Development and prospects of predicting drug polymorphs technology / 药学学报

Most chemical medicines have polymorphs. The difference of medicine polymorphs in physicochemical properties directly affects the stability, efficacy, and safety of solid medicine products. Polymorphs is incomparably important to pharmaceutical chemistry, manufacturing, and control. Meantime polymorphs is a key factor for the quality of high-end drug and formulations. Polymorph prediction technology can effectively guide screening of trial experiments, and reduce the risk of missing stable crystal form in the traditional experiment. Polymorph prediction technology was firstly based on theoretical calculations such as quantum mechanics and computational chemistry, and then was developed by the key technology of machine learning using the artificial intelligence. Nowadays, the popular trend is to combine the advantages of theoretical calculation and machine learning to jointly predict crystal structure. Recently, predicting medicine polymorphs has still been a challenging problem. It is expected to learn from and integrate existing technologies to predict medicine polymorphs more accurately and efficiently.

Application progress of artificial intelligence in the screening and identification of drug targets / 中国药科大学学报

@#In recent years, artificial intelligence (AI) has developed rapidly, with improved computing power and algorithms, which has greatly facilitated the collection and processing of biological, chemical information and clinical data, injecting new vitality into the research and development of new drugs.In this review, we began with a brief overview of the development and the main algorithms of AI in drug discovery.Then we elaborated through several specific cases on the various scenarios of AI application, including target identification, protein structure prediction, hit generation and optimization etc.Finally, we focused on a recent example to discuss the high efficiency of "end-to-end" application of AI.

Analysis of a thermostable lipase from CTG-clade yeast: Molecular expression, characterization and structure prediction

Aims@#This study was aimed to express Meyerozyma guilliermondii strain RT lipase using Komagataella phaffii X-33 expression system and its biochemical characterization and analyse the predicted structure of the product.@*Methodology and results@#Meyerozyma guilliermondii strain RT obtained from the previous study was used as the source of RT lipase gene. Extracellular M. guilliermondii strain RT lipase expression has significantly been improved up to 56 U/mg at 24 h cultivation in Yeast extract-Peptone-Dextrose (YPD) medium containing (in w/v): 1% yeast extract, 2% peptone, 2% dextrose with 0.5% v/v methanol induction. Characterization of RT lipase showed optimum activity at 45 °C and pH 9. It exhibited stability in the alkaline pH range (8 to 10) and retained 50% of its residual activity at 30 °C for 30 min. Substrate specificity analysis revealed that it preferred short to medium-chain triacylglycerols (C2-C12) with the highest activity towards caprylic acid (C8). Pairwise alignment revealed three substitutions (S2L, S92L and S193L) present in non-CTG-clade hosts (K. phaffii). Homology modelling (YASARA) was used to predict the structures of RT lipase [wild type (wt) and recombinant (rc)]. Mutational analysis of the structures showed the differences in loops that might attribute to the reduction of the optimum temperature from 75 °C (wt) to 45 °C (rc).@*Conclusion, significance and impact of study@#RT lipase was successfully overexpressed extracellularly using K. phaffii expression system with 91.8-fold higher specific activity than the native host. The conceptual advances on the importance of codon optimization before expressing a protein from a CTG-clade species in a non-CTG-clade yeast have been highlighted and the effect of the rare codon usage in recombinant protein characteristics has been evident.

Intrinsically disordered proteins (IDPs) and the impact on cell stress resistance / 生物工程学报

Intrinsically disordered proteins (IDPs) are proteins or protein regions that fail to get folded into definite three-dimensional structures but participate in various biological processes and perform specific functions. Defying the traditional protein "sequence-structure-function" paradigm, they enrich the protein "structure-function" diversity. Ubiquitous in organisms, they show extreme hydrophilicity, charged amino acids, and highly repetitive amino acid sequences, with simple arrangement. As a result, they feature highly variable binding affinities and high coordination, which facilitate their functions. IDPs play an important role in cell stress response, which can improve the tolerance to a variety of stresses, such as freezing, high salt, heat shock, and desiccation. In this study, we briefed the characteristics, classifications, and identification of IDPs, summarized the molecular mechanism in improving cell stress resistance, and described the potential applications.

Protein modeling and design based on deep learning / 生物工程学报

The accumulation of protein sequence and structure data allows researchers to obtain large amount of descriptive information, simultaneously it poses an urgent need for researchers to extract information from existing data efficiently and apply it to downstream tasks. Protein design enables the development of novel proteins that are no longer restricted by experimental conditions, which is of great significance for drug target prediction, drug discovery, and material design. As an efficient method for data feature extraction, deep learning can be used to model protein data, and further add a priori information to design novel proteins. Therefore, protein design based on deep learning has become a promising approach despite of many challenges. This review summarizes the deep learning-based modeling and design methods of protein sequence and structure data, highlighting the strategies, principle, scope of application and case studies, with the aim to provide a valuable reference for relevant researchers.

