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A New Hybrid Model of Amino Acid Substitution for Protein Functional Classification

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Chinese Chemical Letters 2005年第8期16卷 1133-1136页

作者： Ke long WANG Zhi Ning WEN Fu Sheng NIE meng long li College of Chemistry Sichuan University Chengdu 610064 State Key Laboratory of Chemo/Biosensing and Chemometrics Hunan University Changsha 410082

In this paper, a new hybrid model of amino acid substitution is developed and compared with the others in previous works. The results show that the new hybrid model can characterize the protein sequences very well by ... 详细信息

关键词： Hybrid model of amino acid substitution protein functional classification Fisher weights.

Alcohols＇ Classification by Infrared Spectra Segment Based on Support Vector Machines

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Chinese Chemical Letters 2006年第7期17卷 929-932页

作者： Wei XIE Fu Sheng NIE meng long li Guang Ming li Min Chun LU College of Chemistry Sichuan University Chengdu 610064

This paper studies various classifiers to identify primary, secondary or tertiary alcohols by using segmental spectra and their combinations to support vector machines （SVMs）. The results showed that the O-H in-plane bending absorption contributed most to identification their substitute. This conclusion disagrees with related known research results.

关键词： Infrared spectra alcohols support vector machine information extraction.

Substructure Prediction from Infrared Spectra by Using Support Vector Machines

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Chinese Chemical Letters 2005年第10期16卷 1354-1356页

作者： Jun Hong liU Min Chun LU Fu Sheng NIE Xiao Yu FENG meng long li College of Chemistry Sichuan University Chengdu 610064

The potential of support vector machines （SVMs） for the substructure elucidation of infrared spectra have been investigated. The trained SVMs can identify the 16 substructures with high accuracy.

关键词： Infrared spectra substructures support vector machines.

Prediction of Multicomponent Reaction Yields Using Machine Learning

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Chinese Journal of Chemistry 2021年第12期39卷 3231-3237页

作者： Xing-Yong Zhu Chuan-Kun Ran Ming Wen Gui-ling Guo Yuan liu li-li liao Yi-Zhou li meng-long li Da-Gang Yu Key Laboratory of Green Chemistry&Technology of Ministry of Education College of ChemistrySichuan UniversityChengduSichuan 610064China College of Cybersecurity Sichuan UniversityChengduSichuan 610064China Beijing National Laboratory for Molecular Sciences Beijing 100190China

Prediction of reaction yields using machine learning(ML)can help chemists select high-yielding reactions and provide prior experience before wet-lab experimenting to improve ***,the exploration of a multicomponent organic reaction features many complex variables and limited number of experimental data,which are challenging for the application of ***,we perform yield prediction for the synthesis of 2-oxazolidones via Cu-catalyzed radical-type oxy-alkylation of allylamines and herteroaryl-methylamines with CO_(2),which is a three-component *** physicochemical descriptors as features to launch ML modelling,we find that XGBoost shows significantly improved performance over linear models and these features are effective for the yield ***,out-of-sample prediction indicates the application potential of the *** study demonstrates great potential of regression-modelling-based ML in organic synthesis even with complex factors and a general small size of reaction data,which are generated from the classical research pattern of method for the inquiry of multicomponent reactions.

关键词： Machine,learning Yield prediction Radical reactions Density functional calculations Multicomponent reactions

Effects of Non-specific and Specific Solvation on Adsorption of BPTI on Au Surface： Insight from Molecular Dynamics Simulation

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Chinese Journal of Chemical Physics 2013年第5期26卷 558-568,I0003,I0004页

作者： Wei Yang li-yun Zhang meng-long li Xue-mei Pu Nan-rong Zhao Faculty of Chemistry Sichuan University Chengdu 610064 China

Proteins adsorption at solid surfaces are of paramount important for many natural processes. However, the role of specific water in influencing the adsorption process has not been well understood. We used molecular dynamics simulation to study the adsorption of BPTI on Au surface in three water environments （dielectric constant model, partial and full solvation models）. The result shows that a fast and strong adsorption can occur in the dielectric environment, which leads to significant structure changes, as confirmed by great deviation from the crystal structure, largely spreading along the Au surface, rapid lose in all secondary structures and the great number of atoms in contact with the surface. Compared to the dielectric model, slower adsorption and fewer changes in the calculated properties above are observed in the partial solvation system since the specific water layer weakens the adsorption effects. However, in the partial solvation system, the adsorption of polar Au surface causes a significant decrease in the specific hydration around the protein, which still results in large structure changes similar to the dielectric system, but with much less adsorption extent. Enough water molecules in the full solvation system could allow the protein to rotate, and to large extent preserve the protein native structure, thus leading to the slowest and weakest adsorption. On the whole, the effects of non-specific and specific solvation on the protein structure and adsorption dynamics are significantly different, highlighting the importance of the specific water molecule in the protein adsorption.

