Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins.

http://robotics.stanford.edu/~latombe/projects/itr-biogeo/JMB_fragments.pdf

Small libraries of protein fragments model native protein structures accurately.

A new optimization protocol is proposed which is based on a combination of a mean field approximation and simulated annealing. Instead of optimizing the energy of the real system the energy of a new mean field system is minimized. The global minimum of the new system and the original system is the same. The mean‐field optimization is advantageous to the optimization of the real system since (a) More statistics are obtained for alternative solutions and (b) the barrier heights separating the minima are reduced compared to the real system. Computational examples are provided for placement of side chains in tetrapeptides and in a small protein BPTI.

Modeling side chains in peptides and proteins: Application of the locally enhanced sampling and the simulated annealing methods to find minimum energy conformations

This review attempts a critical stock-taking of the current state of the science aimed at predicting structural features of proteins from their amino acid sequences. At the primary structure level, methods are considered for detection of remotely related sequences and for recognizing amino acid patterns to predict posttranslational modifications and binding sites. The techniques involving secondary structural features include prediction of secondary structure, membrane-spanning regions, and secondary structural class. At the tertiary structural level, methods for threading a sequence into a mainchain fold, homology modeling and assigning sequences to protein families with similar folds are discussed. A literature analysis suggests that, to date, threading techniques are not able to show their superiority over sequence pattern recognition methods. Recent progress in the state of ab initio structure calculation is reviewed in detail. The analysis shows that many structural features can be predicted from the amino acid sequence much better than just a few years ago and with attendant utility in experimental research. Best prediction can be achieved for new protein sequences that can be assigned to well-studied protein families. For single sequences without homologues, the folding problem has not yet been solved.

Protein structure prediction: recognition of primary, secondary, and tertiary structural features from amino acid sequence.

Empirical conformational energy functions are used to try to compute the three-dimensional structures of polypeptides and proteins. The conformational energy surfaces of such molecules have many local minima, and conventional energy minimization procedures reach only a local minimum (near the starting point of the optimization algorithm) instead of the global minimum (the multiple-minima problem). Several procedures have been developed to surmount this problem. A summary is given here of five of these methods, (i) build-up, (ii) Monte Carlo-plus- minimization (MCM), (iii) relaxation of dimensionality, (iv) pattern-recognition-based importance-sampling minimization (PRISM), and (v) the diffusion equation method which smoothes out the potential surface, leaving only the potential well containing the global minimum. These and other procedures have been applied to a variety of polypeptide structural problems. These include the computation of the structures of open-chain and cyclic peptides, fibrous proteins and globular proteins. Present efforts are being devoted to scaling up these procedures from small polypeptides to proteins, to try to compute the three-dimensional structure of a protein from its amino sequence.

Conformational Energy Calculations on Polypeptides and Proteins

Conformations of bovine pancreatic trypsin inhibitor were calculated by assuming that the final structure as well as properly chosen overlapping segments thereof are simultaneously in low-energy (not necessarily the lowest-energy) conformational states. Therefore, the whole chain can be built up from building blocks whose conformations are determined primarily by short-range interactions. Our earlier buildup procedure was modified by taking account of a statistical analysis of known amino acid sequences that indicates that there is nonrandom pairing of amino acid residues in short segments along the chain, and by carrying out energy minimization on only these segments and on the whole chain [without minimizing the energies of intermediate-size segments (20-30 residues long)]. Results of this statistical analysis were used to determine the variable sizes of the overlapping oligopeptide building blocks used in the calculations; these varied from tripeptides to octapeptides, depending on the amino acid sequence. Successive stages of approximations were used to combine the low-energy conformations of these building blocks in order to keep the number of variables in the computations to a manageable size. The calculations led to a limited number of conformations of the protein (only two different groups, with very similar structure within each group), most residues of which were in the same conformational state as in the native structure.

https://www.pnas.org/content/pnas/88/9/3661.full.pdf

Calculation of protein conformation as an assembly of stable overlapping segments: application to bovine pancreatic trypsin inhibitor.

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Calculation of protein conformation as an assembly of stable overlapping segments: application to bovine pancreatic trypsin inhibitor.