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Search of the optimal strategy for refinement of atomic models

 

P. Afonine,*, V.Y. Lunin#,* & A. Urzhumtsev*

 

$ Centre Charles Hermite, LORIA, Villers-ls-Nancy, 54602 France

# IMPB, Russian Academy of Sciences, Pushchino, 142290, Moscow Region, Russia

* LCM3B, UPRESA 7036 CNRS, Universit Henri Poincar, Nancy 1, B.P. 239, Facult des Sciences, Vandoeuvre-ls-Nancy, 54506 France

 

e-mail: afonine@lcm3b.uhp-nancy.fr

 

1.      Introduction

 

Recently it has been shown (Afonine et al, 2001; Lunin et al., 2002) that the approximation of the maximum likelihood criterion (ML) by a quadratic functional (Lunin & Urzhumtsev, 1999) allows to understand the features of the ML refinement and its advantages with respect to the traditional least-squares (LS) refinement. In this latter, the magnitudes of structure factors calculated from the current atomic model are fitted to the observed structure factor magnitudes by minimisation of

, (1)

The weights may reflect the accuracy of the observed magnitudes or some other effects, but most frequently the unit weights are used.

In the procedure that is usually referenced as ML-refinement the minimised criterion is the negative logarithm of the likelihood, the model-dependent part of which may be presented as

(2)

with

. (3)

For every reflection, its parameter depends on the reflection indexes and particular space group and the statistical parameters and , being the functions of the resolution, reflect the precision of the atomic parameters and the completeness of the model (see for example Lunin & Urzhumtsev, 1984; Read, 1986; Lunin & Skovoroda, 1997; Skovoroda & Lunin, 2000).

The approximation of the criterion (2,3) by a quadratic functional means its substitution by a functional

. (4)

where the target values are no longer the observed magnitudes and the non-unit weights are crucial for a successful refinement. The minimisation of this function we will call LS*-refinement.

Previously (Lunin et al., 2002) we have discussed that and in (4) may be represented as

, , (5)

where m(p) and n(p) are some functions defined in Lunin et al. (2002) and whose behaviour explains the features of the ML refinement.

Formula (5) shows that the parameters and , play the key role in the estimation of and and therefore in the whole refinement. In this article we discuss the best choice of and .

 

2.      Estimation of and

 

Several approaches can be suggested to estimate the parameters and . If there exists some probabilistic hypothesis about irremovable errors in the atomic model (for example, about a missing part of the model) then for several particular cases these parameters may be calculated explicitly (Urzhumtsev et al., 1996). In particular, in the case of an incomplete model, if the absent atoms are supposed to be distributed uniformly in the unit cell, these parameters may be calculated as

and , (6)

where fk(s) are atomic scattering factors of the absent atoms. It should be noted that in practice the exact number of missed atoms and their scattering factors can be known only approximately (for example, it is difficult to know the exact number of missed ordered solvent molecules).

Another way is to use likelihood-based estimates of these parameters when comparing the observed structure factor magnitudes with the ones corresponding to a starting atomic model (Lunin & Urzhumtsev, 1984; Read, 1986). It is important to note that the test set reflections (Brnger, 1992) only should be used (Lunin & Skovoroda, 1995; Skovoroda & Lunin, 2000). Eventually, these estimates can be recalculated iteratively during refinement.

These different ways to estimate and have been tested by comparison of LS-, ML- and various LS*-refinement approaches in order to suggest the best refinement strategy.

 

3.      Models and programs used for tests

 

Similarly to the previous work (Afonine et al., 2001; Lunin et al., 2002), the tests were carried out with CNS complex (Brnger et al., 1998) using the model of Fab fragment of monoclonal antibody (Fokine et al., 2000) which consists of 439 amino acid residues and 213 water molecules, 3593 atoms in total. The crystal belongs to the space group P212121 with the unit cell parameters a = 72.24 , b = 72.01 , c = 86.99 , one molecule per asymmetric unit.

For test purposes the values of Fobs at 2.2 resolution were simulated by the corresponding values calculated from the complete exact model and were used for all refinements. The errors in the atomic co-ordinates were introduced randomly and independently. Incomplete models were obtained by random deletion of atoms, both from the macromolecule and from the solvent.

 

4.      Choice of and

 

Several refinement strategies based on different choice of and through different estimation of and have been compared.

