Structure prediction and binding sites analysis of curcin protein of Jatropha curcas using computational approaches

Ribosome inactivating proteins (RIPs) are defense proteins in a number of higher-plant species that are directly targeted toward herbivores. Jatropha curcas is one of the biodiesel plants having RIPs. The Jatropha seed meal, after extraction of oil, is rich in curcin, a highly toxic RIP similar to ricin, which makes it unsuitable for animal feed. Although the toxicity of curcin is well documented in the literature, the detailed toxic properties and the 3D structure of curcin has not been determined by X-ray crystallography, NMR spectroscopy or any in silico techniques to date. In this pursuit, the structure of curcin was modeled by a composite approach of 3D structure prediction using threading and ab initio modeling. Assessment of model quality was assessed by methods which include Ramachandran plot analysis and Qmean score estimation. Further, we applied the protein-ligand docking approach to identify the r-RNA binding residue of curcin. The present work provides the first structural insight into the binding mode of r-RNA adenine to the curcin protein and forms the basis for designing future inhibitors of curcin. Cloning of a future peptide inhibitor within J. curcas can produce non-toxic varieties of J. curcas, which would make the seed-cake suitable as animal feed without curcin detoxification.     

A computational method for the analysis and prediction of protein:phosphopeptide-binding sites

Phosphopeptide-binding domains, including the FHA, SH2, WW, WD40, MH2, and Polo-box domains, as well as the 14-3-3 proteins, exert control functions in important processes such as cell growth, division, differentiation, and apoptosis. Structures and mechanisms of phosphopeptide binding are generally diverse, revealing few general principles. A computational method for analysis of phosphopeptide-binding domains was therefore developed to elucidate the physical and chemical nature of phosphopeptide binding, given this lack of structural similarity. The surfaces of nine phosphopeptide-binding proteins, representing seven distinct classes of phosphopeptide-binding modules, were discretized, and encoded with information about amino acid identity, surface curvature, and electrostatic potential at every point on the surface in order to identify local surface properties enriched in phosphoresidue contact sites. Cross-validation indicated that propensities corresponding to this enrichment calculated from a subset of the training data could be used to predict the phosphoresidue contact site on proteins not used in training with no false negative results, and with few unconfirmed positive predictions. The locations of phosphoresidue contact sites were then predicted on the surfaces of the checkpoint kinase Chk1 and the BRCA1 BRCT repeat domain, and these predictions are consistent with recent experimental evidence.     

面向蛋白质功能位点识别的机器学习平台构建

有关蛋白质功能的研究是解析生命奥秘的基础,机器学习技术在该领域已有广泛应用。利用支持向量机(support vectormachine,SVM)方法,构建一个预测蛋白质功能位点的通用平台。该平台先提取非同源蛋白质序列,再对这些序列进行特征编码(包括序列的基本信息、物化特征、结构信息及序列保守性特征等),以编码好的样本作为训练数据,利用SVM进行训练,得到敏感性、特异性、Matthew相关系数、准确率及ROC曲线等评价指标,反复测试,得到评价指标最优的SVM模型后,便可以用来预测蛋白质序列上的功能位点。该平台除了应用在预测蛋白质功能位点之外,还可以应用于疾病相关单核苷酸多态性(SNP)预测分析、预测蛋白质结构域分析、生物分子间的相互作用等。     

Structure-based druggability assessment--identifying suitable targets for small molecule therapeutics

A target is druggable if it can be modulated in vivo by a drug-like molecule. The general properties of oral drugs are summarized by the 'rule of 5' which specifies parameters related to size and lipophilicity. Structure-based target druggability assessment consists of predicting ligand-binding sites on the protein that are complementary to these drug-like properties. Automated identification of ligand-binding sites can use geometrical considerations alone or include specific physicochemical properties of the protein surface. Features of a pocket's size and shape, together with measures of its hydrophobicity, are most informative in identifying suitable drug-binding pockets. The recent availability of several validation sets of druggable versus undruggable targets has helped fuel the development of more elaborate methods.     

