Linking the kinome and phosphorylome--a comprehensive review of approaches to find kinase targets

Protein phosphorylation is associated with most cell signaling and developmental processes in eukaryotes. Despite the vast extent of the phosphoproteome within the cell, connecting specific kinases with relevant targets remains a significant experimental frontier. The challenge of linking kinases and their substrates reflects the complexity of kinase function. For example, kinases tend to exert their biological effects through supernumerary, redundant phosphorylation, often on multiple protein complex components. Although these types of phosphorylation events are biologically significant, those kinases responsible are often difficult to identify. Recent methods for global analysis of protein phosphorylation promise to substantially accelerate efforts to map the dynamic phosphorylome. Here, we review both conventional methods to identify kinase targets and more comprehensive genomic and proteomic approaches to connect the kinome and phosphorylome.     

Computational approaches to selecting and optimising targets for structural biology

Selection of protein targets for study is central to structural biology and may be influenced by numerous factors. A key aim is to maximise returns for effort invested by identifying proteins with the balance of biophysical properties that are conducive to success at all stages (e.g. solubility, crystallisation) in the route towards a high resolution structural model. Selected targets can be optimised through construct design (e.g. to minimise protein disorder), switching to a homologous protein, and selection of experimental methodology (e.g. choice of expression system) to prime for efficient progress through the structural proteomics pipeline. Here we discuss computational techniques in target selection and optimisation, with more detailed focus on tools developed within the Scottish Structural Proteomics Facility (SSPF); namely XANNpred, ParCrys, OB-Score (target selection) and TarO (target optimisation). TarO runs a large number of algorithms, searching for homologues and annotating the pool of possible alternative targets. This pool of putative homologues is presented in a ranked, tabulated format and results are also visualised as an automatically generated and annotated multiple sequence alignment. The target selection algorithms each predict the propensity of a selected protein target to progress through the experimental stages leading to diffracting crystals. This single predictor approach has advantages for target selection, when compared with an approach using two or more predictors that each predict for success at a single experimental stage. The tools described here helped SSPF achieve a high (21%) success rate in progressing cloned targets to diffraction-quality crystals.     

Computational approaches to predict protein functional families and functional sites

Understanding the mechanisms of protein function is indispensable for many biological applications, such as protein engineering and drug design. However, experimental annotations are sparse, and therefore, theoretical strategies are needed to fill the gap. Here, we present the latest developments in building functional subclassifications of protein superfamilies and using evolutionary conservation to detect functional determinants, for example, catalytic-, binding- and specificity-determining residues important for delineating the functional families. We also briefly review other features exploited for functional site detection and new machine learning strategies for combining multiple features.     

Mapping the binding sites of challenging drug targets

An increasing number of medically important proteins are challenging drug targets because their binding sites are too shallow or too polar, are cryptic and thus not detectable without a bound ligand or located in a protein–protein interface. While such proteins may not bind druglike small molecules with sufficiently high affinity, they are frequently druggable using novel therapeutic modalities. The need for such modalities can be determined by experimental or computational fragment based methods. Computational mapping by mixed solvent molecular dynamics simulations or the FTMap server can be used to determine binding hot spots. The strength and location of the hot spots provide very useful information for selecting potentially successful approaches to drug discovery.     

A multi-objective genetic algorithm to find active modules in multiplex biological networks

The identification of subnetworks of interest—or active modules—by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in MUltiplex biological Networks. MOGAMUN optimizes both the density of interactions and the scores of the nodes (e.g., their differential expression). We compare MOGAMUN with state-of-the-art methods, representative of different algorithms dedicated to the identification of active modules in single networks. MOGAMUN identifies dense and high-scoring modules that are also easier to interpret. In addition, to our knowledge, MOGAMUN is the first method able to use multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins. Each layer is associated to its own meaning, topology, and biases; the multiplex framework allows exploiting this diversity of biological networks. We applied MOGAMUN to identify cellular processes perturbed in Facio-Scapulo-Humeral muscular Dystrophy, by integrating RNA-seq expression data with a multiplex biological network. We identified different active modules of interest, thereby providing new angles for investigating the pathomechanisms of this disease.Availability: MOGAMUN is available at https://github.com/elvanov/MOGAMUN and as a Bioconductor package at https://bioconductor.org/packages/release/bioc/html/MOGAMUN.html. Contact: rf.uma-vinu@toduab.siana     

Docking dinucleotides to HIV-1 integrase carboxyl-terminal domain to find possible DNA binding sites

HIV-1 integrase mediates a processed viral DNA into the host genome DNA. The binding modes between HIV-1 integrase and viral/host DNA have not been clarified until now. The C-terminal domain of integrase has been found to be a DNA-binding domain. In this work, we have explored the possible DNA binding sites of dimeric C-terminal domain by docking dinucleotides to HIV-1 integrase. The docking results suggest that two symmetrical DNA-binding sites are likely located on the outside surface of the dimeric C-terminal domain and not located in the groove. Those sites are in agreement with the experimental data.     

