Michael Feig

发表

Extending the treatment of backbone energetics in protein force fields: Limitations of gas‐phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations

Alexander D. MacKerell, Charles L. Brooks, Michael Feig, 2004, J. Comput. Chem..

New parallel computing algorithm of molecular dynamics for extremely huge scale biological systems

Cheng Tan, Yutaka Ishikawa, Yuji Sugita, 2020, J. Comput. Chem..

The NASA/IPAC Teacher Archive Research Program (NITARP)

K. T. McCarron, Chelen H. Johnson, Michael Feig, 2012, Robotic Telescopes, Student Research and Education Proceedings, Vol 1, No 1.

Einführung in GNU

Michael Feig, 1996, UNIX easy.

New analytic approximation to the standard molecular volume definition and its application to generalized born calculations

Charles L. Brooks, Michael Feig, Michael S. Lee, 2003, J. Comput. Chem..

2SD-01 Dynamics and stability of proteins in cellular environments(2SD Supercomputing in Molecular Network to Cellular Dynamics,Symposium,The 50th Annual Meeting of the Biophysical Society of Japan)

Michael Feig, 2012 .

High-Accuracy Protein Structures by Combining Machine-Learning with Physics-Based Refinement

Michael Feig, Lim Heo, M. Feig, 2019, bioRxiv.

SimDB: A Problem Solving Environment for Molecular Dynamics Simulation and Analysis

S. Lennart Johnsson, Michael Feig, Matin Abdullah, 2000 .

Molecular Dynamics Trajectory Compression with a Coarse-Grained Model

Michael Feig, Sean M. Law, Yi-Ming Cheng, 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

CHARMM: The biomolecular simulation program

Jianpeng Ma, Charles L. Brooks, Themis Lazaridis, 2009, J. Comput. Chem..

Local Protein Structure Refinement via Molecular Dynamics Simulations with locPREFMD

Michael Feig, M. Feig, 2016, J. Chem. Inf. Model..

New analytic approximation to the standard molecular volume definition and its application to generalized Born calculations

Charles L. Brooks, Michael Feig, Michael S. Lee, 2003, J. Comput. Chem..

Driven to near‐experimental accuracy by refinement via molecular dynamics simulations

Michael Feig, Lim Heo, Collin F Arbour, 2019, Proteins.

Performance comparison of generalized born and Poisson methods in the calculation of electrostatic solvation energies for protein structures

Charles L. Brooks, David A. Case, Michael Feig, 2004, J. Comput. Chem..

Prediction of protein loop conformations using multiscale modeling methods with physical energy scoring functions

Charles L. Brooks, Mark A. Olson, Michael Feig, 2008, J. Comput. Chem..

What makes it difficult to refine protein models further via molecular dynamics simulations?

Michael Feig, Lim Heo, M. Feig, 2018, Proteins.

Comprar Modeling Solvent Environments: Applications to Simulations of Biomolecules | Emma Baker | 9783527324217 | Wiley

Michael Feig, Emma H. Baker, Dilys Lai, 2010 .

Experimental accuracy in protein structure refinement via molecular dynamics simulations

Michael Feig, Lim Heo, M. Feig, 2018, Proceedings of the National Academy of Sciences.

Evaluating CASP4 predictions with physical energy functions

Michael Feig, Charles L Brooks, C. Brooks, 2002, Proteins.

High‐accuracy protein structures by combining machine‐learning with physics‐based refinement

Michael Feig, Lim Heo, M. Feig, 2020, Proteins.

Heterogeneous dielectric generalized Born model with a van der Waals term provides improved association energetics of membrane‐embedded transmembrane helices

Michael Feig, Bercem Dutagaci, Maryam Sayadi, 2017, J. Comput. Chem..

PREFMD: a web server for protein structure refinement via molecular dynamics simulations

Michael Feig, Lim Heo, M. Feig, 2018, Bioinform..

GENESIS: a hybrid-parallel and multi-scale molecular dynamics simulator with enhanced sampling algorithms for biomolecular and cellular simulations

Yasuhiro Matsunaga, Yuji Sugita, Jaewoon Jung, 2015, Wiley interdisciplinary reviews. Computational molecular science.

Physics‐based protein structure refinement through multiple molecular dynamics trajectories and structure averaging

Vahid Mirjalili, Michael Feig, Keenan Noyes, 2014, Proteins.

Sampling of near‐native protein conformations during protein structure refinement using a coarse‐grained model, normal modes, and molecular dynamics simulations

Michael Feig, Michael S Lee, Andrew W Stumpff-Kane, 2007, Proteins.

Modeling of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Proteins by Machine Learning and Physics-Based Refinement

Michael Feig, Lim Heo, M. Feig, 2020, bioRxiv.

