All-atom empirical potential for molecular modeling and dynamics studies of proteins

New protein parameters are reported for the all-atom empirical energy function in the CHARMM program. The parameter evaluation was based on a self-consistent approach designed to achieve a balance between the internal (bonding) and interaction (nonbonding) terms of the force field and among the solvent-solvent, solvent-solute, and solute-solute interactions. Optimization of the internal parameters used experimental gas-phase geometries, vibrational spectra, and torsional energy surfaces supplemented with ab initio results. The peptide backbone bonding parameters were optimized with respect to data for N-methylacetamide and the alanine dipeptide. The interaction parameters, particularly the atomic charges, were determined by fitting ab initio interaction energies and geometries of complexes between water and model compounds that represented the backbone and the various side chains. In addition, dipole moments, experimental heats and free energies of vaporization, solvation and sublimation, molecular volumes, and crystal pressures and structures were used in the optimization. The resulting protein parameters were tested by applying them to noncyclic tripeptide crystals, cyclic peptide crystals, and the proteins crambin, bovine pancreatic trypsin inhibitor, and carbonmonoxy myoglobin in vacuo and in crystals. A detailed analysis of the relationship between the alanine dipeptide potential energy surface and calculated protein φ, χ angles was made and used in optimizing the peptide group torsional parameters. The results demonstrate that use of ab initio structural and energetic data by themselves are not sufficient to obtain an adequate backbone representation for peptides and proteins in solution and in crystals. Extensive comparisons between molecular dynamics simulations and experimental data for polypeptides and proteins were performed for both structural and dynamic properties. Energy minimization and dynamics simulations for crystals demonstrate that the latter are needed to obtain meaningful comparisons with experimental crystal structures. The presented parameters, in combination with the previously published CHARMM all-atom parameters for nucleic acids and lipids, provide a consistent set for condensed-phase simulations of a wide variety of molecules of biological interest.

Abstract A291: A highly selective dual insulin receptor (IR)/insulin-like growth factor 1 receptor (IGF-1R) inhibitor derived from an ERK inhibitor

Dual inhibitors of the closely related receptor tyrosine kinases insulin-like growth factor 1 receptor (IGF-1R) and insulin receptor (IR) are promising therapeutic agents in cancer. Here we report an unusually selective class of dual inhibitors of IGF-1R and IR identified in a parallel screen of known kinase inhibitors against a panel of 300 human protein kinases. Biochemical and structural studies indicate that this class achieves its high selectivity by binding to the ATP-binding pocket of inactive, unphosphorylated IGF-1R/IR and stabilizing the activation loop in a native-like inactive conformation. One member of this compound family was originally reported as an inhibitor of the serine/threonine kinase ERK, a kinase that is distinct in the structure of its unphosphorylated/inactive form from IR/IGF-1R. Remarkably, this compound binds to the ATP-binding pocket of ERK in an entirely different conformation to that of IGF-1R/IR, explaining the potency against these two structurally distinct kinase families. These findings suggest a novel approach to polypharmacology in which two or more unrelated kinases are inhibited by a single compound that targets different conformations of each target kinase.

Citation Information: Mol Cancer Ther 2013;12(11 Suppl):A291.

Citation Format: Krisna C. Duong-Ly, Theonie Anastassiadis, Sean W. Deacon, Alec Lafontant, Haiching Ma, Karthik Devarajan, Roland L. Dunbrack, Jinhua Wu, Jeffrey R. Peterson. A highly selective dual insulin receptor (IR)/insulin-like growth factor 1 receptor (IGF-1R) inhibitor derived from an ERK inhibitor. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr A291.

