scholarly journals Prediction of residue-residue contacts in CASP12 targets from its predicted tertiary structures

2017 ◽  
Author(s):  
Piyush Agrawal ◽  
Sandeep Singh ◽  
Gandharva Nagpal ◽  
Deepti Sethi ◽  
Gajendra P.S. Raghava
Keyword(s):  
Structural Biology ◽  
Tertiary Structure ◽  
Protein Structures ◽  
Simple Approach ◽  
Structural Proteomics ◽  
List Size ◽  
Tertiary Structures ◽  
Residue Contact ◽  
Residue Contacts ◽  
Better Than

AbstractOne of the challenges in the field of structural proteomics is to predict residue-residue contacts in a protein. It is an integral part of CASP competitions due to its importance in the field of structural biology. This manuscript describes RRCPred 2.0 a method participated in CASP12 and predicted residue-residue contact in targets with high precision. In this approach, firstly 150 predicted protein structures were obtained from CASP12 Stage 2 tarball and ranked using clustering-based quality assessment software. Secondly, residue-residue contacts were assigned in top 10 protein structures based on distance between residues. Finally, residue-residue contacts were predicted in target protein based on consensus/average in top 10 predicted structures. This simple approach performs better than most of CASP12 methods in the categories of TBM and TBM/FM. It ranked 1st in following categories; i) TBM domain on list size L/5, ii) TBM/FM domain on list size L/5 and iii) TBM/FM domain on Top 10. These observations indicate that predicted tertiary structure of a protein can be used for predicting residue-residue contacts in protein with high accuracy.

A Review of Protein Inter-residue Distance Prediction

2021 ◽  
Vol 15 (8) ◽  
pp. 821-830
Author(s):  
He Huang ◽  
Xinqi Gong
Keyword(s):  
Structure Prediction ◽  
Tertiary Structure ◽  
Contact Map ◽  
Distance Information ◽  
Distance Map ◽  
Large Molecules ◽  
Residue Contact ◽  
Linear Sequence ◽  
Residue Contacts ◽  
Distance Prediction

Proteins are large molecules consisting of a linear sequence of amino acids. Protein performs biological functions with specific 3D structures. The main factors that drive proteins to form these structures are constraint between residues. These constraints usually lead to important inter-residue relationships, including short-range inter-residue contacts and long-range interresidue distances. Thus, a highly accurate prediction of inter-residue contact and distance information is of great significance for protein tertiary structure computations. Some methods have been proposed for inter-residue contact prediction, most of which focus on contact map prediction and some reviews have summarized the progresses. However, inter-residue distance prediction is found to provide better guidance for protein structure prediction than contact map prediction in recent years. The methods for inter-residue distance prediction can be roughly divided into two types according to the consideration of distance value: one is based on multi-classification with discrete value and the other is based on regression with continuous value. Here, we summarize these algorithms and show that they have obtained good results. Compared to contact map prediction, distance map prediction is in its infancy. There is a lot to do in the future including improving distance map prediction precision and incorporating them into residue-residue distanceguided ab initio protein folding.

scholarly journals Immobilization of urease enzyme on nanoceria modifies secondary and tertiary protein structures

2016 ◽  
Vol 9 (1) ◽  
pp. 44-53 ◽  
Author(s):  
Hussein Kadhem Al-Hakeim ◽  
Muneer Kadhem Khudhair ◽  
Eric Anderson Grulke
Keyword(s):  
Tertiary Structure ◽  
Protein Structures ◽  
Chemical Properties ◽  
Maximum Velocity ◽  
Tertiary Structures ◽  
Covalent Bonds ◽  
Transmission Electron ◽  
Urease Enzyme ◽  
Physical Forces ◽  
Hydrolysis Of

Abstract Urease catalyzes the hydrolysis of urea to form ammonia and carbon dioxide. The increase in pH from the urease reaction causes a broad range of deleterious effects. Nanoceria (cerium oxide) possesses unique chemical properties under a redox reaction. This study investigated the synthesis of nanoceria via a hydrothermal method and determined its interaction with urease enzyme. Transmission electron microscopy results showed a cubic-figured nanoceria with a size of ~15 nm. Urease was immobilized on nanoceria through adsorption. The maximum velocity (Vmax) and Michaelis constant (Km) of the free urease and urease immobilized on nanoceria decreased after interaction with nanoceria, and the Lineweaver-Burk plot showed an uncompetitive inhibition. The thermodynamic study of the adsorption process showed an endothermic reaction. The interaction changed the secondary and tertiary structures of urease as demonstrated by the circular dichroism study (the decrease in both α- and β-structure percentages). The fluorescence study revealed a change in the tertiary structure. The FTIR for the nanoceria—urease complex showed no changes in the covalent bonds, which indicated the involvement of physical forces in the interaction between urease and nanoceria.

