Review
Endocrinology & Metabolism
Balamurugan Dhayalan, Deepak Chatterjee, Yen-Shan Chen, Michael A. Weiss
Summary: Analysis of diabetes-associated mutations in the human insulin gene has provided insights into the folding mechanisms of proinsulin, revealing the impact of mutations on pancreatic beta-cell dysfunction and insulin secretion. Studies suggest that conserved residues play a crucial role in folding efficiency and the susceptibility of proinsulin to impaired foldability can contribute to the development of diseases. This highlights the molecular links between biophysical principles and the impact on diseases such as diabetes and obesity.
FRONTIERS IN ENDOCRINOLOGY
(2021)
Article
Chemistry, Multidisciplinary
Patrick Bryant, Arne Elofsson
Summary: This article describes a computational method for designing peptide binders towards specific protein interfaces. By combining multiple methods, including Foldseek, ESM-IF1, and AlphaFold2, the researchers developed a peptide binder design tool and demonstrated its ability to improve the success rate.
COMMUNICATIONS CHEMISTRY
(2023)
Article
Endocrinology & Metabolism
Yanwu Yang, Michael D. Glidden, Balamurugan Dhayalan, Alexander N. Zaykov, Yen-Shan Chen, Nalinda P. Wickramasinghe, Richard D. DiMarchi, Michael A. Weiss
Summary: This article investigates the toxic misfolding of diabetes-associated proteins in beta-cells, and proposes a peptide model for classifying related mutations. The study found that the mutant variants exhibit successive structural perturbations, which are correlated with the phenotypic differences in diabetes.
FRONTIERS IN ENDOCRINOLOGY
(2022)
Article
Multidisciplinary Sciences
R. Charlotte Eccleston, David D. Pollock, Richard A. Goldstein
Summary: Epistasis and cooperativity in protein folding are both influenced by networks of energetic interactions within proteins, and their selection can affect each other. Selection for cooperativity may be crucial for predicting protein structure using epistasis measurements.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Review
Biochemistry & Molecular Biology
Mohammed AlQuraishi
Summary: Prediction of protein structure from sequence has made significant progress in the past two years, driven by the increasing use of neural networks in structure prediction pipelines. These neural networks have optimized the previous energy models and sampling procedures, resulting in algorithms that can now predict protein structures with a median accuracy of 2.1 angstroms.
CURRENT OPINION IN CHEMICAL BIOLOGY
(2021)
Article
Endocrinology & Metabolism
Balamurugan Dhayalan, Michael D. Glidden, Alexander N. Zaykov, Yen-Shan Chen, Yanwu Yang, Nelson B. Phillips, Faramarz Ismail-Beigi, Mark A. Jarosinski, Richard D. DiMarchi, Michael A. Weiss
Summary: The mutant proinsulin syndrome is a monogenic cause of diabetes mellitus due to toxic misfolding of insulin's biosynthetic precursor. This study used a peptide model to investigate representative clinical mutations and found striking correlations between peptide properties, ER stress, and age of diabetes onset.
FRONTIERS IN ENDOCRINOLOGY
(2022)
Article
Biochemistry & Molecular Biology
Yang Yang, Zhang Chong, Mauno Vihinen
Summary: Most proteins fold into unique three-dimensional structures and their folding rates can be influenced by variations in proteins. We developed a machine-learning-based method, PON-Fold, to predict the folding rate effects of amino acid substitutions in two-state folding proteins. PON-Fold outperformed existing tools in blind tests, showing higher specificity, sensitivity, and correlation coefficient. The tool was also tested for protein domain substitutions and showed varying predictions depending on protein conformations and structures.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Chemistry, Physical
Qinglu Zhong, Guohui Li
Summary: Protein structure prediction is vital for understanding new protein functions, but predicting the effects of proteins with no detectable templates remains challenging. AIMS, a universal multiscale simulation strategy, allows simulations to iteratively switch among multiple resolutions to adaptively balance AA accuracy and CG efficiency. Through AIMS, faster and more accurate predictions of protein structures can be achieved, providing special insights on folding metastable states.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2021)
Article
Biochemistry & Molecular Biology
Pavel Rachitskii, Ivan Kruglov, Alexei V. Finkelstein, Artem R. Oganov
Summary: Protein structure prediction is a major problem in modern biophysics. Current methods that use big data and machine learning have achieved success in recognizing tertiary protein structure from amino acid sequences. In this study, we extended the evolutionary algorithm USPEX to predict protein structure based on global optimization. Our algorithm achieved high accuracy in predicting the tertiary structures of proteins.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Almasul Alfi, Aleksandr Popov, Ashutosh Kumar, Kam Y. J. Zhang, Svetlana Dubiley, Konstantin Severinov, Shunsuke Tagami
Summary: The study combined rapid cell-free mutant analysis with precise structure prediction to successfully elucidate the structure of difficult-to-purify proteins. A hydrophobic patch on the protein surface was identified as the binding site of its partner protein.
