Fully automated ab initio protein structure prediction using I-SITES,HMMSTR and ROSETTA

MOTIVATION: The Monte Carlo fragment insertion method for protein tertiary structure prediction (ROSETTA) of Baker and others, has been merged with the I-SITES library of sequence structure motifs and the HMMSTR model for local structure in proteins, to form a new public server for the ab initio prediction of protein structure. The server performs several tasks in addition to tertiary structure prediction, including a database search, amino acid profile generation, fragment structure prediction, and backbone angle and secondary structure prediction. Meeting reasonable service goals required improvements in the efficiency, in particular for the ROSETTA algorithm. RESULTS: The new server was used for blind predictions of 40 protein sequences as part of the CASP4 blind structure prediction experiment. The results for 31 of those predictions are presented here. 61% of the residues overall were found in topologically correct predictions, which are defined as fragments of 30 residues or more with a root-mean-square deviation in superimposed alpha carbons of less than 6A. HMMSTR 3-state secondary structure predictions were 73% correct overall. Tertiary structure predictions did not improve the accuracy of secondary structure prediction.     

A new topological method to measure protein structure similarity

A method for the quantitative evaluation of structural similarity between protein pairs is developed that makes use of a Delaunay-based topological mapping. The result of the mapping is a three-dimensional array which is representative of the global structural topology and whose elements can be used to construe an integral scoring scheme. This scoring scheme was tested for its dependence on the protein length difference in a pairwise comparison, its ability to provide a reasonable means for structural similarity comparison within a family of structural neighbors of similar length, and its sensitivity to the differences in protein conformation. It is shown that such a topological evaluation of similarity is capable of providing insight into these points of interest. Protein structure comparison using the method is computationally efficient and the topological scores, although providing different information about protein similarity, correlate well with the distance root-mean-square deviation values calculated by rigid-body structural alignment.     

Fully automated protein purification

Obtaining highly purified proteins is essential to begin investigating their functional and structural properties. The steps that are typically involved in purifying proteins can include an initial capture, intermediate purification, and a final polishing step. Completing these steps can take several days and require frequent attention to ensure success. Our goal was to design automated protocols that would allow the purification of proteins with minimal operator intervention. Separate methods have been produced and tested that automate the sample loading, column washing, sample elution and peak collection steps for ion exchange, metal affinity, hydrophobic interaction, and gel filtration chromatography. These individual methods are designed to be coupled and run sequentially in any order to achieve a flexible and fully automated protein purification protocol.     

Gene structure conservation aids similarity based gene prediction

One of the primary tasks in deciphering the functional contents of a newly sequenced genome is the identification of its protein coding genes. Existing computational methods for gene prediction include ab initio methods which use the DNA sequence itself as the only source of information, comparative methods using multiple genomic sequences, and similarity based methods which employ the cDNA or protein sequences of related genes to aid the gene prediction. We present here an algorithm implemented in a computer program called Projector which combines comparative and similarity approaches. Projector employs similarity information at the genomic DNA level by directly using known genes annotated on one DNA sequence to predict the corresponding related genes on another DNA sequence. It therefore makes explicit use of the conservation of the exon–intron structure between two related genes in addition to the similarity of their encoded amino acid sequences. We evaluate the performance of Projector by comparing it with the program Genewise on a test set of 491 pairs of independently confirmed mouse and human genes. It is more accurate than Genewise for genes whose proteins are <80% identical, and is suitable for use in a combined gene prediction system where other methods identify well conserved and non-conserved genes, and pseudogenes.     

Identifying miRNA-disease association based on integrating miRNA topological similarity and functional similarity

Background: MicroRNAs (miRNAs) are a significant type of non-coding RNAs, which usually were encoded by endogenous genes with about ~22 nt nucleotides. Accumulating biological experiments have shown that miRNAs have close associations with various human diseases. Although traditional experimental methods achieve great successes in miRNA-disease interaction identification, these methods also have some limitations. Therefore, it is necessary to develop computational method to predict miRNA-disease interactions. Methods: Here, we propose a computational framework (MDVSI) to predict interactions between miRNAs and diseases by integrating miRNA topological similarity and functional similarity. Firstly, the CosRA index is utilized to measure miRNA similarity based on network topological feature. Then, in order to enhance the reliability of miRNA similarity, the functional similarity and CosRA similarity are integrated based on linear weight method. Further, the potential miRNA-disease associations are predicted by using recommendation method. In addition, in order to overcome limitation of recommendation method, for new disease, a new strategy is proposed to predict potential interactions between miRNAs and new disease based on disease functional similarity. Results: To evaluate the performance of different methods, we conduct ten-fold cross validation and de novo test in experiment and compare MDVSI with two the-state-of-art methods. The experimental result shows that MDVSI achieves an AUC of 0.91, which is at least 0.012 higher than other compared methods. Conclusions: In summary, we propose a computational framework (MDSVI) for miRNA-disease interaction prediction. The experiment results demonstrate that it outperforms other the-state-of-the-art methods. Case study shows that it can effectively identify potential miRNA-disease interactions.     

Deep learning for protein complex structure prediction

Recent developments in the structure prediction of protein complexes have resulted in accuracies rivalling experimental methods in many cases. The high accuracy is mainly observed in dimeric complexes and other problems such as protein disorder and predicting the structure of host-pathogen interactions remain. This review highlights the foundation for current accurate structure prediction of protein complexes and possible ways to address the remaining limitations.     

Analysis and prediction of protein local structure based on structure alphabets

In recent years, protein structure prediction using local structure information has made great progress. Many fragment libraries or structure alphabets have been developed. In this study, the entropies and correlations of local structures are first calculated. The results show that neighboring local structures are strongly correlated. Then, a dual-layer model has been designed for protein local structure prediction. The position-specific score matrix, generated by PSI-BLAST, is inputted to the first-layer classifier, whose output is further enhanced by a second-layer classifier. The neural network is selected as the classifier. Two structure alphabets are explored, which are represented in Cartesian coordinate space and in torsion angles space respectively. Testing on the nonredundant dataset shows that the dual-layer model is an efficient method for protein local structure prediction. The Q-scores are 0.456 and 0.585 for the two structure alphabets, which is a significant improvement in comparison with related works.     

An algorithm for protein secondary structure prediction based on class prediction

An algorithm has been developed to improve the success rate in the prediction of the secondary structure of proteins by taking into account the predicted class of the proteins. This method has been called the 'double prediction method' and consists of a first prediction of the secondary structure from a new algorithm which uses parameters of the type described by Chou and Fasman, and the prediction of the class of the proteins from their amino acid composition. These two independent predictions allow one to optimize the parameters calculated over the secondary structure database to provide the final prediction of secondary structure. This method has been tested on 59 proteins in the database (i.e. 10,322 residues) and yields 72% success in class prediction, 61.3% of residues correctly predicted for three states (helix, sheet and coil) and a good agreement between observed and predicted contents in secondary structure.     

基于遗传算法的蛋白质结构预测方法

遗传算法源于自然界的进化规律,是一种自适应启发式概率性迭代式全局搜索算法。本文主要介绍了GA的基本原理,算法及优点;总结GA在蛋白质结构预测中建立模型和执行策略,以及多种算法相互结合预测蛋白质结构的研究进展。     

Fast similarity search for protein 3D structures using topological pattern matching based on spatial relations

Similarity search for protein 3D structures become complex and computationally expensive due to the fact that the size of protein structure databases continues to grow tremendously. Recently, fast structural similarity search systems have been required to put them into practical use in protein structure classification whilst existing comparison systems do not provide comparison results on time. Our approach uses multi-step processing that composes of a preprocessing step to represent geometry of protein structures with spatial objects, a filter step to generate a small candidate set using approximate topological string matching, and a refinement step to compute a structural alignment. This paper describes the preprocessing and filtering for fast similarity search using the discovery of topological patterns of secondary structure elements based on spatial relations. Our system is fully implemented by using Oracle 8i spatial. We have previously shown that our approach has the advantage of speed of performance compared with other approach such as DALI. This work shows that the discovery of topological relations of secondary structure elements in protein structures by using spatial relations of spatial databases is practical for fast structural similarity search for proteins.     

Induced fit with replica exchange improves protein complex structure prediction

Despite the progress in prediction of protein complexes over the last decade, recent blind protein complex structure prediction challenges revealed limited success rates (less than 20% models with DockQ score > 0.4) on targets that exhibit significant conformational change upon binding. To overcome limitations in capturing backbone motions, we developed a new, aggressive sampling method that incorporates temperature replica exchange Monte Carlo (T-REMC) and conformational sampling techniques within docking protocols in Rosetta. Our method, ReplicaDock 2.0, mimics induced-fit mechanism of protein binding to sample backbone motions across putative interface residues on-the-fly, thereby recapitulating binding-partner induced conformational changes. Furthermore, ReplicaDock 2.0 clocks in at 150-500 CPU hours per target (protein-size dependent); a runtime that is significantly faster than Molecular Dynamics based approaches. For a benchmark set of 88 proteins with moderate to high flexibility (unbound-to-bound iRMSD over 1.2 Å), ReplicaDock 2.0 successfully docks 61% of moderately flexible complexes and 35% of highly flexible complexes. Additionally, we demonstrate that by biasing backbone sampling particularly towards residues comprising flexible loops or hinge domains, highly flexible targets can be predicted to under 2 Å accuracy. This indicates that additional gains are possible when mobile protein segments are known.     

MaxSub: an automated measure for the assessment of protein structure prediction quality

MOTIVATION: Evaluating the accuracy of predicted models is critical for assessing structure prediction methods. Because this problem is not trivial, a large number of different assessment measures have been proposed by various authors, and it has already become an active subfield of research (Moult et al. (1997,1999) and CAFASP (Fischer et al. 1999) prediction experiments have demonstrated that it has been difficult to choose one single, 'best' method to be used in the evaluation. Consequently, the CASP3 evaluation was carried out using an extensive set of especially developed numerical measures, coupled with human-expert intervention. As part of our efforts towards a higher level of automation in the structure prediction field, here we investigate the suitability of a fully automated, simple, objective, quantitative and reproducible method that can be used in the automatic assessment of models in the upcoming CAFASP2 experiment. Such a method should (a) produce one single number that measures the quality of a predicted model and (b) perform similarly to human-expert evaluations. RESULTS: MaxSub is a new and independently developed method that further builds and extends some of the evaluation methods introduced at CASP3. MaxSub aims at identifying the largest subset of C(alpha) atoms of a model that superimpose 'well' over the experimental structure, and produces a single normalized score that represents the quality of the model. Because there exists no evaluation method for assessment measures of predicted models, it is not easy to evaluate how good our new measure is. Even though an exact comparison of MaxSub and the CASP3 assessment is not straightforward, here we use a test-bed extracted from the CASP3 fold-recognition models. A rough qualitative comparison of the performance of MaxSub vis-a-vis the human-expert assessment carried out at CASP3 shows that there is a good agreement for the more accurate models and for the better predicting groups. As expected, some differences were observed among the medium to poor models and groups. Overall, the top six predicting groups ranked using the fully automated MaxSub are also the top six groups ranked at CASP3. We conclude that MaxSub is a suitable method for the automatic evaluation of models.     

LiveBench-8: the large-scale, continuous assessment of automated protein structure prediction

We present the results of the evaluation of the latest LiveBench-8 experiment. These results provide a snapshot view of the state of the art in automated protein structure prediction, just before the 2004 CAFASP-4/CASP-6 experiments begin. The last CAFASP/CASP experiments demonstrated that automated meta-predictors entail a significant advance in the field, already challenging most human expert predictors. LiveBench-8 corroborates the superior performance of meta-predictors, which are able to produce useful predictions for over one-half of the test targets. More importantly, LiveBench-8 identifies a handful of recently developed autonomous (nonmeta) servers that perform at the very top, suggesting that further progress in the individual methods has recently been obtained.     

