Search

Ferredoxins, iron-sulfur (Fe-S) cluster proteins, play a key role in oxidoreduction reactions. To date, evolutionary analysis of these proteins across the domains of life have been confined to observing the abundance of Fe-S cluster types (2Fe-2S, 3Fe-4S, 4Fe-4S, 7Fe-8S (3Fe-4s and 4Fe-4S) and 2[4Fe-4S]) and the diversity of ferredoxins within these cluster types was not studied. To address this research gap, here we propose a subtype classification and nomenclature for ferredoxins based on the characteristic spacing between the cysteine amino acids of the Fe-S binding motif as a subtype signature to assess the diversity of ferredoxins across the living organisms. To test this hypothesis, comparative analysis of ferredoxins between bacterial groups, Alphaproteobacteria and Firmicutes and ferredoxins collected from species of different domains of life that are reported in the literature has been carried out. Ferredoxins were found to be highly diverse within their types. Large numbers of alphaproteobacterial species ferredoxin subtypes were found in Firmicutes species and the same ferredoxin subtypes across the species of Bacteria, Archaea, and Eukarya, suggesting shared common ancestral origin of ferredoxins between Archaea and Bacteria and lateral gene transfer of ferredoxins from prokaryotes (Archaea/Bacteria) to eukaryotes. This study opened new vistas for further analysis of diversity of ferredoxins in living organisms.

Species belonging to the bacterial phyla Bacteroidetes and Firmicutes represent over 90% of the gastrointestinal microbiota. Changes in the ratio of these two bacterial groups were found to have contrasting health effects, including obesity and inflammatory diseases. Despite the availability of many bacterial genomes, comparative genomic studies on the gene pools of these two bacterial groups concerning cytochrome P450 monooxygenases (P450s), ferredoxins, and secondary metabolite biosynthetic gene clusters (smBGCs) are not reported. This study is aimed to address this research gap. The study revealed the presence of diverse sets of P450s, ferredoxins, and smBGCs in their genomes. Bacteroidetes species have the highest number of P450 families, ferredoxin cluster-types, and smBGCs compared to Firmicutes species. Only four P450 families, three ferredoxin cluster types, and five smBGCs are commonly shared between these two bacterial groups. Considering the above facts, we propose that the contrasting effects of these two bacterial groups on the host are partly due to the distinct nature of secondary metabolites produced by these organisms. Thus, the cause of the contrasting health effects of these two bacterial groups lies in their gene pools.

We describe common approaches to atomistic structure modeling with single particle analysis derived cryo-EM maps. Several strategies for atomistic model building and atomistic model fitting methods are discussed, including selection criteria and implementation procedures. In covering basic concepts and caveats, this short perspective aims to help facilitate active discussion between scientists at different levels with diverse backgrounds.

For the last six decades, cytochrome P450 monooxygenases (CYPs/P450s), heme thiolate proteins, have been under the spotlight due to their regio- and stereo-selective oxidation activities, which has led to the exploration of their applications in almost all known areas of biology. The availability of many genome sequences allows us to understand the evolution of P450s in different organisms, especially in the Bacteria domain. The phenomenon that "P450s play a key role in organisms' adaptation vis a vis lifestyle of organisms impacts P450 content in their genome" was proposed based on studies on a handful of individual bacterial groups. To have conclusive evidence, one must analyze P450s and their role in secondary metabolism in species with diverse lifestyles but that belong to the same category. We selected species of the phylum

While quantum dots (QDs) are useful as fluorescent labels, their application in biosciences is limited due to the stability and hydrophobicity of their surface. In this study, we tested two types of proteins for use as a cover for spherical QDs, composed of cadmium selenide. Pumilio homology domain (Puf), which is mostly α-helical, and leucine-rich repeat (LRR) domain, which is rich in β-sheets, were selected to determine if there is a preference for one of these secondary structure types for nanoparticle covers. The protein sequences were optimized to improve their interaction with the surface of QDs. The solubilization of the apoproteins and their assembly with nanoparticles required the application of a detergent, which was removed in subsequent steps. Finally, only the Puf-based cover was successful enough as a QD hydrophilic cover. We showed that a single polypeptide dimer of Puf, PufPuf, can form a cover. We characterized the size and fluorescent properties of the obtained QD:protein assemblies. We showed that the secondary structure of the Puf proteins was not destroyed upon contact with the QDs. We demonstrated that these assemblies do not promote the formation of reactive oxygen species during illumination of the nanoparticles. The data represent advances in the effort to obtain a stable biocompatible cover for QDs.

