Ab initio prediction of the three-dimensional structure of a de novo designed protein: A double-blind case study

Ab initio structure prediction and de novo protein design are two problems at the forefront of research in the fields of structural biology and chemistry. The goal of ab initio structure prediction of proteins is to correctly characterize the 3D structure of a protein using only the amino acid sequence as input. De novo protein design involves the production of novel protein sequences that adopt a desired fold. In this work, the results of a double-blind study are presented in which a new ab initio method was successfully used to predict the 3D structure of a protein designed through an experimental approach using binary patterned combinatorial libraries of de novo sequences. The predicted structure, which was produced before the experimental structure was known and without consideration of the design goals, and the final NMR analysis both characterize this protein as a 4-helix bundle. The similarity of these structures is evidenced by both small RMSD values between the coordinates of the two structures and a detailed analysis of the helical packing.

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.

Theoretical and computational studies of the glucose signaling pathways in yeast using global gene expression data

We have combined DNA microarray experiments with novel computational methods as a means of defining the topology of a biological signal transduction pathway. By DNA microarray techniques, we previously acquired data on expression over time of all genes in the yeast Saccharomyces following addition of glucose to wild-type cells and to cells mutated in one or more components of the Ras signaling network. In addition, we examined the time course of expression following activation of components of the Ras signaling network in the absence of glucose addition. In this current study, we have applied a novel theoretical and computational framework to these data to identify the network topology of the glucose signaling pathway in yeast and the role of Ras components in that network. The computational approach involves clustering genes by expression pattern, postulating a signaling network topology superstructure that includes all possible component interconnections and then evaluating the feasibility of the superstructure interconnections by optimization methods using Mixed Integer Linear Programming techniques. This approach is the first rigorous mathematical framework for addressing the biological network topology issue, and the novel formulation features the introduction of discrete variables for the connectivity and logical expressions that connect the experimental observations to the network structure. This analysis yields a topology for the glucose signaling pathway that is consistent with, and an extension of, known biological interactions in glucose signaling.

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.

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.

Predicting peptide binding to MHC pockets via molecular modeling, implicit solvation, and global optimization

Development of a computational prediction method based on molecular modeling, global optimization, and implicit solvation has produced accurate structure and relative binding affinity predictions for peptide amino acids binding to five pockets of the MHC molecule HLA-DRB1*0101. Because peptide binding to MHC molecules is essential to many immune responses, development of such a method for understanding and predicting the forces that drive binding is crucial for pharmaceutical design and disease treatment. Underlying the development of this prediction method are two hypotheses. The first is that pockets formed by the peptide binding groove of MHC molecules are independent, separating the prediction of peptide amino acids that bind within individual pockets from those that bind between pockets. The second hypothesis is that the native state of a system composed of an amino acid bound to a protein pocket corresponds to the system's lowest free energy. The prediction method developed from these hypotheses uses atomistic-level modeling, deterministic global optimization, and three methods of implicit solvation: solvent-accessible area, solvent-accessible volume, and Poisson-Boltzmann electrostatics. The method predicts relative binding affinities of peptide amino acids for pockets of HLA-DRB1*0101 by determining computationally an amino acid's global minimum energy conformation. Prediction results from the method are in agreement with X-ray crystallography data and experimental binding assays.

Integrated computational and experimental approach for lead optimization and design of compstatin variants with improved activity

A novel structure-activity-based combinatorial computational optimization methodology for the design of peptides that are candidates to become therapeutics is presented. This methodology has been successfully applied in the design of a 7-fold more active analogue, among other active analogues, in the case of the complement inhibitor compstatin. The main steps of the approach involve the availability of NMR-derived structural templates, combinatorial selection of sequences based on optimization of parametrized pairwise residue interaction potentials, prediction of fold stabilities using deterministic global optimization, and experimental validation with immunological activity measurements. This work is direct evidence that an integrated experimental and theoretical approach can make the engineering of compounds with enhanced immunological properties possible.

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.

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).

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.