scholarly journals PYTHIA: Deep Learning Approach for Local Protein Conformation Prediction

2021 ◽  
Vol 22 (16) ◽  
pp. 8831
Author(s):  
Gabriel Cretin ◽  
Tatiana Galochkina ◽  
Alexandre G. de Brevern ◽  
Jean-Christophe Gelly
Keyword(s):  
Amino Acids ◽  
Protein Structure ◽  
Structure Prediction ◽  
Reference Method ◽  
Structure Alignment ◽  
Evolutionary Information ◽  
Protein Structure Alignment ◽  
Structural Alphabet ◽  
Local Conformations ◽  
Local Protein

Protein Blocks (PBs) are a widely used structural alphabet describing local protein backbone conformation in terms of 16 possible conformational states, adopted by five consecutive amino acids. The representation of complex protein 3D structures as 1D PB sequences was previously successfully applied to protein structure alignment and protein structure prediction. In the current study, we present a new model, PYTHIA (predicting any conformation at high accuracy), for the prediction of the protein local conformations in terms of PBs directly from the amino acid sequence. PYTHIA is based on a deep residual inception-inside-inception neural network with convolutional block attention modules, predicting 1 of 16 PB classes from evolutionary information combined to physicochemical properties of individual amino acids. PYTHIA clearly outperforms the LOCUSTRA reference method for all PB classes and demonstrates great performance for PB prediction on particularly challenging proteins from the CASP14 free modelling category.

Algorithmic Aspects of Protein Threading

2011 ◽  
pp. 605-619
Author(s):  
Tatsuya Akutsu
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Three Dimensional ◽  
Structure Alignment ◽  
Protein Structure Alignment ◽  
Amino Acid Residues ◽  
Optimal Solutions ◽  
Score Functions ◽  
Protein Threading

This chapter provides an overview of computational problems and techniques for protein threading. Protein threading is one of the most powerful approaches to protein structure prediction, where protein structure prediction is to infer three-dimensional (3-D) protein structure for a given protein sequence. Protein threading can be modeled as an optimization problem. Optimal solutions can be obtained in polynomial time using simple dynamic programming algorithms if profile type score functions are employed. However, this problem is computationally hard (NP-hard) if score functions include pairwise interaction preferences between amino acid residues. Therefore, various algorithms have been developed for finding optimal or near-optimal solutions. This chapter explains the ideas employed in these algorithms. This chapter also gives brief explanations of related problems: protein threading with constraints, comparison of RNA secondary structures and protein structure alignment.

ALIGNING MULTIPLE PROTEIN STRUCTURES BY DETERMINISTIC ANNEALING

2005 ◽  
Vol 03 (04) ◽  
pp. 837-860 ◽  
Author(s):  
TIANSHOU ZHOU ◽  
LUONAN CHEN ◽  
YUN TANG ◽  
XIANGSUN ZHANG
Keyword(s):  
Protein Structure ◽  
Structure Prediction ◽  
Protein Structures ◽  
Structure Alignment ◽  
Deterministic Annealing ◽  
Protein Structure Alignment ◽  
Protein Chain ◽  
Fold Family ◽  
Multiple Protein ◽  
Wide Range

Protein structure alignment plays a key role in protein structure prediction and fold family classification. An efficient method for multiple protein structure alignment in a mathematical manner is presented, based on deterministic annealing technique. The alignment problem is mapped onto a nonlinear continuous optimization problem (NCOP) with common consensus chain, matching assignment matrices and atomic coordinates as variables. At each step in the annealing procedure, the NCOP is decomposed into as many subproblems as the number of protein chains, each of which is actually an independent pairwise structure alignment between a protein chain and the consensus chain and hence can be efficiently solved by the parallel computation technique. The proposed method is robust with respect to choice of iteration parameters for a wide range of proteins, and performs well in both multiple and pairwise structure alignment cases, compared with existing alignment methods.

Algorithmic Aspects of Protein Threading

2011 ◽  
pp. 118-135
Author(s):  
Tatsuya Akutsu
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Three Dimensional ◽  
Structure Alignment ◽  
Protein Structure Alignment ◽  
Amino Acid Residues ◽  
Optimal Solutions ◽  
Score Functions ◽  
Protein Threading

