HKL-3000R

HKL-3000R [1,2] represents a new approach, integrating data collection, data reduction, phasing and model building to significantly accelerate the process of structure determination and on average minimize the number of data sets and synchrotron time required for structure solution.

The system, when run in semiautomatic mode, provides the experimenter the ability to check the most important parameters defining the quality of the diffraction data and gives insight into the particular steps of the structure elucidation process. In most cases, using the default settings of the program results in a highly complete model of a macromolecule. Moreover, such a semiautomatic pipeline of structure determination at every step provides feedback to the experimenter and, in the worst case, shows why a particular experiment failed, which is not possible in the case of "brute force" automation.

Raw-image data reduction and analysis is performed by the standard HKL-2000® package. For projects of known protein sequence, the system suggests the optimal input parameters for each step and provides sophisticated analysis of the outcome of each step. The analysis of results from each step is used to optimize input parameters for every subsequent step. The experimenter has the ability to adjust hundreds of parameters. Moreover, results can be sorted in several ways, one solution may be compared with others and any solution can be selected for subsequent steps. 

The HKL-3000R pipeline incorporates many formidable and widely used programs like CCP4 [3], SOLVE [4], RESOLVE [5], MLPHARE [6], SHELXD and SHELXE [7], ARP/wARP [8], DM [9], MOLREP [10], REFMAC [11], and Molprobity [12]. These programs must be obtained directly from their respective authors.

References: 

1. Minor W, Cymborowski M, Otwinowski Z, Chruszcz M (2006) HKL-3000: the integration of data reduction and structure solution—from diffraction images to an initial model in minutes. Acta Crystallogr D Biol Crystallogr 62:859-866
2. M. Cymborowski, M. Klimecka, M. Chruszcz, M. D. Zimmerman, I. A. Shumilin, D. Borek, K. Lazarski, A. Joachimiak, Z. Otwinowski, W. Anderson, W. Minor (2010) To automate or not to automate: this is the question. J Struct Funct Genomics; DOI 10.1007/s10969-010-9092-9
3. CCP4 (1994) The CCP4 suite: programs for protein crystallography. Acta Crystallogr D Biol Crystallogr 50:760-763
4. Terwilliger TC, Berendzen J (1999) Automated MAD and MIR structure solution. Acta Crystallogr D Biol Crystallogr 55: 849-861
5. Terwilliger TC (2003) Automated main-chain model building by template matching and iterative fragment extension. Acta Crystallogr D Biol Crystallogr 59:38-44 v
6. Otwinowski Z (1991) ML-PHARE. in CCP4, SERC Daresbury Laboratory, Warrington, UK
7. Sheldrick G (2008) A short history of SHELX. Acta Crystallogr A 64:112-122
8. Perrakis A, Morris R, Lamzin VS (1999) Automated protein model building combined with iterative structure refinement. Nat Struct Biol 6:458-463
9. Cowtan K (1994) DM: an automated procedure for phase improvement by density modification. Joint CCP4 and ESFEACBM Newsletter on Protein Crystallogr 31:34-38
10. Vagin A, Teplyakov A (1997) MOLREP: an automatic program for molecular replacement. J Appl Cryst 30:1022-1025 
11. Murshudov GN, Vagin AA, Dodson EJ (1997) Refinement of macromolecular structures by the maximum-likelihood method. Acta Crystallogr D Biol Crystallogr 53:240-255
12. Davis IW, Leaver-Fay A, Chen VB, Block JN, Kapral GJ, Wang X, Murray LW, Arendall WB III, Snoeyink J, Richardson JS, Richardson DC (2007) MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Res 35:W375-W383