In molecular simulations performed by Markov Chain Monte Carlo (typically employing the Metropolis criterion), each state of a system is obtained by a small random modification of the previous state. Therefore, the process consists of an immense number of small, quick to calculate steps, which are inherently sequential and hence considered to be very hard to parallelise. Here, we present a novel protocol for efficient calculation of multiple sequential steps in parallel. To this end, we first precompute in parallel energy components of all states achievable in a sequence of steps. Then we select a single path through all achievable states, which is identical with the path obtained with the sequential algorithm. As an example, we carried out simulations of the TIP5P water model with the new protocol and compared results with those obtained using the standard Metropolis Monte Carlo scheme. The implementation on the Titan X (Pascal) graphic processor (GPU) architectures allows for a 30-fold speedup in comparison with a simulation on a single core of a multicore CPU. The protocol is general and not limited to the GPU; it can also be used on multicore CPU when the longest possible length of the single simulation is required.

Cytochrome P450 monooxygenase CYP51 (sterol 14α-demethylase) is a well-known target of the azole drug fluconazole for treating cryptococcosis, a life-threatening fungal infection in immune-compromised patients in poor countries. Studies indicate that mutations in CYP51 confer fluconazole resistance on cryptococcal species. Despite the importance of CYP51 in these species, few studies on the structural analysis of CYP51 and its interactions with different azole drugs have been reported. We therefore performed in silico structural analysis of 11 CYP51s from cryptococcal species and other Tremellomycetes. Interactions of 11 CYP51s with nine ligands (three substrates and six azoles) performed by Rosetta docking using 10,000 combinations for each of the CYP51-ligand complex (11 CYP51s × 9 ligands = 99 complexes) and hierarchical agglomerative clustering were used for selecting the complexes. A web application for visualization of CYP51s’ interactions with ligands was developed (http://bioshell.pl/azoledocking/). The study results indicated that Tremellomycetes CYP51s have a high preference for itraconazole, corroborating the in vitro effectiveness of itraconazole compared to fluconazole. Amino acids interacting with different ligands were found to be conserved across CYP51s, indicating that the procedure employed in this study is accurate and can be automated for studying P450-ligand interactions to cater for the growing number of P450s.

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.

The impact of lifestyle on shaping the genome content of an organism is a well-known phenomenon and cytochrome P450 enzymes (CYPs/P450s), heme-thiolate proteins that are ubiquitously present in organisms, are no exception. Recent studies focusing on a few bacterial species such as Streptomyces, Mycobacterium, Cyanobacteria and Firmicutes revealed that the impact of lifestyle affected the P450 repertoire in these species. However, this phenomenon needs to be understood in other bacterial species. We therefore performed genome data mining, annotation, phylogenetic analysis of P450s and their role in secondary metabolism in the bacterial class Gammaproteobacteria. Genome-wide data mining for P450s in 1261 Gammaproteobacterial species belonging to 161 genera revealed that only 169 species belonging to 41 genera have P450s. A total of 277 P450s found in 169 species grouped into 84 P450 families and 105 P450 subfamilies, where 38 new P450 families were found. Only 18% of P450s were found to be involved in secondary metabolism in Gammaproteobacterial species, as observed in Firmicutes as well. The pathogenic or commensal lifestyle of Gammaproteobacterial species influences them to such an extent that they have the lowest number of P450s compared to other bacterial species, indicating the impact of lifestyle on shaping the P450 repertoire. This study is the first report on comprehensive analysis of P450s in Gammaproteobacteria.

