f prediction accuracy? We shall see. 2001 Academic Press INTRODUCTION History. Linus Pauling correctly guessed the formation of helices and strands (14, 15) (and falsely hypothesized other structures). Three years before Pauling's guess was verified by the publications of the first X-ray structures (16, 17), one group had already ventured to predict secondary structure from sequence (18). The first-generation prediction methods following in the 1960s and 1970s were all based on single amino acid propensities (19). The second-generation methods dominating the scene until the early 1990s used propensities for segments of 3--51 adjacent residues (19). Basically any imaginable theoretical algorithm had been applied to the problem of predicting secondary structure from sequence. However, it seemed that prediction accuracy stalled at levels slightly above 60% (percentage of residues predicted correctly in one of the three states: helix, strand, and other). The reason for this limit was the

We present the results of a large-scale testing of the ROSETTA method for ab initio protein structure prediction. Models were generated for two independently generated lists of small proteins (up to 150 amino acid residues), and the results were evaluated using traditional rmsd based measures and a novel measure based on the structure-based comparison of the models to the structures in the PDB using DALI. For 111 of 136 all a and a/b proteins 50 to 150 residues in length, the method produced at least one model within 7 AÊ rmsd of the native structure in 1000 attempts. For 60 of these proteins, the closest structure match in the PDB to at least one of the ten most frequently generated conformations was found to be structurally related (four standard deviations above background) to the native protein. These results suggest that ab initio structure prediction approaches may soon be useful for generating low resolution models and identifying distantly related proteins with similar structures and perhaps functions for these classes of proteins on the genome scale.

As the number of gene sequences in databases, public and private, increase dramatically, so do the number of genes of unknown function. Of the protein sequences currently available approximately

uses information from known proteins to predict secondary structure, but not in the tertiary structure predictions or in generating the terms of the physics-based energy function. Our approach is also characterized by the use of an all atom energy function that includes a novel hydrophobic solvation function derived from experiments that shows promising ability for energy discrimination against misfolded structures. We present the results obtained using our SPSC method and energy function for blind prediction in the 4 th Critical Assessment of Techniques for Protein Structure Prediction competition, and show that our approach is more effective on targets for which less information from known proteins is available. In fact our SPSC method produced the best prediction for one of the most difficult targets of the competition, a new fold protein of 240 amino acids.

s, creating a variety of possible templates for 3D structures of 7TM proteins, including GPCRs. These templates may provide experimentalists with various plausible options for 3D structure of a given GPCR; in our view, only experiments will determine the final choice of the most reasonable 3D template. G-protein coupled receptors (GPCRs) are transmembrane proteins with 7-membered transmembrane helical bundles (7TM proteins). Until recently, this view has been based on indirect evi- Vol. 48 No. 1/2001 53--64 QUARTERLY # Presented at the International Conference on "Conformation of Peptides, Proteins and Nucleic Acids", Debrzyno, Poland, 2000. # The authors wish to thank the Monsanto Company and the US National Institutes of Health for grant support (EY12113, GM48184 and HL54085). # Correspondence should be addressed to: Gregory V. Nikiforovich, phone: (314) 362 1566; fax: (314) 362 0234; e-mail: gregory@ibc

A phenomenon as complex as protein folding requires a complex model to approximate it. This thesis presents a bottom-up approach for building complex probabilistic models of protein secondary structure by incorporating the multiple information sources which we call experts. Expert opinions are represented by probability distributions over the set of possible structures. Bayesian treatment of a group of experts results in a consensus opinion that combines the experts’ probability distributions using the operators of normalized product, quotient and exponentiation. The expression of this consensus opinion simplifies to a product of the expert opinions with two assumptions: (1) balanced training of experts, i.e., uniform prior probability over all structures, and (2) conditional independence between expert opinions, given the structure. This research also studies how Markov chains and hidden Markov models may be used to

Ab initio protein structure prediction remains one of the most important unsolved problems in molecular biophysics after 30 years of intensive research. This problem is in principle solvable: if we know the exact formulation of the physical micro-environment within a cell where proteins fold, we will be able to mimic the folding process in nature by computing the molecular dynamics based on our knowledge of the physical laws (McCarmmon & Harvey, 1987; van Gunsteren, 1998; Duan & Kollman, 1998). Complementarily, we can rely on the much-debated thermodynamic hypothesis, i.e. that the native protein structure is thermodynamically stable and is located at the global free energy minimum (Annsen, 1973). However, we do not yet have a complete understanding of the driving forces behind protein folding. Perturbations introduced by errors in the potential energy landscape may possibly result in a different folding pathway and a different folded structure. Even if we have an accurat...

this report we illustrate how ab initio protein structure prediction can potentially contribute to genome annotation using as examples several of our blind protein structure predictions from CASP3 and CASP4. As many of the structures of the CASP4 prediction targets are not currently available publicly, we focus on the small number of proteins whose structures have already been published. A more complete description of the CASP4 ab initio structure predictions will be published in an upcoming supplemental issue of Proteins: Structure Function and Genetics

this paper to discuss the mathematical properties of MAX-CMO in detail as this has been dealt elsewhere [13],[23], [1]. In this paper we compare three algorithms that can be used to obtain maximum contact map overlaps between protein structures. We will point to the weaknesses and strengths of each one. It is our hope that this paper will encourage researchers to develop new and improve methods for protein comparison based on MAX-CMO. (Protein Structure Comparison; Local-Global Alignment; Genetic Algorithms; Memetic Algorithms; Lagrangian Relaxation) 1