Prediction of protein structure by simulating coarse-grained folding pathways: a preliminary report

In silico analysis of missense variants of the C1qA gene related to infection and autoimmune diseases

Objectives

C1q is a key activator of the classical pathway of the complement system and exerts consequences relating to opsonization and phagocytosis. The C1qA gene is one of three genes encoding the C1q molecule. Defects in C1q, and especially in C1qA, have been linked to an increased susceptibility to infection, sepsis, and systemic lupus erythematosus. These defects could arise from missense single nucleotide polymorphisms (SNPs) and their deleterious impacts on protein structure and function. Thus, identifying high-risk missense SNPs in C1qA has become a necessity if we are to identify appropriate measures for prevention and management of affected patients.

Methods

A comprehensive in silico study was conducted to screen the 184 missense SNPs in the C1qA gene using different tools with different algorithms and approaches. We investigated the impact of SNPs on protein function, stability, and structure. In addition, we identified the location of the SNPs on protein domains, secondary structure alignment, and the phylogenetic conservation of their positions.

Results

Of the 184 missense SNPs, 10 SNPs were predicted to be the most damaging to protein function and structure.

Conclusion

Ten missense SNPs were predicted to have the highest risk of damaging protein function and structure, thus leading to infection, sepsis, and systemic lupus erythematosus. These 10 SNPs constitute the best candidates for further experimental investigations.

Determination of genetic effects and functional SNPs of bovine HTR1B gene on milk fatty acid traits

BACKGROUND

Our previous genome-wide association study (GWAS) on milk fatty acid traits in Chinese Holstein cows revealed, the SNP, BTB-01556197, was significantly associated with C10:0 at genome-wide level (P = 0.0239). It was located in the down-stream of 5-hydroxytryptamine receptor 1B (HTR1B) gene that has been shown to play an important role in the regulation of fatty acid oxidation. Hence, we considered it as a promising candidate gene for milk fatty acids in dairy cattle. In this study, we aimed to investigate whether the HTR1B gene had significant genetic effects on milk fatty acid traits.

RESULTS

We re-sequenced the entire coding region and 3000 bp of 5' and 3' flanking regions of HTR1B gene. A total of 13 SNPs was identified, containing one in 5' flanking region, two in 5' untranslated region (UTR), two in exon 1, five in 3' UTR, and three in 3' flanking region. By performing genotype-phenotype association analysis with SAS9.2 software, we observed that 13 SNPs were significantly associated with medium-chain saturated fatty acids such as C6:0, C8:0 and C10:0 (P < 0.0001 ~ 0.042). With Haploview 4.1 software, linkage disequilibrium (LD) analysis was performed. Two haplotype blocks formed by two and ten SNPs were observed. Haplotype-based association analysis indicated that both haplotype blocks were strongly associated with C6:0, C8:0 and C10:0 as well (P < 0.0001 ~ 0.0071). With regards to the missense mutation in exon 1 (g.17303383G > T) that reduced amino acid change from alanine to serine, we predicted that it altered the secondary structure of HTR1B protein with SOPMA. In addition, we predicted that three SNPs in promoter region, g.17307103A > T, g.17305206 T > G and g.17303761C > T, altered the binding sites of transcription factors (TFs) HMX2, PAX2, FOXP1ES, MIZ1, CUX2, DREAM, and PPAR-RXR by Genomatix. Of them, luciferase assay experiment further confirmed that the allele T of g.17307103A > T significantly increased the transcriptional activity of HTR1B gene than allele A (P = 0.0007).

CONCLUSIONS

In conclusion, our findings provided first evidence that the HTR1B gene had significant genetic effects on milk fatty acids in dairy cattle.

Minimalist representations and the importance of nearest neighbor effects in protein folding simulations

In order to investigate the level of representation required to simulate folding and predict structure, we test the ability of a variety of reduced representations to identify native states in decoy libraries and to recover the native structure given the advanced knowledge of the very broad native Ramachandran basin assignments. Simplifications include the removal of the entire side-chain or the retention of only the Cbeta atoms. Scoring functions are derived from an all-atom statistical potential that distinguishes between atoms and different residue types. Structures are obtained by minimizing the scoring function with a computationally rapid simulated annealing algorithm. Results are compared for simulations in which backbone conformations are sampled from a Protein Data Bank-based backbone rotamer library generated by either ignoring or including a dependence on the identity and conformation of the neighboring residues. Only when the Cbeta atoms and nearest neighbor effects are included do the lowest energy structures generally fall within 4 A of the native backbone root-mean square deviation (RMSD), despite the initial configuration being highly expanded with an average RMSD > or = 10 A. The side-chains are reinserted into the Cbeta models with minimal steric clash. Therefore, the detailed, all-atom information lost in descending to a Cbeta-level representation is recaptured to a large measure using backbone dihedral angle sampling that includes nearest neighbor effects and an appropriate scoring function.

Protein structure prediction using mutually orthogonal Latin squares and a genetic algorithm

We combine a new, extremely fast technique to generate a library of low energy structures of an oligopeptide (by using mutually orthogonal Latin squares to sample its conformational space) with a genetic algorithm to predict protein structures. The protein sequence is divided into oligopeptides, and a structure library is generated for each. These libraries are used in a newly defined mutation operator that, together with variation, crossover, and diversity operators, is used in a modified genetic algorithm to make the prediction. Application to five small proteins has yielded near native structures.

Early-stage folding in proteins (in silico) sequence-to-structure relation

A sequence-to-structure library has been created based on the complete PDB database. The tetrapeptide was selected as a unit representing a well-defined structural motif. Seven structural forms were introduced for structure classification. The early-stage folding conformations were used as the objects for structure analysis and classification. The degree of determinability was estimated for the sequence-to-structure and structure-to-sequence relations. Probability calculus and informational entropy were applied for quantitative estimation of the mutual relation between them. The structural motifs representing different forms of loops and bends were found to favor particular sequences in structure-to-sequence analysis.