FPGA accelerator for protein structure prediction algorithm

2009 ◽  
Author(s):  
Advait Jain ◽  
Pulkit Gambhir ◽  
Priyanka Jindal ◽  
M Balakrishnan ◽  
Kolin Paul
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Prediction Algorithm ◽  
Protein Structure Prediction Algorithm ◽  
Structure Prediction Algorithm

scholarly journals A Probabilistic Fragment-Based Protein Structure Prediction Algorithm

PLoS ONE ◽  
2012 ◽  
Vol 7 (7) ◽  
pp. e38799 ◽  
Author(s):  
David Simoncini ◽  
Francois Berenger ◽  
Rojan Shrestha ◽  
Kam Y. J. Zhang
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Prediction Algorithm ◽  
Protein Structure Prediction Algorithm ◽  
Structure Prediction Algorithm

Improved estimation of model quality using predicted inter-residue distance

2021 ◽  
Author(s):  
Lisha Ye ◽  
Peikun Wu ◽  
Zhenling Peng ◽  
Jianzhao Gao ◽  
Jian Liu ◽  
...  
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Superior Performance ◽  
Supplementary Information ◽  
Prediction Algorithm ◽  
Structure Model ◽  
Single Model ◽  
Model Quality ◽  
Reference Models

Abstract Motivation Protein model quality assessment (QA) is an essential component in protein structure prediction, which aims to estimate the quality of a structure model and/or select the most accurate model out from a pool of structure models, without knowing the native structure. QA remains a challenging task in protein structure prediction. Results Based on the inter-residue distance predicted by the recent deep learning-based structure prediction algorithm trRosetta, we developed QDistance, a new approach to the estimation of both global and local qualities. QDistance works for both single-model and multi-models inputs. We designed several distance-based features to assess the agreement between the predicted and model-derived inter-residue distances. Together with a few widely used features, they are fed into a simple yet powerful linear regression model to infer the global QA scores. The local QA scores for each structure model are predicted based on a comparative analysis with a set of selected reference models. For multi-models input, the reference models are selected from the input based on the predicted global QA scores. For single-model input, the reference models are predicted by trRosetta. With the informative distance-based features, QDistance can predict the global quality with satisfactory accuracy. Benchmark tests on the CASP13 and the CAMEO structure models suggested that QDistance was competitive other methods. Blind tests in the CASP14 experiments showed that QDistance was robust and ranked among the top predictors. Especially, QDistance was the top 3 local QA method and made the most accurate local QA prediction for unreliable local region. Analysis showed that this superior performance can be attributed to the inclusion of the predicted inter-residue distance. Availability and Implementation http://yanglab.nankai.edu.cn/QDistance Supplementary information Supplementary data are available at Bioinformatics online.

The study of unfoldable self-avoiding walks — Application to protein structure prediction software

2015 ◽  
Vol 13 (04) ◽  
pp. 1550009 ◽  
Author(s):  
Christophe Guyeux ◽  
Jean-Marc Nicod ◽  
Laurent Philippe ◽  
Jacques M. Bahi
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Prediction Algorithm ◽  
Enumerative Combinatorics ◽  
Open Questions ◽  
Straight Line ◽  
First Results ◽  
Self Avoiding Walks

Self-avoiding walks (SAWs) are the source of very difficult problems in probability and enumerative combinatorics. They are of great interest as, for example, they are the basis of protein structure prediction (PSP) in bioinformatics. The authors of this paper have previously shown that, depending on the prediction algorithm, the sets of obtained walk conformations differ: For example, all the SAWs can be generated using stretching-based algorithms whereas only the unfoldable SAWs can be obtained with methods that iteratively fold the straight line. A deeper study of (non-)unfoldable SAWs is presented in this paper. The contribution is first a survey of what is currently known about these sets. In particular, we provide clear definitions of various subsets of SAWs related to pivot moves (unfoldable and non-unfoldable SAWs, etc.) and the first results that we have obtained, theoretically or computationally, on these sets. Then a new theorem on the number of non-unfoldable SAWs is demonstrated. Finally, a list of open questions is provided and the consequences on the PSP problem is proposed.

scholarly journals A comparative study of the reported performance of ab initio protein structure prediction algorithms