Improving protein structure similarity searches using domain boundaries based on conserved sequence information

Background  

The identification of protein domains plays an important role in protein structure comparison. Domain query size and composition are critical to structure similarity search algorithms such as the Vector Alignment Search Tool (VAST), the method employed for computing related protein structures in NCBI Entrez system. Currently, domains identified on the basis of structural compactness are used for VAST computations. In this study, we have investigated how alternative definitions of domains derived from conserved sequence alignments in the Conserved Domain Database (CDD) would affect the domain comparisons and structure similarity search performance of VAST.     

A genetic similarity rule determines arthropod community structure

We define a genetic similarity rule that predicts how genetic variation in a dominant plant affects the structure of an arthropod community. This rule applies to hybridizing cottonwood species where plant genetic variation determines plant-animal interactions and structures a dependent community of leaf-modifying arthropods. Because the associated arthropod community is expected to respond to important plant traits, we also tested whether plant chemical composition is one potential intermediate link between plant genes and arthropod community composition. Two lines of evidence support our genetic similarity rule. First, in a common garden experiment we found that trees with similar genetic compositions had similar chemical compositions and similar arthropod compositions. Second, in a wild population, we found a similar relationship between genetic similarity in cottonwoods and the dependent arthropod community. Field data demonstrate that the relationship between genes and arthropods was also significant when the hybrids were analysed alone, i.e. the pattern is not dependent upon the inclusion of both parental species. Because plant-animal interactions and natural hybridization are common to diverse plant taxa, we suggest that a genetic similarity rule is potentially applicable, and may be extended, to other systems and ecological processes. For example, plants with similar genetic compositions may exhibit similar litter decomposition rates. A corollary to this genetic similarity rule predicts that in systems with low plant genetic variability, the environment will be a stronger factor structuring the dependent community. Our findings argue that the genetic composition of a dominant plant can structure higher order ecological processes, thus placing community and ecosystem ecology within a genetic and evolutionary framework. A genetic similarity rule also has important conservation implications because the loss of genetic diversity in one species, especially dominant or keystone species that define many communities, may cascade to negatively affect the rest of the dependent community.     

MANIFOLD: protein fold recognition based on secondary structure, sequence similarity and enzyme classification

We present a protein fold recognition method, MANIFOLD, which uses the similarity between target and template proteins in predicted secondary structure, sequence and enzyme code to predict the fold of the target protein. We developed a non-linear ranking scheme in order to combine the scores of the three different similarity measures used. For a difficult test set of proteins with very little sequence similarity, the program predicts the fold class correctly in 34% of cases. This is an over twofold increase in accuracy compared with sequence-based methods such as PSI-BLAST or GenTHREADER, which score 13-14% correct first hits for the same test set. The functional similarity term increases the prediction accuracy by up to 3% compared with using the combination of secondary structure similarity and PSI-BLAST alone. We argue that using functional and secondary structure information can increase the fold recognition beyond sequence similarity.     

Fully automated two-dimensional capillary electrophoresis for high sensitivity protein analysis

We report a system for automated protein analysis. In the system, proteins are labeled with the fluorogenic reagent 3-(2-furoyl)quinoline-2-carboxaldehyde, which reacts with lysine residues and creates a highly fluorescent product. These labeled proteins are analyzed by submicellar capillary electrophoresis at pH 7.5 to perform a first dimension separation. Once the first components migrate from the capillary, a fraction is transferred to a second dimension capillary, where electrophoresis is performed at pH 11.1 to further separate the proteins. Laser-induced fluorescence is used as an ultrasensitive detector of the separated proteins. Successive fractions are transferred from the first dimension capillary to the second dimension capillary for further separation to generate, in serial fashion, a two-dimensional electropherogram. The transfer of fractions is computer-controlled; there is no operator intervention once the sample has been injected. Zeptomoles of labeled proteins are detected, providing exquisite sensitivity.