First of all, the parameters and have been calculated using the technique described previously (Lunin & Skovoroda, 1995; Skovoroda & Lunin, 2000) through the comparison of the magnitudes with the structure factors calculated from the starting model. These values were kept for the whole refinement process consisted of 800 cycles.

Secondly, the same method of the estimation of and has been applied but their values were recalculated every 400 or 200 refinement cycles, depending on the test.

Alternatively, the refinement was carried out using the estimations (6). In these tests the exact number of missed atoms and their scattering factors were supposed to be known.

Finally, the refinement was carried out with the mixed parameter values, = 1 for all reflection as in (6) and estimated from the comparison of with .

The start models with the mean coordinate errors of 0.5 and 0.7 respectively and with 0.5% and 3.0% of incompleteness were optimised using LS*-criterion (4). For comparison, corresponding LS- and ML-refinements were also done. The results of these tests are shown in Table 1. It can be remarked that, as it has been discussed (Afonine et al., 2001; Lunin et al., 2002), even a small quantity of absent atoms can already strongly influence on the quality of the refined model.

 

Table 1. Mean coordinate errors in the model after refinement using different criteria. Starting models have mean coordinate errors of Dst . The incompleteness Dabs of the models of 0.5% and 3.0% correspond to 18 and 108 atoms deleted, respectively. The number of cycles indicates the frequency with which the parameters of the corresponding criterion were recalculated (the frequency of parameters updating is not definitely known for ML). aF and bF stand for the parameters estimated from the magnitude comparison and bC stands for values calculated from (6). The final coordinate errors shown in italic indicate the cases where this error is higher than the starting error. The numbers in bold indicate the best refinement protocol for the given model.

 

criterion

LS*

aF,bF

 

 

LS*

a=1,bF

 

LS*

a=1,bC

LS

ML

No of cycles

1*800

2*400

4*200

1*800

2*400

4*200

1*800

1*800

800*1?

Dst Dabs

 

 

 

final

error

 

 

 

 

0.5 0.5%

0.320

0.140

0.103

0.358

0.156

0.127

0.111

0.212

0.108

3.0%

0.453

0.345

0.397

0.475

0.353

0.311

0.247

0.375

0.305

0.7 0.5%

0.784

0.636

0.468

0.633

0.491

0.388

0.284

0.397

0.353

3.0%

0.803

0.711

0.592

0.700

0.599

0.527

0.404

0.530

0.537

 

5. Influence of errors in the estimation of

 

The LS*-refinement with the parameters estimated through (6) gives systematically better results in comparison with other known strategies and was chosen as the best one for further tests. The estimation of in (6) depends on the number of missed atoms, on their type and on their temperature factors. The influence of possible errors in the estimation of these parameters from these 3 sources on the results of refinement has been studied.

First of all, the missed atoms were simulated by oxygens or by carbons with temperature factors as they were in the corresponding deleted atoms. No significant influence of such modification of the atomic type has been found (Table 2).

 

Table 2. Mean coordinate errors after LS*-refinement with the estimations (6) for different type assigned to missed atoms; CNO stands for the exact (mixed) type of atoms. Starting models have mean coordinate errors of Dst (in ). Dabs is incompleteness of the models in percents; the number in parenthesis is the corresponding number of deleted atoms. The final coordinate errors shown in italic indicate the cases where this error is higher than the starting error.

 

 

Dst

Dabs

Type

0.5 (18)

1.0 (36)

3.0 (108)

5.0 (180)

7.0 (252)

9.0 (325)

 

CNO

0.105

0.133

0.256

0.343

0.447

0.513

0.5

O

0.111

0.138

0.247

0.357

0.450

0.521

 

C

0.113

0.136

0.256

0.343

0.439

0.499

 

CNO

0.289

0.321

0.422

0.498

0.579

0.649

0.7

O

0.284

0.278

0.404

0.468

0.598

0.645

 

C

0.285

0.334

0.425

0.494

0.609

0.656

 

 

Table 3. Mean coordinate errors for different values <B> of the mean temperature factor assigned to missed atoms. Starting models have mean coordinate errors of Dst (in ). Dabs is incompleteness of the models; the number in parenthesis is the corresponding number of deleted atoms. The final coordinate errors shown in italic indicate the cases where this error is higher than the starting error.