ACS SYNTHETIC BIOLOGY
(2022)
Article
Biology
Balachandran Manavalan, Jooyoung Lee
Summary: Protein folding rate is crucial for understanding the protein folding process and designing proteins. This study presents FRTpred, a novel approach that accurately predicts the logarithmic protein folding rate constant and folding type from the provided sequence. FRTpred outperforms existing methods and can accelerate the characterization of protein data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Biochemistry & Molecular Biology
Nan Xiao, Hongming Ma, Hong Gao, Jing Yang, Dan Tong, Dingzhu Gan, Jinhua Yang, Chi Li, Kang Liu, Yingxin Li, Zhibo Chen, Chaoqun Yin, Xingqi Li, Hongwu Wang
Summary: Liver cancer can be primary or secondary, with higher incidence of the latter. Despite advances in molecular biology and treatments, liver cancer still has a poor prognosis with no cure. Many questions remain regarding the mechanisms of liver cancer occurrence and development, as well as tumor reoccurrence after treatment.
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
(2023)
Article
Biochemistry & Molecular Biology
Nuno F. B. Oliveira, Filipe E. P. Rodrigues, Joao N. M. Vitorino, Rui J. S. Loureiro, Patricia F. N. Faisca, Miguel Machuqueiro
Summary: The study used the D76N mutant as a model to investigate protein aggregation, finding that hydrophobic interactions play a major role in stabilizing interfaces in a series of dimers at physiological pH. The most stable binding mode exhibits self-limited growth properties, while less stable interfaces can propagate indefinitely to form long polymerized chains.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2021)
Article
Biochemistry & Molecular Biology
Andriy Kryshtafovych, Torsten Schwede, Maya Topf, Krzysztof Fidelis, John Moult
Summary: CASP is a community experiment aimed at advancing methods for computing three-dimensional protein structure, including rigorous blind testing and evaluation by independent assessors. In the recent CASP14 experiment, deep-learning methods from one research group consistently delivered computed structures rivaling the corresponding experimental ones in accuracy. These results represent a solution to the classical protein-folding problem, at least for single proteins.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2021)
Article
Biochemistry & Molecular Biology
Jiaan Yang, Wen Xiang Cheng, Xiao Fei Zhao, Gang Wu, Shi Tong Sheng, Qiyue Hu, Hu Ge, Qianshan Qin, Xinshen Jin, Lianshan Zhang, Peng Zhang
Summary: Protein folding is a challenging subject, and the discovery of protein structural flexibility is another major hurdle. Researchers have developed a novel approach, protein structure fingerprint, to expose local folding variations and construct folding conformations for the entire protein. Through the creation of a database and the use of Protein Folding Variation Matrix (PFVM), all folding variations for an entire protein can be simultaneously understood, and the most likely folding conformation and 3D structure can be determined. This approach provides a significant means for investigating the protein folding problem.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2022)
Article
Chemistry, Multidisciplinary
Ashley Vater, Jaime Mayoral, Janelle Nunez-Castilla, Jason W. Labonte, Laura A. Briggs, Jeffrey J. Gray, Irina Makarevitch, Sharif M. Rumjahn, Justin B. Siegel
Summary: Including undergraduate research in STEM education is a high-impact practice that can be scaled more easily by integrating experiences into the classroom. The new biochemistry Course-based Undergraduate Research Experience (CURE) connects students to a worldwide community of protein modeling software developers and has shown positive effects on psychosocial developments associated with STEM persistence. The design of the CURE's curriculum, resources, and instructor materials aim to facilitate widespread implementation and growth.