3-State Protein Secondary Structure Prediction based on SCOPe Classes

Abstract Improving the accuracy of protein secondary structure prediction has been an important task in bioinformatics since it is not only the starting point in obtaining tertiary structure in hierarchical modeling but also enhances sequence analysis and sequence-structure threading to help determine structure and function. Herein we present a model based on DSPRED classifier, a hybrid method composed of dynamic Bayesian networks and a support vector machine to predict 3-state secondary structure information of proteins. We used the SCOPe (Structural Classification of Proteins-extended) database to train and test the model. The results show that DSPRED reached a Q3 accuracy rate of 82.36% when trained and tested using proteins from all SCOPe classes. We compared our method with the popular PSIPRED on the SCOPe test datasets and found that our method outperformed PSIPRED.

Antimicrobial Activity of Cationic Peptides Designed from Neutral Peptide / Actividad antimicrobiana de péptidos catiónicos diseñados a partir de un péptido neutro

RESUMEN Los péptidos antimicrobianos (PAMs) juegan un papel importante en la inmunidad innata de la mayoría de los organismos; ellos pueden tener actividad en bacterias, hongos, virus y parásitos. El mecanismo de acción de los PAMs catiónicos yace en la capacidad de interactuar con membranas microbianas, debido a la superficie aniónica de dichas membranas. La familia de las cecropinas fue identificada como una de las familias peptídicas más importantes en los insectos. Los péptidos de esta familia, no contienen residuos de cisteína y son clasificados como helicoidales. Para estudiar el efecto de la carga sobre la estructura, nosotros introducimos residuos cargados positivamente en los primeros 18 aminoácidos de la región N-terminal de la cecropina-D (WT), y se evaluó la actividad biológica de los péptidos modificados. Dos análogos de la cecropina-D con cargas netas de +5 y +9, fueron obtenidos por síntesis de fase sólida (SSP). Los cambios en los péptidos análogos fueron generados de la siguiente manera: péptido +5 con tres sustituciones (E6R, E8R and Q12K) y péptido +9 con cinco sustituciones (E1R, E6R, E8R, Q12K, and D16K). La actividad antibacteriana fue evaluada en dos grupos de bacterias, con el fin de investigar los efectos de las cargas positivas en dicha actividad. Los péptidos catiónicos mostraron una mayor actividad antimicrobiana tanto en bacterias Gram-negativas como en Gram-positivas, a diferencia del péptido WT. Las representaciones en 3D de los péptidos mostraron que ellos tienen una estructura α-hélice. Nuestros resultados demostraron que cambios en la carga de los péptidos incrementa la actividad antibacteriana.

ABSTRACT Antimicrobial peptides (PAMs) play an important role in the innate defense systems of most organisms; they act against bacteria, fungi, viruses and parasites. The mechanism of action of cationic PAMs rely on their capacity to interact with the anionic microbial membrane surface. The cecropin family was identified as one of the most important peptides in insects. Such peptides do not contain cysteine residues and are classified as α-helical. To study the effect of the charge on the peptide structure, we introduced positive charge residues in the last 18 residues at the N-end of cecropin-D (WT) and evaluated the biological activity of the modified peptides. Two analogous peptides from cecropin-D were obtained by synthesis of a solid phase (SSP) with charges of+5 and +9. The analogous peptides were generated as followed: peptide +5 with three substitutions (E6R, E8R and Q12K) and peptide +9 with five substitutions (E1R, E6R, E8R, Q12K, and D16K). Antibacterial activity was evaluated to investigate the effects of the positive charge in these two analogue peptides against two groups of bacteria. The cationic peptides showed higher antimicrobial activity against Gram-negative and Gram-positive bacteria than the WT peptide. The 3D representations of the peptides showed that they have α-helical structure. Our results demonstrate that changes in the charge of peptides increase the antibacterial activity.

Sequence analysis and structure prediction of the methyltransferase of duck Tembusu virus / 中国人兽共患病学报

In order to determine the amino acid sequence of functionthat play a role of methyltransferase (MTase) activity in duck Tembusu virus (DTMUV),we have made sequence analysis and structure prediction of the MTase in DTMUV by bioin formatics methods,as well as using the sequence and structure of MTase in other flaviviruses which have been reported.The bioinformatical analysis results showed that the MTase and the MTase of other three kinds of flaviviruses had the higher homology in nucleotide sequence and amino acid sequence,for 64.76％,64.55％,67.33％ and 74.71％,68.45％,75.15％ respectively,and the same basic structural characteristics which contained SAM binding site and had a typical of 4 alpha screw and 7 beta folding.At the same time,there were classical conservative sites K-D-K-E of flavivirus MTase in DTMUV MTase sequence.In conclusion,they imply that the MTase of DTMUV might belong to MTase family of flaviviruses.

