Trends in template/fragment-free protein structure prediction

Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward

Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images

Protein structure prediction and analysis are more significant for living organs to perfect asses the livingorgan functionalities. Several protein structure prediction methods use neural network (NN). However,the Hidden Markov model is more interpretable and effective for more biological data analysis comparedto the NN. It employs statistical data analysis to enhance the prediction accuracy. The current workproposed a protein prediction approach from protein images based on Hidden Markov Model andChapman Kolmogrov equation. Initially, a preprocessing stage was applied for protein imagesbinarization using Otsu technique in order to convert the protein image into binary matrix. Subsequently,two counting algorithms, namely the Flood fill and Warshall are employed to classify the proteinstructures. Finally, Hidden Markov model and Chapman Kolmogrov equation are applied on the classifiedstructures for predicting the protein structure. The execution time and algorithmic performances aremeasured to evaluate the primary, secondary and tertiary protein structure prediction

The detection of meningococcal disease through identification of antimicrobial peptides using an in silico model creation

Philosophiae Doctor - PhDNeisseria meningitidis (the meningococcus), the causative agent of meningococcal disease (MD) was identified in 1887 and despite effective antibiotics and partially effective vaccines, Neisseria meningitidis (N. meningitidis) is the leading cause worldwide of meningitis and rapidly fatal sepsis usually in otherwise healthy individuals. Over 500 000 meningococcal cases occur every year. These numbers have made bacterial meningitis a top ten infectious cause of death worldwide. MD primarily affects children under 5 years of age, although in epidemic outbreaks there is a shift in disease to older children, adolescents and adults. MD is also associated with marked morbidity including limb loss, hearing loss, cognitive dysfunction, visual impairment, educational difficulties, developmental delays, motor nerve deficits, seizure disorders and behavioural problems. Antimicrobial peptides (AMPs) are molecules that provide protection against environmental pathogens, acting against a large number of microorganisms, including bacteria, fungi, yeast and virus. AMPs production is a major component of innate immunity against infection. The chemical properties of AMPs allow them to insert into the anionic cell wall and phospholipid membranes of microorganisms or bind to the bacteria making it easily detectable for diagnostic purposes. AMPs can be exploited for the generation of novel antibiotics, as biomarkers in the diagnosis of inflammatory conditions, for the manipulation of the inflammatory process, wound healing, autoimmunity and in the combat of tumour cells. Due to the severity of meningitis, early detection and identification of the strain of N. meningitidis is vital. Rapid and accurate diagnosis is essential for optimal management of patients and a major problem for MD is its diagnostic difficulties and experts conclude that with an early intervention the patient’ prognosis will be much improved. It is becoming increasingly difficult to confirm the diagnosis of meningococcal infection by conventional methods. Although polymerase chain reaction (PCR) has the potential advantage of providing more rapid confirmation of the presence of the bacterium than culturing, it is still time consuming as well as costly. Introduction of AMPs to bind to N. meningitidis receptors could provide a less costly and time consuming solution to the current diagnostic problems. World Health Organization (WHO) meningococcal meningitis program activities encourage laboratory strengthening to ensure prompt and accurate diagnosis to rapidly confirm the presence of MD. This study aimed to identify a list of putative AMPs showing antibacterial activity to N. meningitidis to be used as ligands against receptors uniquely expressed by the bacterium and for the identified AMPs to be used in a Lateral Flow Device (LFD) for the rapid and accurate diagnosis of MD

