Recent developments in deep learning, coupled with an increasing number of sequenced proteins, have led to a breakthrough in life science applications, in particular in protein property prediction. There is hope that deep learning can close the gap between the number of sequenced proteins and proteins with known properties based on lab experiments. Language models from the field of natural language processing have gained popularity for protein property predictions and have led to a new computational revolution in biology, where old prediction results are being improved regularly. Such models can learn useful multipurpose representations of proteins from large open repositories of protein sequences and can be used, for instance, to predict protein properties. The field of natural language processing is growing quickly because of developments in a class of models based on a particular model-the Transformer model. We review recent developments and the use of large-scale Transformer models in applications for predicting protein characteristics and how such models can be used to predict, for example, post-translational modifications. We review shortcomings of other deep learning models and explain how the Transformer models have quickly proven to be a very promising way to unravel information hidden in the sequences of amino acids. 

Transformer-based deep learning for predicting protein properties in the life sciences

Among different post-translational modifications (PTMs), one of the most important one is the lysine crotonylation in proteins. Its importance cannot be undermined related to different diseases and essential biological practice. The key step for finding the hidden mechanisms of crotonylation along with their occurrence sites is to completely apprehend the mechanism behind this biological process. In previously reported studies, researchers have used different techniques, like position weighted matrix (PWM), support vector machine (SVM), k nearest neighbors (KNN), and many others. However, the maximum prediction accuracy achieved was not such high. To address this, herein, we propose an improved predictor for lysine crotonylation sites named iCrotoK-PseAAC, in which we have incorporated various position and composition relative features along with statistical moments into PseAAC. The results of self-consistency testing were 100% accurate, while the 10-fold cross validation gave 99.0% accuracy. Based on the validation and comparison of model, it is concluded that the iCrotoK-PseAAC is more accurate than the previously proposed models.

/pdf/icrotok-pseaac-identify-lysine-crotonylation-sites-by-1ax1xcazkl.pdf

iCrotoK-PseAAC: Identify lysine crotonylation sites by blending position relative statistical features according to the Chou's 5-step rule

Recent developments in deep learning, coupled with an increasing number of sequenced proteins, have led to a breakthrough in life science applications, in particular in protein property prediction. There is hope that deep learning can close the gap between the number of sequenced proteins and proteins with known properties based on lab experiments. Language models from the field of natural language processing have gained popularity for protein property predictions and have led to a new computational revolution in biology, where old prediction results are being improved regularly. Such models can learn useful multipurpose representations of proteins from large open repositories of protein sequences and can be used, for instance, to predict protein properties. The field of natural language processing is growing quickly because of developments in a class of models based on a particular model—the Transformer model. We review recent developments and the use of large-scale Transformer models in applications for predicting protein characteristics and how such models can be used to predict, for example, post-translational modifications. We review shortcomings of other deep learning models and explain how the Transformer models have quickly proven to be a very promising way to unravel information hidden in the sequences of amino acids.

Post-translation modification (PTM) of cysteine S-sulfenylation sites in protein is important in cellular biology. S-sulfenylation plays a significant role in protein functioning, cell signaling and transcriptional regulation. Cysteine, S-sulfenylation site prediction is crucial in order to interpret the S-sulfenylation molecular mechanisms. In this study, statistical moments based methodology is proposed for cysteine S-sulfenylation site predictions. The system proposed has achieved accuracy far better than current state-of-the-art methods using tenfold cross validations and independent tests. The outcomes from the proposed method revealed that using statistical moments based features could produce more efficient and effective results. For the accessibility of the scientific community, we have developed a GitHub repository for cysteine S-sulfenylation sites prediction system which is freely accessible at 
https://www.github.com/ahmad-umt/S-Sulfenylation

.

Prediction of S-Sulfenylation Sites Using Statistical Moments Based Features via CHOU’S 5-Step Rule

Cancerlectins are significantly important group of lectins that have an inhibitory effect on cancer cells with respect to their growth. They have a vital role in various tumor cell interactions like adhesion, growth, metastasis, differentiation and mainly in cellular infection. The investigations associated with cancerlectins are applicable to relevant studies in laboratories, diagnostics and therapy in clinical applications, and drug discoveries in targeting cancers. Prediction of cancerlectins is considered a helpful task due to the fact that they are specifically useful in dissecting cancers. Although, several Bioinformatics tools have been developed to predict cancerlectins, however, the need for improvement in the quality of its prediction model requires enhancements in the annotation and determination process of cancerlectins. In this study, a new model is proposed that builds on statistical moments based features to distinguish cancerlectins from non-cancerlectins. The currently proposed model achieved an accuracy of 88.36% using jackknife test which is better than current state-of-the-art models. These outcomes suggest that the use of statistical moments could bear more effective and efficient results. For the accessibility of the scientific community, a user-friendly web server has been developed which will associate the researchers in medical science. Web server is freely accessible at https://www.biopred.org/canlect.

/pdf/canlect-pred-a-cancer-therapeutics-tool-for-prediction-of-39dr0kjjz2.pdf

CanLect-Pred: A Cancer Therapeutics Tool for Prediction of Target Cancerlectins Using Experiential Annotated Proteomic Sequences

Identification of the sites of post-translational modifications (PTMs) in protein, RNA, and DNA sequences is currently a very hot topic. This is because the information thus obtained is very useful for in-depth understanding the biological processes at the cellular level and for developing effective drugs against major diseases including cancers as well. Although this can be done by means of various experimental techniques, it is both time-consuming and costly to determine the PTM sites purely based on experiments. With the avalanche of biological sequences generated in the post-genomic age, it is highly desired to develop bioinformatics tools for rapidly and effectively identifying the PTM sites. In the last few years, many efforts have been made in this regard, and considerable progresses have been achieved. This review is focused on those prediction methods that have the following two features. (1) They have been developed by strictly observing the 5-steps rule so that they each have a user-friendly web-server for the majority of experimental scientists to easily get their desired data without the need to go through the detailed mathematics involved. (2) Their cornerstones have been based on Pseudo Amino Acid Composition (PseAAC) or Pseudo K-tuple Nucleotide Composition (PseKNC), and hence the prediction quality is generally higher than most of the other PTM prediction methods.

Progresses in Predicting Post-translational Modification

In this minireview paper it has been elucidated that the proposal of pseudo amino acid components represents a very important milestone for the disciplines of proteome and genome. This has been concluded by observing and analyzing the developments in the following six different sub-disciplines: (1) proteome analysis; (2) genome analysis; (3) protein structural classification; (4) protein subcellular location prediction; (5) post-translational modification (PTM) site prediction; (6) stimulating the birth of the renowned and very powerful 5-steps rule.

Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses

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Progresses in Predicting Post-translational Modification

Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses