Scott R. McAllister

A novel high resolution CαCα distance dependent force field based on a high quality decoy set

This work presents a novel CαCα 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. Proteins 2006.

Towards accurate residue–residue hydrophobic contact prediction for α helical proteins via integer linear optimization

A new optimization‐based method is presented to predict the hydrophobic residue contacts in α‐helical proteins. The proposed approach uses a high resolution distance dependent force field to calculate the interaction energy between different residues of a protein. The formulation predicts the hydrophobic contacts by minimizing the sum of these contact energies. These residue contacts are highly useful in narrowing down the conformational space searched by protein structure prediction algorithms. The proposed algorithm also offers the algorithmic advantage of producing a rank ordered list of the best contact sets. This model was tested on four independent α‐helical protein test sets and was found to perform very well. The average accuracy of the predictions (separated by at least six residues) obtained using the presented method was ∼66% for single domain proteins. The average true positive and false positive distances were also calculated for each protein test set and they are 8.87 and 14.67 Å, respectively. Proteins 2009.

Novel approach for α-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 α‐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 α‐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 α‐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 Å 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 α‐helical globular proteins is highlighted. Proteins 2006.

A network flow model for biclustering via optimal re-ordering of data matrices

The analysis of large-scale data sets using clustering techniques arises in many different disciplines and has important applications. Most traditional clustering techniques require heuristic methods for finding good solutions and produce suboptimal clusters as a result. In this article, we present a rigorous biclustering approach, OREO, which is based on the Optimal RE-Ordering of the rows and columns of a data matrix. The physical permutations of the rows and columns are accomplished via a network flow model according to a given objective function. This optimal re-ordering model is used in an iterative framework where cluster boundaries in one dimension are used to partition and re-order the other dimensions of the corresponding submatrices. The performance of OREO is demonstrated on metabolite concentration data to validate the ability of the proposed method and compare it to existing clustering methods.

Descriptor-free molecular discovery in large libraries by adaptive substituent reordering

Molecular discovery often involves identification of the best functional groups (substituents) on a scaffold. When multiple substitution sites are present, the number of possible substituent combinations can be very large. This article introduces a strategy for efficiently optimizing the substituent combinations by iterative rounds of compound sampling, substituent reordering to produce the most regular property landscape, and property estimation over the landscape. Application of this approach to a large pharmaceutical compound library demonstrates its ability to find active compounds with a threefold reduction in synthetic and assaying effort, even without knowing the molecular identity of any compound.

α-Helical Topology Prediction and Generation of Distance Restraints in Membrane Proteins

The field of protein structure prediction has seen significant advances in recent years. Researchers have followed a multitude of approaches, including methods based on comparative modeling, fold recognition and threading, and first-principles techniques. It is noteworthy that the structure prediction of membrane proteins is comparatively less studied by researchers in the field. A membrane protein is characterized by a protein structure that extends into or through the lipid-lipid bilayer of a cell. The structure is influenced by the combination of the hydrophobic bilayer region, the direct interaction with the bilayer, and the aqueous external environment. Due to the difficulty in obtaining reliable experimental structures, accurate computational prediction of membrane proteins is of paramount importance. An optimization model has been developed to predict the interhelical interactions in alpha-helical membrane proteins. A database of alpha-helical membrane proteins of known structure and limited sequence identity can be constructed to develop interaction probabilities. By then maximizing the occurrence of highly probable pairwise or three-residue interactions, realistic contacts can be predicted by imposing a number of geometrical constraints. The development of these low distance contacts can provide additional distance restraints for first principles-based approaches to the tertiary structure prediction problem. The proposed approach is shown to successfully predict interhelical contacts in several membrane protein systems, including bovine rhodopsin and the recently released human beta2 adrenergic receptor protein structure.

Enhanced bounding techniques to reduce the protein conformational search space

The complexity and enormous size of the conformational space that must be explored for the protein tertiary structure prediction problem has led to the development of a wide assortment of algorithmic approaches. In this study, we apply state-of-the-art tertiary structure prediction algorithms and instead focus on the development of bounding techniques to reduce the conformational search space. Dihedral angle bounds on the φ and ψ angles are established based on the predicted secondary structure and studies of the allowed regions of φ/ψ space. Distance bounds are developed based on predicted secondary structure information (including β-sheet topology predictions) to further reduce the search space. This bounding strategy is entirely independent of the degree of homology between the target protein and the database of proteins with experimentally-determined structures. The proposed approach is applied to the structure prediction of protein G as an illustrative example, yielding a significantly higher number of near-native protein tertiary structure predictions.

Global pairwise sequence alignment through mixed-integer linear programming: a template-free approach

The problem of protein sequence alignment is the main starting point for biological analysis of genomic information. A new approach to global pairwise sequence alignment is proposed. This approach is formulated in a mathematically rigorous way, as a mixed integer linear optimization (MILP) problem. Not only does the proposed formulation guarantee the identification of the global optimal alignment, but it also allows for a complete rank-ordered list of any number of user specified top ranking pairwise alignments and for the incorporation of restraints based on restrictions necessary to maintain biological function. This approach has been applied to the alignment of transmembrane helices and serine proteases to demonstrate its utility and advantages over other algorithms.

Mathematical modeling and efficient optimization methods for the distance-dependent rearrangement clustering problem

In this article we present a computational study for solving the distance-dependent rearrangement clustering problem using mixed-integer linear programming (MILP). To address sparse data sets, we present an objective function for evaluating the pair-wise interactions between two elements as a function of the distance between them in the final ordering. The physical permutations of the rows and columns of the data matrix can be modeled using mixed-integer linear programming and we present three models based on (1) the relative ordering of elements, (2) the assignment of elements to a final position, and (3) the assignment of a distance between a pair of elements. These models can be augmented with the use of cutting planes and heuristic methods to increase computational efficiency. The performance of the models is compared for three distinct re-ordering problems corresponding to glass transition temperature data for polymers and two drug inhibition data matrices. The results of the comparative study suggest that the assignment model is the most effective for identifying the optimal re-ordering of rows and columns of sparse data matrices.

A path selection approach to global pairwise sequence alignment using integer linear optimization

An important and well-studied problem in the area of computational biology is the sequence alignment problem. A novel integer linear programming (ILP) model has been developed to rigorously address the global pairwise sequence alignment problem. The important components of the model formulation, in addition to its rigour are (a) the natural introduction of functionally important conservation constraints, (b) the creation of a rank-ordered list of the highest scoring alignments and (c) the possible refinement of alignments by pairwise interaction scores. By using a path selection approach that employs some of the algorithmic advantages of dynamic programming methods, this integer linear optimization model gains efficiency while maintaining the rigour of the combinatorial optimization approach. †Dedicated to H. Th. Jongen on the occasion of his 60th birthday.