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

Dual inhibitors of the closely related receptor tyrosine kinases insulin-like growth factor 1 receptor (IGF-1R) and insulin receptor (IR) are promising therapeutic agents in cancer. Here, we report an unusually selective class of dual inhibitors of IGF-1R and IR identified in a parallel screen of known kinase inhibitors against a panel of 300 human protein kinases. Biochemical and structural studies indicate that this class achieves its high selectivity by binding to the ATP-binding pocket of inactive, unphosphorylated IGF-1R/IR and stabilizing the activation loop in a native-like inactive conformation. One member of this compound family was originally reported as an inhibitor of the serine/threonine kinase ERK, a kinase that is distinct in the structure of its unphosphorylated/inactive form from IR/IGF-1R. Remarkably, this compound binds to the ATP-binding pocket of ERK in an entirely different conformation to that of IGF-1R/IR, explaining the potency against these two structurally distinct kinase families. These findings suggest a novel approach to polypharmacology in which two or more unrelated kinases are inhibited by a single compound that targets different conformations of each target kinase.

The role of balanced training and testing data sets for binary classifiers in bioinformatics

Training and testing of conventional machine learning models on binary classification problems depend on the proportions of the two outcomes in the relevant data sets. This may be especially important in practical terms when real-world applications of the classifier are either highly imbalanced or occur in unknown proportions. Intuitively, it may seem sensible to train machine learning models on data similar to the target data in terms of proportions of the two binary outcomes. However, we show that this is not the case using the example of prediction of deleterious and neutral phenotypes of human missense mutations in human genome data, for which the proportion of the binary outcome is unknown. Our results indicate that using balanced training data (50% neutral and 50% deleterious) results in the highest balanced accuracy (the average of True Positive Rate and True Negative Rate), Matthews correlation coefficient, and area under ROC curves, no matter what the proportions of the two phenotypes are in the testing data. Besides balancing the data by undersampling the majority class, other techniques in machine learning include oversampling the minority class, interpolating minority-class data points and various penalties for misclassifying the minority class. However, these techniques are not commonly used in either the missense phenotype prediction problem or in the prediction of disordered residues in proteins, where the imbalance problem is substantial. The appropriate approach depends on the amount of available data and the specific problem at hand.

Charge asymmetry in the proteins of the outer membrane

MOTIVATION

Outer membrane beta-barrels (OMBBs) are the proteins found in the outer membrane of bacteria, mitochondria and chloroplasts. There are thousands of beta-barrels reported in genomic databases with ∼2-3% of the genes in gram-negative bacteria encoding these proteins. These proteins have a wide variety of biological functions including active and passive transport, cell adhesion, catalysis and structural anchoring. Of the non-redundant OMBB structures in the Protein Data Bank, half have been solved during the past 5 years. This influx of information provides new opportunities for understanding the chemistry of these proteins. The distribution of charges in proteins in the outer membrane has implications for how the mechanism of outer membrane protein insertion is understood. Understanding the distribution of charges might also assist in organism selection for the heterologous expression of mitochondrial OMBBs.

RESULTS

We find a strong asymmetry in the charge distribution of these proteins. For the outward-facing residues of the beta-barrel within regions of similar amino acid density for both membrane leaflets, the external side of the outer membrane contains almost three times the number of charged residues as the internal side of the outer membrane. Moreover, the lipid bilayer of the outer membrane is asymmetric, and the overall preference for amino acid types to be in the external leaflet of the membrane correlates roughly with the hydrophobicity of the membrane lipids. This preference is demonstrably related to the difference in lipid composition of the external and internal leaflets of the membrane.

