Parsers, Data Structures and Algorithms for Macromolecular Analysis Toolkit (MAT): Design and Implementation

Kalyan, Gazal; Junghare,; SJ, Sushanth; Chattopadhyay, Aditya; Mitra, Pralay; Hazra, Saugata

doi:10.1101/605907

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…In Silico Workflow. The package that has been developed to predict peptides with potential ACE inhibitory activity is written in the C++ language in conjugation with Macromolecular Analysis Toolkit (MAT), 35 accessible at http://mat.iitr.ac.in. The input from the user can be taken in the FASTA (.fasta) format or Uniprot ID.…”

Section: ■ Methodsmentioning

confidence: 99%

Anti-hypertensive Peptide Predictor: A Machine Learning-Empowered Web Server for Prediction of Food-Derived Peptides with Potential Angiotensin-Converting Enzyme-I Inhibitory Activity

Kalyan

Junghare

Khan

et al. 2021

J. Agric. Food Chem.

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Angiotensin converting enzyme-I (ACE-I) is a key therapeutic target of the renin−angiotensin−aldosterone system (RAAS), the central pathway of blood pressure regulation. Food-derived peptides with ACE-I inhibitory activities are receiving significant research attention. However, identification of ACE-I inhibitory peptides from different food proteins is a labor-intensive, lengthy, and expensive process. For successful identification of potential ACE-I inhibitory peptides from food sources, a machine learning and structural bioinformatics-based web server has been developed and reported in this study. The web server can take input in the FASTA format or through UniProt ID to perform the in silico gastrointestinal digestion and then screen the resulting peptides for ACE-I inhibitory activity. This unique platform provides elaborated structural and functional features of the active peptides and their interaction with ACE-I. Thus, it can potentially enhance the efficacy and reduce the time and cost in identifying and characterizing novel ACE-I inhibitory peptides from food proteins. URL: http://hazralab.iitr.ac.in/ahpp/index.php.

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Section: ■ Methodsmentioning

confidence: 99%

Anti-hypertensive Peptide Predictor: A Machine Learning-Empowered Web Server for Prediction of Food-Derived Peptides with Potential Angiotensin-Converting Enzyme-I Inhibitory Activity

Kalyan

Junghare

Khan

et al. 2021

J. Agric. Food Chem.

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“…The interactions were calculated using the MAT web server. [57] The visualization and figure preparation was performed by using PyMOL. [58]…”

Section: Structural Preparation and Admet Screening For In-silico Studymentioning

confidence: 99%

Synthesis of Dihydrobenzofuro[3,2‐b]chromenes as Potential 3CLpro Inhibitors of SARS‐CoV‐2: A Molecular Docking and Molecular Dynamics Study

et al. 2022

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The recent emergence of pandemic of coronavirus (COVID‐19) caused by SARS‐CoV‐2 has raised significant global health concerns. More importantly, there is no specific therapeutics currently available to combat against this deadly infection. The enzyme 3‐chymotrypsin‐like cysteine protease (3CLpro) is known to be essential for viral life cycle as it controls the coronavirus replication. 3CLpro could be a potential drug target as established before in the case of severe acute respiratory syndrome coronavirus (SARS‐CoV) and Middle East respiratory syndrome coronavirus (MERS‐CoV). In the current study, we wanted to explore the potential of fused flavonoids as 3CLpro inhibitors. Fused flavonoids (5a,10a‐dihydro‐11H‐benzofuro[3,2‐b]chromene) are unexplored for their potential bioactivities due to their low natural occurrences. Their synthetic congeners are also rare due to unavailability of general synthetic methodology. Here we designed a simple strategy to synthesize 5a,10a‐dihydro‐11H‐benzofuro[3,2‐b]chromene skeleton and it's four novel derivatives. Our structural bioinformatics study clearly shows excellent potential of the synthesized compounds in comparison to experimentally validated inhibitor N3. Moreover, in‐silico ADMET study displays excellent druggability and extremely low level of toxicity of the synthesized molecules. Further, for better understanding, the molecular dynamic approach was implemented to study the change in dynamicity after the compounds bind to the protein. A detailed investigation through clustering analysis and distance calculation gave us sound comprehensive data about their molecular interaction. In summary, we anticipate that the currently synthesized molecules could not only be a potential set of inhibitors against 3CLpro but also the insights acquired from the current study would be instrumental in further developing novel natural flavonoid based anti‐COVID therapeutic spectrums.

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Split-bucket partition (SBP): a novel execution model for top-K and selection algorithms on GPUs

Yang,

Zhang,

et al. 2024

J Supercomput

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Top-K and selection operations are critical in data processing and analysis, and their efficient implementation on GPUs is increasingly important due to the growing demands of data analysis. Existing methods, primarily relying on the bucket partition execution model, encounter challenges such as uneven bucket distribution and latency in merging processes. To address these issues, we introduce a novel Split-Bucket Partition (SBP) execution model that specifically addresses these challenges. Additionally, we propose task and control flow optimizations targeted at top-K and selection algorithms, which further contribute to performance improvements. Our optimized algorithms significantly outperform existing approaches, delivering performance gains of up to $$2.3$$ 2.3 times and $$1.6$$ 1.6 times for different bucket partitioning rules. Our algorithms show robust performance improvements in non-uniform data scenarios, with gains ranging from $$1.9$$ 1.9 times to $$15.5$$ 15.5 times. However, it should be noted that the SBP model has limitations related to shared memory and register utilization, potentially impacting performance. Tests on TU102 and A100 GPU architectures validate the effectiveness of our approach, achieving a maximum speedup of $$2.9$$ 2.9 times. The study suggests that while the SBP model is effective for top-K and selection algorithms, it also holds promise for other computational tasks, setting the stage for future research.

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Parsers, Data Structures and Algorithms for Macromolecular Analysis Toolkit (MAT): Design and Implementation

Cited by 4 publications

References 38 publications

Anti-hypertensive Peptide Predictor: A Machine Learning-Empowered Web Server for Prediction of Food-Derived Peptides with Potential Angiotensin-Converting Enzyme-I Inhibitory Activity

Anti-hypertensive Peptide Predictor: A Machine Learning-Empowered Web Server for Prediction of Food-Derived Peptides with Potential Angiotensin-Converting Enzyme-I Inhibitory Activity

Synthesis of Dihydrobenzofuro[3,2‐b]chromenes as Potential 3CLpro Inhibitors of SARS‐CoV‐2: A Molecular Docking and Molecular Dynamics Study

Split-bucket partition (SBP): a novel execution model for top-K and selection algorithms on GPUs

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