Dunbrack Lab

The Dunbrack group concentrates on research in computational structural biology, including homology modeling, fold recognition, molecular dynamics simulations, statistical analysis of the PDB, and bioinformatics. In developing these methods, we use methods from various areas of mathematics and computer science, including Bayesian statistics and computational geometry. We place an emphasis on large-scale benchmarking of new methods and comparison with existing methods. We are interested in applying comparative modeling to important problems in various areas of biology. Areas of particular interest include DNA repair, proteases and other peptide-binding protein families, and membrane proteins.

Our software development uses visual programming environments, such as Visual Studio, and modern programming languages such as object-oriented C++ and C#. Our goal is to produce easy-to-use, professional software for use in our own research as well as to be distributed to other research groups around the world.

The research in my group is concentrated on statistical structural bioinformatics, the development of methods for structure prediction of proteins and protein complexes, and the application of these methods to problems in cancer biology. Our statistical studies include Bayesian and non-parametric statistical analyses of main-chain and side-chain bond angles and dihedral angles used in methods for protein structure prediction and structure determination and validation. We also analyze the structures in particular protein families, such as kinases and antibodies, across the whole structure database to develop insights that will lead to better design of cancer therapeutics. We have performed clustering and statistical analysis of protein-protein interactions in crystals as a means of determining accurate biological assemblies for each PDB entry and developing hypotheses on biologically relevant interactions that may have been overlooked by authors of structures. Our structure prediction methods include recently developed software for predicting the structures of protein complexes including both homo- and heterooligomers based on the biological assemblies of templates in the Protein Data Bank. I believe strongly in making statistical analyses, databases, and software available to all non-profit research groups, which we do via our website, http://dunbrack.fccc.edu.

I am very fortunate to work in one of the country’s leading cancer research centers and cancer hospitals, allowing me to extend and apply my interests in structural biology to problems in cancer biology. Through the Molecular Modeling Facility at Fox Chase, which I direct, we have worked with nearly all of my colleagues who work in every area of cellular or molecular biology, using structure prediction to help them explain or design experiments in any system of interest. With the advent of germline and tumor gene sequencing in cancer patients, I believe there is an opportunity to apply knowledge gained from structural bioinformatics and structure prediction in guiding the design of therapeutics in precision medicine.

1. Statistical functions of the Ramachandran map variables f,y. We have utilized modern methods of statistics to derive density estimates, classification functions, and regression surfaces as functions of the backbone dihedrals f and y for use in structure determination, validation, and prediction as well as protein design. We have derived smoothed density estimates of f,y for each residue type with kernel density estimates using periodic von Mises kernels. These density estimates are used in the program Rosetta to model the torsional energy potential of the backbone. We have also used a hierarchical Bayesian non-parametric method to produce neighbor-dependent Ramachandran maps, which provide f,y densities for each residue type given the amino acid type of its neighbor (http://dunbrack.fccc.edu/ndrd/). These are also used in Rosetta and other programs for loop structure prediction. From density estimates conditional on a discrete variable X (such as residue type or rotamer), p(f,y|X_i), it is possible to derive classification functions of the form p(X_i|f,y) with Bayes’ rule. The backbone-dependent rotamer library is an example of such a function, which I first derived as a graduate student in 1991. We have produced new versions with increasingly powerful statistical methods in a series of papers (1993-2011) with a total of 2,740 citations on Google scholar and 551 licenses (http://dunbrack.fccc.edu/bbdep2010). Finally, we have used kernel regression on f and y to derive smooth functions for backbone bond angles and the dihedral angle w and side-chain bond angles and dihedral angles on data sets of structures with resolution <1.0 Å.
   1. Ting D, Wang G, Shapovalov M, Mitra R, Jordan MI, Dunbrack RL Jr. Neighbor-dependent Ramachandran probability distributions of amino acids developed from a hierarchical Dirichlet process model. PLoS Comput Biol. 2010 Apr 29;6(4):e1000763. PubMed PMID: 20442867; PubMed Central PMCID: PMC2861699.
   2. Shapovalov MV, Dunbrack RL Jr. A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions. Structure. 2011 Jun 8;19(6):844-58. PubMed PMID: 21645855; PubMed Central PMCID: PMC3118414.
   3. Berkholz DS, Driggers CM, Shapovalov MV, Dunbrack RL Jr, Karplus PA. Nonplanar peptide bonds in proteins are common and conserved but not biased toward active sites. Proc Natl Acad Sci U S A. 2012 Jan 10;109(2):449-53. PubMed PMID: 22198840; PubMed Central PMCID: PMC3258596.
   4. Berkholz DS, Shapovalov MV, Dunbrack RL Jr., Karplus PA. Conformation dependence of backbone geometry in proteins. Structure 17:1316-1325, 2009. PMID: 19836332; PMCID: PMC2810841.
2. Algorithms and software for protein structure prediction: We have developed graph-theory based algorithms for prediction of protein side-chain conformations. Our program SCWRL is by far the most used program for prediction of the three-dimensional structure of side chains given an input protein backbone structure. It was published in five papers (1997, 1999, 2003, 2008, 2009) with 2,407 citations and 6,733 licenses. The latest version predicts >97% of c1 dihedral angles within 40° for residues that are buried within protein crystals (http://dunbrack.fccc.edu/scwrl4). We developed the cyclic coordinate descent algorithm (CCD) for closing protein loops during loop modeling procedures. This simple, very fast algorithm is implemented in Rosetta for connecting backbone segments in protein structure prediction. It is also used in many other programs for loop structure prediction (372 citations). Finally, we have developed two graphical user interfaces for structure prediction using SCWRL, one for modeling monomeric protein structures (MolIDE) and one for modeling biological assemblies, such as homo- and heterooligomers (BioAssemblyModeler or BAM). MolIDE was published in two papers (2005, 2008: 142 citations; licensed 2,122 times) and BAM was published in 2014 (335sc licenses). BAM takes up to six protein sequences, assigns protein domain families to the queries, and then searches in a precompiled database of biological assemblies in the Protein Data Bank for any structures that contain one or more of the query domains. With the push of a few buttons, protein oligomeric structures can be predicted with profile-profile alignment and SCWRL side-chain predictions.
   1. Canutescu AA, Dunbrack RL Jr. Cyclic coordinate descent: A robotics algorithm for protein loop closure. Protein Sci. 2003 May;12(5):963-72. PubMed PMID: 12717019; PubMed Central PMCID: PMC2323867.
   2. Wang Q, Canutescu AA, Dunbrack RL Jr. SCWRL and MolIDE: computer programs for side-chain conformation prediction and homology modeling. Nat Protoc. 2008;3(12):1832-47. PubMed PMID: 18989261; PubMed Central PMCID: PMC2682191.
   3. Krivov GG, Shapovalov MV, Dunbrack RL Jr. Improved prediction of protein side-chain conformations with SCWRL4. Proteins. 2009 Dec;77(4):778-95. PubMed PMID: 19603484; PubMed Central PMCID: PMC2885146.
   4. Shapovalov MV, Wang Q, Xu Q, Andrake MD, Dunbrack RL Jr. BioAssemblyModeler (BAM): User-friendly homology modeling of protein homo- and heterooligomers. PLOS ONE 9:e98309, 2014. PMID: 24922057. PubMed Central PMCID: PMC4055448.
3. Biological assemblies of proteins. Many if not most proteins exist in homo- or heterooligomeric assemblies, and these assemblies can be observed in protein crystals. Annotations for biological assemblies in the PDB are less than 80% accurate. One form of evidence in favor of the biological relevance of interfaces in crystals is whether they have been observed in multiple crystal forms of the same protein or of homologous proteins. For example, we found a small dimer of cytosolic sulfotransferases (previously identified in 2001 by crosslinking and mass spec) in 24 crystal forms and 47 PDB entries (currently), but it is contained in only 6 of the deposited biological assemblies in the PDB. We extended this idea to the entire PDB first by benchmarking the idea (2008, 75 citations) and second by developing ProtCID: the Protein Common Interface Database (http://dunbrack2.fccc.edu/protcid). ProtCID provides evidence for correct protein biological assemblies by clustering similar interfaces in multiple crystal forms of homologous proteins or protein pairs, which is highly correlated with biological relevance. The database (48 citations; ~1,000 annual users) is useful for generating novel biological hypotheses, and also contains information on Pfam domains (http://dunbrack2.fccc.edu/ProtCiD/PDBfam/), bound peptides, DNA/RNA, and ligands. One important consequence of knowing the correct biological assemblies of a protein or protein family in the PDB is improving the accuracy of predicting the phenotypes of missense mutations. We demonstrated in 2013 that the location of missense mutations within biological assemblies improves the prediction of missense phenotypes. We have been applying these methods to exome sequencing in clinical research projects at Fox Chase Cancer Center. We have recently used the interface database to identify 15 complexes of kinases in crystals in the act of trans-autophosphorylation, published in Science Signaling.
   1. Xu Q, Canutescu AA, Wang G, Shapovalov M, Obradovic Z, Dunbrack RL Jr. Statistical analysis of interface similarity in crystals of homologous proteins. J Mol Biol. 2008 Aug 29;381(2):487-507. PubMed PMID: 18599072; PubMed Central PMCID: PMC2573399.
   2. Xu Q, Dunbrack RL Jr. The protein common interface database (ProtCID)--a comprehensive database of interactions of homologous proteins in multiple crystal forms. Nucleic Acids Res. 2011 Jan;39(Database issue):D761-70. PubMed PMID: 21036862; PubMed Central PMCID: PMC3013667.
   3. Wei Q, Xu Q, Dunbrack RL Jr. Prediction of phenotypes of missense mutations in human proteins from biological assemblies. Proteins. 2013 Feb;81(2):199-213. PubMed PMID: 22965855; PubMed Central PMCID: PMC3552143.
   4. Xu Q, Malecka KL, Fink L, Jordan JJ, Duffy E, Kolander S, Peterson J, Dunbrack RL Jr. Three-dimensional structures of autophosphorylation complexes in crystals of protein kinases. Science Signaling 8:rs13, 2015. doi:10.1126/scisignal.aaa6711. Pubmed PMID: 26628682. PubMed Central PMCID: PMC4766099.
4. Structural bioinformatics of antibodies and computational antibody design. Antibodies contain six complementarity-determining regions (CDRs), which are highly variable loops that bind antigen. In 2011, we updated the classification and clustering of antibody CDR conformations utilizing strict quality criteria, a dihedral angle metric, and an affinity propagation algorithm (139 citations). This was the first comprehensive classification since Chothia’s efforts in 1997, and the first quantitative clustering of antibody CDRs. We have also presented this as a publicly available database with weekly updates on the PDB (http://dunbrack2.fccc.edu/pyigclassify). We are developing computational methods for antibody design (NIH R01 GM111819). With Jeff Gray at Johns Hopkins, we identified non-antibody protein structures that contain loops resembling the CDR H3 of antibodies, which contains a kink at its C-terminus that was thought to be unusual and specific to antibodies. It is present in at least 600 other protein families, and in PDZ domains appears to play a conserved role in defining the conformation of a loop involved in substrate recognition. These non-antibody loops will provide templates for structure prediction and potentially for computational design of antibodies.
   1. North B, Lehmann A, Dunbrack RL Jr. A new clustering of antibody CDR loop conformations. J Mol Biol. 2011 Feb 18;406(2):228-56. PubMed PMID: 21035459; PubMed Central PMCID: PMC3065967.
   2. Adolf-Bryfogle J, Xu Q, North B, Lehmann A, Dunbrack RL Jr. PyIgClassify: a database of antibody CDR structural classifications. Nucleic Acids Res. 2015 Jan 28;43(Database issue):D432-8. PubMed PMID: 25392411. PubMed Central PMCID: PMC4383924
   3. Weitzner BD, Dunbrack RL Jr*, Gray JJ*. The Origin of CDR H3 Structural Diversity. Structure. 2015 Feb 3;23(2):302-11. PubMed PMID: 25579815; PubMed Central PMCID: PMC4318709.
   4. Lehmann AK, Wixted JHF, Shapovalov MV, Roder H, Dunbrack RL Jr.,* Robinson MK.* Stability engineering of anti-EGFR scFv antibodies by rational design of a l-to-k swap of the VL framework using a structure-guided approach. mAbs 7:1058-1071, 2015. doi:10.1080/19420862.2015.1088618. PubMed PMID: 26337947. PMCID: in process by journal.