This chapter provides an overview of computational problems and techniques for protein threading. Protein threading is one of the most powerful approaches to protein structure prediction, where protein structure prediction is to infer three-dimensional (3-D) protein structure for a given protein sequence. Protein threading can be modeled as an optimization problem. Optimal solutions can be obtained in polynomial time using simple dynamic programming algorithms if profile type score functions are employed. However, this problem is computationally hard (NP-hard) if score functions include pairwise interaction preferences between amino acid residues. Therefore, various algorithms have been developed for finding optimal or near-optimal solutions. This chapter explains the ideas employed in these algorithms. This chapter also gives brief explanations of related problems: protein threading with constraints, comparison of RNA secondary structures and protein structure alignment.

scholarly journals Efficient Protein Structure Alignment Methods Based on a Structural Alphabet

2014 ◽  
Vol 106 (2) ◽  
pp. 54a
Author(s):  
Agnel Praveen Joseph ◽  
Jean-Christophe Gelly ◽  
Sylvain Leonard ◽  
Pierrick Craveur ◽  
Jeremy Esque ◽  
...  
Keyword(s):  
Protein Structure ◽  
Structure Alignment ◽  
Protein Structure Alignment ◽  
Structural Alphabet

Algorithmic Aspects of Protein Threading

2008 ◽  
pp. 103-118
Author(s):  
Tatsuya Akutsu
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Three Dimensional ◽  
Structure Alignment ◽  
Protein Structure Alignment ◽  
Amino Acid Residues ◽  
Optimal Solutions ◽  
Score Functions ◽  
Protein Threading

This chapter provides an overview of computational problems and techniques for protein threading. Protein threading is one of the most powerful approaches to protein structure prediction, where protein structure prediction is to infer three-dimensional (3-D) protein structure for a given protein sequence. Protein threading can be modeled as an optimization problem. Optimal solutions can be obtained in polynomial time using simple dynamic programming algorithms if profile type score functions are employed. However, this problem is computationally hard (NP-hard) if score functions include pairwise interaction preferences between amino acid residues. Therefore, various algorithms have been developed for finding optimal or near-optimal solutions. This chapter explains the ideas employed in these algorithms. This chapter also gives brief explanations of related problems: protein threading with constraints, comparison of RNA secondary structures and protein structure alignment.

scholarly journals mulPBA: an efficient multiple protein structure alignment method based on a structural alphabet

2013 ◽  
Vol 32 (4) ◽  
pp. 661-668 ◽  
Author(s):  
Sylvain Léonard ◽  
Agnel Praveen Joseph ◽  
Narayanaswamy Srinivasan ◽  
Jean-Christophe Gelly ◽  
Alexandre G. de Brevern
Keyword(s):  
Protein Structure ◽  
Structure Alignment ◽  
Protein Structure Alignment ◽  
Alignment Method ◽  
Structural Alphabet ◽  
Multiple Protein

A pair-to-pair amino acids substitution matrix and its applications for protein structure prediction

2007 ◽  
Vol 67 (1) ◽  
pp. 142-153 ◽  
Author(s):  
Eran Eyal ◽  
Milana Frenkel-Morgenstern ◽  
Vladimir Sobolev ◽  
Shmuel Pietrokovski
Keyword(s):  
Amino Acids ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Substitution Matrix

PROTEIN STRUCTURE ALIGNMENT AND FAST SIMILARITY SEARCH USING LOCAL SHAPE SIGNATURES

2004 ◽  
Vol 02 (01) ◽  
pp. 215-239 ◽  
Author(s):  
TOLGA CAN ◽  
YUAN-FANG WANG
Keyword(s):  
Protein Structure ◽  
Protein Structures ◽  
Structure Alignment ◽  
Protein Structure Alignment ◽  
Specific Information ◽  
Alignment Algorithm ◽  
Screening Process ◽  
Domain Specific ◽  
Local Sequence ◽  
Shape Signatures

We present a new method for conducting protein structure similarity searches, which improves on the efficiency of some existing techniques. Our method is grounded in the theory of differential geometry on 3D space curve matching. We generate shape signatures for proteins that are invariant, localized, robust, compact, and biologically meaningful. The invariancy of the shape signatures allows us to improve similarity searching efficiency by adopting a hierarchical coarse-to-fine strategy. We index the shape signatures using an efficient hashing-based technique. With the help of this technique we screen out unlikely candidates and perform detailed pairwise alignments only for a small number of candidates that survive the screening process. Contrary to other hashing based techniques, our technique employs domain specific information (not just geometric information) in constructing the hash key, and hence, is more tuned to the domain of biology. Furthermore, the invariancy, localization, and compactness of the shape signatures allow us to utilize a well-known local sequence alignment algorithm for aligning two protein structures. One measure of the efficacy of the proposed technique is that we were able to perform structure alignment queries 36 times faster (on the average) than a well-known method while keeping the quality of the query results at an approximately similar level.

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