scholarly journals Influence of Disease-Causing Mutations on Protein Structural Networks

2021 ◽  
Vol 7 ◽  
Author(s):  
Vasam Manjveekar Prabantu ◽  
Nagarajan Naveenkumar ◽  
Narayanaswamy Srinivasan
Keyword(s):  
Protein Structure ◽  
Tertiary Structure ◽  
Protein Structures ◽  
Data Bank ◽  
Disease Mutations ◽  
Structural Network ◽  
Structural Divergence ◽  
Disease Variant ◽  
Structural Networks ◽  
Global And Local

The interactions between residues in a protein tertiary structure can be studied effectively using the approach of protein structure network (PSN). A PSN is a node-edge representation of the structure with nodes representing residues and interactions between residues represented by edges. In this study, we have employed weighted PSNs to understand the influence of disease-causing mutations on proteins of known 3D structures. We have used manually curated information on disease mutations from UniProtKB/Swiss-Prot and their corresponding protein structures of wildtype and disease variant from the protein data bank. The PSNs of the wildtype and disease-causing mutant are compared to analyse variation of global and local dissimilarity in the overall network and at specific sites. We study how a mutation at a given site can affect the structural network at a distant site which may be involved in the function of the protein. We have discussed specific examples of the disease cases where the protein structure undergoes limited structural divergence in their backbone but have large dissimilarity in their all atom networks and vice versa, wherein large conformational alterations are observed while retaining overall network. We analyse the effect of variation of network parameters that characterize alteration of function or stability.

scholarly journals A COMPARATIVE STUDY OF PROTEIN TERTIARY STRUCTURE PREDICTION METHODS

2014 ◽  
pp. 15-18
Author(s):  
CHANDRAYANI N. ROKDE ◽  
DR.MANALI KSHIRSAGAR
Keyword(s):  
Protein Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Sequence Data ◽  
Protein Structures ◽  
Three Dimensional ◽  
Data Bank ◽  
Dimensional Structure ◽  
X Ray Crystallography ◽  
Protein Tertiary Structure Prediction

Protein structure prediction (PSP) from amino acid sequence is one of the high focus problems in bioinformatics today. This is due to the fact that the biological function of the protein is determined by its three dimensional structure. The understanding of protein structures is vital to determine the function of a protein and its interaction with DNA, RNA and enzyme. Thus, protein structure is a fundamental area of computational biology. Its importance is intensed by large amounts of sequence data coming from PDB (Protein Data Bank) and the fact that experimentally methods such as X-ray crystallography or Nuclear Magnetic Resonance (NMR)which are used to determining protein structures remains very expensive and time consuming. In this paper, different types of protein structures and methods for its prediction are described.

Prediction of Structural and Functional Aspects of Protein

2014 ◽  
pp. 317-333
Author(s):  
Arun G. Ingale
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Protein Structures ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Sequence Information ◽  
Predict Protein Structure ◽  
Basic Ideas

To predict the structure of protein from a primary amino acid sequence is computationally difficult. An investigation of the methods and algorithms used to predict protein structure and a thorough knowledge of the function and structure of proteins are critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this chapter sheds light on the methods used for protein structure prediction. This chapter covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, it presents an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction, giving unique insight into the future applications of the modeled protein structures. In this chapter, current protein structure prediction methods are reviewed for a milieu on structure prediction, the prediction of structural fundamentals, tertiary structure prediction, and functional imminent. The basic ideas and advances of these directions are discussed in detail.

scholarly journals Different Synergy in Amyloids and Biologically Active Forms of Proteins

2019 ◽  
Vol 20 (18) ◽  
pp. 4436 ◽  
Author(s):  
Piotr Fabian ◽  
Katarzyna Stapor ◽  
Mateusz Banach ◽  
Magdalena Ptak-Kaczor ◽  
Leszek Konieczny ◽  
...  
Keyword(s):  
Amino Acids ◽  
Comparative Analysis ◽  
Protein Structure ◽  
Tertiary Structure ◽  
Protein Structures ◽  
Water Environment ◽  
Biologically Active ◽  
Radius Of Curvature ◽  
Hydrophobic Core ◽  
Structural Form

