RosettaBackrub--a web server for flexible backbone protein structure modeling and design.

The RosettaBackrub server (http://kortemmelab.ucsf.edu/backrub) implements the Backrub method, derived from observations of alternative conformations in high-resolution protein crystal structures, for flexible backbone protein modeling. Backrub modeling is applied to three related applications using the Rosetta program for structure prediction and design: (I) modeling of structures of point mutations, (II) generating protein conformational ensembles and designing sequences consistent with these conformations and (III) predicting tolerated sequences at protein-protein interfaces. The three protocols have been validated on experimental data. Starting from a user-provided single input protein structure in PDB format, the server generates near-native conformational ensembles. The predicted conformations and sequences can be used for different applications, such as to guide mutagenesis experiments, for ensemble-docking approaches or to generate sequence libraries for protein design

Practically Useful: What the Rosetta Protein Modeling Suite Can Do for You

The objective of this review is to enable researchers to use the software package ROSETTA for biochemical and biomedicinal studies. We provide a brief review of the six most frequent research problems tackled with ROSETTA. For each of these six tasks, we provide a tutorial that illustrates a basic ROSETTA protocol. The ROSETTA method was originally developed for de novo protein structure prediction and is regularly one of the best performers in the community-wide biennial Critical Assessment of Structure Prediction. Predictions for protein domains with fewer than 125 amino acids regularly have a backbone root-mean-square deviation of better than 5.0 A ˚. More impressively, there are several cases in which ROSETTA has been used to predict structures with atomic level accuracy better than 2.5 A ˚. In addition to de novo structure prediction, ROSETTA also has methods for molecular docking, homology modeling, determining protein structures from sparse experimental NMR or EPR data, and protein design. ROSETTA has been used to accurately design a novel protein structure, predict the structure of protein-protein complexes, design altered specificity protein-protein and protein-DNA interactions, and stabilize proteins and protein complexes. Most recently, ROSETTA has been used to solve the X-ray crystallographic phase problem. ROSETTA is a unified software package for protein structure prediction and functional design. It has been used to predic

Transferable coarse-grained potential for de novo\textit{de novo} protein folding and design

Protein folding and design are major biophysical problems, the solution of which would lead to important applications especially in medicine. Here a novel protein model capable of simultaneously provide quantitative protein design and folding is introduced. With computer simulations it is shown that, for a large set of real protein structures, the model produces designed sequences with similar physical properties to the corresponding natural occurring sequences. The designed sequences are not yet fully realistic and require further experimental testing. For an independent set of proteins, notoriously difficult to fold, the correct folding of both the designed and the natural sequences is also demonstrated. The folding properties are characterized by free energy calculations. which not only are consistent among natural and designed proteins, but we also show a remarkable precision when the folded structures are compared to the experimentally determined ones. Ultimately, this novel coarse-grained protein model is unique in the combination of its fundamental three features: its simplicity, its ability to produce natural foldable designed sequences, and its structure prediction precision. The latter demonstrated by free energy calculations. It is also remarkable that low frustration sequences can be obtained with such a simple and universal design procedure, and that the folding of natural proteins shows funnelled free energy landscapes without the need of any potentials based on the native structure

Formulation of Hybrid Knowledge-Based/Molecular Mechanics Potentials for Protein Structure Refinement and a Novel Graph Theoretical Protein Structure Comparison and Analysis Technique

Proteins are the fundamental machinery that enables the functions of life. It is critical to understand them not just for basic biology, but also to enable medical advances. The field of protein structure prediction is concerned with developing computational techniques to predict protein structure and function from a protein’s amino acid sequence, encoded for directly in DNA, alone. Despite much progress since the first computational models in the late 1960’s, techniques for the prediction of protein structure still cannot reliably produce structures of high enough accuracy to enable desired applications such as rational drug design. Protein structure refinement is the process of modifying a predicted model of a protein to bring it closer to its native state. In this dissertation a protein structure refinement technique, that of potential energy minimization using hybrid molecular mechanics/knowledge based potential energy functions is examined in detail. The generation of the knowledge-based component is critically analyzed, and in the end, a potential that is a modest improvement over the original is presented. This dissertation also examines the task of protein structure comparison. In evaluating various protein structure prediction techniques, it is crucial to be able to compare produced models against known structures to understand how well the technique performs. A novel technique is proposed that allows an in-depth yet intuitive evaluation of the local similarities between protein structures. Based on a graph analysis of pairwise atomic distance similarities, multiple regions of structural similarity can be identified between structures independently of relative orientation. Multidomain structures can be evaluated and this technique can be combined with global measures of similarity such as the global distance test. This method of comparison is expected to have broad applications in rational drug design, the evolutionary study of protein structures, and in the analysis of the protein structure prediction effort