关键词： Adsorption Au surface Implicit water Partial solvation Full solvation

EFFICACY AND SAFETY OF GEFITINIB AS MONOTHERAPY FOR CHINESE PATIENTS WITH ADVANCED NON-SMALL CELL LUNG CANCER

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Chinese Medical Journal 2006年第1期119卷 63-68页

作者： WANG meng-zhao li long-yun WANG Shu-lan ZHANG Xiao-tong ZHONG Wei ZHANG li Department of Respiratory Diseases Peking Union Medical College Hospital Peking Union Medical College and Chinese Academy of Medical Sciences Beijing 100730 China

Platinum-based chemotherapy can improve the survival and quality of life of patients with locally advanced and metastatic lung cancer. Second-line docetaxel monotherapy can improve overall survival following the failure of first line chemotherapy. However, many limiting factors such as poor performance status, advanced age, adverse effects of chemotherapy and reluctance to receive cytotoxic chemotherapeutic agents render patients unable to accept chemotherapy.

关键词： non-small cell lung cancer gefitinib treatment

Fast Fourier Transform-based Support Vector Machine for Prediction of G-protein Coupled Receptor Subfamilies

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Acta Biochimica et Biophysica Sinica 2005年第11期37卷 759-766页

作者： Yan-Zhi GUO meng-long li Ke-long WANG Zhi-Ning WEN Min-Chun LU li-Xia liU lin JIANG College of Chemistry,Sichuan University,Chengdu 610064,China College of Chemistry Sichuan UniversityChengdu 610064China

Although the sequence information on G-protein coupled receptors(GPCRs)continues togrow,many GPCRs remain orphaned(*** specificity unknown)or poorly characterized with littlestructural information available,so an automated and reliable method is badly needed to facilitate the identifi-cation of novel *** this study,a method of fast Fourier transform-based support vector machinehas been developed for predicting GPCR subfamilies according to protein’s *** classifyingClass B,C,D and F subfamilies,the method achieved an overall Matthew’s correlation coefficient andaccuracy of 0.95 and 93.3%,respectively,when evaluated using the jackknife *** method achieved anaccuracy of 100% on the Class B independent *** results show that this method can classify GPCRsubfamilies as well as their functional classification with high accuracy.A web server implementing theprediction is available at http://***/blast/Pred-GPCR.

关键词： G-protein coupled receptor subfamily fast Fourier transform support vector machine prediction

Local Sequence Information-based Support Vector Machine to Classify Voltage-gated Potassium Channels

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Acta Biochimica et Biophysica Sinica 2006年第6期38卷 363-371页

作者： li-Xia liU meng-long li Fu-Yuan TAN Min-Chun LU Ke-long WANG Yan-Zhi GUO Zhi-Ning WEN lin JIANG College of Chemistry,Sichuan University.Chengdu 610064,China CollegeofChemistry SichuanUniversityChengdu610064China

In our previous work,we developed a computational tool,PreK-ClassK-ClassKv,to predictand classify potassium (K~+) *** K+ channel prediction (PreK) and classification at family level(ClassK),this method performs ***,it does not perform so well in classifying voltage-gatedpotassium (Kv) channels (ClassKv).In this paper,a new method based on the local sequence information ofKv channels is introduced to classify Kv *** transmembrane domains of a Kv channel protein areused to define a protein,and the dipeptide composition technique is used to transform an amino acid sequenceto a numerical sequence.A Kv channel protein is represented by a vector with 2000 elements,and a supportvector machine algorithm is applied to classify Kv *** method shows good performance withaverages of total accuracy (Acc),sensitivity (SE),specificity (SP),reliability (R) and Matthews correlationcoefficient (MCC) of 98.0%,89.9%,100%,0.95 and 0.94 *** results indicate that the localsequence information-based method is better than the global sequence information-based method to classifyKv channels.

关键词： voltage-gated potassium channel classification transmembrane domain dipeptide composition support vector machine

Using position specific scoring matrix and auto covariance to predict protein subnuclear localization

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Journal of Biomedical Science and Engineering 2009年第1期2卷 51-56页

作者： Rong-Quan Xiao Yan-Zhi Guo Yu-Hong Zeng Hai-Feng Tan Hai-Feng Tan Xue-Mei Pu meng-long li College of Chemistry Sichuan University Chengdu 610064 P.R. China College of life Sciences Sichuan University College of life Sciences Sichuan University Chengdu 610064

The knowledge of subnuclear localization in eukaryotic cells is indispensable for under-standing the biological function of nucleus, genome regulation and drug discovery. In this study, a new feature representation was pro-posed by combining position specific scoring matrix (PSSM) and auto covariance (AC). The AC variables describe the neighboring effect between two amino acids, so that they incorpo-rate the sequence-order information;PSSM de-scribes the information of biological evolution of proteins. Based on this new descriptor, a support vector machine (SVM) classifier was built to predict subnuclear localization. To evaluate the power of our predictor, the benchmark dataset that contains 714 proteins localized in nine subnuclear compartments was utilized. The total jackknife cross validation ac-curacy of our method is 76.5%, that is higher than those of the Nuc-PLoc (67.4%), the OET- KNN (55.6%), AAC based SVM (48.9%) and ProtLoc (36.6%). The prediction software used in this article and the details of the SVM parameters are freely available at http://***/ predict_SubNL/*** and the dataset used in our study is from Shen and Chou’s work by downloading at http://***/ bioinf/Nuc-PLoc/***.

关键词： Position Specific Scoring Matrix Auto Covariance Support Vector Machine Protein Subnuclear Localization Prediction