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A New Generation of the IMAGIC Image Processing System

MARIN VAN HEEL, GEORGE HARAUZ,1 AND ELENA V. ORLOVA

Fritz Haber Institute of the Max Planck Society, Faradayweg 4-6, D-14195 Berlin, Germany

RALF SCHMIDT

Fritz Haber Institute of th e Max Planck S ociety, Faradayweg 4-6, D-14195 B erlin, Germ any; and Im age S cience Software GmbH,

Mecklenburgische Strasse 27, D-14197 Berlin, Germany

AND

MICHAEL SCHATZ

Image Science Software GmbH, Mecklenburgische Strasse 27, D-14197 Berlin, Germany

Received May 15, 1995, and in revised form July 6, 1995

O n e o f t h e a i m s o f m o d e r n m i c r o s c o p y i s t o q u a n -

t i f y t w o - , t h r e e - , o r e v e n f o u r - d i m e n si o n a l p h e n o m -

e n a i n b i o l o g y , m e d i c i n e , a n d m a t e r i a l sc i e n c e s. Th e

r e q u i r e m e n t s i m p o s e d o n s o f t w a r e b y s u c h d a t a

p r o c e s s i n g a r e e x e m p l i fi e d b y t h e d e s i g n c o n s i d e r -

a t i o n s o f t h e I MAG IC -5 s o f t w a re s y s t e m . T h i s s y s -

t e m i n c l u d e s f a c i l i t i e s f o r m u l t i v a r i a t e s t a t i s t i c a l

a n a l y si s o f l a rg e d a t a s e t s, f o r c o r r e l a t i o n a v e r a g i n g

o f t w o - d i m e n s i o n a l c r y s t a l s , a n d f o r t h r e e -

d i m e n s i o n a l r e c o n s t r u c t i o n o f m a c r o m o l e c u l a r

s t r u c t u re s . Th e m o l e c u l e s m a y b e a r ra n g e d a s t w o -

d i m e n s i o n a l c r y s t a l s , a s h e l i c e s , o r a s s i n g l e p a r t i -

cles with arbitrary pointgroup symmetry. IMAGICs

n o v e l a n g u l a r r e c o n s t i t u t i o n a p p r o a c h a l l o w s f o r

t h e r a p i d d e t e r m i n a t i o n o f t h r e e - d i m e n si o n a l st r u c -

t u r e s o f u n c r y st a l l i z e d m o l e c u l e s t o h i g h r e so l u t i o n .

Th e g e n e r a l o r g a n i z a t i o n , u se r i n t e r a c t i o n st r a t e g y ,

fi l e st r u c t u r e , a n d e x t e n d i b i l i t y o f IMA G IC a r e d i s-

c u s s e d a n d i l l u s t r a t e d w i t h s o m e p r a c t i c a l e x a m -

p l e s . 1 99 6 A c a d e m i c P r e s s , I n c .

INTRODUCTION

Advanced microscopy without extensive imageprocessing can no longer be imagined. We here focuson problems of t hr ee-dimensiona l (3D) reconstr uc-tion of biological ma cromolecules ba sed on t heir t wo-dimen siona l (2D) electron microscopic (EM) p rojec-tions. The basic principles of 3D reconstruction fromprojections (cf. DeRosier and Klug, 1968) and its ap-

plicat ion to th e r econst ru ction of helically organizedmacromolecular assemblies (DeRosier and Moore,1970), of icosah edra l viru ses (Crowther et al., 1970),and of 2D protein crystals (Henderson and Unwin,1975) were pioneered largely by the group aroundAar on Klug at th e MRC in Ca mbridge, England, inth e lat e sixties a nd early seventies. A different phi-losophy, th at of an alyzing isolat ed biological ma cro-molecules, was pioneered by the group of Walter

Hoppe (cf. Hoppe et al., 1974). Much of the presentwork in quantitative biological electron microscopycan be traced to the foundations laid at that time.