 

<B>, Ų

Dst Dabs=

0.5 (18)

1.0 (36)

3.0 (108)

5.0 (180)

7.0 (252)

9.0 (325)

5

 

0.085

0.129

0.281

0.386

0.528

0.582

15

 

0.083

0.120

0.259

0.342

0.455

0.508

25

0.5

0.109

0.144

0.258

0.347

0.440

0.506

35

 

0.144

0.167

0.272

0.353

0.449

0.483

45

 

0.170

0.207

0.290

0.380

0.470

0.507

5

 

0.178

0.233

0.378

0.508

0.626

0.693

15

 

0.264

0.274

0.377

0.474

0.565

0.610

25

0.7

0.304

0.356

0.431

0.522

0.605

0.655

35

 

0.374

0.432

0.494

0.595

0.677

0.703

45

 

0.517

0.581

0.599

0.719

0.774

0.781

 

To study the influence of the estimated temperature factor on the minimisation process, the known values of B-factors of missed atoms (following the results of previous test, all these atoms were assigned to be carbons) were considered to be equal to the same value which varied from 5 to 80 2 in a series of runs while the mean value of the temperature factor for the deleted atoms varied in the limits 27-29 2. Table 3 shows that the variation of the estimated temperature factors of missing atoms by 15 2 around the mean values does not seriously affect the quality of the refined model.

Finally, the influence of a wrong estimation of the number of missed atoms has been studied. For this purpose the start model with 5.0% (180 atoms) of deleted atoms and introduced error of 0.5 was generated. Different estimations of the number of missing atoms were used to get the values and corresponding and . The error in this number of order of at least 25% practically did not influence the final coordinate errors.

 

 

6.                  Conclusions

 

The quadratic approximation of the maximum-likelihood-based criterion allows to understand better the features of the ML-based refinement and its advantages. Even more, this approximation allows to choose a better refinement strategy and to build its new quadratic functional the minimisation of which leads to better models that those obtained both by traditional LS and ML-based refinement.

In this quadratic functional, the corresponding target values and the weights are calculated using formulas (6) for the parameters and of the variable part of the likelihood function (2,3). These formulas allow to get such estimation for the ideally refined model without knowing directly its parameters and therefore to build the quadratic approximation of (2,3) at the point of its minimum and thus to improve the refinement criterion.

These estimations of and are quite insensible to the choice of the type of atoms supposed to be missed, to their mean B-factor estimation and to the estimation of the number of such missed atoms making such new refinement strategy quite robust.

 

Acknowledgment

 

The work was supported partially by RFBR grants 00-04-48175 and 01-07-90317, by CNRS, UHP and Region Lorraine through financial support. The authors thank C. Lecomte and E. Dodson for their interest to the project.

 

 

References

 

Afonine, P., Lunin, V.Y. & Urzhumtsev, A.G. (2001). CCP4 Newsletter on Protein Crystallography, 39, 52-56.

Brnger, A.T. (1992). Nature, 355, 472-474.

Brnger, A.T., Adams, P.D., Clore, G.M., DeLabo, W.L., Gros, P., Grosse-Kunstleve, R.W., Jiang, J.-S., Kuszewski, J., Nilges, M., Pannu, N.S., Read, R.J., Rice, L.M., Simonson, T. & Warren, G.L. (1998) Acta Cryst. D54, 905-921.

Fokine, A.V., Afonine, P.V., Mikhailova, I.Yu., Tsygannik, I.N., Mareeva, T.Yu., Nesmeyanov, V.A., Pangborn, W., Li, N., Duax, W., Siszak, E., Pletnev, V.Z. (2000). Rus. J. Bioorgan. Chem., 26, 512-519.

Lunin, V.Y., Afonine, P.V., Urzhumtsev, A. (2002). Acta Cryst., A, in press.

Lunin, V.Y. & Skovoroda, T.P. (1995). Acta Cryst., A51, 880-887.

Lunin, V.Y. & Urzhumtsev, A. (1984). Acta Cryst., A40, 269-277

Lunin, V.Y. & Urzhumtsev, A. (1999). CCP4 Newsletter on Protein Crystallography, 37, 14-28.

Skovoroda, T.P. & Lunin, V.Y. (2000). Crystallography Reports 45, part. 2, 195-198.

Urzhumtsev, A.G., Skovoroda, T.P. & Lunin, V.Y (1996). J.Appl.Cryst., 29, 741-744.

 


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