Higher-throughput approaches to crystallization and crystal structure determination

2008 ◽  
Vol 36 (4) ◽  
pp. 771-775 ◽  
Author(s):  
Mark J. Fogg ◽  
Anthony J. Wilkinson
Keyword(s):  
Crystal Structure ◽  
Structural Biology ◽  
Protein Structures ◽  
Data Bank ◽  
Structural Proteomics ◽  
Structural Determination ◽  
Ligation Independent Cloning ◽  
Structure Solution ◽  
Biology Laboratory ◽  
Automated Methods

In recent times, there has been a large increase in the number of protein structures deposited in the Protein Data Bank. Structural genomics initiatives have contributed to this expansion through their focus on high-throughput structural determination. This has fuelled advances in many of the techniques in the pipeline from gene to protein to crystal to structure. These include ligation-independent cloning methods, parallel purification systems, robotic crystallization devices and automated methods of crystal identification, data collection and, in some cases, structure solution. Some of these advances are described and discussed briefly with an emphasis on activities in the York Structural Biology Laboratory through its participation in the Structural Proteomics in Europe consortium.

scholarly journals A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers

2021 ◽  
Author(s):  
Raj Shekhor Roy ◽  
Farhan Quadir ◽  
Elham Soltanikazemi ◽  
Jianlin Cheng
Keyword(s):  
Deep Learning ◽  
Tertiary Structure ◽  
Quaternary Structure ◽  
High Accuracy ◽  
Residual Network ◽  
Sequence Alignments ◽  
Learning Methods ◽  
Tertiary Structures ◽  
Residue Contacts ◽  
Contact Predictions

Deep learning has revolutionized protein tertiary structure prediction recently. The cutting-edge deep learning methods such as AlphaFold can predict high-accuracy tertiary structures for most individual protein chains. However, the accuracy of predicting quaternary structures of protein complexes consisting of multiple chains is still relatively low due to lack of advanced deep learning methods in the field. Because interchain residue-residue contacts can be used as distance restraints to guide quaternary structure modeling, here we develop a deep dilated convolutional residual network method (DRCon) to predict interchain residue-residue contacts in homodimers from residue-residue co-evolutionary signals derived from multiple sequence alignments of monomers, intrachain residue-residue contacts of monomers extracted from true/predicted tertiary structures or predicted by deep learning, and other sequence and structural features. Tested on three homodimer test datasets (Homo_std dataset, DeepHomo dataset, and CASP14-CAPRI dataset), the precision of DRCon for top L/5 interchain contact predictions (L: length of monomer in a homodimer) is 43.46%, 47.15%, and 24.81% respectively, which is substantially better than two existing deep learning interchain contact prediction methods. Moreover, our experiments demonstrate that using predicted tertiary structure or intrachain contacts of monomers in the unbound state as input, DRCon still performs reasonably well, even though its accuracy is lower than when true tertiary structures in the bound state are used as input. Finally, our case study shows that good interchain contact predictions can be used to build high-accuracy quaternary structure models of homodimers.

scholarly journals ISSEC: inferring contacts among protein secondary structure elements using deep object detection

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Qi Zhang ◽  
Jianwei Zhu ◽  
Fusong Ju ◽  
Lupeng Kong ◽  
Shiwei Sun ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Object Detection ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Confidence Score ◽  
Contact Map ◽  
Residue Contact ◽  
Residue Contacts