Enzyme active sites: Identification and prediction of function using computational chemistry

Understanding the biochemically active amino acids in proteins is a key factor to improve the knowledge of how enzymes work, to predict the function of newly discovered protein structures of unknown function, and to establish design principles for enzyme engineering. Here, we explore recently reported computational chemistry-based methods for the prediction of active amino acids in protein 3D structures, including biochemically important distal residues, and their implications for functional genomics, for enzyme design, and for enhancing understanding of the function of enzymes.     

Statistical analysis and prediction of bacterial sites for ribosomal binding]

An algorithm for recognition of prokaryotic ribosomal binding sites is suggested. The parameter library contains weight matrices for mapping of gene starts in various bacterial genomes. Comparison of the ribosome binding starts in different taxonomic groups demonstrates that the signals in Gram-positive bacteria are stronger than in Gram-negative bacteria, and in particular, Enterobacteria. The recognition matrices are available by e-mail misha@imb.imb.ac.ru.     

Structure-based analysis of VDAC1 protein: defining oligomer contact sites

The outer mitochondrial membrane protein, the voltage-dependent anion channel (VDAC), is increasingly implicated in the control of apoptosis. Oligomeric assembly of VDAC1 was shown to be coupled to apoptosis induction, with oligomerization increasing substantially upon apoptosis induction and inhibited by apoptosis blockers. In this study, structure- and computation-based selection of the predicated VDAC1 dimerization site, in combination with site-directed mutagenesis, cysteine replacement, and chemical cross-linking, were employed to identify contact sites between VDAC1 molecules in dimers and higher oligomers. The predicted weakly stable β-strands were experimentally found to represent the interfaces between VDAC1 monomers composing the oligomer. Replacing hydrophobic amino acids with charged residues in β-strands 1, 2, and 19 interfered with VDAC1 oligomerization. The proximity of β-strands 1, 2, and 19 within the VDAC1 dimer and the existence of other association sites involving β-strand 16 were confirmed when a cysteine was introduced at defined positions in cysteineless VDAC1 mutants, together with the use of cysteine-specific cross-linker bis(maleimido)ethane. Moreover, the results suggest that VDAC1 also exists as a dimer that upon apoptosis induction undergoes conformational changes and that its oligomerization proceeds through a series of interactions involving two distinct interfaces. Dissection of VDAC1 dimerization/oligomerization as presented here provides structural insight into the oligomeric status of cellular VDAC1 under physiological and apoptotic conditions.     

Structure-based prediction of DNA target sites by regulatory proteins

Regulatory proteins play a critical role in controlling complex spatial and temporal patterns of gene expression in higher organism, by recognizing multiple DNA sequences and regulating multiple target genes. Increasing amounts of structural data on the protein-DNA complex provides clues for the mechanism of target recognition by regulatory proteins. The analyses of the propensities of base-amino acid interactions observed in those structural data show that there is no one-to-one correspondence in the interaction, but clear preferences exist. On the other hand, the analysis of spatial distribution of amino acids around bases shows that even those amino acids with strong base preference such as Arg with G are distributed in a wide space around bases. Thus, amino acids with many different geometries can form a similar type of interaction with bases. The redundancy and structural flexibility in the interaction suggest that there are no simple rules in the sequence recognition, and its prediction is not straightforward. However, the spatial distributions of amino acids around bases indicate a possibility that the structural data can be used to derive empirical interaction potentials between amino acids and bases. Such information extracted from structural databases has been successfully used to predict amino acid sequences that fold into particular protein structures. We surmised that the structures of protein-DNA complexes could be used to predict DNA target sites for regulatory proteins, because determining DNA sequences that bind to a particular protein structure should be similar to finding amino acid sequences that fold into a particular structure. Here we demonstrate that the structural data can be used to predict DNA target sequences for regulatory proteins. Pairwise potentials that determine the interaction between bases and amino acids were empirically derived from the structural data. These potentials were then used to examine the compatibility between DNA sequences and the protein-DNA complex structure in a combinatorial "threading" procedure. We applied this strategy to the structures of protein-DNA complexes to predict DNA binding sites recognized by regulatory proteins. To test the applicability of this method in target-site prediction, we examined the effects of cognate and noncognate binding, cooperative binding, and DNA deformation on the binding specificity, and predicted binding sites in real promoters and compared with experimental data. These results show that target binding sites for several regulatory proteins are successfully predicted, and our data suggest that this method can serve as a powerful tool for predicting multiple target sites and target genes for regulatory proteins.     

