Andrzej Kolinski

Simulations of the folding of a globular protein

Dynamic Monte Carlo simulations of the folding of a globular protein, apoplastocyanin, have been undertaken in the context of a new lattice model of proteins that includes both side chains and a-carbon backbone atoms and that can approximate native conformations at the level of 2 angstroms (root mean square) or better. Starting from random-coil unfolded states, the model apoplastocyanin was folded to a native conformation that is topologically similar to the real protein. The present simulations used a marginal propensity for local secondary structure consistent with but by no means enforcing the native conformation and a full hydrophobicity scale in which any nonbonded pair of side chains could interact. These molecules folded through a punctuated on-site mechanism of assembly where folding initiated at or near one of the turns ultimately found in the native conformation. Thus these simulations represent a partial solution to the globular-protein folding problem.

Structural genomics and its importance for gene function analysis

Structural genomics projects aim to solve the experimental structures of all possible protein folds. Such projects entail a conceptual shift from traditional structural biology in which structural information is obtained on known proteins to one in which the structure of a protein is determined first and the function assigned only later. Whereas the goal of converting protein structure into function can be accomplished by traditional sequence motif-based approaches, recent studies have shown that assignment of a proteins biochemical function can also be achieved by scanning its structure for a match to the geometry and chemical identity of a known active site. Importantly, this approach can use low-resolution structures provided by contemporary structure prediction methods. When applied to genomes, structural information (either experimental or predicted) is likely to play an important role in high-throughput function assignment.

Dynamic Monte Carlo simulations of a new lattice model of globular protein folding, structure and dynamics

A long-standing problem of molecular biology is the prediction of globular protein tertiary structure from the primary sequence. In the context of a new, 24-nearest-neighbor lattice model of proteins that includes both alpha and beta-carbon atoms, the requirements for folding to a unique four-member beta-barrel, four-helix bundles and a model alpha/beta-bundle have been explored. A number of distinct situations are examined, but the common requirements for the formation of a unique native conformation are tertiary interactions plus the presence of relatively small (but not irrelevant) intrinsic turn preferences that select out the native conformer from a manifold of compact states. When side-chains are explicitly included, there are many conformations having the same or a slightly greater number of side-chain contacts as in the native conformation, and it is the local intrinsic turn preferences that produce the conformational selectivity on collapse. The local preference for helix or beta-sheet secondary structure may be at odds with the secondary structure ultimately found in the native conformation. The requisite intrinsic turn populations are about 0.3% for beta-proteins, 2% for mixed alpha/beta-proteins and 6% for helix bundles. In addition, an idealized model of an allosteric conformational transition has been examined. Folding occurs predominantly by a sequential on-site assembly mechanism with folding initiating either at a turn or from an isolated helix or beta-strand (where appropriate). For helical and beta-protein models, similar folding pathways were obtained in diamond lattice simulations, using an entirely different set of local Monte Carlo moves. This argues strongly that the results are universal; that is, they are independent of lattice, protein model or the particular realization of Monte Carlo dynamics. Overall, these simulations demonstrate that the folding of all known protein motifs can be achieved in the context of a single class of lattice models that includes realistic backbone structures and idealized side-chains.

A general method for the prediction of the three dimensional structure and folding pathway of globular proteins: Application to designed helical proteins

Starting from amino acid sequence alone, a general approach for simulating folding into the molten globule or rigid, native state depending on sequence is described. In particular, the 3D folds of two simple designed proteins have been predicted using a Monte Carlo folding algorithm. The model employs a very flexible hybrid lattice representation of the protein conformation, and fast lattice dynamics. A full rotamer library for side group conformations, and potentials of mean force of short and long range interactions have been extracted from the statistics of a high resolution set of nonhomologous, 3D structures of globular proteins. The simulated folding process starts from an arbitrary random conformation and relatively rapidly assembles a well defined four helix bundle. The very cooperative folding of the model systems is facilitated by the proper definition of the model protein hydrogen bond network, and multibody interactions of the side groups. The two sequences studied exhibit very different behav...

Ab initio folding of proteins using restraints derived from evolutionary information.

We present our predictions in the ab initio structure prediction category of CASP3. Eleven targets were folded, using a method based on a Monte Carlo search driven by secondary and tertiary restraints derived from multiple sequence alignments. Our results can be qualitatively summarized as follows: The global fold can be considered “correct” for targets 65 and 74, “almost correct” for targets 64, 75, and 77, “half‐correct” for target 79, and “wrong” for targets 52, 56, 59, and 63. Target 72 has not yet been solved experimentally. On average, for small helical and alpha/beta proteins (on the order of 110 residues or smaller), the method predicted low resolution structures with a reasonably good prediction of the global topology. Most encouraging is that in some situations, such as with target 75 and, particularly, target 77, the method can predict a substantial portion of a rare or even a novel fold. However, the current method still fails on some beta proteins, proteins over the 110‐residue threshold, and sequences in which only a poor multiple sequence alignment can be built. On the other hand, for small proteins, the method gives results of quality at least similar to that of threading, with the advantage of not being restricted to known folds in the protein database. Overall, these results indicate that some progress has been made on the ab initio protein folding problem. Detailed information about our results can be obtained by connecting to http://www.bioinformatics.danforthcenter.org/CASP3. Proteins Suppl 1999;3:177–185.