Abstract Background The formation of contacts among protein secondary structure elements (SSEs) is an important step in protein folding as it determines topology of protein tertiary structure; hence, inferring inter-SSE contacts is crucial to protein structure prediction. One of the existing strategies infers inter-SSE contacts directly from the predicted possibilities of inter-residue contacts without any preprocessing, and thus suffers from the excessive noises existing in the predicted inter-residue contacts. Another strategy defines SSEs based on protein secondary structure prediction first, and then judges whether each candidate SSE pair could form contact or not. However, it is difficult to accurately determine boundary of SSEs due to the errors in secondary structure prediction. The incorrectly-deduced SSEs definitely hinder subsequent prediction of the contacts among them. Results We here report an accurate approach to infer the inter-SSE contacts (thus called as ISSEC) using the deep object detection technique. The design of ISSEC is based on the observation that, in the inter-residue contact map, the contacting SSEs usually form rectangle regions with characteristic patterns. Therefore, ISSEC infers inter-SSE contacts through detecting such rectangle regions. Unlike the existing approach directly using the predicted probabilities of inter-residue contact, ISSEC applies the deep convolution technique to extract high-level features from the inter-residue contacts. More importantly, ISSEC does not rely on the pre-defined SSEs. Instead, ISSEC enumerates multiple candidate rectangle regions in the predicted inter-residue contact map, and for each region, ISSEC calculates a confidence score to measure whether it has characteristic patterns or not. ISSEC employs greedy strategy to select non-overlapping regions with high confidence score, and finally infers inter-SSE contacts according to these regions. Conclusions Comprehensive experimental results suggested that ISSEC outperformed the state-of-the-art approaches in predicting inter-SSE contacts. We further demonstrated the successful applications of ISSEC to improve prediction of both inter-residue contacts and tertiary structure as well.

scholarly journals Evolutionary Models of Amino Acid Substitutions Based on the Tertiary Structure of their Neighborhoods

2018 ◽  
Author(s):  
Elias Primetis ◽  
Spyridon Chavlis ◽  
Pavlos Pavlidis
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Amino Acid Substitution ◽  
Tertiary Structure ◽  
Protein Structures ◽  
Amino Acid Substitutions ◽  
Tertiary Structures ◽  
Substitution Matrices ◽  
Amino Acid Substitution Model ◽  
Amino Acid Substitution Matrices

AbstractIntra-protein residual vicinities depend on the involved amino acids. Energetically favorable vicinities (or interactions) have been preserved during evolution, while unfavorable vicinities have been eliminated. We describe, statistically, the interactions between amino acids using resolved protein structures. Based on the frequency of amino acid interactions, we have devised an amino acid substitution model that implements the following idea: amino acids that have similar neighbors in the protein tertiary structure can replace each other, while substitution is more difficult between amino acids that prefer different spatial neighbors. Using known tertiary structures for α-helical membrane (HM) proteins, we build evolutionary substitution matrices. We constructed maximum likelihood phylogenies using our amino acid substitution matrices and compared them to widely-used methods. Our results suggest that amino acid substitutions are associated with the spatial neighborhoods of amino acid residuals, providing, therefore, insights into the amino acid substitution process.

scholarly journals Predicting interchain contacts for homodimeric and homomultimeric protein complexes using multiple sequence alignments of monomers and deep learning

2020 ◽  
Author(s):  
Farhan Quadir ◽  
Raj Roy ◽  
Randal Halfmann ◽  
Jianlin Cheng
Keyword(s):  
Deep Learning ◽  
Tertiary Structure ◽  
Protein Complexes ◽  
Great Success ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Residue Contact ◽  
Residue Contacts ◽  
Evolutionary Features

AbstractDeep learning methods that achieved great success in predicting intrachain residue-residue contacts have been applied to predict interchain contacts between proteins. However, these methods require multiple sequence alignments (MSAs) of a pair of interacting proteins (dimers) as input, which are often difficult to obtain because there are not many known protein complexes available to generate MSAs of sufficient depth for a pair of proteins. In recognizing that multiple sequence alignments of a monomer that forms homomultimers contain the co-evolutionary signals of both intrachain and interchain residue pairs in contact, we applied DNCON2 (a deep learning-based protein intrachain residue-residue contact predictor) to predict both intrachain and interchain contacts for homomultimers using multiple sequence alignment (MSA) and other co-evolutionary features of a single monomer followed by discrimination of interchain and intrachain contacts according to the tertiary structure of the monomer. Allowing true-positive predictions within two residue shifts, the best average precision was obtained for the Top-L/10 predictions of DNCON2: 22.9% for homodimers, and 17.0% for higher order homomultimers. In some instances, especially where interchain contact densities are high, the approach predicted interchain contacts with 100% precision. We show that the predicted contacts can be used to accurately construct the structure of some complexes. Our experiment demonstrates that monomeric multiple sequence alignments can be used with deep learning to predict interchain contacts of homomeric proteins.

