Expanding Direct Coupling Analysis to Identify Heterodimeric Interfaces from Limited Protein Sequence Data

2021 ◽  
Author(s):  
Kareem M. Mehrabiani ◽  
Ryan R. Cheng ◽  
José N. Onuchic
Keyword(s):  
Protein Sequence ◽  
Sequence Data ◽  
Direct Coupling ◽  
Coupling Analysis ◽  
Protein Sequence Data ◽  
Direct Coupling Analysis ◽  
Limited Protein

scholarly journals pydca v1.0: a comprehensive software for direct coupling analysis of RNA and protein sequences

2019 ◽  
Vol 36 (7) ◽  
pp. 2264-2265 ◽  
Author(s):  
Mehari B Zerihun ◽  
Fabrizio Pucci ◽  
Emanuel K Peter ◽  
Alexander Schug
Keyword(s):  
Structure Prediction ◽  
Sequence Data ◽  
Direct Coupling ◽  
Supplementary Information ◽  
Spatial Proximity ◽  
Homologous Proteins ◽  
Coupling Analysis ◽  
Multiple Sequence ◽  
Wide Range ◽  
Direct Coupling Analysis

Abstract Motivation The ongoing advances in sequencing technologies have provided a massive increase in the availability of sequence data. This made it possible to study the patterns of correlated substitution between residues in families of homologous proteins or RNAs and to retrieve structural and stability information. Direct coupling analysis (DCA) infers coevolutionary couplings between pairs of residues indicating their spatial proximity, making such information a valuable input for subsequent structure prediction. Results Here, we present pydca, a standalone Python-based software package for the DCA of protein- and RNA-homologous families. It is based on two popular inverse statistical approaches, namely, the mean-field and the pseudo-likelihood maximization and is equipped with a series of functionalities that range from multiple sequence alignment trimming to contact map visualization. Thanks to its efficient implementation, features and user-friendly command line interface, pydca is a modular and easy-to-use tool that can be used by researchers with a wide range of backgrounds. Availability and implementation pydca can be obtained from https://github.com/KIT-MBS/pydca or from the Python Package Index under the MIT License. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals pydca v1.0: a comprehensive software for Direct Coupling Analysis of RNA and Protein Sequences

2019 ◽  
Author(s):  
Mehari B. Zerihun ◽  
Fabrizio Pucci ◽  
Emanuel Karl Peter ◽  
Alexander Schug
Keyword(s):  
Structure Prediction ◽  
Sequence Data ◽  
Mean Field ◽  
Direct Coupling ◽  
Spatial Proximity ◽  
Homologous Proteins ◽  
Coupling Analysis ◽  
Multiple Sequence ◽  
Wide Range ◽  
Direct Coupling Analysis

AbstractThe ongoing advances in sequencing technologies have provided a massive increase in the availability of sequence data. This made it possible to study the patterns of correlated substitution between residues in families of homologous proteins or RNAs and to retrieve structural and stability information. Direct coupling Analysis (DCA) infers coevolutionary couplings between pairs of residues indicating their spatial proximity, making such information a valuable input for subsequent structure prediction. Here we present pydca, a standalone Python-based software package for the DCA of protein- and RNA-homologous families. It is based on two popular inverse statistical approaches, namely, the mean-field and the pseudo-likelihood maximization and is equipped with a series of functionalities that range from multiple sequence alignment trimming to contact map visualization. Thanks to its efficient implementation, features and user-friendly command line interface, pydca is a modular and easy-to-use tool that can be used by researchers with a wide range of backgrounds.Availabilityhttps://github.com/KIT-MBS/pydca

scholarly journals Protein structure without structure determination: direct coupling analysis based on in vitro evolution

2019 ◽  
Author(s):  
Marco Fantini ◽  
Simonetta Lisi ◽  
Paolo De Los Rios ◽  
Antonino Cattaneo ◽  
Annalisa Pastore
Keyword(s):  
Structural Information ◽  
Sequence Data ◽  
Protein Structures ◽  
Direct Coupling ◽  
In Vitro Mutagenesis ◽  
Evolutionary Analysis ◽  
Protein Families ◽  
Coupling Analysis ◽  
Direct Coupling Analysis

