Synthetic peptide arrays and peptide combinatorial libraries for the exploration of protein-ligand interactions and the design of protein inhibitors

Synthetic peptides have a long tradition as molecular tools in biomedical research and drug discovery. The introduction of high-throughput synthesis and screening technologies for synthetic peptides, such as arrays and combinatorial libraries, enabled the large-scale and detailed exploration of protein-ligand interactions, as well as the discovery of novel biologically active peptides. This review summarizes currently available synthetic peptide array and library technologies, in particular mixture-based peptide libraries, which are illustrated by numerous applications in various fields of biomedical research.     

Synthesis of 3,5-difluorotyrosine-containing peptides: application in substrate profiling of protein tyrosine phosphatases

Fully protected 3,5-difluorotyrosine (F2Y), Fmoc-F2Y(tBu)-OH, is efficiently prepared by a chemoenzymatic process and incorporated into individual peptides and combinatorial peptide libraries. The F2Y-containing peptides display kinetic properties toward protein tyrosine phosphatases (PTPs) similar to their corresponding tyrosine-containing counterparts but are resistant to tyrosinase action. These properties make F2Y a useful tyrosine surrogate during peptide library screening for optimal PTP substrates.     

Comparison of bacterial and phage display peptide libraries in search of target-binding motif

Genetic engineering allows modification of bacterial and bacteriophage genes, which code for surface proteins, enabling display of random peptides on the surface of these microbial vectors. Biologic peptide libraries thus formed are used for high-throughput screening of clones bearing peptides with high affinity for target proteins. There are reports of many successful affinity selections performed with phage display libraries and substantially fewer cases describing the use of bacterial display systems. In theory, bacterial display has some advantages over phage display, but the two systems have never been experimentally compared. We tested both techniques in selecting streptavidin-binding peptides from two commercially available libraries. Under similar conditions, selection of phage-displayed peptides to model protein streptavidin proved convincingly better.     

ZINC--a free database of commercially available compounds for virtual screening

A critical barrier to entry into structure-based virtual screening is the lack of a suitable, easy to access database of purchasable compounds. We have therefore prepared a library of 727,842 molecules, each with 3D structure, using catalogs of compounds from vendors (the size of this library continues to grow). The molecules have been assigned biologically relevant protonation states and are annotated with properties such as molecular weight, calculated LogP, and number of rotatable bonds. Each molecule in the library contains vendor and purchasing information and is ready for docking using a number of popular docking programs. Within certain limits, the molecules are prepared in multiple protonation states and multiple tautomeric forms. In one format, multiple conformations are available for the molecules. This database is available for free download (http://zinc.docking.org) in several common file formats including SMILES, mol2, 3D SDF, and DOCK flexibase format. A Web-based query tool incorporating a molecular drawing interface enables the database to be searched and browsed and subsets to be created. Users can process their own molecules by uploading them to a server. Our hope is that this database will bring virtual screening libraries to a wide community of structural biologists and medicinal chemists.     

ω-芋螺毒素的定量构效关系与虚拟筛选

ω-芋螺毒素属于海洋生物活性多肽,由24-31个氨基酸残基组成.特异性作用于电压敏感的钙离子通道(VGCCs),能够直接开发成药物或作为先导化合物进行新药开发.本文应用新型氨基酸残基结构描述符cscales和遗传偏最小二乘算法,对ω-芋螺毒素进行定量构效关系(QSAR)研究,并设计、构建了容量为2244个化合物的N-型和P/Q-型VGCC拮抗剂虚拟组合多肽库,然后分别采用QSAR模型预测和相似性搜索方法对组合多肽库进行了虚拟筛选.研究结果表明,建立的N-型和P/Q-型VGCC拮抗剂QSAR模型均具有较好的预测能力,交叉验证相关系数(CV-r2)均大于0.89.主成分分析和聚类分析结果表明,虚拟组合多肽库中化合物具有较好的结构多样性和差异性.通过虚拟筛选,得到了具有高预测活性的6个N-型和19个P/Q-型钙离子通道拮抗剂,为进一步的合成和活性评价奠定了理论基础.同时,本文建立的多肽QSAR预测模型和虚拟筛选策略,为其它多肽类化合物的定量构效关系研究和虚拟筛选提供了参考.     

