356 research outputs found

    Specific protein-protein binding in many-component mixtures of proteins

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    Proteins must bind to specific other proteins in vivo in order to function. The proteins must bind only to one or a few other proteins of the of order a thousand proteins typically present in vivo. Using a simple model of a protein, specific binding in many component mixtures is studied. It is found to be a demanding function in the sense that it demands that the binding sites of the proteins be encoded by long sequences of bits, and the requirement for specific binding then strongly constrains these sequences. This is quantified by the capacity of proteins of a given size (sequence length), which is the maximum number of specific-binding interactions possible in a mixture. This calculation of the maximum number possible is in the same spirit as the work of Shannon and others on the maximum rate of communication through noisy channels.Comment: 13 pages, 3 figures (changes for v2 mainly notational - to be more in line with notation in information theory literature

    Today\u27s Fibromyalgia

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    Fibromyalgia is a chronic musculoskeletal disorder which results in widespread pain, fatigue, cognitive difficulties, and emotional distress (CDC, 2017). It is also associated with abnormal pain processing. About 2% of US adults have Fibromyalgia, and the disease is more common in middle-aged women, particularly those who have other illnesses like Lupus or Rheumatoid Arthritis (CDC, 2017). The exact cause of the disease is unknown, but it is likely a combination of genetics, infections and physical and emotional trauma. Since the exact cause of Fibromyalgia is unknown, treatment options vary. Medication, stress management techniques and an exercise plan are all examples of treatments used for Fibromyalgia. Treatment is important due to the negative impact Fibromyalgia has on life functioning. Fibromyalgia research has progressed in recent years as more clinicians put definitive diagnostic measures in place, researchers investigated various theories on causes and they have continued to explore new treatment options

    In silico Analyses of Immune System Protein Interactome Network, Single-Cell RNA Sequencing of Human Tissues, and Artificial Neural Networks Reveal Potential Therapeutic Targets for Drug Repurposing Against COVID-19

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    Background: There is pressing urgency to identify therapeutic targets and drugs that allow treating COVID-19 patients effectively.Methods: We performed in silico analyses of immune system protein interactome network, single-cell RNA sequencing of human tissues, and artificial neural networks to reveal potential therapeutic targets for drug repurposing against COVID-19.Results: We screened 1,584 high-confidence immune system proteins in ACE2 and TMPRSS2 co-expressing cells, finding 25 potential therapeutic targets significantly overexpressed in nasal goblet secretory cells, lung type II pneumocytes, and ileal absorptive enterocytes of patients with several immunopathologies. Then, we performed fully connected deep neural networks to find the best multitask classification model to predict the activity of 10,672 drugs, obtaining several approved drugs, compounds under investigation, and experimental compounds with the highest area under the receiver operating characteristics.Conclusion: After being effectively analyzed in clinical trials, these drugs can be considered for treatment of severe COVID-19 patients. Scripts can be downloaded at

    The evolution and functional repertoire of translation proteins following the origin of life

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    <p>Abstract</p> <p>Background</p> <p>The RNA world hypothesis posits that the earliest genetic system consisted of informational RNA molecules that directed the synthesis of modestly functional RNA molecules. Further evidence suggests that it was within this RNA-based genetic system that life developed the ability to synthesize proteins by translating genetic code. Here we investigate the early development of the translation system through an evolutionary survey of protein architectures associated with modern translation.</p> <p>Results</p> <p>Our analysis reveals a structural expansion of translation proteins immediately following the RNA world and well before the establishment of the DNA genome. Subsequent functional annotation shows that representatives of the ten most ancestral protein architectures are responsible for all of the core protein functions found in modern translation.</p> <p>Conclusions</p> <p>We propose that this early robust translation system evolved by virtue of a positive feedback cycle in which the system was able to create increasingly complex proteins to further enhance its own function.</p> <p>Reviewers</p> <p>This article was reviewed by Janet Siefert, George Fox, and Antonio Lazcano (nominated by Laura Landweber)</p

    Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

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    With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

    A systematic approach for peptide characterization of B-cell receptor in chronic lymphocytic leukemia cells

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    A wide variety of immunoglobulins (Ig) is produced by the immune system thanks to different mechanisms (V(D)J recombination, somatic hypermutation, and antigen selection). The profiling of Ig sequences (at both DNA and peptide levels) are of great relevance to developing targeted vaccines or treatments for specific diseases or infections. Thus, genomics and proteomics techniques (such as Next- Generation Sequencing (NGS) and mass spectrometry (MS)) have notably increased the knowledge in Ig sequencing and serum Ig peptide profiling in a high-throughput manner. However, the peptide characterization of membrane-bound Ig (e.g., B-cell receptors, BCR) is still a challenge mainly due to the poor recovery of mentioned Ig. Herein, we have evaluated three different sample processing methods for peptide sequencing of BCR belonging to chronic lymphocytic leukemia (CLL) B cells identifying up to 426 different peptide sequences (MS/MS data are available via ProteomeXchange with identifier PXD004466). Moreover, as a consequence of the results here obtained, recommended guidelines have been described for BCR-sequencing of B-CLL samples by MS approaches. For this purpose, an in-house algorithm has been designed and developed to compare the MS/MS results with those obtained by molecular biology in order to integrate both proteomics and genomics results and establish the steps to follow when sequencing membrane-bound Ig by MS/MS.We gratefully acknowledge financial support from the Spanish Health Institute Carlos III (ISCIII) for the grants: FIS PI11/02114 and FIS PI114/01538. We also acknowledge Fondos FEDER (EU) and Junta Castilla LeĂłn (grant BIO/SA07/15). This work has been also sponsored by FundaciĂłn SolĂłrzano (FS/23-2015). The Proteomics Unit belongs to ProteoRed, PRB2-ISCIII, supported by grant PT13/0001, of the PE I+D+I 2013-2016, funded by ISCIII and FEDER. The authors would like to thank all the clinicians and technicians in the Cytometry and Cell Purification Services of the University of Salamanca, the Spanish National DNA Bank (Banco Nacional de DNA Carlos III, University of Salamanca) and the Genomic Unit of Cancer Research Centre (IBMCC, USAL-CSIC) for their support in the data collection for the preparation of this manuscript. P.D. is supported by a JCYL-EDU/346/2013 Ph.D. scholarship.Peer Reviewe
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