Abstract 1683: Autophagy inhibition for cancer therapy

In tumor but not normal cells, the stress-inducible heat shock protein 70 (HSP70) is abundantly expressed and becomes incorporated into lysosome membranes, where it serves to stabilize and protect lysosome function. Inhibition of HSP70 thus results in defective lysosome function, along with impaired autophagy. We previously reported that the HSP70 inhibitor phenylethynesulfonamide (PES) is a potent and effective autophagy inhibitor (1). PES shows marked cytotoxicity to tumor cells, but minimal effects in normal, non-transformed cells. We previously reported that PES binds to both HSP70 and to the constitutively-expressed family member HSC70; these are critical co-chaperones for HSP90, and we previously reported that treatment of tumor cells with PES leads to decreased function of HSP90 client proteins like HER2, AKT and CDK4 (2). We have recently performed a preliminary structure-activity relationship for PES. These studies revealed a novel PES analogue that we call PES-Cl, which shows 10-fold decreased IC50 for tumor cells, minimal cytotoxicity to normal cells, and a greatly enhanced ability to inhibit autophagy. In vitro we show that PES-Cl is cytotoxic to melanoma cells, including those with both intrinsic and acquired resistance to BRAF inhibitors, but shows minimal cytotoxicity to primary melanocytes. In a pre-clinical model for B-cell lymphoma (Eu-myc transgenic mouse), we show that PES-Cl demonstrates significant ability to extend the life of mice (p=0.000006), with no evidence for liver pathology or toxicity. We report that PES binds to the substrate-binding domain of HSP70, and requires the C-terminal helical ‘lid’ of this protein (amino acids 573-616) in order to bind. Using molecular modeling and in silico docking, we have identified a candidate binding site for PES in HSP70, and we identify point mutants that fail to interact with this compound. Our cell cycle analyses of PES-Cl-treated cells show that this compound induces G2/M arrest. Interestingly, we also show that this HSP70 inhibitor impairs the activity of the Anaphase Promoting Complex/Cyclosome (APC/C) in cell-free extracts. PES-Cl is thus a promising new HSP70 inhibitor that binds to the C-terminal ‘lid’ of HSP70, and multiple mechanisms of action: inhibition of autophagy, inhibition of HSP90 function, and inhibition of the Anaphase Promoting Complex/Cyclosome (APC/C).

1. Leu et al, Molecular Cell, 6(1):15-27, 2009

2. Leu et al, Molecular Cancer Research, 9(7):936-47, 2011

Citation Format: Gregor Balaburski, Julia I-Ju Leu, Neil Beeharry, Seth Hayik, Mark D. Andrake, Gao Zhang, Meenhard Herlyn, Jessie Villanueva, Roland L. Dunbrack, Tim Yen, Donna L. George, Maureen E. Murphy. Autophagy inhibition for cancer therapy. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1683. doi:10.1158/1538-7445.AM2013-1683

A new model for allosteric regulation of phenylalanine hydroxylase: implications for disease and therapeutics

The structural basis for allosteric regulation of phenylalanine hydroxylase (PAH), whose dysfunction causes phenylketonuria (PKU), is poorly understood. A new morpheein model for PAH allostery is proposed to consist of a dissociative equilibrium between two architecturally different tetramers whose interconversion requires a ∼90° rotation between the PAH catalytic and regulatory domains, the latter of which contains an ACT domain. This unprecedented model is supported by in vitro data on purified full length rat and human PAH. The conformational change is both predicted to and shown to render the tetramers chromatographically separable using ion exchange methods. One novel aspect of the activated tetramer model is an allosteric phenylalanine binding site at the intersubunit interface of ACT domains. Amino acid ligand-stabilized ACT domain dimerization follows the multimerization and ligand binding behavior of ACT domains present in other proteins in the PDB. Spectroscopic, chromatographic, and electrophoretic methods demonstrate a PAH equilibrium consisting of two architecturally distinct tetramers as well as dimers. We postulate that PKU-associated mutations may shift the PAH quaternary structure equilibrium in favor of the low activity assemblies. Pharmacological chaperones that stabilize the ACT:ACT interface can potentially provide PKU patients with a novel small molecule therapeutic.

Aurora A kinase (AURKA) in normal and pathological cell division

Temporally and spatially controlled activation of the Aurora A kinase (AURKA) regulates centrosome maturation, entry into mitosis, formation and function of the bipolar spindle, and cytokinesis. Genetic amplification and mRNA and protein overexpression of Aurora A are common in many types of solid tumor, and associated with aneuploidy, supernumerary centrosomes, defective mitotic spindles, and resistance to apoptosis. These properties have led Aurora A to be considered a high-value target for development of cancer therapeutics, with multiple agents currently in early-phase clinical trials. More recently, identification of additional, non-mitotic functions and means of activation of Aurora A during interphase neurite elongation and ciliary resorption have significantly expanded our understanding of its function, and may offer insights into the clinical performance of Aurora A inhibitors. Here we review the mitotic and non-mitotic functions of Aurora A, discuss Aurora A regulation in the context of protein structural information, and evaluate progress in understanding and inhibiting Aurora A in cancer.

