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- Allosteric regulation (2)
- Deep learning (2)
- Drug discovery (2)
- Allosteric binding pockets (1)
- Allosteric communications (1)
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- Automated cell tracking (1)
- Binding energetics (1)
- Cancer driver mutations (1)
- Convolutional neural networks (1)
- Deep machine learning (1)
- Deep mutational scanning (1)
- Ensemble-based machine learning features (1)
- Imaging (1)
- Machine learning classifiers (1)
- Markov state models (1)
- Molecular dynamics (1)
- Multiscale modeling (1)
- Network analysis (1)
- Network modeling (1)
- Protein stability (1)
- Random forest (1)
- Reinforcement learning (1)
Articles 1 - 8 of 8
Full-Text Articles in Life Sciences
From Deep Mutational Mapping Of Allosteric Protein Landscapes To Deep Learning Of Allostery And Hidden Allosteric Sites: Zooming In On “Allosteric Intersection” Of Biochemical And Big Data Approaches, Gennady M. Verkhivker, Mohammed Alshahrani, Grace Gupta, Sian Xiao, Peng Tao
From Deep Mutational Mapping Of Allosteric Protein Landscapes To Deep Learning Of Allostery And Hidden Allosteric Sites: Zooming In On “Allosteric Intersection” Of Biochemical And Big Data Approaches, Gennady M. Verkhivker, Mohammed Alshahrani, Grace Gupta, Sian Xiao, Peng Tao
Mathematics, Physics, and Computer Science Faculty Articles and Research
The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric …
Dissecting Mutational Allosteric Effects In Alkaline Phosphatases Associated With Different Hypophosphatasia Phenotypes: An Integrative Computational Investigation, Fei Xiao, Ziyun Zhou, Xingyu Song, Mi Gan, Jie Long, Gennady M. Verkhivker, Guang Hu
Dissecting Mutational Allosteric Effects In Alkaline Phosphatases Associated With Different Hypophosphatasia Phenotypes: An Integrative Computational Investigation, Fei Xiao, Ziyun Zhou, Xingyu Song, Mi Gan, Jie Long, Gennady M. Verkhivker, Guang Hu
Mathematics, Physics, and Computer Science Faculty Articles and Research
Hypophosphatasia (HPP) is a rare inherited disorder characterized by defective bone mineralization and is highly variable in its clinical phenotype. The disease occurs due to various loss-of-function mutations in ALPL, the gene encoding tissue-nonspecific alkaline phosphatase (TNSALP). In this work, a data-driven and biophysics-based approach is proposed for the large-scale analysis of ALPL mutations-from nonpathogenic to severe HPPs. By using a pipeline of synergistic approaches including sequence-structure analysis, network modeling, elastic network models and atomistic simulations, we characterized allosteric signatures and effects of the ALPL mutations on protein dynamics and function. Statistical analysis of molecular features computed for the …
Allosteric Regulation At The Crossroads Of New Technologies: Multiscale Modeling, Networks, And Machine Learning, Gennady M. Verkhivker, Steve Agajanian, Guang Hu, Peng Tao
Allosteric Regulation At The Crossroads Of New Technologies: Multiscale Modeling, Networks, And Machine Learning, Gennady M. Verkhivker, Steve Agajanian, Guang Hu, Peng Tao
Mathematics, Physics, and Computer Science Faculty Articles and Research
Allosteric regulation is a common mechanism employed by complex biomolecular systems for regulation of activity and adaptability in the cellular environment, serving as an effective molecular tool for cellular communication. As an intrinsic but elusive property, allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity and is considered as the “second secret of life.” The fundamental biological importance and complexity of these processes require a multi-faceted platform of synergistically integrated approaches for prediction and characterization of allosteric functional states, atomistic reconstruction of allosteric regulatory mechanisms and discovery of …
Integration Of Random Forest Classifiers And Deep Convolutional Neural Networks For Classification And Biomolecular Modeling Of Cancer Driver Mutations, Steve Agajanian, Odeyemi Oluyemi, Gennady M. Verkhivker
Integration Of Random Forest Classifiers And Deep Convolutional Neural Networks For Classification And Biomolecular Modeling Of Cancer Driver Mutations, Steve Agajanian, Odeyemi Oluyemi, Gennady M. Verkhivker
Mathematics, Physics, and Computer Science Faculty Articles and Research
Development of machine learning solutions for prediction of functional and clinical significance of cancer driver genes and mutations are paramount in modern biomedical research and have gained a significant momentum in a recent decade. In this work, we integrate different machine learning approaches, including tree based methods, random forest and gradient boosted tree (GBT) classifiers along with deep convolutional neural networks (CNN) for prediction of cancer driver mutations in the genomic datasets. The feasibility of CNN in using raw nucleotide sequences for classification of cancer driver mutations was initially explored by employing label encoding, one hot encoding, and embedding to …
