and death in two unrelated neonates. WES, Sanger sequencing, homology modeling, and in silico bioinformatics analysis were employed to assess the effects of variants on protein structure and function. WES revealed a novel homozygous variant, p.G303Afs*40 and p.R156P, in the PC gene of each neonate, which both were confirmed by Sanger sequencing. Based on the ACMG guidelines, the p.G303Afs*40 was likely pathogenic, and the p.R156P was a VUS. Nevertheless, a known variant at position 156, the p.R156Q, was also a VUS. Protein secondary structure prediction showed changes in p.R156P and p.R156Q variants compared to the wild-type protein. However, p.G303Afs*40 depicted significant changes at C-terminal. Furthermore, comparing the interaction of wild-type and variant proteins with the ATP ligand during simulations, revealed a decreased affinity to the ATP in all the variants. Moreover, analysis of SNP impacts on PC protein using Polyphen-2, SNAP2, FATHMM, and SNPs&GO servers predicted both R156P and R156Q as damaging variants. Likewise, free energy calculations demonstrated the destabilizing effect of both variants on PC. This study confirmed the pathogenicity of both variants and suggested them as a cause of type B PCD. The results of this study would provide the family with prenatal diagnosis and expand the variant spectrum in the PC gene,which is beneficial for geneticists and endocrinologists.

Prediction of Protein Secondary Structure based on Multi-scale Convolutional Neural Network

In the field of bioinformatics, the prediction of secondary structure of proteins is very important. It can be obtained from the prediction of primary structure (amino acid sequence) and can provide reference for the prediction of tertiary structure of proteins. Amino acid sequences of proteins are encoded with several features and then combined into the prediction network. Convolutional neural network has excellent performance in text and sequence information extraction. The amino acid sequence of protein is also a special sequence, so the convolutional neural network can be used to extract the information in the sequence. Moreover, the influence of amino acids on the formation of secondary structure varies with different distances, so in the experiment, convolutional neural networks with convolution nuclei of different sizes were used to form multi-scale convolution blocks to extract amino acid sequence information. At the same time, the sliding window technique is also used to show the interaction between the sequences, and a long amino acid sequence is divided into some amino acid fragments and input into the model. Finally, the accuracy of Q8 on the dataset CB6133_filtered reaches 71%.

Artificial Neural Networks and their Use in Agricultural Genetic Engineering and Protein Estimation

A mathematical model called an artificial neural network uses certain algorithms to anticipate and predict various events. This model has multiple layers, including input, hidden, and output layers. By altering its algorithms, various outputs can be produced based on the input utilized. Biological neuron mechanization has an impact on artificial neural networks. As biological neurons have a propensity to learn and train sets of data for producing biased outputs by spotting and removing variances in them, ANN also works on these principles. Although this model has many uses, it has historically been employed in biological experiments using the supervised learning method, one of which is to predict protein secondary structure. This allows one to identify the positions of different amino acids that are ordered complicated protein structures, which are very appealing in genetic engineering. By this model scientists can map out and isolate a desirable gene in genetic material without going through intense laborious experiments. This report summarizes all the objectives linked to artificial neural networks as well as their applications in bioengineering by examining many related studies.

AttSec: protein secondary structure prediction by capturing local patterns from attention map

BackgroundProtein secondary structures that link simple 1D sequences to complex 3D structures can be used as good features for describing the local properties of protein, but also can serve as key features for predicting the complex 3D structures of protein. Thus, it is very important to accurately predict the secondary structure of the protein, which contains a local structural property assigned by the pattern of hydrogen bonds formed between amino acids. In this study, we accurately predict protein secondary structure by capturing the local patterns of protein. For this objective, we present a novel prediction model, AttSec, based on transformer architecture. In particular, AttSec extracts self-attention maps corresponding to pairwise features between amino acid embeddings and passes them through 2D convolution blocks to capture local patterns. In addition, instead of using additional evolutionary information, it uses protein embedding as an input, which is generated by a language model.ResultsFor the ProteinNet DSSP8 dataset, our model showed 11.8% better performance on the entire evaluation datasets compared with other no-evolutionary-information-based models. For the NetSurfP-2.0 DSSP8 dataset, it showed 1.2% better performance on average. There was an average performance improvement of 9.0% for the ProteinNet DSSP3 dataset and an average of 0.7% for the NetSurfP-2.0 DSSP3 dataset.ConclusionWe accurately predict protein secondary structure by capturing the local patterns of protein. For this objective, we present a novel prediction model, AttSec, based on transformer architecture. Although there was no dramatic accuracy improvement compared with other models, the improvement on DSSP8 was greater than that on DSSP3. This result implies that using our proposed pairwise feature could have a remarkable effect for several challenging tasks that require finely subdivided classification. Github package URL is https://github.com/youjin-DDAI/AttSec.

