Prediction of dinucleotide-specific RNA-binding sites in proteins

<p>Abstract</p> <p>Background</p> <p>Regulation of gene expression, protein synthesis, replication and assembly of many viruses involve RNA–protein interactions. Although some successful computational tools have been reported to recognize RNA binding sites in proteins, the problem of specificity remains poorly investigated. After the nucleotide base composition, the dinucleotide is the smallest unit of RNA sequence information and many RNA-binding proteins simply bind to regions enriched in one dinucleotide. Interaction preferences of protein subsequences and dinucleotides can be inferred from protein-RNA complex structures, enabling a training-based prediction approach.</p> <p>Results</p> <p>We analyzed basic statistics of amino acid-dinucleotide contacts in protein-RNA complexes and found their pairing preferences could be identified. Using a standard approach to represent protein subsequences by their evolutionary profile, we trained neural networks to predict multiclass target vectors corresponding to 16 possible contacting dinucleotide subsequences. In the cross-validation experiments, the accuracies of the optimum network, measured as areas under the curve (AUC) of the receiver operating characteristic (ROC) graphs, were in the range of 65-80%.</p> <p>Conclusions</p> <p>Dinucleotide-specific contact predictions have also been extended to the prediction of interacting protein and RNA fragment pairs, which shows the applicability of this method to predict targets of RNA-binding proteins. A web server predicting the 16-dimensional contact probability matrix directly from a user-defined protein sequence was implemented and made available at: <url>http://tardis.nibio.go.jp/netasa/srcpred</url>.</p

Short-Range Interactions and Decision Tree-Based Protein Contact Map Predictor

In this paper, we focus on protein contact map prediction, one of the most important intermediate steps of the protein folding prob lem. The objective of this research is to know how short-range interac tions can contribute to a system based on decision trees to learn about the correlation among the covalent structures of a protein residues. We propose a solution to predict protein contact maps that combines the use of decision trees with a new input codification for short-range in teractions. The method’s performance was very satisfactory, improving the accuracy instead using all information of the protein sequence. For a globulin data set the method can predict contacts with a maximal accu racy of 43%. The presented predictive model illustrates that short-range interactions play the predominant role in determining protein structur

Prediction of GTP interacting residues, dipeptides and tripeptides in a protein from its evolutionary information

Background: Guanosine triphosphate (GTP)-binding proteins play an important role in regulation of G-protein. Thus prediction of GTP interacting residues in a protein is one of the major challenges in the field of the computational biology. In this study, an attempt has been made to develop a computational method for predicting GTP interacting residues in a protein with high accuracy (Acc), precision (Prec) and recall (Rc). Result: All the models developed in this study have been trained and tested on a non-redundant (40% similarity) dataset using five-fold cross-validation. Firstly, we have developed neural network based models using single sequence and PSSM profile and achieved maximum Matthews Correlation Coefficient (MCC) 0.24 (Acc 61.30%) and 0.39 (Acc 68.88%) respectively. Secondly, we have developed a support vector machine (SVM) based models using single sequence and PSSM profile and achieved maximum MCC 0.37 (Prec 0.73, Rc 0.57, Acc 67.98%) and 0.55 (Prec 0.80, Rc 0.73, Acc 77.17%) respectively. In this work, we have introduced a new concept of predicting GTP interacting dipeptide (two consecutive GTP interacting residues) and tripeptide (three consecutive GTP interacting residues) for the first time. We have developed SVM based model for predicting GTP interacting dipeptides using PSSM profile and achieved MCC 0.64 with precision 0.87, recall 0.74 and accuracy 81.37%. Similarly, SVM based model have been developed for predicting GTP interacting tripeptides using PSSM profile and achieved MCC 0.70 with precision 0.93, recall 0.73 and accuracy 83.98%. Conclusion: These results show that PSSM based method performs better than single sequence based method. The prediction models based on dipeptides or tripeptides are more accurate than the traditional model based on single residue. A web server "GTPBinder" http://www.imtech.res.in/raghava/gtpbinder/ webcite based on above models has been developed for predicting GTP interacting residues in a protein

Identification, analysis and inference of point mutations associated to drug resistance in bacteria: a lesson learnt from the resistance of Streptococcus pneumoniae to quinolones

