ISDTool: A computational model for predicting immunosuppressive domain of HERVs

Human endogenous retroviruses (HERVs) have been found to act as etiological cofactors in several chronic diseases, including cancer, autoimmunity and neurological dysfunction. Immunosuppressive domain (ISD) is a conserved region of transmembrane protein (TM) in envelope gene (env) of retroviruses. In vitro and vivo, evidence has shown that retroviral TM is highly immunosuppressive and a synthetic peptide (CKS-17) that shows homology to ISD inhibits immune function. ISD is probably a potential pathogenic element in HERVs. However, only less than one hundred ISDs of HERVs have been annotated by researchers so far, and universal software for domain prediction could not achieve sufficient accuracy for specific ISD. In this paper, a computational model is proposed to identify ISD in HERVs based on genome sequences only. It has a classification accuracy of 97.9% using Jack-knife test. 117 HERVs families were scanned with the model, 1002 new putative ISDs have been predicted and annotated in the human chromosomes. This model is also applicable to search for ISDs in human T-lymphotropic virus (HTLV), simian T-lymphotropic virus (STLV) and murine leukemia virus (MLV) because of the evolutionary relationship between endogenous and exogenous retroviruses. Furthermore, software named ISDTool has been developed to facilitate the application of the model. Datasets and the software involved in the paper are all available at https://sourceforge.net/projects/isdtool/files/ISDTool-1.0.     

A hidden Markov model with molecular mechanics energy-scoring function for transmembrane helix prediction

A range of methods has been developed to predict transmembrane helices and their topologies. Although most of these algorithms give good predictions, no single method consistently outperforms the others. However, combining different algorithms is one approach that can potentially improve the accuracy of the prediction. We developed a new method that initially uses a hidden Markov model to predict alternative models for membrane spanning helices in proteins. The algorithm subsequently identifies the best among models by ranking them using a novel scoring function based on the folding energy of transmembrane helical fragments. This folding of helical fragments and the incorporation into membrane is modeled using CHARMm, extended with the Generalized Born surface area solvent model (GBSA/IM) with implicit membrane. The combined method reported here, TMHGB significantly increases the accuracy of the original hidden Markov model-based algorithm.     

预测混合煤燃烧性能的数学模型

用热重分析法研究了单种煤的DTG和TG曲线特征，建立了预测混合煤燃烧性能的数学模型。结果表明，各单种煤的DTG曲线呈单峰，峰值温度tmax与其燃料比F的对数呈线性相关关系，tmax=68.86 ln（F）+512.2 （℃）。由组成混合煤的单种煤的燃料比F可推断出混合煤的DTG曲线特征，从而预测混合煤的燃烧性能。如果混合煤的煤质指标满足标准煤的煤质指标要求，则当Fmax/Fmin≤3.20时，混合煤的燃烧性能好；当3.205.71时，混合煤的燃烧性能较差。     

A fast method for large‐scale De Novo peptide and miniprotein structure prediction

Although peptides have many biological and biomedical implications, an accurate method predicting their equilibrium structural ensembles from amino acid sequences and suitable for large‐scale experiments is still missing. We introduce a new approach—PEP‐FOLD—to the de novo prediction of peptides and miniproteins. It first predicts, in the terms of a Hidden Markov Model‐derived structural alphabet, a limited number of local conformations at each position of the structure. It then performs their assembly using a greedy procedure driven by a coarse‐grained energy score. On a benchmark of 52 peptides with 9–23 amino acids, PEP‐FOLD generates lowest‐energy conformations within 2.8 and 2.3 Å Cα root‐mean‐square deviation from the full nuclear magnetic resonance structures (NMR) and the NMR rigid cores, respectively, outperforming previous approaches. For 13 miniproteins with 27–49 amino acids, PEP‐FOLD reaches an accuracy of 3.6 and 4.6 Å Cα root‐mean‐square deviation for the most‐native and lowest‐energy conformations, using the nonflexible regions identified by NMR. PEP‐FOLD simulations are fast—a few minutes only—opening therefore, the door to in silico large‐scale rational design of new bioactive peptides and miniproteins. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images

