Guide tree optimization with genetic algorithm to improve multiple protein 3D-structure alignment.

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.

parMATT: parallel multiple alignment of protein 3D-structures with translations and twists for distributed-memory systems.

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.

Mustguseal: a server for multiple structure-guided sequence alignment of protein families.

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: vytas@belozersky.msu.ru. Supplementary information: Supplementary data are available at Bioinformatics online.

Molecular and Mechanical Causes of Microtubule Catastrophe and Aging.

Tubulin polymers, microtubules, can switch abruptly from the assembly to shortening. These infrequent transitions, termed "catastrophes", affect numerous cellular processes but the underlying mechanisms are elusive. We approached this complex stochastic system using advanced coarse-grained molecular dynamics modeling of tubulin-tubulin interactions. Unlike in previous simplified models of dynamic microtubules, the catastrophes in this model arise owing to fluctuations in the composition and conformation of a growing microtubule tip, most notably in the number of protofilament curls. In our model, dynamic evolution of the stochastic microtubule tip configurations over a long timescale, known as the system's "aging", gives rise to the nonexponential distribution of microtubule lifetimes, consistent with experiment. We show that aging takes place in the absence of visible changes in the microtubule wall or tip, as this complex molecular-mechanical system evolves slowly and asymptotically toward the steady-state level of the catastrophe-promoting configurations. This new, to our knowledge, theoretical basis will assist detailed mechanistic investigations of the mechanisms of action of different microtubule-binding proteins and drugs, thereby enabling accurate control over the microtubule dynamics to treat various pathologies.

Parallel workflow manager for non-parallel bioinformatic applications to solve large-scale biological problems on a supercomputer.

Rapid expansion of online resources providing access to genomic, structural, and functional information associated with biological macromolecules opens an opportunity to gain a deeper understanding of the mechanisms of biological processes due to systematic analysis of large datasets. This, however, requires novel strategies to optimally utilize computer processing power. Some methods in bioinformatics and molecular modeling require extensive computational resources. Other algorithms have fast implementations which take at most several hours to analyze a common input on a modern desktop station, however, due to multiple invocations for a large number of subtasks the full task requires a significant computing power. Therefore, an efficient computational solution to large-scale biological problems requires both a wise parallel implementation of resource-hungry methods as well as a smart workflow to manage multiple invocations of relatively fast algorithms. In this work, a new computer software mpiWrapper has been developed to accommodate non-parallel implementations of scientific algorithms within the parallel supercomputing environment. The Message Passing Interface has been implemented to exchange information between nodes. Two specialized threads - one for task management and communication, and another for subtask execution - are invoked on each processing unit to avoid deadlock while using blocking calls to MPI. The mpiWrapper can be used to launch all conventional Linux applications without the need to modify their original source codes and supports resubmission of subtasks on node failure. We show that this approach can be used to process huge amounts of biological data efficiently by running non-parallel programs in parallel mode on a supercomputer. The C++ source code and documentation are available from http://biokinet.belozersky.msu.ru/mpiWrapper .

Robust enzyme design: bioinformatic tools for improved protein stability.

The ability of proteins and enzymes to maintain a functionally active conformation under adverse environmental conditions is an important feature of biocatalysts, vaccines, and biopharmaceutical proteins. From an evolutionary perspective, robust stability of proteins improves their biological fitness and allows for further optimization. Viewed from an industrial perspective, enzyme stability is crucial for the practical application of enzymes under the required reaction conditions. In this review, we analyze bioinformatic-driven strategies that are used to predict structural changes that can be applied to wild type proteins in order to produce more stable variants. The most commonly employed techniques can be classified into stochastic approaches, empirical or systematic rational design strategies, and design of chimeric proteins. We conclude that bioinformatic analysis can be efficiently used to study large protein superfamilies systematically as well as to predict particular structural changes which increase enzyme stability. Evolution has created a diversity of protein properties that are encoded in genomic sequences and structural data. Bioinformatics has the power to uncover this evolutionary code and provide a reproducible selection of hotspots - key residues to be mutated in order to produce more stable and functionally diverse proteins and enzymes. Further development of systematic bioinformatic procedures is needed to organize and analyze sequences and structures of proteins within large superfamilies and to link them to function, as well as to provide knowledge-based predictions for experimental evaluation.

Evaluation of Docking Target Functions by the Comprehensive Investigation of Protein-Ligand Energy Minima.

The adequate choice of the docking target function impacts the accuracy of the ligand positioning as well as the accuracy of the protein-ligand binding energy calculation. To evaluate a docking target function we compared positions of its minima with the experimentally known pose of the ligand in the protein active site. We evaluated five docking target functions based on either the MMFF94 force field or the PM7 quantum-chemical method with or without implicit solvent models: PCM, COSMO, and SGB. Each function was tested on the same set of 16 protein-ligand complexes. For exhaustive low-energy minima search the novel MPI parallelized docking program FLM and large supercomputer resources were used. Protein-ligand binding energies calculated using low-energy minima were compared with experimental values. It was demonstrated that the docking target function on the base of the MMFF94 force field in vacuo can be used for discovery of native or near native ligand positions by finding the low-energy local minima spectrum of the target function. The importance of solute-solvent interaction for the correct ligand positioning is demonstrated. It is shown that docking accuracy can be improved by replacement of the MMFF94 force field by the new semiempirical quantum-chemical PM7 method.