cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20765822
title: Systems biology approach delineates critical pathways associated with disease progression in rheumatoid arthritis

description: Rheumatoid Arthritis (RA) is a chronic systemic autoimmune disease leading to inflammation, cartilage cell death, synoviocyte proliferation, and increased and impaired differentiation of osteoclasts and osteoblasts leading to joint erosions and deformities. Transcriptomics, proteomics, and metabolomics datasets were analyzed to identify the critical pathways that drive the RA pathophysiology. Single nucleotide polymorphisms (SNPs) associated with RA were analyzed for the functional implications, clinical outcomes, and blood parameters later validated by literature. SNPs associated with RA were grouped into pathways that drive the immune response and cytokine production. Further gene set enrichment analysis (GSEA) was performed on gene expression omnibus (GEO) data sets of peripheral blood mononuclear cells (PBMCs), synovial macrophages, and synovial biopsies from RA patients showed enrichment of Th1, Th2, Th17 differentiation, viral and bacterial infections, metabolic signalling and immunological pathways with potential implications for RA. The proteomics data analysis presented pathways with genes involved in immunological signaling and metabolic pathways, including vitamin B12 and folate metabolism. Metabolomics datasets analysis showed significant pathways like amino-acyl tRNA biosynthesis, metabolism of amino acids (arginine, alanine aspartate, glutamate, glutamine, phenylalanine, and tryptophan), and nucleotide metabolism. Furthermore, our commonality analysis of multi-omics datasets identified common pathways with potential implications for joint remodeling in RA. Disease-modifying anti-rheumatic drugs (DMARDs) and biologics treatments were found to modulate many of the pathways that were deregulated in RA. Overall, our analysis identified molecular signatures associated with the observed symptoms, joint erosions, potential biomarkers, and therapeutic targets in RA. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.20465476
title: <i>Ab initio</i> modelling of an essential mammalian protein: Transcription Termination Factor 1 (TTF1)

description: Transcription Termination Factor 1 (TTF1) is an essential mammalian protein that regulates transcription, replication fork arrest, DNA damage repair, chromatin remodelling etc. TTF1 interacts with numerous cellular proteins to regulate various cellular phenomena which play a crucial role in maintaining normal cellular physiology, and dysregulation of this protein has been reported to induce oncogenic transformation of the cells. However, despite its key role in many cellular processes, the complete structure of human TTF1 has not been elucidated to date, neither experimentally nor computationally. Therefore, understanding the structure of human TTF1 is crucial for studying its functions and interactions with other cellular factors. The aim of this study was to construct the complete structure of human TTF1 protein, using molecular modelling approaches. Owing to the lack of suitable homologues in the Protein Data Bank (PDB), the complete structure of human TTF1 was constructed by <i>ab initio</i> modelling. The structural stability was determined with molecular dynamics (MD) simulations in explicit solvent, and trajectory analyses. The frequently occurring conformation of human TTF1 was selected by trajectory clustering, and the central residues of this conformation were determined by centrality analyses of the Residue Interaction Network (RIN) of TTF1. Two residue clusters, one in the oligomerization domain and other in the C-terminal domain, were found to be central to the structural stability of human TTF1. To the best of our knowledge, this study is the first to report the complete structure of this essential mammalian protein, and the results obtained herein will provide structural insights for future research including that in cancer biology and related studies. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.20465476.v1
title: <i>Ab initio</i> modelling of an essential mammalian protein: Transcription Termination Factor 1 (TTF1)

description: Transcription Termination Factor 1 (TTF1) is an essential mammalian protein that regulates transcription, replication fork arrest, DNA damage repair, chromatin remodelling etc. TTF1 interacts with numerous cellular proteins to regulate various cellular phenomena which play a crucial role in maintaining normal cellular physiology, and dysregulation of this protein has been reported to induce oncogenic transformation of the cells. However, despite its key role in many cellular processes, the complete structure of human TTF1 has not been elucidated to date, neither experimentally nor computationally. Therefore, understanding the structure of human TTF1 is crucial for studying its functions and interactions with other cellular factors. The aim of this study was to construct the complete structure of human TTF1 protein, using molecular modelling approaches. Owing to the lack of suitable homologues in the Protein Data Bank (PDB), the complete structure of human TTF1 was constructed by <i>ab initio</i> modelling. The structural stability was determined with molecular dynamics (MD) simulations in explicit solvent, and trajectory analyses. The frequently occurring conformation of human TTF1 was selected by trajectory clustering, and the central residues of this conformation were determined by centrality analyses of the Residue Interaction Network (RIN) of TTF1. Two residue clusters, one in the oligomerization domain and other in the C-terminal domain, were found to be central to the structural stability of human TTF1. To the best of our knowledge, this study is the first to report the complete structure of this essential mammalian protein, and the results obtained herein will provide structural insights for future research including that in cancer biology and related studies. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21120240
title: Assessment of tools for RNA secondary structure prediction and extraction: a final-user perspective

