Biomedical subjects

Adrian A Canutescu

Publications and source records attributed to Adrian A Canutescu.

6 recordsLinked to original sources

PubMed2005

Analysis of KIT mutations in sporadic and familial gastrointestinal stromal tumors: therapeutic implications through protein modeling.

PURPOSE: Gastrointestinal stromal tumors (GIST) are characterized by expressing a gain-of-function mutation in KIT, and to a lesser extent, PDGFR. Imatinib mesylate, a tyrosine kinase inhibitor, has activity against GISTs that contain oncogenic mutations of KIT. In this study, KIT and PDGFRalpha mutation status was analyzed and protein modeling approaches were used to assess the potential effect of KIT mutations in response to imatinib therapy. EXPERIMENTAL DESIGN: Genomic DNA was isolated from GIST tumors. Exons 9, 11, 13, and 17 of c-KIT and exons 12, 14, and 18 of PDGFRalpha were evaluated for oncogenic mutations. Protein modeling was used to assess mutations within the juxtamembrane region and the kinase domain of KIT. RESULTS: Mutations in KIT exons 9, 11, and 13 were identified in GISTs with the majority of changes involving the juxtamembrane region of KIT. Molecular modeling indicates that mutations in this region result in disruption of the KIT autoinhibited conformation, and lead to gain-of-function activation of the kinase. Furthermore, a novel germ-line mutation in KIT was identified that is associated with an autosomal dominant predisposition to the development of GIST. CONCLUSIONS: We have used protein modeling and structural analyses to elucidate why patients with GIST tumors containing exon 11 mutations are the most responsive to imatinib mesylate treatment. Importantly, mutations detected in this exon and others displayed constitutive activation of KIT. Furthermore, we have found tumors that are both KIT and PDGFRalpha mutation negative, suggesting that additional, yet unidentified, abnormalities may contribute to GIST tumorigenesis.

Adult↗

PubMed2005

MollDE: a homology modeling framework you can click with.

UNLABELLED: Molecular Integrated Development Environment (MolIDE) is an integrated application designed to provide homology modeling tools and protocols under a uniform, user-friendly graphical interface. Its main purpose is to combine the most frequent modeling steps in a semi-automatic, interactive way, guiding the user from the target protein sequence to the final three-dimensional protein structure. The typical basic homology modeling process is composed of building sequence profiles of the target sequence family, secondary structure prediction, sequence alignment with PDB structures, assisted alignment editing, side-chain prediction and loop building. All of these steps are available through a graphical user interface. MolIDE's user-friendly and streamlined interactive modeling protocol allows the user to focus on the important modeling questions, hiding from the user the raw data generation and conversion steps. MolIDE was designed from the ground up as an open-source, cross-platform, extensible framework. This allows developers to integrate additional third-party programs to MolIDE. AVAILABILITY: http://dunbrack.fccc.edu/molide/molide.php CONTACT: rl_dunbrack@fccc.edu.

Algorithms↗

PubMed2005

Formation of MacroH2A-containing senescence-associated heterochromatin foci and senescence driven by ASF1a and HIRA.

In senescent cells, specialized domains of transcriptionally silent senescence-associated heterochromatic foci (SAHF), containing heterochromatin proteins such as HP1, are thought to repress expression of proliferation-promoting genes. We have investigated the composition and mode of assembly of SAHF and its contribution to cell cycle exit. SAHF is enriched in a transcription-silencing histone H2A variant, macroH2A. As cells approach senescence, a known chromatin regulator, HIRA, enters PML nuclear bodies, where it transiently colocalizes with HP1 proteins, prior to incorporation of HP1 proteins into SAHF. A physical complex containing HIRA and another chromatin regulator, ASF1a, is rate limiting for formation of SAHF and onset of senescence, and ASF1a is required for formation of SAHF and efficient senescence-associated cell cycle exit. These data indicate that HIRA and ASF1a drive formation of macroH2A-containing SAHF and senescence-associated cell cycle exit, via a pathway that appears to depend on flux of heterochromatic proteins through PML bodies.

Amino Acid Sequence↗

PubMed2005

Abrogation of fibroblast activation protein enzymatic activity attenuates tumor growth.