Architecture and assembly of HIV integrase multimers in the absence of DNA substrates

We have applied small angle x-ray scattering and protein cross-linking coupled with mass spectrometry to determine the architectures of full-length HIV integrase (IN) dimers in solution. By blocking interactions that stabilize either a core-core domain interface or N-terminal domain intermolecular contacts, we show that full-length HIV IN can form two dimer types. One is an expected dimer, characterized by interactions between two catalytic core domains. The other dimer is stabilized by interactions of the N-terminal domain of one monomer with the C-terminal domain and catalytic core domain of the second monomer as well as direct interactions between the two C-terminal domains. This organization is similar to the "reaching dimer" previously described for wild type ASV apoIN and resembles the inner, substrate binding dimer in the crystal structure of the PFV intasome. Results from our small angle x-ray scattering and modeling studies indicate that in the absence of its DNA substrate, the HIV IN tetramer assembles as two stacked reaching dimers that are stabilized by core-core interactions. These models of full-length HIV IN provide new insight into multimer assembly and suggest additional approaches for enzyme inhibition.

A modified HSP70 inhibitor shows broad activity as an anticancer agent

The stress-induced HSP70 is an ATP-dependent molecular chaperone that plays a key role in refolding misfolded proteins and promoting cell survival following stress. HSP70 is marginally expressed in nontransformed cells, but is greatly overexpressed in tumor cells. Silencing HSP70 is uniformly cytotoxic to tumor but not normal cells; therefore, there has been great interest in the development of HSP70 inhibitors for cancer therapy. Here, we report that the HSP70 inhibitor 2-phenylethynesulfonamide (PES) binds to the substrate-binding domain of HSP70 and requires the C-terminal helical "lid" of this protein (amino acids 573-616) to bind. Using molecular modeling and in silico docking, we have identified a candidate binding site for PES in this region of HSP70, and we identify point mutants that fail to interact with PES. A preliminary structure-activity relationship analysis has revealed a derivative of PES, 2-(3-chlorophenyl) ethynesulfonamide (PES-Cl), which shows increased cytotoxicity and ability to inhibit autophagy, along with significantly improved ability to extend the life of mice with pre-B-cell lymphoma, compared with the parent compound (P = 0.015). Interestingly, we also show that these HSP70 inhibitors impair the activity of the anaphase promoting complex/cyclosome (APC/C) in cell-free extracts, and induce G2-M arrest and genomic instability in cancer cells. PES-Cl is thus a promising new anticancer compound with several notable mechanisms of action.

Prediction of phenotypes of missense mutations in human proteins from biological assemblies

Single nucleotide polymorphisms (SNPs) are the most frequent variation in the human genome. Nonsynonymous SNPs that lead to missense mutations can be neutral or deleterious, and several computational methods have been presented that predict the phenotype of human missense mutations. These methods use sequence-based and structure-based features in various combinations, relying on different statistical distributions of these features for deleterious and neutral mutations. One structure-based feature that has not been studied significantly is the accessible surface area within biologically relevant oligomeric assemblies. These assemblies are different from the crystallographic asymmetric unit for more than half of X-ray crystal structures. We find that mutations in the core of proteins or in the interfaces in biological assemblies are significantly more likely to be disease-associated than those on the surface of the biological assemblies. For structures with more than one protein in the biological assembly (whether the same sequence or different), we find the accessible surface area from biological assemblies provides a statistically significant improvement in prediction over the accessible surface area of monomers from protein crystal structures (P = 6e-5). When adding this information to sequence-based features such as the difference between wildtype and mutant position-specific profile scores, the improvement from biological assemblies is statistically significant but much smaller (P = 0.018). Combining this information with sequence-based features in a support vector machine leads to 82% accuracy on a balanced dataset of 50% disease-associated mutations from SwissVar and 50% neutral mutations from human/primate sequence differences in orthologous proteins.

Assignment of protein sequences to existing domain and family classification systems: Pfam and the PDB

MOTIVATION

Automating the assignment of existing domain and protein family classifications to new sets of sequences is an important task. Current methods often miss assignments because remote relationships fail to achieve statistical significance. Some assignments are not as long as the actual domain definitions because local alignment methods often cut alignments short. Long insertions in query sequences often erroneously result in two copies of the domain assigned to the query. Divergent repeat sequences in proteins are often missed.

RESULTS

We have developed a multilevel procedure to produce nearly complete assignments of protein families of an existing classification system to a large set of sequences. We apply this to the task of assigning Pfam domains to sequences and structures in the Protein Data Bank (PDB). We found that HHsearch alignments frequently scored more remotely related Pfams in Pfam clans higher than closely related Pfams, thus, leading to erroneous assignment at the Pfam family level. A greedy algorithm allowing for partial overlaps was, thus, applied first to sequence/HMM alignments, then HMM-HMM alignments and then structure alignments, taking care to join partial alignments split by large insertions into single-domain assignments. Additional assignment of repeat Pfams with weaker E-values was allowed after stronger assignments of the repeat HMM. Our database of assignments, presented in a database called PDBfam, contains Pfams for 99.4% of chains >50 residues.

AVAILABILITY

The Pfam assignment data in PDBfam are available at http://dunbrack2.fccc.edu/ProtCid/PDBfam, which can be searched by PDB codes and Pfam identifiers. They will be updated regularly.

Assignment of protonation states in proteins and ligands: combining pKa prediction with hydrogen bonding network optimization

Among the many applications of molecular modeling, drug design is probably the one with the highest demands on the accuracy of the underlying structures. During lead optimization, the position of every atom in the binding site should ideally be known with high precision to identify those chemical modifications that are most likely to increase drug affinity. Unfortunately, X-ray crystallography at common resolution yields an electron density map that is too coarse, since the chemical elements and their protonation states cannot be fully resolved.This chapter describes the steps required to fill in the missing knowledge, by devising an algorithm that can detect and resolve the ambiguities. First, the pK (a) values of acidic and basic groups are predicted. Second, their potential protonation states are determined, including all permutations (considering for example protons that can jump between the oxygens of a phosphate group). Third, those groups of atoms are identified that can adopt alternative but indistinguishable conformations with essentially the same electron density. Fourth, potential hydrogen bond donors and acceptors are located. Finally, all these data are combined in a single "configuration energy function," whose global minimum is found with the SCWRL algorithm, which employs dead-end elimination and graph theory. As a result, one obtains a complete model of the protein and its bound ligand, with ambiguous groups rotated to the best orientation and with protonation states assigned considering the current pH and the H-bonding network. An implementation of the algorithm has been available since 2008 as part of the YASARA modeling & simulation program.

Protein-intrinsic and signaling network-based sources of resistance to EGFR- and ErbB family-targeted therapies in head and neck cancer

Agents targeting EGFR and related ErbB family proteins are valuable therapies for the treatment of many cancers. For some tumor types, including squamous cell carcinomas of the head and neck (SCCHN), antibodies targeting EGFR were the first protein-directed agents to show clinical benefit, and remain a standard component of clinical strategies for management of the disease. Nevertheless, many patients display either intrinsic or acquired resistance to these drugs; hence, major research goals are to better understand the underlying causes of resistance, and to develop new therapeutic strategies that boost the impact of EGFR/ErbB inhibitors. In this review, we first summarize current standard use of EGFR inhibitors in the context of SCCHN, and described new agents targeting EGFR currently moving through pre-clinical and clinical development. We then discuss how changes in other transmembrane receptors, including IGF1R, c-Met, and TGF-β, can confer resistance to EGFR-targeted inhibitors, and discuss new agents targeting these proteins. Moving downstream, we discuss critical EGFR-dependent effectors, including PLC-γ; PI3K and PTEN; SHC, GRB2, and RAS and the STAT proteins, as factors in resistance to EGFR-directed inhibitors and as alternative targets of therapeutic inhibition. We summarize alternative sources of resistance among cellular changes that target EGFR itself, through regulation of ligand availability, post-translational modification of EGFR, availability of EGFR partners for hetero-dimerization and control of EGFR intracellular trafficking for recycling versus degradation. Finally, we discuss new strategies to identify effective therapeutic combinations involving EGFR-targeted inhibitors, in the context of new system level data becoming available for analysis of individual tumors.

