scholarly journals Dark Proteome Database: Studies on Dark Proteins

2019 ◽  
Author(s):  
Nelson Perdigão
Keyword(s):  
Natural Killer Cells ◽  
Homology Modeling ◽  
3D Structure ◽  
Model Organisms ◽  
Human Organism ◽  
Experimental Characterization ◽  
Proteome Database ◽  
Protein Universe ◽  
Cervical Mucosa ◽  
Higher Eukaryotes

The dark proteome as we define it, is the part of the proteome where 3D structure has not been observed either by homology modeling or by experimental characterization in the protein universe. From the 550.116 proteins available in Swiss-Prot (as of July 2016) 43.2% of the Eukarya universe and 49.2% of the Virus universe are part of the dark proteome. In Bacteria and Archaea, the percentage of the dark proteome presence is significantly less, with 12.6% and 13.3% respectively. In this work, we present the map of the dark proteome in Human and in other model organisms. The most significant result is that around 40%- 50% of the proteome of these organisms are still in the dark, where the higher percentages belong to higher eukaryotes (mouse and human organisms). Due to the amount of darkness present in the human organism being more than 50%, deeper studies were made, including the identification of ‘dark’ genes that are responsible for the production of the so-called dark proteins, as well as, the identification of the ‘dark’ organs where dark proteins are over represented, namely heart, cervical mucosa and natural killer cells. This is a step forward in the direction of the human dark proteome.

scholarly journals Dark Proteome Database: Studies on Dark Proteins

2019 ◽  
Vol 8 (2) ◽  
pp. 8 ◽  
Author(s):  
Nelson Perdigão ◽  
Agostinho Rosa
Keyword(s):  
Homology Modeling ◽  
3D Structure ◽  
Model Organisms ◽  
Human Organism ◽  
Special Importance ◽  
Experimental Characterization ◽  
Proteome Database ◽  
Protein Universe ◽  
Cervical Mucosa ◽  
Higher Eukaryotes

The dark proteome, as we define it, is the part of the proteome where 3D structure has not been observed either by homology modeling or by experimental characterization in the protein universe. From the 550.116 proteins available in Swiss-Prot (as of July 2016), 43.2% of the eukarya universe and 49.2% of the virus universe are part of the dark proteome. In bacteria and archaea, the percentage of the dark proteome presence is significantly less, at 12.6% and 13.3% respectively. In this work, we present a necessary step to complete the dark proteome picture by introducing the map of the dark proteome in the human and in other model organisms of special importance to mankind. The most significant result is that around 40% to 50% of the proteome of these organisms are still in the dark, where the higher percentages belong to higher eukaryotes (mouse and human organisms). Due to the amount of darkness present in the human organism being more than 50%, deeper studies were made, including the identification of ‘dark’ genes that are responsible for the production of so-called dark proteins, as well as the identification of the ‘dark’ tissues where dark proteins are over represented, namely, the heart, cervical mucosa, and natural killer cells. This is a step forward in the direction of gaining a deeper knowledge of the human dark proteome.

scholarly journals FAMSD: A Powerful Protein Modeling Platform that Combines Alignment Methods, Homology Modeling, 3D Structure Quality Estimation and Molecular Dynamics

2009 ◽  
Vol 57 (12) ◽  
pp. 1335-1342 ◽  
Author(s):  
Kazuhiko Kanou ◽  
Mitsuo Iwadate ◽  
Tomoko Hirata ◽  
Genki Terashi ◽  
Hideaki Umeyama ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Homology Modeling ◽  
3D Structure ◽  
Protein Modeling ◽  
Quality Estimation ◽  
Structure Quality

scholarly journals Binding site identification of COVID-19 main protease 3D structure by homology modeling

2021 ◽  
Vol 21 (3) ◽  
pp. 1713
Author(s):  
Marion Adebiyi ◽  
Oludayo O. Olugbara
Keyword(s):  
Secondary Structure ◽  
Homology Modeling ◽  
Binding Site ◽  
Drug Target ◽  
3D Structure ◽  
Random Coil ◽  
Local Alignment ◽  
Potential Drug Target ◽  
Potential Drug ◽  
Main Protease