Structural Analysis of Recombinant Human Preproinsulins by Structure Prediction, Molecular Dynamics, and Protein-Protein Docking

More effective production of human insulin is important, because insulin is the main medication that is used to treat multiple types of diabetes and because many people are suffering from diabetes. The current system of insulin production is based on recombinant DNA technology, and the expression vector is composed of a preproinsulin sequence that is a fused form of an artificial leader peptide and the native proinsulin. It has been reported that the sequence of the leader peptide affects the production of insulin. To analyze how the leader peptide affects the maturation of insulin structurally, we adapted several in silico simulations using 13 artificial proinsulin sequences. Three-dimensional structures of models were predicted and compared. Although their sequences had few differences, the predicted structures were somewhat different. The structures were refined by molecular dynamics simulation, and the energy of each model was estimated. Then, protein-protein docking between the models and trypsin was carried out to compare how efficiently the protease could access the cleavage sites of the proinsulin models. The results showed some concordance with experimental results that have been reported; so, we expect our analysis will be used to predict the optimized sequence of artificial proinsulin for more effective production.

The introduction of PKS-NRPS databases / 中国药学杂志

Polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs) are two kinds of multi-modular enzymes, which biosynthesize highly complex polyketides and nonribosonmal peptides, respectively. Both of these two secondary metabolites are of considerable pharmaceutical relevance and are thought to cover diverse biological functions. With the development of sequencing and bioinformatics, data about PKS/NRPS are increasing rapidly. New PKS/NRPS databases are created to analyze gene sequence and predict the functions and structures of natural products. In this article, we introduce five newest databases including PKMiner, NRPSsp, NaPDoS, ClusterMine360, and IMG-ABC, with the goal to help researchers choose databases.

Structure-function analysis of two variants of mumps virus hemagglutinin-neuraminidase protein

A point mutation from guanine (G) to adenine (A) at nucleotide position 1081 in the hemagglutinin-neuraminidase (HN) gene has been associated with neurovirulence of Urabe AM9 mumps virus vaccine. This mutation corresponds to a glutamic acid (E) to lysine (K) change at position 335 in the HN glycoprotein. We have experimentally demonstrated that two variants of Urabe AM9 strain (HN-A1081 and HN-G1081) differ in neurotropism, sialic acidbinding affinity and neuraminidase activity. In the present study, we performed a structure-function analysis of that amino acid substitution; the structures of HN protein of both Urabe AM9 strain variants were predicted. Based on our analysis, the E/K mutation changes the protein surface properties and to a lesser extent their conformations, which in turn reflects in activity changes. Our modeling results suggest that this E/K interchange does not affect the structure of the sialic acid binding motif; however, the electrostatic surface differs drastically due to an exposed short alpha helix. Consequently, this mutation may affect the accessibility of HN to substrates and membrane receptors of the host cells. Our findings appear to explain the observed differences in neurotropism of these vaccine strains.

Effective scoring function for protein structure prediction / 上海第二医科大学学报

Objective To design a good scoring function for predicting the tertiary structures of proteins. Methods A scoring function,which was composed of simple sum of pairwise distance-dependent potentials of C?-C?,N-N,C′-C′ and backbone dihedral angle-dependent potentials,was proposed in this paper. Results The performance of the scoring function was verified with experiment to be improved. Conclusion The proposed scoring function is effective and valid in predicting the tertiary structures of proteins.

Prediction and comparison of the secondary structure of legume lectins.

Secondary structure prediction for the 4 legume lectins: Concanavalin A, soybean agglutinin, favabean lectin and lentil lectin, was done by the method of Chou and Fasman. This prediction shows that these four lectins fall into a structurally distinct class of proteins, containing high amounts of β-sheet and β-turns. There is a notable similarity in the gross structure of these proteins; all four of them contain about 40–50% of β-sheet, 35–45 % β-turn and 0–10% of α-helix. When the secondary structure of corresponding residues in each pair of these lectins was compared, there was a striking similarity in the Concanavalin A-soybean agglutinin and favabean lectin-lentil lectin pairs, and considerably less similarity in the other pairs, suggesting that these legume lectins have probably evolved in a divergent manner from a common ancestor. A comparison of the predicted potential β-turn sites also supports the hypothesis of divergent evolution in this class of lectins.

Physical principles of protein structure and protein folding.

Physical principles determining the protein structure and protein folding are reviewed: (i) the molecular theory of protein secondary structure and the method of its prediction based on this theory; (ii) the existence of a limited set of thermodynamically favourable folding patterns of α- and ß-regions in a compact globule which does not depend on the details of the amino acid sequence; (iii) the moderns approaches to the prediction of the folding patterns of α- and ß-regions in concrete proteins; (iv) experimental approaches to the mechanism of protein folding. The review reflects theoretical and experimental works of the author and his collaborators as well as those of other groups.