The growing number of t wo- a nd three-dimen-siona l ana lysis techniques an d th e intera ctive cha r-acter of the minicomputer, which appeared in themid sevent ies, crea ted a n eed for a coherent compu t-ing environment in which to perform these tasksand to develop new image processing techniques.Various image processing software systems thuswere designed (see review: Hegerl, 1992) includingour IMAGIC system (Van Heel, 1979; Van H eel and

Keegstra, 1981). Since 1981 the IMAGIC systemgrew from 22 000 lines to 500 000 lines of codeand went through major revisions associated withits porting to the VMS and UN IX opera tin g system s,its ada pta tion to th e X Windows (X11) sta nda rd, an dchanges in its user-interaction philosophy.

USER P ERSP ECTI VE

Pr oblem orientation. The IM AGIC sys tem isproblem oriented a nd t he questions a ddressed to theuser while solving a problem concern that one task.

It is our philosophy to assemble specialized pro-

1 Cur ren t addr ess: College of Biological Science, Un iversity of

Guelph, Guelph On tar io N1G 2W1, Canada .

JOURNAL OF STRUCTURAL BIOLOGY 116, 1724 (1996)ARTICLE NO. 0004

17

1047-8477/96 $18.00Copyright 1996 by Academic P ress, In c.

All rights of reproduction in any form reserved.

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grams for each more complicated task, since high-level programs allow much better interactive guid-ance for the user than do image processing com-ma nd files or scripts.

User interaction. The user experiences the con-v e r s a t i o n a l I M A G I C s y s t e m i n a n i n t e r a c t i v esess ion . IM AGIC command names are long ands e l f - e x p l a n a t o r y ( s u c h a s C O R R E L A T I O N -AVE RAGIN G or TH RE E D-TES T-IMAGE ) a n d

may be abbreviated. The ? or HELP answer toany question will provide the user with additionalinformation specific to that part icular question.Well-form ula ted int era ctive help is cru cial t o explor-ing the possibilities of the programs or as a memoryaid and is used frequently even by experiencedIMAGIC users. For some very interactive tasks, avisually oriented u ser int erface is more appr opriat e

th an a convers at iona l one, an d cont rol windows withpush buttons, sliders, etc. are used (cf. Fig. 1b).

Default processing. Whenever the user types anan swer t o a specific question, t ha t answer becomesth e defau lt th e next time t he question is posed. Thesecond time one then starts a command, one needmodify only the par am eters t ha t one is not sa tisfiedwith. The default values ar e a substantial memorysupport wh ich help ret ra ce a line of reasoning.

Multiple images. An image file in IMAGIC in-cludes both a header file and a data file (Van Heelan d Keegstr a, 1981). The file ma y cont ain t housa ndsof images and th e system t reat s them a s a un it. Thecomm an d BAND-PASS-FILTE R, for exam ple, aut o-matically processes all the images in the file, unlessother wise specified. The u ser n eed not form ula te ex-plicit loops over t he ima ges to be pr ocessed.

F IG . 1. (a) Continuous stereo surface representation of the ice-embedded portal protein of bacteriophage SPP1. By calculating asequence of surface-view images with an interimage angle compatible with stereo viewing (about 6), one can create a continuous stereorepr esent at ion conveying a good 3D imp ression even in pr inted form (Van Heel, 1983). Typically, one will precalculate a full 360 set ofimages covering a great circle on th e un it sphere. Sh own here a re some contiguous par ts of such a great circle set. The portal proteindepicted was found to possess an unusual 13-fold rotational symmetry (Dube et al., 1993). (b) By presen ting su ch a gr eat circle sequenceof sur face repr esenta tions in ra pid succession, one can creat e the impr ession of a r otatin g 3D object. Better st ill is to present t wo adjacentmovies, wher eby the two frames form a pa ir of ster eo ima ges (see illustr at ion). The user can t hen m erge th e two ima ges visua lly (possibly

using a stereo viewer) and t hus be presented simu ltaneously with complementa ry 3D st ereoscopic and r otation cues.

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Many sm all programs. The IMAGIC system is acol le ct i on of m a n y p r og r a m s s t a r t e d b y t h eIMAGIC supervising program and not a singlelarge ent ity. Thus, th e IMAGIC system is compat i-ble with the basic concepts of the multitasking, XWindows-based workstation. The user may s tar tany number of program s, image display windows,term inals, and plot windows.