description: The study of RNA structure is fundamental to clarify the RNA molecular functioning. The flexible RNA nature, the huge number of expressed RNAs, and the variety of functions make it challenging to obtain a quantity of structural information comparable to what is already available for proteins. The <i>in silico</i> prediction of RNA 3D structures is of particular relevance, to understand the fundamental features of the structure-function relationship, because the 3D structure drives the molecular interaction with DNA or protein complexes. The quality of the prediction of the RNA 3D structure is determined by the knowledge of a properly predicted or measured secondary structure. In this paper, we comparatively evaluate computational tools to model RNA secondary structure, focusing our investigation, among the dozens of methods in literature, on tools which are freely available and implemented in web-server versions, providing a more direct access to the final users, not necessarily bioinformatics experts. Our focus is on assessing performances for long sequences, with the final aim of selecting best methods for perspective lncRNAs investigation. Indeed, among RNAs, the non-coding and long non-coding RNAs (lncRNAs, with sequence length larger than 200 nts) assume special relevance, due to their function in regulatory mechanisms, which is still largely unexplored in the case of lncRNAs. As lncRNA experimental structures are at present missing, other families of large RNAs are here used as test cases, to establish the reliability of predictive bioinformatics tools and their perspective applicability to the case of lncRNAs. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.21120240.v1
title: Assessment of tools for RNA secondary structure prediction and extraction: a final-user perspective

description: The study of RNA structure is fundamental to clarify the RNA molecular functioning. The flexible RNA nature, the huge number of expressed RNAs, and the variety of functions make it challenging to obtain a quantity of structural information comparable to what is already available for proteins. The <i>in silico</i> prediction of RNA 3D structures is of particular relevance, to understand the fundamental features of the structure-function relationship, because the 3D structure drives the molecular interaction with DNA or protein complexes. The quality of the prediction of the RNA 3D structure is determined by the knowledge of a properly predicted or measured secondary structure. In this paper, we comparatively evaluate computational tools to model RNA secondary structure, focusing our investigation, among the dozens of methods in literature, on tools which are freely available and implemented in web-server versions, providing a more direct access to the final users, not necessarily bioinformatics experts. Our focus is on assessing performances for long sequences, with the final aim of selecting best methods for perspective lncRNAs investigation. Indeed, among RNAs, the non-coding and long non-coding RNAs (lncRNAs, with sequence length larger than 200 nts) assume special relevance, due to their function in regulatory mechanisms, which is still largely unexplored in the case of lncRNAs. As lncRNA experimental structures are at present missing, other families of large RNAs are here used as test cases, to establish the reliability of predictive bioinformatics tools and their perspective applicability to the case of lncRNAs. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.20448707.v1
title: A computational study to target necroptosis via RIPK1 inhibition

description: The human receptor-interacting serine/threonine-protein kinase 1 (RIPK1) is a critical necroptosis regulator implicated in cancer, psoriasis, ulcerative colitis, rheumatoid arthritis, Alzheimer’s disease, and multiple sclerosis. Currently, there are no specific RIPK1 antagonists in clinical practice. In this study, we took a target-based computational approach to identify blood-brain-barrier-permeable potent RIPK1 ligands with novel chemotypes. Using molecular docking, we virtually screened the Marine Natural Products (MNP) library of 14,492 small molecules. Initial 18 hits were subjected to detailed ADMET profiling for bioavailability, brain penetration, druglikeness, and toxicities and eventually yielded 548773-66-6 as the best ligand. RIPK1 548773-66-6 binding was validated through duplicated molecular dynamics (MD) simulations where the co-crystallized ligand L8D served as a reference. Trajectory analysis indicated negligible Root-Mean-Square-Deviations (RMSDs) of the best ligand 548773-66-6 relative to the protein backbone: 0.156 ± 0.043 nm and 0.296 ± 0.044 nm (mean ± standard deviations) in two individual simulations. Visual inspection confirmed that 548773-66-6 occupied the RIPK1 ligand-binding pocket associated with the kinase activation loop. Further computations demonstrated consistent hydrogen bond interactions of the ligand with the residue ASP156. Binding free energy estimation also supported stable interactions of 548773-66-6 and RIPK1. Together, our <i>in silico</i> analysis predicted 548773-66-6 as a novel ligand for RIPK1. Therefore, 548773-66-6 could be a viable lead for inhibiting necroptosis in central nervous system inflammatory disorders. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.20528774
title: Interdisciplinary <i>in silico</i> studies to understand in-depth molecular level mechanism of drug resistance involving NS3-4A protease of HCV

