Ongoing shuffling of protein fragments diversifies core viral functions linked to interactions with bacterial hosts.

Biological modularity enhances evolutionary adaptability. This principle is vividly exemplified by bacterial viruses (phages), which display extensive genomic modularity. Phage genomes are composed of independent functional modules that evolve separately and recombine in various configurations. While genomic modularity in phages has been extensively studied, less attention has been paid to protein modularity-proteins consisting of distinct building blocks that can evolve and recombine, enhancing functional and genetic diversity. Here, we use a set of 133,574 representative phage proteins and highly sensitive homology detection to capture instances of domain mosaicism, defined as fragment sharing between two otherwise unrelated proteins, and to understand its relationship with functional diversity in phage genomes. We discover that unrelated proteins from diverse functional classes frequently share homologous domains. This phenomenon is particularly pronounced within receptor-binding proteins, endolysins, and DNA polymerases. We also identify multiple instances of recent diversification via domain shuffling in receptor-binding proteins, neck passage structures, endolysins and some members of the core replication machinery, often transcending distant taxonomic and ecological boundaries. Our findings suggest that ongoing diversification via domain shuffling is reflective of a co-evolutionary arms race, driven by the need to overcome various bacterial resistance mechanisms against phages.

Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann fold proteins.

The Rossmann fold enzymes are involved in essential biochemical pathways such as nucleotide and amino acid metabolism. Their functioning relies on interaction with cofactors, small nucleoside-based compounds specifically recognized by a conserved ßαß motif shared by all Rossmann fold proteins. While Rossmann methyltransferases recognize only a single cofactor type, the S-adenosylmethionine, the oxidoreductases, depending on the family, bind nicotinamide (nicotinamide adenine dinucleotide, nicotinamide adenine dinucleotide phosphate) or flavin-based (flavin adenine dinucleotide) cofactors. In this study, we showed that despite its short length, the ßαß motif unambiguously defines the specificity towards the cofactor. Following this observation, we trained two complementary deep learning models for the prediction of the cofactor specificity based on the sequence and structural features of the ßαß motif. A benchmark on two independent test sets, one containing ßαß motifs bearing no resemblance to those of the training set, and the other comprising 38 experimentally confirmed cases of rational design of the cofactor specificity, revealed the nearly perfect performance of the two methods. The Rossmann-toolbox protocols can be accessed via the webserver at https://lbs.cent.uw.edu.pl/rossmann-toolbox and are available as a Python package at https://github.com/labstructbioinf/rossmann-toolbox.

A library of coiled-coil domains: from regular bundles to peculiar twists.

MOTIVATION: Coiled coils are widespread protein domains involved in diverse processes ranging from providing structural rigidity to the transduction of conformational changes. They comprise two or more α-helices that are wound around each other to form a regular supercoiled bundle. Owing to this regularity, coiled-coil structures can be described with parametric equations, thus enabling the numerical representation of their properties, such as the degree and handedness of supercoiling, rotational state of the helices, and the offset between them. These descriptors are invaluable in understanding the function of coiled coils and designing new structures of this type. The existing tools for such calculations require manual preparation of input and are therefore not suitable for the high-throughput analyses. RESULTS: To address this problem, we developed SamCC-Turbo, a software for fully automated, per-residue measurement of coiled coils. By surveying Protein Data Bank with SamCC-Turbo, we generated a comprehensive atlas of ∼50 000 coiled-coil regions. This machine learning-ready dataset features precise measurements as well as decomposes coiled-coil structures into fragments characterized by various degrees of supercoiling. The potential applications of SamCC-Turbo are exemplified by analyses in which we reveal general structural features of coiled coils involved in functions requiring conformational plasticity. Finally, we discuss further directions in the prediction and modeling of coiled coils. AVAILABILITY AND IMPLEMENTATION: SamCC-Turbo is available as a web server (https://lbs.cent.uw.edu.pl/samcc_turbo) and as a Python library (https://github.com/labstructbioinf/samcc_turbo), whereas the results of the Protein Data Bank scan can be browsed and downloaded at https://lbs.cent.uw.edu.pl/ccdb. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Application of crowdfunding to video game projects financing.

After the financial crisis in 2007 started, many of the new innovative companies didn't have the access to the capital. The chance for them was crowdfunding. Nowadays, facing the coronavirus pandemic, this innovative method of financing becomes a mainstay in development of video games as well. The video game market has developed rapidly over the last over a dozen years, but not all of the creators have the possibility for the traditional funding. This article presents the results of research aimed at examining the availability and use of crowdfunding - the innovative source of financing of projects on the video games market. The research methods used in the article are an observational method and a method of individual cases as well as the rational reasoning on the basis of achieved results.

ANO5 mutations in the Polish limb girdle muscular dystrophy patients: Effects on the protein structure.

