Article
Biology
Leila Fattel, Dennis Psaroudakis, Colleen F. Yanarella, Kevin O. Chiteri, Haley A. Dostalik, Parnal Joshi, Dollye C. Starr, Ha Vu, Kokulapalan Wimalanathan, Carolyn J. Lawrence-Dill
Summary: Genome-wide functional annotations are crucial for studying gene function and traits in plants. By comparing functional annotation data from different species, similarities and differences can be identified, leading to the generation of novel hypotheses about gene function and evolutionary relationships.
Article
Biochemistry & Molecular Biology
Carlos P. Cantalapiedra, Ana Hernandez-Plaza, Ivica Letunic, Peer Bork, Jaime Huerta-Cepas
Summary: The article introduces a major upgrade of the eggNOG-mapper tool, optimized for functional annotation of vast genomic and metagenomic datasets, including database updates, efficiency enhancements, and new features such as de novo gene prediction and fast protein domain discovery.
MOLECULAR BIOLOGY AND EVOLUTION
(2021)
Article
Multidisciplinary Sciences
Jonathan Yaacov Weinstein, Carlos Marti-Gomez, Rosalie Lipsh-Sokolik, Shlomo Yakir Hoch, Demian Liebermann, Reinat Nevo, Haim Weissman, Ekaterina Petrovich-Kopitman, David Margulies, Dmitry Ivankov, David M. McCandlish, Sarel J. Fleishman
Summary: Mutations in a protein's active site can lead to dramatic and useful changes in protein activity. However, due to a high density of molecular interactions, the active site is sensitive to mutations, making it difficult to obtain functional multipoint mutants. A new approach called high-throughput Functional Libraries (htFuncLib) has been introduced to design a sequence space where mutations can form low-energy combinations, reducing the risk of incompatible interactions. This approach has been successfully applied to GFP, resulting in the discovery of thousands of unique designs with improved functional properties.
NATURE COMMUNICATIONS
(2023)
Article
Biochemical Research Methods
Jose Antonio Barbero-Aparicio, Santiago Cuesta-Lopez, Cesar Ignacio Garcia-Osorio, Javier Perez-Rodriguez, Nicolas Garcia-Pedrajas
Summary: This article explores the use of a physical model as an additional information source to improve the prediction of transcription start sites. The study shows that the physical model can accurately predict these sites and opens up new possibilities for research at the intersection of statistical mechanics and machine learning.
BMC BIOINFORMATICS
(2022)
Article
Chemistry, Multidisciplinary
Ting Zhang, Xu Han, Hong Liu, Marti Biset-Peiro, Jian Li, Xuan Zhang, Pengyi Tang, Bo Yang, Lirong Zheng, Joan Ramon Morante, Jordi Arbiol
Summary: Regulating the coordination environment with heteroatoms allows the construction of metal-nitrogen-carbon catalysts for electrochemical CO2 reduction. This study presents a facile method to create axial O-coordinated FeN4 active sites using an oxygen- and nitrogen-rich metal-organic framework as the precursor. The O-coordinated active sites exhibit improved catalytic performance compared to non-O-coordinated sites.
ADVANCED FUNCTIONAL MATERIALS
(2022)
Article
Computer Science, Information Systems
Waseem Ullah, Khan Muhammad, Ijaz Ul Haq, Amin Ullah, Saeed Ullah Khattak, Muhammad Sajjad
Summary: Accurate splice site prediction is crucial in medical sciences and biochemistry as mutations can lead to genetic diseases and cancer. The study utilized machine learning algorithms and a composite features-based model to improve the accuracy of splice site prediction. The results showed high accuracy rates for donor and acceptor sites datasets using SVM classifier.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Yu Xiong, Shibin Wang, Wenxing Chen, Jian Zhang, Qiheng Li, Han-Shi Hu, Lirong Zheng, Wensheng Yan, Lin Gu, Dingsheng Wang, Yadong Li
Summary: This study demonstrates the high catalytic performance of a dual-active-site copper catalyst in the oxyphosphorylation reaction of alkenes, as well as the roles of two different Cu active sites in this reaction. The findings provide valuable insights for the rational design of better-performing heterogeneous catalysts.
Article
Engineering, Geological
Yong-Gook Lee, Sang-Jin Kim, Zeinep Achmet, Oh-Sung Kwon, Duhee Park, Luigi Di Sarno
Summary: Prediction models for site amplification are developed using two machine learning algorithms, random forest (RF) and deep neural network (DNN). By utilizing matrix data containing the response spectrum of the input ground motion and shear wave velocity profile, both machine learning models achieve exceptional accuracy in predicting both the linear and nonlinear amplifications, providing accurate estimates for the mean and standard deviation of site amplification. Among the two techniques, the DNN-based model demonstrates better performance.
SOIL DYNAMICS AND EARTHQUAKE ENGINEERING
(2023)
Article
Multidisciplinary Sciences
Sabit Ahmed, Afrida Rahman, Md. Al Mehedi Hasan, Md Khaled Ben Islam, Julia Rahman, Shamim Ahmad
Summary: The study introduces a computational tool, predPhogly-Site, that predicts phosphoglycerylation sites in proteins with around 99% accuracy, outperforming existing prediction tools.
