doi.bio/peter_l_freddolino


Peter L Freddolino

Early Life and Education

Peter L Freddolino is a researcher in the field of biochemistry and genomics. He obtained his B.S. with Honors in Biology from the California Institute of Technology in 2004 and went on to complete his Ph.D. in Biophysics and Computational Biology at the University of Illinois at Urbana-Champaign in 2009.

Career and Research

Freddolino has held several postdoctoral research positions, including at the Tavazoie Lab, Joint Centers for Systems Biology, Columbia University (2011-2014), and the Tavazoie Lab, Lewis-Sigler Institute for Integrative Genomics, Princeton University (2009-2011). He has also been a research fellow and graduate research assistant at the University of Illinois at Urbana-Champaign.

Freddolino's research combines computational and experimental approaches to understand how cells sense and respond to their environment. He utilizes microbial population genetics, systems biology tools, bioinformatic analysis, and molecular and atomistic-level simulations.

Some of his notable publications include:

Freddolino is currently an Assistant Professor of Biological Chemistry, Computational Medicine, and Bioinformatics at the University of Michigan. He is also affiliated with the Department of Biological Chemistry, Department of Computational Medicine and Bioinformatics, Cellular and Molecular Biology Program, and the Center for RNA Biomedicine.

Google Scholar Profile

Peter L Freddolino)

Google Scholar

Lydia Freddolino Associate Professor http://freddolino-lab.med.umich.edu/people/lydsf Accelerating molecular modeling applications with graphics processors JE Stone, JC Phillips, PL Freddolino, DJ Hardy, LG Trabuco, K Schulten Journal of computational chemistry 28 (16), 2618-2640, 2007 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:u5HHmVD_uO8C Cited by: 925

Molecular dynamics simulations of the complete satellite tobacco mosaic virus PL Freddolino, AS Arkhipov, SB Larson, A McPherson, K Schulten Structure 14 (3), 437-449, 2006 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:d1gkVwhDpl0C Cited by: 613

COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information C Zhang, PL Freddolino, Y Zhang Nucleic acids research 45 (W1), W291-W299, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:isC4tDSrTZIC Cited by: 565

Ten-microsecond molecular dynamics simulation of a fast-folding WW domain PL Freddolino, F Liu, M Gruebele, K Schulten Biophysical journal 94 (10), L75-L77, 2008 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:2osOgNQ5qMEC Cited by: 434

Challenges in protein-folding simulations PL Freddolino, CB Harrison, Y Liu, K Schulten Nature physics 6 (10), 751-758, 2010 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:hqOjcs7Dif8C Cited by: 411

Prediction of structure and function of G protein-coupled receptors N Vaidehi, WB Floriano, R Trabanino, SE Hall, P Freddolino, EJ Choi, … Proceedings of the National Academy of Sciences 99 (20), 12622-12627, 2002 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:u-x6o8ySG0sC Cited by: 375

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens N Zhou, Y Jiang, TR Bergquist, AJ Lee, BZ Kacsoh, AW Crocker, … Genome biology 20, 1-23, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:rO6llkc54NcC Cited by: 364

Coarse grained protein− lipid model with application to lipoprotein particles AY Shih, A Arkhipov, PL Freddolino, K Schulten The Journal of Physical Chemistry B 110 (8), 3674-3684, 2006 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:9yKSN-GCB0IC Cited by: 338

Stability and dynamics of virus capsids described by coarse-grained modeling A Arkhipov, PL Freddolino, K Schulten Structure 14 (12), 1767-1777, 2006 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:IjCSPb-OGe4C Cited by: 330

Ab initio protein structure prediction J Lee, PL Freddolino, Y Zhang From protein structure to function with bioinformatics, 3-35, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:M3NEmzRMIkIC Cited by: 292

Bacterial adaptation through loss of function AK Hottes, PL Freddolino, A Khare, ZN Donnell, JC Liu, S Tavazoie PLoS genetics 9 (7), e1003617, 2013 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:qUcmZB5y_30C Cited by: 275

Force field bias in protein folding simulations PL Freddolino, S Park, B Roux, K Schulten Biophysical journal 96 (9), 3772-3780, 2009 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:Y0pCki6q_DkC Cited by: 233

The predicted 3D structure of the human D2 dopamine receptor and the binding site and binding affinities for agonists and antagonists MYS Kalani, N Vaidehi, SE Hall, RJ Trabanino, PL Freddolino, MA Kalani, … Proceedings of the national academy of sciences 101 (11), 3815-3820, 2004 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:UeHWp8X0CEIC Cited by: 206