Im portexport. M a n y i m a g e f o r m a t s s u c h a s

TIFF and GIF, used by commercial densitome-ters and scanners, and various specific formats inuse in electron microscopy are accepted as input oroutput for the IMPORTEXPORT program. Mostdata-only formats can be made directly accessibleto IM AGIC s imply by creat ing a cor respondingheader file and renam ing the dat a file. The on-the-fly conversion of floating-point data formats allowsthe use of IMAGIC within mixed-hardware environ-ments.

Edu cational an d t esting aspects. IMAGIC can beused to model real data processing runs for educa-

tional or test ing purp oses. Extensive model buildingfacilities a llow th e user to crea te a ll kinds of 2D an d3D data sets. 3D stru ctur es may be generat ed witharbitrary point-group symmetry and may then berepeat ed int o helical a ggregates or into 2D crysta lswith a ll possible plan e groups. The model stru ctu rescan be projected in various directions, for example,to emulat e t ilt-series r econstr uction experiment s inthe electron microscope. Noise may be added; im-ages may be randomly rotated and shifted.

TECHNICAL ORGANIZATION

The IMAGIC system, although primarily aimed atan alyzing large electr on m icroscopical dat a set s, is ageneral purpose floating-point-oriented scientificdata ana lysis system which has been used in suchdiverse fields as light microscopy, medical positronemission tomography (PET), raster tunneling mi-croscopy (RTM; Haiss et a l . , 1994), holography(Harscher et al., 1995), Fourier transform infraredspectroscopy (Naumann et al., 1988), and pr otein se-quence analysis (Van Heel, 1991b). Written in thestandard languages FORTRAN and C, and ad-hering to the X11 standa rd, the system ru ns on mostmodern computer systems (AIX, SOLARIS, IRIX,ULTRIX, OpenVMS, OSF/1, etc.). The IMAGIC sys-tem is a structured, modular network of programsand routines in which software transparency andmaintainability have been optimized. All system-dependent routines are concentrated in a few envi-ronm ent -specific librar ies, such as t he operat ing sys-tem interface library and the display device inter-face libraries.

Horizontal and vertical m odu larity. A verticalmodular organizat ion exists in t he IMAGIC system

to distinguish between the higher levels of software

intera cting directly with th e user a nd t he lower lev-els of the software interacting with the operatingsystem, the hardware, etc. IMAGIC also has a hor-izontal modularity referring to programs, libraries,and command definitions which logically belong to-gether . The MSA module, for exam ple, consists of aset of multivariate sta tistical a na lysis progra ms fordata compression and classification (see below), themsa lib libra ry cont ainin g specific MSA subrou-

tines, the msa.icm command definitions file, andth e compilation files needed to inst all th e MSA mod-ule on different operating systems. Locally devel-oped programs aimed at performing a given taskwill also typically be grouped in such a module. Amodule as a self-cont ain ed entit y ma y be tra nsferr eda n d c o m p i l e d i n d e p e n d e n t l y i n t o a n e x i s t i n gIMAGIC system.

Input/ output . An important aspect of the high-volume image processing problems that IMAGICaims at is the balance between pure calculations andinputoutput operations to disk. It is not uncommon

for an I/O-intensive computer program to exploitonly 510% of th e a vailable CPU capacity becau se itis continuously waiting for data to be read in fromdisk, especially when the disk is accessed through aslow and overloaded network. A number of I /O-minimizing str at egies ar e implemented in IMAGIC:a. When open ing an I MAGIC ima ge file, a line buffermemory is reserved which m ay be as lar ge as a fullima ge. Ph ysical I/O opera tions ta ke place only whent h e i m a g e l i n e s n e e d e d a r e n o t a l r e a d y i n t h a tbuffer. b. Sma ll images (up t o 512 512 or 768 768on a 1995 work sta tion) can be rea d in once and t henbe pr ocessed by memory-to-memory operat ions. E x-ten sive in-core processing libra ries ar e implemen tedan d include all single-par ticle alignmen t operat ions.c. The IMAGIC 3D bufferin g scheme (Borla nd et al.,1988) allows fast random access within large 3D vol-um es with limited a vailable memory.