Antibiotic resistance is one of the biggest public health challenges of our time. Bacterial chemoresistance is the phenomenon whereby bacteria develop the ability to survive and multiply in the presence of an antibacterial drug; the expression of a resistant phenotype may be due to three fundamental mechanisms, including the expression of enzymes that inactivate the antibacterial drug, changes in the membrane permeability to antibiotics and the onset of point mutations causing the physical-chemical alteration of the antimicrobial targets. In recent decades, new antibiotic resistance mechanisms have emerged and are spreading globally, threatening human health and the ability to fight the most common infectious diseases. Quinolones, a novel class of antibiotics that bind bacterial topoisomerases and inhibit cell replication, have been important in limiting the spread of penicillin- and macrolides-resistant Streptococcus pneumoniae. However, alarmingly, resistance to quinolones is spreading recently. Resistance is caused by the appearance of point mutations in the bacterial topoisomerase and gyrase. Some mutations are well known, but some are not and the information about known molecular mechanisms causing resistance is sparse and not systematically collected and organised. This means that it cannot be used to infer new mutations in newly sequenced bacterial genes and study how they may affect the drug binding. The lack of structured, organized, and reusable information about point mutations associated with antibiotic resistance represents a critical issue and is a common pattern in the field. Here, we present a structural analysis of point mutations involved in the resistance to quinolones affecting the gyrase and topoisomerase genes in Streptococcus pneumoniae. Results, extended to other bacterial species, have been collected in a database, Quinores3D db, and can now be used – through a web server, Quinores3D finder - to analyze both known and yet unknown mutations occurring in bacterial topoisomerases and gyrases. The development, testing and deployment of Quinores3D db and Quinores3D finder are further results of this PhD thesis. Furthermore, structural data about point mutations associated with antibiotic resistance were used to train, test and validate a machine learning algorithm for the inference of still unknown mutations potentially involved in bacterial resistance to quinolone. As the performance of the algorithm, measured in terms of accuracy, sensitivity and specificity, is very promising, we plan to incorporate it in the web server to allow users to predict new mutations associated with bacterial resistance to quinolones

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network\u27s prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine

Multiscale Modeling of RNA Structures Using NMR Chemical Shifts

Structure determination is an important step in understanding the mechanisms of functional non-coding ribonucleic acids (ncRNAs). Experimental observables in solution-state nuclear magnetic resonance (NMR) spectroscopy provide valuable information about the structural and dynamic properties of RNAs. In particular, NMR-derived chemical shifts are considered structural "fingerprints" of RNA conformational state(s). In my thesis, I have developed computational tools to model RNA structures (mainly secondary structures) using structural information extracted from NMR chemical shifts. Inspired by methods that incorporate chemical-mapping data into RNA secondary structure prediction, I have developed a framework, CS-Fold, for using assigned chemical shift data to conditionally guide secondary structure folding algorithms. First, I developed neural network classifiers, CS2BPS (Chemical Shift to Base Pairing Status), that take assigned chemical shifts as input and output the predicted base pairing status of individual residues in an RNA. Then I used the base pairing status predictions as folding restraints to guide RNA secondary structure prediction. Extensive testing indicates that from assigned NMR chemical shifts, we could accurately predict the secondary structures of RNAs and map distinct conformational states of a single RNA. Another way to utilize experimental data like NMR chemical shifts in structure modeling is probabilistic modeling, that is, using experimental data to recover native-like structure from a structural ensemble that contains a set of low energy structure models. I first developed a model, SS2CS (Secondary Structure to Chemical Shift), that takes secondary structure as input and predicts chemical shifts with high accuracies. Using Bayesian/maximum entropy (BME), I was able to reweight secondary structure models based on the agreement between the measured and reweighted ensemble-averaged chemical shifts. Results indicate that BME could identify the native or near-native structure from a set of low energy structure models as well as recover some of the non-canonical interactions in tertiary structures. We could also probe the conformational landscape by studying the weight pattern assigned by BME. Finally, I explored RNA structural annotation using assigned NMR chemical shifts. Using multitask learning, eleven structural properties were annotated by classifying individual residues in terms of each structural property. The results indicate that our method, CS-Annotate, could predict the structural properties with reasonable accuracy. We believe that CS-Annotate could be used for assessing the quality of a structure model by comparing the structure derived structural properties with the CS-Annotate derived structural properties. One major limitation of the tools developed is that they require assigned chemical shifts. And to assign chemical shifts, a secondary structure model is typically assumed. However, with the recent advances in singly labeled RNA synthesis, chemical shifts could be assigned without the assumption about the secondary structure. We envision that using the chemical shifts derived from singly labeled NMR experiments, CS-Fold could be used for modeling the secondary structure of RNA. We also believe that unassigned chemical shifts could be used for selecting structure models. Native-like structures could be recovered by comparing optimally assigned chemical shifts with computed chemical shifts (generated by SS2CS). Overall, the results presented in this thesis indicate we could extract crucial structural information of the residues in an RNA based on its NMR chemical shifts. Moreover, with the tools like CS-Fold, SS2CS, and CS-Annotate, we could accurately predict the secondary structure, model conformational landscape, and study structural properties of an RNA.PHDChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163247/1/kexin_1.pd