Allosteric Mechanism Of The Circadian Protein Vivid Resolved Through Markov State Model And Machine Learning Analysis, Hongyu Zhou, Zheng Dong, Gennady M. Verkhivker, Brian D. Zoltowski, Peng Tao
Allosteric Mechanism Of The Circadian Protein Vivid Resolved Through Markov State Model And Machine Learning Analysis, Hongyu Zhou, Zheng Dong, Gennady M. Verkhivker, Brian D. Zoltowski, Peng Tao
Mathematics, Physics, and Computer Science Faculty Articles and Research
The fungal circadian clock photoreceptor Vivid (VVD) contains a photosensitive allosteric light, oxygen, voltage (LOV) domain that undergoes a large N-terminal conformational change. The mechanism by which a blue-light driven covalent bond formation leads to a global conformational change remains unclear, which hinders the further development of VVD as an optogenetic tool. We answered this question through a novel computational platform integrating Markov state models, machine learning methods, and newly developed community analysis algorithms. Applying this new integrative approach, we provided a quantitative evaluation of the contribution from the covalent bond to the protein global conformational change, and proposed an …
Atomistic Simulations And Network-Based Modeling Of The Hsp90-Cdc37 Chaperone Binding With Cdk4 Client Protein: A Mechanism Of Chaperoning Kinase Clients By Exploiting Weak Spots Of Intrinsically Dynamic Kinase Domains, John Czemeres, Kurt Buse, Gennady M. Verkhivker
Atomistic Simulations And Network-Based Modeling Of The Hsp90-Cdc37 Chaperone Binding With Cdk4 Client Protein: A Mechanism Of Chaperoning Kinase Clients By Exploiting Weak Spots Of Intrinsically Dynamic Kinase Domains, John Czemeres, Kurt Buse, Gennady M. Verkhivker
Mathematics, Physics, and Computer Science Faculty Articles and Research
A fundamental role of the Hsp90 and Cdc37 chaperones in mediating conformational development and activation of diverse protein kinase clients is essential in signal transduction. There has been increasing evidence that the Hsp90-Cdc37 system executes its chaperoning duties by recognizing conformational instability of kinase clients and modulating their folding landscapes. The recent cryo-electron microscopy structure of the Hsp90-Cdc37- Cdk4 kinase complex has provided a framework for dissecting regulatory principles underlying differentiation and recruitment of protein kinase clients to the chaperone machinery. In this work, we have combined atomistic simulations with protein stability and network-based rigidity decomposition analyses to characterize dynamic …
Computational Analysis Of Residue Interaction Networks And Coevolutionary Relationships In The Hsp70 Chaperones: A Community- Hopping Model Of Allosteric Regulation And Communication, Gabrielle Stetz, Gennady M. Verkhivker
Computational Analysis Of Residue Interaction Networks And Coevolutionary Relationships In The Hsp70 Chaperones: A Community- Hopping Model Of Allosteric Regulation And Communication, Gabrielle Stetz, Gennady M. Verkhivker
Mathematics, Physics, and Computer Science Faculty Articles and Research
Allosteric interactions in the Hsp70 proteins are linked with their regulatory mechanisms and cellular functions. Despite significant progress in structural and functional characterization of the Hsp70 proteins fundamental questions concerning modularity of the allosteric interaction networks and hierarchy of signaling pathways in the Hsp70 chaperones remained largely unexplored and poorly understood. In this work, we proposed an integrated computational strategy that combined atomistic and coarse-grained simulations with coevolutionary analysis and network modeling of the residue interactions. A novel aspect of this work is the incorporation of dynamic residue correlations and coevolutionary residue dependencies in the construction of allosteric interaction networks …
Detection And Tracking Of T Cells In Time-Lapse Imaging, Cody Arbuckle, Milton L. Greenberg, Erik J. Linstead
Detection And Tracking Of T Cells In Time-Lapse Imaging, Cody Arbuckle, Milton L. Greenberg, Erik J. Linstead
Mathematics, Physics, and Computer Science Faculty Articles and Research
The effective classification and tracking of cells obtained from modern staining techniques has significant limitations due to the necessity of having to train and utilize a human expert in the field who must manually identify each cell in each slide. Often times these slides are filled with noise cells that are not of particular interest to the researcher. The use of computational methods has the ability to effectively and efficiently enhance image quality, as well as identify and track target cell types over large data sets. Here we present a computational approach to the in vitro tracking of T cells …