description: Hepatitis C virus (HCV) causes hepatitis, a life-threatening disease responsible for liver cirrhosis. Urgent measures have been taken to develop therapeutics against this deadly pathogen. NS3/4A protease is an extremely important target. A series of inhibitors have been developed against this viral protease including Faldaprevir. Unfortunately, the error-prone viral RNA polymerase causes the emergence of resistance, thereby causing reduced effectiveness of those peptidomimetic inhibitors. Among the drug resistant variants, three single amino acid residues (R155, A156 and D168) are notable for their presence in clinical isolates and also their effectivity against most of the known inhibitors in clinical development. Therefore, it is crucial to understand the mechanistic role of those drug resistant variants while designing potent novel inhibitors. In this communication, we have deeply analyzed through using <i>in silico</i> studies to understand the molecular mechanism of alteration of inhibitor binding between wild type and its R155K, A156V and D168V variants. Principal component analysis was carried to identify the backbone fluctuations of important residues in HCV NS3/4A responsible for the inhibitor binding and maintaining drug resistance. Free energy landscape as a function of the principal components has been used to identify the stability and conformation of the key residues that regulate inhibitor binding and their impact in developing drug resistance. Our findings are consistent with the trend of experimental results. The observations are also true in case of other Faldaprevir-like peptidomimetic inhibitors. Understanding this binding mechanism would be significant for the development of novel inhibitors with less susceptibility towards drug resistance. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.20528774.v2
title: Interdisciplinary <i>in silico</i> studies to understand in-depth molecular level mechanism of drug resistance involving NS3-4A protease of HCV

description: Hepatitis C virus (HCV) causes hepatitis, a life-threatening disease responsible for liver cirrhosis. Urgent measures have been taken to develop therapeutics against this deadly pathogen. NS3/4A protease is an extremely important target. A series of inhibitors have been developed against this viral protease including Faldaprevir. Unfortunately, the error-prone viral RNA polymerase causes the emergence of resistance, thereby causing reduced effectiveness of those peptidomimetic inhibitors. Among the drug resistant variants, three single amino acid residues (R155, A156 and D168) are notable for their presence in clinical isolates and also their effectivity against most of the known inhibitors in clinical development. Therefore, it is crucial to understand the mechanistic role of those drug resistant variants while designing potent novel inhibitors. In this communication, we have deeply analyzed through using <i>in silico</i> studies to understand the molecular mechanism of alteration of inhibitor binding between wild type and its R155K, A156V and D168V variants. Principal component analysis was carried to identify the backbone fluctuations of important residues in HCV NS3/4A responsible for the inhibitor binding and maintaining drug resistance. Free energy landscape as a function of the principal components has been used to identify the stability and conformation of the key residues that regulate inhibitor binding and their impact in developing drug resistance. Our findings are consistent with the trend of experimental results. The observations are also true in case of other Faldaprevir-like peptidomimetic inhibitors. Understanding this binding mechanism would be significant for the development of novel inhibitors with less susceptibility towards drug resistance. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.20448707
title: A computational study to target necroptosis via RIPK1 inhibition

description: The human receptor-interacting serine/threonine-protein kinase 1 (RIPK1) is a critical necroptosis regulator implicated in cancer, psoriasis, ulcerative colitis, rheumatoid arthritis, Alzheimer’s disease, and multiple sclerosis. Currently, there are no specific RIPK1 antagonists in clinical practice. In this study, we took a target-based computational approach to identify blood-brain-barrier-permeable potent RIPK1 ligands with novel chemotypes. Using molecular docking, we virtually screened the Marine Natural Products (MNP) library of 14,492 small molecules. Initial 18 hits were subjected to detailed ADMET profiling for bioavailability, brain penetration, druglikeness, and toxicities and eventually yielded 548773-66-6 as the best ligand. RIPK1 548773-66-6 binding was validated through duplicated molecular dynamics (MD) simulations where the co-crystallized ligand L8D served as a reference. Trajectory analysis indicated negligible Root-Mean-Square-Deviations (RMSDs) of the best ligand 548773-66-6 relative to the protein backbone: 0.156 ± 0.043 nm and 0.296 ± 0.044 nm (mean ± standard deviations) in two individual simulations. Visual inspection confirmed that 548773-66-6 occupied the RIPK1 ligand-binding pocket associated with the kinase activation loop. Further computations demonstrated consistent hydrogen bond interactions of the ligand with the residue ASP156. Binding free energy estimation also supported stable interactions of 548773-66-6 and RIPK1. Together, our <i>in silico</i> analysis predicted 548773-66-6 as a novel ligand for RIPK1. Therefore, 548773-66-6 could be a viable lead for inhibiting necroptosis in central nervous system inflammatory disorders. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.20528774.v1
title: Interdisciplinary <i>in silico</i> studies to understand in-depth molecular level mechanism of drug resistance involving NS3-4A protease of HCV