QwikMD—integrative molecular dynamics toolkit for novices and experts JV Ribeiro, RC Bernardi, T Rudack, JE Stone, JC Phillips, PL Freddolino, … Scientific reports 6 (1), 26536, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:j3f4tGmQtD8C Cited by: 196

Common structural transitions in explicit-solvent simulations of villin headpiece folding PL Freddolino, K Schulten Biophysical journal 97 (8), 2338-2347, 2009 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:W7OEmFMy1HYC Cited by: 187

Assembly of lipoprotein particles revealed by coarse-grained molecular dynamics simulations AY Shih, PL Freddolino, A Arkhipov, K Schulten Journal of structural biology 157 (3), 579-592, 2007 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:zYLM7Y9cAGgC Cited by: 169

Introducing a spectrum of long-range genomic deletions in human embryonic stem cells using type I CRISPR-Cas AE Dolan, Z Hou, Y Xiao, MJ Gramelspacher, J Heo, SE Howden, … Molecular cell 74 (5), 936-950. e5, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:SeFeTyx0c_EC Cited by: 166

Predicted 3D structure for the human β2 adrenergic receptor and its binding site for agonists and antagonists PL Freddolino, MYS Kalani, N Vaidehi, WB Floriano, SE Hall, … Proceedings of the national academy of sciences 101 (9), 2736-2741, 2004 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:qjMakFHDy7sC Cited by: 160

Molecular mechanism of ligand recognition by NR3 subtype glutamate receptors Y Yao, CB Harrison, PL Freddolino, K Schulten, ML Mayer The EMBO journal 27 (15), 2158-2170, 2008 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:LkGwnXOMwfcC Cited by: 151

Interfacial Activation of Candida antarctica Lipase B: Combined Evidence from Experiment and Simulation T Zisis, PL Freddolino, P Turunen, MCF van Teeseling, AE Rowan, … Biochemistry 54 (38), 5969-5979, 2015 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:TQgYirikUcIC Cited by: 149

Google Scholar

Lydia Freddolino Associate Professor http://freddolino-lab.med.umich.edu/people/lydsf Accelerating molecular modeling applications with graphics processors JE Stone, JC Phillips, PL Freddolino, DJ Hardy, LG Trabuco, K Schulten Journal of computational chemistry 28 (16), 2618-2640, 2007 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:u5HHmVD_uO8C Cited by: 925

Molecular dynamics simulations of the complete satellite tobacco mosaic virus PL Freddolino, AS Arkhipov, SB Larson, A McPherson, K Schulten Structure 14 (3), 437-449, 2006 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:d1gkVwhDpl0C Cited by: 613

COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information C Zhang, PL Freddolino, Y Zhang Nucleic acids research 45 (W1), W291-W299, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:isC4tDSrTZIC Cited by: 565

Ten-microsecond molecular dynamics simulation of a fast-folding WW domain PL Freddolino, F Liu, M Gruebele, K Schulten Biophysical journal 94 (10), L75-L77, 2008 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:2osOgNQ5qMEC Cited by: 434

Challenges in protein-folding simulations PL Freddolino, CB Harrison, Y Liu, K Schulten Nature physics 6 (10), 751-758, 2010 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:hqOjcs7Dif8C Cited by: 411

Prediction of structure and function of G protein-coupled receptors N Vaidehi, WB Floriano, R Trabanino, SE Hall, P Freddolino, EJ Choi, … Proceedings of the National Academy of Sciences 99 (20), 12622-12627, 2002 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:u-x6o8ySG0sC Cited by: 375

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens N Zhou, Y Jiang, TR Bergquist, AJ Lee, BZ Kacsoh, AW Crocker, … Genome biology 20, 1-23, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:rO6llkc54NcC Cited by: 364

Coarse grained protein− lipid model with application to lipoprotein particles AY Shih, A Arkhipov, PL Freddolino, K Schulten The Journal of Physical Chemistry B 110 (8), 3674-3684, 2006 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:9yKSN-GCB0IC Cited by: 338

Stability and dynamics of virus capsids described by coarse-grained modeling A Arkhipov, PL Freddolino, K Schulten Structure 14 (12), 1767-1777, 2006 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:IjCSPb-OGe4C Cited by: 330

Ab initio protein structure prediction J Lee, PL Freddolino, Y Zhang From protein structure to function with bioinformatics, 3-35, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:M3NEmzRMIkIC Cited by: 292

