Ancient positive selection on CD155 as a possible cause for susceptibility to poliovirus infection in simians

Predicting receptor functionality of signaling lymphocyte activation molecule for measles virus hemagglutinin by docking simulation

Predicting susceptibility of various species to a virus assists assessment of risk of interspecies transmission. Evaluation of receptor functionality may be useful in screening for susceptibility. In this study, docking simulation was conducted for measles virus hemagglutinin (MV‐H) and immunoglobulin‐like variable domain of signaling lymphocyte activation molecule (SLAM‐V). It was observed that the docking scores for MV‐H and SLAM‐V correlated with the activity of SLAM as an MV receptor. These results suggest that the receptor functionality may be predicted from the docking scores of virion surface proteins and cellular receptor molecules.

Receptor modification as a therapeutic approach against viral diseases

Poliovirus causes flaccid paralysis through the destruction of motor neurons in the CNS. Susceptibility to its infection is mainly due to the interaction in between the surface capsid proteins and its receptors on the host cell surface, important for binding, penetration and other necessary events during early infection. Receptor modification is a new approach to treat viral diseases by the modification of target proteins structure. Binding domains are modified in an effective way to make it difficult for the virus to recognize it. In this study, tolerant and intolerant induced mutations in the poliovirus receptor, VP1 and VP2 were identified and substituted in the seed sequence to get the modified versions. Substitutions causing changes in initial folding were short listed and further analyzed for high level folding, physiochemical properties and interactions. Highest RMSD values were observed in between the seed and the mutant K90F (3.265 Å) and Q130W (3.270Å) respectively. The proposed substitutions were found to have low functional impact and thus can be further tested and validated by the experimental researchers. Interactions analyses proved most of the substitutions having decreased affinity for both the VP1 and VP2 and thus are of significant importance against poliovirus. This study will play an important role for bridging computational biology to other fields of applied biology and also will provide an insight to develop resistance against viral diseases. It is also expected that same approach can also be applicable against other viruses like HCV, HIV and other in near future.

Statistical methods for detecting natural selection from genomic data

In the study of molecular and phenotypic evolution, understanding the relative importance of random genetic drift and positive selection as the mechanisms for driving divergences between populations and maintaining polymorphisms within populations has been a central issue. A variety of statistical methods has been developed for detecting natural selection operating at the amino acid and nucleotide sequence levels. These methods may be largely classified into those aimed at detecting recurrent and/or recent/ongoing natural selection by utilizing the divergence and/or polymorphism data. Using these methods, pervasive positive selection has been identified for protein-coding and non-coding sequences in the genomic analysis of some organisms. However, many of these methods have been criticized by using computer simulation and real data analysis to produce excessive false-positives and to be sensitive to various disturbing factors. Importantly, some of these methods have been invalidated experimentally. These facts indicate that many of the statistical methods for detecting natural selection are unreliable. In addition, the signals that have been believed as the evidence for fixations of advantageous mutations due to positive selection may also be interpreted as the evidence for fixations of deleterious mutations due to random genetic drift. The genomic diversity data are rapidly accumulating in various organisms, and detection of natural selection may play a critical role for clarifying the relative role of random genetic drift and positive selection in molecular and phenotypic evolution. It is therefore important to develop reliable statistical methods that are unbiased as well as robust against various disturbing factors, for inferring natural selection.