Article
Chemistry, Medicinal
Jannis Born, Yoel Shoshan, Tien Huynh, Wendy D. Cornell, Eric J. Martin, Matteo Manica
Summary: Recent research has shown that incorporating active site information improves the accuracy of predicting the binding affinity between kinases and ligands. In this study, we propose and compare multiple definitions of the active site, and provide significant evidence to support the superiority of our novel definition over previous ones, especially in modeling ATP-noncompetitive inhibitors. Additionally, we utilize the discontiguity of the active site sequence to develop novel protein sequence augmentation strategies, which further enhance the predictive performance.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Letter
Chemistry, Medicinal
Jannis Born, Yoel Shoshan, Tien Huynh, Wendy D. Cornell, Eric J. Martin, Matteo Manica
Summary: Recent work suggests that using active site information instead of full protein sequence improves the accuracy of predicting kinase-ligand binding affinity. In this study, we propose and compare multiple definitions of the active site, and find that our novel definition outperforms previous definitions and better models ATP noncompetitive inhibitors. Additionally, by leveraging the discontiguity of the active site sequence, we develop novel protein-sequence augmentation strategies that further enhance performance.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Article
Chemistry, Multidisciplinary
Shichao Ding, Zhaoyuan Lyu, Lingzhe Fang, Tao Li, Wenlei Zhu, Suiqiong Li, Xin Li, Jin-Cheng Li, Dan Du, Yuehe Lin
Summary: Developing novel substitutes for heme enzymes, such as Fe-N-C based single-atomic site catalysts (SASCs), can provide high catalytic activity and selectivity in biosensing applications. Synthesizing a SASC (Fe-SASC/NW) by doping single iron atoms into carbon nanowires shows promising heme enzyme-like catalytic performance for hydrogen peroxide detection. The electrochemical sensor based on Fe-SASC/NW exhibits excellent detection capabilities for H2O2 due to the isolated Fe-N-x active sites and their structural similarity with natural metalloproteases.
Article
Chemistry, Physical
Dongxiao Chen, Pei-Lin Kang, Zhi-Pan Liu
Summary: By using machine-learning reaction exploration, it was found that ethene oxidation on silver metal surfaces has three low-energy pathways, with the dehydrogenation of oxometallacycle intermediate (OMC-DH) being the most important one. The dominance of the dehydrogenation path for ethene oxidation on both Ag(100) and Ag(111) metal surfaces regardless of the reaction conditions rationalizes the low selectivity to combustion products in low oxygen pressure experiments. The presence of the OMC-DH pathway and the general low selectivity on metal sites were confirmed by evaluating this mechanism on different catalysts, pointing towards revealing the true active site of Ag-based catalyst in ethene oxidation.
Review
Chemistry, Multidisciplinary
Christopher Uyeda, Conner M. Farley
Summary: Multinuclear catalysts show unique abilities to cooperatively engage substrates, while binuclear catalysts provide new electronic configurations and covalent bonding to effectively address challenges not easily solved by single-metal systems. Novel ligands can stabilize metal-metal bonds, activate strong bonds, and have significant implications in catalytic reactions.
ACCOUNTS OF CHEMICAL RESEARCH
(2021)
Article
Biochemistry & Molecular Biology
Kelly K. Barnsley, Mary Jo Ondrechen
Summary: Understanding the biochemically active amino acids in proteins is crucial for improving our understanding of enzyme function, predicting the function of unknown protein structures, and guiding enzyme engineering. This article explores recent computational chemistry-based methods for predicting active amino acids in protein 3D structures, including distal residues, and discusses their implications for functional genomics, enzyme design, and enhancing our understanding of enzyme function.
CURRENT OPINION IN STRUCTURAL BIOLOGY
(2022)
Article
Biochemistry & Molecular Biology
Heather R. Brodkin, Nicholas A. DeLateur, Srinivas Somarowthu, Caitlyn L. Mills, Walter R. Novak, Penny J. Beuning, Dagmar Ringe, Mary Jo Ondrechen
Article
Biochemical Research Methods
Ramya Parasuram, Caitlyn L. Mills, Zhouxi Wang, Saroja Somasundaram, Penny J. Beuning, Mary Jo Ondrechen
Article
Biochemistry & Molecular Biology
Ramya Parasuram, Timothy A. Coulther, Judith M. Hollander, Elise Keston-Smith, Mary Jo Ondrechen, Penny J. Beuning
Article
Biochemistry & Molecular Biology
Derek J. MacPherson, Caitlyn L. Mills, Mary Jo Ondrechen, Jeanne A. Hardy
JOURNAL OF BIOLOGICAL CHEMISTRY
(2019)
Article
Multidisciplinary Sciences
Lisa Ngu, Jenifer N. Winters, Kien Nguyen, Kevin E. Ramos, Nicholas A. DeLateur, Lee Makowski, Paul C. Whitford, Mary Jo Ondrechen, Penny J. Beuning
Article
Chemistry, Medicinal
Gengyang Yuan, Xiying Qu, Baohui Zheng, Ramesh Neelamegam, Sepideh Afshar, Suhasini Iyengar, Chuzhi Pan, Junfeng Wang, Hye Jin Kang, Mary Jo Ondrechen, Pekka Poutiainen, Georges El Fakhri, Zhaoda Zhang, Anna-Liisa Brownell
JOURNAL OF MEDICINAL CHEMISTRY
(2020)
Article
Chemistry, Physical
Timothy A. Coulther, Jaeju Ko, Mary Jo Ondrechen
Summary: Interactions between catalytic and neighboring amino acids can increase catalytic rates by elongating the buffer range and enhancing coupling of protonation equilibria. Differences in intrinsic pK(a)s and energy of interaction between residues play a key role in optimizing this coupling for efficient catalysis.