Bacterial adaptation through loss of function AK Hottes, PL Freddolino, A Khare, ZN Donnell, JC Liu, S Tavazoie PLoS genetics 9 (7), e1003617, 2013 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:qUcmZB5y_30C Cited by: 275

Force field bias in protein folding simulations PL Freddolino, S Park, B Roux, K Schulten Biophysical journal 96 (9), 3772-3780, 2009 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:Y0pCki6q_DkC Cited by: 233

The predicted 3D structure of the human D2 dopamine receptor and the binding site and binding affinities for agonists and antagonists MYS Kalani, N Vaidehi, SE Hall, RJ Trabanino, PL Freddolino, MA Kalani, … Proceedings of the national academy of sciences 101 (11), 3815-3820, 2004 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:UeHWp8X0CEIC Cited by: 206

QwikMD—integrative molecular dynamics toolkit for novices and experts JV Ribeiro, RC Bernardi, T Rudack, JE Stone, JC Phillips, PL Freddolino, … Scientific reports 6 (1), 26536, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:j3f4tGmQtD8C Cited by: 196

Common structural transitions in explicit-solvent simulations of villin headpiece folding PL Freddolino, K Schulten Biophysical journal 97 (8), 2338-2347, 2009 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:W7OEmFMy1HYC Cited by: 187

Assembly of lipoprotein particles revealed by coarse-grained molecular dynamics simulations AY Shih, PL Freddolino, A Arkhipov, K Schulten Journal of structural biology 157 (3), 579-592, 2007 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:zYLM7Y9cAGgC Cited by: 169

Introducing a spectrum of long-range genomic deletions in human embryonic stem cells using type I CRISPR-Cas AE Dolan, Z Hou, Y Xiao, MJ Gramelspacher, J Heo, SE Howden, … Molecular cell 74 (5), 936-950. e5, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:SeFeTyx0c_EC Cited by: 166

Predicted 3D structure for the human β2 adrenergic receptor and its binding site for agonists and antagonists PL Freddolino, MYS Kalani, N Vaidehi, WB Floriano, SE Hall, … Proceedings of the national academy of sciences 101 (9), 2736-2741, 2004 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:qjMakFHDy7sC Cited by: 160

Molecular mechanism of ligand recognition by NR3 subtype glutamate receptors Y Yao, CB Harrison, PL Freddolino, K Schulten, ML Mayer The EMBO journal 27 (15), 2158-2170, 2008 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:LkGwnXOMwfcC Cited by: 151

Interfacial Activation of Candida antarctica Lipase B: Combined Evidence from Experiment and Simulation T Zisis, PL Freddolino, P Turunen, MCF van Teeseling, AE Rowan, … Biochemistry 54 (38), 5969-5979, 2015 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:TQgYirikUcIC Cited by: 149

Google Scholar

Lydia Freddolino Associate Professor http://freddolino-lab.med.umich.edu/people/lydsf Accelerating molecular modeling applications with graphics processors JE Stone, JC Phillips, PL Freddolino, DJ Hardy, LG Trabuco, K Schulten Journal of computational chemistry 28 (16), 2618-2640, 2007 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:u5HHmVD_uO8C Cited by: 925

Molecular dynamics simulations of the complete satellite tobacco mosaic virus PL Freddolino, AS Arkhipov, SB Larson, A McPherson, K Schulten Structure 14 (3), 437-449, 2006 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:d1gkVwhDpl0C Cited by: 613

COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information C Zhang, PL Freddolino, Y Zhang Nucleic acids research 45 (W1), W291-W299, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:isC4tDSrTZIC Cited by: 565

Ten-microsecond molecular dynamics simulation of a fast-folding WW domain PL Freddolino, F Liu, M Gruebele, K Schulten Biophysical journal 94 (10), L75-L77, 2008 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:2osOgNQ5qMEC Cited by: 434

Challenges in protein-folding simulations PL Freddolino, CB Harrison, Y Liu, K Schulten Nature physics 6 (10), 751-758, 2010 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:hqOjcs7Dif8C Cited by: 411

Prediction of structure and function of G protein-coupled receptors N Vaidehi, WB Floriano, R Trabanino, SE Hall, P Freddolino, EJ Choi, … Proceedings of the National Academy of Sciences 99 (20), 12622-12627, 2002 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:u-x6o8ySG0sC Cited by: 375