Toward a Database of Geometric Interrelationships of Protein Secondary Structure Elements for De Novo Protein Design, Prediction and Analysis

Computational methods of analyzing, simulating, and modeling proteins are essential towards understanding protein structure and its interactions. Computational methods are easier as not all protein structures can be determined experimentally due to the inherent difficultly of working with some proteins. In order to predict, design, analyze, simulate or model a protein, data from experimentally determined proteins such as those located in the repository of the Protein Data Bank (PDB) are essential. The assumption here is that we can use pieces of known proteins to piece together a new protein hence, de novo protein design. The analysis of the geometric relationships between secondary structure elements in proteins can be extremely useful to protein prediction, analysis, and de novo design. This thesis project involves creating a database of protein secondary structure elements and geometric information for rapid protein assembly, de novo protein design, prediction and analysis

Toward a Database of Geometric Interrelationships of Protein Secondary Structure Elements for De Novo Protein Design, Prediction and Analysis

Computational methods of analyzing, simulating, and modeling proteins are essential towards understanding protein structure and its interactions. Computational methods are easier as not all protein structures can be determined experimentally due to the inherent difficultly of working with some proteins. In order to predict, design, analyze, simulate or model a protein, data from experimentally determined proteins such as those located in the repository of the Protein Data Bank (PDB) are essential. The assumption here is that we can use pieces of known proteins to piece together a new protein hence, de novo protein design. The analysis of the geometric relationships between secondary structure elements in proteins can be extremely useful to protein prediction, analysis, and de novo design. This thesis project involves creating a database of protein secondary structure elements and geometric information for rapid protein assembly, de novo protein design, prediction and analysis

Protein Secondary Structure Prediction and Perceptions of Complexities using Deep Neural Network

The Protein molecule is known as the large biological molecule in a living organism. The protein performs several works like transporting molecules, catalysing metabolic reaction, responding to stimuli etc in a human body. Protein Structure analysis and prediction is very much essential to make any research about the same protein molecule. The basic intention of protein structure prediction (PSP) is to predict the three dimensional structure that generate by the amino acid sequence. The very peculiar matter is only twenty amino acid found in a living body where as approximately one lakh protein molecules can be framed from the same amino acid compositions in different percentages.  The three dimensional structure framed by the amino acid compositions generally changes its shape and size due to the effect of external agents or medicines that comes in contact with these protein molecules. The basic intention behind the prediction of structure of the protein is to design new drugs or medicines. From the structures the medicine researchers working for the development of medicines may easily detect the changes in the living body or the requirement of drugs or medicines. The detection of the structure and the prediction of perfect structure is always a challenging task. The protein structure is basically a three dimensional structure in its secondary transformation. The structure may be in the form of ? Helix, ? sheets or loop etc. In this paper the identification of the secondary structures and the percentages of ? Helix, ? sheets or loop structures are being predicted and the probable complexities that may occur during the prediction is discussed. Deep neural network is a deep structured learning process is an application of the broader family machine learning. Deep learning architectures has a number application in various fields like medical science, bioinformatics, medical image analysis etc. A novel method is being proposed in this research article for the detection, correction and removal of various complexities during prediction using deep neural network. This technique will be helpful for different researchers working in the field for drug design and medicine research

Generative Tertiary Structure-based RNA Design

Learning from 3D biological macromolecules with artificial intelligence technologies has been an emerging area. Computational protein design, known as the inverse of protein structure prediction, aims to generate protein sequences that will fold into the defined structure. Analogous to protein design, RNA design is also an important topic in synthetic biology, which aims to generate RNA sequences by given structures. However, existing RNA design methods mainly focus on the secondary structure, ignoring the informative tertiary structure, which is commonly used in protein design. To explore the complex coupling between RNA sequence and 3D structure, we introduce an RNA tertiary structure modeling method to efficiently capture useful information from the 3D structure of RNA. For a fair comparison, we collect abundant RNA data and split the data according to tertiary structures. With the standard dataset, we conduct a benchmark by employing structure-based protein design approaches with our RNA tertiary structure modeling method. We believe our work will stimulate the future development of tertiary structure-based RNA design and bridge the gap between the RNA 3D structures and sequences