qNABpredict: Quick, accurate, and taxonomy-aware sequence-based prediction of content of nucleic acid binding amino acids

Accurate Prediction of Protein-Binding Residues in Protein Sequences Using SCRIBER

Deciphering molecular-level mechanisms that govern protein-protein interactions (PPIs) relies in part on the accurate prediction of protein-binding partners and protein-binding residues. These predictions can be used to support a wide spectrum of applications that include development of PPI networks and protein docking programs, drug design studies, and investigations of molecular details that underlie certain diseases. Computational methods that predict protein-binding residues offer convenient, inexpensive, and relatively accurate data that can aid these efforts. We introduce and describe a user-friendly webserver for the SCRIBER method that conveniently provides state-of-the-art predictions of protein-binding residues and that minimizes cross-predictions, i.e., incorrect prediction of residues that bind other/non-protein ligands as protein binding. SCRIBER relies on a two-layer architecture that is specifically designed to reduce the cross-predictions. We motivate and explain this predictive architecture. We describe how to use the webserver, interact with its web interface, and collect, read, and understand results generated by SCRIBER. The SCRIBER webserver is available at http://biomine.cs.vcu.edu/servers/SCRIBER/ .

Accurate and Fast Prediction of Intrinsic Disorder Using flDPnn

Intrinsically disordered proteins (IDPs) that include one or more intrinsically disordered regions (IDRs) are abundant across all domains of life and viruses and play numerous functional roles in various cellular processes. Due to a relatively low throughput and high cost of experimental techniques for identifying IDRs, there is a growing need for fast and accurate computational algorithms that accurately predict IDRs/IDPs from protein sequences. We describe one of the leading disorder predictors, flDPnn. Results from a recent community-organized Critical Assessment of Intrinsic Disorder (CAID) experiment show that flDPnn provides fast and state-of-the-art predictions of disorder, which are supplemented with the predictions of several major disorder functions. This chapter provides a practical guide to flDPnn, which includes a brief explanation of its predictive model, descriptions of its web server and standalone versions, and a case study that showcases how to read and understand flDPnn's predictions.

Engineering of the Lrp/AsnC-type transcriptional regulator DecR as a genetically encoded biosensor for multilevel optimization of L-cysteine biosynthesis pathway in Escherichia coli

L-cysteine is an important sulfur-containing amino acid being difficult to produce by microbial fermentation. Due to the lack of high-throughput screening methods, existing genetically engineered bacteria have been developed by simply optimizing the expression of L-cysteine-related genes one by one. To overcome this limitation, in this study, a biosensor-based approach for multilevel biosynthetic pathway optimization of L-cysteine from the DecR regulator variant of Escherichia coli was applied. Through protein engineering, we obtained the DecRN29Y/C81E/M90Q/M99E variant-based biosensor with improved specificity and an 8.71-fold increase in dynamic range. Using the developed biosensor, we performed high-throughput screening of the constructed promoter and RBS combination library, and successfully obtained the optimized strain, which resulted in a 6.29-fold increase in L-cysteine production. Molecular dynamics (MD) simulations and electrophoretic mobility shift analysis (EMSA) showed that the N29Y/C81E/M90Q/M99E variant had enhanced induction activity. This enhancement may be due to the increased binding of the variant to DNA in the presence of L-cysteine, which enhances transcriptional activation. Overall, our biosensor-based strategy provides a promising approach for optimizing biosynthetic pathways at multiple levels. The successful implementation of this strategy demonstrates its potential for screening improved recombinant strains.

A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment

Accurate diagnosis is the key to providing prompt and explicit treatment and disease management. The recognized biological method for the molecular diagnosis of infectious pathogens is polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identifying disease-related genes for diagnosis, prognosis, and treatment. The models reduce the time and cost used by wet-lab experimental procedures. Consequently, sophisticated computational approaches have been developed to facilitate the detection of cancer, a leading cause of death globally, and other complex diseases. In this review, we systematically evaluate the recent trends in multi-omics data analysis based on deep learning techniques and their application in disease prediction. We highlight the current challenges in the field and discuss how advances in deep learning methods and their optimization for application is vital in overcoming them. Ultimately, this review promotes the development of novel deep-learning methodologies for data integration, which is essential for disease detection and treatment.