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens N Zhou, Y Jiang, TR Bergquist, AJ Lee, BZ Kacsoh, AW Crocker, … Genome biology 20, 1-23, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:rO6llkc54NcC Cited by: 364

Coarse grained protein− lipid model with application to lipoprotein particles AY Shih, A Arkhipov, PL Freddolino, K Schulten The Journal of Physical Chemistry B 110 (8), 3674-3684, 2006 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:9yKSN-GCB0IC Cited by: 338

Stability and dynamics of virus capsids described by coarse-grained modeling A Arkhipov, PL Freddolino, K Schulten Structure 14 (12), 1767-1777, 2006 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:IjCSPb-OGe4C Cited by: 330

Ab initio protein structure prediction J Lee, PL Freddolino, Y Zhang From protein structure to function with bioinformatics, 3-35, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:M3NEmzRMIkIC Cited by: 292

Bacterial adaptation through loss of function AK Hottes, PL Freddolino, A Khare, ZN Donnell, JC Liu, S Tavazoie PLoS genetics 9 (7), e1003617, 2013 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:qUcmZB5y_30C Cited by: 275

Force field bias in protein folding simulations PL Freddolino, S Park, B Roux, K Schulten Biophysical journal 96 (9), 3772-3780, 2009 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:Y0pCki6q_DkC Cited by: 233

The predicted 3D structure of the human D2 dopamine receptor and the binding site and binding affinities for agonists and antagonists MYS Kalani, N Vaidehi, SE Hall, RJ Trabanino, PL Freddolino, MA Kalani, … Proceedings of the national academy of sciences 101 (11), 3815-3820, 2004 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:UeHWp8X0CEIC Cited by: 206

QwikMD—integrative molecular dynamics toolkit for novices and experts JV Ribeiro, RC Bernardi, T Rudack, JE Stone, JC Phillips, PL Freddolino, … Scientific reports 6 (1), 26536, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:j3f4tGmQtD8C Cited by: 196

Common structural transitions in explicit-solvent simulations of villin headpiece folding PL Freddolino, K Schulten Biophysical journal 97 (8), 2338-2347, 2009 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:W7OEmFMy1HYC Cited by: 187

Assembly of lipoprotein particles revealed by coarse-grained molecular dynamics simulations AY Shih, PL Freddolino, A Arkhipov, K Schulten Journal of structural biology 157 (3), 579-592, 2007 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:zYLM7Y9cAGgC Cited by: 169

Introducing a spectrum of long-range genomic deletions in human embryonic stem cells using type I CRISPR-Cas AE Dolan, Z Hou, Y Xiao, MJ Gramelspacher, J Heo, SE Howden, … Molecular cell 74 (5), 936-950. e5, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:SeFeTyx0c_EC Cited by: 166

Predicted 3D structure for the human β2 adrenergic receptor and its binding site for agonists and antagonists PL Freddolino, MYS Kalani, N Vaidehi, WB Floriano, SE Hall, … Proceedings of the national academy of sciences 101 (9), 2736-2741, 2004 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:qjMakFHDy7sC Cited by: 160

Molecular mechanism of ligand recognition by NR3 subtype glutamate receptors Y Yao, CB Harrison, PL Freddolino, K Schulten, ML Mayer The EMBO journal 27 (15), 2158-2170, 2008 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:LkGwnXOMwfcC Cited by: 151

Interfacial Activation of Candida antarctica Lipase B: Combined Evidence from Experiment and Simulation T Zisis, PL Freddolino, P Turunen, MCF van Teeseling, AE Rowan, … Biochemistry 54 (38), 5969-5979, 2015 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:TQgYirikUcIC Cited by: 149

Google Scholar

Lydia Freddolino

Associate Professor

http://freddolino-lab.med.umich.edu/people/lydsf

Accelerating molecular modeling applications with graphics processors JE Stone, JC Phillips, PL Freddolino, DJ Hardy, LG Trabuco, K Schulten Journal of computational chemistry 28 (16), 2618-2640, 2007 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:u5HHmVD_uO8C

Molecular dynamics simulations of the complete satellite tobacco mosaic virus PL Freddolino, AS Arkhipov, SB Larson, A McPherson, K Schulten Structure 14 (3), 437-449, 2006 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:d1gkVwhDpl0C

COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information C Zhang, PL Freddolino, Y Zhang Nucleic acids research 45 (W1), W291-W299, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:isC4tDSrTZIC

Ten-microsecond molecular dynamics simulation of a fast-folding WW domain PL Freddolino, F Liu, M Gruebele, K Schulten Biophysical journal 94 (10), L75-L77, 2008 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:2osOgNQ5qMEC