AbstractDirect Coupling Analysis (DCA) is a powerful technique that enables to extract structural information of proteins belonging to large protein families exclusively by in silico analysis. This method is however limited by sequence availability and various biases. Here, we propose a method that exploits molecular evolution to circumvent these limitations: instead of relying on existing protein families, we used in vitro mutagenesis of TEM-1 beta lactamase combined with in vivo functional selection to generate the sequence data necessary for evolutionary analysis. We could reconstruct by this strategy, which we called CAMELS (CouplingAnalysis byMolecularEvolutionLibrarySequencing), the lactamase fold exclusively from sequence data. Through generating and sequencing large libraries of variants, we can deal with any protein, ancient or recent, from any species, having the only constraint of setting up a functional phenotypic selection of the protein. This method allows us to obtain protein structures without solving the structure experimentally.

scholarly journals Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis

2016 ◽  
Vol 113 (43) ◽  
pp. 12186-12191 ◽  
Author(s):  
Thomas Gueudré ◽  
Carlo Baldassi ◽  
Marco Zamparo ◽  
Martin Weigt ◽  
Andrea Pagnani
Keyword(s):  
Protein Interactions ◽  
Multiple Scales ◽  
Sequence Data ◽  
Direct Coupling ◽  
Physical Contact ◽  
Protein Protein Interactions ◽  
Homologous Proteins ◽  
Coupling Analysis ◽  
Large Joint ◽  
Direct Coupling Analysis

Understanding protein−protein interactions is central to our understanding of almost all complex biological processes. Computational tools exploiting rapidly growing genomic databases to characterize protein−protein interactions are urgently needed. Such methods should connect multiple scales from evolutionary conserved interactions between families of homologous proteins, over the identification of specifically interacting proteins in the case of multiple paralogs inside a species, down to the prediction of residues being in physical contact across interaction interfaces. Statistical inference methods detecting residue−residue coevolution have recently triggered considerable progress in using sequence data for quaternary protein structure prediction; they require, however, large joint alignments of homologous protein pairs known to interact. The generation of such alignments is a complex computational task on its own; application of coevolutionary modeling has, in turn, been restricted to proteins without paralogs, or to bacterial systems with the corresponding coding genes being colocalized in operons. Here we show that the direct coupling analysis of residue coevolution can be extended to connect the different scales, and simultaneously to match interacting paralogs, to identify interprotein residue−residue contacts and to discriminate interacting from noninteracting families in a multiprotein system. Our results extend the potential applications of coevolutionary analysis far beyond cases treatable so far.

scholarly journals Direct Coupling Analysis of Epistasis in Allosteric Materials

2019 ◽  
Author(s):  
Barbara Bravi ◽  
Riccardo Ravasio ◽  
Carolina Brito ◽  
Matthieu Wyart
Keyword(s):  
Drug Design ◽  
Long Range ◽  
Active Site ◽  
De Novo ◽  
Sequence Data ◽  
Direct Coupling ◽  
Coupling Analysis ◽  
Promising Tool ◽  
Direct Coupling Analysis ◽  
The Impact

AbstractIn allosteric proteins, the binding of a ligand modifies function at a distant active site. Such al-losteric pathways can be used as target for drug design, generating considerable interest in inferring them from sequence alignment data. Currently, different methods lead to conflicting results, in particular on the existence of long-range evolutionary couplings between distant amino-acids mediating allostery. Here we propose a resolution of this conundrum, by studying epistasis and its inference in models where an allosteric material is evolved in silico to perform a mechanical task. We find four types of epistasis (Synergistic, Sign, Antagonistic, Saturation), which can be both short or long-range and have a simple mechanical interpretation. We perform a Direct Coupling Analysis (DCA) and find that DCA predicts well mutation costs but is a rather poor generative model. Strikingly, it can predict short-range epistasis but fails to capture long-range epistasis, in agreement with empirical findings. We propose that such failure is generic when function requires subparts to work in concert. We illustrate this idea with a simple model, which suggests that other methods may be better suited to capture long-range effects.Author summaryAllostery in proteins is the property of highly specific responses to ligand binding at a distant site. To inform protocols of de novo drug design, it is fundamental to understand the impact of mutations on allosteric regulation and whether it can be predicted from evolutionary correlations. In this work we consider allosteric architectures artificially evolved to optimize the cooperativity of binding at allosteric and active site. We first characterize the emergent pattern of epistasis as well as the underlying mechanical phenomena, finding four types of epistasis (Synergistic, Sign, Antagonistic, Saturation), which can be both short or long-range. The numerical evolution of these allosteric architectures allows us to benchmark Direct Coupling Analysis, a method which relies on co-evolution in sequence data to infer direct evolutionary couplings, in connection to allostery. We show that Direct Coupling Analysis predicts quantitatively mutation costs but underestimates strong long-range epistasis. We provide an argument, based on a simplified model, illustrating the reasons for this discrepancy and we propose neural networks as more promising tool to measure epistasis.

