SpeeDB: fast structural protein searches

PIF - A Java library for finding atomic interactions and extracting geometric features supporting the analysis of protein structures

Proteins play an essential role in the functioning of living organisms. The enormity of the atomic interactions in proteins is essential in controlling their spatial structures and dynamics. It can also provide scientists with valuable information that help to determine the native structures of proteins. This paper presents the PIF (Protein Interaction Finder) library for the Java language, enabling the identification of selected atomic interactions (hydrogen and disulfide bonds, ionic, hydrophobic, aromatic-aromatic, sulfur-aromatic, and amino-aromatic interactions) based on the three-dimensional structure of proteins. The interaction calculation rules applied in PIF rely on documented theoretical foundations gathered from experimental studies of interactions in native protein structures. The library has a universal purpose, supporting drug discovery and development processes and protein structure modeling. Finding the atomic interactions can also deliver numerical features for various Artificial Intelligence (AI) models built for protein analysis. The conducted research comparing the results obtained with the use of the PIF library and competing tools has shown that our solution can effectively determine the interactions occurring in protein structures for entire collections of proteins. Moreover, as a solution that provides a programming interface, the PIF library can be used in any Java project, making it a universal tool.

Leber's hereditary optic neuropathy (LHON)-associated ND6 14 484 T > C mutation caused pleiotropic effects on the complex I, RNA homeostasis, apoptosis and mitophagy

Leber's hereditary optic neuropathy (LHON) is a maternally inherited eye disease due to mitochondrial DNA (mtDNA) mutations. LHON-linked ND6 14 484 T > C (p.M64V) mutation affected structural components of complex I but its pathophysiology is poorly understood. The structural analysis of complex I revealed that the M64 forms a nonpolar interaction Y59 in the ND6, Y59 in the ND6 interacts with E34 of ND4L, and L60 of ND6 interacts with the Y114 of ND1. These suggested that the m.14484 T > C mutation may perturb the structure and function of complex I. Mutant cybrids constructed by transferring mitochondria from lymphoblastoid cell lines of one Chinese LHON family into mtDNA-less (ρo) cells revealed decreases in the levels of ND6, ND1 and ND4L. The m.14484 T > C mutation may affect mitochondrial mRNA homeostasis, supported by reduced levels of SLIRP and SUPV3L1 involved in mRNA degradation and increasing expression of ND6, ND1 and ND4L genes. These alterations yielded decreased activity of complex I, respiratory deficiency, diminished mitochondrial ATP production and reduced membrane potential, and increased production of reactive oxygen species in the mutant cybrids. Furthermore, the m.14484 T > C mutation promoted apoptosis, evidenced by elevating Annexin V-positive cells, release of cytochrome c into cytosol, levels in apoptotic proteins BAX, caspases 3, 7, 9 and decreasing levels in anti-apoptotic protein Bcl-xL in the mutant cybrids. Moreover, the cybrids bearing the m.14484 T > C mutation exhibited the reduced levels of autophagy protein LC3, increased levels of substrate P62 and impaired PINK1/Parkin-dependent mitophagy. Our findings highlighted the critical role of m.14484 T > C mutation in the pathogenesis of LHON.

Scalable Extraction of Big Macromolecular Data in Azure Data Lake Environment

Calculation of structural features of proteins, nucleic acids, and nucleic acid-protein complexes on the basis of their geometries and studying various interactions within these macromolecules, for which high-resolution structures are stored in Protein Data Bank (PDB), require parsing and extraction of suitable data stored in text files. To perform these operations on large scale in the face of the growing amount of macromolecular data in public repositories, we propose to perform them in the distributed environment of Azure Data Lake and scale the calculations on the Cloud. In this paper, we present dedicated data extractors for PDB files that can be used in various types of calculations performed over protein and nucleic acids structures in the Azure Data Lake. Results of our tests show that the Cloud storage space occupied by the macromolecular data can be successfully reduced by using compression of PDB files without significant loss of data processing efficiency. Moreover, our experiments show that the performed calculations can be significantly accelerated when using large sequential files for storing macromolecular data and by parallelizing the calculations and data extractions that precede them. Finally, the paper shows how all the calculations can be performed in a declarative way in U-SQL scripts for Data Lake Analytics.

Efficient Multicriteria Protein Structure Comparison on Modern Processor Architectures

Fast increasing computational demand for all-to-all protein structures comparison (PSC) is a result of three confounding factors: rapidly expanding structural proteomics databases, high computational complexity of pairwise protein comparison algorithms, and the trend in the domain towards using multiple criteria for protein structures comparison (MCPSC) and combining results. We have developed a software framework that exploits many-core and multicore CPUs to implement efficient parallel MCPSC in modern processors based on three popular PSC methods, namely, TMalign, CE, and USM. We evaluate and compare the performance and efficiency of the two parallel MCPSC implementations using Intel's experimental many-core Single-Chip Cloud Computer (SCC) as well as Intel's Core i7 multicore processor. We show that the 48-core SCC is more efficient than the latest generation Core i7, achieving a speedup factor of 42 (efficiency of 0.9), making many-core processors an exciting emerging technology for large-scale structural proteomics. We compare and contrast the performance of the two processors on several datasets and also show that MCPSC outperforms its component methods in grouping related domains, achieving a high F-measure of 0.91 on the benchmark CK34 dataset. The software implementation for protein structure comparison using the three methods and combined MCPSC, along with the developed underlying rckskel algorithmic skeletons library, is available via GitHub.