LGMD2L is a subtype of limb-girdle muscular dystrophy (LGMD), caused by recessive mutations in ANO5, encoding anoctamin-5 (ANO5). We present the analysis of five patients with skeletal muscle weakness for whom heterozygous mutations within ANO5 were identified by whole exome sequencing (WES). Patients varied in the age of the disease onset (from 22 to 38 years) and severity of the morphological and clinical phenotypes. Out of the nine detected mutations one was novel (missense p.Lys132Met, accompanied by p.His841Asp) and one was not yet characterized in the literature (nonsense, p.Trp401Ter, accompanied by p.Asp81Gly). The p.Asp81Gly mutation was also identified in another patient carrying a p.Arg758Cys mutation as well. Also, a c.191dupA frameshift (p.Asn64LysfsTer15), the first described and common mutation was identified. Mutations were predicted by in silico tools to have damaging effects and are likely pathogenic according to criteria of the American College of Medical Genetics and Genomics (ACMG). Indeed, molecular modeling of mutations revealed substantial changes in ANO5 conformation that could affect the protein structure and function. In addition, variants in other genes associated with muscle pathology were identified, possibly affecting the disease progress. The presented data indicate that the identified ANO5 mutations contribute to the observed muscle pathology and broaden the genetic spectrum of LGMD myopathies.

PiPred - a deep-learning method for prediction of π-helices in protein sequences.

Canonical π-helices are short, relatively unstable secondary structure elements found in proteins. They comprise seven or more residues and are present in 15% of all known protein structures, often in functionally important regions such as ligand- and ion-binding sites. Given their similarity to α-helices, the prediction of π-helices is a challenging task and none of the currently available secondary structure prediction methods tackle it. Here, we present PiPred, a neural network-based tool for predicting π-helices in protein sequences. By performing a rigorous benchmark we show that PiPred can detect π-helices with a per-residue precision of 48% and sensitivity of 46%. Interestingly, some of the α-helices mispredicted by PiPred as π-helices exhibit a geometry characteristic of π-helices. Also, despite being trained only with canonical π-helices, PiPred can identify 6-residue-long α/π-bulges. These observations suggest an even higher effective precision of the method and demonstrate that π-helices, α/π-bulges, and other helical deformations may impose similar constraints on sequences. PiPred is freely accessible at: https://toolkit.tuebingen.mpg.de/#/tools/quick2d . A standalone version is available for download at: https://github.com/labstructbioinf/PiPred , where we also provide the CB6133, CB513, CASP10, and CASP11 datasets, commonly used for training and validation of secondary structure prediction methods, with correctly annotated π-helices.

DeepCoil-a fast and accurate prediction of coiled-coil domains in protein sequences.

MOTIVATION: Coiled coils are protein structural domains that mediate a plethora of biological interactions, and thus their reliable annotation is crucial for studies of protein structure and function. RESULTS: Here, we report DeepCoil, a new neural network-based tool for the detection of coiled-coil domains in protein sequences. In our benchmarks, DeepCoil significantly outperformed current state-of-the-art tools, such as PCOILS and Marcoil, both in the prediction of canonical and non-canonical coiled coils. Furthermore, in a scan of the human genome with DeepCoil, we detected many coiled-coil domains that remained undetected by other methods. This higher sensitivity of DeepCoil should make it a method of choice for accurate genome-wide detection of coiled-coil domains. AVAILABILITY AND IMPLEMENTATION: DeepCoil is written in Python and utilizes the Keras machine learning library. A web server is freely available at https://toolkit.tuebingen.mpg.de/#/tools/deepcoil and a standalone version can be downloaded at https://github.com/labstructbioinf/DeepCoil. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Variability of the core geometry in parallel coiled-coil bundles.

In protein modelling and design, an understanding of the relationship between sequence and structure is essential. Using parallel, homotetrameric coiled-coil structures as a model system, we demonstrated that machine learning techniques can be used to predict structural parameters directly from the sequence. Coiled coils are regular protein structures, which are of great interest as building blocks for assembling larger nanostructures. They are composed of two or more alpha-helices wrapped around each other to form a supercoiled bundle. The coiled-coil bundles are defined by four basic structural parameters: topology (parallel or antiparallel), radius, degree of supercoiling, and the rotation of helices around their axes. In parallel coiled coils the latter parameter, describing the hydrophobic core packing geometry, was assumed to show little variation. However, we found that subtle differences between structures of this type were not artifacts of structure determination and could be predicted directly from the sequence. Using this information in modelling narrows the structural parameter space that must be searched and thus significantly reduces the required computational time. Moreover, the sequence-structure rules can be used to explain the effects of point mutations and to shed light on the relationship between hydrophobic core architecture and coiled-coil topology.

Identification of protein structural elements responsible for the diversity of sequence preferences among Mini-III RNases.

Many known endoribonucleases select their substrates based on the presence of one or a few specific nucleotides at or near the cleavage site. In some cases, selectivity is also determined by the structural features of the substrate. We recently described the sequence-specific cleavage of double-stranded RNA by Mini-III RNase from Bacillus subtilis in vitro. Here, we characterized the sequence specificity of eight other members of the Mini-III RNase family from different bacterial species. High-throughput analysis of the cleavage products of Φ6 bacteriophage dsRNA indicated subtle differences in sequence preference between these RNases, which were confirmed and characterized by systematic analysis of the cleavage kinetics of a set of short dsRNA substrates. We also showed that the sequence specificities of Mini-III RNases are not reflected by different binding affinities for cognate and non-cognate sequences, suggesting that target selection occurs predominantly at the cleavage step. We were able to identify two structural elements, the α4 helix and α5b-α6 loop that were involved in target selection. Characterization of the sequence specificity of the eight Mini-III RNases may provide a basis for better understanding RNA substrate recognition by Mini-III RNases and adopting these enzymes and their engineered derivatives as tools for RNA research.