scholarly journals AC2: An Efficient Protein Sequence Compression Tool Using Artificial Neural Networks and Cache-Hash Models

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 530
Author(s):  
Milton Silva ◽  
Diogo Pratas ◽  
Armando J. Pinho
Keyword(s):  
Protein Sequence ◽  
Sequence Data ◽  
Specific Protein ◽  
General Purpose ◽  
Amino Acid Sequences ◽  
Input Size ◽  
Protein Sequence Data ◽  
Analysis Application ◽  
Straightforward Solution ◽  
Human Coronaviruses

Recently, the scientific community has witnessed a substantial increase in the generation of protein sequence data, triggering emergent challenges of increasing importance, namely efficient storage and improved data analysis. For both applications, data compression is a straightforward solution. However, in the literature, the number of specific protein sequence compressors is relatively low. Moreover, these specialized compressors marginally improve the compression ratio over the best general-purpose compressors. In this paper, we present AC2, a new lossless data compressor for protein (or amino acid) sequences. AC2 uses a neural network to mix experts with a stacked generalization approach and individual cache-hash memory models to the highest-context orders. Compared to the previous compressor (AC), we show gains of 2–9% and 6–7% in reference-free and reference-based modes, respectively. These gains come at the cost of three times slower computations. AC2 also improves memory usage against AC, with requirements about seven times lower, without being affected by the sequences’ input size. As an analysis application, we use AC2 to measure the similarity between each SARS-CoV-2 protein sequence with each viral protein sequence from the whole UniProt database. The results consistently show higher similarity to the pangolin coronavirus, followed by the bat and human coronaviruses, contributing with critical results to a current controversial subject. AC2 is available for free download under GPLv3 license.

scholarly journals Techniques for the verification of minimal phylogenetic trees illustrated with ten mammalian haemoglobin sequences

1980 ◽  
Vol 187 (1) ◽  
pp. 65-74 ◽  
Author(s):  
D Penny ◽  
M D Hendy ◽  
L R Foulds
Keyword(s):  
Amino Acid ◽  
Phylogenetic Tree ◽  
Protein Sequence ◽  
Phylogenetic Trees ◽  
Sequence Data ◽  
Protein Sequences ◽  
Nucleotide Sequences ◽  
Amino Acid Sequences ◽  
Minimal Tree ◽  
Protein Sequence Data

We have recently reported a method to identify the shortest possible phylogenetic tree for a set of protein sequences [Foulds Hendy & Penny (1979) J. Mol. Evol. 13. 127–150; Foulds, Penny & Hendy (1979) J. Mol. Evol. 13, 151–166]. The present paper discusses issues that arise during the construction of minimal phylogenetic trees from protein-sequence data. The conversion of the data from amino acid sequences into nucleotide sequences is shown to be advantageous. A new variation of a method for constructing a minimal tree is presented. Our previous methods have involved first constructing a tree and then either proving that it is minimal or transforming it into a minimal tree. The approach presented in the present paper progressively builds up a tree, taxon by taxon. We illustrate this approach by using it to construct a minimal tree for ten mammalian haemoglobin alpha-chain sequences. Finally we define a measure of the complexity of the data and illustrate a method to derive a directed phylogenetic tree from the minimal tree.

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