JOURNAL OF CHEMICAL EDUCATION
(2021)
Article
Biochemistry & Molecular Biology
Johnathan D. Guest, Thom Vreven, Jing Zhou, Iain Moal, Jeliazko R. Jeliazkov, Jeffrey J. Gray, Zhiping Weng, Brian G. Pierce
Summary: The study aims to accurately predict antibody-antigen complex structures and conduct structure-based antibody design, which are crucial for biotherapeutics, immunity, and vaccines. By assembling a non-redundant set of test cases for antibody-antigen docking and affinity prediction, this research provides insights into the determinants of antibody recognition and molecular flexibility, showcasing the challenges faced in this diverse set of cases.
Article
Chemistry, Physical
Sai Pooja Mahajan, Yashes Srinivasan, Jason W. Labonte, Matthew P. DeLisa, Jeffrey J. Gray
Summary: This study decoded the sequence and structural motifs determining peptide substrate preferences for the GalNAc-T2 isoform, revealing enzyme features that lead to finely tuned specificity for a broad range of peptide substrates. Computational scanning and experimental validation successfully discriminated glycosylatable peptides with high efficiency, providing insights for designing enzyme variants with tailored specificity in the future.
Article
Biochemistry & Molecular Biology
Ameya Harmalkar, Jeffrey J. Gray
Summary: Computational docking methods can provide structural models of protein-protein complexes, but protein backbone flexibility can hinder accurate predictions. Recent advances include enhanced sampling techniques and internal coordinate formulations to address the issue of protein backbone flexibility.
CURRENT OPINION IN STRUCTURAL BIOLOGY
(2021)
Article
Multidisciplinary Sciences
Jeliazko R. Jeliazkov, Rahel Frick, Jing Zhou, Jeffrey J. Gray
Summary: In recent years, advances in high-throughput sequencing have led to exponential growth in the observed antibody sequence space, but not in the number of structures. Computational modeling has the potential to bridge this gap, but further improvements are needed, particularly in accuracy and speed, especially in modeling CDR-H3 loops.
Article
Chemistry, Physical
Rebecca F. Alford, Rituparna Samanta, Jeffrey J. Gray
Summary: Energy functions are crucial for biomolecular modeling, with membrane protein energy functions lagging behind soluble protein ones due to sparse data and overfitting issues. To address this challenge, a suite of 12 tests on diverse independent data sets was conducted, evaluating the ability of energy functions to capture membrane protein orientation, stability, sequence, and structure. Findings point out areas for improvement in energy functions and potential integration with machine-learning-based optimization methods in the future.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2021)
Article
Chemistry, Physical
Morgan L. Nance, Jason W. Labonte, Jared Adolf-Bryfogle, Jeffrey J. Gray
Summary: Carbohydrate chains play a crucial role in regulating biological functions through interactions with protein receptors. The GlycanDock algorithm developed for protein-glycoligand docking refinement shows potential in high-resolution structural studies and can address experimental challenges. This algorithm has the ability to sample and discriminate among protein-glycoligand models with statistical reliability, making it a valuable tool in glycosciences research acceleration.
JOURNAL OF PHYSICAL CHEMISTRY B
(2021)
Article
Multidisciplinary Sciences
Julia Koehler Leman, Sergey Lyskov, Steven M. Lewis, Jared Adolf-Bryfogle, Rebecca F. Alford, Kyle Barlow, Ziv Ben-Aharon, Daniel Farrell, Jason Fell, William A. Hansen, Ameya Harmalkar, Jeliazko Jeliazkov, Georg Kuenze, Justyna D. Krys, Ajasja Ljubetic, Amanda L. Loshbaugh, Jack Maguire, Rocco Moretti, Vikram Khipple Mulligan, Morgan L. Nance, Phuong T. Nguyen, Shane O. Conchuir, Shourya S. Roy Burman, Rituparna Samanta, Shannon T. Smith, Frank Teets, Johanna K. S. Tiemann, Andrew Watkins, Hope Woods, Brahm J. Yachnin, Christopher D. Bahl, Chris Bailey-Kellogg, David Baker, Rhiju Das, Frank DiMaio, Sagar D. Khare, Tanja Kortemme, Jason W. Labonte, Kresten Lindorff-Larsen, Jens Meiler, William Schief, Ora Schueler-Furman, Justin B. Siegel, Amelie Stein, Vladimir Yarov-Yarovoy, Brian Kuhlman, Andrew Leaver-Fay, Dominik Gront, Jeffrey J. Gray, Richard Bonneau
Summary: Vast international resources are wasted on irreproducible research each year, but reproducible scientific software applications can be created by meeting simple design goals. This reproducible design framework is valuable for developers and users of any scientific software, helping the scientific community create reproducible methods.