Minimal ensembles of side chain conformers for modeling protein-protein interactions

The goal of this article is to reduce the complexity of the side chain search within docking problems. We apply six methods of generating side chain conformers to unbound protein structures and determine their ability of obtaining the bound conformation in small ensembles of conformers. Methods are evaluated in terms of the positions of side chain end groups. Results for 68 protein complexes yield two important observations. First, the end-group positions change less than 1 Å on association for over 60% of interface side chains. Thus, the unbound protein structure carries substantial information about the side chains in the bound state, and the inclusion of the unbound conformation into the ensemble of conformers is very beneficial. Second, considering each surface side chain separately in its protein environment, small ensembles of low-energy states include the bound conformation for a large fraction of side chains. In particular, the ensemble consisting of the unbound conformation and the two highest probability predicted conformers includes the bound conformer with an accuracy of 1 Å for 78% of interface side chains. As more than 60% of the interface side chains have only one conformer and many others only a few, these ensembles of low-energy states substantially reduce the complexity of side chain search in docking problems. This approach was already used for finding pockets in protein-protein interfaces that can bind small molecules to potentially disrupt protein-protein interactions. Side-chain search with the reduced search space will also be incorporated into protein docking algorithms.

A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions

Rotamer libraries are used in protein structure determination, prediction, and design. The backbone-dependent rotamer library consists of rotamer frequencies, mean dihedral angles, and variances as a function of the backbone dihedral angles. Structure prediction and design methods that employ backbone flexibility would strongly benefit from smoothly varying probabilities and angles. A new version of the backbone-dependent rotamer library has been developed using adaptive kernel density estimates for the rotamer frequencies and adaptive kernel regression for the mean dihedral angles and variances. This formulation allows for evaluation of the rotamer probabilities, mean angles, and variances as a smooth and continuous function of phi and psi. Continuous probability density estimates for the nonrotameric degrees of freedom of amides, carboxylates, and aromatic side chains have been modeled as a function of the backbone dihedrals and rotamers of the remaining degrees of freedom. New backbone-dependent rotamer libraries at varying levels of smoothing are available from http://dunbrack.fccc.edu.

Testing computational prediction of missense mutation phenotypes: functional characterization of 204 mutations of human cystathionine beta synthase

Predicting the phenotypes of missense mutations uncovered by large-scale sequencing projects is an important goal in computational biology. High-confidence predictions can be an aid in focusing experimental and association studies on those mutations most likely to be associated with causative relationships between mutation and disease. As an aid in developing these methods further, we have derived a set of random mutations of the enzymatic domains of human cystathionine beta synthase. This enzyme is a dimeric protein that catalyzes the condensation of serine and homocysteine to produce cystathionine. Yeast missing this enzyme cannot grow on medium lacking a source of cysteine, while transfection of functional human CBS into yeast strains missing endogenous enzyme can successfully complement for the missing gene. We used PCR mutagenesis with error-prone Taq polymerase to produce 948 colonies and compared cell growth in the presence or absence of a cysteine source as a measure of CBS function. We were able to infer the phenotypes of 204 single-site mutants, 79 of them deleterious and 125 neutral. This set was used to test the accuracy of six publicly available prediction methods for phenotype prediction of missense mutations: SIFT, PolyPhen, PMut, SNPs3D, PhD-SNP, and nsSNPAnalyzer. The top methods are PolyPhen, SIFT, and nsSNPAnalyzer, which have similar performance. Using kernel discriminant functions, we found that the difference in position-specific scoring matrix values is more predictive than the wild-type PSSM score alone, and that the relative surface area in the biologically relevant complex is more predictive than that of the monomeric proteins.