Fast dynamics perturbation analysis for prediction of protein functional sites

Background  

We present a fast version of the dynamics perturbation analysis (DPA) algorithm to predict functional sites in protein structures. The original DPA algorithm finds regions in proteins where interactions cause a large change in the protein conformational distribution, as measured using the relative entropy D _x . Such regions are associated with functional sites.     

A computational pipeline for protein structure prediction and analysis at genome scale

MOTIVATION: Experimental techniques alone cannot keep up with the production rate of protein sequences, while computational techniques for protein structure predictions have matured to such a level to provide reliable structural characterization of proteins at large scale. Integration of multiple computational tools for protein structure prediction can complement experimental techniques. RESULTS: We present an automated pipeline for protein structure prediction. The centerpiece of the pipeline is our threading-based protein structure prediction system PROSPECT. The pipeline consists of a dozen tools for identification of protein domains and signal peptide, protein triage to determine the protein type (membrane or globular), protein fold recognition, generation of atomic structural models, prediction result validation, etc. Different processing and prediction branches are determined automatically by a prediction pipeline manager based on identified characteristics of the protein. The pipeline has been implemented to run in a heterogeneous computational environment as a client/server system with a web interface. Genome-scale applications on Caenorhabditis elegans, Pyrococcus furiosus and three cyanobacterial genomes are presented. AVAILABILITY: The pipeline is available at http://compbio.ornl.gov/proteinpipeline/     

Structure-based exploration of sevoflurane binding sites in cognition-related synaptotagmin-1

Synaptotagmin 1 (Syt1) is a Ca²⁺ sensor in the membrane of pre-synaptic axon terminal, which functions as an essential regulator of neurotransmitter release and hormone secretion and plays an important role in learning, memory and thinking. The sevoflurane, a general anaesthetics used widely in surgery, has been reported to modulate intracellular calcium flux and downstream neural events by targeting Syt1 C2A domain, exhibiting potential to reshape cognition. In order to explore the binding sites of sevoflurane in Syt1 C2A domain, we herein conducted a systematic computational investigation that integrated ligand pocket mapping, molecular docking calculations and molecular dynamics simulations to perform conformational sampling in the interaction space of sevoflurane with the domain. With the protocol, we were able to identify a number of ‘hotspots’ where sevoflurane can potentially bind to the domain. Subsequently, the location, geometry and physicochemical property of these putative binding sites were examined in detail using a variety of bioinformatics tools, from which three promising candidates were selected and investigated in vitro. Consequently, one was confirmed as specific binding site that can be bound tightly by sevoflurane ligand, while another was suggested to form a relatively weak, non-specific interaction with the ligand. This work would help to understand the molecular mechanism and biological implication underlying Syt1-sevoflurane recognition, and to design molecular aptamers to intervene with cognitive behaviour.     

Proteome-wide prediction of overlapping small molecule and protein binding sites using structure

Small molecules that modulate protein-protein interactions are of great interest for chemical biology and therapeutics. Here I present a structure-based approach to predict 'bi-functional' sites able to bind both small molecule ligands and proteins, in proteins of unknown structure. First, I develop a homology-based annotation method that transfers binding sites of known three-dimensional structure onto protein sequences, predicting residues in ligand and protein binding sites with estimated true positive rates of 98% and 88%, respectively, at 1% false positive rates. Applying this method to the human proteome predicts 8463 proteins with bi-functional residues and correctly recovers the targets of known interaction modulators. Proteins with significantly (p < 0.01) more bi-functional residues than expected were found to be enriched in regulatory and depleted in metabolism functions. Finally, I demonstrate the utility of the method by describing examples of predicted overlap and evidence of their biological and therapeutic relevance. The results suggest that combining the structures of known binding sites with established fold detection algorithms can predict regions of protein-protein interfaces that are amenable to small molecule modulation. Open-source software and the results for several complete proteomes are available at http://pibase.janelia.org/homolobind.     

Rapid and accurate prediction and scoring of water molecules in protein binding sites

Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity.