Jeffrey Skolnick

Last updated
Jeffrey Skolnick
Born
Brooklyn, New York
Known forcomputational techniques for protein structure analysis
Awards
Scientific career
Fields
Institutions
Thesis Investigations on a Rod Like Polyelectrolyte Model  (1978)
Doctoral advisor Professor Marshall Fixman; Ph.D.
Website www.biology.gatech.edu/people/jeffrey-skolnick

Jeffrey Skolnick is an American computational biologist. He is currently a Georgia Institute of Technology School of Biology Professor, the Director of the Center for the Study of Systems Biology, the Mary and Maisie Gibson Chair, the Georgia Research Alliance Eminent Scholar in Computational Systems Biology, the Director of the Integrative BioSystems Institute, and was previously the Scientific Advisor at Intellimedix. [1]

Contents

He has focused on the development of computational algorithms and their application to proteomes for the prediction of protein structure and function, the prediction of small molecule ligand-protein interactions with applications to drug discovery, the prediction of off-target uses of existing drugs, and the exploration of the interplay between protein physics and evolution in determining protein structure and function. He is a pioneer in the field of protein structure prediction, including the development of CABS and CAS methods of lattice based conformation sampling, and the algorithms Touchstone II and TASSER.

Skolnick is most known for demonstrating that the number of ligand binding pockets in proteins is quite small, thereby justifying the likelihood that large scale drug repurposing will work. This combined with the ability to use predicted as well as experimental structures in virtual ligand screening at higher accuracy and precision than existing approaches will enable FDA approved drugs with novel mechanisms of action to be identified computationally with a high likelihood of experimental success. [2] [3] [4]

He is also known for his unique teaching methodology and interactive pedagogy to simplify the comprehension of complex concepts in computational chemistry.

Major Discoveries

Completeness of the library of protein structures and interactions

Skolnick was first to demonstrate that the library of single domain protein structures is likely complete and that the observed folds in nature arise from the confinement of dense polymer chains. He further demonstrated that the confinement of these dense polymer chains plus hydrodynamic interactions were the dominant contributor to diffusive processes in cells. Moreover, that the hydrodynamic interactions introduced large scale temporal and spatial correlations that may have important functional consequences. [2] [3] [5] [6]

Ligand homology modeling

He also pioneered the field of ligand homology modeling with his threading based, FINDSITE approach for protein function inference, binding site prediction and virtual ligand screening. The research showed that remotely related proteins identified by threading often share a common ligand binding site occupied by chemically similar ligands that contain strongly conserved anchor functional groups as well as a variable region that accounts for their binding specificity. These insights enable low-resolution predicted structures to be used for ligand screening/binding pose prediction, with comparable accuracy as with high-resolution experimental structures. In virtual ligand screening, the latest version, FINDSITEcomb, was shown to work far better than more traditional virtual screening approaches on both predicted and high resolution experimental structures. [7] [8]

TASSER protein structure prediction

He also developed the TASSER protein structure prediction approach, whose variants were among the top performers in CASP in the 2000s and the basis for the I-TASSER service. TASSER was among the first methods whose models were closer to the native structure than the starting template. [9] [10]

Odijk-Skolnick-Fixman electrostatic persistence length

Skolnick's Ph.D. thesis " “Investigations on a Rod Like Polyelectrolyte Model", along with Fixman and Odijk, developed a theory for the electrostatic persistence length in polyelectrolytes now known as the Odijk-Skolnick-Fixman electrostatic persistence length which is still considered the classical benchmark. [11] [12]

Education

Skolnick graduated summa cum laude from Washington University in 1975 with a Bachelor of Arts degree in chemistry. After Washington University, he moved on to Yale, where he graduated with a Master of Philosophy in Chemistry in 1977 and a Ph.D. in Chemistry just one year later in 1978.

