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CASP14 Abstract Book CRITICAL ASSESSMENT OF TECHNIQUES FOR PROTEIN STRUCTURE PREDICTION 14 ABSTRACT BOOK Fourteenth round May-September 2020 1 TABLE OF CONTENTS 191227 .............................................................................................................. 13 iPhord: A protein structure prediction system based on deep learning ................................................ 13 3DCNN_prof ..................................................................................................... 16 Single model quality assessment using 3DCNN with profile-based features ......................................... 16 A2I2Prot ........................................................................................................... 17 Fusion of sequence embedding and sequence alignments for protein contact predictions ................. 17 AILON ............................................................................................................... 19 Protein tertiary structure prediction driven by deep neural network and cmFinder from its amino acid sequence ................................................................................................................................................. 19 AIR .................................................................................................................... 21 AIR: An artificial intelligence-based protocol for protein structure refinement using multi-objective particle swarm optimization ................................................................................................................... 21 AlphaFold2 ....................................................................................................... 22 High Accuracy Protein Structure Prediction Using Deep Learning ......................................................... 22 angleQA ............................................................................................................ 25 AngleQA: protein single-model quality assessment based on torsion angles ........................................ 25 AP_1 ................................................................................................................. 27 AP_1 structure predictions in CASP14 .................................................................................................... 27 BAKER-experimental (Assembly)....................................................................... 28 Protein oligomer structure predictions guided by predicted inter-chain contacts ................................ 28 BAKER-ROSETTASERVER, BAKER (TS) ................................................................ 30 Protein structure prediction guided by predicted inter-residue geometries ......................................... 30 BAKER-ROSETTASERVER, BAKER-experimental (EMA) ...................................... 32 Estimation of Model Quality via Deep Residual Learning ....................................................................... 32 BAKER, BAKER-experimental (Refinement) ....................................................... 34 Model refinement guided by an interplay between Deep-learning and Rosetta................................... 34 Bates_BMM ...................................................................................................... 36 Protein fold construction and complex assembly by employing particle swarm optimization.............. 36 Bhattacharya .................................................................................................... 38 Protein tertiary structure prediction by Bhattacharya group in CASP14 ............................................... 38 1 Bhattacharya-QDeep, Bhattacharya-QDeepU, Bhattacharya-Server ................. 39 Protein model accuracy estimation by Bhattacharya groups in CASP14 ................................................ 39 Bhattacharya, Bhattacharya-Server .................................................................. 40 Protein structure refinement by Bhattacharya groups in CASP14 ......................................................... 40 Bioinsilico_sbi ................................................................................................... 42 Three-dimensional prediction of proteins using a collection of sMotifs ................................................ 42 Bioinsilico_sbi ................................................................................................... 44 Protein contact predictions using a reduced alphabet and direct-coupling analysis ............................. 44 Bioinsilico_sbi, Bioinsilico_sbi_PAIR .................................................................. 46 Assessing the quality of protein structural models using split-statistical potentials ............................. 46 BrainFold .......................................................................................................... 48 Contact Pair Prediction Using a Deep Neural Net................................................................................... 48 CAO-QA1 (High Accuracy Modeling) ................................................................. 51 Collaborative protein structure prediction with deep learning based de novo prediction and model selection .................................................................................................................................................. 51 CAO-QA1(Accuracy Estimation) ........................................................................ 54 AngularQA: Protein Model Quality Assessment with LSTM Networks ................................................... 54 CAO-SERVER(Accuracy Estimation) ................................................................... 57 TopQA: a topological representation for single-model protein quality assessment with machine learning ................................................................................................................................................... 57 CAO-SERVER(Topology) .................................................................................... 60 de novo protein structure prediction using stepwise fragment sampling with...................................... 60 contact prediction and model selection based on deep learning techniques ........................................ 60 ClusPro ............................................................................................................. 62 Hybrid ClusPro server in 2020 CASP/CAPRI rounds ................................................................................ 62 CMH1971 .......................................................................................................... 64 Structure Prediction, Quality Assessment and Contact Prediction by EMAP_CLUST ............................. 64 CUTSP ............................................................................................................... 65 Morphing semi-supervised protein structures predicted using distance and torsion representations with deep graph ranking ......................................................................................................................... 65 DATE ................................................................................................................. 67 2 Template-based Structure Prediction and Interresidue Distances and Orientations Prediction-based Structure Prediction ................................................................................................................................ 67 DeepML ............................................................................................................ 69 Quality Assesment of Protein Models using Graph Convolutional Networks ....................................... 69 DeepMUSICS ..................................................................................................... 69 A novel deep learning framework for protein structure prediction ....................................................... 69 DeepPotential ................................................................................................... 72 Learning deep statistical potentials for protein folding.......................................................................... 72 DELCLAB ........................................................................................................... 74 DellaCorteLab ................................................................................................... 76 Refinement with Improved Restrained Molecular Dynamics ................................................................. 76 DellaCorteLab ................................................................................................... 78 De novo structure prediction with deep learning and molecular mechanics simulation....................... 78 DellaCorteLab ................................................................................................... 80 ProSPr: Protein Structure Prediction via Inter-Residue Distances .......................................................... 80 DESTINI ............................................................................................................. 82 Deep-learning the protein folding code for structure prediction and sequence comparison ............... 82 DMP2................................................................................................................ 84 Tertiary structure and distance predictions with DMPfold2 .................................................................. 84 E2E...................................................................................................................
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