Jooyoung Lee

Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8.

A correct alignment is an essential requirement in homology modeling. Yet in order to bridge the structural gap between template and target, which may not only involve loop rearrangements, but also shifts of secondary structure elements and repacking of core residues, high‐resolution refinement methods with full atomic details are needed. Here, we describe four approaches that address this “last mile of the protein folding problem” and have performed well during CASP8, yielding physically realistic models: YASARA, which runs molecular dynamics simulations of models in explicit solvent, using a new partly knowledge‐based all atom force field derived from Amber, whose parameters have been optimized to minimize the damage done to protein crystal structures. The LEE‐SERVER, which makes extensive use of conformational space annealing to create alignments, to help Modeller build physically realistic models while satisfying input restraints from templates and CHARMM stereochemistry, and to remodel the side‐chains. ROSETTA, whose high resolution refinement protocol combines a physically realistic all atom force field with Monte Carlo minimization to allow the large conformational space to be sampled quickly. And finally UNDERTAKER, which creates a pool of candidate models from various templates and then optimizes them with an adaptive genetic algorithm, using a primarily empirical cost function that does not include bond angle, bond length, or other physics‐like terms. Proteins 2009.

New optimization method for conformational energy calculations on polypeptides: Conformational space annealing

A new optimization method is presented to search for the global minimum‐energy conformations of polypeptides. The method combines essential aspects of the build‐up procedure and the genetic algorithm, and it introduces the important concept of “conformational space annealing.” Instead of considering a single conformation, attention is focused on a population of conformations while new conformations are obtained by modifying a “seed conformation.” The annealing is carried out by introducing a distance cutoff, Dcut, which is defined in the conformational space; Dcut effectively divides the whole conformational space of local minima into subdivisions. The value of Dcut is set to a large number at the beginning of the algorithm to cover the whole conformational space, and annealing is achieved by slowly reducing it. Many distinct local minima designed to be distributed as far apart as possible in conformational space are investigated simultaneously. Therefore, the new method finds not only the global minimum‐energy conformation but also many other distinct local minima as by‐products. The method is tested on Met‐enkephalin, a 24‐dihedral angle problem. For all 100 independent runs, the accepted global minimum‐energy conformation was obtained after about 2600 minimizations on average.

Recent improvements in prediction of protein structure by global optimization of a potential energy function.

Recent improvements of a hierarchical ab initio or de novo approach for predicting both α and β structures of proteins are described. The united-residue energy function used in this procedure includes multibody interactions from a cumulant expansion of the free energy of polypeptide chains, with their relative weights determined by Z-score optimization. The critical initial stage of the hierarchical procedure involves a search of conformational space by the conformational space annealing (CSA) method, followed by optimization of an all-atom model. The procedure was assessed in a recent blind test of protein structure prediction (CASP4). The resulting lowest-energy structures of the target proteins (ranging in size from 70 to 244 residues) agreed with the experimental structures in many respects. The entire experimental structure of a cyclic α-helical protein of 70 residues was predicted to within 4.3 Å α-carbon (Cα) rms deviation (rmsd) whereas, for other α-helical proteins, fragments of roughly 60 residues were predicted to within 6.0 Å Cα rmsd. Whereas β structures can now be predicted with the new procedure, the success rate for α/β- and β-proteins is lower than that for α-proteins at present. For the β portions of α/β structures, the Cα rmsds are less than 6.0 Å for contiguous fragments of 30–40 residues; for one target, three fragments (of length 10, 23, and 28 residues, respectively) formed a compact part of the tertiary structure with a Cα rmsd less than 6.0 Å. Overall, these results constitute an important step toward the ab initio prediction of protein structure solely from the amino acid sequence.

Unbiased global optimization of Lennard-Jones clusters for N < or =201 using the conformational space annealing method.

We apply the conformational space annealing method to the Lennard-Jones clusters and find all known lowest energy configurations up to 201 atoms, without using extra information of the problem such as the structures of the known global energy minima. In addition, the robustness of the algorithm with respect to the randomness of initial conditions of the problem is demonstrated by ten successful independent runs up to 183 atoms. Our results indicate that this method is a general and yet efficient global optimization algorithm applicable to many systems.

Structural studies of a bacterial condensin complex reveal ATP-dependent disruption of intersubunit interactions.

Condensins are key mediators of chromosome condensation across organisms. Like other condensins, the bacterial MukBEF condensin complex consists of an SMC family protein dimer containing two ATPase head domains, MukB, and two interacting subunits, MukE and MukF. We report complete structural views of the intersubunit interactions of this condensin along with ensuing studies that reveal a role for the ATPase activity of MukB. MukE and MukF together form an elongated dimeric frame, and MukFs C-terminal winged-helix domains (C-WHDs) bind MukB heads to constitute closed ring-like structures. Surprisingly, one of the two bound C-WHDs is forced to detach upon ATP-mediated engagement of MukB heads. This detachment reaction depends on the linker segment preceding the C-WHD, and mutations on the linker restrict cell growth. Thus ATP-dependent transient disruption of the MukB-MukF interaction, which creates openings in condensin ring structures, is likely to be a critical feature of the functional mechanism of condensins.