Fast Fourier Tran sform s. Very importa nt for vir-tu ally all processing with in th e IMAGIC system ar eFa st Four ier Tran sform s (FF Ts) in one, two, or thr eedimensions. We u se t he Singleton m ixed-ra dix FFTalgorit hm (Singleton 1969) for efficiency. The m ixed-radix FFT allows sampling of the data in a problem-oriented way. For example, if th e sam pling of a 3Dvolume on a 1283 grid is insufficient, with a radix-2algorithm one is forced to migrate to a 2563 gridrepresent ing an eight fold increase in file space andcomputat ional requirements , whereas a mixed-ra dix tra nsform allows one to use a , say, 1603 grid.A p a r t f r o m t h e S i n g l e t o n F F T a l g o r i t h m t h eIMAGIC 2D-FFT a nd 3D-FFT algorith ms a re ba sedon the TRANSPO algorithm (Van Heel, 1991a),one of the fastest ways of transposing large disk-based mu ltidimensiona l dat a sets. The combinat ion

of th ese algorit hm s allows for th e rout ine calcula tion

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of large m ultidimensiona l F FTs, like 10242 imagesor 6403 volumes.

Extending the system. The IMAGIC system is de-signed as a development platform for implementingnew image processing ideas . Some 10 differentSAMPLE program s, ea ch cont ain ing a ll n ecessar yIMAGIC infrastructure but differing in bufferingphilosophy, are available. These programs can beedited t o perform new t ask s. In-core libra ries for t he

fast processing of sma ll images, an d ex-core librar iescontaining equivalent operations on larger images,can be called from th e new pr ogram s.

DATA VISUALIZATION

F o r d i s p l a y i n g i m a g e s a n d p l o t t i n g c u r v e sIMAGIC r elies lar gely on th e X Windows routines.Like all IMAGIC programs, th e DISPLAY programby default can loop over all images present in a fileto produce galleries of images (Fig. 1a). The firstsurface rendering programs for the presentation of3D maps in electron microscopy were developed

within IMAGIC (Van Heel, 1983) and were updatedto include more elaborat e rend ering options (Saxton,1985). An effective procedure for visualizing 3Dstr uctureswhich can even be used in pr intis th econt inuous ster eo sequence (Van Heel, 1983) cover-ing, for example, a great circle of viewing directionsaround an object. By displaying the images in a gal-lery such t ha t a ll leftright n eighbors r epresent st e-reo pairs, a visual stereo ma tching of two neighborscauses al l pairs in the gallery to match s imulta-neously (Fig. 1a). Many sets of gradua lly cha ngingimages may be viewed in rapid succession using theMOVIE facility. An appea ling option is t he d isplay-ing of a pair of stereo images, each of which rotateswith tim e (Fig. 1b). This techn ique is one of the best3D stereo visualization techniques not requir ingspecial hardware.

ADVANCED METHODS

Multivariate statistical analysis. The MSA ap-proach, which allows the analysis of mixed popula-tions of images, was introduced to electron micros-copy some 15 years ago and is n ow an integral pa rtof many image analysis procedures (Van Heel andFrank, 1980; 1981). With the MSA techniques oneconsiders images as a l inear combination of thema in eigenvectors (eigenim ages) of t he set, th usreducing the total amount of data and facilitatinginterpretation. The eigenvector analysis was origi-nally performed using the 2 metric (Lebart et al.,1984) which is a good metr ic for h istogram dat a wit hinh eren t positivity but is not so good for genera l sig-nal processing. General signals, including phase-contrast EM images, may have a zero average den-sity, a situation which cannot be dealt with in strict

correspondence analysis other than either by adding

a const an t or by thr esholding th e negative densitiesaway. Thus, we n ow preferably use th e modulationmet ric (Borla nd a nd Van H eel, 1990; Van Heel et al.,1992b), alth ough th e MSA progra m a llows a flexiblechoice of metrics.