Protein structure is the result of the high synergy of all amino acids present in the protein. This synergy is the result of an overall strategy for adapting a specific protein structure. It is a compromise between two trends: The optimization of non-binding interactions and the directing of the folding process by an external force field, whose source is the water environment. The geometric parameters of the structural form of the polypeptide chain in the form of a local radius of curvature that is dependent on the orientation of adjacent peptide bond planes (result of the respective Phi and Psi rotation) allow for a comparative analysis of protein structures. Certain levels of their geometry are the criteria for comparison. In particular, they can be used to assess the differences between the structural form of biologically active proteins and their amyloid forms. On the other hand, the application of the fuzzy oil drop model allows the assessment of the role of amino acids in the construction of tertiary structure through their participation in the construction of a hydrophobic core. The combination of these two models—the geometric structure of the backbone and the determining of the participation in the construction of the tertiary structure that is applied for the comparative analysis of biologically active and amyloid forms—is presented.

TOP: a new method for protein structure comparisons and similarity searches

2000 ◽  
Vol 33 (1) ◽  
pp. 176-183 ◽  
Author(s):  
Guoguang Lu
Keyword(s):  
User Interface ◽  
Protein Structure ◽  
Protein Structures ◽  
Three Dimensional ◽  
Data Bank ◽  
Structure Alignment ◽  
Dimensional Structure ◽  
Protein Structure Alignment ◽  
Protein Structure Analysis ◽  
Structure Comparison

In order to facilitate the three-dimensional structure comparison of proteins, software for making comparisons and searching for similarities to protein structures in databases has been developed. The program identifies the residues that share similar positions of both main-chain and side-chain atoms between two proteins. The unique functions of the software also include database processingviaInternet- and Web-based servers for different types of users. The developed method and its friendly user interface copes with many of the problems that frequently occur in protein structure comparisons, such as detecting structurally equivalent residues, misalignment caused by coincident match of Cαatoms, circular sequence permutations, tedious repetition of access, maintenance of the most recent database, and inconvenience of user interface. The program is also designed to cooperate with other tools in structural bioinformatics, such as the 3DB Browser software [Prilusky (1998).Protein Data Bank Q. Newslett.84, 3–4] and the SCOP database [Murzin, Brenner, Hubbard & Chothia (1995).J. Mol. Biol.247, 536–540], for convenient molecular modelling and protein structure analysis. A similarity ranking score of `structure diversity' is proposed in order to estimate the evolutionary distance between proteins based on the comparisons of their three-dimensional structures. The function of the program has been utilized as a part of an automated program for multiple protein structure alignment. In this paper, the algorithm of the program and results of systematic tests are presented and discussed.

scholarly journals Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction

2021 ◽  
Author(s):  
SM Bargeen Alam Turzo ◽  
Justin Thomas Seffernick ◽  
Amber D Rolland ◽  
Micah T Donor ◽  
Sten Heinze ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Structure ◽  
Ion Mobility ◽  
Structure Determination ◽  
Tertiary Structure ◽  
De Novo ◽  
Structural Information ◽  
Collision Cross Section ◽  
Protein Structures ◽  
Protein Shape

Among a wide variety of mass spectrometry (MS) methodologies available for structural characterizations of proteins, ion mobility (IM) provides structural information about protein shape and size in the form of an orientationally averaged collision cross-section (CCS). While IM data have been predominantly employed for the structural assessment of protein complexes, CCS data from IM experiments have not yet been used to predict tertiary structure from sequence. Here, we are showing that IM data can significantly improve protein structure determination using the modeling suite Rosetta. The Rosetta Projection Approximation using Rough Circular Shapes (PARCS) algorithm was developed that allows for fast and accurate prediction of CCS from structure. Following successful rigorous testing for accuracy, speed, and convergence of PARCS, an integrative modelling approach was developed in Rosetta to use CCS data from IM experiments. Using this method, we predicted protein structures from sequence for a benchmark set of 23 proteins. When using IM data, the predicted structure improved or remained unchanged for all 23 proteins, compared to the predicted models in the absence of CCS data. For 15/23 proteins, the RMSD (root-mean-square deviation) of the predicted model was less than 5.50 Å, compared to only 10/23 without IM data. We also developed a confidence metric that successfully identified near-native models in the absence of a native structure. These results demonstrate the ability of IM data in de novo structure determination.

