Analysis of non-structural proteins,NSPs of SARS-CoV-2 as targets for computational drug designing

BackgroundThe ORF1ab of Severe Acute Respiratory Syndrome, SARS Corona Virus, SARS-CoV-2 genome is processed into 15 non-structural proteins, NSPs by proteases and each NSP has a specific role in the life cycle and pathogenicity of the virus. This research analyzes possible drugs for these proteins as targets in computational drug designing using already available experimental drugs from the drug bank database.MethodsOut of 471 proteins and 8820 drugs download from Protein Data Bank, PDB and Drug Bank database respectively, 16 proteins similar to NSP 1–15 and 31 drugs as per the “Rule of three” were selected for docking. Out of 88 docking results using PyRx, 18 proteins/chains with three promising drugs, DB01977, DB07132 and DB07535 were analyzed using PyMOL for final results.ResultsNSPs 3, 5, 11, 14 and 15 were identified as targets for the drugs, DB01977, BD07132 and DB07535. Drugs, DB01977 and DB07535 bind in the same binding pockets of NSP 5 and NSP 15. Drug, DB07132 binds with more number of residues when compared with the other two drugs and this indicates that the strength of protein-drug association is more by this drug with the NSPs than other drugs. Binding pockets of NSPs for these three drugs are very close with many sharing residues in common suggesting of similarity of pharmacophore of these drugs with the target binding pockets.ConclusionThe binding pockets of NSPs are well matched with the pharmacophore of drugs and with polar surface of drugs less than or equal to 100 A², drugs, DB01977, DB07132 and DB07535 bind individually and effectively with NSPs 3, 5, 11, 14 and 15 of ORF1ab of SARS-CoV-2 genome to bring changes in the activity of SARS-CoV-2 which may be useful for biological and clinical considerations.     

Staphylococcus aureus protects its immune‐evasion proteins against degradation by neutrophil serine proteases

Neutrophils store large quantities of neutrophil serine proteases (NSPs) that contribute, via multiple mechanisms, to antibacterial immune defences. Even though neutrophils are indispensable in fighting Staphylococcus aureus infections, the importance of NSPs in anti‐staphylococcal defence is yet unknown. However, the fact that S. aureus produces three highly specific inhibitors for NSPs [the extracellular adherence proteins (EAPs) Eap, EapH1 and EapH2], suggests that these proteases are important for host defences against this bacterium. In this study we demonstrate that NSPs can inactivate secreted virulence factors of S. aureus and that EAP proteins function to prevent this degradation. Specifically, we find that a large group of S. aureus immune‐evasion proteins is vulnerable to proteolytic inactivation by NSPs. In most cases, NSP cleavage leads to functional inactivation of virulence proteins. Interestingly, proteins with similar immune‐escape functions appeared to have differential cleavage sensitivity towards NSPs. Using targeted mutagenesis and complementation analyses in S. aureus, we demonstrate that all EAP proteins can protect other virulence factors from NSP degradation in complex bacterial supernatants. These findings show that NSPs inactivate S. aureus virulence factors. Moreover, the protection by EAP proteins can explain why this antibacterial function of NSPs was masked in previous studies. Furthermore, our results indicate that therapeutic inactivation of EAP proteins can help to restore the natural host immune defences against S. aureus.     

Classifying Ten Types of Major Cancers Based on Reverse Phase Protein Array Profiles

Gathering vast data sets of cancer genomes requires more efficient and autonomous procedures to classify cancer types and to discover a few essential genes to distinguish different cancers. Because protein expression is more stable than gene expression, we chose reverse phase protein array (RPPA) data, a powerful and robust antibody-based high-throughput approach for targeted proteomics, to perform our research. In this study, we proposed a computational framework to classify the patient samples into ten major cancer types based on the RPPA data using the SMO (Sequential minimal optimization) method. A careful feature selection procedure was employed to select 23 important proteins from the total of 187 proteins by mRMR (minimum Redundancy Maximum Relevance Feature Selection) and IFS (Incremental Feature Selection) on the training set. By using the 23 proteins, we successfully classified the ten cancer types with an MCC (Matthews Correlation Coefficient) of 0.904 on the training set, evaluated by 10-fold cross-validation, and an MCC of 0.936 on an independent test set. Further analysis of these 23 proteins was performed. Most of these proteins can present the hallmarks of cancer; Chk2, for example, plays an important role in the proliferation of cancer cells. Our analysis of these 23 proteins lends credence to the importance of these genes as indicators of cancer classification. We also believe our methods and findings may shed light on the discoveries of specific biomarkers of different types of cancers.     

Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts

The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions. The study of these maps provides opportunities for future toxicogenomics and drug discovery applications. We developed a computational framework to build disease-specific drug-protein connectivity maps. We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples. We described the development and application of this computational framework using Alzheimer''s Disease (AD) as a primary example in three steps. First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins. Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies. Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature. We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment. Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts. To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results. Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for drug development, in which therapeutic/toxicological profiles of candidate drugs can be checked computationally before costly clinical trials begin.     

Degradation of Newly Synthesized Polypeptides by Ribosome-Associated RACK1/c-Jun N-Terminal Kinase/Eukaryotic Elongation Factor 1A2 Complex

Folding of newly synthesized polypeptides (NSPs) into functional proteins is a highly regulated process. Rigorous quality control ensures that NSPs attain their native fold during or shortly after completion of translation. Nonetheless, signaling pathways that govern the degradation of NSPs in mammals remain elusive. We demonstrate that the stress-induced c-Jun N-terminal kinase (JNK) is recruited to ribosomes by the receptor for activated protein C kinase 1 (RACK1). RACK1 is an integral component of the 40S ribosome and an adaptor for protein kinases. Ribosome-associated JNK phosphorylates the eukaryotic translation elongation factor 1A isoform 2 (eEF1A2) on serines 205 and 358 to promote degradation of NSPs by the proteasome. These findings establish a role for a RACK1/JNK/eEF1A2 complex in the quality control of NSPs in response to stress.     

Probabilistic model of the human protein-protein interaction network

A catalog of all human protein-protein interactions would provide scientists with a framework to study protein deregulation in complex diseases such as cancer. Here we demonstrate that a probabilistic analysis integrating model organism interactome data, protein domain data, genome-wide gene expression data and functional annotation data predicts nearly 40,000 protein-protein interactions in humans-a result comparable to those obtained with experimental and computational approaches in model organisms. We validated the accuracy of the predictive model on an independent test set of known interactions and also experimentally confirmed two predicted interactions relevant to human cancer, implicating uncharacterized proteins into definitive pathways. We also applied the human interactome network to cancer genomics data and identified several interaction subnetworks activated in cancer. This integrative analysis provides a comprehensive framework for exploring the human protein interaction network.     

Towards an efficient and robust foot classification from pedobarographic images

This paper presents a new computational framework for automatic foot classification from digital plantar pressure images. It classifies the foot as left or right and simultaneously calculates two well-known footprint indices: the Cavanagh's arch index (AI) and the modified AI. The accuracy of the framework was evaluated using a set of plantar pressure images from two common pedobarographic devices. The results were outstanding, as all feet under analysis were correctly classified as left or right and no significant differences were observed between the footprint indices calculated using the computational solution and the traditional manual method. The robustness of the proposed framework to arbitrary foot orientations and to the acquisition device was also tested and confirmed.     

Interactions in native binding sites cause a large change in protein dynamics

Cellular functions are regulated by molecules that interact with proteins and alter their activities. To enable such control, protein activity, and therefore protein conformational distributions, must be susceptible to alteration by molecular interactions at functional sites. Here we investigate whether interactions at functional sites cause a large change in the protein conformational distribution. We apply a computational method, called dynamics perturbation analysis (DPA), to identify sites at which interactions have a large allosteric potential D(x), which is the Kullback-Leibler divergence between protein conformational distributions with and without an interaction. In DPA, a protein is decorated with surface points that interact with neighboring protein atoms, and D(x) is calculated for each of the points in a coarse-grained model of protein vibrations. We use DPA to examine hundreds of protein structures from a standard small-molecule docking test set, and find that ligand-binding sites have elevated values of D(x): for 95% of proteins, the probability of randomly obtaining values as high as those in the binding site is 10(-3) or smaller. We then use DPA to develop a computational method to predict functional sites in proteins, and find that the method accurately predicts ligand-binding-site residues for proteins in the test set. The performance of this method compares favorably with that of a cleft analysis method. The results confirm that interactions at small-molecule binding sites cause a large change in the protein conformational distribution, and motivate using DPA for large-scale prediction of functional sites in proteins. They also suggest that natural selection favors proteins whose activities are capable of being regulated by molecular interactions.     