After the eigenvector eigenvalue data compres-sion, an au toma tic classification or cluster ing proce-dure operating on th e compressed dat a is essential.Our favored a pproach is t he hiera rchical ascendant

classification scheme (Lebart et al., 1984) in combi-nation with a moving elements postprocessor (VanHe el, 1984a , 1989; Borlan d an d Van He el, 1990). Theeigenvector and classification programs in the MSAmodule a re developed specifically for an alyzing verylarge disk-based data sets (100 000 molecular im-ages). An important application of MSA techniquesis to perform an exhaustive search for characteris-tic views of a molecule p resen t in a mixed popula-tion of m olecular images (Van Heel an d Stoffler-Meilicke, 1985) (Fig. 2). Similar molecular imagesare averaged into these characteristic views thus

str ongly reducing the n oise in t he ima ges (Ha ra uz etal., 1987b; Dube et al., 1993; Serysheva et al., 1995;Schatz et al., 1995).

Alignment techniques. The IMAGIC system hasextended facilities for rotational, translational, hor-izont al, and vertical alignmen ts concentra ted in t heALIGN module. These iterative schemes are basedon the classical cross-correlation function (CCF)(Steinkilberg a nd Schra mm, 1980) or, a lterna tively,on the improved mutual correlation function (MCF,Van Heel et al., 1992a). Exten sive facilities for mu l-t i r eferen ce a l ignmen t (Van Heel an d S t offler -Meilicke, 1985) allow a dat a set to be a ligned withrespect to a la rge set of reference images and ar e ofgrowing importance in the context of angular recon-stitution (see below, and Serysheva et al., 1995). AllIMAGIC alignment algorithms calculate the finalrotat ed an d shifted ima ge using only a single int er-polat ion step, even when a sequ ence of tr an slat iona land rotational alignments have been applied to theima ges. Equ ivalent rota tion a nd shift pa ra met ersare computed t hroughout th e alignment steps thusallowing the calculat ion of the en d r esults from th einput images directly, thus alleviating the loss ofresolution enta iled by mult iple int erpolat ions.

Reference-free alignm ents and sym m etry analysis.

Conventional alignments of a set of images with re-spect to a given reference, however, may bias thatdata set toward the properties of that reference im-age (Boekem a et al., 1986). To avoid such dat a bia s,a n umber of approaches have been implement ed in-cluding: r otat ionally and tra nslationally invariantfunction classification (Schatz and Van Heel, 1990,1992) , ro ta t ional a l ignment by c lass ificat ion of tra nslational invariant functions (Van Heel et al.,

1992b), and alignment by classification of transla-

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tionally centered molecular images (Dube et al.,1993). The latter approach is, at the same time, agood m ethod for determ ining th e rotat iona l symme-try of a large set of molecular images (Fig. 2) andsupersedes t he popular rotat ional power spectru mtechn ique of Crowther an d Amos (1971). The u se ofimage moments for reference-free classification ofdark-field and electron spectroscopic images hasalso been implemented and investigated (Beniacand Harauz, 1995).

Correlation averaging. Crystallographic averag-ing t aking imperfections of th e crystal into a ccountby unbending was one of the first options to beimplemented in t he IMAGIC system (Van Heel an dHollenberg, 1980). Convent iona l corr elat ion avera g-ing (Saxton and Baumeister, 1982) can now be usedto generate 3D reconstructions of 2D crystallo-gra phic specimens (Boekema et al., 1984; Schu ltz etal., 1993). These procedures, including transfer-function corr ection, were used to find 2D pr ojectionima ges of porin to 4 resolution (Sa et al., 1989).The newest IMAGIC corr elat ion a veraging appr oachallows the averaging of low-dose high-resolution im-ages of 2D crystals whereby the statistical differ-ences existing between un it cells are t aken into ac-count using the MSA classification facilities. Thesecorrelation averaging procedures may also exploitth e a dvanta ges of nonsquared corr elation functionslike the MCF.

A n g u l a r r ec on s t i t u t i o n . T h e m u l t i r e f e r e n c ealignment and MSA classification approaches de-scribed above allow us to obtain the various charac-

teristic projections of macromolecules in an EM

specimen . However, since th ese char acter istic viewsare not obtained through any tilting of the specimenholder, we do not a priori know what their relativeangular orientations are. Early methods for findingthese unknown Euler angles (Van Heel , 1984b;Ha ra uz a nd Van Heel, 1986) led to the developmentof the angular reconstitution approach (Van Heel,1987; Goncharov and Gelfand, 1988). Angular recon-stit ut ion is based on th e common line projection t he-orem sta tin g tha t t wo 2D projections of th e same 3Dobject ha ve at least one (1D) line pr ojection in com-mon. With th ree or more projections of an asym met -ric object, the relative orientation of all projections isfixed.

