Development of electrophysiological properties of fusiform neurons from the dorsal cochlear nucleus of mice before and after hearing onset.

The dorsal cochlear nucleus (DCN) in the auditory brainstem integrates auditory and somatosensory information. Mature DCN fusiform neurons fall into two qualitatively distinct types: quiet, with no spontaneous regular action potential firing, or active, with regular spontaneous action potential firing. However, how these firing states and other electrophysiological properties of fusiform neurons develop during early postnatal days to adulthood is not known. Thus, we recorded fusiform neurons from mice from P4 to P21 and analyzed their electrophysiological properties. In the prehearing phase (P4-P13), we found that most fusiform neurons are quiet, with active neurons emerging after hearing onset at P14. Subthreshold properties underwent significant changes before hearing onset, whereas changes to the action potential waveform occurred mainly after P14, with the depolarization and repolarization phases becoming markedly faster and half-width significantly decreased. The activity threshold in posthearing neurons was more negative than in prehearing cells. Persistent sodium current (INaP) was increased after P14, coinciding with the emergence of spontaneous firing. Thus, we suggest that posthearing expression of INaP leads to hyperpolarization of the activity threshold and the active state of the fusiform neuron. At the same time, other changes refine the passive membrane properties and increase the speed of action potential firing of fusiform neurons.NEW & NOTEWORTHY Auditory brainstem neurons express unique electrophysiological properties adapted for their complex physiological functions that develop before hearing onset. Fusiform neurons of the DCN present two firing states, quiet and active, but the origin of these states is not known. Here, we showed that the quiet and active states develop after hearing onset at P14, along with changes in action potentials, suggesting an influence of auditory input on the refining of fusiform neuron's excitability.

Profile of Myracrodruon urundeuva Volatile Compounds Ease of Extraction and Biodegradability and In Silico Evaluation of Their Interactions with COX-1 and iNOS.

Myracrodruon urundeuva Fr. Allem. (Anacardiaceae) is a tree popularly known as the "aroeira-do-sertão", native to the caatinga and cerrado biomes, with a natural dispersion ranging from the Northeast, Midwest, to Southeast Brazil. Its wood is highly valued and overexploited, due to its characteristics such as durability and resistance to decaying. The diversity of chemical constituents in aroeira seed has shown biological properties against microorganisms and helminths. As such, this work aimed to identify the profile of volatile compounds present in aroeira seeds. Headspace solid phase microextraction was employed (HS-SPME) using semi-polar polydimethylsiloxane-divinylbenzene fiber (PDMS/DVB) for the extraction of VOCs. 22 volatile organic compounds were identified: nine monoterpenes and eight sesquiterpenes, in addition to six compounds belonging to different chemical classes such as fatty acids, terpenoids, salicylates and others. Those that stood out were p-mentha-1,4, 4(8)-diene, 3-carene (found in all samples), caryophyllene and cis-geranylacetone. A virtual docking analysis suggested that around 65% of the VOCs molar content from the aroeiras seeds present moderate a strong ability to bind to cyclooxygenase I (COX-I) active site, oxide nitric synthase (iNOS) active site (iNOSas) or to iNOS cofactor site (iNOScs), corroborating an anti-inflamatory potential. A pharmacophoric descriptor analysis allowed to infer the more determinant characteristics of these compounds' conferring affinity to each site. Taken together, our results illustrate the high applicability for the integrated use of SPME, in silico virtual screening and chemoinformatics tools at the profiling of the biotechnological and pharmaceutical potential of natural sources.

A higher flexibility at the SARS-CoV-2 main protease active site compared to SARS-CoV and its potentialities for new inhibitor virtual screening targeting multi-conformers.

The main-protease (Mpro) catalyzes a crucial step for the SARS-CoV-2 life cycle. The recent SARS-CoV-2 presents the main protease (MCoV2pro) with 12 mutations compared to SARS-CoV (MCoV1pro). Recent studies point out that these subtle differences lead to mobility variances at the active site loops with functional implications. We use metadynamics simulations and a sort of computational analysis to probe the dynamic, pharmacophoric and catalytic environment differences between the monomers of both enzymes. So, we verify how much intrinsic distinctions are preserved in the functional dimer of MCoV2pro, as well as its implications for ligand accessibility and optimized drug screening. We find a significantly higher accessibility to open binding conformers in the MCoV2pro monomer compared to MCoV1pro. A higher hydration propensity for the MCoV2pro S2 loop with the A46S substitution seems to exercise a key role. Quantum calculations suggest that the wider conformations for MCoV2pro are less catalytically active in the monomer. However, the statistics for contacts involving the N-finger suggest higher maintenance of this activity at the dimer. Docking analyses suggest that the ability to vary the active site width can be important to improve the access of the ligand to the active site in different ways. So, we carry out a multiconformational virtual screening with different ligand bases. The results point to the importance of taking into account the protein conformational multiplicity for new promissors anti MCoV2pro ligands. We hope these results will be useful in prospecting, repurposing and/or designing new anti SARS-CoV-2 drugs.Communicated by Ramaswamy H. Sarma.