Detection of Human Protein Structures by Select Deep Learning Models and Dynamic Systems

Peptide bonds bind amino acids together to form proteins. Because of the way amino acids fold, proteins have a three-dimensional structure. Increased data, or samples, improves machine learning models that learn from examples. ANNs, a single-layer learning system, require sophisticated systems and time to learn massive volumes of data over time. Today, the multi-layer deep learning technique is preferred. Deep learning uses artificial neural networks. This part of the work provides technical details about the deep learning methods used. This research's deep learning methods include CNN, Hidden Markov Model (HMM), Recurrent Weighted Average (RWA), and Conditional Random Fields (CRF). The training and testing results of these models are presented in this work. For this work, the CNN, HMM, RWA, and CRF algorithms were all compared using the CB513 dataset. In addition, each method was analyzed and contrasted with other research conducted previously. In this particular research endeavour, the respective success rates of the four models that were built for predicting the protein secondary structure were as follows: 82.54%, 81.06%, 81.10%, and 81.48%. The models and working environment produced in this study can be used to predict protein secondary structure quickly. In deep learning experiments, data amount affects learning. Increasing data will test the study's models
  

Protein Secondary Structure Prediction Using Cascaded Feature Learning Model

The protein secondary structure prediction (PSSP) is pivotal for predicting tertiary structure, which is proliferating in demand for drug design and development. Further, it can be used to learn different protein functions. Although there are many computational methods for protein structure prediction, none of them have succeeded 100% in solving the protein structure prediction problem. This paper proposes a deep learning model, namely Cascaded Feature Learning Model (CFLM) that uses a multi-stage transfer learning approach based on the Residual Dense Network (RDN) for predicting protein secondary structure. The model is trained with different protein datasets at various levels of transfer learning and accepts selected protein features such as solvent accessibility, physicochemical properties, PSSM (position-specific scoring matrix), and PSFM (position-specific frequency matrix) as input. The Q3 and Q8 accuracy obtained on CASP 13 and 14 benchmark datasets with the proposed approach are 91.23% and 91.45%, and 76.83% and 78.04%, respectively. The performance of CFLM is also compared with some of the recent PSSP approaches and it is observed that the proposed approach improves the prediction accuracy by 2.8% in terms of Q3 and 1.81% in terms of Q8 in the CASP 14 dataset.

Investigating Native Metal Ion Binding Sites in Mammalian Histidine-Rich Glycoprotein.

Mammalian histidine-rich glycoprotein (HRG) is a highly versatile and abundant blood plasma glycoprotein with a diverse range of ligands that is involved in regulating many essential biological processes, including coagulation, cell adhesion, and angiogenesis. Despite its biomedical importance, structural information on the multi-domain protein is sparse, not least due to intrinsically disordered regions that elude high-resolution structural characterization. Binding of divalent metal ions, particularly ZnII, to multiple sites within the HRG protein is of critical functional importance and exerts a regulatory role. However, characterization of the ZnII binding sites of HRG is a challenge; their number and composition as well as their affinities and stoichiometries of binding are currently not fully understood. In this study, we explored modern electron paramagnetic resonance (EPR) spectroscopy methods supported by protein secondary and tertiary structure prediction to assemble a holistic picture of native HRG and its interaction with metal ions. To the best of our knowledge, this is the first time that this suite of EPR techniques has been applied to count and characterize endogenous metal ion binding sites in a native mammalian protein of unknown structure.