Research Article: In silico analysis of PFN1 related to amyotrophic lateral sclerosis

Date Published: June 19, 2019

Publisher: Public Library of Science

Author(s): Gabriel Rodrigues Coutinho Pereira, Giovanni Henrique Almeida Silva Tellini, Joelma Freire De Mesquita, Salvatore Adinolfi.

http://doi.org/10.1371/journal.pone.0215723

Abstract

Profilin 1 (PFN1) protein plays key roles in neuronal growth and differentiation, membrane trafficking, and regulation of the actin cytoskeleton. Four natural variants of PFN1 were described as related to ALS, the most common adult-onset motor neuron disorder. However, the pathological mechanism of PFN1 in ALS is not yet completely understood. The goal of this work is to thoroughly analyze the effects of the ALS-related mutations on PFN1 structure and function using computational simulations. Here, PhD-SNP, PMUT, PolyPhen-2, SIFT, SNAP, SNPS&GO, SAAP, nsSNPAnalyzer, SNPeffect4.0 and I-Mutant2.0 were used to predict the functional and stability effects of PFN1 mutations. ConSurf was used for the evolutionary conservation analysis, and GROMACS was used to perform the MD simulations. The mutations C71G, M114T, and G118V, but not E117G, were predicted as deleterious by most of the functional prediction algorithms that were used. The stability prediction indicated that the ALS-related mutations could destabilize PFN1. The ConSurf analysis indicated that the mutation C71G, M114T, E117G, and G118V occur in highly conserved positions. The MD results indicated that the studied mutations could affect the PFN1 flexibility at the actin and PLP-binding domains, and consequently, their intermolecular interactions. It may be therefore related to the functional impairment of PFN1 upon C71G, M114T, E117G and G118V mutations, and their involvement in ALS development. We also developed a database, SNPMOL (http://www.snpmol.org/), containing the results presented on this paper for biologists and clinicians to exploit PFN1 and its natural variants.

Partial Text

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that progressively affects the upper and lower motor neurons, leading to muscular atrophy and paralysis due to neuron injury and death [1]. ALS is the most common adult-onset motor neuron disorder [2] with an estimated economic burden of over one billion dollars a year in the United States only [3]. Due to the lack of effective treatments, ALS leads to death within 2 to 5 years after the diagnosis, usually due to respiratory paralysis [4]. Most ALS cases are sporadic (sALS); however, 5–10% of the ALS cases are familial (fALS) and related to genetic causes [5].

In this paper, we analyzed the effects of PFN1 nsSNVs using ten functional and stability prediction algorithms, an evolutionary algorithm, and MD simulations. The functional prediction algorithms used here showed high accuracy in detecting the known deleterious potential of the C71G, M114T, and G118V mutations, but not E117G. The functional prediction analysis also showed that it is important to use a variety of algorithms to determine the deleterious effects of mutations. The stability prediction suggested that the ALS-related mutations could destabilize PFN1. The evolutionary conservation analysis indicated that the mutations C71G, M114T, E117G, and G118V occur in highly conserved positions. The MD analyses suggested that the studied mutations could affect the PFN1 flexibility at the actin and PLP-binding domains, and consequently, their intermolecular interactions. It may be therefore related to the functional impairment of PFN1 upon C71G, M114T, E117G and G118V mutations, and their involvement in ALS development. We also developed a human-curated database, SNPMOL (http://www.snpmol.org/), containing the results presented in this paper for biologists and clinicians to exploit PFN1 and its natural variants. Furthermore, we can conclude that computational simulations are an effective approach for the study of disease-related mutations, as well as an important ally of the experimental methods.

 

Source:

http://doi.org/10.1371/journal.pone.0215723

 

Leave a Reply

Your email address will not be published.