Nucleic Acids Research, 2006, Vol. 34, Web Server issue W237 Job options Users can choose either to redesign the whole protein with all 20 amino acids considered at each sequence position, or to redesign part of the target protein as speciﬁed in a resﬁle. Because RosettaDesign uses a stochastic sampling algorithm to identify low energy sequences, different simulations will not necessarily give identical results. Users can choose to repeat the same simulation up to 10 times with a single job submission. Output files The simulation results are compressed as a zip ﬁle that unzips into three ﬁles: a log ﬁle indicating what commands were used for the simulation, a text ﬁle with a list of the mutations that were made, and a third ﬁle that provides the coordinates in PDB format along with the energies of the redesigned protein. If a run does not ﬁnish, the server will email the user the suspected reason for failure. There are three sections of the PDB ﬁle pertaining to the energy of the redesigned protein: The ﬁrst part is a list of scores. Except for the reference energies, a lower score is better. The second section of ener- gies is a table with the energy of each residue in the protein (Table 1). In the cases in which an energy depends on two atoms in separate residues (for instance the Lennard–Jones energy), half of the energy is assigned to each residue. The Table 1. The scores relevant to protein design Energy type Description Total The total score for the designed protein (lower is better) LJatr Attractive portion of the Lennard–Jones potential (rewards close contacts) LJrep Repulsive portion of the Lennard–Jones potential (penalizes overlaps) LKsol Lazaridis–Karplus solvation model (penalizes buried polars) (13) Erot lnP(rot|aa,phi,psi), internal energy of side chain rotamers as derived from Dunbrack’s statistics (15) Eintra Intra-residue steric clashes Ehbnd Kortemme hydrogen bonding potential (14) Epair Pair score based on the probability of seeing two amino acids near each other in the PDB (favors salt bridges) (16) Eaa_phipsi lnP(aa|phi,psi), amino acid phi,psi preferences Hb_sc Sidechain-sidechain and sidechain-backbone hydrogen bond energy Hb_srbb Backbone-backbone hbonds close in primary sequence Hb_lrbb Backbone-backbone hbonds distant in primary sequence Eref Reference energy derived from amino acid composition Egb Generalized born solvation energy (this is not used by the server) Eh2o, Eh2o_hb Energies from explicit waters (this is not used by the server) Ecst Constraint energies (this is not used by the server) Eres Total energy for the residue (lower is better) SASApack SASApack is related to the void volume in a protein. Surface areas are computed with a 1.4 s probe and 0.5 s probe and the difference (ASA_0.5 - ASA_1.4) is compared to the expected difference for a particular residue type in a particular environment. A negative value is favorable and indicates that the residue is more tightly packed than is seen in average pdb files. third section is a table of measured energies—expected ener- gies. Expected energies are derived by calculating the average energies of the different amino acids as a function of buried- ness in a large set of proteins from the PDB. For instance, in the PDB leucines with 20 neighbors (residues within 10 s) have an average Lennard–Jones score of 3.79 kcal/mol. If a leucine in the redesigned protein has 20 neighbors and has a Lennard–Jones energy of 4.2 kcal/mol, then it indicates that leucine is more tightly packed than the average leucine in the PDB. In general, we have found this table especially useful in the design of new protein structures, as it allows one to estim- ate how much the designed protein resembles proteins found in nature. SERVER PERFORMANCE There have been 3000 jobs submitted by more than 320 clients to the RosettaDesign web server since March 2005. The server can accept proteins as large as 1000 residues and can redesign up to 200 residues in one simulation. The web site is set up as an apache server with a daemon that automatically invokes the Rosetta++ executable with the users input ﬁle and options obtained from the web interface. The user’s input ﬁles, job options, and the results are recorded in a mySQL database via a php-http module. A maximum of two jobs can be run at the same time. The daemon checks the mySQL database for pend- ing jobs every minute. For proteins between 100 and 200 residues, the simulation typically ﬁnishes in 5–30 min. Accuracy of the RosettaDesign server In a large scale test of RosettaDesign, the program was used to completely redesign nine naturally occurring proteins (9). The redesigned sequences were on average 35% identical to the wild-type sequence. Five out of the nine proteins were well- folded as evidenced by NMR and thermal and chemical dena- turation experiments. All ﬁve of the well-folded proteins had higher thermal unfolding midpoints than the wild-type sequence. RosettaDesign has also been used to redesign small regions of a protein to increase protein stability or bind- ing afﬁnities (10,17,18). In many of these cases, lower free energies were obtained by building additional hydrophobic interactions. RosettaDesign has had less success with creating buried hydrogen bond networks. This is presumably because hydrogen bonds are very sensitive to small changes in distance and orientation, and desolvation penalties are difﬁcult to cal- culate accurately. Because the RosettaDesign energy function favors like amino acids being near other (polars with polars, hydrophobics with hydrophobics) it will in some cases design large patches of hydrophobic amino acids on the surface of a protein. Although this may be favorable for protein stability, it can lead to aggregation of the protein. In this event, the user can force a small set of residues in the center of the patch to be polar, and this in general will encourage RosettaDesign to put polar residues at the neighboring positions as well. Possible uses for the RosettaDesign server Over the last 10 years protein design software has been applied to a large number of interesting problems. Several laboratories have used sequence optimization algorithms to explore the