文本成果組會

1、Work Report1Supervisor: Tingjun HouReporter:Qian Zhang2Scoring:Dist-basedAi+1Ai+2Ai+3Ai3Scoring:Markov Chain-based4t1t2t3t4t5t6t7t8t9t10Scoring:Markov Chain-based5Scoring:Markov Chain-based 6t1t2t3t4t5TOTALt100.20.30.30.61.4t20.200.50.40.51.6t30.30.500.230.541.57t40.30.40.2300.611.54t50.60.50.540.61

2、02.25Scoring:Markov Chain-based7t1t2t3t4t5TOTALt100.20.30.30.61.4t20.200.50.40.51.6t30.30.500.230.541.57t40.30.40.2300.611.54t50.60.50.540.6102.251Scoring:Markov Chain-based8t1t2t3t4t5TOTALt100.20.30.30.61.4t20.200.50.40.51.6t30.30.500.230.541.57t40.30.40.2300.611.54t50.60.50.540.6102.251Scoring:Mar

3、kov Chain-based29t1t2t3t4t5TOTALt100.20.30.30.61.4t20.200.50.40.51.6t30.30.500.230.541.57t40.30.40.2300.611.54t50.60.50.540.6102.251Scoring:Markov Chain-based2310t1t2t3t4t5TOTALt100.20.30.30.61.4t20.200.50.40.51.6t30.30.500.230.541.57t40.30.40.2300.611.54t50.60.50.540.6102.251Scoring:Markov Chain-ba

4、sed2345t1 , t2 , t4 , t3 , t511Select Rules:1. A protein with Two asymmetric chain2. Resolution is better than 2.5 23358 pdbs remain3. Remove pdbs with more than one structures4. Remove pdbs with abnormal residues in its interface10716 pdbs remainScoring:Markov Chain-based12GALIVPFM.G0.0046210.00378

5、10.0049940.0029750.0030770.0031820.0028380.001109A0.003820.0054170.0057370.0031920.0036050.0034630.0027430.001565L0.0050680.0056950.00950.004860.0050940.005060.0041080.002569I0.0027820.003170.0049360.0029330.0034610.002540.0022640.001377V0.0032820.0038120.0050990.0030310.0040590.002840.0028960.00141

6、6P0.0030650.0030090.005190.0026350.0029310.0030720.0027060.001436F0.0024620.0030310.0042470.0022690.0027770.002620.0027080.001307M0.0013950.00180.0025790.0012770.0015070.0011530.0011940.001514Scoring:Markov Chain-based13Scoring:Markov Chain-basedRAWMC1AHW:89841AHW:2191AHW:29651AVX:37691AVX:39171AVX:

7、34541AVX:55251AVX:42681AVX:12281AVX:36441AVX:44471AVX:22801AVX:8561AVX:52271AVX:59881AVX:45431AVX:6361AVX:95521AVX:3131AVX:68421AVX:71571AZS:15501B6C:26271B6C:44431B6C:18511B6C:14371B6C:4126Average47.7052023141.93352601TOP 100 from GA/414Scoring:Hidden Markov ModelSi+1Si+2Si+3SiOiOi+1Oi+2Oi+3Hidden

8、nodeObserved node15For one hidden markov model, what do we need?Supposing hidden node value = 1,2N(N is not sure), observed node value = G,A,L,I,V,P,F,MScoring:Hidden Markov Model16Scoring:Hidden Markov Model-EM1. Stochastic step: Initialize the parameters, (X), A(X), B(X) and hidden node values (N)

9、.2. Expectation step: determine the distribution of the latent variables by using current parameters and observations.3. Maximization step: computes parameters maximizing the expected log-likelihood with current variables.17Scoring:Hidden Markov Model-EMUsing Hidden Markov Model(HMM) Toolbox for Mat

10、lab developed by Kevins Group 18Folding:PHAISTOSA Framework for Markov Chain Monte Carlo Simulation and Inference of Protein Structure.Two methods dominate the field of molecular simulation:Molecular Dynamics (MD)Markov Chain Monte Carlo(MCMC)difference?The way to update the system in each iteration

11、Newtons EquationGenarate samples from a probability distribution19Folding:PHAISTOSObjective: 1. accelarate the calculation of SASA(implemented in GB/SA) by using GPU2. integrate Entropy into energy calculationWork flow:1. test PHAISTOSs current performance(time/accuracy).2. accelarate the process of calculating SASA, and compare the time consumed.3. integrate Entropy, and compare the accuracy.20Folding:PHAISTOSiterations = 107time 1d/corePDB:1L2Y(20 residues)21Folding:PHAISTOSLastLowest_RmsdLowest_EnergyT4500.2251430.49076510.3668167T3000.2802280.480360.399351922Fold