Influence of the SNPs on the structural stability of CBS protein: Insight from molecular dynamics simulations

Molecular insights of the G2019S substitution in LRRK2 kinase domain associated with Parkinson's disease: A molecular dynamics simulation approach

The G2019S substitution in the Leucine-rich repeat kinase 2 (LRRK2) is significantly associated with Parkinson's disease (PD). This substitution was identified in both familial and sporadic forms of PD with a higher frequency. Few computational studies have reported the impact of G2019S substitution on inhibitors of the kinase domain of LRRK2. However, no computational study deeply investigated the possible impact of the G2019S substitution on the kinase domain in its Apo conformation. Therefore, in this study, we used 200 ns molecular dynamic simulation using the GROMACS 5.1.4 package software to investigate the impact of the G2019S substitution on the structure of the kinase domain of LRRK2. Our results indicate that the G2019S substitution affects the dynamics and stability of LRRK2 by decreasing the flexibility and increasing the compactness of the kinase domain and showing its tendency to be in an active conformation for long time interval because of the high energy barrier between active and inactive conformation. This study predicts the molecular pathogenicity mechanism of the G2019S on patients with PD and provides a potential platform for developing therapeutics for patients with PD that harbor this amino acid substitution.

Identification and structural characterization of deleterious non-synonymous single nucleotide polymorphisms in the human SKP2 gene

In SCF (Skp, Cullin, F-box) ubiquitin-protein ligase complexes, S-phase kinase 2 (SKP2) is one of the major players of F-box family, that is responsible for the degradation of several important cell regulators and tumor suppressor proteins. Despite of having significant evidence for the role of SKP2 on tumorgenesis, there is a lack of available data regarding the effect of non-synonymous polymorphisms. In this communication, the structural and functional consequences of non-synonymous single nucleotide polymorphisms (nsSNPs) of SKP2 have been reported by employing various computational approaches and molecular dynamics simulation. Initially, several computational tools like SIFT, PolyPhen-2, PredictSNP, I-Mutant 2.0 and ConSurf have been implicated in this study to explore the damaging SNPs. In total of 172 nsSNPs, 5 nsSNPs were identified as deleterious and 3 of them were predicted to be decreased the stability of protein. Guided from ConSurf analysis, P101L (rs761253702) and Y346C (rs755010517) were categorized as the highly conserved and functional disrupting mutations. Therefore, these mutations were subjected to three dimensional model building and molecular dynamics simulation study for the detailed structural consequences upon the mutations. The study revealed that P101L and Y346C mutations increased the flexibility and changed the structural dynamics. As both these mutations are located in the most functional regions of SKP2 protein, these computational insights might be helpful to consider these nsSNPs for wet-lab confirmatory analysis as well as in rationalizing future population based studies and structure based drug design against SKP2.

Substitution impact of highly conserved arginine residue at position 75 in GJB1 gene in association with X-linked Charcot-Marie-tooth disease: A computational study

X-linked Charcot-Marie-Tooth type 1 X (CMTX1) disease is a subtype of Charcot-Marie-Tooth (CMT), which is mainly caused by mutations in the GJB1 gene. It is also known as connexin 32 (Cx32) that leads to Schwann cell abnormalities and peripheral neuropathy. CMTX1 is considered as the second most common form of CMT disease. The aim of this study is to computationally predict the potential impact of different single amino acid substitutions at position 75 of Cx32, from arginine (R) to proline (P), glutamine (Q) and tryptophan (W). This position is known to be highly conserved among the family of connexin. To understand the structural and functional changes due to these single amino acid substitutions, we employed a homology-modeling technique to build the three-dimensional structure models for the native and mutant proteins. The protein structures were further embedded into a POPC lipid bilayer, inserted into a water box, and subjected to molecular dynamics simulation for 50 ns. Our results show that the mutants R75P, R75Q and R75W display variable structural conformation and dynamic behavior compared to the native protein. Our data proves useful in predicting the potential pathogenicity of the mutant proteins and is expected to serve as a platform for drug discovery for patients with CMT.

Structural and functional effects of nucleotide variation on the human TB drug metabolizing enzyme arylamine N-acetyltransferase 1

The human arylamine N-acetyltransferase 1 (NAT1) enzyme plays a vital role in determining the duration of action of amine-containing drugs such as para-aminobenzoic acid (PABA) by influencing the balance between detoxification and metabolic activation of these drugs. Recently, four novel single nucleotide polymorphisms (SNPs) were identified within a South African mixed ancestry population. Modeling the effects of these SNPs within the structural protein was done to assess possible structure and function changes in the enzyme. The use of molecular dynamics simulations and stability predictions indicated less thermodynamically stable protein structures containing E264K and V231G, while the N245I change showed a stabilizing effect. Coincidently the N245I change displayed a similar free energy landscape profile to the known R64W amino acid substitution (slow acetylator), while the R242M displayed a similar profile to the published variant, I263V (proposed fast acetylator), and the wild type protein structure. Similarly, principal component analysis indicated that two amino acid substitutions (E264K and V231G) occupied less conformational clusters of folded states as compared to the WT and were found to be destabilizing (may affect protein function). However, two of the four novel SNPs that result in amino acid changes: (V231G and N245I) were predicted by both SIFT and POLYPHEN-2 algorithms to affect NAT1 protein function, while two other SNPs that result in R242M and E264K substitutions showed contradictory results based on SIFT and POLYPHEN-2 analysis. In conclusion, the structural methods were able to verify that two non-synonymous substitutions (E264K and V231G) can destabilize the protein structure, and are in agreement with mCSM predictions, and should therefore be experimentally tested for NAT1 activity. These findings could inform a strategy of incorporating genotypic data (i.e., functional SNP alleles) with phenotypic information (slow or fast acetylator) to better prescribe effective treatment using drugs metabolized by NAT1.

On human disease-causing amino acid variants: statistical study of sequence and structural patterns

Statistical analysis was carried out on large set of naturally occurring human amino acid variations, and it was demonstrated that there is a preference for some amino acid substitutions to be associated with diseases. At an amino acid sequence level, it was shown that the disease-causing variants frequently involve drastic changes in amino acid physicochemical properties of proteins such as charge, hydrophobicity, and geometry. Structural analysis of variants involved in diseases and being frequently observed in human population showed similar trends: disease-causing variants tend to cause more changes in hydrogen bond network and salt bridges as compared with harmless amino acid mutations. Analysis of thermodynamics data reported in the literature, both experimental and computational, indicated that disease-causing variants tend to destabilize proteins and their interactions, which prompted us to investigate the effects of amino acid mutations on large databases of experimentally measured energy changes in unrelated proteins. Although the experimental datasets were linked neither to diseases nor exclusory to human proteins, the observed trends were the same: amino acid mutations tend to destabilize proteins and their interactions. Having in mind that structural and thermodynamics properties are interrelated, it is pointed out that any large change in any of them is anticipated to cause a disease.