Cytochrome P450 monooxygenase CYP51 (sterol 14α-demethylase) is a well-known target of the azole drug fluconazole for treating cryptococcosis, a life-threatening fungal infection in immune-compromised patients in poor countries. Studies indicate that mutations in CYP51 confer fluconazole resistance on cryptococcal species. Despite the importance of CYP51 in these species, few studies on the structural analysis of CYP51 and its interactions with different azole drugs have been reported. We therefore performed in silico structural analysis of 11 CYP51s from cryptococcal species and other Tremellomycetes. Interactions of 11 CYP51s with nine ligands (three substrates and six azoles) performed by Rosetta docking using 10,000 combinations for each of the CYP51-ligand complex (11 CYP51s × 9 ligands = 99 complexes) and hierarchical agglomerative clustering were used for selecting the complexes. A web application for visualization of CYP51s’ interactions with ligands was developed (http://bioshell.pl/azoledocking/). The study results indicated that Tremellomycetes CYP51s have a high preference for itraconazole, corroborating the in vitro effectiveness of itraconazole compared to fluconazole. Amino acids interacting with different ligands were found to be conserved across CYP51s, indicating that the procedure employed in this study is accurate and can be automated for studying P450-ligand interactions to cater for the growing number of P450s.

The assignment of secondary structure elements in protein conformations is necessary to interpret a protein model that has been established by computational methods. The process essentially involves labeling the amino acid residues with H (Helix), E (Strand), or C (Coil, also known as Loop). When particular atoms are absent from an input protein structure, the procedure becomes more complicated, especially when only the alpha carbon locations are known. Various techniques have been tested and applied to this problem during the last forty years. The application of machine learning techniques is the most recent trend. This contribution presents the HECA classifier, which uses neural networks to assign protein secondary structure types. The technique exclusively employs Cα coordinates. The Keras (TensorFlow) library was used to implement and train the neural network model. The BioShell toolkit was used to calculate the neural network input features from raw coordinates. The study’s findings show that neural network-based methods may be successfully used to take on structure assignment challenges when only Cα trace is available. Thanks to the careful selection of input features, our approach’s accuracy (above 97%) exceeded that of the existing methods.

Unraveling the role of cytochrome P450 monooxygenases (CYPs/P450s), heme-thiolate proteins present in living and non-living entities, in secondary metabolite synthesis is gaining momentum. In this direction, in this study, we analyzed the genomes of 203 Streptomyces species for P450s and unraveled their association with secondary metabolism. Our analyses revealed the presence of 5460 P450s, grouped into 253 families and 698 subfamilies. The CYP107 family was found to be conserved and highly populated in Streptomyces and Bacillus species, indicating its key role in the synthesis of secondary metabolites. Streptomyces species had a higher number of P450s than Bacillus and cyanobacterial species. The average number of secondary metabolite biosynthetic gene clusters (BGCs) and the number of P450s located in BGCs were higher in Streptomyces species than in Bacillus, mycobacterial, and cyanobacterial species, corroborating the superior capacity of Streptomyces species for generating diverse secondary metabolites. Functional analysis via data mining confirmed that many Streptomyces P450s are involved in the biosynthesis of secondary metabolites. This study was the first of its kind to conduct a comparative analysis of P450s in such a large number (203) of Streptomyces species, revealing the P450s&rsquo, association with secondary metabolite synthesis in Streptomyces species. Future studies should include the selection of Streptomyces species with a higher number of P450s and BGCs and explore the biotechnological value of secondary metabolites they produce.

Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities.