Torus Circumference and Thickness Parameters From a Linear DNA Size Series Suggest How Toruses Self-Assemble

1985 ◽  
Vol 43 ◽  
pp. 522-523
Author(s):  
George C. Ruben ◽  
Kenneth A. Marx
Keyword(s):  
Tertiary Structure ◽  
Dna Complexes ◽  
Tertiary Structures ◽  
Self Assembling ◽  
Double Stranded Dna ◽  
Calf Thymus ◽  
Dna Organization ◽  
Condensed Dna ◽  
Dna Size

Certain double stranded DNA bacteriophage and viruses are thought to have their DNA organized into large torus shaped structures. Morphologically, these poorly understood biological DNA tertiary structures resemble spermidine-condensed DNA complexes formed in vitro in the total absence of other macromolecules normally synthesized by the pathogens for the purpose of their own DNA packaging. Therefore, we have studied the tertiary structure of these self-assembling torus shaped spermidine- DNA complexes in a series of reports. Using freeze-etch, low Pt-C metal (10-15Å) replicas, we have visualized the microscopic DNA organization of both calf Thymus( CT) and linear 0X-174 RFII DNA toruses. In these structures DNA is circumferentially wound, continuously, around the torus into a semi-crystalline, hexagonal packed array of parallel DNA helix sections.

Automated Assessment of Binding Affinity via Alchemical Free Energy Calculations

2020 ◽  
Author(s):  
Maximilian Kuhn ◽  
Stuart Firth-Clark ◽  
Paolo Tosco ◽  
Antonia S. J. S. Mey ◽  
Mark Mackey ◽  
...  
Keyword(s):  
Free Energy ◽  
Protein Structures ◽  
Free Energy Calculations ◽  
Distinct Advantage ◽  
Binding Affinities ◽  
Structure Based Drug Design ◽  
Energy Calculations ◽  
Kendall’S Τ ◽  
Experimental Values ◽  
Better Than

Free energy calculations have seen increased usage in structure-based drug design. Despite the rising interest, automation of the complex calculations and subsequent analysis of their results are still hampered by the restricted choice of available tools. In this work, an application for automated setup and processing of free energy calculations is presented. Several sanity checks for assessing the reliability of the calculations were implemented, constituting a distinct advantage over existing open-source tools. The underlying workflow is built on top of the software Sire, SOMD, BioSimSpace and OpenMM and uses the AMBER14SB and GAFF2.1 force fields. It was validated on two datasets originally composed by Schrödinger, consisting of 14 protein structures and 220 ligands. Predicted binding affinities were in good agreement with experimental values. For the larger dataset the average correlation coefficient Rp was 0.70 ± 0.05 and average Kendall’s τ was 0.53 ± 0.05 which is broadly comparable to or better than previously reported results using other methods. <br>

Protein Inter-Residue Contacts Prediction: Methods, Performances and Applications

2019 ◽  
Vol 14 (3) ◽  
pp. 178-189 ◽  
Author(s):  
Xiaoyang Jing ◽  
Qimin Dong ◽  
Ruqian Lu ◽  
Qiwen Dong
Keyword(s):  
Machine Learning ◽  
Protein Structure ◽  
Tertiary Structure ◽  
Prediction Methods ◽  
Learning Methods ◽  
Typical Application ◽  
Machine Learning Methods ◽  
Residue Contacts ◽  
Fusion Methods ◽  
Correlated Mutations

Background:Protein inter-residue contacts prediction play an important role in the field of protein structure and function research. As a low-dimensional representation of protein tertiary structure, protein inter-residue contacts could greatly help de novo protein structure prediction methods to reduce the conformational search space. Over the past two decades, various methods have been developed for protein inter-residue contacts prediction.Objective:We provide a comprehensive and systematic review of protein inter-residue contacts prediction methods.Results:Protein inter-residue contacts prediction methods are roughly classified into five categories: correlated mutations methods, machine-learning methods, fusion methods, templatebased methods and 3D model-based methods. In this paper, firstly we describe the common definition of protein inter-residue contacts and show the typical application of protein inter-residue contacts. Then, we present a comprehensive review of the three main categories for protein interresidue contacts prediction: correlated mutations methods, machine-learning methods and fusion methods. Besides, we analyze the constraints for each category. Furthermore, we compare several representative methods on the CASP11 dataset and discuss performances of these methods in detail.Conclusion:Correlated mutations methods achieve better performances for long-range contacts, while the machine-learning method performs well for short-range contacts. Fusion methods could take advantage of the machine-learning and correlated mutations methods. Employing more effective fusion strategy could be helpful to further improve the performances of fusion methods.

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