We propose an accurate potential which combines useful feat - PowerPoint Presentation

Slide1

We propose an accurate potential which combines useful features

HP, HH and PP interactions among the amino acidsSequence based accessibility obtained for each amino acids3D Structure based property i.e. uPhi and uPsiThe improved potential can be used forProtein-Ligand binding site predictionAb Initio protein structure predictionFold recognitionDrug design andEnzyme designThe proposed potential outperforms all the stat-of-arts approaches.

3D structure prediction is useful in drug and novel enzymes design.Energy functions can aid inProtein structure prediction andFold recognitionWe propose, 3DIGARS3.0 potential for improved accuracy.We introduce two 3D structural featuresuPhi based energyuPsi based energyMotivation comes from the fact that the 3D structural features assists the advancement of the accuracy.uPhi and uPsi are linearly combined with prior energy components3DIGARS energy which is based on HP, HH and PP interactions and their respective ideal gas reference stateASA energy computed by modeling real and predicted accessibility obtained from protein sequencesThe linearly combined energies are optimized using GAThree decoy sets were used in optimizationMoulderRosetta and I-TasserFive independent test decoy sets were used to evaluate the accuracy4state_reducedfisa_casp3hg_structalig_structal andig_structural hires3DIGARS3.0 outperformed the state-of-the-arts approachesDFIRE by 440.91%RWplus by 440.91%dDFIRE by 72.46%GOAP by 20.20%3DIGARS by 417.39%3DIGARS2.0 by 440.91% based on independent test datasets.The percentage weighted average improvement is calculated as where, yi represents new value and xi represents old value

Figure 1: (a) Native like protein conformation, presented in a 3D hexagonal-close-packing (HCP) configuration using hydrophobic (H) and hydrophilic or polar (P) residues. The H-H interactions space is relatively smaller than P-P interactions space, since hydrophobic residues (black ball) being afraid of water tends to remain inside of the central space. (b) 3D metaphoric HP folding kernels, depicted based on HCP configuration based HP model, showing the 3 layers of distributions of amino-acids.

Figure 5: Process flow of the design and development of 3DIGARS3.0 energy function.

3DIGARS potentialCore statistical function based on HP, HH and PP interactions (see Fig. 1)Segregated ideal gas reference state and libraries for HP, HH and PP groupsBetter training dataset (100% sequence identity cutoff can capture natural frequency distribution)Three shape parameters (αhp, αhh and αpp) controls shape of assumed spherical protein surfaceThree contribution parameters (βhp, βhh and βpp) controls the contribution of each group3DIGARS2.0 potentialIntegration of the core energy and sequence specific featuresSequence specific feature is computed by modeling error between the real and predicted ASA (see Fig. 2)Real and predicted ASA are obtained from DSSP and REGAd3p respectively3DIGARS2.0 is a linearly weighted accumulation of 3DIGARS and mined ASA3DIGARS3.0 potentialIntegration of core energy, sequence specific energy and 3D structural features (see Fig. 5)3D structural features added are attained based on uPhi and uPsi anglesuPhi and uPsi are computed using Cartesian coordinates of set of 4 atoms (see Fig. 3 and 4)uPhi and uPsi based energies are computed based on following steps (see Fig. 4)Cosine value range (-1 to 1) of angles uPhi and uPsi are divided into 20 bins, each of width 0.1Individual frequency tables for uPhi and uPsi are computedFrequency tables are further used to compute individual energy score librariesEnergy score are then used to compute uPhi and uPsi energies for a given protein

Protein folding and structure prediction problems relies on an accurate energy function.Accuracy of the potential function depends onInteraction distance between atom pairsHydrophobic (H) and hydrophilic (P) propertiesSequence-specific informationOrientation-dependent interactions andOptimization techniquesWe develop a potential function, which is an optimized linearly weighted accumulation of 3-Dimensional Ideal Gas Reference State based Energy Function (3DIGARS) It is formulated using an idea of HP, HH and PP properties of amino acidsMined accessible surface area (ASA) andUbiquitously computed Phi (uPhi) and Psi (uPsi) energiesOptimization is performed using a Genetic Algorithm (GA).Based on independent test dataset, the proposed energy function outperformed state-of-the-art approaches significantly.

An

Eclectic Energy Function to Discriminate Native From DecoysAvdesh Mishra, Sumaiya Iqbal, Md Tamjidul Hoqueemail: {amishra2, siqbal1, thoque}@uno.eduDepartment of Computer Science, University of New Orleans, New Orleans, LA, USA

Methods

Introduction

Results

Discussions

Conclusions

Acknowledgements

Figure

4: (a) Shows atoms arrangement as well as vectors created using the Cartesian coordinates of the atoms. (b) Shows the dihedral angle ϴ involving the four atoms.

Figure

3:

Definition of the angle ϴ formed by four atoms (At

1

, At

2

, At

3

and At4). uPhi is computed using At1 belonging to one residue and a set of atoms, At2, At3, At4 belonging to some other residues. Similarly, uPsi is computed using a set of atoms, At1, At2, At3 belonging to some residues and an atom At4 belonging to some other residue.

Figure

2: The dark central area, composed of atoms, can be thought of a 3D proteins and the outline around the area in green and red can be thought of real and predicted accessible surface area respectively. The error between real and predicted ASA is modelled as an energy feature.

Table 1: Performance comparison of different energy functions on optimization datasets based on correct native count.

Decoy Sets(No. of targets)MethodsDFIRERWplusdDFIREGOAP3DIGARS3DIGARS2.03DIGARS3.0Moulder(20)19(-2.97)19(-2.84)18(-2.74)19(-3.58)19(-2.99)19(-2.68)20(-3.851)Rosetta(58)20(-1.82)20(-1.47)12(-0.83)45(-3.70)31(-2.023)49(-2.987)46(-2.683)I-Tasser(56)49(-4.02)56(-5.77)48(-5.03)45(-5.36)53(-4.036)56(-4.296)56(-5.573)Weighted Average in %38.6428.4256.4111.9318.45-1.61 Legend: Entry format is native-count (z-score). Bold indicates best scores. Underscore indicates close to best scores.

Table 2: Performance comparison of different energy functions on independent test datasets based on correct native count.

Decoy Sets(No. of targets)MethodsDFIRERWplusdDFIREGOAP3DIGARS3DIGARS2.03DIGARS3.04state_reduced(7)6(-3.48)6(-3.51)7(-4.15)7(-4.38)6(-3.371)4(-2.642)7(-3.456)fisa_casp3(5)4(-4.80)4(-5.17)4(-4.83)5(-5.27)5(-4.319)5(-4.682)4(-4.076)hg_structal(29)12(-1.97)12(-1.74)16(-1.33)22(-2.73)12(-1.914)12(-1.589)28(-3.678)ig_structal(61)0(0.92)0(1.11)26(-1.02)47(-1.62)0(0.645)0(0.268)60(-2.526)ig_structal_hires(20)0(0.17)0(0.32)16(-2.05)18(-2.35)0(-0.002)1(0.030)20(-2.378)Weighted Average in %440.91440.9172.4620.20417.39440.91 Legend: Entry format is native-count (z-score). Bold indicates best scores. Underscore indicates close to best scores.

We gratefully acknowledge the Louisiana Board of Regents through the Board of Regents Support Fund, LEQSF (2013-16)-RD-A-19.