The protein common interface database (ProtCID)--a comprehensive database of interactions of homologous proteins in multiple crystal forms

The protein common interface database (ProtCID) is a database that contains clusters of similar homodimeric and heterodimeric interfaces observed in multiple crystal forms (CFs). Such interfaces, especially of homologous but non-identical proteins, have been associated with biologically relevant interactions. In ProtCID, protein chains in the protein data bank (PDB) are grouped based on their PFAM domain architectures. For a single PFAM architecture, all the dimers present in each CF are constructed and compared with those in other CFs that contain the same domain architecture. Interfaces occurring in two or more CFs comprise an interface cluster in the database. The same process is used to compare heterodimers of chains with different domain architectures. By examining interfaces that are shared by many homologous proteins in different CFs, we find that the PDB and the Protein Interfaces, Surfaces, and Assemblies (PISA) are not always consistent in their annotations of biological assemblies in a homologous family. Our data therefore provide an independent check on publicly available annotations of the structures of biological interactions for PDB entries. Common interfaces may also be useful in studies of protein evolution. Coordinates for all interfaces in a cluster are downloadable for further analysis. ProtCiD is available at http://dunbrack2.fccc.edu/protcid.

A new clustering of antibody CDR loop conformations

Previous analyses of the complementarity-determining regions (CDRs) of antibodies have focused on a small number of "canonical" conformations for each loop. This is primarily the result of the work of Chothia and coworkers, most recently in 1997. Because of the widespread utility of antibodies, we have revisited the clustering of conformations of the six CDR loops with the much larger amount of structural information currently available. In this work, we were careful to use a high-quality data set by eliminating low-resolution structures and CDRs with high B-factors or high conformational energies. We used a distance function based on directional statistics and an effective clustering algorithm with affinity propagation. With this data set of over 300 nonredundant antibody structures, we were able to cover 28 CDR-length combinations (e.g., L1 length 11, or "L1-11" in our CDR-length nomenclature) for L1, L2, L3, H1, and H2. The Chothia analysis covered only 20 CDR-lengths. Only four of these had more than one conformational cluster, of which two could easily be distinguished by gene source (mouse/human; κ/λ) and one could easily be distinguished purely by the presence and the positions of Pro residues (L3-9). Thus, using the Chothia analysis does not require the complicated set of "structure-determining residues" that is often assumed. Of our 28 CDR-lengths, 15 have multiple conformational clusters, including 10 for which the Chothia analysis had only one canonical class. We have a total of 72 clusters for non-H3 CDRs; approximately 85% of the non-H3 sequences can be assigned to a conformational cluster based on gene source and/or sequence. We found that earlier predictions of "bulged" versus "nonbulged" conformations based on the presence or the absence of anchor residues Arg/Lys94 and Asp101 of H3 have not held up, since all four combinations lead to a majority of conformations that are bulged. Thus, the earlier analyses have been significantly enhanced by the increased data. We believe that the new classification will lead to improved methods for antibody structure prediction and design.

Rapid calcium-dependent activation of Aurora-A kinase

Oncogenic hyperactivation of the mitotic kinase Aurora-A (AurA) in cancer is associated with genomic instability. Increasing evidence indicates that AurA also regulates critical processes in normal interphase cells, but the source of such activity has been obscure. We report here that multiple stimuli causing release of Ca²⁺ from intracellular endoplasmic reticulum stores rapidly and transiently activate AurA, without requirement for second messengers. This activation is mediated by direct Ca²⁺-dependent calmodulin (CaM) binding to multiple motifs on AurA. On the basis of structure–function analysis and molecular modelling, we map two primary regions of CaM-AurA interaction to unfolded sequences in the AurA N- and C-termini. This unexpected mechanism for AurA activation provides a new context for evaluating the function of AurA and its inhibitors in normal and cancerous cells.