His Ph.D. thesis, “Investigations on a Rod Like Polyelectrolyte Model”, focused on polymer statistical mechanics with Dr. Marshall Fixman. The methods described by Skolnick and Fixman and independently developed by Theo Odijk are still used as the basis for the electrostatic persistence length of polyelectrolytes. [11]

Academic Recognition

Awards

Skolnick has been recognized as a Fellow with the American Association for the Advancement of Science, the Biophysical Society, and the St. Louis Academy of Science. He has also been awarded an Alfred P. Sloan Research Fellowship. [13] [14] [15] [16]

Journals and Editorial Boards

DatesJournal
2012–presentEditorial Board, PeerJ [17]
2012–presentStructural Biology Section Editor, Biology Direct [18]
2011–presentEditorial Board, Current Bioinformatics [19]
2005–presentEditorial Board, Biology Direct [18]
2005–presentEditorial Board, Protein Science [20]
1998–presentEditorial Board, Proteins [21]

He is also a cofounder of an early stage structural proteomics company, GeneFormatics, and his software has been commercialized by Tripos. [22]

Professional career

DatesPosition
2010–presentDirector, Integrated Biosystems Institute, Georgia Institute of Technology [1]
2008–presentMary and Maisie Gibson Chair in Computational Systems Biology [23]
2008–2010Associate Director, Integrated Biosystems Institute, Georgia Institute of Technology [1]
2007–2012Adjunct Professor, School of Chemistry & Biochemistry, Georgia Institute of Technology
2006–presentDirector, Center for the Study of Systems Biology, Georgia Institute of Technology [1]
2006–presentGRA Eminent Scholar, Computational Systems Biology [1]
2006–presentProfessor, School of Biology, Georgia Institute of Technology [24]
2002–2005Director, Buffalo Center of Excellence in Bioinformatics [25]
2002–2005Professor, Structural Biology, University at Buffalo [26]
1999–2002Director, Computational/Structural Biology, Danforth Plant Science Center [27]

Patents

Related Research Articles

<span class="mw-page-title-main">Bioinformatics</span> Computational analysis of large, complex sets of biological data

Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The subsequent process of analyzing and interpreting data is often referred to as computational biology, though the distinction between the two terms is often disputed.

<span class="mw-page-title-main">Protein</span> Biomolecule consisting of chains of amino acid residues

Proteins are large biomolecules and macromolecules that comprise one or more long chains of amino acid residues. Proteins perform a vast array of functions within organisms, including catalysing metabolic reactions, DNA replication, responding to stimuli, providing structure to cells and organisms, and transporting molecules from one location to another. Proteins differ from one another primarily in their sequence of amino acids, which is dictated by the nucleotide sequence of their genes, and which usually results in protein folding into a specific 3D structure that determines its activity.

<span class="mw-page-title-main">Structural bioinformatics</span> Bioinformatics subfield

Structural bioinformatics is the branch of bioinformatics that is related to the analysis and prediction of the three-dimensional structure of biological macromolecules such as proteins, RNA, and DNA. It deals with generalizations about macromolecular 3D structures such as comparisons of overall folds and local motifs, principles of molecular folding, evolution, binding interactions, and structure/function relationships, working both from experimentally solved structures and from computational models. The term structural has the same meaning as in structural biology, and structural bioinformatics can be seen as a part of computational structural biology. The main objective of structural bioinformatics is the creation of new methods of analysing and manipulating biological macromolecular data in order to solve problems in biology and generate new knowledge.

<span class="mw-page-title-main">Binding site</span> Molecule-specific coordinate bonding area in biological systems

In biochemistry and molecular biology, a binding site is a region on a macromolecule such as a protein that binds to another molecule with specificity. The binding partner of the macromolecule is often referred to as a ligand. Ligands may include other proteins, enzyme substrates, second messengers, hormones, or allosteric modulators. The binding event is often, but not always, accompanied by a conformational change that alters the protein's function. Binding to protein binding sites is most often reversible, but can also be covalent reversible or irreversible.

<span class="mw-page-title-main">Drug design</span> Invention of new medications based on knowledge of a biological target

Drug design, often referred to as rational drug design or simply rational design, is the inventive process of finding new medications based on the knowledge of a biological target. The drug is most commonly an organic small molecule that activates or inhibits the function of a biomolecule such as a protein, which in turn results in a therapeutic benefit to the patient. In the most basic sense, drug design involves the design of molecules that are complementary in shape and charge to the biomolecular target with which they interact and therefore will bind to it. Drug design frequently but not necessarily relies on computer modeling techniques. This type of modeling is sometimes referred to as computer-aided drug design. Finally, drug design that relies on the knowledge of the three-dimensional structure of the biomolecular target is known as structure-based drug design. In addition to small molecules, biopharmaceuticals including peptides and especially therapeutic antibodies are an increasingly important class of drugs and computational methods for improving the affinity, selectivity, and stability of these protein-based therapeutics have also been developed.

