Procleave: Predicting Protease-specific Substrate Cleavage Sites by Combining Sequence and Structural Information

Proteases are enzymes that cleave and hydrolyse the peptide bonds between two specific amino acid residues of target substrate proteins.Protease-controlled proteolysis plays a key role in the degradation and recycling of proteins,which is essential for various physiological processes.Thus,solving the substrate identification problem will have important implications for the precise understanding of functions and physiological roles of proteases,as well as for therapeutic target identification and pharmaceutical applicability.Consequently,there is a great demand for bioinformatics methods that can predict novel substrate cleavage events with high accuracy by utilizing both sequence and structural information.In this study,we present Procleave,a novel bioinformatics approach for predicting protease-specific substrates and specific cleavage sites by taking into account both their sequence and 3D structural information.Structural features of known cleavage sites were represented by discrete values using a LOWESS data-smoothing optimization method,which turned out to be critical for the performance of Procleave.The optimal approximations of all structural parameter values were encoded in a conditional random field(CRF)computational framework,alongside sequence and chemical group-based features.Here,we demonstrate the outstanding performance of Procleave through extensive benchmarking and independent tests.Procleave is capable of correctly identifying most cleavage sites in the case study.Importantly,when applied to the human structural proteome encompassing 17,628 protein structures,Procleave suggests a number of potential novel target substrates and their corresponding cleavage sites of different proteases.Procleave is implemented as a webserver and is freely accessible at http://procleave.erc.monash.edu/.

Exploring technologies to better link physical evidence and digital information for disaster victim identification

Disaster victim identification(DVI)entails a protracted process of evidence collection and data matching to reconcile physical remains with victim identity.Technology is critical to DVI by enabling the linkage of physical evidence to information.However,labelling physical remains and collecting data at the scene are dominated by low-technology paper-based practices.We ask,how can technology help us tag and track the victims of disaster?Our response to this question has two parts.First,we conducted a human–computer interaction led investigation into the systematic factors impacting DVI tagging and tracking processes.Through interviews with Australian DVI practitioners,we explored how technologies to improve linkage might fit with prevailing work practices and preferences;practical and social considerations;and existing systems and processes.We focused on tagging and tracking activities throughout the DVI process.Using insights from these interviews and relevant literature,we identified four critical themes:protocols and training;stress and stressors;the plurality of information capture and management systems;and practicalities and constraints.Second,these findings were iteratively discussed by the authors,who have combined expertise across electronics,data science,cybersecurity,human–computer interaction and forensic pathology.We applied the themes identified in the first part of the investigation to critically review technologies that could support DVI practitioners by enhancing DVI processes that link physical evidence to information.This resulted in an overview of candidate technologies matched with consideration of their key attributes.This study recognises the importance of considering human factors that can affect technology adoption into existing practices.Consequently,we provide a searchable table(as Supplementary information)that relates technologies to the key considerations and attributes relevant to DVI practice,for readers to apply to their own context.While this research directly contributes to DVI,it also has applications to other domains in which a physical/digital linkage is required,and particularly within high stress environments with little room for error.

Comparative Analysis of Vaccine Inequity and COVID-19 Transmission Amid the Omicron Variant Among Countries--Six Countries,Asia-Pacific Region,2022

What is already known about this topic?The coronavirus disease 2019(COVID-19)persists as a significant global public health crisis.The predominant strain,severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),notably the Omicron variant,continues to undergo mutations.While vaccination is heralded as the paramount solution to cease the pandemic,challenges persist in providing equitable access to COVID-19 vaccines.What is added by this report?The distribution of vaccine coverage exhibited disparities between high-income and middle-income countries,with middle-income countries evidencing lower levels of vaccination.The data further suggested that countries with lesser vaccination levels tended to display a higher case fatality rate.Findings indicated that an increase in population-wide vaccination was effective in mitigating COVID-19 related mortalities.What are the implications for public health practice?The findings of this research underscore the pressing necessity for equitable access to vaccines to effectively mitigate the COVID-19 pandemic within the Asia-Pacific region.

Understanding the impact of disease and vaccine mechanisms on the importance of optimal vaccine allocation

Vaccination is an important epidemic intervention strategy.However,it is generally unclear how the outcomes of different vaccine strategies change depending on population characteristics,vaccine mechanisms and allocation objective.In this paper we develop a conceptual mathematical model to simulate strategies for pre-epidemic vaccination.We extend the SEIR model to incorporate a range of vaccine mechanisms and disease characteristics.We then compare the outcomes of optimal and suboptimal vaccination strategies for three public health objectives(total infections,total symptomatic infections and total deaths)using numerical optimisation.Our comparison shows that the difference in outcomes between vaccinating optimally and suboptimally depends on vaccine mechanisms,disease characteristics,and objective considered.Our modelling finds vaccines that impact transmission produce better outcomes as transmission is reduced for all strategies.For vaccines that impact the likelihood of symptomatic disease or dying due to infection,the improvement in outcome as we decrease these variables is dependent on the strategy implemented.Through a principled model-based process,this work highlights the importance of designing effective vaccine allocation strategies.We conclude that efficient allocation of resources can be just as crucial to the success of a vaccination strategy as the vaccine effectiveness and/or amount of vaccines available.