STRUCTURAL MODELING OF PROTEIN-PROTEIN INTERACTIONS USING MULTIPLE-CHAIN THREADING AND FRAGMENT ASSEMBLY

Since its birth, the study of protein structures has made progress with leaps and bounds. However, owing to the expenses and difficulties involved, the number of protein structures has not been able to catch up with the number of protein sequences and in fact has steadily lost ground. This necessitated the development of high-throughput but accurate computational algorithms capable of predicting the three dimensional structure of proteins from its amino acid sequence. While progress has been made in the realm of protein tertiary structure prediction, the advancement in protein quaternary structure prediction has been limited by the fact that the degree of freedom for protein complexes is even larger and even fewer number of protein complex structures are present in the PDB library. In fact, protein complex structure prediction till date has largely remained a docking problem where automated algorithms aim to predict the protein complex structure starting from the unbound crystal structure of its component subunits and thus has remained largely limited in terms of scope. Secondly, since docking essentially treats the unbound subunits as "rigid-bodies" it has limited accuracy when conformational change accompanies protein-protein interaction. In one of the first of its kind effort, this study aims for the development of protein complex structure algorithms which require only the amino acid sequence of the interacting subunits as input. The study aimed to adapt the best features of protein tertiary structure prediction including template detection and ab initio loop modeling and extend it for protein-protein complexes thus requiring simultaneous modeling of the three dimensional structure of the component subunits as well as ensuring the correct orientation of the chains at the protein-protein interface. Essentially, the algorithms are dependent on knowledge-based statistical potentials for both fold recognition and structure modeling. First, as a way to compare known structure of protein-protein complexes, a complex structure alignment program MM-align was developed. MM-align joins the chains of the complex structures to be aligned to form artificial monomers in every possible order. It then aligns them using a heuristic dynamic programming based approach using TM-score as the objective function. However, the traditional NW dynamic programming was redesigned to prevent the cross alignment of chains during the structure alignment process. Driven by the knowledge obtained from MM-align that protein complex structures share evolutionary relationships and the current protein complex structure library already contains homologous/structurally analogous protein quaternary structure families, a dimeric threading approach, COTH was designed. The new threading-recombination approach boosts the protein complex structure library by combining tertiary structure templates with complex alignments. The query sequences are first aligned to complex templates using the modified dynamic programming algorithm, guided by a number of predicted structural features including ab initio binding-site predictions. Finally, a template-based complex structure prediction approach, TACOS, was designed to build full-length protein complex structures starting from the initial templates identified by COTH. TACOS, fragments the templates aligned regions of templates and reassembles them while building the structure of the threading unaligned region ab inito using a replica-exchange monte-carlo simulation procedure. Simultaneously, TACOS also searches for the best orientation match of the component structures driven by a number of knowledge-based potential terms. Overall, TACOS presents the one of the first approach capable of predicting full length protein complex structures from sequence alone and introduces a new paradigm in the field of protein complex structure modeling

Protein Fold Recognition Using Neural Networks

To predict accurately the three-dimensional (3D) structures of proteins from their amino acid sequences alone remains a challenging problem. However, using protein fold recognition tools, it is often possible to achieve good models or at least to gain some more information, to aid scientists in their research. This thesis describes development of TUNE (Threading Using Neural Networks), a fold recognition program using artificial neural network (ANN) models. A new method to generate amino acid substitution matrices is described in chapter two. It uses an ANN to generalise amino acid substitutions observed in protein structure alignments. Matrices for alignment scoring from this approach were compared with classic alignment scoring schemes. From these neural network models, a series of encoding schemes were constructed. These schemes describe the amino acid types with a few numbers. They were generated to replace the orthogonal encoding scheme, so that smaller, faster and more accurate neural network models can be applied on bioinformatic problems. The TUNE model was introduced in chapter four to measure protein sequence-structure compatibility. Given the integrated residue structural environment descriptions, the model predicts probabilities of observing amino acid types in such environments. Using this model, a scoring function to measure the fitness of a residue in a protein structure model can be made for protein threading programs. The model in chapter two was extended by including the residue structural environment descriptions for predictions. A simple protein fold recognition program with a dynamic programming algorithm was developed using this model. The program was then tested in the fourth round of the Critical Assessment of protein Structure Prediction methods (CASP4) and produced reasonably good results