Repression of dpp targets by binding of brinker to mad sites

Signaling by decapentaplegic (Dpp), a Drosophila member of the transforming growth factor (TGF) beta superfamily of growth factors, has recently been shown to activate targets such as vestigial (vg) indirectly through negative regulation of brinker (brk). Here we show that the Brk protein functions as a repressor by binding to Dpp response elements. The Brk DNA binding activity was localized to an amino-terminal region containing a putative homeodomain. Brk bound to a Dpp response element of the Ultrabithorax (Ubx) midgut enhancer at a sequence that overlaps a binding site for the Smad protein, Mothers Against Dpp (Mad). Furthermore, Brk was able to compete with Mad for occupancy of this binding site. This recognition of overlapping binding sites provides a potential explanation for why the G/C-rich Mad binding site consensus differs the Smad3/Smad4 binding site consensus. We also found that the Dpp response element from Ubx was more sensitive than the vg quadrant enhancer to repression by Brk. This difference correlates with short-range activation of Ubx by Dpp in the visceral mesoderm, whereas vg exhibits a long-range response to Dpp in the wing imaginal disc, indicating that Brk binding sites may play a critical role in limiting thresholds for activation by Dpp. Finally, we provide evidence that Brk is capable of functioning as an active repressor. Thus, whereas Brk and Mad compete for regulation of Ubx and vg, Brk may regulate other Dpp targets without direct involvement of Mad.     

Using multiple microenvironments to find similar ligand-binding sites: application to kinase inhibitor binding

The recognition of cryptic small-molecular binding sites in protein structures is important for understanding off-target side effects and for recognizing potential new indications for existing drugs. Current methods focus on the geometry and detailed chemical interactions within putative binding pockets, but may not recognize distant similarities where dynamics or modified interactions allow one ligand to bind apparently divergent binding pockets. In this paper, we introduce an algorithm that seeks similar microenvironments within two binding sites, and assesses overall binding site similarity by the presence of multiple shared microenvironments. The method has relatively weak geometric requirements (to allow for conformational change or dynamics in both the ligand and the pocket) and uses multiple biophysical and biochemical measures to characterize the microenvironments (to allow for diverse modes of ligand binding). We term the algorithm PocketFEATURE, since it focuses on pockets using the FEATURE system for characterizing microenvironments. We validate PocketFEATURE first by showing that it can better discriminate sites that bind similar ligands from those that do not, and by showing that we can recognize FAD-binding sites on a proteome scale with Area Under the Curve (AUC) of 92%. We then apply PocketFEATURE to evolutionarily distant kinases, for which the method recognizes several proven distant relationships, and predicts unexpected shared ligand binding. Using experimental data from ChEMBL and Ambit, we show that at high significance level, 40 kinase pairs are predicted to share ligands. Some of these pairs offer new opportunities for inhibiting two proteins in a single pathway.     

Computational intelligence approaches for pattern discovery in biological systems

Biology, chemistry and medicine are faced by tremendous challenges caused by an overwhelming amount of data and the need for rapid interpretation. Computational intelligence (CI) approaches such as artificial neural networks, fuzzy systems and evolutionary computation are being used with increasing frequency to contend with this problem, in light of noise, non-linearity and temporal dynamics in the data. Such methods can be used to develop robust models of processes either on their own or in combination with standard statistical approaches. This is especially true for database mining, where modeling is a key component of scientific understanding. This review provides an introduction to current CI methods, their application to biological problems, and concludes with a commentary about the anticipated impact of these approaches in bioinformatics.     

应用化学基因组信息预测小分子化合物的潜在生物靶标的理论方法

在后基因组时代,化学基因组技术在药物作用靶点的确认、小分子化合物对通路的作用,以及小分子先导化合物的识别等方面都有着广泛的应用,为新药研发提供了新的技术方法。本文主要介绍了当前几种基于化学基因组信息来预测小分子化合物潜在生物靶标的理论方法（包括化学相似性搜索方法、反向分子对接方法、数据挖掘方法以及生物活性谱图分析方法）,并分析了这些方法的优缺点以及应用前景。     

microRNA计算发现方法的研究进展

microRNA (miRNA)是近几年发现的一类长度为～21 nt的内源非编码小RNA, 在植物和动物中发挥着重要而广泛的调控功能。它的发现主要有cDNA克隆测序和计算发现两条途径。由于cDNA克隆测序方法受miRNA表达的时间和组织特异性以及表达水平的影响, 而计算发现可以弥补其不足, 因此miRNA的计算发现方法研究受到了广泛的重视。文章对近几年计算发现miRNA的研究进展进行了综述, 根据计算发现方法的本质, 将计算发现方法归纳为5类, 分别是同源片段搜索方法、基于比较基因组学的预测方法、基于序列和结构特征打分的预测方法、结合作用靶标的预测方法和基于机器学习的预测方法, 并对各类方法的原理、核心思想、优点和局限性进行了分析, 最后探讨了进一步的发展方向。     

Computational approaches to molecular recognition

Recent advances in the computation of free energies have facilitated the understanding of host—guest and protein—ligand recognition. Rigorous perturbation methods have been assessed and expanded, and more approximate techniques have been developed that allow faster treatment of diverse systems.     