State-of-the-art web services for de novo protein structure prediction

2020 ◽  
Author(s):  
Luciano A Abriata ◽  
Matteo Dal Peraro
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
De Novo ◽  
State Of The Art ◽  
Data Bank ◽  
End Users ◽  
Model Quality ◽  
Uncharacterized Protein

Abstract Residue coevolution estimations coupled to machine learning methods are revolutionizing the ability of protein structure prediction approaches to model proteins that lack clear homologous templates in the Protein Data Bank (PDB). This has been patent in the last round of the Critical Assessment of Structure Prediction (CASP), which presented several very good models for the hardest targets. Unfortunately, literature reporting on these advances often lacks digests tailored to lay end users; moreover, some of the top-ranking predictors do not provide webservers that can be used by nonexperts. How can then end users benefit from these advances and correctly interpret the predicted models? Here we review the web resources that biologists can use today to take advantage of these state-of-the-art methods in their research, including not only the best de novo modeling servers but also datasets of models precomputed by experts for structurally uncharacterized protein families. We highlight their features, advantages and pitfalls for predicting structures of proteins without clear templates. We present a broad number of applications that span from driving forward biochemical investigations that lack experimental structures to actually assisting experimental structure determination in X-ray diffraction, cryo-EM and other forms of integrative modeling. We also discuss issues that must be considered by users yet still require further developments, such as global and residue-wise model quality estimates and sources of residue coevolution other than monomeric tertiary structure.

scholarly journals Efficient protein tertiary structure retrievals and classifications using content based comparison algorithms

2007 ◽  
Author(s):  
◽  
Pin-Hao Chi
Keyword(s):  
Protein Structure ◽  
Real Time ◽  
High Throughput ◽  
High Efficiency ◽  
Tertiary Structure ◽  
Protein Structures ◽  
Three Dimensional ◽  
Comparison Algorithms ◽  
High Level ◽  
Protein Structure Retrieval

Functionally important sites of proteins are potentially conserved to specific three-dimensional structural folds. To understand the structure-to-function relationship, life sciences researchers and biologists have a great need to retrieve similar structures from protein databases and classify these structures into the same protein fold. Traditional protein structure retrieval and classification methods are known to be either computationally expensive or labor intensive. In the past decade, more than 35000 protein structures have been identified. To meet the needs of fast retrieval and classifying high-throughput protein data, our research covers three main subjects: (1) Real-time global protein structure retrieval: We introduce an image-based approach that extracts signatures of three-dimensional protein structures. Our high-level protein signatures are then indexed by multi-dimensional indexing trees for fast retrieval. (2) Real-time global protein structure classification: An advanced knowledge discovery and data mining (KDD) model is proposed to convert high-level protein signature into itemsets for mining association rules. The advantage of this KDD approach is to effectively reveal the hidden knowledge from similar protein tertiary structures and quickly suggest possible SCOP domains for a newly-discovered protein. In addition, we develop a non-parametric classifier, E-Predict, that can rapidly assign known SCOP folds and recognize novel folds for newly-discovered proteins. (3) Efficient local protein structure retrieval and classification: We propose a novel algorithm, namely, the Index-based Protein Substructure Alignment (IPSA), that constructs a two-layer indexing tree to capture the obscured similarity of protein substructures in a timely fashion. Our research works exhibit significantly high efficiency with reasonably high accuracy and will benefit the study of high-throughput protein structure-function evolutionary relationships.

scholarly journals Ancient gene duplications in RNA viruses revealed by protein tertiary structure comparisons

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Alejandro Miguel Cisneros-Martínez ◽  
Arturo Becerra ◽  
Antonio Lazcano
Keyword(s):  
Tertiary Structure ◽  
Rna Viruses ◽  
Rna Virus ◽  
Protein Structures ◽  
Data Bank ◽  
Cysteine Proteases ◽  
Gene Duplications ◽  
Virus Family ◽  
Protein Tertiary Structure ◽  
Duplication Events

