In this paper, seventeen different fish Antifreeze Proteins (AFPs) retrieved from Swiss-Prot database are analysed and characterized using in silico tools. Primary structure analysis shows that most of the AFPs are hydrophobic in nature due to the high content of non-polar residues. The presence of 11 cysteines in the rainbow smelt fish and sea raven fish AFPs infer that these proteins may form disulphide (SS) bonds, which are regarded as a positive factor for stability. The aliphatic index computed by ExPasy's ProtParam infers that AFPs may be stable for a wide range of temperature. Secondary structure analysis shows that most of the fish AFPs have predominant α-helical structures and rest of the AFPs have mixed secondary structure. The very high coil structural content of rainbow smelt fish and sea raven fish AFPs are due to the rich content of more flexible glycine and hydrophobic proline amino acids. Proline has a special property of creating kinks in polypetide chains and disrupting ordered secondary structure. SOSUI server predicts one transmembrane region in winter flounder fish and atlantic cod and two transmembrane regions in yellowtail flounder fish AFP. The predicted transmembrane regions were visualized and analysed using helical wheel plots generated by EMBOSS pepwheel tool. The presence of disulphide (SS) bonds in the AFPs Q01758 and P05140 are predicted by CYS_REC tool and also identified from the three-dimensional structure using Rasmol tool. The disulphide bonds identified from the three-dimensional structure using the Rasmol tool might be correct as the evaluation parameters are within the acceptable limits for the modelled 3D structures.
The set covering problem (SCP) is a well known classic combinatorial NP-hard problem, having practical application in many fields. To optimize the objective function of the SCP, many heuristic, meta heuristic, greedy and approximation approaches have been proposed in the recent years. In the development of swarm intelligence, the particle swarm optimization is a nature inspired optimization technique for continuous problems and for discrete problems we have the well known discrete particle swarm optimization (DPSO) method. Aiming towards the best solution for discrete problems, we have the recent method called jumping particle swarm optimization (JPSO). In this DPSO the improved solution is based on the particles attraction caused by attractor. In this paper, a new approach based on JPSO is proposed to solve the SCP. The proposed approach works in three phases: for selecting attractor, refining the feasible solution given by the attractor in order to reach the optimality and for removing redundancy in the solution. The proposed approach has been tested on the benchmark instances of SCP and compared with best known methods. Computational results show that it produces high quality solution in very short running times when compared to other algorithms.
Abstract:Obtaining structural information about Vif is of interest for several reasons that include the study of the interaction of Vif with APOBEC3G, a resistance factor. Vif is a potential drug target and its function is essential for the HIV-1 infectivity process. To study Vif mechanism of action, we need to decipher its structure. Pivotal in this approach is the painstaking prediction of its protein structure. The three-dimensional (3D) crystal structure for Vif has not been established. In order to understand its mechanism of action, information on the structure of Vif is very much needed. Therefore we undertook this study based on the hypothesis that information from structurally homologous proteins can be used to predict the 3D structure of Vif by computer modeling and threading. As a result the structure of HIV-1 Vif has been modeled and deposited in the theoretical models section and accepted with the PDB code 1VZF. Here, we present the results of the comparative modeling strategy we used to predict the 3D structure of Vif.
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