Computational approaches to protein-protein interaction

The interactions between proteins allow the cell's life. A number of experimental, genome-wide, high-throughput studies have been devoted to the determination of protein-protein interactions and the consequent interaction networks. Here, the bioinformatics methods dealing with protein-protein interactions and interaction network are overviewed. 1. Interaction databases developed to collect and annotate this immense amount of data; 2. Automated data mining techniques developed to extract information about interactions from the published literature; 3. Computational methods to assess the experimental results developed as a consequence of the finding that the results of high-throughput methods are rather inaccurate; 4. Exploitation of the information provided by protein interaction networks in order to predict functional features of the proteins; and 5. Prediction of protein-protein interactions.     

Computational approaches to motor control

New concepts and computational models that integrate behavioral and neurophysiological observations have addressed several of the most fundamental long-standing problems in motor control. These problems include the selection of particular trajectories among the large number of possibilities, the solution of inverse kinematics and dynamics problems, motor adaptation and the learning of sequential behaviors.     

Computational approaches to gene prediction

The problems associated with gene identification and the prediction of gene structure in DNA sequences have been the focus of increased attention over the past few years with the recent acquisition by large-scale sequencing projects of an immense amount of genome data. A variety of prediction programs have been developed in order to address these problems. This paper presents a review of the computational approaches and gene-finders used commonly for gene prediction in eukaryotic genomes. Two approaches, in general, have been adopted for this purpose: similarity-based and ab initio techniques. The information gleaned from these methods is then combined via a variety of algorithms, including Dynamic Programming (DP) or the Hidden Markov Model (HMM), and then used for gene prediction from the genomic sequences.     

Cyclothiazide binding to functionally active AMPA receptor reveals genuine allosteric interaction with agonist binding sites

The agonist, [3H](-)[S]-1-(2-amino-2-carboxyethyl)-5-fluoro-pyrimidine-2,4-dione ([3H](S)F-Willardiine) binding to functional alpha-amino-3-hydroxy-5-methylisoxazole-4-propionic acid (AMPA) receptors of resealed plasma membrane vesicles and nerve endings freshly isolated from the rat cerebral cortex displayed two binding sites (K(D1)=33+/-7 nM, B(MAX1)=1.6+/-0.3 pmol/mg protein, K(D2)=720+/-250 nM and B(MAX2)=7.8+/-4.0 pmol/mg protein). The drug which impairs AMPA receptor desensitisation, 6-chloro-3,4-dihydro-3-(2-norbornene-5-yl)-2H-1,2,4-benzothiadiazine-7-sulphonamide-1,1-dioxide (cyclothiazide, CTZ) fully displaced the [3H](S)F-Willardiine binding at a concentration of 500 microM. In the presence of 100 microM CTZ (K(I(CTZ))=60+/-6 microM), both the antagonist [3H]-1,2,3,4-tetrahydro-6-nitro-2,3-dioxo-benzo(F)quinoxaline-7-sulfonamide ([3H]NBQX: K(D)=24+/-4 nM, B(MAX)=12.0+/-0.1 pmol/mg protein) and the high-affinity agonist binding showed similar affinity reduction ([3H](S)F-Willardiine: K(D)=140+/-19 nM, B(MAX)=2.9+/-0.5 pmol/mg protein; [3H]NBQX: K(D)=111+/-34 nM, B(MAX)=12+/-3 pmol/mg protein). To disclose structural correlates underlying genuine allosteric binding interactions, molecular mechanics calculations of CTZ-induced structural changes were performed with the use of PDB data on extracellular GluR2 binding domain dimeric crystals available by now. Hydrogen-bonding and root mean square (rms) values of amino acid residues recognising receptor agonists showed minor alterations in the agonist binding sites itself. Moreover, CTZ binding did not affect dimeric subunit structures significantly. These findings indicated that the structural changes featuring the non-desensitised state could possibly occur to a further site of the extracellular GluR2 binding domain. The increase of agonist efficacy on allosteric CTZ binding may be interpreted in terms of a mechanism involving AMPA receptor desensitisation sequential to activation.