EasyVS: a user-friendly web-based tool for molecule library selection and structure-based virtual screening.

SUMMARY: EasyVS is a web-based platform built to simplify molecule library selection and virtual screening. With an intuitive interface, the tool allows users to go from selecting a protein target with a known structure and tailoring a purchasable molecule library to performing and visualizing docking in a few clicks. Our system also allows users to filter screening libraries based on molecule properties, cluster molecules by similarity and personalize docking parameters. AVAILABILITY AND IMPLEMENTATION: EasyVS is freely available as an easy-to-use web interface at http://biosig.unimelb.edu.au/easyvs. CONTACT: douglas.pires@unimelb.edu.au or david.ascher@unimelb.edu.au. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

visGReMLIN: graph mining-based detection and visualization of conserved motifs at 3D protein-ligand interface at the atomic level.

BACKGROUND: Interactions between proteins and non-proteic small molecule ligands play important roles in the biological processes of living systems. Thus, the development of computational methods to support our understanding of the ligand-receptor recognition process is of fundamental importance since these methods are a major step towards ligand prediction, target identification, lead discovery, and more. This article presents visGReMLIN, a web server that couples a graph mining-based strategy to detect motifs at the protein-ligand interface with an interactive platform to visually explore and interpret these motifs in the context of protein-ligand interfaces. RESULTS: To illustrate the potential of visGReMLIN, we conducted two cases in which our strategy was compared with previous experimentally and computationally determined results. visGReMLIN allowed us to detect patterns previously documented in the literature in a totally visual manner. In addition, we found some motifs that we believe are relevant to protein-ligand interactions in the analyzed datasets. CONCLUSIONS: We aimed to build a visual analytics-oriented web server to detect and visualize common motifs at the protein-ligand interface. visGReMLIN motifs can support users in gaining insights on the key atoms/residues responsible for protein-ligand interactions in a dataset of complexes.

PDBest: a user-friendly platform for manipulating and enhancing protein structures.

UNLABELLED: PDBest (PDB Enhanced Structures Toolkit) is a user-friendly, freely available platform for acquiring, manipulating and normalizing protein structures in a high-throughput and seamless fashion. With an intuitive graphical interface it allows users with no programming background to download and manipulate their files. The platform also exports protocols, enabling users to easily share PDB searching and filtering criteria, enhancing analysis reproducibility. AVAILABILITY AND IMPLEMENTATION: PDBest installation packages are freely available for several platforms at http://www.pdbest.dcc.ufmg.br CONTACT: wellisson@dcc.ufmg.br, dpires@dcc.ufmg.br, raquelcm@dcc.ufmg.br SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

aCSM: noise-free graph-based signatures to large-scale receptor-based ligand prediction.

MOTIVATION: Receptor-ligand interactions are a central phenomenon in most biological systems. They are characterized by molecular recognition, a complex process mainly driven by physicochemical and structural properties of both receptor and ligand. Understanding and predicting these interactions are major steps towards protein ligand prediction, target identification, lead discovery and drug design. RESULTS: We propose a novel graph-based-binding pocket signature called aCSM, which proved to be efficient and effective in handling large-scale protein ligand prediction tasks. We compare our results with those described in the literature and demonstrate that our algorithm overcomes the competitor's techniques. Finally, we predict novel ligands for proteins from Trypanosoma cruzi, the parasite responsible for Chagas disease, and validate them in silico via a docking protocol, showing the applicability of the method in suggesting ligands for pockets in a real-world scenario. AVAILABILITY AND IMPLEMENTATION: Datasets and the source code are available at http://www.dcc.ufmg.br/∼dpires/acsm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cutoff Scanning Matrix (CSM): structural classification and function prediction by protein inter-residue distance patterns.

