Predicting nucleic acid binding interfaces from structural models of proteins

The function of DNA‐ and RNA‐binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However, the main pitfall of various structure‐based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high‐resolution three‐dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I‐TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high‐resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I‐TASSER produces high‐quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared with patches extracted from independent models. Overall, these results suggest that combining information from a collection of low‐resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure. Proteins 2012. © 2011 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90296/1/PROT_23214_sm_SuppFig4.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/90296/2/PROT_23214_sm_SuppTab1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/90296/3/PROT_23214_sm_SuppFig1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/90296/4/PROT_23214_sm_SuppFig5.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/90296/5/PROT_23214_sm_SuppFig2.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/90296/6/PROT_23214_sm_SuppTab2.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/90296/7/PROT_23214_sm_SuppFig3.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/90296/8/PROT_23214_sm_SuppTab3.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/90296/9/23214_ftp.pd

Selecting Intermittent Fasting Type to Improve Health in Type 2 Diabetes: A Machine Learning Approach

Intermittent fasting (IF) is the cycling between periods of eating and fasting. The two most popular forms of IER are: the 5: 2 diet characterized by two consecutive or non-consecutive “fast” days and the alternate-day energy restriction, commonly called alternate-day fasting (ADF). The second form is time-restricted feeding (TRF), eating within specific time frames such as the most prevalent 16: 8 diet, with 16 hours of fasting and 8 hours for eating. It is already known that IF can bring about changes in metabolic parameters related with type 2 diabetes (T2D). Furthermore, IF can be effective in improving health by reducing metabolic disorders and age-related diseases. However, it is not clear yet whether the age at which fasting begins, gender and severity of T2D influence on the effectiveness of the different types of IF in reducing metabolic disorders. In this chapter I will present the risk factors of T2D, the different types of IF interventions and the research-based knowledge regarding the effect of IF on T2D. Furthermore, I will describe several machine learning approaches to provide a recommendation system which reveals a set of rules that can assist selecting a successful IF intervention for a personal case. Finally, I will discuss the question: Can we predict the optimal IF intervention for a prediabetes patient

From face to interface recognition: a differential geometric approach to distinguish DNA from RNA binding surfaces

Protein nucleic acid interactions play a critical role in all steps of the gene expression pathway. Nucleic acid (NA) binding proteins interact with their partners, DNA or RNA, via distinct regions on their surface that are characterized by an ensemble of chemical, physical and geometrical properties. In this study, we introduce a novel methodology based on differential geometry, commonly used in face recognition, to characterize and predict NA binding surfaces on proteins. Applying the method on experimentally solved three-dimensional structures of proteins we successfully classify double-stranded DNA (dsDNA) from single-stranded RNA (ssRNA) binding proteins, with 83% accuracy. We show that the method is insensitive to conformational changes that occur upon binding and can be applicable for de novo protein-function prediction. Remarkably, when concentrating on the zinc finger motif, we distinguish successfully between RNA and DNA binding interfaces possessing the same binding motif even within the same protein, as demonstrated for the RNA polymerase transcription-factor, TFIIIA. In conclusion, we present a novel methodology to characterize protein surfaces, which can accurately tell apart dsDNA from an ssRNA binding interfaces. The strength of our method in recognizing fine-tuned differences on NA binding interfaces make it applicable for many other molecular recognition problems, with potential implications for drug design

Structure-based prediction of RNA-binding domains and RNA-binding sites and application to structural genomics targets