Challenges in protein-folding simulations PL Freddolino, CB Harrison, Y Liu, K Schulten Nature physics 6 (10), 751-758, 2010 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:hqOjcs7Dif8C

Prediction of structure and function of G protein-coupled receptors N Vaidehi, WB Floriano, R Trabanino, SE Hall, P Freddolino, EJ Choi, … Proceedings of the National Academy of Sciences 99 (20), 12622-12627, 2002 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:u-x6o8ySG0sC

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens N Zhou, Y Jiang, TR Bergquist, AJ Lee, BZ Kacsoh, AW Crocker, … Genome biology 20, 1-23, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:rO6llkc54NcC

Coarse grained protein− lipid model with application to lipoprotein particles AY Shih, A Arkhipov, PL Freddolino, K Schulten The Journal of Physical Chemistry B 110 (8), 3674-3684, 2006 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:9yKSN-GCB0IC

Stability and dynamics of virus capsids described by coarse-grained modeling A Arkhipov, PL Freddolino, K Schulten Structure 14 (12), 1767-1777, 2006 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:IjCSPb-OGe4C

Ab initio protein structure prediction J Lee, PL Freddolino, Y Zhang From protein structure to function with bioinformatics, 3-35, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:M3NEmzRMIkIC

Bacterial adaptation through loss of function AK Hottes, PL Freddolino, A Khare, ZN Donnell, JC Liu, S Tavazoie PLoS genetics 9 (7), e1003617, 2013 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:qUcmZB5y_30C

Force field bias in protein folding simulations PL Freddolino, S Park, B Roux, K Schulten Biophysical journal 96 (9), 3772-3780, 2009 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:Y0pCki6q_DkC

The predicted 3D structure of the human D2 dopamine receptor and the binding site and binding affinities for agonists and antagonists MYS Kalani, N Vaidehi, SE Hall, RJ Trabanino, PL Freddolino, MA Kalani, … Proceedings of the national academy of sciences 101 (11), 3815-3820, 2004 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:UeHWp8X0CEIC

QwikMD—integrative molecular dynamics toolkit for novices and experts JV Ribeiro, RC Bernardi, T Rudack, JE Stone, JC Phillips, PL Freddolino, … Scientific reports 6 (1), 26536, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:j3f4tGmQtD8C

Common structural transitions in explicit-solvent simulations of villin headpiece folding PL Freddolino, K Schulten Biophysical journal 97 (8), 2338-2347, 2009 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:W7OEmFMy1HYC

Assembly of lipoprotein particles revealed by coarse-grained molecular dynamics simulations AY Shih, PL Freddolino, A Arkhipov, K Schulten Journal of structural biology 157 (3), 579-592, 2007 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:zYLM7Y9cAGgC

Introducing a spectrum of long-range genomic deletions in human embryonic stem cells using type I CRISPR-Cas AE Dolan, Z Hou, Y Xiao, MJ Gramelspacher, J Heo, SE Howden, … Molecular cell 74 (5), 936-950. e5, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:SeFeTyx0c_EC

Predicted 3D structure for the human β2 adrenergic receptor and its binding site for agonists and antagonists PL Freddolino, MYS Kalani, N Vaidehi, WB Floriano, SE Hall, … Proceedings of the national academy of sciences 101 (9), 2736-2741, 2004 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:qjMakFHDy7sC

Molecular mechanism of ligand recognition by NR3 subtype glutamate receptors Y Yao, CB Harrison, PL Freddolino, K Schulten, ML Mayer The EMBO journal 27 (15), 2158-2170, 2008 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:LkGwnXOMwfcC

Interfacial Activation of Candida antarctica Lipase B: Combined Evidence from Experiment and Simulation T Zisis, PL Freddolino, P Turunen, MCF van Teeseling, AE Rowan, … Biochemistry 54 (38), 5969-5979, 2015 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=w2MQZREAAAAJ&citationforview=w2MQZREAAAAJ:TQgYirikUcIC

Youtube Videos

Youtube Title: CCMB SEMINAR 10/29/2014 - Peter Freddolino, PhD

Youtube Link: link

Youtube Channel Name: University of Michigan Computational Medicine and Bioinformatics

Youtube Channel Link: https://www.youtube.com/@universityofmichigancomput8877

CCMB SEMINAR 10/29/2014 - Peter Freddolino, PhD

Youtube Title: Large-scale Computational Mapping of Protein-DNA Binding Affinity Landscapes -- Peter Freddolino

