scholarly journals High coordination lattice models of protein structure, dynamics and thermodynamics.

1997 ◽  
Vol 44 (3) ◽  
pp. 389-422 ◽  
Author(s):  
A Koliński ◽  
J Skolnick
Keyword(s):  
Structure Prediction ◽  
Tertiary Structure ◽  
Stochastic Dynamics ◽  
Lattice Models ◽  
Globular Proteins ◽  
Conformational Space ◽  
Evolutionary Information ◽  
Time Dynamics ◽  
Protein Models ◽  
High Coordination

A high coordination lattice discretization of protein conformational space is described. The model allows discrete representation of polypeptide chains of globular proteins and small macromolecular assemblies with an accuracy comparable to the accuracy of crystallographic structures. Knowledge based force field, that consists of sequence specific short range interactions, cooperative model of hydrogen bond network and tertiary one body, two body and multibody interactions, is outlined and discussed. A model of stochastic dynamics for these protein models is also described. The proposed method enables moderate resolution tertiary structure prediction of simple and small globular proteins. Its applicability in structure prediction increases significantly when evolutionary information is exploited or/and when sparse experimental data are available. The model responds correctly to sequence mutations and could be used at early stages of a computer aided protein design and protein redesign. Computational speed, associated with the discrete structure of the model, enables studies of the long time dynamics of polypeptides and proteins and quite detailed theoretical studies of thermodynamics of nontrivial protein models.

Increasing the accuracy of protein loop structure prediction with evolutionary constraints

2018 ◽  
Vol 35 (15) ◽  
pp. 2585-2592 ◽  
Author(s):  
Claire Marks ◽  
Charlotte M Deane
Keyword(s):  
Structure Prediction ◽  
De Novo ◽  
Conformational Space ◽  
Supplementary Information ◽  
Evolutionary Constraints ◽  
Final Model ◽  
Loop Conformations ◽  
Loop Regions ◽  
Accuracy Of Prediction ◽  
Protein Models

Abstract Motivation Accurate prediction of loop structures remains challenging. This is especially true for long loops where the large conformational space and limited coverage of experimentally determined structures often leads to low accuracy. Co-evolutionary contact predictors, which provide information about the proximity of pairs of residues, have been used to improve whole-protein models generated through de novo techniques. Here we investigate whether these evolutionary constraints can enhance the prediction of long loop structures. Results As a first stage, we assess the accuracy of predicted contacts that involve loop regions. We find that these are less accurate than contacts in general. We also observe that some incorrectly predicted contacts can be identified as they are never satisfied in any of our generated loop conformations. We examined two different strategies for incorporating contacts, and on a test set of long loops (10 residues or more), both approaches improve the accuracy of prediction. For a set of 135 loops, contacts were predicted and hence our methods were applicable in 97 cases. Both strategies result in an increase in the proportion of near-native decoys in the ensemble, leading to more accurate predictions and in some cases improving the root-mean-square deviation of the final model by more than 3 Å. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A multi-objective evolutionary approach to the protein structure prediction problem

2005 ◽  
Vol 3 (6) ◽  
pp. 139-151 ◽  
Author(s):  
Vincenzo Cutello ◽  
Giuseppe Narzisi ◽  
Giuseppe Nicosia
Keyword(s):  
Protein Structure ◽  
Potential Energy ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Research Effort ◽  
Potential Energy Function ◽  
Conformational Space ◽  
Multi Objective ◽  
Protein Structure Prediction Problem

The protein structure prediction (PSP) problem is concerned with the prediction of the folded, native, tertiary structure of a protein given its sequence of amino acids. It is a challenging and computationally open problem, as proven by the numerous methodological attempts and the research effort applied to it in the last few years. The potential energy functions used in the literature to evaluate the conformation of a protein are based on the calculations of two different interaction energies: local (bond atoms) and non-local (non-bond atoms). In this paper, we show experimentally that those types of interactions are in conflict, and do so by using the potential energy function Chemistry at HARvard Macromolecular Mechanics. A multi-objective formulation of the PSP problem is introduced and its applicability studied. We use a multi-objective evolutionary algorithm as a search procedure for exploring the conformational space of the PSP problem.

Secondary and tertiary structure prediction of fenugreek (Trigonella foenum-graecum) protein

2016 ◽  
Vol 39 (1) ◽  
Author(s):  
Sharda Choudhary ◽  
Ravindra Singh ◽  
R. S. Meena ◽  
Geetika Jethra
Keyword(s):  
Structure Prediction ◽  
Tertiary Structure ◽  
Transmembrane Protein ◽  
Protein Domain ◽  
E Coli ◽  
Trigonella Foenum Graecum ◽  
Lab Experiments ◽  
Wet Lab ◽  
Protein Models ◽  
Secondary And Tertiary Structure

<italic>In-silico</italic> development of protein models in fenugreek (<italic>Trigonella foenum-graecum</italic>) has opened up new vistas using the modern computational tools. The identification of protein in fenugreek having homology with the protein domain of humans and E-coli shows that the database available on Apiaceae family are very low. The estimated molecular weight of identified for fenugreek AM-1 protein was 11372.8 and was predicted as basic. A channel in fenugreek transmembrane protein has been identified through which a ligand (an ion or a small molecule) might pass. The present finding may be a valuable addition to the proteomic information available on fenugreek. Further validation can be performed using wet lab experiments.

scholarly journals Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aashish Jain ◽  
Genki Terashi ◽  
Yuki Kagaya ◽  
Sai Raghavendra Maddhuri Venkata Subramaniya ◽  
Charles Christoffer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
3D Structure ◽  
Evolutionary Information ◽  
Learning Approaches ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Novel Approach

AbstractProtein 3D structure prediction has advanced significantly in recent years due to improving contact prediction accuracy. This improvement has been largely due to deep learning approaches that predict inter-residue contacts and, more recently, distances using multiple sequence alignments (MSAs). In this work we present AttentiveDist, a novel approach that uses different MSAs generated with different E-values in a single model to increase the co-evolutionary information provided to the model. To determine the importance of each MSA’s feature at the inter-residue level, we added an attention layer to the deep neural network. We show that combining four MSAs of different E-value cutoffs improved the model prediction performance as compared to single E-value MSA features. A further improvement was observed when an attention layer was used and even more when additional prediction tasks of bond angle predictions were added. The improvement of distance predictions were successfully transferred to achieve better protein tertiary structure modeling.

Fast closure of long loops at the initiation of the folding transition of globular proteins studied by time-resolved FRET-based methods

2014 ◽  
Vol 10 (4) ◽  
Author(s):  
Tomer Orevi ◽  
Gil Rahamim ◽  
Sivan Shemesh ◽  
Eldad Ben Ishay ◽  
Dan Amir ◽  
...  
Keyword(s):  
Stochastic Dynamics ◽  
Sequence Data ◽  
Random Search ◽  
Excitation Energy Transfer ◽  
Globular Proteins ◽  
Conformational Space ◽  
Resonance Excitation ◽  
Time Resolved ◽  
Non Local

AbstractThe protein folding problem would be considered “solved” when it will be possible to “read genes”, i.e., to predict the native fold of proteins, their dynamics, and the mechanism of fast folding based solely on sequence data. The long-term goal should be the creation of an algorithm that would simulate the stepwise mechanism of folding, which constrains the conformational space and in which random search for stable interactions is possible. Here, we focus attention on the initial phases of the folding transition starting with the compact disordered collapsed ensemble, in search of the initial sub-domain structural biases that direct the otherwise stochastic dynamics of the backbone. Our studies are designed to test the “loop hypothesis”, which suggests that fast closure of long loop structures by non-local interactions between clusters of mainly non-polar residues is an essential conformational step at the initiation of the folding transition of globular proteins. We developed and applied experimental methods based on time-resolved resonance excitation energy transfer (trFRET) measurements combined with fast mixing methods and studied the initial phases of the folding of

PROTEIN SECONDARY STRUCTURE PREDICTION USING SUPPORT VECTOR MACHINES AND A NEW FEATURE REPRESENTATION

2006 ◽  
Vol 06 (04) ◽  
pp. 551-567 ◽  
Author(s):  
JAYAVARDHANA GUBBI ◽  
DANIEL T. H. LAI ◽  
MARIMUTHU PALANISWAMI ◽  
MICHAEL PARKER
Keyword(s):  
Support Vector Machines ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Feature Representation ◽  
Evolutionary Information ◽  
Support Vector ◽  
Vector Machines

