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Modeling the Tertiary Structure of the Rift Valley Fever Virus L Protein.

Gideon K Gogovi^1,2, Fahad Almsned³, Nicole Bracci^4,5

¹Center for Simulation and Modeling, George Mason University, 4400 University Drive, MSN 6A12, Fairfax, VA 22030, USA. ggogovi@masonlive.gmu.edu.

Molecules (Basel, Switzerland)

|May 10, 2019

Summary

Researchers elucidated the Rift Valley fever virus (RVFV) L protein structure using computational methods. This structural insight aids understanding of viral replication and transcription processes.

Keywords:

Rift Valley fever virus computational structure determination multidomain protein tertiary structure

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Computational Biology
Bioinformatics
Structural Biology

Background:

Protein tertiary structure is crucial for biological activity and cellular processes.
The Rift Valley fever virus (RVFV) L protein's large size and multiple domains have hindered previous structural studies.
Understanding RVFV L protein structure is key to investigating viral replication and transcription.

Purpose of the Study:

To elucidate the tertiary structure of the RVFV L protein using in silico techniques.
To develop a generalizable modeling protocol for large, multidomain proteins.

Main Methods:

Leveraged complementary computational-molecular-biology and bioinformatics tools.
Constructed, refined, and evaluated multiple atomistic structural models.
Analyzed protein properties including potential energy, radius of gyration, and flexibility.

Main Results:

Generated physically realistic structural models of the RVFV L protein.
Identified flexible termini (approx. 200 amino acids each) and a high proportion of helical regions.
Reported key structural properties relevant to protein function.

Conclusions:

The structural characterization of the RVFV L protein facilitates further studies on viral replication and transcription.
The presented workflow offers a generalizable approach for modeling complex, multidomain proteins.