Some Metacomputing Experiences for Scientific Applications

S. Lennart Johnsson, Michael Feig, Olle Larsson, 1999, Parallel Process. Lett..

Highly accurate biomolecular electrostatics in continuum dielectric environments

Guo-Wei Wei, Michael Feig, Yongcheng Zhou, 2008, J. Comput. Chem..

Accurate prediction of protonation state as a prerequisite for reliable MM‐PB(GB)SA binding free energy calculations of HIV‐1 protease inhibitors

Supot Hannongbua, Michael Feig, Kitiyaporn Wittayanarakul, 2008, J. Comput. Chem..

Molecular simulation methods: Standard practices and modern challenges

Michael Feig, 2010 .

Scoring confidence index: statistical evaluation of ligand binding mode predictions

Maria I. Zavodszky, Michael Feig, David J. Lee, 2009, J. Comput. Aided Mol. Des..

Protein structure refinement via molecular‐dynamics simulations: What works and what does not?

Vahid Mirjalili, Michael Feig, M. Feig, 2016, Proteins.

Accelerating the Generalized Born with Molecular Volume and Solvent Accessible Surface Area Implicit Solvent Model Using Graphics Processing Units

Michael Feig, Jianhan Chen, Xiping Gong, 2019, J. Comput. Chem..

Balancing an accurate representation of the molecular surface in generalized born formalisms with integrator stability in molecular dynamics simulations

Michael Feig, Jana Chocholousová, J. Chocholousová, 2006, J. Comput. Chem..

Simulating Biomolecules in Cellular Environments

Jacob Clifford, Brian D. Connelly, Michael Feig, 2008 .

Prediction of Membrane Permeation of Drug Molecules by Combining an Implicit Membrane Model with Machine Learning

Alexander D. MacKerell, Michael Feig, Alexander D MacKerell, 2018, J. Chem. Inf. Model..

Long-Range Signaling in MutS and MSH Homologs via Switching of Dynamic Communication Pathways

Beibei Wang, Monika Sharma, Michael Feig, 2016, PLoS Comput. Biol..

On the correlation between drug-resistant pattern of HIV-1 protease inhibitors and binding free energy and structural changes

Supot Hannongbua, Michael Feig, Kitiyaporn Wittayanarakul, 2007, BMC Systems Biology.

High order matched interface and boundary method for elliptic equations with discontinuous coefficients and singular sources

Shan Zhao, Guo-Wei Wei, Michael Feig, 2006, J. Comput. Phys..

MMTSB Tool Set: enhanced sampling and multiscale modeling methods for applications in structural biology.

Michael Feig, John Karanicolas, Charles L Brooks, 2004, Journal of molecular graphics & modelling.

Large scale distributed data repository: design of a molecular dynamics trajectory database

B. Montgomery Pettitt, S. Lennart Johnsson, Michael Feig, 1999, Future Gener. Comput. Syst..

Protein Structure Refinement through Structure Selection and Averaging from Molecular Dynamics Ensembles.

Vahid Mirjalili, Michael Feig, M. Feig, 2013, Journal of chemical theory and computation.

Transferring the PRIMO Coarse-Grained Force Field to the Membrane Environment: Simulations of Membrane Proteins and Helix–Helix Association

Michael Feig, Afra Panahi, Parimal Kar, 2014, Journal of chemical theory and computation.

RNA polymerase II with open and closed trigger loops: active site dynamics and nucleic acid translocation.

Michael Feig, Zachary F Burton, M. Feig, 2010, Biophysical journal.

Dynamic error correction and regulation of downstream bubble opening by human RNA polymerase II.

Michael Feig, Zachary F Burton, Chunfen Zhang, 2005, Molecular cell.

Conformational coupling, bridge helix dynamics and active site dehydration in catalysis by RNA polymerase.

Michael Feig, Michael Hampsey, Benoit Coulombe, 2010, Biochimica et biophysica acta.

Determination of Hydrophobic Lengths of Membrane Proteins with the HDGB Implicit Membrane Model

Michael Feig, Bercem Dutagaci, M. Feig, 2017, J. Chem. Inf. Model..

Computational protein structure refinement: almost there, yet still so far to go

Michael Feig, M. Feig, 2017, Wiley interdisciplinary reviews. Computational molecular science.

PRIMO/PRIMONA: A coarse‐grained model for proteins and nucleic acids that preserves near‐atomistic accuracy

Shayantani Mukherjee, Michael Feig, Yi-Ming Cheng, 2010, Proteins.

Thermodynamics of Macromolecular Association in Heterogeneous Crowding Environments: Theoretical and Simulation Studies with a Simplified Model.

Yuji Sugita, Michael Feig, Tadashi Ando, 2016, The journal of physical chemistry. B.

High-resolution 3D models of Caulobacter crescentus chromosome reveal genome structural variability and organization

Asli Yildirim, Michael Feig, M. Feig, 2018, Nucleic acids research.