Mechanistic understanding of many key cellular processes often involves identification of RNA binding proteins (RBPs) and RNA binding sites in two separate steps. Here, they are predicted simultaneously by structural alignment to known protein–RNA complex structures followed by binding assessment with a DFIRE-based statistical energy function. This method achieves 98% accuracy and 91% precision for predicting RBPs and 93% accuracy and 78% precision for predicting RNA-binding amino-acid residues for a large benchmark of 212 RNA binding and 6761 non-RNA binding domains (leave-one-out cross-validation). Additional tests revealed that the method makes no false positive prediction from 311 DNA binding domains but correctly detects six domains binding with both DNA and RNA. In addition, it correctly identified 31 of 75 unbound RNA-binding domains with 92% accuracy and 65% precision for predicted binding residues and achieved 86% success rate in its application to SCOP RNA binding domain superfamily (Structural Classification Of Proteins). It further predicts 25 targets as RBPs in 2076 structural genomics targets: 20 of 25 predicted ones (80%) are putatively RNA binding. The superior performance over existing methods indicates the importance of dividing structures into domains, using a Z-score to measure relative structural similarity, and a statistical energy function to measure protein–RNA binding affinity

Patch Finder Plus (PFplus): A web server for extracting and displaying positive electrostatic patches on protein surfaces

Positively charged electrostatic patches on protein surfaces are usually indicative of nucleic acid binding interfaces. Interestingly, many proteins which are not involved in nucleic acid binding possess large positive patches on their surface as well. In some cases, the positive patches on the protein are related to other functional properties of the protein family. PatchFinderPlus (PFplus) http://pfp.technion.ac.il is a web-based tool for extracting and displaying continuous electrostatic positive patches on protein surfaces. The input required for PFplus is either a four letter PDB code or a protein coordinate file in PDB format, provided by the user. PFplus computes the continuum electrostatics potential and extracts the largest positive patch for each protein chain in the PDB file. The server provides an output file in PDB format including a list of the patch residues. In addition, the largest positive patch is displayed on the server by a graphical viewer (Jmol), using a simple color coding

PRIDB: a protein–RNA interface database

The Protein–RNA Interface Database (PRIDB) is a comprehensive database of protein–RNA interfaces extracted from complexes in the Protein Data Bank (PDB). It is designed to facilitate detailed analyses of individual protein–RNA complexes and their interfaces, in addition to automated generation of user-defined data sets of protein–RNA interfaces for statistical analyses and machine learning applications. For any chosen PDB complex or list of complexes, PRIDB rapidly displays interfacial amino acids and ribonucleotides within the primary sequences of the interacting protein and RNA chains. PRIDB also identifies ProSite motifs in protein chains and FR3D motifs in RNA chains and provides links to these external databases, as well as to structure files in the PDB. An integrated JMol applet is provided for visualization of interacting atoms and residues in the context of the 3D complex structures. The current version of PRIDB contains structural information regarding 926 protein–RNA complexes available in the PDB (as of 10 October 2010). Atomic- and residue-level contact information for the entire data set can be downloaded in a simple machine-readable format. Also, several non-redundant benchmark data sets of protein–RNA complexes are provided. The PRIDB database is freely available online at http://bindr.gdcb.iastate.edu/PRIDB

Warong Aiskrim D’NANZ: case study: company analysis / Mohd Ishraffil Ezzrail Mohd Jusni … [et al.]

Warong Aiskrim D’NANZ is a family business operated by two married couples, Mrs. Azliza and her husband, Mr. Adnan and help by their 3 children as a worker. Their first outlet was launched in November 2020 in Klebang Beach area. They started this business since 2014 by provide a service such as kiosk, order from home and for private events such as wedding and birthday party. After brainstormed the idea of the product that they want to sell, they decided on producing ice cream business and took a course of making ice cream to build their own business empire. For D’NANZ Ice cream’s business strategy, they sell their ice cream at the cheapest price so that everyone can afford to buy their product. Starting with using flavours that bought at a bakery, they started to improve their recipes by using the real fruit itself such as “kelapa pandan” and “durian” because they figured out that using the real fruit makes the ice cream taste better. They came out with this idea so that their customers could eat the ice cream as well as the coconut while they scoop the ice cream inside the coconut. As for case study, we conduct an interview with the founder of Warong Aiskrim D’NANZ to collect information that related with syllabus of Principles of Entrepreneurship. There are nine (9) elements of Business Model Canvas that we used to identify how exactly this family business works and how they manage the marketing and operations strategy to survive in the business market despite the condition of economics and other issues faced by Warong Aiskrim D’NANZ. The major problem facing by Warong Aiskrim D’NANZ is a sharp decline in sales revenue for 2 years since Movement Control Order was announced on March 2020. In addition of the major problems facing by this business is lack of marketing. Main causes of this major problems are lack of skills in managing social media and market segmentation. This business is open to all types of consumers as they sell variety of homemade ice-cream yet it is less well known in the market. There are some advantages and disadvantages for each solution that we provide for Warong Aiskrim D’NANZ such as building a sales and marketing team within the company is that the company can determine the most successful approaches with a more well-defined strategy and building more engagements with social media audiences is the risk of negative publicity

