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Protein Residue Contact Prediction Based on Deep Learning and Massive Statistical Features from Multi-Sequence Alignment

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Tsinghua Science and Technology 2022年第5期27卷 843-854页

作者： Huiling Zhang Min Hao Hao Wu Hing-Fung Ting Yihong Tang Wenhui Xi Yanjie Wei Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China College of Electronic and Information Engineering Southwest UniversityChongqing 400715China School of Software Engineering University of Science and Technology of ChinaHefei 230051China Department of Computer Science The University of Hong KongHong Kong 999077China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China

Sequence-based protein tertiary structure prediction is of fundamental importance because the function of a protein ultimately depends on its 3 D *** accurate residue-residue contact map is one of the essential elements for current ab initio prediction protocols of 3 D structure ***,with the combination of deep learning and direct coupling techniques,the performance of residue contact prediction has achieved significant ***,a considerable number of current Deep-Learning(DL)-based prediction methods are usually time-consuming,mainly because they rely on different categories of data types and third-party *** this research,we transformed the complex biological problem into a pure computational problem through statistics and artificial *** have accordingly proposed a feature extraction method to obtain various categories of statistical information from only the multi-sequence alignment,followed by training a DL model for residue-residue contact prediction based on the massive statistical *** proposed method is robust in terms of different test sets,showed high reliability on model confidence score,could obtain high computational efficiency and achieve comparable prediction precisions with DL methods that relying on multi-source inputs.

关键词： multi-sequence alignment residue-residue contact prediction feature extraction statistical information Deep Learning(DL) high computational efficiency

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Constitutive modeling of ultra?fine?grained titanium flow stress for machining temperature prediction

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Bio-Design and Manufacturing 2019年第3期2卷 153-160页

作者： Jinqiang Ning Vinh Nguyen Yong Huang Karl T.Hartwig Steven Y.Liang George W.Woodruff School of Mechanical Engineering Georgia Institute of Technology801 Ferst DriveAtlantaGA 30332USA Department of Mechanical and Aerospace Engineering University of Florida231 MAE-AP.O.Box 116250GainesvilleFL 32611USA Department of Mechanical Engineering Texas A&M University3123 TAMUCollege StationTX 77843?3123USA

This work investigates the machining temperatures of ultra-fine-grained titanium(UFG Ti),prepared by equal channel angular extrusion,through analytical *** Ti has great usefulness in biomedical applications because of its high mechanical strength,sufficient manufacturability,and high *** temperatures were predicted using a physics-based predictive model based on material constitutive relation and mechanics of the orthogonal cutting *** minimization between the stress calculated using Johnson–Cook constitutive model and the same stress calculated using mechanics model yields the estimation of machining temperatures at two deformation *** agreements are observed upon validation to the values reported in the *** machinability of UFG Ti is investigated by comparing its machining temperature to that of Ti–6Al–4V alloy under the same cutting *** lower temperatures are observed in machining UFG *** computational efficiency of the presented model is investigated by comparing its average computational time(~0.5 s)to that of a widely used modified chip formation model(8900 s)with comparable prediction *** work extends the applicability of the presented temperature model to a broader class of materials,specifically ultra-fine-grained *** high computational efficiency allows the in situ temperature prediction and optimization of temperature condition with process parameters planning.

关键词： Ultra-fine-grained titanium Analytical modeling high computational efficiency Johnson–Cook model Cutting mechanics

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