The influx of coronavirus in 2019 (COVID-19) from Wuhan of China has led to a global pandemic, undesirable hiatus, and recorded millions of infection cases with several deaths worldwide. The strain of COVID-19 has neither known treatments nor vaccines, but recent studies have shown that a few of its enzymes may have been considered as potential drug target. Since its influx, the virus has been well-studied, but a lot is not known about its protease yet.  The purpose of this work was to identify the binding site in-silico and present 3D structure of COVID-19 main protease (Mpro) by homology modeling through multiple alignment followed by optimization and validation. The modeling was done by Swiss-Model template library and basic local alignment search tool (BLAST). The obtained homotrimer oligo-state model was verified for reliability using structural validation software such as PROCHECK, Verify3D, MolProbity and QMEAN. The HHBlits software was used to determine the structures that matched the target sequence by evolution. Best template, 6u7h.1.A was used to build a tertiary structure for Mpro with ProMod3 3.0.0 on the Swiss-Model workspace. Self-optimized prediction method with alignment (SOPMA) was applied to compute features of the secondary structure. The verification of quality of COVID-19 structure through Ramachandran plot showed an abundance of 99.3% of amino acid residues in allowed regions while 0.1% in disallowed region. The Verify3D rated the structure a 90.87% PASS of residues having an average 3D-1D score of at least 0.2, which validates a good environment profile for the COVID-19 Mpro model. The features of the secondary structure indicated that the modeled 3D structure of Mpro contains 32.05% α-helix and 37.17% random coil with 25.92 extended strand. DeepSite algorithm elucidates the binding site area that captured local patterns in the structure and exposed the surface cavity of the binding pocket of this protein. The main result of this study suggests that blocking expression of the protein may constitute an efficient approach for transmission blockage. Hence, our thought is that Mpro of COVID-19 may be considered a potential drug target. Nevertheless, more experimental analyses, verification and validation experiments will be required as a targeted drug or vaccine design against COVID-19 virus.

Combined approach of homology modeling, molecular dynamics, and docking: computer-aided drug discovery

2019 ◽  
Vol 4 (10) ◽  
Author(s):  
Varun Chahal ◽  
Sonam Nirwan ◽  
Rita Kakkar
Keyword(s):  
Drug Design ◽  
Homology Modeling ◽  
3D Structure ◽  
Structure Based Drug Design ◽  
Combined Application ◽  
Cytoplasmic Proteins ◽  
Software Algorithms ◽  
Computer Aided ◽  
Drug Designing ◽  
Process Structure

Abstract With the continuous development in software, algorithms, and increase in computer speed, the field of computer-aided drug design has been witnessing reduction in the time and cost of the drug designing process. Structure based drug design (SBDD), which is based on the 3D structure of the enzyme, is helping in proposing novel inhibitors. Although a number of crystal structures are available in various repositories, there are various proteins whose experimental crystallization is difficult. In such cases, homology modeling, along with the combined application of MD and docking, helps in establishing a reliable 3D structure that can be used for SBDD. In this review, we have reported recent works, which have employed these three techniques for generating structures and further proposing novel inhibitors, for cytoplasmic proteins, membrane proteins, and metal containing proteins. Also, we have discussed these techniques in brief in terms of the theory involved and the various software employed. Hence, this review can give a brief idea about using these tools specifically for a particular problem.

scholarly journals Analysis of the Thermostability Determinants of Hyperthermophilic Esterase EstE1 Based on Its Predicted Three-Dimensional Structure

2006 ◽  
Vol 72 (4) ◽  
pp. 3021-3025 ◽  
Author(s):  
Jin-Kyu Rhee ◽  
Do-Yun Kim ◽  
Dae-Gyun Ahn ◽  
Jung-Hyuk Yun ◽  
Seung-Hwan Jang ◽  
...  
Keyword(s):  
Homology Modeling ◽  
Hydrophobic Interactions ◽  
3D Structure ◽  
Three Dimensional ◽  
Ion Pair ◽  
Archaeoglobus Fulgidus ◽  
Dimensional Structure ◽  
Three Dimensional Structure ◽  
Structure Relationship

ABSTRACT The three-dimensional (3D) structure of the hyperthermophilic esterase EstE1 was constructed by homology modeling using Archaeoglobus fulgidus esterase as a reference, and the thermostability-structure relationship was analyzed. Our results verified the predicted 3D structure of EstE1 and identified the ion pair networks and hydrophobic interactions that are critical determinants for the thermostability of EstE1.

Protein Structure Prediction using Homology Modeling

2016 ◽  
pp. 339-359 ◽  
Author(s):  
Akanksha Gupta ◽  
Pallavi Mohanty ◽  
Sonika Bhatnagar
Keyword(s):  
Homology Modeling ◽  
Structure Prediction ◽  
High Throughput Sequencing ◽  
Tertiary Structure ◽  
Structural Information ◽  
Sequence Similarity ◽  
3D Structure ◽  
Comparative Modeling ◽  
Spatial Arrangement ◽  
Good Sequence

Sequence-structure deficit marks one of the critical problems in today's scenario where high-throughput sequencing has resulted in large datasets of protein sequences but their corresponding 3D structures still needs to be determined. Homology modeling, also termed as Comparative modeling refers to modeling of 3D structure of a protein by exploiting structural information from other known protein structures with good sequence similarity. Homology models contain sufficient information about the spatial arrangement of important residues in the protein and are often used in drug design for screening of large libraries by molecular docking techniques. This chapter provides a brief description about protein tertiary structure prediction and Homology modeling. The authors provide a description of the steps involved in homology modeling protocols and provide information on the various resources available for the same.

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