A number of refinements (Van Heel et al., 1992b;Orlova and Van Heel, 1994; Serysheva et al., 1995;Schatz et al., 1995) of angular reconstitution haveren dered th e techn ique one of th e most pr actical an droutine techniques for deter mining the 3D str uctureof lar ge ma cromolecula r assem blies (Fig. 1). The r e-finements include a better assignment of Euler an -gle to a cha ra cteristic projection image th rough th euse of anchor sets of reprojections, and improve-ments of the overall alignment of the data set bymulti-reference alignment of the full data set withrespect to a large set of reprojections covering theasymmet ric trian gle for th e given point-group sym-metry (Schatz et al., 1995). Since no tilt-series datacollection with mu ltiple exposur es of th e sa me spec-imen area is required, the angular reconstitutiontechnique is simple experimentally. No theoreticalupper limit to the resolution attainable by the an-

gular reconstitu tion technique is in sight. A 3D res-

F IG . 2. MSA symmetry analysis of a Lu m bricus terrestris hem oglobin d at a set collected from electr on images of specimen s embedd edin vitreous ice. The first step of this an alysis encompasses the centering of all molecular images using a n iterative tra nslational-onlyalignment procedure performed with r espect t o rotationally symmetrized total sums of the d ata set (Dube et al., 1993). Because of thepointgroup symmetry of the molecule an d of the prevalence of specific views, the main symmetry components of the dat a set emer geddirectly from the ana lysis of the full data set. The predominant symmetry property of annelid hemoglobins is sixfold a s illustr ated byeigenvectors N os. 2 and 3 (top row) which ar e 90 out of phase in a r otationa l sense. The t wofold symm etry a xes of th is 622 pointgroupsymm etric molecule (Royer et al., 1987; Boekema and Van Heel, 1989) are perpendicular to th e main sixfold axis and are described byhigher eigenvectors (Nos. 5, 6). Our symmetry analysis often serves a double purpose since this alignment by classification also is a

reference-free and unbiased first step toward finding the characteristic views of a molecule present in a mixed population of images(bottom row; Schatz et al., 1995).

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olution of around 15 (in all directions since thereis no missing cone) ha s alr eady been a chieved in th ecase of th e Lumbricus terrestris hemoglobin (publi-cat ion in pr epara tion).

T hree-dim ensional reconstruction. In electronmicroscopy th e first 3D r econstr uction schem es weredesigned for helically arranged polymers (DeRosieran d Moore, 1970) an d icosah edra l viru ses (Crowth eret al., 1970) an d were implemented in Fourier space

using polar coordinates . For 3D reconstructionsfrom tilt series of 2D crystals (Henderson and Un-win, 1975; Henderson et al., 1986; Kuhlbra ndt an dWang, 1991) methods are used which are based ontechniques originally developed for X-ray crystallog-raphy.

In the IMAGIC system the main current 3D re-cons t ruct ion a lgor i thm is the exact fi l ter back-projection algorithm (Harauz and Van Heel, 1986;Radermacher , 1988). In filtered back-projectiontechniques, the Fourier space filtering is generallyperformed using an analytical filter. This filter is

implicitly based on the a priori assumption that aninfinit e num ber of 2D projections of th e 3D stru ctur eis available and that their projection directions areu n i f o r m l y d i s t r i b u t e d o v e r a l l p o s s i b l e a n g l e s(Harauz and Van Heel, 1986). For the exact filteralgorithm, in contrast, a unique filter is applied toeach of the 2D projections taking into account theexplicit Fourier space overlap between all availableprojections. The algorithm was first used to solvevery large single-tilt-axis 3D reconst ru ctions of a h u-man chromosome (Harauz et al., 1987a; Borland etal., 1988). Our general-geometry algorithm can dealwith all possible reconstruction geometries, includ-ing t he conventiona l t omogra phy geometr y.

