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.
One example: The severe acute respiratory syndrome (SARS) epidemic originating from China in 2002 was caused by a previously uncharacterized coronavirus that could be identified by specific RT-PCR amplification. Efforts to control future SARS outbreaks depend on the accurate and early identification of SARS-CoV infected patients. A real-time fluorogenic RT-PCR assay based on the 3 -noncoding region (3 -NCR) of SARS-CoV genome was developed as a quantitative SARS diagnostic tool. The ideal amplification efficiency of a sensitive SARS-CoV RT-PCR assay should yield an E value (PCR product concentration increase per amplification cycle) equal to 2.0. It was demonstrated that the 3 -NCR SARS-CoV based RT-PCR reactions could be formulated to reach excellent E values of 1.81, or 91% amplification efficacy. The SARS-CoV cDNA preparations derived from viral RNA extract and the cloned recombinant plasmid both exhibit the identical amplification characteristics, i.e. amplification efficacy using the same PCR formulation developed in this study. The viral genomic copy (or genomic equivalences, GE) per infectious unit (GE/pfu) of SARS-CoV used in this study was also established to be approximate 1200-1600:1. The assay's detection sensitivity could reach 0.005 pfu or 6-8 GE per assay. It was preliminarily demonstrated that the assay could efficiently detect SARS-CoV from clinical specimens of SARS probable and suspected patients identified in Taiwan. The 3 -NCR based SARS-CoV assay demonstrated 100% diagnostic specificity testing samples of patients with acute respiratory disease from a non-SARS epidemic region.
Solution is here: Development and evaluation of an efficient 3 -noncoding region-based SARS coronavirus (SARS-CoV) RT-PCR assay for detection of SARS-CoV
Explanation: It's a correct title because the paragraph described the development of 3 -noncoding region of SARS-CoV genome and assay efficiently detecting SARS-CoV from clinical specimens.

Now, solve this: The viral RNA-dependent RNA polymerase (RdRp) is essential for multiplication of all RNA viruses. The sequence diversity of an RNA virus population contributes to its ability to infect the host. This diversity emanates from errors made by the RdRp during RNA synthesis. The physical basis for RdRp fidelity is unclear but is linked to conformational changes occurring during the nucleotide-addition cycle. To understand RdRp dynamics that might influence RdRp function, we have analyzed all-atom molecular dynamics (MD) simulations on the nanosecond timescale of four RdRps from the picornavirus family that exhibit 30-74% sequence identity. Principal component analysis showed that the major motions observed during the simulations derived from conserved structural motifs and regions of known function. Dynamics of residues participating in the same biochemical property, for example RNA binding, nucleotide binding or catalysis, were correlated even when spatially distant on the RdRp structure. The conserved and correlated dynamics of functional, structural elements suggest co-evolution of dynamics with structure and function of the RdRp. Crystal structures of all picornavirus RdRps exhibit a template-nascent RNA duplex channel too small to fully accommodate duplex RNA. Simulations revealed opening and closing motions of the RNA and NTP channels, which might be relevant to NTP entry, PP i exit and translocation. A role for nanosecond timescale dynamics in RdRp fidelity is supported by altered dynamics of the high-fidelity G64S derivative of PV RdRp relative to wild-type enzyme. . Table S1 lists the amino acid sequence numbering for the different subdomains, motifs A-G, and functional regions I-III of the RdRps used in the study. Figure S1 shows the structure-based sequence alignment for the different RdRps. Figure S2 shows the time series plot of RMSD for the different simulated systems. Figure S3 shows representative structures for the clusters C1-C4 of PV RdRp in two views. Figure S4 shows snapshots corresponding to the beginning, middle, and end of the simulation trajectories of the four RdRps. Figure S5 compares analyzed B-factors for the different simulated RdRps. Figure S6 compares the per-residue RMSD analysis for the different simulated RdRps. Figure S7 compares the radius of gyration analysis for the different RdRps. Figure S8 compares the RNA channel width in the different simulated RdRps. Figure S9 compares PCA and DCCM analyses from 25 and 52 ns simulations of PV RdRp. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript.
Solution:
Author manuscript; available in PMC