Motivation Visualization is a powerful tool to analyze, understand and present big data. Computational biology, bioinformatics and molecular modeling require dedicated tools, tailored to very complex, highly multidimensional data. Over the recent years, numerous tools have been developed for online presentation, but new challenges like the COVID-19 pandemic require new libraries which will guarantee fast development of online tools for a better understanding of biomedical data/results. Results VisuaLife is a Python library that provides a new approach to visualization in a web browser. It offers 2D and 3D plotting capabilities as well as widgets designed to display the most common biological data types: nucleotide or protein sequences, 3D biomolecular structures and multiple sequence alignments. Components provided by the VisuaLife library can be assembled into a web application to create an analysis tool tailored to provide multidimensional analysis of a specific research problem. VisuaLife, to our best knowledge, is the most modern solution that allows one to implement such a client-side interactivity in Python. Availability and implementation The git repository of the library is hosted at BitBucket: https://bitbucket.org/dgront/visualife/. PyPI distribution is also provided for MacOS and Linux. While basic examples are provided in the supporting materials, the full documentation is available at ReadTheDocs website: https://visualife.readthedocs.io/. Supplementary information Supplementary data are available at Bioinformatics online.

Sporotrichosis is an emerging chronic, granulomatous, subcutaneous, mycotic infection caused by Sporothrix species. Sporotrichosis is treated with the azole drug itraconazole as ketoconazole is ineffective. It is a well-known fact that azole drugs act by inhibiting cytochrome P450 monooxygenases (P450s), heme-thiolate proteins. To date, nothing is known about P450s in Sporothrix schenckii and the molecular basis of its resistance to ketoconazole. Here we present genome-wide identification, annotation, phylogenetic analysis and comprehensive P450 family-level comparative analysis of S. schenckii P450s with pathogenic fungi P450s, along with a rationale for ketoconazole resistance by S. schenckii based on in silico structural analysis of CYP51. Genome data-mining of S. schenckii revealed 40 P450s in its genome that can be grouped into 32 P450 families and 39 P450 subfamilies. Comprehensive comparative analysis of P450s revealed that S. schenckii shares 11 P450 families with plant pathogenic fungi and has three unique P450 families: CYP5077, CYP5386 and CYP5696 (novel family). Among P450s, CYP51, the main target of azole drugs was also found in S. schenckii. 3D modeling of S. schenckii CYP51 revealed the presence of characteristic P450 motifs with exceptionally large reductase interaction site 2. In silico analysis revealed number of mutations that can be associated with ketoconazole resistance, especially at the channel entrance to the active site. One of possible reason for better stabilization of itraconazole, compared to ketoconazole, is that the more extended molecule of itraconazole may form a hydrogen bond with ASN-230. This in turn may explain its effectiveness against S. schenckii vis-a-vis resistant to ketoconazole. This article is part of a Special Issue entitled: Cytochrome P450 biodiversity and biotechnology, edited by Erika Plettner, Gianfranco Gilardi, Luet Wong, Vlada Urlacher, Jared Goldstone.

Unraveling the role of cytochrome P450 monooxygenases (CYPs/P450s), heme-thiolate proteins present in living and non-living entities, in secondary metabolite synthesis is gaining momentum. In this direction, in this study, we analyzed the genomes of 203 Streptomyces species for P450s and unraveled their association with secondary metabolism. Our analyses revealed the presence of 5460 P450s, grouped into 253 families and 698 subfamilies. The CYP107 family was found to be conserved and highly populated in Streptomyces and Bacillus species, indicating its key role in the synthesis of secondary metabolites. Streptomyces species had a higher number of P450s than Bacillus and cyanobacterial species. The average number of secondary metabolite biosynthetic gene clusters (BGCs) and the number of P450s located in BGCs were higher in Streptomyces species than in Bacillus, mycobacterial, and cyanobacterial species, corroborating the superior capacity of Streptomyces species for generating diverse secondary metabolites. Functional analysis via data mining confirmed that many Streptomyces P450s are involved in the biosynthesis of secondary metabolites. This study was the first of its kind to conduct a comparative analysis of P450s in such a large number (203) of Streptomyces species, revealing the P450s’ association with secondary metabolite synthesis in Streptomyces species. Future studies should include the selection of Streptomyces species with a higher number of P450s and BGCs and explore the biotechnological value of secondary metabolites they produce.

