MOTIVATION: With the increasing availability of 3D-data, the focus of comparative bioinformatic analysis is shifting from protein sequence alignments toward more content-rich 3D-alignments. This raises the need for new ways to improve the accuracy of 3D-superimposition. RESULTS: We proposed guide tree optimization with genetic algorithm (GA) as a universal tool to improve the alignment quality of multiple protein 3D-structures systematically. As a proof of concept, we implemented the suggested GA-based approach in popular Matt and Caretta multiple protein 3D-structure alignment (M3DSA) algorithms, leading to a statistically significant improvement of the TM-score quality indicator by up to 220-1523% on 'SABmark Superfamilies' (in 49-77% of cases) and 'SABmark Twilight' (in 59-80% of cases) datasets. The observed improvement in collections of distant homologies highlights the potentials of GA to optimize 3D-alignments of diverse protein superfamilies as one plausible tool to study the structure-function relationship. AVAILABILITY AND IMPLEMENTATION: The source codes of patched gaCaretta and gaMatt programs are available open-access at https://github.com/n-canter/gamaps. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Accurate structural alignment of proteins is crucial at studying structure-function relationship in evolutionarily distant homologues. Various software tools were proposed to align multiple protein 3D-structures utilizing one CPU and thus are of limited productivity at large-scale analysis of protein families/superfamilies. RESULTS: The parMATT is a hybrid MPI/pthreads/OpenMP parallel re-implementation of the MATT algorithm to align multiple protein 3D-structures by allowing translations and twists. The parMATT can be faster than MATT on a single multi-core CPU, and provides a much greater speedup when executed on distributed-memory systems, i.e. computing clusters and supercomputers hosting memory-independent computing nodes. The most computationally demanding steps of the MATT algorithm-the initial construction of pairwise alignments between all input structures and further iterative progression of the multiple alignment-were parallelized using MPI and pthreads, and the concluding refinement step was optimized by introducing the OpenMP support. The parMATT can significantly accelerate the time-consuming process of building a multiple structural alignment from a large set of 3D-records of homologous proteins. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://biokinet.belozersky.msu.ru/parMATT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Comparative analysis of homologous proteins in a functionally diverse superfamily is a valuable tool at studying structure-function relationship, but represents a methodological challenge. Results: The Mustguseal web-server can automatically build large structure-guided sequence alignments of functionally diverse protein families that include thousands of proteins basing on all available information about their structures and sequences in public databases. Superimposition of protein structures is implemented to compare evolutionarily distant relatives, whereas alignment of sequences is used to compare close homologues. The final alignment can be downloaded for a local use or operated on-line with the built-in interactive tools and further submitted to the integrated sister web-servers of Mustguseal to analyze conserved, subfamily-specific and co-evolving residues at studying a protein function and regulation, designing improved enzyme variants for practical applications and selective ligands to modulate functional properties of proteins. Availability and implementation: Freely available on the web at https://biokinet.belozersky.msu.ru/mustguseal. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

The AlgoWiki open encyclopedia of parallel algorithmic features enables the entire computing community to work together to describe the properties of a multitude of mathematical algorithms and their implementation for various software and hardware platforms. As part of the AlgoWiki project, a structure has been suggested for providing universal descriptions of algorithm properties. Along with the first part of the description, dedicated to machine-independent properties of the algorithms, it is extremely important to study and describe the dynamic characteristics of their software implementation. By studying fundamental algorithm properties such as execution time, performance, data locality, efficiency and scalability, we can give some estimate of the potential implementation quality for a given algorithm on a specific computer and lay the foundation for comparative analysis of various computing platforms with regards to the algorithms presented in AlgoWiki.

In this work we propose a design for a new distributed modular monitoring system framework, which allows combining both monitoring tasks (supercomputer health and performance monitoring) in one monitoring system. Our approach allows different part of monitoring system to process only the data needed for the task assigned to this part. Another feature of our framework is the ability to calculate performance metrics on-the-fly, dynamically creating processing modules for every job or other objects of interest.

Cluster systems usually run several applications-often from different users-concurrently, with individual applications competing for access to shared resources such as the file system or the network. Low application performance is therefore not always the result of inefficient program design, but may instead be caused by interference from outside. However, knowing the difference is essential for an appropriate response. Unfortunately, traditional performance-analysis techniques consider an application always in isolation, without the ability to compare its performance to the overall performance conditions on the system when it was executed. In this paper, we present a novel approach of how to correlate the performance behavior of applications running side by side. To accomplish this, we divide the application runtime into fine-grained time slices whose boundaries are synchronized across the entire system. Mapping performance data related to shared resources onto these time slices, we are able to establish the simultaneity of their usage across jobs, which can be indicative of inter-application interference. Our experiments show that such interference effects, for which the developer is usually not to blame, can degrade application performance significantly.