In silico characterization and prediction of global protein–mRNA interactions in yeast

Post-transcriptional gene regulation is mediated through complex networks of protein–RNA interactions. The targets of only a few RNA binding proteins (RBPs) are known, even in the well-characterized budding yeast. In silico prediction of protein–RNA interactions is therefore useful to guide experiments and to provide insight into regulatory networks. Computational approaches have identified RBP targets based on sequence binding preferences. We investigate here to what extent RBP–RNA interactions can be predicted based on RBP and mRNA features other than sequence motifs. We analyze global relationships between gene and protein properties in general and between selected RBPs and known mRNA targets in particular. Highly translated RBPs tend to bind to shorter transcripts, and transcripts bound by the same RBP show high expression correlation across different biological conditions. Surprisingly, a given RBP preferentially binds to mRNAs that encode interaction partners for this RBP, suggesting coordinated post-transcriptional auto-regulation of protein complexes. We apply a machine-learning approach to predict specific RBP targets in yeast. Although this approach performs well for RBPs with known targets, predictions for uncharacterized RBPs remain challenging due to limiting experimental data. We also predict targets of fission yeast RBPs, indicating that the suggested framework could be applied to other species once more experimental data are available

Dissecting the protein–RNA interface: the role of protein surface shapes and RNA secondary structures in protein–RNA recognition

Protein–RNA interactions are essential for many biological processes. However, the structural mechanisms underlying these interactions are not fully understood. Here, we analyzed the protein surface shape (dented, intermediate or protruded) and the RNA base pairing properties (paired or unpaired nucleotides) at the interfaces of 91 protein–RNA complexes derived from the Protein Data Bank. Dented protein surfaces prefer unpaired nucleotides to paired ones at the interface, and hydrogen bonds frequently occur between the protein backbone and RNA bases. In contrast, protruded protein surfaces do not show such a preference, rather, electrostatic interactions initiate the formation of hydrogen bonds between positively charged amino acids and RNA phosphate groups. Interestingly, in many protein–RNA complexes that interact via an RNA loop, an aspartic acid is favored at the interface. Moreover, in most of these complexes, nucleotide bases in the RNA loop are flipped out and form hydrogen bonds with the protein, which suggests that aspartic acid is important for RNA loop recognition through a base-flipping process. This study provides fundamental insights into the role of the shape of the protein surface and RNA secondary structures in mediating protein–RNA interactions

Exploiting structural and topological information to improve prediction of RNA-protein binding sites

The breast and ovarian cancer susceptibility gene BRCA1 encodes a multifunctional tumor suppressor protein BRCA1, which is involved in regulating cellular processes such as cell cycle, transcription, DNA repair, DNA damage response and chromatin remodeling. BRCA1 protein, located primarily in cell nuclei, interacts with multiple proteins and various DNA targets. It has been demonstrated that BRCA1 protein binds to damaged DNA and plays a role in the transcriptional regulation of downstream target genes. As a key protein in the repair of DNA double-strand breaks, the BRCA1-DNA binding properties, however, have not been reported in detail