Information recovery in the biological sciences : protein structure determination by constraint satisfaction, simulation and automated image processing

Regardless of the field of study or particular problem, any experimental science always poses the same question: ÒWhat object or phenomena generated the data that we see, given what is known?Ó In the field of 2D electron crystallography, data is collected from a series of two-dimensional images, formed either as a result of diffraction mode imaging or TEM mode real imaging. The resulting dataset is acquired strictly in the Fourier domain as either coupled Amplitudes and Phases (as in TEM mode) or Amplitudes alone (in diffraction mode). In either case, data is received from the microscope in a series of CCD or scanned negatives of images which generally require a significant amount of pre-processing in order to be useful. Traditionally, processing of the large volume of data collected from the microscope was the time limiting factor in protein structure determination by electron microscopy. Data must be initially collected from the microscope either on film-negatives, which in turn must be developed and scanned, or from CCDs of sizes typically no larger than 2096x2096 (though larger models are in operation). In either case, data are finally ready for processing as 8-bit, 16-bit or (in principle) 32-bit grey-scale images. Regardless of data source, the foundation of all crystallographic methods is the presence of a regular Fourier lattice. Two dimensional cryo-electron microscopy of proteins introduces special challenges as multiple crystals may be present in the same image, producing in some cases several independent lattices. Additionally, scanned negatives typically have a rectangular region marking the film number and other details of image acquisition that must be removed prior to processing. If the edges of the images are not down-tapered, vertical and horizontal ÒstreaksÓ will be present in the Fourier transform of the image --arising from the high-resolution discontinuities between the opposite edges of the image. These streaks can overlap with lattice points which fall close to the vertical and horizontal axes and disrupt both the information they contain and the ability to detect them. Lastly, SpotScanning (Downing, 1991) is a commonly used process where-by circular discs are individually scanned in an image. The large-scale regularity of the scanning patter produces a low frequency lattice which can interfere and overlap with any protein crystal lattices. We introduce a series of methods packaged into 2dx (Gipson, et al., 2007) which simultaneously addresses these problems, automatically detecting accurate crystal lattice parameters for a majority of images. Further a template is described for the automation of all subsequent image processing steps on the road to a fully processed dataset. The broader picture of image processing is one of reproducibility. The lattice parameters, for instance, are only one of hundreds of parameters which must be determined or provided and subsequently stored and accessed in a regular way during image processing. Numerous steps, from correct CTF and tilt-geometry determination to the final stages of symmetrization and optimal image recovery must be performed sequentially and repeatedly for hundreds of images. The goal in such a project is then to automatically process as significant a portion of the data as possible and to reduce unnecessary, repetitive data entry by the user. Here also, 2dx (Gipson, et al., 2007), the image processing package designed to automatically process individual 2D TEM images is introduced. This package focuses on reliability, ease of use and automation to produce finished results necessary for full three-dimensional reconstruction of the protein in question. Once individual 2D images have been processed, they contribute to a larger project-wide 3-dimensional dataset. Several challenges exist in processing this dataset, besides simply the organization of results and project-wide parameters. In particular, though tilt-geometry, relative amplitude scaling and absolute orientation are in principle known (or obtainable from an individual image) errors, uncertainties and heterogeneous data-types provide for a 3D-dataset with many parameters to be optimized. 2dx_merge (Gipson, et al., 2007) is the follow-up to the first release of 2dx which had originally processed only individual images. Based on the guiding principles of the earlier release, 2dx_merge focuses on ease of use and automation. The result is a fully qualified 3D structure determination package capable of turning hundreds of electron micrograph images, nearly completely automatically, into a full 3D structure. Most of the processing performed in the 2dx package is based on the excellent suite of programs termed collectively as the MRC package (Crowther, et al., 1996). Extensions to this suite and alternative algorithms continue to play an essential role in image processing as computers become faster and as advancements are made in the mathematics of signal processing. In this capacity, an alternative procedure to generate a 3D structure from processed 2D images is presented. This algorithm, entitled ÒProjective Constraint OptimizationÓ (PCO), leverages prior known information, such as symmetry and the fact that the protein is bound in a membrane, to extend the normal boundaries of resolution. In particular, traditional methods (Agard, 1983) make no attempt to account for the Òmissing coneÓ a vast, un-sampled, region in 3D Fourier space arising from specimen tilt limitations in the microscope. Provided sufficient data, PCO simultaneously refines the dataset, accounting for error, as well as attempting to fill this missing cone. Though PCO provides a near-optimal 3D reconstruction based on data, depending on initial data quality and amount of prior knowledge, there may be a host of solutions, and more importantly pseudo-solutions, which are more-or-less consistent with the provided dataset. Trying to find a global best-fit for known information and data can be a daunting challenge mathematically, to this end the use of meta-heuristics is addressed. Specifically, in the case of many pseudo-solutions, so long as a suitably defined error metric can be found, quasi-evolutionary swarm algorithms can be used that search solution space, sharing data as they go. Given sufficient computational power, such algorithms can dramatically reduce the search time for global optimums for a given dataset. Once the structure of a protein has been determined, many questions often remain about its function. Questions about the dynamics of a protein, for instance, are not often readily interpretable from structure alone. To this end an investigation into computationally optimized structural dynamics is described. Here, in order to find the most likely path a protein might take through Òconformation spaceÓ between two conformations, a graphics processing unit (GPU) optimized program and set of libraries is written to speed of the calculation of this process 30x. The tools and methods developed here serve as a conceptual template as to how GPU coding was applied to other aspects of the work presented here as well as GPU programming generally. The final portion of the thesis takes an apparent step in reverse, presenting a dramatic, yet highly predictive, simplification of a complex biological process. Kinetic Monte Carlo simulations idealize thousands of proteins as interacting agents by a set of simple rules (i.e. react/dissociate), offering highly-accurate insights into the large-scale cooperative behavior of proteins. This work demonstrates that, for many applications, structure, dynamics or even general knowledge of a protein may not be necessary for a meaningful biological story to emerge. Additionally, even in cases where structure and function is known, such simulations can help to answer the biological question in its entirety from structure, to dynamics, to ultimate function

