Does inclusion of residue‐residue contact information boost protein threading?

2019 ◽  
Vol 87 (7) ◽  
pp. 596-606 ◽  
Author(s):  
Sutanu Bhattacharya ◽  
Debswapna Bhattacharya
Keyword(s):  
Residue Contact ◽  
Contact Information ◽  
Protein Threading

scholarly journals Exploiting residue–residue contact information beyond structure predictions in molecular replacement

2018 ◽  
Vol 74 (a2) ◽  
pp. e46-e46
Author(s):  
Felix Simkovic ◽  
Saulo de Oliveira ◽  
Charlotte Deane ◽  
Daniel J. Rigden
Keyword(s):  
Molecular Replacement ◽  
Residue Contact ◽  
Contact Information

scholarly journals The Compendium of U.S. jails: creating and conducting research with the first comprehensive contact database of U.S. jails

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Chelsea M. A. Foudray ◽  
Camille Kramer ◽  
Danielle S. Rudes ◽  
Carolyn Sufrin ◽  
Eliza Burr ◽  
...  
Keyword(s):  
Health Care Services ◽  
Opioid Use Disorder ◽  
Opioid Use ◽  
Case Examples ◽  
Treatment Practices ◽  
Care Services ◽  
Contact Information ◽  
Conducting Research ◽  
Pass Through ◽  
Policies And Practices

Abstract Background Millions of people pass through U.S. jails annually. Conducting research about these public institutions is critical to understanding on-the-ground policies and practices, especially health care services, affecting millions of people. However, there is no existing database of the number, location, or contact information of jails. We created the National Jails Compendium to address this gap. In this paper, we detail our comprehensive methodology for identifying jail locations and contact information. We then describe the first research project to use the Compendium, a survey assessing jails’ treatment practices for incarcerated pregnant people with opioid use disorder. Results This study sent surveys electronically or in paper form to all 2986 jails in the Compendium, with 1139 surveys returned. We outline the process for using the Compendium, highlighting challenges in reaching contacts through case examples, cataloging responses and non-responses, and defining what counts as a jail. Conclusion We aim to provide tools for future researchers to use the Compendium as well as a pathway for keeping it current. The Compendium provides transparency that aids in understanding jail policies and practices. Such information may help devise interventions to ensure humane, evidence-based treatment of incarcerated people.

scholarly journals Template-based prediction of protein structure with deep learning

BMC Genomics ◽  
2020 ◽  
Vol 21 (S11) ◽  
Author(s):  
Haicang Zhang ◽  
Yufeng Shen
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Query Sequence ◽  
Dynamic Programming Algorithm ◽  
Tertiary Structure Prediction ◽  
Protein Tertiary Structure ◽  
Protein Threading ◽  
Protein Tertiary Structure Prediction

Abstract Background Accurate prediction of protein structure is fundamentally important to understand biological function of proteins. Template-based modeling, including protein threading and homology modeling, is a popular method for protein tertiary structure prediction. However, accurate template-query alignment and template selection are still very challenging, especially for the proteins with only distant homologs available. Results We propose a new template-based modelling method called ThreaderAI to improve protein tertiary structure prediction. ThreaderAI formulates the task of aligning query sequence with template as the classical pixel classification problem in computer vision and naturally applies deep residual neural network in prediction. ThreaderAI first employs deep learning to predict residue-residue aligning probability matrix by integrating sequence profile, predicted sequential structural features, and predicted residue-residue contacts, and then builds template-query alignment by applying a dynamic programming algorithm on the probability matrix. We evaluated our methods both in generating accurate template-query alignment and protein threading. Experimental results show that ThreaderAI outperforms currently popular template-based modelling methods HHpred, CNFpred, and the latest contact-assisted method CEthreader, especially on the proteins that do not have close homologs with known structures. In particular, in terms of alignment accuracy measured with TM-score, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 56, 13, and 11%, respectively, on template-query pairs at the similarity of fold level from SCOPe data. And on CASP13’s TBM-hard data, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 16, 9 and 8% in terms of TM-score, respectively. Conclusions These results demonstrate that with the help of deep learning, ThreaderAI can significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins.

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