Amino-Acid Substitutions In Membrane Proteins: Applications To Homology Recognition And Comparative Modelling

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Partner-Aware Prediction of Interacting Residues in Protein-Protein Complexes from Sequence Data

Computational prediction of residues that participate in protein-protein interactions is a difficult task, and state of the art methods have shown only limited success in this arena. One possible problem with these methods is that they try to predict interacting residues without incorporating information about the partner protein, although it is unclear how much partner information could enhance prediction performance. To address this issue, the two following comparisons are of crucial significance: (a) comparison between the predictability of inter-protein residue pairs, i.e., predicting exactly which residue pairs interact with each other given two protein sequences; this can be achieved by either combining conventional single-protein predictions or making predictions using a new model trained directly on the residue pairs, and the performance of these two approaches may be compared: (b) comparison between the predictability of the interacting residues in a single protein (irrespective of the partner residue or protein) from conventional methods and predictions converted from the pair-wise trained model. Using these two streams of training and validation procedures and employing similar two-stage neural networks, we showed that the models trained on pair-wise contacts outperformed the partner-unaware models in predicting both interacting pairs and interacting single-protein residues. Prediction performance decreased with the size of the conformational change upon complex formation; this trend is similar to docking, even though no structural information was used in our prediction. An example application that predicts two partner-specific interfaces of a protein was shown to be effective, highlighting the potential of the proposed approach. Finally, a preliminary attempt was made to score docking decoy poses using prediction of interacting residue pairs; this analysis produced an encouraging result

Homology-based prediction of interactions between proteins using Averaged One-Dependence Estimators

BACKGROUND: Identification of protein-protein interactions (PPIs) is essential for a better understanding of biological processes, pathways and functions. However, experimental identification of the complete set of PPIs in a cell/organism (“an interactome”) is still a difficult task. To circumvent limitations of current high-throughput experimental techniques, it is necessary to develop high-performance computational methods for predicting PPIs. RESULTS: In this article, we propose a new computational method to predict interaction between a given pair of protein sequences using features derived from known homologous PPIs. The proposed method is capable of predicting interaction between two proteins (of unknown structure) using Averaged One-Dependence Estimators (AODE) and three features calculated for the protein pair: (a) sequence similarities to a known interacting protein pair (F(Seq)), (b) statistical propensities of domain pairs observed in interacting proteins (F(Dom)) and (c) a sum of edge weights along the shortest path between homologous proteins in a PPI network (F(Net)). Feature vectors were defined to lie in a half-space of the symmetrical high-dimensional feature space to make them independent of the protein order. The predictability of the method was assessed by a 10-fold cross validation on a recently created human PPI dataset with randomly sampled negative data, and the best model achieved an Area Under the Curve of 0.79 (pAUC(0.5%) = 0.16). In addition, the AODE trained on all three features (named PSOPIA) showed better prediction performance on a separate independent data set than a recently reported homology-based method. CONCLUSIONS: Our results suggest that F(Net), a feature representing proximity in a known PPI network between two proteins that are homologous to a target protein pair, contributes to the prediction of whether the target proteins interact or not. PSOPIA will help identify novel PPIs and estimate complete PPI networks. The method proposed in this article is freely available on the web at http://mizuguchilab.org/PSOPIA

Systems biology approaches to a rational drug discovery paradigm

The published manuscript is available at EurekaSelect via http://www.eurekaselect.com/openurl/content.php?genre=article&doi=10.2174/1568026615666150826114524.Prathipati P., Mizuguchi K.. Systems biology approaches to a rational drug discovery paradigm. Current Topics in Medicinal Chemistry, 16, 9, 1009. https://doi.org/10.2174/1568026615666150826114524

An Open Framework for Extensible Multi-Stage Bioinformatics Software

In research labs, there is often a need to customise software at every step in a given bioinformatics workflow, but traditionally it has been difficult to obtain both a high degree of customisability and good performance. Performance-sensitive tools are often highly monolithic, which can make research difficult. We present a novel set of software development principles and a bioinformatics framework, Friedrich, which is currently in early development. Friedrich applications support both early stage experimentation and late stage batch processing, since they simultaneously allow for good performance and a high degree of flexibility and customisability. These benefits are obtained in large part by basing Friedrich on the multiparadigm programming language Scala. We present a case study in the form of a basic genome assembler and its extension with new functionality. Our architecture has the potential to greatly increase the overall productivity of software developers and researchers in bioinformatics.Comment: 12 pages, 1 figure, to appear in proceedings of PRIB 201

Attention network for predicting T-cell receptor–peptide binding can associate attention with interpretable protein structural properties

