[show abstract][hide abstract] ABSTRACT: Accurate comparative analysis tools for low-homology proteins remains a difficult challenge in computational biology, especially sequence alignment and consensus folding problems. We presentpartiFold-Align, the first algorithm for simultaneous alignment and consensus folding of unaligned protein sequences; the algorithm’s complexity is polynomial in time and space. Algorithmically,partiFold-Align exploits sparsity in the set of super-secondary structure pairings and alignment candidates to achieve an effectively cubic running time for simultaneous pairwise alignment and folding. We demonstrate the efficacy of these techniques on transmembrane β-barrel proteins, an important yet difficult class of proteins with few known three-dimensional structures. Testing against structurally derived sequence alignments,partiFold-Align significantly outperforms state-of-the-art pairwise sequence alignment tools in the most difficult low sequence homology case and improves secondary structure prediction where current approaches fail. Importantly, partiFold-Align requires no prior training. These general techniques are widely applicable to many more protein families. partiFold-Align is available at http://partiFold.csail.mit.edu.
[show abstract][hide abstract] ABSTRACT: The comparative sequence analysis is the most reliable method for RNA secondary structure prediction, and many algorithms based on it have been developed in last several decades. This paper considers RNA structure prediction as a 2-classes classification problem: given a sequence alignment, to decide whether or not two columns of alignment form a base pair. We employed Support Vector Machine (SVM) to predict potential paired sites, and selected co-variation information, thermodynamic information and the fraction of complementary bases as feature vectors. Considering the effect of sequence similarity upon co-variation score, we introduced a similarity weight factor, which could adjust the contribution of co-variation and thermodynamic information toward prediction according to sequence similarity. The test on 49 Rfam-seed alignments showed the effectiveness of our method, and the accuracy was better than many similar algorithms. Furthermore, this method could predict simple pseudoknot.
Sheng wu gong cheng xue bao = Chinese journal of biotechnology 08/2008; 24(7):1140-8.
[show abstract][hide abstract] ABSTRACT: The present investigation includes in Silico sequence analysis, three-dimensional (3D) structure prediction and evolutionary profile of growth hormone (GH) from 14 ornamental freshwater fishes. The analyses were performed using the sequence data of growth hormone gene (gh) and its encoded GH protein. The evolutionary analyses were performed using maximum likelihood (ML) estimate and maximum parsimony (MP) methods. Bootstrap test (1000 replicates) was performed to validate the phylogenetic tree. The tertiary structures of GH were predicted using the comparative modelling method. The suitable template for comparative modeling protein databank (PDB IDs: 1HWG A) has been selected on the basis of basic local alignment search tool (BLASTp) and fast analysis (FASTA) results. The target-template alignment, model building, loop modelling and evaluation have been performed in Modeller 9.10. The tertiary structure of GH is α-helix structure connected by loops, which forms a compressed complex maintained by two disulfide bridges. The resultant 3D models are verified by ERRAT and ProCheck programmes. After fruitful verification, the tertiary structures of GH have been deposited to protein model database (PMDB). Sequence analyses and RNA secondary structure prediction was performed by CLC genomics workbench version 4.0. The computational models of GH could be of use for further evaluation of molecular mechanism of function
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