Biomedical subjects

C A Floudas

Publications and source records attributed to C A Floudas.

10 recordsLinked to original sources

PubMed2006

Novel approach for alpha-helical topology prediction in globular proteins: generation of interhelical restraints.

The protein folding problem represents one of the most challenging problems in computational biology. Distance constraints and topology predictions can be highly useful for the folding problem in reducing the conformational space that must be searched by deterministic algorithms to find a protein structure of minimum conformational energy. We present a novel optimization framework for predicting topological contacts and generating interhelical distance restraints between hydrophobic residues in alpha-helical globular proteins. It should be emphasized that since the model does not make assumptions about the form of the helices, it is applicable to all alpha-helical proteins, including helices with kinks and irregular helices. This model aims at enhancing the ASTRO-FOLD protein folding approach of Klepeis and Floudas (Journal of Computational Chemistry 2003;24:191-208), which finds the structure of global minimum conformational energy via a constrained nonlinear optimization problem. The proposed topology prediction model was evaluated on 26 alpha-helical proteins ranging from 2 to 8 helices and 35 to 159 residues, and the best identified average interhelical distances corresponding to the predicted contacts fell below 11 A in all 26 of these systems. Given the positive results of applying the model to several protein systems, the importance of interhelical hydrophobic-to-hydrophobic contacts in determining the folding of alpha-helical globular proteins is highlighted.

Animals↗

PubMed2006

A novel high resolution Calpha--Calpha distance dependent force field based on a high quality decoy set.

This work presents a novel C(alpha)--C(alpha) distance dependent force field which is successful in selecting native structures from an ensemble of high resolution near-native conformers. An enhanced and diverse protein set, along with an improved decoy generation technique, contributes to the effectiveness of this potential. High quality decoys were generated for 1489 nonhomologous proteins and used to train an optimization based linear programming formulation. The goal in developing a set of high resolution decoys was to develop a simple, distance-dependent force field that yields the native structure as the lowest energy structure and assigns higher energies to decoy structures that are quite similar as well as those that are less similar. The model also includes a set of physical constraints that were based on experimentally observed physical behavior of the amino acids. The force field was tested on two sets of test decoys not in the training set and was found to excel on all the metrics that are widely used to measure the effectiveness of a force field. The high resolution force field was successful in correctly identifying 113 native structures out of 150 test cases and the average rank obtained for this test was 1.87. All the high resolution structures (training and testing) used for this work are available online and can be downloaded from http://titan.princeton.edu/HRDecoys.

Algorithms↗

PubMed2005

Protein loop structure prediction with flexible stem geometries.

The structure prediction of loops with flexible stem residues is addressed in this article. While the secondary structure of the stem residues is assumed to be known, the geometry of the protein into which the loop must fit is considered to be unknown in our methodology. As a consequence, the compatibility of the loop with the remainder of the protein is not used as a criterion to reject loop decoys. The loop structure prediction with flexible stems is more difficult than fitting loops into a known protein structure in that a larger conformational space has to be covered. The main focus of the study is to assess the precision of loop structure prediction if no information on the protein geometry is available. The proposed approach is based on (1) dihedral angle sampling, (2) structure optimization by energy minimization with a physically based energy function, (3) clustering, and (4) a comparison of strategies for the selection of loops identified in (3). Steps (1) and (2) have similarities to previous approaches to loop structure prediction with fixed stems. Step (3) is based on a new iterative approach to clustering that is tailored for the loop structure prediction problem with flexible stems. In this new approach, clustering is not only used to identify conformers that are likely to be close to the native structure, but clustering is also employed to identify far-from-native decoys. By discarding these decoys iteratively, the overall quality of the ensemble and the loop structure prediction is improved. Step (4) provides a comparative study of criteria for loop selection based on energy, colony energy, cluster density, and a hybrid criterion introduced here. The proposed method is tested on a large set of 3215 loops from proteins in the Pdb-Select25 set and to 179 loops from proteins from the Casp6 experiment.

Cluster Analysis↗

PubMed2004

A new pairwise folding potential based on improved decoy generation and side-chain packing.

A new force field for pairwise residue interactions as a function of C(alpha) to C(alpha) distances is presented. The force field was developed through the solution of a linear programming formulation with large sets of constraints. The constraints are based on the construction of >80,000 low-energy decoys for a set of proteins and requiring the decoy energies for each protein system to be higher than the native conformation of that particular protein. The generation of a robust force field was facilitated by the use of a novel decoy generation process, which involved the rational selection of proteins to add to the training set and included a significant energy minimization of the decoys. The force field was tested on a large set of decoys for various proteins not included in the training set and shown to perform well compared with a leading force field in identifying the native conformation for these proteins.

Algorithms↗

PubMed2004

Improvement of the anti-C3 activity of compstatin using rational and combinatorial approaches.

Compstatin is a 13-residue cyclic peptide that has the ability to inhibit the cleavage of C3 to C3a and C3b. The effects of targeting C3 cleavage are threefold, and result in hindrance of: (i) the generation of the pro-inflammatory peptide C3a, (ii) the generation of opsonin C3b (or its fragment C3d), and (iii) further complement activation of the common pathway (beyond C3) with the end result of the generation of the membrane attack complex. We will report on our progress on: (i) rational design of more active compstatin analogues based on the three-dimensional structure of compstatin, (ii) experimental combinatorial design based on the generation of a phage-displayed peptide library partially randomized with the implementation of structure-induced restraints, and (iii) theoretical combinatorial design based on a novel computational optimization method, structure-induced restraints and flexible structural templates. All three approaches have resulted in analogues with improved activities. Currently, the lead analogue has the sequence acetyl-I[CVYQDWGAHRC]T-NH(2) (where the brackets denote cyclization), and is 16-fold more active than the parent peptide. We will also report on our progress towards understanding the dynamic character of compstatin using molecular dynamics simulations. The identification of an ensemble of interconverting conformers of compstatin with variable populations is a first step towards the incorporation of dynamic elements in the design of new analogues using dynamics-activity relationships in addition to structure-activity relationships.