Tumor-associated fibroblasts are functionally and phenotypically distinct from normal fibroblasts that are not in the tumor microenvironment. Fibroblast activation protein is a 95 kDa cell surface glycoprotein expressed by tumor stromal fibroblasts, and has been shown to have dipeptidyl peptidase and collagenase activity. Site-directed mutagenesis at the catalytic site of fibroblast activation protein, Ser624 --> Ala624, resulted in an approximately 100,000-fold loss of fibroblast activation protein dipeptidyl peptidase (DPP) activity. HEK293 cells transfected with wild-type fibroblast activation protein, enzymatic mutant (S624A) fibroblast activation protein, or vector alone, were inoculated subcutaneously into immunodeficient mouse to assess the contribution of fibroblast activation protein enzymatic activity to tumor growth. Overexpression of wild-type fibroblast activation protein showed growth potentiation and enhanced tumorigenicity compared with both fibroblast activation protein S624A and vector-transfected HEK293 xenografts. HEK293 cells transfected with fibroblast activation protein S624A showed tumor growth rates and tumorigenicity potential similar only to vector-transfected HEK293. In vivo assessment of fibroblast activation protein DPP activity of these tumors showed enhanced enzymatic activity of wild-type fibroblast activation protein, with only baseline levels of fibroblast activation protein DPP activity in either fibroblast activation protein S624A or vector-only xenografts. These results indicate that the enzymatic activity of fibroblast activation protein is necessary for fibroblast activation protein-driven tumor growth in the HEK293 xenograft model system. This establishes the proof-of-principle that the enzymatic activity of fibroblast activation protein plays an important role in the promotion of tumor growth, and provides an attractive target for therapeutics designed to alter fibroblast activation protein-induced tumor growth by targeting its enzymatic activity.

Alanine↗

PubMed2003

Cyclic coordinate descent: A robotics algorithm for protein loop closure.

In protein structure prediction, it is often the case that a protein segment must be adjusted to connect two fixed segments. This occurs during loop structure prediction in homology modeling as well as in ab initio structure prediction. Several algorithms for this purpose are based on the inverse Jacobian of the distance constraints with respect to dihedral angle degrees of freedom. These algorithms are sometimes unstable and fail to converge. We present an algorithm developed originally for inverse kinematics applications in robotics. In robotics, an end effector in the form of a robot hand must reach for an object in space by altering adjustable joint angles and arm lengths. In loop prediction, dihedral angles must be adjusted to move the C-terminal residue of a segment to superimpose on a fixed anchor residue in the protein structure. The algorithm, referred to as cyclic coordinate descent or CCD, involves adjusting one dihedral angle at a time to minimize the sum of the squared distances between three backbone atoms of the moving C-terminal anchor and the corresponding atoms in the fixed C-terminal anchor. The result is an equation in one variable for the proposed change in each dihedral. The algorithm proceeds iteratively through all of the adjustable dihedral angles from the N-terminal to the C-terminal end of the loop. CCD is suitable as a component of loop prediction methods that generate large numbers of trial structures. It succeeds in closing loops in a large test set 99.79% of the time, and fails occasionally only for short, highly extended loops. It is very fast, closing loops of length 8 in 0.037 sec on average.

Algorithms↗

PubMed2003

A graph-theory algorithm for rapid protein side-chain prediction.

Fast and accurate side-chain conformation prediction is important for homology modeling, ab initio protein structure prediction, and protein design applications. Many methods have been presented, although only a few computer programs are publicly available. The SCWRL program is one such method and is widely used because of its speed, accuracy, and ease of use. A new algorithm for SCWRL is presented that uses results from graph theory to solve the combinatorial problem encountered in the side-chain prediction problem. In this method, side chains are represented as vertices in an undirected graph. Any two residues that have rotamers with nonzero interaction energies are considered to have an edge in the graph. The resulting graph can be partitioned into connected subgraphs with no edges between them. These subgraphs can in turn be broken into biconnected components, which are graphs that cannot be disconnected by removal of a single vertex. The combinatorial problem is reduced to finding the minimum energy of these small biconnected components and combining the results to identify the global minimum energy conformation. This algorithm is able to complete predictions on a set of 180 proteins with 34342 side chains in <7 min of computer time. The total chi(1) and chi(1 + 2) dihedral angle accuracies are 82.6% and 73.7% using a simple energy function based on the backbone-dependent rotamer library and a linear repulsive steric energy. The new algorithm will allow for use of SCWRL in more demanding applications such as sequence design and ab initio structure prediction, as well addition of a more complex energy function and conformational flexibility, leading to increased accuracy.

Algorithms↗