The Role of Pressure in Inverse Design for Assembly

Isotropic pairwise interactions that promote the self assembly of complex particle morphologies have been discovered by inverse design strategies derived from the molecular coarse-graining literature. While such approaches provide an avenue to reproduce structural correlations, thermodynamic quantities such as the pressure have typically not been considered in self-assembly applications. In this work, we demonstrate that relative entropy optimization can be used to discover potentials that self-assemble into targeted cluster morphologies with a prescribed pressure when the iterative simulations are performed in the isothermal-isobaric ensemble. By tuning the pressure in the optimization, we generate a family of simple pair potentials that all self-assemble the same structure. Selecting an appropriate simulation ensemble to control the thermodynamic properties of interest is a general design strategy that could also be used to discover interaction potentials that self-assemble structures having, for example, a specified chemical potential.Comment: 29 pages, 8 figure

Structure of the mature Rous sarcoma virus lattice reveals a role for IP6 in the formation of the capsid hexamer

Inositol hexakisphosphate (IP6) is an assembly cofactor for HIV-1. We report here that IP6 is also used for assembly of Rous sarcoma virus (RSV), a retrovirus from a different genus. IP6 is ~100-fold more potent at promoting RSV mature capsid protein (CA) assembly than observed for HIV-1 and removal of IP6 in cells reduces infectivity by 100-fold. Here, visualized by cryo-electron tomography and subtomogram averaging, mature capsid-like particles show an IP6-like density in the CA hexamer, coordinated by rings of six lysines and six arginines. Phosphate and IP6 have opposing effects on CA in vitro assembly, inducing formation of T = 1 icosahedrons and tubes, respectively, implying that phosphate promotes pentamer and IP6 hexamer formation. Subtomogram averaging and classification optimized for analysis of pleomorphic retrovirus particles reveal that the heterogeneity of mature RSV CA polyhedrons results from an unexpected, intrinsic CA hexamer flexibility. In contrast, the CA pentamer forms rigid units organizing the local architecture. These different features of hexamers and pentamers determine the structural mechanism to form CA polyhedrons of variable shape in mature RSV particles

The prediction and analysis of protein structure using specialized database techniques

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 1995.Includes bibliographical references.by Tau-Mu Yi.Ph.D