DI SCUSSI O N

Hardware developments. In the las t 20 years ,the speed of computing has increased 1000-fold,while t he m arket cost of data storage on ma gneticdisks ha s decreased by as mu ch. As a consequence,computer har dware considerat ions have shifted tothe background and software considerations havecome t o the foreground. Ten years ago manu factur -ers offered image processing systems based on spe-cial ha rdwar e to achieve a high processing t hr ough-put for specific tasks. The speed of processing withstandard workstation computers has now increasedso much that general purpose computers runningflexible software, written in portable high-level lan-guages such a s FORTRAN a nd C, often surpa ss theearlier ha rd-wired solutions.

Software continuity. The short lifespan of anycomputer hardwar e, and thu s of software orientedtoward that hardware, highlights the continuity as-pects of image processing projects which typically

ru n over m uch longer t ime scales. In th is light it is

notewort hy t ha t th e image processing softwar e sys-tems in use 15 years ago are still the main systemstoday (Hegerl, 1992). Man y projects ar e ru n by doc-toral students and continuity is endangered whenthe students leave the labs. The software tools cre-at ed in th e cour se of a project m ust be genera ted ina systematic way so t hey ar e overviewable by thenext generation of students. Developing the soft-ware in a s t ructured environment l ike IM AGIC

helps in consolidating know-how. Maintena nce an dcontinuity of software have become at least as im-port an t as th e ma intena nce of the m icroscope itself.We ha ve found it difficult to achieve long-term con-tinu ity in a strictly academic environm ent, an d th usin 1990 decided to commercialize the IMAGIC sys-tem.

C u r ren t a n d f u t u r e d e vel op m en t s . One of themost active corners of the IMAGIC system is theANGREC module, containing all the angular re-constitution programs. This module is being usedextensively to determ ine 3D str uctu res of symmet ric

particles such as the (622 symmetric) L. terrestrishem oglobin (Schat z et al., 1995), t he (222) L i m u l u s

polyphemus hemocyanin (Van Heel et al., 1994), th e(52) keyhole limpet hem ocyanin (Dube et al., 1995),th e port al protein str uctures with 13-fold rotationalsymmetr y (Fig. 1, and Dube et al., 1993), th e 4-foldsymmetr ic Ca2+-release channel (Serysheva et al.,1995), and for asymmetric part icles such as the

Escherichia coli ribosome (Sta rk et al., 1995). Theprograms are formulated in a Cartesian coordinatesystem and work for al l pointgroup symmetries .With our new approach one ma y relax the symmetr yrequirements and thus , for example, analyze anicosahedral virus (532) using only 5, 52, 32, or 222pointgroup symmetry. The angular reconstitutionapproach is a flexible tool for studying structurefunction relations in biology without crystallizingth e protein. In cases where crystals do exist t ha t a resuita ble for high-resolut ion X-ra y crysta llography,the low-resolution (1030 ) EM map of the oligo-mer ma y help find t he h igh-resolution ph ases of th eX-ra y diffra ction pat tern , a technique current ly be-ing explored with the giant hemoglobin ofL. terres-tris (Royer et al., 1987).

A unique development th at is tak ing place in oursystem is t he u nificat ion of the r econstr uction tech-niques for specific macromolecular organizationsinto a s ingle framework. Whereas earl ier recon-str uction program s were designed sp ecifically for he-lically arranged polymers (DeRosier and Moore,1970) or icosahedr al viru ses (Crowth er et al., 1970),th e progra ms in IMAGIC ar e form ula ted for th e gen-eral case. An icosahedral virus is a single particlewith 532 pointgroup symmetry and a helical fiber isa single particlethat repeats itself infinitely in one

direction. All tha t is required to an alyze h elical a s-

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semblies with the single-particle programs is a con-sistent approach on reinterpolating the data suchtha t the linear r epeats in th e helical assembly syn-chronize with the sampling raster and frame usedfor pr ocessin g (Fig. 3). By th e sam e token , a 2D crys-tal is a single particle that repeats itself in twodirections. Th e unificat ion of th ese a pproaches, u s-ing Car tesian coordina tes, cont ributes considerablyto the ease-of-use and ease-of-maintenance of thesepowerful str uctur e an alysis t echniques.

The IMAGIC system h as grea tly profited from continu ous feed-back from its man y users. We thank Professor E lmar Zeitler andthe Max Planck Society for supporting the development of theIMAGIC system.

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