Parallel Algorithm for Discovering and Comparing Three-Dimensional Proteins Patterns

Identifying conserved (similar) three-dimensional patterns among a set of proteins can be helpful for the rational design of polypharmacological drugs. Some available tools allow this identification from a limited perspective, only considering the available information, such as known binding sites o...

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Published in:IEEE/ACM transactions on computational biology and bioinformatics Vol. 21; no. 3; pp. 508 - 515
Main Authors: Valdes-Jimenez, Alejandro, Reyes-Parada, Miguel, Nunez-Vivanco, Gabriel, Jimenez-Gonzalez, Daniel
Format: Journal Article
Language:English
Published: United States IEEE 01.05.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Allosteric properties
Amino acids
Binding sites
Computational Biology - methods
CUDA
Databases, Protein
Distributed memory
Drug development
Drugs
Fingers
Graphics processing units
Hybrid systems
Models, Molecular
MPI
OpenMP
parallel and distributed programming
Protein Conformation
Proteins
Proteins - chemistry
Proteins - metabolism
Sequence Analysis, Protein - methods
Similarity
Structural proteins
Task analysis
Three-dimensional displays
three-dimensional patterns discovering
ISSN:1545-5963, 1557-9964, 1557-9964
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Abstract Identifying conserved (similar) three-dimensional patterns among a set of proteins can be helpful for the rational design of polypharmacological drugs. Some available tools allow this identification from a limited perspective, only considering the available information, such as known binding sites or previously annotated structural motifs. Thus, these approaches do not look for similarities among all putative orthosteric and or allosteric bindings sites between protein structures. To overcome this tech-weakness Geomfinder was developed, an algorithm for the estimation of similarities between all pairs of three-dimensional amino acids patterns detected in any two given protein structures, which works without information about their known patterns. Even though Geomfinder is a functional alternative to compare small structural proteins, it is computationally unfeasible for the case of large protein processing and the algorithm needs to improve its performance. This work presents several parallel versions of the Geomfinder to exploit SMPs, distributed memory systems, hybrid version of SMP and distributed memory systems, and GPU based systems. Results show significant improvements in performance as compared to the original version and achieve up to 24.5x speedup when analyzing proteins of average size and up to 95.4x in larger proteins.
AbstractList Identifying conserved (similar) three-dimensional patterns among a set of proteins can be helpful for the rational design of polypharmacological drugs. Some available tools allow this identification from a limited perspective, only considering the available information, such as known binding sites or previously annotated structural motifs. Thus, these approaches do not look for similarities among all putative orthosteric and or allosteric bindings sites between protein structures. To overcome this tech-weakness Geomfinder was developed, an algorithm for the estimation of similarities between all pairs of three-dimensional amino acids patterns detected in any two given protein structures, which works without information about their known patterns. Even though Geomfinder is a functional alternative to compare small structural proteins, it is computationally unfeasible for the case of large protein processing and the algorithm needs to improve its performance. This work presents several parallel versions of the Geomfinder to exploit SMPs, distributed memory systems, hybrid version of SMP and distributed memory systems, and GPU based systems. Results show significant improvements in performance as compared to the original version and achieve up to 24.5x speedup when analyzing proteins of average size and up to 95.4x in larger proteins.
Identifying conserved (similar) three-dimensional patterns among a set of proteins can be helpful for the rational design of polypharmacological drugs. Some available tools allow this identification from a limited perspective, only considering the available information, such as known binding sites or previously annotated structural motifs. Thus, these approaches do not look for similarities among all putative orthosteric and or allosteric bindings sites between protein structures. To overcome this tech-weakness Geomfinder was developed, an algorithm for the estimation of similarities between all pairs of three-dimensional amino acids patterns detected in any two given protein structures, which works without information about their known patterns. Even though Geomfinder is a functional alternative to compare small structural proteins, it is computationally unfeasible for the case of large protein processing and the algorithm needs to improve its performance. This work presents several parallel versions of the Geomfinder to exploit SMPs, distributed memory systems, hybrid version of SMP and distributed memory systems, and GPU based systems. Results show significant improvements in performance as compared to the original version and achieve up to 24.5x speedup when analyzing proteins of average size and up to 95.4x in larger proteins.Identifying conserved (similar) three-dimensional patterns among a set of proteins can be helpful for the rational design of polypharmacological drugs. Some available tools allow this identification from a limited perspective, only considering the available information, such as known binding sites or previously annotated structural motifs. Thus, these approaches do not look for similarities among all putative orthosteric and or allosteric bindings sites between protein structures. To overcome this tech-weakness Geomfinder was developed, an algorithm for the estimation of similarities between all pairs of three-dimensional amino acids patterns detected in any two given protein structures, which works without information about their known patterns. Even though Geomfinder is a functional alternative to compare small structural proteins, it is computationally unfeasible for the case of large protein processing and the algorithm needs to improve its performance. This work presents several parallel versions of the Geomfinder to exploit SMPs, distributed memory systems, hybrid version of SMP and distributed memory systems, and GPU based systems. Results show significant improvements in performance as compared to the original version and achieve up to 24.5x speedup when analyzing proteins of average size and up to 95.4x in larger proteins.
Author Valdes-Jimenez, Alejandro
Nunez-Vivanco, Gabriel
Reyes-Parada, Miguel
Jimenez-Gonzalez, Daniel
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Snippet Identifying conserved (similar) three-dimensional patterns among a set of proteins can be helpful for the rational design of polypharmacological drugs. Some...
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SubjectTerms Algorithms
Allosteric properties
Amino acids
Binding sites
Computational Biology - methods
CUDA
Databases, Protein
Distributed memory
Drug development
Drugs
Fingers
Graphics processing units
Hybrid systems
Models, Molecular
MPI
OpenMP
parallel and distributed programming
Protein Conformation
Proteins
Proteins - chemistry
Proteins - metabolism
Sequence Analysis, Protein - methods
Similarity
Structural proteins
Task analysis
Three-dimensional displays
three-dimensional patterns discovering
Title Parallel Algorithm for Discovering and Comparing Three-Dimensional Proteins Patterns
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