Calculation of protein conformation by global optimization of a potential energy function.

A novel hierarchical approach to protein folding has been applied to compute the unknown structures of seven target proteins provided by CASP3. The approach is based exclusively on the global optimization of a potential energy function for a united‐residue model by conformational space annealing, followed by energy refinement using an all‐atom potential. Comparison of the submitted models for five globular proteins with the experimental structures shows that the conformations of large fragments (∼60 aa) were predicted with rmsds of 4.2–6.8 Å for the Cα atoms. Our lowest‐energy models for targets T0056 and T0061 were particularly successful, producing the correct fold of approximately 52% and 80% of the structures, respectively. These results support the thermodynamic hypothesis that protein structure can be computed solely by global optimization of a potential energy function for a given amino acid sequence. Proteins Suppl 1999;3:204–208.

PPRODO: Prediction of protein domain boundaries using neural networks

Successful prediction of protein domain boundaries provides valuable information not only for the computational structure prediction of multidomain proteins but also for the experimental structure determination. Since protein sequences of multiple domains may contain much information regarding evolutionary processes such as gene–exon shuffling, this information can be detected by analyzing the position‐specific scoring matrix (PSSM) generated by PSI‐BLAST. We have presented a method, PPRODO (Prediction of PROtein DOmain boundaries) that predicts domain boundaries of proteins from sequence information by a neural network. The network is trained and tested using the values obtained from the PSSM generated by PSI‐BLAST. A 10‐fold cross‐validation technique is performed to obtain the parameters of neural networks using a nonredundant set of 522 proteins containing 2 contiguous domains. PPRODO provides good and consistent results for the prediction of domain boundaries, with accuracy of about 66% using the ±20 residue criterion. The PPRODO source code, as well as all data sets used in this work, are available from http://gene.kias.re.kr/∼jlee/pprodo/. Proteins 2005.

Prediction of protein tertiary structure using PROFESY, a novel method based on fragment assembly and conformational space annealing

A novel method for ab initio prediction of protein tertiary structures, PROFESY (PROFile Enumerating SYstem), is proposed. This method utilizes the secondary structure prediction information of a query sequence and the fragment assembly procedure based on global optimization. Fifteen‐residue‐long fragment libraries are constructed using the secondary structure prediction method PREDICT, and fragments in these libraries are assembled to generate full‐length chains of a query protein. Tertiary structures of 50 to 100 conformations are obtained by minimizing an energy function for proteins, using the conformational space annealing method that enables one to sample diverse low‐lying local minima of the energy. We apply PROFESY for benchmark tests to proteins with known structures to demonstrate its feasibility. In addition, we participated in CASP5 and applied PROFESY to four new‐fold targets for blind prediction. The results are quite promising, despite the fact that PROFESY was in its early stages of development. In particular, PROFESY successfully provided us the best model‐one structure for the target T0161. Proteins 2004.

High accuracy template based modeling by global optimization.

For high‐accuracy template‐based‐modeling of CASP7 targets, we have applied a procedure based on the rigorous optimization of score functions at three stages: multiple alignment, chain building, and side‐chain modeling. We applied the conformational space annealing method to a newly developed consistency based score function for multiple alignment. For chain building, we optimized the MODELLER energy function. For side‐chain modeling, we optimized a SCWRL‐like energy function using a rotamer library constructed specifically for a given target sequence. By rigorous optimization, we have achieved significant improvement in backbone as well as side‐chain modeling for TBM and TBM/HA targets. For most TBM/HA targets (17/26), the predicted model was more accurate than the model one can construct from the best template in a posteriori fashion. It appears that the current method can extract relevant information out of multiple templates. Proteins 2007.

Efficient parallel algorithms in global optimization of potential energy functions for peptides, proteins, and crystals

Global optimization is playing an increasing role in physics, chemistry, and biophysical chemistry. One of the most important applications of global optimization is to find the global minima of the potential energy of molecules or molecular assemblies, such as crystals. The solution of this problem typically requires huge computational effort. Even the fastest processor available is not fast enough to carry out this kind of computation in real time for the problems of real interest, e.g., protein and crystal structure prediction. One way to circumvent this problem is to take advantage of massively parallel computing. In this paper, we provide several examples of parallel implementations of global optimization algorithms developed in our laboratory. All of these examples follow the master/worker approach. Most of the methods are parallelized on the algorithmic (coarse-grain) level and one example of fine-grain parallelism is given, in which the function evaluation itself is computationally expensive. All parallel algorithms were initially implemented on an IBM/SP2 (distributed-memory) machine. In all cases, however, message passing is handled through the standard Message Passing Interface (MPI); consequently the algorithms can also be implemented on any distributed- or shared-memory system that runs MPI. The efficiency of these implementations is discussed.