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causesthe infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformaticstools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection,understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to getinsight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for theroutine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemicand evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets anddevelopment of therapeutic strategies. For each tool, we briefly describe its use case and how it advances researchspecifically for SARS-CoV-2.Fil: Hufsky, Franziska. Friedrich Schiller University Jena; AlemaniaFil: Lamkiewicz, Kevin. Friedrich Schiller University Jena; AlemaniaFil: Almeida, Alexandre. the Wellcome Sanger Institute; Reino UnidoFil: Aouacheria, Abdel. Centre National de la Recherche Scientifique; FranciaFil: Arighi, Cecilia. Biocuration and Literature Access at PIR; Estados UnidosFil: Bateman, Alex. European Bioinformatics Institute. Head of Protein Sequence Resources; Reino UnidoFil: Baumbach, Jan. Universitat Technical Zu Munich; AlemaniaFil: Beerenwinkel, Niko. Universitat Technical Zu Munich; AlemaniaFil: Brandt, Christian. Jena University Hospital; AlemaniaFil: Cacciabue, Marco Polo Domingo. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Chuguransky, Sara Rocío. European Bioinformatics Institute; Reino Unido. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Drechsel, Oliver. Robert Koch-Institute; AlemaniaFil: Finn, Robert D.. Biocurator for Pfam and InterPro databases; Reino UnidoFil: Fritz, Adrian. Helmholtz Centre for Infection Research; AlemaniaFil: Fuchs, Stephan. Robert Koch-Institute; AlemaniaFil: Hattab, Georges. University Marburg; AlemaniaFil: Hauschild, Anne Christin. University Marburg; AlemaniaFil: Heider, Dominik. University Marburg; AlemaniaFil: Hoffmann, Marie. Freie Universität Berlin; AlemaniaFil: Hölzer, Martin. Friedrich Schiller University Jena; AlemaniaFil: Hoops, Stefan. University of Virginia; Estados UnidosFil: Kaderali, Lars. University Medicine Greifswald; AlemaniaFil: Kalvari, Ioanna. European Bioinformatics Institute; Reino UnidoFil: von Kleist, Max. Robert Koch-Institute; AlemaniaFil: Kmiecinski, Renó. Robert Koch-Institute; AlemaniaFil: Kühnert, Denise. Max Planck Institute for the Science of Human History; AlemaniaFil: Lasso, Gorka. Albert Einstein College of Medicine; Estados UnidosFil: Libin, Pieter. Hasselt University; BélgicaFil: List, Markus. Universitat Technical Zu Munich; AlemaniaFil: Löchel, Hannah F.. University Marburg; Alemani

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories.Peer Reviewe

Computational methods to design biophysical experiments for the study of protein dynamics

In recent years, new software and automated instruments have enabled us to imagine autonomous or "self-driving" laboratories of the future. However, ways to design new scientific studies remain unexplored due to challenges such as minimizing associated time, labor, and expense of sample preparation and data acquisition. In the field of protein biophysics, computational simulations such as molecular dynamics and spectroscopy-based experiments such as double electron-electron resonance and Fluorescence resonance energy transfer techniques have emerged as critical experimental tools to capture protein dynamic behavior, a change in protein structure as a function of time which is important for their cellular functions. These techniques can lead to the characterization of key protein conformations and can capture protein motions over a diverse range of timescales. This work addresses the problem of the choice of probe positions in a protein, which residue-pairs should experimentalists choose for spectroscopy experiments. For this purpose, molecular dynamics simulations and Markov state models of protein conformational dynamics are utilized to rank sets of labeled residue-pairs in terms of their ability to capture the conformational dynamics of the protein. The applications of our experimental study design methodology called OptimalProbes on different types of proteins and experimental techniques are examined. In order to utilize this method for a previously uncharacterized protein, atomistic molecular dynamics simulations are performed to study a bacterial di/tri-peptide transporter a typical representative of the Major Facilitator Superfamily of membrane proteins. This was followed by ideal double electron-electron resonance experimental choice predictions based on the simulation data. The predicted choices are superior to the residue-pair choices made by experimentalists which failed to capture the slowest dynamical processes in the conformational ensemble obtained from our long timescale simulations. For molecular dynamics simulations based design of experimental studies to succeed both ensembles need to be comparable. Since this has not been the case for double electron-electron resonance distance distributions and molecular simulations, we explore possible reasons that can lead to mismatches between experiments and simulations in order to reconcile simulated ensembles with experimentally obtained distance traces. This work is one of the first studies towards integrating spectroscopy experiment design into a computational method systematically based on molecular simulations