Four-body scoring function for mutagenesis

MOTIVATION: There is a need for an efficient and accurate computational method to identify the effects of single- and multiple-residue mutations on the stability and reactivity of proteins. Such a method should ideally be consistent and yet applicable in a widespread manner, i.e. it should be applied to various proteins under the same parameter settings, and have good predictive power for all of them. RESULTS: We develop a Delaunay tessellation-based four-body scoring function to predict the effects of single- and multiple-residue mutations on the stability and reactivity of proteins. We test our scoring function on sets of single-point mutations used by several previous studies. We also assemble a new, diverse set of 237 single- and multiple-residue mutations, from over 24 different publications. The four-body scoring function correctly predicted the changes to the stability of 169 out of 210 mutants (80.5%), and the changes to the reactivity of 17 out of 27 mutants (63%). For the mutants that had the changes in stability/reactivity quantified (using reaction rates, temperatures, etc.), an average Spearman rank correlation coefficient of 0.67 was achieved with the four-body scores. We also develop an efficient method for screening huge numbers of mutants of a protein, called combinatorial mutagenesis. In one study, 64 million mutants of a cold-shock nucleus binding domain protein 1CSQ, with six of its residues being changed to all possible (20) amino acids, were screened within a few hours on a PC, and all five stabilizing mutants reported were correctly identified as stabilizing by combinatorial mutagenesis.     

Structural Characterization of Mouse Neutrophil Serine Proteases and Identification of Their Substrate Specificities: RELEVANCE TO MOUSE MODELS OF HUMAN INFLAMMATORY DISEASES*

It is widely accepted that neutrophil serine proteases (NSPs) play a critical role in neutrophil-associated lung inflammatory and tissue-destructive diseases. To investigate NSP pathogenic role(s), various mouse experimental models have been developed that mimic acutely or chronically injured human lungs. We and others are using mouse exposure to cigarette smoke as a model for chronic obstructive pulmonary disease with or without exacerbation. However, the relative contribution of NSPs to lung disease processes as well as their underlying mechanisms remains still poorly understood. And the lack of purified mouse NSPs and their specific substrates have hampered advances in these studies. In this work, we compared mouse and human NSPs and generated three-dimensional models of murine NSPs based on three-dimensional structures of their human homologs. Analyses of these models provided compelling evidence that peptide substrate specificities of human and mouse NSPs are different despite their conserved cleft and close structural resemblance. These studies allowed us to synthesize for the first time novel sensitive fluorescence resonance energy transfer substrates for individual mouse NSPs. Our findings and the newly identified substrates should better our understanding about the role of NSPs in the pathogenesis of cigarette-associated chronic obstructive pulmonary disease as well as other neutrophils-associated inflammatory diseases.     

Reduction of Soybean Meal Non-Starch Polysaccharides and ��-Galactosides by Solid-State Fermentation Using Cellulolytic Bacteria Obtained from Different Environments

Soybean meal (SBM) is an important protein source in animal feed. However, the levels of SBM inclusion are restricted in some animal species by the presence of antinutritional factors (ANFs), including non-starch polysaccharides (NSPs) and α-galactosides (GOSs). The aim of this study was to reduce the soybean meal NSPs and GOSs by solid-state fermentation (SSF) using a combination of cellulolytic bacteria isolated from different environments (termites, earthworms, corn silage and bovine ruminal content). To analyse the key enzymatic activities, the isolates were grown in minimal media containing NSPs extracted from SBM. The selected bacterial strains belonged to the genera Streptomyces, Cohnella and Cellulosimicrobium. SSF resulted in a reduction of nearly 24% in the total NSPs, 83% of stachyose and 69% of raffinose and an increase in the protein content. These results suggest that cellulolytic bacteria-based SSF processing facilitates SBM nutritional improvement. In addition, the use of fermented SBM in animal diets can be recommended.     