NATURE COMMUNICATIONS
(2021)
Article
Education, Scientific Disciplines
Olivia A. Erickson, Rebecca B. Cole, Jared M. Isaacs, Silvia Alvarez-Clare, Jonathan Arnold, Allison Augustus-Wallace, Joseph C. Ayoob, Alan Berkowitz, Janet Branchaw, Kevin R. Burgio, Charles H. Cannon, Ruben Michael Ceballos, C. Sarah Cohen, Hilary Coller, Jane Disney, Van A. Doze, Margaret J. Eggers, Stacy Farina, Edwin L. Ferguson, Jeffrey J. Gray, Jean T. Greenberg, Alexander Hoffmann, Danielle Jensen-Ryan, Robert M. Kao, Alex C. Keene, Johanna E. Kowalko, Steven A. Lopez, Camille Mathis, Mona Minkara, Courtney J. Murren, Mary Jo Ondrechen, Patricia Ordonez, Anne Osano, Elizabeth Padilla-Crespo, Soubantika Palchoudhury, Hong Qin, Juan Ramirez-Lugo, Jennifer Reithel, Colin A. Shaw, Amber Smith, Rosemary Smith, Adam P. Summers, Fern Tsien, Erin L. Dolan
Summary: This paragraph describes the impact of the COVID-19 pandemic on undergraduate research programs in the United States and the implementation of remote undergraduate research programs in the life sciences. Through surveys and discussions, the strengths, weaknesses, and recommendations for improvement of these programs were identified. Despite coinciding with a peak in awareness of racial inequities and structural racism, students reported lower focus on these topics.
CBE-LIFE SCIENCES EDUCATION
(2022)
Article
Multidisciplinary Sciences
Deniz Akpinaroglu, Jeffrey A. Ruffolo, Sai Pooja Mahajan, Jeffrey J. Gray
Summary: Antibody engineering is widely used in medicine and accurate modeling of antibody structures is crucial for effective engineering and design. We developed DeepSCAb, a deep learning method that predicts both backbone and side-chain conformations of antibodies, improving the accuracy of antibody structure prediction. This method is particularly useful for antibodies without known backbone structures.
Article
Biochemical Research Methods
Ameya Harmalkar, Sai Pooja Mahajan, Jeffrey J. Gray
Summary: We developed a new sampling method, ReplicaDock 2.0, for protein complex structure prediction that successfully captures binding-induced conformational changes. Our method mimics the induced-fit mechanism of protein binding and is significantly faster than Molecular Dynamics based approaches. It achieves high success rates for moderately and highly flexible targets.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Biochemistry & Molecular Biology
Veda Sheersh Boorla, Ratul Chowdhury, Ranjani Ramasubramanian, Brandon Ameglio, Rahel Frick, Jeffrey J. Gray, Costas D. Maranas
Summary: This study focuses on the computational design of neutralizing antibodies for emerging SARS-CoV-2 variants. By recombining VDJ genes and optimizing amino acid substitutions, high-affinity antibody variable regions were designed. Computational evaluation identified a promising candidate for experimental testing.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Jeffrey A. Ruffolo, Jeremias Sulam, Jeffrey J. Gray
Summary: DeepAb is a deep learning method that accurately predicts antibody FV structures from sequences. It outperforms alternative methods on diverse and therapeutically relevant antibodies and offers interpretable predictions by introducing an attention mechanism. Additionally, a mutant scoring metric derived from network confidence improves binding affinity for a specific antibody.
Review
Computer Science, Artificial Intelligence
Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, Jeffrey J. Gray
Meeting Abstract
Biophysics
Taylor P. Light, Kelly Karl, Jeffrey J. Gray, Kalina Hristova
BIOPHYSICAL JOURNAL
(2020)