Protein structure prediction and analysis are more significant for living organs to perfect asses the living organ functionalities. Several protein structure prediction methods use neural network (NN). However, the Hidden Markov model is more interpretable and effective for more biological data analysis compared to the NN. It employs statistical data analysis to enhance the prediction accuracy. The current work proposed a protein prediction approach from protein images based on Hidden Markov Model and Chapman Kolmogrov equation. Initially, a preprocessing stage was applied for protein images’ binarization using Otsu technique in order to convert the protein image into binary matrix. Subsequently, two counting algorithms, namely the Flood fill and Warshall are employed to classify the protein structures. Finally, Hidden Markov model and Chapman Kolmogrov equation are applied on the classified structures for predicting the protein structure. The execution time and algorithmic performances are measured to evaluate the primary, secondary and tertiary protein structure prediction.     

THPep: A machine learning-based approach for predicting tumor homing peptides

In the present era, a major drawback of current anti-cancer drugs is the lack of satisfactory specificity towards tumor cells. Despite the presence of several therapies against cancer, tumor homing peptides are gaining importance as therapeutic agents. In this regard, the huge number of therapeutic peptides generated in recent years, demands the need to develop an effective and interpretable computational model for rapidly, effectively and automatically predicting tumor homing peptides. Therefore, a sequence-based approach referred herein as THPep has been developed to predict and analyze tumor homing peptides by using an interpretable random forest classifier in concomitant with amino acid composition, dipeptide composition and pseudo amino acid composition. An overall accuracy and Matthews correlation coefficient of 90.13% and 0.76, respectively, were achieved from the independent test set on an objective benchmark dataset. Upon comparison, it was found that THPep was superior to the existing method and holds high potential as a useful tool for predicting tumor homing peptides. For the convenience of experimental scientists, a web server for this proposed method is provided publicly at http://codes.bio/thpep/.     

A kinetic model for predicting biodegradation

Biodegradation plays a key role in the environmental risk assessment of organic chemicals. The need to assess biodegradability of a chemical for regulatory purposes supports the development of a model for predicting the extent of biodegradation at different time frames, in particular the extent of ultimate biodegradation within a ‘10?day window’ criterion as well as estimating biodegradation half-lives. Conceptually this implies expressing the rate of catabolic transformations as a function of time. An attempt to correlate the kinetics of biodegradation with molecular structure of chemicals is presented. A simplified biodegradation kinetic model was formulated by combining the probabilistic approach of the original formulation of the CATABOL model with the assumption of first order kinetics of catabolic transformations. Nonlinear regression analysis was used to fit the model parameters to OECD 301F biodegradation kinetic data for a set of 208 chemicals. The new model allows the prediction of biodegradation multi-pathways, primary and ultimate half-lives and simulation of related kinetic biodegradation parameters such as biological oxygen demand (BOD), carbon dioxide production, and the nature and amount of metabolites as a function of time. The model may also be used for evaluating the OECD ready biodegradability potential of a chemical within the ‘10-day window’ criterion.     

A new method for predicting conduction anesthesia

A new method for predicting conduction anesthesia has been suggested. The method is based on calculation of theP matrix probabilities of interatomic contacts for each molecule of the compounds considered. TheP matrix enables one to evaluate the main tendencies of atoms and atomic groups to interact in biochemical sorption on the nerve fiber surface. The minimum effective concentrations calculated for 25 compounds are in good agreement with the experimental data. The correlation coefficient between the experimental and calculated values is 0.98 when the standard deviation is 0.1 mmol L⁻¹. Translated fromIzvestiya Akademii Nauk. Seriya Khimicheskaya, No. 10, pp. 1781–1784, October, 1997.     