description: Hepatitis C virus (HCV) causes hepatitis, a life-threatening disease responsible for liver cirrhosis. Urgent measures have been taken to develop therapeutics against this deadly pathogen. NS3/4A protease is an extremely important target. A series of inhibitors have been developed against this viral protease including Faldaprevir. Unfortunately, the error-prone viral RNA polymerase causes the emergence of resistance, thereby causing reduced effectiveness of those peptidomimetic inhibitors. Among the drug resistant variants, three single amino acid residues (R155, A156 and D168) are notable for their presence in clinical isolates and also their effectivity against most of the known inhibitors in clinical development. Therefore, it is crucial to understand the mechanistic role of those drug resistant variants while designing potent novel inhibitors. In this communication, we have deeply analyzed through using <i>in silico</i> studies to understand the molecular mechanism of alteration of inhibitor binding between wild type and its R155K, A156V and D168V variants. Principal component analysis was carried to identify the backbone fluctuations of important residues in HCV NS3/4A responsible for the inhibitor binding and maintaining drug resistance. Free energy landscape as a function of the principal components has been used to identify the stability and conformation of the key residues that regulate inhibitor binding and their impact in developing drug resistance. Our findings are consistent with the trend of experimental results. The observations are also true in case of other Faldaprevir-like peptidomimetic inhibitors. Understanding this binding mechanism would be significant for the development of novel inhibitors with less susceptibility towards drug resistance. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.23720985
title: Rawdata for Zhao et al., 2023 Journal of Hydrology

description: Raw data for Zhao et al., 2023, Journal of Hydrology
DOI: 10.5281/zenodo.8169707
title: Simulation data for the office cell building energy model with the attached overhang

description: Simulation data for 729,000 variants of the office cell building model with the overhang attached over the window. The variants are determined by the overhang depth and height, location, presence of obstacles, orientation and cooling and heating set points. The office cell model is described in the manuscript "Predicting the shape of loads for an office cell with an overhang from a small number of building energy simulations".
DOI: 10.5281/zenodo.8169706
title: Simulation data for the office cell building energy model with the attached overhang

description: Simulation data for 729,000 variants of the office cell building model with the overhang attached over the window. The variants are determined by the overhang depth and height, location, presence of obstacles, orientation and cooling and heating set points. The office cell model is described in the manuscript "Predicting the shape of loads for an office cell with an overhang from a small number of building energy simulations".
DOI: 10.5517/ccdc.csd.cc26c7nh
title: CCDC 2036596: Experimental Crystal Structure Determination

description: Related Article: Yankun Xu, Mingjie Sun, Weiwei Xu, Guobo Deng, Yun Liang, Yuan Yang|2023|J.Am.Chem.Soc.|145|15303|doi:10.1021/jacs.3c02875
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bsqrm
title: CCDC 2168721: Experimental Crystal Structure Determination

description: Related Article: Qing-Dou Xu, Chen Zeng, Shao-Dong Su, Yong He, Yang Liu, Sheng-Min Hu, Xin-Tao Wu, Tian-Lu Sheng|2022|Dalton Trans.|51|13938|doi:10.1039/D2DT01313D
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c572w
title: CCDC 2179767: Experimental Crystal Structure Determination

description: Related Article: Wenjun Du, Lirong Jiang, Shuang Chen, Shan Jin, Manzhou Zhu|2022|Inorg.Chem.|61|14233|doi:10.1021/acs.inorgchem.2c01602
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.23664273
title: rd16 genotypes_ACD.csv

description: The datasets contain the raw phenotype and genotype data that was used in the R/qtl program to identify 6 new quantitative trait loci (QTL) associated with the anterior chamber depth (ACD) phenotype in mice.
DOI: 10.5281/zenodo.7289667
title: Subsurface variogram data

description: This is a data set that contains world-wide estimates of variogram of covariancve functions for aquifer and soil sites.
description: added scripts for processing data
DOI: 10.5281/zenodo.7289668
title: Subsurface variogram data

description: This is a data set that contains world-wide estimates of variogram of covariancve functions for aquifer and soil sites.
DOI: 10.5281/zenodo.8169429
title: Subsurface variogram data

description: This is a data set that contains world-wide estimates of variogram of covariancve functions for aquifer and soil sites.
description: added scripts for processing data
DOI: 10.5517/ccdc.csd.cc2cpw98
title: CCDC 2195770: Experimental Crystal Structure Determination

description: Related Article: Yankun Xu, Mingjie Sun, Weiwei Xu, Guobo Deng, Yun Liang, Yuan Yang|2023|J.Am.Chem.Soc.|145|15303|doi:10.1021/jacs.3c02875
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.