Systematic prediction of human membrane receptor interactions

Membrane receptor‐activated signal transduction pathways are integral to cellular functions and disease mechanisms in humans. Identification of the full set of proteins interacting with membrane receptors by high‐throughput experimental means is difficult because methods to directly identify protein interactions are largely not applicable to membrane proteins. Unlike prior approaches that attempted to predict the global human interactome, we used a computational strategy that only focused on discovering the interacting partners of human membrane receptors leading to improved results for these proteins. We predict specific interactions based on statistical integration of biological data containing highly informative direct and indirect evidences together with feedback from experts. The predicted membrane receptor interactome provides a system‐wide view, and generates new biological hypotheses regarding interactions between membrane receptors and other proteins. We have experimentally validated a number of these interactions. The results suggest that a framework of systematically integrating computational predictions, global analyses, biological experimentation and expert feedback is a feasible strategy to study the human membrane receptor interactome.     

Barriers and Potential Improvements for Needle and Syringe Exchange Programs (NSPs) in China: A Qualitative Study from Perspectives of Both Health and Public Security Sectors

This study explores the acceptability, the barriers to the implementation of needle and syringe exchange programs (NSPs) and the potential improvement strategies in China from the perspectives of governmental health and public security officials. Purposive sampling was used for recruitment of participants who had been involved in NSPs implementation. Semi-Structured individual interviews were conducted in Mandarin to address three aspects of NSPs: (1) participants’ attitudes towards NSPs, (2) participants’ opinions on the effectiveness and barriers of NSPs, and (3) suggestions for improving the program. Content analysis was used to analyse the translated interview data. A total of 68 participants from 12 Hunan counties were interviewed (34 from each of the Bureau of Health and the Narcotic Division). Both groups recognised the importance and effectiveness of NSPs in HIV prevention, but public security officials regarded NSPs as a temporary intervention in place of punitive measures. Most health officials (32/34) regarded the main barriers to its implementation as administrative and structural, whereas participants from Narcotics Division (n=24) questioned the legitimacy of NSPs and concerned about the poor management of drug users’ risk behaviours. Close cooperation between the health and public security sectors, engagement of the drug user community and an enabling policy environment were reportedly to be critical for potential improvements of NSPs in China. Misconceptions about NSPs encourage drug users’ addictive behaviour, and an unclear leadership and insufficient support de-motivate the participants from the Bureau of Health and the Narcotics Division to actively support the program implementation.     

PreSPI: a domain combination based prediction system for protein-protein interaction

With the accumulation of protein and its related data on the Internet, many domain-based computational techniques to predict protein interactions have been developed. However, most techniques still have many limitations when used in real fields. They usually suffer from low accuracy in prediction and do not provide any interaction possibility ranking method for multiple protein pairs. In this paper, we propose a probabilistic framework to predict the interaction probability of proteins and develop an interaction possibility ranking method for multiple protein pairs. Using the ranking method, one can discern the protein pairs that are more likely to interact with each other in multiple protein pairs. The validity of the prediction model was evaluated using an interacting set of protein pairs in yeast and an artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in the DIP (Database of Interacting Proteins) was used as a learning set of interacting protein pairs, high sensitivity (77%) and specificity (95%) were achieved for the test groups containing common domains with the learning set of proteins within our framework. The stability of the prediction model was also evident when tested over DIP CORE, HMS-PCI and TAP data. In the validation of the ranking method, we reveal that some correlations exist between the interacting probability and the accuracy of the prediction.     

171 Protein structure prediction with limited data

Proteins need to interact with other molecules in order to carry out their biological role. Knowing the protein structure is crucial to study these interactions and it can for example lead to drug design. However, the cost of determining the structure of a protein with current experimental techniques is very high – both in time and money. In the absence of experimental structures, current computational tools are not always able to correctly predict the native fold. We are using a physics based computational framework to determine the structure of proteins. The pipeline is designed to handle sparse data coming from evolution, bioinformatics or experiments (solid state NMR, cross linking, …). The data is transformed into a set of restraints used in our physical simulations. However, we require that only a subset of the input information is satisfied. This is done to account for uncertainties in the input data. The framework uses a Hamiltonian-temperature replica exchange formalism that allows the system to choose what data is compatible with the physics of the system. I will show some results on how this methodology can help us in both protein structure refinement and protein structure prediction.     

Towards an efficient and robust foot classification from pedobarographic images

This paper presents a new computational framework for automatic foot classification from digital plantar pressure images. It classifies the foot as left or right and simultaneously calculates two well-known footprint indices: the Cavanagh's arch index (AI) and the modified AI. The accuracy of the framework was evaluated using a set of plantar pressure images from two common pedobarographic devices. The results were outstanding, as all feet under analysis were correctly classified as left or right and no significant differences were observed between the footprint indices calculated using the computational solution and the traditional manual method. The robustness of the proposed framework to arbitrary foot orientations and to the acquisition device was also tested and confirmed.     