JOURNAL OF CHEMICAL PHYSICS
(2021)
Article
Biochemistry & Molecular Biology
Timothy A. Coulther, Moritz Pott, Cathleen Zeymer, Donald Hilvert, Mary Jo Ondrechen
Summary: The study examines local interactions in the laboratory evolution of a highly active, computationally designed retroaldolase (RA) enzyme variants. As evolution progresses, electrostatic coupling between biochemically active amino acids and other residues increases. The charge state of the catalytic lysine K83 becomes more strongly coupled to those of other amino acids as the enzyme evolves into a better catalyst.
Article
Biochemistry & Molecular Biology
Caitlyn L. Mills, Pengcheng Yin, Becky Leifer, Lori Ferrins, George A. O'Doherty, Penny J. Beuning, Mary Jo Ondrechen
Summary: This study investigates the functional characterization of eight structural genomics proteins from the Crotonase superfamily and uses the SALSA method for functional annotation. It is found that most of these proteins exhibit hydrolase activity in addition to their expected enoyl CoA hydratase activity.
ACS CHEMICAL BIOLOGY
(2022)
Article
Chemistry, Multidisciplinary
Marie Little Fawn Agan, Reni Joseph, Armando Rivera-Figueroa, Benny C. Chan, Abby R. O'Connor, Mary Jo Ondrechen, Wayne E. Jr Jr Jones, Peter K. Dorhout, Ann C. Kimble-Hill
Summary: This study examines programs and policies in various chemistry departments to increase faculty diversity, comparing applicant demographics and advertising language effects on applicant pool diversity. The results generate a list of best practices for academic administrations and search committees to enhance their ability to attract diverse talent.
JOURNAL OF CHEMICAL EDUCATION
(2022)
Article
Biochemistry & Molecular Biology
Kelly K. Barnsley, Mary Jo Ondrechen
Summary: Understanding the biochemically active amino acids in proteins is crucial for improving our understanding of enzyme function, predicting the function of unknown protein structures, and guiding enzyme engineering. This article explores recent computational chemistry-based methods for predicting active amino acids in protein 3D structures, including distal residues, and discusses their implications for functional genomics, enzyme design, and enhancing our understanding of enzyme function.
CURRENT OPINION IN STRUCTURAL BIOLOGY
(2022)
Article
Biochemistry & Molecular Biology
Lisa Ngu, Debarpita Ray, Samantha S. Watson, Penny J. Beuning, Mary Jo Ondrechen, George A. O'Doherty
Summary: A diastereoselective synthesis of the beta-anomer of glycinamide ribonucleotide (beta-GAR) has been developed with a high overall yield. The synthetic beta-GAR demonstrated remarkable resistance to anomerization in both solution and solid state.
Article
Biochemistry & Molecular Biology
Suhasini M. Iyengar, Kelly K. Barnsley, Rholee Xu, Aleksandr Prystupa, Mary Jo Ondrechen
Summary: The study reveals that catalytic aspartate and glutamate residues are strongly coupled to other carboxylate residues, while catalytic lysine residues are strongly coupled to tyrosine or cysteine residues. These interactions provide important insights into how weak Bronsted acids or bases in solution can become strong acids, bases, or nucleophiles in an enzymatic environment.
Article
Biochemistry & Molecular Biology
Lakindu S. Pathira Kankanamge, Lydia A. Ruffner, Mong Mary Touch, Manuel Pina, Penny J. Beuning, Mary Jo Ondrechen
Summary: This study predicted and experimentally validated the functions of HAD superfamily proteins from structural genomics using computational techniques based on amino acid properties. Five proteins were successfully predicted and validated, showing important functions in sugar phosphatases and dehalogenases.
BIOCHEMICAL JOURNAL
(2023)
Article
Chemistry, Medicinal
Lakindu S. Pathira Kankanamge, Alexandra Mora, Mary Jo Ondrechen, Penny J. Beuning
Summary: This study used computational tools and bioinformatic methods to predict the effects of single nucleotide polymorphism variants on the activity of DNA polymerase kappa. The results showed that some variants can decrease its catalytic activity, while others have similar catalytic efficiency to the wild-type polymerase.
CHEMICAL RESEARCH IN TOXICOLOGY
(2023)