*=joint corresponding authors

5. Applications of protein structure prediction to cancer biology. Since 2003, I have directed the Molecular Modeling Facility at Fox Chase, an NCI CCSG supported facility that provides molecular modeling services to all investigators at Fox Chase and Temple University School of Medicine. The Facility discusses new projects with principal investigators to determine what questions are being asked and what role structural information may have in the interpretation of existing experimental data, in the design of new experiments, and in the development of hypotheses in molecular and cellular biological studies. With my colleagues, we have published 41 collaborative papers since 1997 that include the application of protein structure prediction and molecular modeling in biological and clinical contexts. In 2014, I was asked to be the assessor of template-based modeling for the 11th Meeting on the Critical Assessment of Protein Structure Prediction (CASP11). CASP meetings allow groups developing structure prediction methods to make predictions on ~100 protein targets before their structures are available. After the structures become available, they are assessed by experts in the field and the best groups are asked to present their results at the biennial meeting. This year the organizers wanted to emphasize the utility of structure prediction in biological studies and for this reason my group was chosen to perform the assessment since we have a long history in applying structure prediction in many areas of biology.
   1. Plimack ER, Dunbrack RL Jr., Brennan TA, Andrake MD, et al. Defects in DNA Repair Genes Predict Response to Neoadjuvant Cisplatin-based Chemotherapy in Muscle-invasive Bladder Cancer. Eur Urol. 68:959-967, 2015. PubMed PMID: 26238431. PubMed Central PMCID: PMC4764095.
   2. Anastassiadis T, Duong-Ly KC, Deacon SW, Lafontant A, Ma H, Devarajan K, Dunbrack RL Jr., Wu J, Peterson JR. A highly selective dual insulin receptor (IR)/insulin-like growth factor 1 receptor (IGF-1R) inhibitor derived from an extracellular signal-regulated kinase (ERK) inhibitor. J Biol Chem. 2013 Sep 27;288(39):28068-77. PubMed PMID: 23935097; PubMed Central PMCID: PMC3784719.
   3. Huwe PJ, Xu Q, Shapovalov MV, Modi V, Andrake, MD, Dunbrack RL. Biological function derived from predicted structures in CASP11. Proteins 10.1002/prot.24997, 2016. NIHMSID: 754633. PMCID: in process.
   4. Modi V, Xu Q, Adhikari S, Dunbrack RL. Assessment of template-based modeling of protein structure in CASP11. Proteins. 2016 Apr 15. doi: 10.1002/prot.25049. PMID:27081927. NIHMSID: 778476. PMCID: in process.

Complete List of Published Work in My Bibliography