Animals↗

PubMed2003

Prediction of beta-sheet topology and disulfide bridges in polypeptides.

An ab initio method has been developed to predict beta architectures in polypeptides. The approach predicts the topology of beta-sheets and disulfide bridges through a novel superstructure-based mathematical framework originally established for chemical process synthesis problems. Two types of superstructure are introduced, both of which emanate from the principle that hydrophobic interactions drive the formation of a beta-structure. The mathematical formulation of the problem results in a set of integer linear programming (ILP) problems that can be solved to global optimality to identify the optimal beta-configuration. These (ILP) models can also predict a ranked ordered list of the best, second-best, third-best, etc., topologies of beta-sheets and disulfide bridges. The approach is shown to perform very well for several benchmark polypeptide systems, as well as polypeptides exhibiting challenging nonsequential beta-sheet topologies folds (56 to 187 amino acids).

Algorithms↗

PubMed2003

ASTRO-FOLD: a combinatorial and global optimization framework for Ab initio prediction of three-dimensional structures of proteins from the amino acid sequence.

The field of computational biology has been revolutionized by recent advances in genomics. The completion of a number of genome projects, including that of the human genome, has paved the way toward a variety of challenges and opportunities in bioinformatics and biological systems engineering. One of the first challenges has been the determination of the structures of proteins encoded by the individual genes. This problem, which represents the progression from sequence to structure (genomics to structural genomics), has been widely known as the structure-prediction-in-protein-folding problem. We present the development and application of ASTRO-FOLD, a novel and complete approach for the ab initio prediction of protein structures given only the amino acid sequences of the proteins. The approach exhibits many novel components and the merits of its application are examined for a suite of protein systems, including a number of targets from several critical-assessment-of-structure-prediction experiments.

Algorithms↗

PubMed2003

Hybrid global optimization algorithms for protein structure prediction: alternating hybrids.

Hybrid global optimization methods attempt to combine the beneficial features of two or more algorithms, and can be powerful methods for solving challenging nonconvex optimization problems. In this paper, novel classes of hybrid global optimization methods, termed alternating hybrids, are introduced for application as a tool in treating the peptide and protein structure prediction problems. In particular, these new optimization methods take the form of hybrids between a deterministic global optimization algorithm, the alphaBB, and a stochastically based method, conformational space annealing (CSA). The alphaBB method, as a theoretically proven global optimization approach, exhibits consistency, as it guarantees convergence to the global minimum for twice-continuously differentiable constrained nonlinear programming problems, but can benefit from computationally related enhancements. On the other hand, the independent CSA algorithm is highly efficient, though the method lacks theoretical guarantees of convergence. Furthermore, both the alphaBB method and the CSA method are found to identify ensembles of low-energy conformers, an important feature for determining the true free energy minimum of the system. The proposed hybrid methods combine the desirable features of efficiency and consistency, thus enabling the accurate prediction of the structures of larger peptides. Computational studies for met-enkephalin and melittin, employing sequential and parallel computing frameworks, demonstrate the promise for these proposed hybrid methods.

Algorithms↗

PubMed2002

Ab initio prediction of helical segments in polypeptides.

An ab initio method has been developed to predict helix formation for polypeptides. The approach relies on the systematic analysis of overlapping oligopeptides to determine the helical propensity for individual residues. Detailed atomistic level modeling, including entropic contributions, and solvation/ionization energies calculated through the solution of the Poisson-Boltzmann equation, is utilized. The calculation of probabilities for helix formation is based on the generation of ensembles of low energy conformers. The approach, which is easily amenable to parallelization, is shown to perform very well for several benchmark polypeptide systems, including the bovine pancreatic trypsin inhibitor, the immunoglobulin binding domain of protein G, the chymotrypsin inhibitor 2, the R69 N-terminal domain of phage 434 repressor, and the wheat germ agglutinin.

Animals↗

PubMed1997

A predictive method for the evaluation of peptide binding in pocket 1 of HLA-DRB1 via global minimization of energy interactions.

Human leukocyte antigens (HLA) or histocompatibility molecules are glycoproteins that play a pivotal role in the development of an effective immune response. An important function of the HLA molecules is the ability to bind and present antigen peptides to T lymphocytes. Presently there is no comprehensive way of predicting and energetically evaluating peptide binding on HLA molecules. To quantitatively determine the binding specificity of a class II HLA molecule interacting with peptides, a novel decomposition approach based on deterministic global optimization is proposed that takes advantage of the topography of HLA binding grove, and examined the interactions of the bound peptide with the five different pockets. In particular, the main focus of this paper is the study of pocket 1 of HLADR1 (DRB1*0101 allele). The determination of the minimum energy conformation is based on the ECEPP/3 potential energy model that describes the energetics of the atomic interactions. The minimization of the total potential energy is formulated on the set of peptide dihedral angles, Euler angles, and translation variables to describe the relative position. The deterministic global optimization algorithm, alpha BB, which has been shown to be epsilon-convergent to the global minimum potential energy through the solution of a series of nonlinear convex optimization problems, is utilized. The PACK conformational energy model that utilizes the ECEPP/3 model but also allows the consideration of protein chain interactions is interfaced with alpha BB. MSEED, a program used to calculate the solvation contribution via the area accessible to the solvent, is also interfaced with alpha BB. Results are presented for the entire array of naturally occurring amino acids binding to pocket 1 of the HLA DR1 molecule and very good agreement with experimental binding assays is obtained.

Algorithms↗