Understanding how a T-cell receptor (TCR) recognizes its specific ligand peptide is crucial for gaining an insight into biological functions and disease mechanisms. Despite its importance, experimentally determining TCR–peptide–major histocompatibility complex (TCR–pMHC) interactions is expensive and time-consuming. To address this challenge, computational methods have been proposed, but they are typically evaluated by internal retrospective validation only, and few researchers have incorporated and tested an attention layer from language models into structural information. Therefore, in this study, we developed a machine learning model based on a modified version of Transformer, a source–target attention neural network, to predict the TCR–pMHC interaction solely from the amino acid sequences of the TCR complementarity-determining region (CDR) 3 and the peptide. This model achieved competitive performance on a benchmark dataset of the TCR–pMHC interaction, as well as on a truly new external dataset. Additionally, by analyzing the results of binding predictions, we associated the neural network weights with protein structural properties. By classifying the residues into large- and small-attention groups, we identified statistically significant properties associated with the largely attended residues such as hydrogen bonds within CDR3. The dataset that we created and the ability of our model to provide an interpretable prediction of TCR–peptide binding should increase our knowledge about molecular recognition and pave the way for designing new therapeutics

Improved pose and affinity predictions using different protocols tailored on the basis of data availability

This is a post-peer-review, pre-copyedit version of an article published in Journal of Computer-Aided Molecular Design. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10822-016-9982-4.Prathipati, P., Nagao, C., Ahmad, S. et al. Improved pose and affinity predictions using different protocols tailored on the basis of data availability. J Comput Aided Mol Des 30, 817–828 (2016). https://doi.org/10.1007/s10822-016-9982-

Prediction of mono- and di-nucleotide-specific DNA-binding sites in proteins using neural networks

<p>Abstract</p> <p>Background</p> <p>DNA recognition by proteins is one of the most important processes in living systems. Therefore, understanding the recognition process in general, and identifying mutual recognition sites in proteins and DNA in particular, carries great significance. The sequence and structural dependence of DNA-binding sites in proteins has led to the development of successful machine learning methods for their prediction. However, all existing machine learning methods predict DNA-binding sites, irrespective of their target sequence and hence, none of them is helpful in identifying specific protein-DNA contacts. In this work, we formulate the problem of predicting specific DNA-binding sites in terms of contacts between the residue environments of proteins and the identity of a mononucleotide or a dinucleotide step in DNA. The aim of this work is to take a protein sequence or structural features as inputs and predict for each amino acid residue if it binds to DNA at locations identified by one of the four possible mononucleotides or one of the 10 unique dinucleotide steps. Contact predictions are made at various levels of resolution viz. in terms of side chain, backbone and major or minor groove atoms of DNA.</p> <p>Results</p> <p>Significant differences in residue preferences for specific contacts are observed, which combined with other features, lead to promising levels of prediction. In general, PSSM-based predictions, supported by secondary structure and solvent accessibility, achieve a good predictability of ~70–80%, measured by the area under the curve (AUC) of ROC graphs. The major and minor groove contact predictions stood out in terms of their poor predictability from sequences or PSSM, which was very strongly (>20 percentage points) compensated by the addition of secondary structure and solvent accessibility information, revealing a predominant role of local protein structure in the major/minor groove DNA-recognition. Following a detailed analysis of results, a web server to predict mononucleotide and dinucleotide-step contacts using PSSM was developed and made available at <url>http://sdcpred.netasa.org/</url> or <url>http://tardis.nibio.go.jp/netasa/sdcpred/</url>.</p> <p>Conclusion</p> <p>Most residue-nucleotide contacts can be predicted with high accuracy using only sequence and evolutionary information. Major and minor groove contacts, however, depend profoundly on the local structure. Overall, this study takes us a step closer to the ultimate goal of predicting mutual recognition sites in protein and DNA sequences.</p

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

Brefeldin A-inhibited guanine nucleotide-exchange protein 3 (BIG3) is predicted to interact with its partner through an ARM-type α-helical structure

Background: Brefeldin A-inhibited guanine nucleotide-exchange protein 3 (BIG3) has been identified recently as a novel regulator of estrogen signalling in breast cancer cells. Despite being a potential target for new breast cancer treatment, its amino acid sequence suggests no association with any well-characterized protein family and provides little clues as to its molecular function. In this paper, we predicted the structure, function and interactions of BIG3 using a range of bioinformatic tools. Results: Homology search results showed that BIG3 had distinct features from its paralogues, BIG1 and BIG2, with a unique region between the two shared domains, Sec7 and DUF1981. Although BIG3 contains Sec7 domain, the lack of the conserved motif and the critical glutamate residue suggested no potential guaninyl-exchange factor (GEF) activity. Fold recognition tools predicted BIG3 to adopt an α-helical repeat structure similar to that of the armadillo (ARM) family. Using state-of-the-art methods, we predicted interaction sites between BIG3 and its partner PHB2. Conclusions: The combined results of the structure and interaction prediction led to a novel hypothesis that one of the predicted helices of BIG3 might play an important role in binding to PHB2 and thereby preventing its translocation to the nucleus. This hypothesis has been subsequently verified experimentally