PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
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Article Citation - Scopus: 30Thermochemistry of Alkali Metal Cation Interactions With Histidine: Influence of the Side Chain(2012-11-26) Armentrout, Peter B.; Citir, Murat; Chen, Yu; Rodgers, Mary T.The interactions of alkali metal cations (M+ = Na+, K+, Rb+, Cs+) with the amino acid histidine (His) are examined in detail. Experimentally, bond energies are determined using threshold collision-induced dissociation of the M+(His) complexes with xenon in a guided ion beam tandem mass spectrometer. Analyses of the energy dependent cross sections provide 0 K bond energies of 2.31 ± 0.11, 1.70 ± 0.08, 1.42 ± 0.06, and 1.22 ± 0.06 eV for complexes of His with Na+, K+, Rb+, and Cs+, respectively. All bond dissociation energy (BDE) determinations include consideration of unimolecular decay rates, internal energy of reactant ions, and multiple ion-neutral collisions. These experimental results are compared to values obtained from quantum chemical calculations conducted previously at the MP2(full)/6-311+G(2d,2p), B3LYP/6-311+G(2d,2p), and B3P86/6-311+G(2d,2p) levels with geometries and zero point energies calculated at the B3LYP/6-311+G(d,p) level where Rb and Cs use the Hay-Wadt effective core potential and basis set augmented with additional polarization functions (HW*). Additional calculations using the def2-TZVPPD basis set with B3LYP geometries were conducted here at all three levels of theory. Either basis set yields similar results for Na+(His) and K+(His), which are in reasonable agreement with the experimental BDEs. For Rb+(His) and Cs +(His), the HW* basis set and ECP underestimate the experimental BDEs, whereas the def2-TZVPPD basis set yields results in good agreement. The effect of the imidazole side chain on the BDEs is examined by comparing the present results with previous thermochemistry for other amino acids. Both polarizability and the local dipole moment of the side chain are influential in the energetics. © 2012 American Chemical Society. © 2013 Elsevier B.V., All rights reserved.; MEDLINE® is the source for the MeSH terms of this document.Article Citation - WoS: 5Citation - Scopus: 5Novel Antimicrobial Peptide Design Using Motif Match Score Representation(IEEE Computer Soc, 2024-11) Soylemez, Ummu Gulsum; Yousef, Malik; Kesmen, Zulal; Bakir-Gungor, BurcuAntimicrobial peptides (AMPs) have drawn the interest of the researchers since they offer an alternative to the traditional antibiotics in the fight against antibiotic resistance and they exhibit additional pharmaceutically significant properties. Recently, computational approaches attemp to reveal how antibacterial activity is determined from a machine learning perspective and they aim to search and find the biological cues or characteristics that control antimicrobial activity via incorporating motif match scores. This study is dedicated to the development of a machine learning framework aimed at devising novel antimicrobial peptide (AMP) sequences potentially effective against Gram-positive/Gram-negative bacteria. In order to design newly generated sequences classified as either AMP or non-AMP, various classification models were trained. These novel sequences underwent validation utilizing the "DBAASP: strain-specific antibacterial prediction based on machine learning approaches and data on AMP sequences" tool. The findings presented herein represent a significant stride in this computational research, streamlining the process of AMP creation or modification within wet lab environments.Article Citation - WoS: 3Citation - Scopus: 6IGPRED-Multitask: A Deep Learning Model to Predict Protein Secondary Structure, Torsion Angles and Solvent Accessibility(IEEE Computer Soc, 2023-03-01) Gormez, Yasin; Aydin, ZaferProtein secondary structure, solvent accessibility and torsion angle predictions are preliminary steps to predict 3D structure of a protein. Deep learning approaches have achieved significant improvements in predicting various features of protein structure. In this study, IGPRED-Multitask, a deep learning model with multi task learning architecture based on deep inception network, graph convolutional network and a bidirectional long short-term memory is proposed. Moreover, hyper-parameters of the model are fine-tuned using Bayesian optimization, which is faster and more effective than grid search. The same benchmark test data sets as in the OPUS-TASS paper including TEST2016, TEST2018, CASP12, CASP13, CASPFM, HARD68, CAMEO93, CAMEO93_HARD, as well as the train and validation sets, are used for fair comparison with the literature. Statistically significant improvements are observed in secondary structure prediction on 4 datasets, in phi angle prediction on 2 datasets and in psi angel prediction on 3 datasets compared to the state-of-the-art methods. For solvent accessibility prediction, TEST2016 and TEST2018 datasets are used only to assess the performance of the proposed model.