Modeling of Protein-RNA Complex Structures Using Computational Docking Methods.

A significant part of biology involves the formation of RNA-protein complexes. X-ray crystallography has added a few solved RNA-protein complexes to the repertoire; however, it remains challenging to capture these complexes and often only the unbound structures are available. This has inspired a growing interest in finding ways to predict these RNA-protein complexes. In this study, we show ways to approach this problem by computational docking methods, either with a fully automated NPDock server or with a workflow of methods for generation of many alternative structures followed by selection of the most likely solution. We show that by introducing experimental information, the structure of the bound complex is rendered far more likely to be within reach. This study is meant to help the user of docking software understand how to grapple with a typical realistic problem in RNA-protein docking, understand what to expect in the way of difficulties, and recognize the current limitations.

The disordered P granule protein LAF-1 drives phase separation into droplets with tunable viscosity and dynamics.

P granules and other RNA/protein bodies are membrane-less organelles that may assemble by intracellular phase separation, similar to the condensation of water vapor into droplets. However, the molecular driving forces and the nature of the condensed phases remain poorly understood. Here, we show that the Caenorhabditis elegans protein LAF-1, a DDX3 RNA helicase found in P granules, phase separates into P granule-like droplets in vitro. We adapt a microrheology technique to precisely measure the viscoelasticity of micrometer-sized LAF-1 droplets, revealing purely viscous properties highly tunable by salt and RNA concentration. RNA decreases viscosity and increases molecular dynamics within the droplet. Single molecule FRET assays suggest that this RNA fluidization results from highly dynamic RNA-protein interactions that emerge close to the droplet phase boundary. We demonstrate than an N-terminal, arginine/glycine rich, intrinsically disordered protein (IDP) domain of LAF-1 is necessary and sufficient for both phase separation and RNA-protein interactions. In vivo, RNAi knockdown of LAF-1 results in the dissolution of P granules in the early embryo, with an apparent submicromolar phase boundary comparable to that measured in vitro. Together, these findings demonstrate that LAF-1 is important for promoting P granule assembly and provide insight into the mechanism by which IDP-driven molecular interactions give rise to liquid phase organelles with tunable properties.

Designability landscape reveals sequence features that define axial helix rotation in four-helical homo-oligomeric antiparallel coiled-coil structures.

Coiled coils are widespread protein domains comprising α-helices wound around each other in a regular fashion. Owing to their regularity, coiled-coil structures can be fully described by parametric equations. This in turn makes them an excellent model for studying sequence-structure relationships in proteins. Here, we used computational design to identify sequence features that determine the degree of helix axial rotation in four-helical homo-oligomeric antiparallel coiled coils. We designed 135,000 artificial sequences for a repertoire of backbone models representing all theoretically possible axial rotation states. Analysis of the designed sequences revealed features that precisely define the rotation of the helices. Based on these features we implemented a bioinformatic tool, which given a coiled-coil sequence, predicts the rotation of the helices in its structure. Moreover, we showed that another structural parameter, helix axial shift, is coupled to helix axial rotation and that dependence between these two parameters narrows the number of possible axial rotation states.

Ferredoxin:NADP+ oxidoreductase in junction with CdSe/ZnS quantum dots: characteristics of an enzymatically active nanohybrid.

Ferredoxin:NADP(+) oxidoreductase (FNR) is a plant and cyanobacterial photosynthetic enzyme, also found in non-photosynthetic tissues, where it is involved in redox reactions of biosynthetic pathways. In vivo it transfers electrons to nicotinamide adenine dinucleotide phosphate (NADP(+)), forming its reduced version, NADPH, while in vitro it can also use NADPH to reduce several substrates, such as ferricyanide, various quinones and nitriles. As an oxidoreductase catalyzing reaction of a broad range of substrates, FNR may be used in biotechnological processes. Quantum dots are semiconductor nanocrystals of a few to several nanometers diameter, having very useful luminescent properties. We present the spectroscopic and functional characteristics of a covalent conjugation of FNR and CdSe/ZnS quantum dots. Two types of quantum dots, of different diameter and emission maximum (550 and 650 nm), were used for comparison. Steady-state fluorescence and gel electrophoresis confirmed efficient conjugation, while fluorescence correlation spectroscopy (FCS) allowed for determination of the conjugates' radii. The nanohybrids sustained enzymatic activity; however, changes in maximal reaction rates and Michaelis constant were found. Detailed analysis of the kinetic parameters showed that the changes in the enzyme activity depend on the substrate used for activity measurement but also on the size of the quantum dots. The presented nanohybrids, as the first example using plant and photosynthetic enzyme as a protein partner, may became a tool to study photosynthesis as well as other biosynthetic and biotechnological processes, involving enzymatically catalyzed electron transfer.