Youtube Link: link

Youtube Channel Name: NCSAatIllinois

Youtube Channel Link: https://www.youtube.com/@NCSAatIllinois

Large-scale Computational Mapping of Protein-DNA Binding Affinity Landscapes -- Peter Freddolino

Youtube Title: Lipoproteins that Circulate in the Blood Collecting Fat

Youtube Link: link

Youtube Channel Name: TCBG UIUC

Youtube Channel Link: https://www.youtube.com/@tcbguiuc

Lipoproteins that Circulate in the Blood Collecting Fat

Youtube Title: Folding of a Five-Helix Protein

Youtube Link: link

Youtube Channel Name: TCBG UIUC

Youtube Channel Link: https://www.youtube.com/@tcbguiuc

Folding of a Five-Helix Protein

Youtube Title: Six Microseconds of Protein Folding

Youtube Link: link

Youtube Channel Name: TCBG UIUC

Youtube Channel Link: https://www.youtube.com/@tcbguiuc

Six Microseconds of Protein Folding

Youtube Title: Four Principles of Relationship-Rich Education

Youtube Link: link

Youtube Channel Name: CELatElon

Youtube Channel Link: https://www.youtube.com/@CenterForEngagedLearning

Four Principles of Relationship-Rich Education

Youtube Title: Quantification of Alpha-synuclein Pathology in Fibril-Injected Mice

Youtube Link: link

Youtube Channel Name: PennInstituteonAging

Youtube Channel Link: https://www.youtube.com/@PennInstituteonAging

Quantification of Alpha-synuclein Pathology in Fibril-Injected Mice

Youtube Title: Build-a-Cell seminar Peter Nguyen: Freeze-Dried Cell-Free Synthetic Biology

Youtube Link: link

Youtube Channel Name: Build-a-Cell

Youtube Channel Link: https://www.youtube.com/@build-a-cell2765

Build-a-Cell seminar Peter Nguyen: Freeze-Dried Cell-Free Synthetic Biology

Youtube Title: Will immunology cure all diseases?

Youtube Link: link

Youtube Channel Name: Scienza in rete

Youtube Channel Link: https://www.youtube.com/@scienzainrete

Will immunology cure all diseases?

Youtube Title: RNA Collaborative Seminar Series, May 6, 2020 w/University of Michigan S. Moon and P. Freddolino

Youtube Link: link

Youtube Channel Name: RNA Collaborative Seminar Series

Youtube Channel Link: https://www.youtube.com/@rnacollaborativeseminarser5993

RNA Collaborative Seminar Series, May 6, 2020 w/University of Michigan S. Moon and P. Freddolino

Youtube Title: THEORETICAL AND COMPUTATIONAL NEUROSCIENCE B 11012018

Youtube Link: link

Youtube Channel Name: ELSC Video

Youtube Channel Link: https://www.youtube.com/@elscvideo

THEORETICAL AND COMPUTATIONAL NEUROSCIENCE B 11012018

Youtube Title: Sculpting Proteins: Art + Science (EMMY Winner)

Youtube Link: link

Youtube Channel Name: Beckman Institute at Illinois

Youtube Channel Link: https://www.youtube.com/@BeckmanIllinois

Sculpting Proteins: Art + Science (EMMY Winner)

Youtube Title: Disordered protein controls formation and stability of the bacterial flagellar hook

Youtube Link: link

Youtube Channel Name: BMC

Youtube Channel Link: https://www.youtube.com/@BioMedCentral

Disordered protein controls formation and stability of the bacterial flagellar hook

Youtube Title: M. Marzullo - Functional characterization of pendolino, a Drosophila gene …..

Youtube Link: link

Youtube Channel Name: Icgeb

Youtube Channel Link: https://www.youtube.com/@icgeb

M. Marzullo - Functional characterization of pendolino, a Drosophila gene .....

Youtube Title: Lorentzian Polynomials Lecture 1

Youtube Link: link

Youtube Channel Name: IPAC Seminar

Youtube Channel Link: https://www.youtube.com/@ipacseminar1495

Lorentzian Polynomials Lecture 1

Youtube Title: DrPetersenMicrobiomeTalk_Pt2

Youtube Link: link

Youtube Channel Name: Loren Launen

Youtube Channel Link: https://www.youtube.com/@lorenlaunen3438

Dr_Petersen_MicrobiomeTalk_Pt2