Refining conformational ensembles of flexible proteins against small-angle x-ray scattering data

Intrinsically disordered proteins and flexible regions in multidomain proteins display substantial conformational heterogeneity. Characterizing the conformational ensembles of these proteins in solution typically requires combining one or more biophysical techniques with computational modeling or simulations. Experimental data can either be used to assess the accuracy of a computational model or to refine the computational model to get a better agreement with the experimental data. In both cases, one generally needs a so-called forward model (i.e., an algorithm to calculate experimental observables from individual conformations or ensembles). In many cases, this involves one or more parameters that need to be set, and it is not always trivial to determine the optimal values or to understand the impact on the choice of parameters. For example, in the case of small-angle x-ray scattering (SAXS) experiments, many forward models include parameters that describe the contribution of the hydration layer and displaced solvent to the background-subtracted experimental data. Often, one also needs to fit a scale factor and a constant background for the SAXS data but across the entire ensemble. Here, we present a protocol to dissect the effect of the free parameters on the calculated SAXS intensities and to identify a reliable set of values. We have implemented this procedure in our Bayesian/maximum entropy framework for ensemble refinement and demonstrate the results on four intrinsically disordered proteins and a protein with three domains connected by flexible linkers. Our results show that the resulting ensembles can depend on the parameters used for solvent effects and suggest that these should be chosen carefully. We also find a set of parameters that work robustly across all proteins.     

Characterization of Neural Stem/Progenitor Cells Expressing VEGF and its Receptors in the Subventricular Zone of Newborn Piglet Brain

Neural stem/progenitor cell (NSP) biology and neurogenesis in adult central nervous system (CNS) are important both towards potential future therapeutic applications for CNS repair, and for the fundamental function of the CNS. In the present study, we report the characterization of NSP population from subventricular zone (SVZ) of neonatal piglet brain using in vivo and in vitro systems. We show that the nestin and vimentin-positive neural progenitor cells are present in the SVZ of the lateral ventricles of neonatal piglet brain. In vitro, piglet NSPs proliferated as neurospheres, expressed the typical protein of neural progenitors, nestin and a range of well-established neurodevelopmental markers. Upon dissociation and subculture, piglet NSPs differentiated into neurons and glial cells. Clonal analysis demonstrates that piglet NSPs are multipotent and retain the capacity to generate both glia and neurons. These cells expressed VEGF, VEGFR1, VEGFR2 and Neuropilin-1 and -2 mRNAs. Real time PCR revealed that SVZ NSPs from newborn piglet expressed total VEGF and all VEGF splice variants. These findings show that piglet NSPs may be helpful to more effectively design growth factor based strategies to enhance endogenous precursor cells for cell transplantation studies potentially leading to the application of this strategy in the nervous system disease and injury.     

Analysis of characters and classification based on information theory in the species of the Greater Antilles of the fern familiy Gleicheniaceae

We define a set of characters for the study of the fern family Gleicheniaceae. These characters have no ordered states. We establish a classification, through a divisive strategy, on the basis of these characters applied to the species occurring in the Greater Antilles. We used a technique based on information theory. A computational program was implemented to obtain this classification. With the same technique a procedure is used in order to compare a set of classifications reached under a different set of characters. The results obtained under three classifications on the family of this study are shown.     

Comparison of proteins based on segments structural similarity

We present here a simple method for fast and accurate comparison of proteins using their structures. The algorithm is based on structural alignment of segments of Calpha chains (with size of 99 or 199 residues). The method is optimized in terms of speed and accuracy. We test it on 97 representative proteins with the similarity measure based on the SCOP classification. We compare our algorithm with the LGscore2 automatic method. Our method has the same accuracy as the LGscore2 algorithm with much faster processing of the whole test set, which is promising. A second test is done using the ToolShop structure prediction evaluation program and shows that our tool is on average slightly less sensitive than the DALI server. Both algorithms give a similar number of correct models, however, the final alignment quality is better in the case of DALI. Our method was implemented under the name 3D-Hit as a web server at http://3dhit.bioinfo.pl/ free for academic use, with a weekly updated database containing a set of 5000 structures from the Protein Data Bank with non-homologous sequences.