Protein design is the rational design of new protein molecules to design novel activity, behavior, or purpose, and to advance basic understanding of protein function. Proteins can be designed from scratch or by making calculated variants of a known protein structure and its sequence. Rational protein design approaches make protein-sequence predictions that will fold to specific structures. These predicted sequences can then be validated experimentally through methods such as peptide synthesis, site-directed mutagenesis, or artificial gene synthesis.

<span class="mw-page-title-main">Protein–protein interaction</span> Physical interactions and constructions between multiple proteins

Protein–protein interactions (PPIs) are physical contacts of high specificity established between two or more protein molecules as a result of biochemical events steered by interactions that include electrostatic forces, hydrogen bonding and the hydrophobic effect. Many are physical contacts with molecular associations between chains that occur in a cell or in a living organism in a specific biomolecular context.

<span class="mw-page-title-main">Docking (molecular)</span> Prediction method in molecular modeling

In the field of molecular modeling, docking is a method which predicts the preferred orientation of one molecule to a second when a ligand and a target are bound to each other to form a stable complex. Knowledge of the preferred orientation in turn may be used to predict the strength of association or binding affinity between two molecules using, for example, scoring functions.

Macromolecular docking is the computational modelling of the quaternary structure of complexes formed by two or more interacting biological macromolecules. Protein–protein complexes are the most commonly attempted targets of such modelling, followed by protein–nucleic acid complexes.

Protein–ligand docking is a molecular modelling technique. The goal of protein–ligand docking is to predict the position and orientation of a ligand when it is bound to a protein receptor or enzyme. Pharmaceutical research employs docking techniques for a variety of purposes, most notably in the virtual screening of large databases of available chemicals in order to select likely drug candidates. There has been rapid development in computational ability to determine protein structure with programs such as AlphaFold, and the demand for the corresponding protein-ligand docking predictions is driving implementation of software that can find accurate models. Once the protein folding can be predicted accurately along with how the ligands of various structures will bind to the protein, the ability for drug development to progress at a much faster rate becomes possible.

<span class="mw-page-title-main">Virtual screening</span>

Virtual screening (VS) is a computational technique used in drug discovery to search libraries of small molecules in order to identify those structures which are most likely to bind to a drug target, typically a protein receptor or enzyme.

<span class="mw-page-title-main">Molecular biophysics</span> Interdisciplinary research area

Molecular biophysics is a rapidly evolving interdisciplinary area of research that combines concepts in physics, chemistry, engineering, mathematics and biology. It seeks to understand biomolecular systems and explain biological function in terms of molecular structure, structural organization, and dynamic behaviour at various levels of complexity. This discipline covers topics such as the measurement of molecular forces, molecular associations, allosteric interactions, Brownian motion, and cable theory. Additional areas of study can be found on Outline of Biophysics. The discipline has required development of specialized equipment and procedures capable of imaging and manipulating minute living structures, as well as novel experimental approaches.

In the fields of computational chemistry and molecular modelling, scoring functions are mathematical functions used to approximately predict the binding affinity between two molecules after they have been docked. Most commonly one of the molecules is a small organic compound such as a drug and the second is the drug's biological target such as a protein receptor. Scoring functions have also been developed to predict the strength of intermolecular interactions between two proteins or between protein and DNA.

<span class="mw-page-title-main">Ram Samudrala</span>

Ram Samudrala is a professor of computational biology and bioinformatics at the University at Buffalo, United States. He researches protein folding, structure, function, interaction, design, and evolution.

Computational Resources for Drug Discovery (CRDD) is an important module of the in silico module of Open Source for Drug Discovery (OSDD). The CRDD web portal provides computer resources related to drug discovery, predicting inhibitors, and predicting the ADME-Tox properties of molecules on a single platform. It caters to researchers researching computer-aided drug design by providing computational resources, and hosting a discussion forum. One of the major objectives of CRDD is to promote open source software in the field of cheminformatics and pharmacoinformatics.