2007 ◽  
Vol 5 (21) ◽  
pp. 387-396 ◽  
Author(s):  
Glennie Helles
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Current Knowledge ◽  
Three Dimensional ◽  
Performance Measure ◽  
Prediction Algorithm ◽  
Dimensional Structure ◽  
Prediction Algorithms ◽  
Small Proteins

Protein structure prediction is one of the major challenges in bioinformatics today. Throughout the past five decades, many different algorithmic approaches have been attempted, and although progress has been made the problem remains unsolvable even for many small proteins. While the general objective is to predict the three-dimensional structure from primary sequence, our current knowledge and computational power are simply insufficient to solve a problem of such high complexity. Some prediction algorithms do, however, appear to perform better than others, although it is not always obvious which ones they are and it is perhaps even less obvious why that is. In this review, the reported performance results from 18 different recently published prediction algorithms are compared. Furthermore, the general algorithmic settings most likely responsible for the difference in the reported performance are identified, and the specific settings of each of the 18 prediction algorithms are also compared. The average normalized r.m.s.d. scores reported range from 11.17 to 3.48. With a performance measure including both r.m.s.d. scores and CPU time, the currently best-performing prediction algorithm is identified to be the I-TASSER algorithm. Two of the algorithmic settings—protein representation and fragment assembly—were found to have definite positive influence on the running time and the predicted structures, respectively. There thus appears to be a clear benefit from incorporating this knowledge in the design of new prediction algorithms.

scholarly journals EFFECTIVE OPTIMIZATION ALGORITHMS FOR FRAGMENT-ASSEMBLY BASED PROTEIN STRUCTURE PREDICTION

2007 ◽  
Vol 05 (02a) ◽  
pp. 335-352 ◽  
Author(s):  
KEVIN W. DERONNE ◽  
GEORGE KARYPIS
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Query Sequence ◽  
Optimization Techniques ◽  
Hill Climbing ◽  
Prediction Algorithm ◽  
Scoring Functions ◽  
Fragment Assembly ◽  
Fold Prediction

Despite recent developments in protein structure prediction, an accurate new fold prediction algorithm remains elusive. One of the challenges facing current techniques is the size and complexity of the space containing possible structures for a query sequence. Traditionally, to explore this space fragment assembly approaches to new fold prediction have used stochastic optimization techniques. Here, we examine deterministic algorithms for optimizing scoring functions in protein structure prediction. Two previously unused techniques are applied to the problem, called the Greedy algorithm and the Hill-climbing (HC) algorithm. The main difference between the two is that the latter implements a technique to overcome local minima. Experiments on a diverse set of 276 proteins show that the HC algorithms consistently outperform existing approaches based on Simulated Annealing optimization (a traditional stochastic technique) in optimizing the root mean squared deviation between native and working structures.

Sequence Specific Dihedral Angle Distribution: Application in Protein Structure Prediction and Evaluation

1970 ◽  
Vol 19 (2) ◽  
pp. 217-226
Author(s):  
S. M. Minhaz Ud-Dean ◽  
Mahdi Muhammad Moosa
Keyword(s):  
Protein Structure ◽  
Dihedral Angle ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Protein Structures ◽  
Angle Distribution ◽  
Ramachandran Plot ◽  
Specific Data ◽  
Specific Distribution ◽  
Structure Evaluation

Protein structure prediction and evaluation is one of the major fields of computational biology. Estimation of dihedral angle can provide information about the acceptability of both theoretically predicted and experimentally determined structures. Here we report on the sequence specific dihedral angle distribution of high resolution protein structures available in PDB and have developed Sasichandran, a tool for sequence specific dihedral angle prediction and structure evaluation. This tool will allow evaluation of a protein structure in pdb format from the sequence specific distribution of Ramachandran angles. Additionally, it will allow retrieval of the most probable Ramachandran angles for a given sequence along with the sequence specific data. Key words: Torsion angle, φ-ψ distribution, sequence specific ramachandran plot, Ramasekharan, protein structure appraisal D.O.I. 10.3329/ptcb.v19i2.5439 Plant Tissue Cult. & Biotech. 19(2): 217-226, 2009 (December)

Protein structure prediction of human connexin 30 and its mutations in hearing system

2014 ◽  
Vol 3 (5) ◽  
Author(s):  
S. Reiisi ◽  
M. Hashemzade-chaleshtori ◽  
S. Reisi ◽  
H. Shahi ◽  
S. Parchami ◽  
...  
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Connexin 30 ◽  
Hearing System
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