Deep Learning for Genomics: A Concise Overview

Advancements in genomic research such as high-throughput sequencing techniques have driven modern genomic studies into "big data" disciplines. This data explosion is constantly challenging conventional methods used in genomics. In parallel with the urgent demand for robust algorithms, deep learning has succeeded in a variety of fields such as vision, speech, and text processing. Yet genomics entails unique challenges to deep learning since we are expecting from deep learning a superhuman intelligence that explores beyond our knowledge to interpret the genome. A powerful deep learning model should rely on insightful utilization of task-specific knowledge. In this paper, we briefly discuss the strengths of different deep learning models from a genomic perspective so as to fit each particular task with a proper deep architecture, and remark on practical considerations of developing modern deep learning architectures for genomics. We also provide a concise review of deep learning applications in various aspects of genomic research, as well as pointing out potential opportunities and obstacles for future genomics applications.Comment: Invited chapter for Springer Book: Handbook of Deep Learning Application

Artificial intelligence used in genome analysis studies

Next Generation Sequencing (NGS) or deep sequencing technology enables parallel reading of multiple individual DNA fragments, thereby enabling the identification of millions of base pairs in several hours. Recent research has clearly shown that machine learning technologies can efficiently analyse large sets of genomic data and help to identify novel gene functions and regulation regions. A deep artificial neural network consists of a group of artificial neurons that mimic the properties of living neurons. These mathematical models, termed Artificial Neural Networks (ANN), can be used to solve artificial intelligence engineering problems in several different technological fields (e.g., biology, genomics, proteomics, and metabolomics). In practical terms, neural networks are non-linear statistical structures that are organized as modelling tools and are used to simulate complex genomic relationships between inputs and outputs. To date, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNN) have been demonstrated to be the best tools for improving performance in problem solving tasks within the genomic field

Towards Parsimonious Generative Modeling of RNA Families

Generative probabilistic models emerge as a new paradigm in data-driven, evolution-informed design of biomolecular sequences. This paper introduces a novel approach, called Edge Activation Direct Coupling Analysis (eaDCA), tailored to the characteristics of RNA sequences, with a strong emphasis on simplicity, efficiency, and interpretability. eaDCA explicitly constructs sparse coevolutionary models for RNA families, achieving performance levels comparable to more complex methods while utilizing a significantly lower number of parameters. Our approach demonstrates efficiency in generating artificial RNA sequences that closely resemble their natural counterparts in both statistical analyses and SHAPE-MaP experiments, and in predicting the effect of mutations. Notably, eaDCA provides a unique feature: estimating the number of potential functional sequences within a given RNA family. For example, in the case of cyclic di-AMP riboswitches (RF00379), our analysis suggests the existence of approximately $\mathbf{10^{39}}$ functional nucleotide sequences. While huge compared to the known $< \mathbf{4,000}$ natural sequences, this number represents only a tiny fraction of the vast pool of nearly $\mathbf{10^{82}}$ possible nucleotide sequences of the same length (136 nucleotides). These results underscore the promise of sparse and interpretable generative models, such as eaDCA, in enhancing our understanding of the expansive RNA sequence space.Comment: 33 pages (including SI