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at ., This Perspective reviews tools developed over the past five years in the macromolecular modeling, docking and design software Rosetta.

Surface-directed passivation of p-doped silicon (Si) substrate was achieved by its biofunctionalisation with hexahistidine (His6)-tagged cytochrome c553 (cyt c553), a soluble electroactive photosynthetic protein responsible for electron donation to photooxidised photosystem I (PSI). Five distinct variants of cyt c553 were genetically engineered by introducing the specific linker peptides of 0–19 amino acids (AA) in length between the cyt c553 holoprotein and a C-terminal His6-tag, the latter being the affinity ‘anchor’ used for the specific immobilisation of this protein on the semiconductor surface. Calculation of 2D Gibbs free energy maps for the five cyt c553 variants showed a significantly higher number of thermodynamically feasible conformations of immobilised cyt c variants containing longer linker peptides. Here we show that the distinct cyt c553-based Si bioelectrodes display some characteristics of the p–n-type diodes, albeit varying in the level of dark saturation current J0 considered as the charge recombination parameter. These combined bioinformatic and electrochemical analyses indicate that the cyt c553 variants with longer linker peptides, up to 19AA in length, allow for more structural flexibility of immobilised cyt c553 in terms of both, orientation and distance of the haem group with respect to the Si surface, and promote the efficient biopassivation of the semiconductor substrate. Incorporation of the specifically immobilised 19AA cyt c553 variant into the all-solid-state biophotoelectrodes containing light harvesting PSI module enhanced biophotovoltaic performance of the PSI biophotoelectrode compared to the analogous device devoid of cyt c553.

The knowledge of the three-dimensional structure of proteins is crucial for understanding many important biological processes. Most of the biologically relevant protein systems are too large for classical, atomistic molecular modeling tools. In such cases, coarse-grained (CG) models offer various opportunities for efficient conformational sampling and thus prediction of the three-dimensional structure. A variety of CG models have been proposed, each based on a similar framework consisting of a set of conceptual components such as protein representation, force field, sampling, etc. In this chapter we discuss these components, highlighting ideas which have proven to be the most successful. As CG methods are usually part of multistage procedures, we also describe approaches used for the incorporation of homology data and all-atom reconstruction methods.

We recently developed a new coarse-grained model of protein structure and dynamics [ Dawid et al. J. Chem. Theory Comput. 2017 , 13 ( 11 ), 5766 - 5779 ]. The model assumed a single bead representation of amino acid residues, where positions of such united residues were defined by centers of mass of four amino acid fragments. Replica exchange Monte Carlo sampling of the model chains provided good pictures of modeled structures and their dynamics. In its generic form the statistical knowledge-based force field of the model has been dedicated for single-domain globular proteins. Sequence-specific interactions are defined by three-letter secondary structure data. In the present work we demonstrate that different assignments and/or predictions of secondary structures are usually sufficient for enforcing cooperative formation of native-like folds of SURPASS chains for the majority of single-domain globular proteins. Simulations of native-like structure assembly for a representative set of globular proteins have shown that the accuracy of secondary structure data is usually not crucial for model performance, although some specific errors can strongly distort the obtained three-dimensional structures.

The mechanism through which nuclear receptors respond differentially to structurally distinct agonists is a poorly understood process. We present a computational method that identifies nuclear receptor amino acids that are likely involved in biological responses triggered by ligand binding. The method involves tracing how structural changes spread from the ligand binding pocket to the sites on the receptor surface, which makes it a good tool for studying allosteric effects. We employ the method to the vitamin D receptor and verify that the identified amino acids are biologically relevant using a broad range of experimental data and a genome browser. We infer that surface vitamin D receptor residues K141, R252, I260, T280, T287 and L417 are likely involved in cell differentiation and antiproliferation, whereas P122, D149, K321, E353 and Q385 are linked to carcinogenesis.