Tools for the automated assignment of high-resolution three-dimensional protein NMR spectra based on pattern recognition techniques

Comprehensive automation for NMR structure determination of proteins

This chapter gives an overview of automated protein structure determination by nuclear magnetic resonance (NMR) with the UNIO protocol that enables high to full automation of all NMR data analysis steps involved. Four established algorithms, namely, the MATCH algorithm for sequence-specific resonance assignment, the ASCAN algorithm for side-chain resonance assignment, the CANDID algorithm for NOE assignment, and the ATNOS algorithm for signal identification in NMR spectra, are assembled into three principal UNIO NMR data analysis components (MATCH, ATNOS/ASCAN, and ATNOS/CANDID) that are accessed thanks to a particularly intuitive and flexible, yet powerful graphical user interface (GUI). UNIO is designed to work independently or in association with other NMR software. The principal data analysis components for sequence-specific backbone, side-chain and NOE assignment may be run separately or out of sequence. User-intervention at individual stages is encouraged and facilitated by graphical tools included for the preparation, analysis, validation, and subsequent presentation of the NMR structure.

Automated structure determination from NMR spectra

Automated methods for protein structure determination by NMR have increasingly gained acceptance and are now widely used for the automated assignment of distance restraints and the calculation of three-dimensional structures. This review gives an overview of the techniques for automated protein structure analysis by NMR, including both NOE-based approaches and methods relying on other experimental data such as residual dipolar couplings and chemical shifts, and presents the FLYA algorithm for the fully automated NMR structure determination of proteins that is suitable to substitute all manual spectra analysis and thus overcomes a major efficiency limitation of the NMR method for protein structure determination.

Graphical interpretation of Boolean operators for protein NMR assignments

We have developed a graphics based algorithm for semi-automated protein NMR assignments. Using the basic sequential triple resonance assignment strategy, the method is inspired by the Boolean operators as it applies "AND"-, "OR"- and "NOT"-like operations on planes pulled out of the classical three-dimensional spectra to obtain its functionality. The method's strength lies in the continuous graphical presentation of the spectra, allowing both a semi-automatic peaklist construction and sequential assignment. We demonstrate here its general use for the case of a folded protein with a well-dispersed spectrum, but equally for a natively unfolded protein where spectral resolution is minimal.

Protein Structure Elucidation from Minimal NMR Data: The CLOUDS Approach

In this chapter we review automated methods of protein NMR data analysis and expand on the assignment-independent CLOUDS approach. As presented, given a set of reliable NOEs it is feasible to derive a spatial H-atom distribution that provides a low-resolution image of the protein structure. In order to generate such a list of unambiguous NOEs, a probabilistic assessment of the NOE identities (in terms of frequency-labeled H-atom sources) was developed on the basis of Bayesian inference. The methodology, encompassing programs SPI and BACUS, provides a list of "clean" NOEs that does not hinge on prior knowledge of sequence-specific resonance assignments or a preliminary structural model. As such, the combined SPI/BACUS approach, intrinsically adaptable to include 13C- and/or 15N-edited experiments, affords a useful tool for the analysis of NMR data irrespective of whether the adopted structure calculation protocol is assignment-dependent.

The NOESY jigsaw: automated protein secondary structure and main-chain assignment from sparse, unassigned NMR data

High-throughput, data-directed computational protocols for Structural Genomics (or Proteomics) are required in order to evaluate the protein products of genes for structure and function at rates comparable to current gene-sequencing technology. This paper presents the JIGSAW algorithm, a novel high-throughput, automated approach to protein structure characterization with nuclear magnetic resonance (NMR). JIGSAW applies graph algorithms and probabilistic reasoning techniques, enforcing first-principles consistency rules in order to overcome a 5-10% signal-to-noise ratio. It consists of two main components: (1) graph-based secondary structure pattern identification in unassigned heteronuclear NMR data, and (2) assignment of spectral peaks by probabilistic alignment of identified secondary structure elements against the primary sequence. Deferring assignment eliminates the bottleneck faced by traditional approaches, which begin by correlating peaks among dozens of experiments. JIGSAW utilizes only four experiments, none of which requires 13C-labeled protein, thus dramatically reducing both the amount and expense of wet lab molecular biology and the total spectrometer time. Results for three test proteins demonstrate that JIGSAW correctly identifies 79-100% of alpha-helical and 46-65% of beta-sheet NOE connectivities and correctly aligns 33-100% of secondary structure elements. JIGSAW is very fast, running in minutes on a Pentium-class Linux workstation. This approach yields quick and reasonably accurate (as opposed to the traditional slow and extremely accurate) structure calculations. It could be useful for quick structural assays to speed data to the biologist early in an investigation and could in principle be applied in an automation-like fashion to a large fraction of the proteome.

MUSIC, selective pulses, and tuned delays: amino acid type-selective (1)H-(15)N correlations, II

Amino acid type-selective experiments help to remove ambiguities in either manual or automated assignment procedures. Here we present modified triple-resonance experiments that yield amino acid type-selective (1)H-(15)N correlations. They are based on the MUSIC coherence transfer scheme which replaces the initial INEPT transfer and is selective for XH(2) or XH(3) (where X is either (15)N or (13)C). Signals of the desired amino acid types are thus selected based on the topology of the side chain. MUSIC is combined with selective pulses and carefully tuned delays to create experiments for Ser (S-HSQC); Val, Ile, and Ala (VIA-HSQC); Leu and Ala (LA-HSQC); Asp, Asn, and Gly (DNG-HSQC), as well as Glu, Gln, and Gly (EQG-HSQC). The new experiments are recorded as two-dimensional spectra and their performance is demonstrated by their application to two protein domains of 83 and 115 residues.

MUSIC in triple-resonance experiments: amino acid type-selective (1)H-(15)N correlations

Amino acid type-selective triple-resonance experiments can be of great help for the assignment of protein spectra, since they help to remove ambiguities in either manual or automated assignment procedures. Here, modified triple-resonance experiments that yield amino acid type-selective (1)H-(15)N correlations are presented. They are based on novel coherence transfer schemes, the MUSIC pulse sequence elements, that replace the initial INEPT transfer and are selective for XH(2) or XH(3) (X can be (15)N or (13)C). The desired amino acid type is thereby selected based on the topology of the side chain. Experiments for Gly (G-HSQC); Ala (A-HSQC); Thr, Val, Ile, and Ala (TAVI-HSQC); Thr and Ala (TA-HSQC), as well as Asn and Gln (N-HSQC and QN-HSQC), are described. The new experiments are recorded as two-dimensional experiments and therefore need only small amounts of spectrometer time. The performance of the experiments is demonstrated with the application to two protein domains. Copyright 1999 Academic Press.

Automated analysis of NMR assignments and structures for proteins

Recent developments in protein NMR technology have provided spectral data that are highly amenable to analysis by advanced computer software systems. Specific data collection strategies, coupled with these computer programs, allow automated analysis of extensive backbone and sidechain resonance assignments and three-dimensional structures for proteins of 50 to 200 amino acids.