BACKGROUND: The unforgiving pace of growth of available biological data has increased the demand for efficient and scalable paradigms, models and methodologies for automatic annotation. In this paper, we present a novel structure-based protein function prediction and structural classification method: Cutoff Scanning Matrix (CSM). CSM generates feature vectors that represent distance patterns between protein residues. These feature vectors are then used as evidence for classification. Singular value decomposition is used as a preprocessing step to reduce dimensionality and noise. The aspect of protein function considered in the present work is enzyme activity. A series of experiments was performed on datasets based on Enzyme Commission (EC) numbers and mechanistically different enzyme superfamilies as well as other datasets derived from SCOP release 1.75. RESULTS: CSM was able to achieve a precision of up to 99% after SVD preprocessing for a database derived from manually curated protein superfamilies and up to 95% for a dataset of the 950 most-populated EC numbers. Moreover, we conducted experiments to verify our ability to assign SCOP class, superfamily, family and fold to protein domains. An experiment using the whole set of domains found in last SCOP version yielded high levels of precision and recall (up to 95%). Finally, we compared our structural classification results with those in the literature to place this work into context. Our method was capable of significantly improving the recall of a previous study while preserving a compatible precision level. CONCLUSIONS: We showed that the patterns derived from CSMs could effectively be used to predict protein function and thus help with automatic function annotation. We also demonstrated that our method is effective in structural classification tasks. These facts reinforce the idea that the pattern of inter-residue distances is an important component of family structural signatures. Furthermore, singular value decomposition provided a consistent increase in precision and recall, which makes it an important preprocessing step when dealing with noisy data.

Analysis of binding properties and specificity through identification of the interface forming residues (IFR) for serine proteases in silico docked to different inhibitors.

BACKGROUND: Enzymes belonging to the same super family of proteins in general operate on variety of substrates and are inhibited by wide selection of inhibitors. In this work our main objective was to expand the scope of studies that consider only the catalytic and binding pocket amino acids while analyzing enzyme specificity and instead, include a wider category which we have named the Interface Forming Residues (IFR). We were motivated to identify those amino acids with decreased accessibility to solvent after docking of different types of inhibitors to sub classes of serine proteases and then create a table (matrix) of all amino acid positions at the interface as well as their respective occupancies. Our goal is to establish a platform for analysis of the relationship between IFR characteristics and binding properties/specificity for bi-molecular complexes. RESULTS: We propose a novel method for describing binding properties and delineating serine proteases specificity by compiling an exhaustive table of interface forming residues (IFR) for serine proteases and their inhibitors. Currently, the Protein Data Bank (PDB) does not contain all the data that our analysis would require. Therefore, an in silico approach was designed for building corresponding complexes. The IFRs are obtained by "rigid body docking" among 70 structurally aligned, sequence wise non-redundant, serine protease structures with 3 inhibitors: bovine pancreatic trypsin inhibitor (BPTI), ecotine and ovomucoid third domain inhibitor. The table (matrix) of all amino acid positions at the interface and their respective occupancy is created. We also developed a new computational protocol for predicting IFRs for those complexes which were not deciphered experimentally so far, achieving accuracy of at least 0.97. CONCLUSIONS: The serine proteases interfaces prefer polar (including glycine) residues (with some exceptions). Charged residues were found to be uniquely prevalent at the interfaces between the "miscellaneous-virus" subfamily and the three inhibitors. This prompts speculation about how important this difference in IFR characteristics is for maintaining virulence of those organisms.Our work here provides a unique tool for both structure/function relationship analysis as well as a compilation of indicators detailing how the specificity of various serine proteases may have been achieved and/or could be altered. It also indicates that the interface forming residues which also determine specificity of serine protease subfamily can not be presented in a canonical way but rather as a matrix of alternative populations of amino acids occupying variety of IFR positions.

Protein cutoff scanning: A comparative analysis of cutoff dependent and cutoff free methods for prospecting contacts in proteins.

In this study, we carried out a comparative analysis between two classical methodologies to prospect residue contacts in proteins: the traditional cutoff dependent (CD) approach and cutoff free Delaunay tessellation (DT). In addition, two alternative coarse-grained forms to represent residues were tested: using alpha carbon (CA) and side chain geometric center (GC). A database was built, comprising three top classes: all alpha, all beta, and alpha/beta. We found that the cutoff value at about 7.0 A emerges as an important distance parameter. Up to 7.0 A, CD and DT properties are unified, which implies that at this distance all contacts are complete and legitimate (not occluded). We also have shown that DT has an intrinsic missing edges problem when mapping the first layer of neighbors. In proteins, it may produce systematic errors affecting mainly the contact network in beta chains with CA. The almost-Delaunay (AD) approach has been proposed to solve this DT problem. We found that even AD may not be an advantageous solution. As a consequence, in the strict range up to 7.0 A, the CD approach revealed to be a simpler, more complete, and reliable technique than DT or AD. Finally, we have shown that coarse-grained residue representations may introduce bias in the analysis of neighbors in cutoffs up to 6.8 A, with CA favoring alpha proteins and GC favoring beta proteins. This provides an additional argument pointing to the value of 7.0 A as an important lower bound cutoff to be used in contact analysis of proteins.