Accurate and efficient generalized born model based on solvent accessibility: derivation and application for LogP octanol/water prediction and flexible peptide docking

A novel method for fast and accurate evaluation of the generalized Born radii in macromolecular solvation electrostatics calculations is proposed, based on the solvent accessibility of the first two solvation layers around an atom. The reverse generalized Born radii calculated by the method have correlation coefficient of 98.7% and RMSD of 0.031 A(-1) with the values obtained using a precise but significantly slower numerical boundary element solution. The method is applied to derive an estimate of the free solvation energy difference between octanol and water and to predict LogP octanol-water. A nine-parameter model is optimized on an 81 compound training set and applied to predict LogP(ow) for an external evaluation set of 19 drug molecules with RMSD of 0.9. The new GB approximation is also tested in Monte Carlo docking simulations of the fully flexible p53 peptide fragment to MDM2. The best energy solution found in the simulations has RMSD of 2.8 A to the X-ray structure.     

A computational model of the 5-HT3 receptor extracellular domain: search for ligand binding sites

A three-dimensional model of the 5-HT₃ receptor extarcellular domain has been derived on the basis of the nicotinic acetylcholine receptor model recently published by Tsigelny et al. Maximum complementarity between the position and characteristics of mutated residues putatively involved in ligand interaction and the pharmacophoric elements derived by the indirect approach applied on several series of 5-HT₃ ligands have been exploited to gain insights into the ligand binding modalities and to speculate on the mechanistic role of the structural components. The analysis of the three-dimensional model allows one to distinguish among amino acids that exert key roles in ligand interactions, subunit architecture, receptor assembly and receptor dynamics. For some of these, alternative roles with respect to the ones hypothesized by experimentalists are assigned. Different binding modalities for agonists and antagonists are highlighted, and residues which probably play a role in the transduction of binding into a change in conformational state of the receptor are suggested. Received: 27 July 2000 / Accepted: 15 September 2000 / Published online: 21 December 2000     

蛋白质组质谱分析中基于串并联支持向量机的肽段色谱保留时间预测方法

基于质谱的大规模蛋白质鉴定中,在线液相色谱分离发挥了重要作用。色谱保留时间(retention time,RT)是肽段鉴定和定量的重要信息。由于整个色谱分析运行时间中,流动相中的有机相采用了非线性浓度曲线以及样品中肽段之间的相互影响等因素,基于肽段序列的RT预测还存在精度不高、模型推广性能差等问题。本文提出了一种基于串并联支持向量机(serial and parallel support vector machine,SP-SVM)的RT预测方法,能够表征洗脱过程中有机相浓度的非线性变化和肽段之间的相互影响,显著提高了肽段保留时间预测的精度。利用复杂样本数据集验证结果表明,预测RT和实验RT之间的决定系数达到了0.95,超过95%的鉴定肽段的RT预测误差范围小于总运行时间的20%,超过70%的鉴定肽段的RT预测误差范围小于总运行时间的10%。本文提出的模型的性能达到了目前已知的最好水平。     

基质金属蛋白酶活性中心肽段的合成及纯化

基质金属蛋白酶(MMPs)是一类生物活性依赖于钙锌离子,能降解细胞外基质(extracellar matrix,ECM)的酶家族.目前已发现26个成员,越来越多的研究表明,MMPs在肿瘤侵袭转移中起着重要作用,此作用不仅仅限于它有利于细胞外基质的降解,还对肿瘤微环境的维持和促进肿瘤生长起着重要作用[1].     

IMSPeptider: A computational peptide collision cross‐section area calculator based on a novel molecular dynamics simulation protocol

Introduction of ion mobility mass spectrometry (IMS/MS) into the proteomic workflow provides an orthogonal separation to the widely used LC‐MS platforms. IMS also provides structural information that could facilitate peptide identification. However, the lack of tools capable of predictive power in a high‐throughput fashion makes peptide global profiling quite challenging. To target this issue, a computational workflow was developed based on biophysical principles to predict the collision cross‐section area (CCS) of peptides as measured from IMS/MS experiments. Hosted on a web server, it allows the user to input a primary sequence (query) and retrieve information on peptide structure, sequence, and corresponding CCS. The current version is designed to identify peptide sequences up to 23 residues in length, in its higher charge state, based on a match of the molecule m/z and CCS. The protocol was validated against a 128‐sequences‐dataset and CCS predicted within 2.8% average error. © 2013 Wiley Periodicals, Inc.     