Aurora-A kinase localizes to centrosomes, is involved in the progression through mitosis and is overexpressed in certain cancers. Here, calcium is shown to induce Aurora-A auto-phosphorylation in a calmodulin-dependent manner, suggesting a novel role for Aurora-A in non-mitotic cells.

Development of a rotamer library for use in beta-peptide foldamer computational design

Foldamers present a particularly difficult challenge for accurate computational design compared to the case for conventional peptide and protein design due to the lack of a large body of structural data to allow parametrization of rotamer libraries and energies. We therefore explored the use of molecular mechanics for constructing rotamer libraries for non-natural foldamer backbones. We first evaluated the accuracy of molecular mechanics (MM) for the prediction of rotamer probability distributions in the crystal structures of proteins is explored. The van der Waals radius, dielectric constant and effective Boltzmann temperature were systematically varied to maximize agreement with experimental data. Boltzmann-weighted probabilities from these molecular mechanics energies compare well with database-derived probabilities for both an idealized alpha-helix (R = 0.95) as well as beta-strand conformations (R = 0.92). Based on these parameters, de novo rotamer probabilities for secondary structures of peptides built from beta-amino acids were determined. To limit computational complexity, it is useful to establish a residue-specific criterion for excluding rare, high-energy rotamers from the library. This is accomplished by including only those rotamers with probability greater than a given threshold (e.g., 10%) of the random value, defined as 1/n where n is the number of potential rotamers for each residue type.

PONDR-FIT: a meta-predictor of intrinsically disordered amino acids

Protein intrinsic disorder is becoming increasingly recognized in proteomics research. While lacking structure, many regions of disorder have been associated with biological function. There are many different experimental methods for characterizing intrinsically disordered proteins and regions; nevertheless, the prediction of intrinsic disorder from amino acid sequence remains a useful strategy especially for many large-scale proteomic investigations. Here we introduced a consensus artificial neural network (ANN) prediction method, which was developed by combining the outputs of several individual disorder predictors. By eight-fold cross-validation, this meta-predictor, called PONDR-FIT, was found to improve the prediction accuracy over a range of 3 to 20% with an average of 11% compared to the single predictors, depending on the datasets being used. Analysis of the errors shows that the worst accuracy still occurs for short disordered regions with less than ten residues, as well as for the residues close to order/disorder boundaries. Increased understanding of the underlying mechanism by which such meta-predictors give improved predictions will likely promote the further development of protein disorder predictors. Access to PONDR-FIT is available at www.disprot.org.

Improved prediction of protein side-chain conformations with SCWRL4

Determination of side-chain conformations is an important step in protein structure prediction and protein design. Many such methods have been presented, although only a small number are in widespread use. SCWRL is one such method, and the SCWRL3 program (2003) has remained popular because of its speed, accuracy, and ease-of-use for the purpose of homology modeling. However, higher accuracy at comparable speed is desirable. This has been achieved in a new program SCWRL4 through: (1) a new backbone-dependent rotamer library based on kernel density estimates; (2) averaging over samples of conformations about the positions in the rotamer library; (3) a fast anisotropic hydrogen bonding function; (4) a short-range, soft van der Waals atom-atom interaction potential; (5) fast collision detection using k-discrete oriented polytopes; (6) a tree decomposition algorithm to solve the combinatorial problem; and (7) optimization of all parameters by determining the interaction graph within the crystal environment using symmetry operators of the crystallographic space group. Accuracies as a function of electron density of the side chains demonstrate that side chains with higher electron density are easier to predict than those with low-electron density and presumed conformational disorder. For a testing set of 379 proteins, 86% of chi(1) angles and 75% of chi(1+2) angles are predicted correctly within 40 degrees of the X-ray positions. Among side chains with higher electron density (25-100th percentile), these numbers rise to 89 and 80%. The new program maintains its simple command-line interface, designed for homology modeling, and is now available as a dynamic-linked library for incorporation into other software programs.