TOUCHSTONE: An ab initio protein structure prediction method that uses threading-based tertiary restraints

The successful prediction of protein structure from amino acid sequence requires two features: an efficient conformational search algorithm and an energy function with a global minimum in the native state. As a step toward addressing both issues, a threading-based method of secondary and tertiary restraint prediction has been developed and applied to ab initio folding. Such restraints are derived by extracting consensus contacts and local secondary structure from at least weakly scoring structures that, in some cases, can lack any global similarity to the sequence of interest. Furthermore, to generate representative protein structures, a reduced lattice-based protein model is used with replica exchange Monte Carlo to explore conformational space. We report results on the application of this methodology, termed TOUCHSTONE, to 65 proteins whose lengths range from 39 to 146 residues. For 47 (40) proteins, a cluster centroid whose rms deviation from native is below 6.5 (5) Å is found in one of the five lowest energy centroids. The number of correctly predicted proteins increases to 50 when atomic detail is added and a knowledge-based atomic potential is combined with clustered and nonclustered structures for candidate selection. The combination of the ratio of the relative number of contacts to the protein length and the number of clusters generated by the folding algorithm is a reliable indicator of the likelihood of successful fold prediction, thereby opening the way for genome-scale ab initio folding.

Assessing Energy Functions for Flexible Docking

A good docking algorithm requires an energy function that is selective, in that it clearly differentiates correctly docked structures from misdocked ones, and that is efficient, meaning that a correctly docked structure can be identified quickly. We assess the selectivity and efficiency of a broad spectrum of energy functions, derived from systematic modifications of the CHARMM param19/toph19 energy function. In particular, we examine the effects of the dielectric constant, the solvation model, the scaling of surface charges, reduction of van der Waals repulsion, and nonbonded cutoffs. Based on an assessment of the energy functions for the docking of five different ligand–receptor complexes, we find that selective energy functions include a variety of distance‐dependent dielectric models together with truncation of the nonbonded interactions at 8 Å. We evaluate the docking efficiency, the mean number of docked structures per unit of time, of the more selective energy functions, using a simulated annealing molecular dynamics protocol. The largest improvements in efficiency come from a reduction of van der Waals repulsion and a reduction of surface charges. We note that the most selective potential is quite inefficient, although a hierarchical approach can be employed to take advantage of both selective and efficient energy functions. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 1612–1622, 1998

Coarse-Grained Protein Models and Their Applications.

The traditional computational modeling of protein structure, dynamics, and interactions remains difficult for many protein systems. It is mostly due to the size of protein conformational spaces and required simulation time scales that are still too large to be studied in atomistic detail. Lowering the level of protein representation from all-atom to coarse-grained opens up new possibilities for studying protein systems. In this review we provide an overview of coarse-grained models focusing on their design, including choices of representation, models of energy functions, sampling of conformational space, and applications in the modeling of protein structure, dynamics, and interactions. A more detailed description is given for applications of coarse-grained models suitable for efficient combinations with all-atom simulations in multiscale modeling strategies.

Generalized protein structure prediction based on combination of fold‐recognition with de novo folding and evaluation of models

To predict the tertiary structure of full‐length sequences of all targets in CASP6, regardless of their potential category (from easy comparative modeling to fold recognition to apparent new folds) we used a novel combination of two very different approaches developed independently in our laboratories, which ranked quite well in different categories in CASP5. First, the GeneSilico metaserver was used to identify domains, predict secondary structure, and generate fold recognition (FR) alignments, which were converted to full‐atom models using the “FRankensteins Monster” approach for comparative modeling (CM) by recombination of protein fragments. Additional models generated “de novo” by fully automated servers were obtained from the CASP website. All these models were evaluated by VERIFY3D, and residues with scores better than 0.2 were used as a source of spatial restraints. Second, a new implementation of the lattice‐based protein modeling tool CABS was used to carry out folding guided by the above‐mentioned restraints with the Replica Exchange Monte Carlo sampling technique. Decoys generated in the course of simulation were subject to the average linkage hierarchical clustering. For a representative decoy from each cluster, a full‐atom model was rebuilt. Finally, five models were selected for submission based on combination of various criteria, including the size, density, and average energy of the corresponding cluster, and the visual evaluation of the full‐atom structures and their relationship to the original templates. The combination of FRankenstein and CABS was one of the best‐performing algorithms over all categories in CASP6 (it is important to note that our human intervention was very limited, and all steps in our method can be easily automated). We were able to generate a number of very good models, especially in the Comparative Modeling and New Folds categories. Frequently, the best models were closer to the native structure than any of the templates used. The main problem we encountered was in the ranking of the final models (the only step of significant human intervention), due to the insufficient computational power, which precluded the possibility of full‐atom refinement and energy‐based evaluation. Proteins 2005;Suppl 7:84–90.

Derivation of protein-specific pair potentials based on weak sequence fragment similarity.

A method is presented for the derivation of knowledge‐based pair potentials that corrects for the various compositions of different proteins. The resulting statistical pair potential is more specific than that derived from previous approaches as assessed by gapless threading results. Additionally, a methodology is presented that interpolates between statistical potentials when no homologous examples to the protein of interest are in the structural database used to derive the potential, to a Go‐likepotential (in which native interactions are favorable and all nonnative interactions are not) when homologous proteins are present. For cases in which no protein exceeds 30% sequence identity, pairs of weakly homologous interacting fragments are employed to enhance the specificity of the potential. In gapless threading, the mean z score increases from −10.4 for the best statistical pair potential to −12.8 when the local sequence similarity, fragment‐based pair potentials are used. Examination of the ab initio structure prediction of four representative globular proteins consistently reveals a qualitative improvement in the yield of structures in the 4 to 6 Å rmsd from native range when the fragment‐based pair potential is used relative to that when the quasichemical pair potential is employed. This suggests that such protein‐specific potentials provide a significant advantage relative to generic quasichemical potentials. Proteins 2000;38:3–16. ©2000 Wiley‐Liss, Inc.