Knowledge of the secondary structure and solvent accessibility of a protein plays a vital role in the prediction of fold, and eventually the tertiary structure of the protein. A challenging issue of predicting protein secondary structure from sequence alone is addressed. Support vector machines (SVM) are employed for the classification and the SVM outputs are converted to posterior probabilities for multi-class classification. The effect of using Chou–Fasman parameters and physico-chemical parameters along with evolutionary information in the form of position specific scoring matrix (PSSM) is analyzed. These proposed methods are tested on the RS126 and CB513 datasets. A new dataset is curated (PSS504) using recent release of CATH. On the CB513 dataset, sevenfold cross-validation accuracy of 77.9% was obtained using the proposed encoding method. A new method of calculating the reliability index based on the number of votes and the Support Vector Machine decision value is also proposed. A blind test on the EVA dataset gives an average Q3 accuracy of 74.5% and ranks in top five protein structure prediction methods. Supplementary material including datasets are available on .

Improved Sampling Strategies for Protein Model Refinement based on Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Lim Heo ◽  
Collin Arbour ◽  
Michael Feig
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Structure Prediction ◽  
Protein Structures ◽  
Conformational Space ◽  
Dynamics Simulation ◽  
Model Refinement ◽  
Protein Model ◽  
Lower Accuracy ◽  
Simulation Based

Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. Those methods utilize protein structure databases for structure prediction via template-based modeling or for training machine-learning models to generate predictions. Structure prediction for proteins distant from proteins with known structures often results in lower accuracy with respect to the true physiological structures. Physics-based protein model refinement methods can be applied to improve model accuracy in the predicted models. Refinement methods rely on conformational sampling around the predicted structures, and if structures closer to the native states are sampled, improvements in the model quality become possible. Molecular dynamics simulations have been especially successful for improving model qualities but although consistent refinement can be achieved, the improvements in model qualities are still moderate. To extend the refinement performance of a simulation-based protocol, we explored new schemes that focus on an optimized use of biasing functions and the application of increased simulation temperatures. In addition, we tested the use of alternative initial models so that the simulations can explore conformational space more broadly. Based on the insight of this analysis we are proposing a new refinement protocol that significantly outperformed previous state-of-the-art molecular dynamics simulation-based protocols in the benchmark tests described here. <br>

In Silico Study of Potential Cross-Kingdom Plant MicroRNA Based Regulation in Chronic Myeloid Leukemia

2020 ◽  
Vol 17 (2) ◽  
pp. 125-132
Author(s):  
Marjanu Hikmah Elias ◽  
Noraziah Nordin ◽  
Nazefah Abdul Hamid
Keyword(s):  
Targeted Therapy ◽  
In Silico ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Target Prediction ◽  
In Silico Analysis ◽  
Microrna Target ◽  
Good Target

Background: Chronic Myeloid Leukaemia (CML) is associated with the BCRABL1 gene, which plays a central role in the pathogenesis of CML. Thus, it is crucial to suppress the expression of BCR-ABL1 in the treatment of CML. MicroRNA is known to be a gene expression regulator and is thus a good candidate for molecularly targeted therapy for CML. Objective: This study aims to identify the microRNAs from edible plants targeting the 3’ Untranslated Region (3’UTR) of BCR-ABL1. Methods: In this in silico analysis, the sequence of 3’UTR of BCR-ABL1 was obtained from Ensembl Genome Browser. PsRNATarget Analysis Server and MicroRNA Target Prediction (miRTar) Server were used to identify miRNAs that have binding conformity with 3’UTR of BCR-ABL1. The MiRBase database was used to validate the species of plants expressing the miRNAs. The RNAfold web server and RNA COMPOSER were used for secondary and tertiary structure prediction, respectively. Results: In silico analyses revealed that cpa-miR8154, csi-miR3952, gma-miR4414-5p, mdm-miR482c, osa-miR1858a and osa-miR1858b show binding conformity with strong molecular interaction towards 3’UTR region of BCR-ABL1. However, only cpa-miR- 8154, osa-miR-1858a and osa-miR-1858b showed good target site accessibility. Conclusion: It is predicted that these microRNAs post-transcriptionally inhibit the BCRABL1 gene and thus could be a potential molecular targeted therapy for CML. However, further studies involving in vitro, in vivo and functional analyses need to be carried out to determine the ability of these miRNAs to form the basis for targeted therapy for CML.

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