Protein function prediction methods are techniques that bioinformatics researchers use to assign biological or biochemical roles to proteins. These proteins are usually ones that are poorly studied or predicted based on genomic sequence data. These predictions are often driven by data-intensive computational procedures. Information may come from nucleic acid sequence homology, gene expression profiles, protein domain structures, text mining of publications, phylogenetic profiles, phenotypic profiles, and protein-protein interaction. Protein function is a broad term: the roles of proteins range from catalysis of biochemical reactions to transport to signal transduction, and a single protein may play a role in multiple processes or cellular pathways.

<span class="mw-page-title-main">Ruth Nussinov</span> Bioinformatician

Ruth Nussinov is an Israeli-American biologist born in Rehovot who works as a professor in the Department of Human Genetics, School of Medicine at Tel Aviv University and is the senior principal scientist and principal investigator at the National Cancer Institute, National Institutes of Health. Nussinov is also the editor in chief of the Current Opinion in Structural Biology and formerly of the journal PLOS Computational Biology.

<span class="mw-page-title-main">I-TASSER</span>

I-TASSER is a bioinformatics method for predicting three-dimensional structure model of protein molecules from amino acid sequences. It detects structure templates from the Protein Data Bank by a technique called fold recognition. The full-length structure models are constructed by reassembling structural fragments from threading templates using replica exchange Monte Carlo simulations. I-TASSER is one of the most successful protein structure prediction methods in the community-wide CASP experiments.

Molecular Operating Environment (MOE) is a drug discovery software platform that integrates visualization, modeling and simulations, as well as methodology development, in one package. MOE scientific applications are used by biologists, medicinal chemists and computational chemists in pharmaceutical, biotechnology and academic research. MOE runs on Windows, Linux, Unix, and macOS. Main application areas in MOE include structure-based design, fragment-based design, ligand-based design, pharmacophore discovery, medicinal chemistry applications, biologics applications, structural biology and bioinformatics, protein and antibody modeling, molecular modeling and simulations, virtual screening, cheminformatics & QSAR. The Scientific Vector Language (SVL) is the built-in command, scripting and application development language of MOE.

FlexAID is a molecular docking software that can use small molecules and peptides as ligands and proteins and nucleic acids as docking targets. As the name suggests, FlexAID supports full ligand flexibility as well side-chain flexibility of the target. It does using a soft scoring function based on the complementarity of the two surfaces.

