In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Overlapping genes represent a fascinating evolutionary puzzle, since they encode two functionally unrelated proteins from the same DNA sequence. They originate by a mechanism of overprinting, in which point mutations in an existing frame allow the expression (the "birth") of a completely new protein from a second frame. In viruses, in which overlapping genes are abundant, these new proteins often play a critical role in infection, yet they are frequently overlooked during genome annotation. This results in erroneous interpretation of mutational studies and in a significant waste of resources. Therefore, overlapping genes need to be correctly detected, especially since they are now thought to be abundant also in eukaryotes. Developing better detection methods and conducting systematic evolutionary studies require a large, reliable benchmark dataset of known cases. We thus assembled a highquality dataset of 80 viral overlapping genes whose expression is experimentally proven. Many of them were not present in databases. We found that overall, overlapping genes differ significantly from non-overlapping genes in their nucleotide and amino acid composition. In particular, the proteins they encode are enriched in high-degeneracy amino acids and depleted in low-degeneracy ones, which may alleviate the evolutionary constraints acting on overlapping genes. Principal component analysis revealed that the vast majority of overlapping genes follow a similar composition bias, despite their heterogeneity in length and function. Six proven mammalian overlapping genes also followed this bias. We propose that this apparently near-universal composition bias may either favour the birth of overlapping genes, or/and result from selection pressure acting on them.
Overlapping genes and the proteins they encode differ significantly in their sequence composition from non-overlapping genes

Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.
In silico pharmacology for drug discovery: applications to targets and beyond

Bovine coronaviruses (BCoVs) isolated in Japan consist of four genetic groups, as determined by phylogenetic analysis using the polymorphic region (aa 456-592) of the S glycoprotein gene. Japanese field isolates of BCoV, reference Kakegawa strain, and vaccine strain 66/H were analyzed for their antigenic properties by indirect immunofluorescence and neutralization testing. There were no significant differences observed among these BCoVs in direct immunofluorescence tests. However, antigenic differences were observed between BCoVs in the neutralization tests, although there was no clear indication of a distinct serotype. A monoclonal antibody, 4H4, against the Kakegawa strain belonging to group 1 lacked significant neutralizing activity for viruses of groups 2, 3, and 4. Therefore, we speculate that the genetic differences between these groups may have altered their antigenicity. Analysis of mutant viruses resistant to neutralization by 4H4 revealed that the antigenic site of the Kakegawa strain maps to amino acid position 284 of the S glycoprotein. This site is not homologous to a known antigenic site (aa 528) of the Quebec strain belonging to group 1, and it is not located in the conformational domain comprising domain I (aa 351-403) and domain II (aa 517-621). This amino acid constitutes a neutralization epitope of BCoV, which is distinct from aa 528 of the Quebec strain. These results indicate antigenic evolution of BCoV between the genetic groups circulating in Japan.
Antigenic variation among recent Japanese isolates of bovine coronaviruses belonging to phylogenetically distinct genetic groups