Abstract To date only a handful of duplicated genes have been described in RNA viruses. This shortage can be attributed to different factors, including the RNA viruses with high mutation rate that would make a large genome more prone to acquire deleterious mutations. This may explain why sequence-based approaches have only found duplications in their most recent evolutionary history. To detect earlier duplications, we performed protein tertiary structure comparisons for every RNA virus family represented in the Protein Data Bank. We present a list of thirty pairs of possible paralogs with <30 per cent sequence identity. It is argued that these pairs are the outcome of six duplication events. These include the α and β subunits of the fungal toxin KP6 present in the dsRNA Ustilago maydis virus (family Totiviridae), the SARS-CoV (Coronaviridae) nsp3 domains SUD-N, SUD-M and X-domain, the Picornavirales (families Picornaviridae, Dicistroviridae, Iflaviridae and Secoviridae) capsid proteins VP1, VP2 and VP3, and the Enterovirus (family Picornaviridae) 3C and 2A cysteine-proteases. Protein tertiary structure comparisons may reveal more duplication events as more three-dimensional protein structures are determined and suggests that, although still rare, gene duplications may be more frequent in RNA viruses than previously thought. Keywords: gene duplications; RNA viruses.

scholarly journals Learning structural motif representations for efficient protein structure search

2017 ◽  
Author(s):  
Yang Liu ◽  
Qing Ye ◽  
Liwei Wang ◽  
Jian Peng
Keyword(s):  
Protein Structure ◽  
Fundamental Problem ◽  
Sequence Similarity ◽  
Structural Alignment ◽  
Protein Structures ◽  
Computational Cost ◽  
Data Bank ◽  
Hierarchical Organization ◽  
Structural Motif ◽  
And Function

AbstractMotivationUnderstanding the relationship between protein structure and function is a fundamental problem in protein science. Given a protein of unknown function, fast identification of similar protein structures from the Protein Data Bank (PDB) is a critical step for inferring its biological function. Such structural neighbors can provide evolutionary insights into protein conformation, interfaces and binding sites that are not detectable from sequence similarity. However, the computational cost of performing pairwise structural alignment against all structures in PDB is prohibitively expensive. Alignment-free approaches have been introduced to enable fast but coarse comparisons by representing each protein as a vector of structure features or fingerprints and only computing similarity between vectors. As a notable example, FragBag represents each protein by a “bag of fragments”, which is a vector of frequencies of contiguous short backbone fragments from a predetermined library.ResultsHere we present a new approach to learning effective structural motif presentations using deep learning. We develop DeepFold, a deep convolutional neural network model to extract structural motif features of a protein structure. Similar to FragBag, DeepFold represents each protein structure or fold using a vector of learned structural motif features. We demonstrate that DeepFold substantially outperforms FragBag on protein structural search on a non-redundant protein structure database and a set of newly released structures. Remarkably, DeepFold not only extracts meaningful backbone segments but also finds important long-range interacting motifs for structural comparison. We expect that DeepFold will provide new insights into the evolution and hierarchical organization of protein structural motifs.Availabilityhttps://github.com/largelymfs/[email protected]

scholarly journals The continuity of protein structure space is an intrinsic property of proteins

2009 ◽  
Vol 106 (37) ◽  
pp. 15690-15695 ◽  
Author(s):  
Jeffrey Skolnick ◽  
Adrian K. Arakaki ◽  
Seung Yup Lee ◽  
Michal Brylinski
Keyword(s):  
Protein Structure ◽  
Protein Structures ◽  
Intrinsic Property ◽  
Data Bank ◽  
Evolutionary Divergence ◽  
Single Domain Protein ◽  
Structure Space ◽  
Functional Analyses ◽  
Related Proteins ◽  
Protein Structure Space

The classical view of the space of protein structures is that it is populated by a discrete set of protein folds. For proteins up to 200 residues long, by using structural alignments and building upon ideas of the completeness and continuity of structure space, we show that nearly any structure is significantly related to any other using a transitive set of no more than 7 intermediate structurally related proteins. This result holds for all structures in the Protein Data Bank, even when structural relationships between evolutionary related proteins (as detected by threading or functional analyses) are excluded. A similar picture holds for an artificial library of compact, hydrogen-bonded, homopolypeptide structures. The 3 sets share the global connectivity features of random graphs, in which the local connectivity of each node (i.e., the number of neighboring structures per protein) is preserved. This high connectivity supports the continuous view of single-domain protein structure space. More importantly, these results do not depend on evolution, rather just on the physics of protein structures. The fact that evolutionary divergence need not be invoked to explain the continuous nature of protein structure space has implications for how the universe of protein structures might have originated, and how function should be transferred between proteins of similar structure.

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