References

  1. 1 2 3 4 5 "SURA Honors Georgia Tech Biologist as Distinguished Scientist". Proteomics Weekly. NewsRX. March 24, 2014. Archived from the original on March 29, 2015. Retrieved 3 August 2014.
  2. 1 2 Zhang, Y.; Hubner, I. A.; Arakaki, A. K.; Shakhnovich, E.; Skolnick, J. (14 February 2006). "On the origin and highly likely completeness of single-domain protein structures". Proceedings of the National Academy of Sciences. 103 (8): 2605–2610. Bibcode:2006PNAS..103.2605Z. doi: 10.1073/pnas.0509379103 . PMC   1413790 . PMID   16478803.
  3. 1 2 Skolnick, Jeffrey; Zhou, Hongyi; Brylinski, Michal (14 June 2012). "Further Evidence for the Likely Completeness of the Library of Solved Single Domain Protein Structures". The Journal of Physical Chemistry B. 116 (23): 6654–6664. doi:10.1021/jp211052j. PMC   3351587 . PMID   22272723.
  4. ↑ "Protein study suggests drug side effects are inevitable". Drug Discovery Today. May 23, 2013.
  5. ↑ Zhang, Yang (2009). "Protein structure prediction: when is it useful?". Current Opinion in Structural Biology. 19 (2): 145–155. doi:10.1016/j.sbi.2009.02.005. PMC   2673339 . PMID   19327982.
  6. ↑ Saltsman, Kirstie. "Modeling How Molecules Move Inside Cells". National Institutes of Health.
  7. ↑ Brylinski, M.; Skolnick, J. (28 December 2007). "A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation". Proceedings of the National Academy of Sciences. 105 (1): 129–134. doi: 10.1073/pnas.0707684105 . PMC   2224172 . PMID   18165317.
  8. ↑ Karypis, George; Rangwala, Huzefa (March 16, 2011). Introduction to Protein Structure Prediction: Methods and Algorithms. John Wiley & Sons. p. 349. ISBN   978-1-118-09946-9.
  9. ↑ Zhang, Yang; Arakaki, Adrian K.; Skolnick, Jeffrey (2005). "TASSER: An automated method for the prediction of protein tertiary structures in CASP6". Proteins: Structure, Function, and Bioinformatics. 61 (S7): 91–98. doi:10.1002/prot.20724. PMID   16187349. S2CID   16802808.
  10. ↑ Roy, Ambrish; Kucukural, Alper; Zhang, Yang (25 March 2010). "I-TASSER: a unified platform for automated protein structure and function prediction". Nature Protocols. 5 (4): 725–738. doi:10.1038/nprot.2010.5. PMC   2849174 . PMID   20360767.
  11. 1 2 Skolnick, Jeffrey; Fixman, Marshall (1977). "Electrostatic Persistence Length of a Wormlike Polyelectrolyte". Macromolecules. 10 (5): 944–948. Bibcode:1977MaMol..10..944S. doi:10.1021/ma60059a011.
  12. ↑ Dias, Rita; Lindman, Bjorn. DNA Interactions with Polymers and Surfactants. John Wiley & Sons. p. 26.
  13. ↑ "Past Fellows". Alfred P. Sloan Foundation. Archived from the original on 25 February 2016. Retrieved 3 August 2014.
  14. ↑ "AAAS News & Notes". American Association for the Advancement of Science. Archived from the original on 30 July 2012.
  15. ↑ "Fellow of the Biophysical Society Award". biophysics.org. Biophysical Society. Retrieved 3 August 2014.
  16. ↑ "Academy Fellows". academyofsciencestl.org. St. Louis Academy of Science. Retrieved 3 August 2014.
  17. ↑ "Advisory Board and Editors Computational Biology". peerj.com. PeerJ. Retrieved 3 August 2014.
  18. 1 2 "Editorial Board". biologydirect.com. Biology Direct. Retrieved 3 August 2014.
  19. ↑ "Editorial Board ::: Current Bioinformatics". Bentham Science. Retrieved 3 August 2014.
  20. ↑ "Protein Science - Editorial Board - Wiley Online Library". Protein Science. doi:10.1002/(ISSN)1469-896X.
  21. ↑ "Proteins: Structure, Function, and Bioinformatics - Editorial Board - Wiley Online Library". Proteins: Structure, Function, and Bioinformatics. doi:10.1002/(ISSN)1097-0134.
  22. ↑ Hoerr, Dorothy. "The Business of Biotech". Albright College. Retrieved 3 August 2014.
  23. ↑ "DRUG DISCOVERY & THERAPY WORLD CONGRESS 2015". ddtwc.com/. Archived from the original on 11 August 2014. Retrieved 3 August 2014.
  24. ↑ "Georgia Tech Research Suggests Drug Side Effects Are Inevitable". Global Biodefense. Stemar Media Group, LLC. Retrieved 3 August 2014.
  25. ↑ Drury, Tracey (1 March 2004). "Bioinformatics scientists stoked by UB's Simpson". Buffalo Business First. The Business Journals. Retrieved 3 August 2014.
  26. ↑ "UB Honors Pataki, Welcomes New Bioinformatics Director". WBFO Buffalo's NPR News Station. 10 May 2002. Retrieved 3 August 2014.
  27. ↑ Hopkin, Karen (12 June 2002). "Computational Biologists Join the Fold". Bio-ITWorld. Cambridge Healthtech Institute. Retrieved 3 August 2014.
  28. ↑ US 5265030,Skolnick, Jeffrey&Kolinski, Andrzej,"System and method for determining three-dimensional structures of proteins",published 1993-11-23, assigned to Scripps Clinic and Research Foundation
  29. ↑ US 5933819,Skolnick, Jeffrey; Milik, Mariusz& Kolinski, Andrezej,"Prediction of relative binding motifs of biologically active peptides and peptide mimetics",published 1999-08-03, assigned to The Scripps Research Institute
  30. ↑ US 6631332,Skolnick, Jeffrey& Fetrow, Jacquelyn S.,"Methods for using functional site descriptors and predicting protein function",published 2003-10-07, assigned to The Scripps Research Institute
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