Suzanne Fortier

Direct methods for solving macromolecular structures

Preface. 1. Introduction. 2. Tools for Solving the Phase Problem. 2.1. Mathematical Methods. 2.2. Computational Methods. 2.3. Experimental Methods. 3. Applications of Direct Methods to Macro-Molecular Structures. 3.1. Integration of Direct Methods with Experimental Phase Information. 3.2. Combining Direct Methods with Structural Information. 3.3 Ab Initio Phasing. 4. Latest Developments. Index.

Macromolecular crystallization in a high throughput laboratory—the search phase

Macromolecular crystallization efforts are frequently divided into a search phase, during which approximate conditions are sought, and an optimization phase, when the approximate conditions are optimized to yield crystals of sufficient quality for diffraction work. Faced with the possibility that, on a yearly basis, many hundreds of proteins might be generated, both in our laboratories and at the laboratories of our collaborators, we have recently designed and commissioned a high throughput robotics lab designed for the search phase. The lab is capable of setting up and photographically evaluating over 60,000 microbatch crystallization experiments per week. In the first four months of operation we have set up crystallization experiments for more than one hundred proteins.

Intelligent decision support for protein crystal growth

Current structural genomics projects are likely to produce hundreds of proteins a year for structural analysis. The primary goal of our research is to speed up the process of crystal growth for proteins in order to enable the determination of protein structure using single crystal X-ray diffraction. We describe Max, a working prototype that includes a high-throughput crystallization and evaluation setup in the wet laboratory and an intelligent software system in the computer laboratory. A robotic setup for crystal growth is able to prepare and evaluate over 40 thousand crystallization experiments a day. Images of the crystallization outcomes captured with a digital camera are processed by an image-analysis component that uses the two-dimensional Fourier transform to perform automated classification of the experiment outcome. An information repository component, which stores the data obtained from crystallization experiments, was designed with an emphasis on correctness, completeness, and reproducibility. A case-based reasoning component provides support for the design of crystal growth experiments by retrieving previous similar cases, and then adapting these in order to create a solution for the problem at hand. While work on Max is still in progress, we report here on the implementation status of its components, discuss how our work relates to other research, and describe our plans for the future.

Synthesis of a new family of water-soluble tertiary phosphine ligands and of their rhodium(I) complexes; olefin hydrogenation in aqueous and biphasic media

Abstract The series of phosphonium phosphines [Ph2P(CH2)nPMe3]X (n = 2, 3, 6, 10; X = NO−3, Cl−, PF−6), henceforth II-, III-, VI-, and X-phosphos, respectively, have been prepared and characterized. The ligands react with [NBDRhCl]2 (NBD = norbornadiene) to form the complexes [NBDRhCl(n-phosphos)]X, one of which, [NBDRhCl(II-phosphos)]PF6, has been characterized crystallographically. The 1:1 complexes [NBDRhCl(n-phosphos)]X react with a second equivalent of ligand to form the complexes [NBDRh(n-phosphos)2]X, which form very active olefin hydrogenation catalysts in aqueous and aqueous—organic biphasic systems. The effect of chain length on activity is very significant, the complex of VI-phosphos forming the most active catalyst.

A Direct-Methods Solution to the Phase Problem in the Single Isomorphous Replacement Case: Theoretical Basis and Initial Applications

The probabilistic theory of the three-phase structure invariants for a pair of isomorphous structures [Hauptman (1982). Acta Cryst. A38, 289-294] is reexamined. The analysis leads to distributions capable of estimating cosine invariants in the full range of -1 to +1. In particular, it is shown that heavy-atom substructure information can be incorporated easily into the distributions. The initial applications, using calculated diffraction data from the protein cytochrome C55o, MR ~--14 500, and its PtC142- derivative show that a remarkable increase in accuracy results from the use of the revised distributions, particularly after the incorporation of heavy-atom substructure information. Finally, it is shown that in the individual phase determinations the redundant cosine invariants play a role identical to that of the multiple isomorphous derivatives and thus provide the basis for the solution of the phase problem in the single isomorphous replacement case.

Analysis of three-dimensional protein images

A fundamental goal of research in molecular biology is to understand protein structure. Protein crystallography is currently the most successful method for determining the three-dimensional (3D) conformation of a protein, yet it remains labor intensive and relies on an experts ability to derive and evaluate a protein scene model. In this paper, the problem of protein structure determination is formulated as an exercise in scene analysis. A computational methodology is presented in which a 3D image of a protein is segmented into a graph of critical points. Bayesian and certainty factor approaches are described and used to analyze critical point graphs and identify meaningful substructures, such as α-helices and β-sheets. Results of applying the methodologies to protein images at low and medium resolution are reported. The research is related to approaches to representation, segmentation and classification in vision, as well as to top-down approaches to protein structure prediction.

Conformational analysis from crystallographic data using conceptual clustering

The rapid growth of crystallographic databases has created a demand for novel and efficient techniques for the analysis of molecular conformations, in order to derive new concepts and rules and to generate useful classifications of the available data. This paper presents a conceptual clustering approach, termed IMEM (image memory), which discovers the conformational diversity present in a dataset of crystal structures. In contrast to numerical clustering methods, IMEM views a molecular structure as comprising qualitative relationships among its parts, i.e. the structure is viewed as a molecular scene. In addition, IMEM does not require the user to have any a priori knowledge of an expected number of conformational classes within a given dataset. The IMEM approach is applied to several datasets derived from the Cambridge Structural Database and, in all cases, chemically correct and sensible conformational classifications were discovered. This is confirmed by a rigorous comparison of IMEM results with published conformational data obtained by energy-minimization and numerical clustering methods. Conformational analysis tools have an important part to play in the conversion of raw molecular databases to knowledge bases.

Molecular scene analysis: the integration of direct-methods and artificial-intelligence strategies for solving protein crystal structure.

A knowledge-based approach to crystal structure determination is presented. The approach integrates direct-methods and artificial-intelligence strategies to rephrase the structure determination process as an exercise in scene analysis. A general joint probability distribution framework, which allows the incorporation of isomorphous replacement, anomalous scattering and a priori structural information, forms the basis of the direct-methods strategies. The accumulated knowledge on crystal and molecular structures is exploited through the use of artificial-intelligence strategies, which include techniques of knowledge representation, search and machine learning.

Knowledge discovery in molecular databases

An approach to knowledge discovery in complex molecular databases is described. The machine learning paradigm used is structured concept formation, in which objects described in terms of components and their interrelationships are clustered and organized in a knowledge base. Symbolic images are used to represent classes of structured objects. A discovered molecular knowledge base is successfully used in the interpretation of a high resolution electron density map. >

CRITICAL-POINT ANALYSIS IN PROTEIN ELECTRON-DENSITY MAP INTERPRETATION

Publisher Summary Critical-point mapping has the potential to become an effective tool for the segmentation of protein electron-density maps and the recognition of structural motifs within the maps. Being built on a well-defined mathematical framework, it is well suited for implementation as a fully automated computer approach to map interpretation. Current efforts are focused on reimplementing the program to gain speed and to allow for the incorporation of intelligence and information. Critical-point mapping takes as input an electron–density map and produces a provisional interpretation consisting of hypothesized three-dimensional structures. The threading algorithm in turn attempts to find the most plausible ways to superimpose a given sequence onto hypothesized structures with a scoring function. Threading, thus, can be used for the evaluation and validation of map interpretation results. Furthermore, it can be used for the classification and identification of individual residues in medium-resolution electron-density maps, providing a potentially very useful tool for model building and refinement.