A correlation‐based method for the enhancement of scoring functions on funnel‐shaped energy landscapes

Michael Feig, Andrew W. Stumpff-Kane, M. Feig, 2006, Proteins.

NTP-driven translocation and regulation of downstream template opening by multi-subunit RNA polymerases.

Michael Feig, Zachary F Burton, Chunfen Zhang, 2005, Biochemistry and cell biology = Biochimie et biologie cellulaire.

Synthetic curcuminoids modulate the arachidonic acid metabolism of human platelet 12-lipoxygenase and reduce sprout formation of human endothelial cells

Michael Feig, Jerzy Jankun, M. Feig, 2006, Molecular Cancer Therapeutics.

Computational Simulation Strategies for Analysis of Multisubunit RNA Polymerases

Michael Feig, Zachary F. Burton, Robert I. Cukier, 2013, Chemical reviews.

The requirement for mechanical coupling between head and S2 domains in smooth muscle myosin ATPase regulation and its implications for dimeric motor function.

Florence Tama, Michael Feig, Charles L Brooks, 2005, Journal of molecular biology.

Molecular dynamics simulations of large integral membrane proteins with an implicit membrane model.

Michael Feig, Seiichiro Tanizaki, M. Feig, 2006, The journal of physical chemistry. B.

Modeling solvent environments : applications to simulations of biomolecules

Michael Feig, M. Feig, 2010 .

Extending the horizon: towards the efficient modeling of large biomolecular complexes in atomic detail

Michael Feig, Seiichiro Tanizaki, J. Chocholousová, 2006 .

Dynamic Heterogeneous Dielectric Generalized Born (DHDGB): An implicit membrane model with a dynamically varying bilayer thickness.

Michael Feig, Afra Panahi, M. Feig, 2013, Journal of chemical theory and computation.

Clustering and dynamics of crowded proteins near membranes and their influence on membrane bending

Yuji Sugita, Michael Feig, Wonpil Im, 2019, Proceedings of the National Academy of Sciences.

Implicit membrane models for membrane protein simulation.

Michael Feig, M. Feig, 2008, Methods in molecular biology.

Is Alanine Dipeptide a Good Model for Representing the Torsional Preferences of Protein Backbones?

Michael Feig, M. Feig, 2008, Journal of chemical theory and computation.

Whole-Cell Models and Simulations in Molecular Detail.

Yuji Sugita, Michael Feig, Y. Sugita, 2019, Annual review of cell and developmental biology.

Force field influence on the observation of π-helical protein structures in molecular dynamics simulations

Alexander D. MacKerell, Charles L. Brooks, Michael Feig, 2003 .

CHARMM36: An Improved Force Field for Folded and Intrinsically Disordered Proteins

Alexander D. MacKerell, Helmut Grubmüller, Jing Huang, 2017 .

Conformational Sampling in Structure Prediction and Refinement with Atomistic and Coarse-Grained Models

Michael Feig, Andrew W. Stumpff-Kane, M. Feig, 2011 .

RNA polymerase II flexibility during translocation from normal mode analysis

Michael Feig, Zachary F Burton, M. Feig, 2010, Proteins.

Structure refinement of membrane proteins via molecular dynamics simulations

Michael Feig, Lim Heo, Bercem Dutagaci, 2018, Proteins.

Implicit solvation based on generalized Born theory in different dielectric environments.

Michael Feig, Wonpil Im, Charles L Brooks, 2004, The Journal of chemical physics.

Discrimination of Native-like States of Membrane Proteins with Implicit Membrane-based Scoring Functions.

Michael Feig, Takaharu Mori, Kitiyaporn Wittayanarakul, 2017, Journal of chemical theory and computation.

Reduced native state stability in crowded cellular environment due to protein-protein interactions.

Yuji Sugita, Michael Feig, Ryuhei Harada, 2013, Journal of the American Chemical Society.

High-Performance Data Analysis on the Big Trajectory Data of Cellular Scale All-atom Molecular Dynamics Simulations.

Yuji Sugita, Michael Feig, Isseki Yu, 2018, Journal of physics. Conference series.

Evolution and Physics in Comparative Protein Structure Modeling

Charles L. Brooks, Andrej Sali, Andras Fiser, 2002 .

Complete atomistic model of a bacterial cytoplasm for integrating physics, biochemistry, and systems biology.

Yuji Sugita, Michael Feig, Ryuhei Harada, 2015, Journal of molecular graphics & modelling.

Erratum: New Analytic Approximation to the Standard Molecular Volume Definition and Its Application to Generalized Born Calculations (Journal of Computational Chemistry (2003) 24:11 (1348-1356))

Charles L. Brooks, Michael Feig, C. Brooks, 2003 .