Common prevalence of alanine and glycine in mobile reactive centre loops of serpins and viral fusion peptides: Do prions possess a fusion peptide?

Summary Serpin reactive centre loops and fusion peptides released by proteolytic cleavage are particularly mobile. Their amino acid compositions reveal a common and unusual abundance of alanine, accompanied by high levels of glycine. These two small residues, which are not simultaneously abundant in stable helices (standard or transmembrane), probably play an important role in mobility. Threonine and valine (also relatively small amino acids) are also abundant in these two kinds of peptides. Moreover, the known 3D structures of an uncleaved serpin reactive centre and a fusion peptide are strikingly similar. Such sequences possess many small residues and are found in several signal peptides and in PrP, a protein associated with spongi-form encephalopathies and resembling virus envelope proteins. These properties may be related to the infection mechanisms of these diseases.     

多肽新药研发策略研究进展

多肽药物具有生物活性高、药用剂量小、产业化开发优势明显等诸多优点,已成为全球关注的创新药物研发热点之一.但是其代谢不稳定、半衰期短及较难通透组织屏障等缺点严重阻碍了多肽新药在临床治疗中的广泛应用.为了解决这些限制多肽药物的瓶颈,本课题组发展了一系列的改造策略.通过这些策略的应用,以期加快多肽药物临床应用的步伐.本文主要结合本课题组的工作对多肽新药创制过程中所遇到的关键问题及解决思路进行综述.     

A computer simulation — Statistical procedure for predicting complexation equilibrium constants

The complexing properties of macrocyclic ligands have been quantitatively studied by the combined use of molecular mechanics, molecular dynamics, and multiple linear regression. The dependent variables in the regression equations are experimental equilibrium constants for known macrocycle complexes in various solvents. The independent variables are theoretical simulation results on the solvent-free ligand and its complex and additional physically motivated empirical variables to describe solvent and other important effects.The systems studied were: (a) 314 metal ion-macrocycle-solvent (including 3 mixed solvents) combinations; (b) 88 ammonium ion-crown ether-solvent (including 1 mixed solvent) combinations; (c) 24 hydrogen ion-crown ether-H₂O combinations; (d) 26 Na⁺ ion-spherand-CDCl₃ combinations; (e) 78 ammonium ion-spherand-CDCl₃ combinations; and (f) 73 complicated host-guest-solvent (including 1 mixed solvent) combinations.For each system, we report the best regression equation obtained using the AMBER force field. The standard errors in logK range from 1.42 in the largest system to 0.36 in the smallest. Regression equations were determined for several of the systems using the MMP2 force field as well, and the equations are shown to be relatively insensitive to the force field.The predictive ability of the method was tested by predicting logK for 20% of the cases chosen at random using equations derived from the remaining 80%. The errors in the predicted values are shown to be consistent with the statistical assumptions of the model.Regression equations obtained with this method can be used to predict the equilibrium constants for new complexes involving some combination of new, possibly unknown macrocycle, new host and, in certain cases new solvent. No X-ray or other structural data for the macrocycle is needed.     

A simple model for predicting the course of photochemical reactions

The possibility of building and using a simplified model with constant (time-invariant) reaction probabilities for rapid simulation of photochemical processes with the required reliability of the prediction has been shown. A relation between the probabilities of transformations and fundamental molecular characteristics (optical transition probabilities and quantum beat frequencies referring to a nonradiative reaction transition) that can be quantitatively determined with a good degree of certainty has been revealed. For monomolecular isomerization reactions as an example, the attainable level of the accuracy of prediction by the simple model has been estimated, which has been shown to be ～10% in most cases. It is important that the prediction of the order of magnitude of the key characteristics of photochemical processes, such as quantum yield and the reaction rate, is definitely correct as is required in most applications of photochemistry.