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The inception of next generations sequencing technologies have exponentially increased the volume of biological sequence data. Protein sequences, being quoted as the 'language of life', has been analyzed for a multitude of applications and inferences. Owing to the rapid development of deep learning, in recent years there have been a number of breakthroughs in the domain of Natural Language Processing. Since these methods are capable of performing different tasks when trained with a sufficient amount of data, off-the-shelf models are used to perform various biological applications. In this study, we investigated the applicability of the popular Skip-gram model for protein sequence analysis and made an attempt to incorporate some biological insights into it. We propose a novel k-mer embedding scheme, Align-gram, which is capable of mapping the similar k-mers close to each other in a vector space. Furthermore, we experiment with other sequence-based protein representations and observe that the embeddings derived from Align-gram aids modeling and training deep learning models better. Our experiments with a simple baseline LSTM model and a much complex CNN model of DeepGoPlus shows the potential of Align-gram in performing different types of deep learning applications for protein sequence analysis. [ABSTRACT FROM AUTHOR]

All proteomes contain both proteins and polypeptide segments that don't form a defined three-dimensional structure yet are biologically active—called intrinsically disordered proteins and regions (IDPs and IDRs). Most of these IDPs/IDRs lack useful functional annotation limiting our understanding of their importance for organism fitness. Here we characterized IDRs using protein sequence annotations of functional sites and regions available in the UniProt knowledgebase ("UniProt features": active site, ligand-binding pocket, regions mediating protein-protein interactions, etc.). By measuring the statistical enrichment of twenty-five UniProt features in 981 IDRs of 561 human proteins, we identified eight features that are commonly located in IDRs. We then collected the genetic variant data from the general population and patient-based databases and evaluated the prevalence of population and pathogenic variations in IDPs/IDRs. We observed that some IDRs tolerate 2 to 12-times more single amino acid-substituting missense mutations than synonymous changes in the general population. However, we also found that 37% of all germline pathogenic mutations are located in disordered regions of 96 proteins. Based on the observed-to-expected frequency of mutations, we categorized 34 IDRs in 20 proteins (DDX3X, KIT, RB1, etc.) as intolerant to mutation. Finally, using statistical analysis and a machine learning approach, we demonstrate that mutation-intolerant IDRs carry a distinct signature of functional features. Our study presents a novel approach to assign functional importance to IDRs by leveraging the wealth of available genetic data, which will aid in a deeper understating of the role of IDRs in biological processes and disease mechanisms. Author summary: Intrinsically disordered regions (IDRs) in proteins are typically not considered to be functionally as important as the structured parts. However, it is becoming evident that both structured and disordered regions are essential for the repertoire of protein functions. Nevertheless, most of these largely flexible and functionally dynamic protein regions remain uncharacterized. Nevertheless, most of these largely flexible and functionally dynamic protein regions remain uncharacterized. Here, informed by human genetic diversity (i.e., genetic variations from the general population and patients), we identified the IDRs that are more frequently mutated in patients than in relatively healthy individuals, and further show that they carry a set of characteristic functional features. This approach provides a different and effective means to identify unannotated disordered protein segments that are biologically important and lead to pathogenesis upon mutation. [ABSTRACT FROM AUTHOR]

Motivation Researchers and practitioners use a number of popular sequence comparison tools that use many alignment-based techniques. Due to high time and space complexity and length-related restrictions, researchers often seek alignment-free tools. Recently, some interesting ideas, namely, Minimal Absent Words (MAW) and Relative Absent Words (RAW), have received much interest among the scientific community as distance measures that can give us alignment-free alternatives. This drives us to structure a framework for analysing biological sequences in an alignment-free manner. Results In this application note, we present Alignment-free Dissimilarity Analysis & Comparison Tool (ADACT), a simple web-based tool that computes the analogy among sequences using a varied number of indexes through the distance matrix, species relation list and phylogenetic tree. This tool basically combines absent word (MAW or RAW) computation, dissimilarity measures, species relationship and thus brings all required software in one platform for the ease of researchers and practitioners alike in the field of bioinformatics. We have also developed a restful API. Availability and implementation ADACT has been hosted at http://research.buet.ac.bd/ADACT/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

A DNA-binding protein (DNA-BP) is a protein that can bind and interact with a DNA. Identification of DNA-BPs using experimental methods is expensive as well as time consuming. As such, fast and accurate computational methods are sought for predicting whether a protein can bind with a DNA or not. In this paper, we focus on building a new computational model to identify DNA-BPs in an efficient and accurate way. Our model extracts meaningful information directly from the protein sequences, without any dependence on functional domain or structural information. After feature extraction, we have employed Random Forest (RF) model to rank the features. Afterwards, we have used Recursive Feature Elimination (RFE) method to extract an optimal set of features and trained a prediction model using Support Vector Machine (SVM) with linear kernel. Our proposed method, named as DNA-binding Protein Prediction model using Chou’s general PseAAC (DPP-PseAAC) , demonstrates superior performance compared to the state-of-the-art predictors on standard benchmark dataset. DPP-PseAAC achieves accuracy values of 93.21%, 95.91% and 77.42% for 10-fold cross-validation test, jackknife test and independent test respectively. The source code of DPP-PseAAC, along with relevant dataset and detailed experimental results, can be found at https://github.com/srautonu/DNABinding . A publicly accessible web interface has also been established at: http://77.68.43.135:8080/DPP-PseAAC/ . [ABSTRACT FROM AUTHOR]