药物发现中常用化合物库特征描述与对比分析

随着计算技术的发展和分子模拟软件的日趋成熟, 虚拟筛选已经在药物发现过程中发挥着越来越重要的作用. 在虚拟筛选过程中, 所使用化合物库的质量对先导化合物发现的成功率起着至关重要的作用. 本文通过对已知药物库、天然产物库、中药原植物化学成分库、筛选常用商业化合物库以及研究者所在实验室建立的化合物库的分析比较, 从化合物库的分子多样性、化学空间和分子骨架等多个方面提取并对比每一种化合物库的特征, 发现了已知药物库与中药原植物化学成分库的特征相似性, 揭示了中药原植物化学成分库作为筛选库的类药性优势, 并且深化了对几种筛选用化合物库特征的认识和理解.     

Conformational selectivity of peptides for single-walled carbon nanotubes

Carbon nanotubes show promising prospects for applications ranging from molecular electronics to ultrasensitive biosensors. An important aspect to understanding carbon nanotube properties is their interactions with biomolecules such as peptides and proteins, as these interactions are important in our understanding of nanotube interactions with the environment, their use in cellular systems, as well as their interface with biological materials for medical and diagnostic applications. Here we report the sequence and conformational requirements of peptides for high-affinity binding to single-walled carbon nanotubes (SWNTs). A new motif, X(1)THX(2)X(3)PWTX(4), where X(1) is G or H, X(2) is H or D or null, X(3) is null or R, and X4 is null or K, was identified from two classes of phage-displayed peptide libraries. The high affinity binding of the motif to SWNTs required constrained conformations which were achieved through either the extension of the amino acid sequence (e.g., LLADTTHHRPWT) or the addition of a constrained disulfide bond (e.g., CGHPWTKC). This motif shows specific high-affinity to the currently studied SWNTs, compared to previously reported peptides. The conformations of the identified peptides in complex with SWNTs were also characterized with a variety of biophysical methodologies including CD, fluorescence, NMR spectroscopy, and molecular modeling.     

Streamlined in vivo selection and screening of human prostate carcinoma avid phage particles for development of peptide based in vivo tumor imaging agents

Bacteriophage (phage) display has been exploited for the purpose of discovering new cancer specific targeting peptides. However, this approach has resulted in only a small number of tumor targeting peptides useful as in vivo imaging agents. We hypothesize that in vivo screening for tumor uptake of fluorescently tagged phage particles displaying multiple copies of an in vivo selected tumor targeting peptide will expedite the development of peptide based imaging agents. In this study, both in vivo selection and in vivo screening of phage displaying foreign peptides were utilized to best predict peptides with the pharmacokinetic properties necessary for translation into efficacious in vivo imaging agents. An in vivo selection of phage display libraries was performed in SCID mice bearing human PC-3 prostate carcinoma tumors. Eight randomly selected phage clones and four control phage clones were fluorescently labeled with AlexaFluor 680 for subsequent in vivo screening and analyses. The corresponding peptides of six of these phage clones were tested as 111In-labeled peptide conjugates for single photon emission computed tomography (SPECT) imaging of PC-3 prostate carcinomas. Two peptide sequences, G1 and H5, were successful as in vivo imaging agents. The affinities of G1 and H5 peptides for cultured PC-3 cells were then analyzed via cell flow cytometry resulting in Kd values of 1.8 μM and 2.2 μM, respectively. The peptides bound preferentially to prostate tumor cell lines compared to that of other carcinoma and normal cell lines, and H5 appeared to possess cytotoxic properties. This study demonstrates the value of in vivo screening of fluorescently labeled phage for the prediction of the efficacy of the corresponding 111In-labeled synthetic peptide as an in vivo SPECT tumor imaging agent.     

Biosynthetic Strategies for Macrocyclic Peptides

Macrocyclic peptides are predominantly peptide structures bearing one or more rings and spanning multiple amino acid residues. Macrocyclization has become a common approach for improving the pharmacological properties and bioactivity of peptides. A variety of ribosomal-derived and non-ribosomal synthesized cyclization approaches have been established. The biosynthesis of backbone macrocyclic peptides using seven new emerging methodologies will be discussed with regard to the features and strengths of each platform rather than medicinal chemistry tools. The mRNA display variant, known as the random nonstandard peptide integrated discovery (RaPID) platform, utilizes flexible in vitro translation (FIT) to access macrocyclic peptides containing nonproteinogenic amino acids (NAAs). As a new discovery approach, the ribosomally synthesized and post-translationally modified peptides (RiPPs) method involves the combination of ribosomal synthesis and the phage screening platform together with macrocyclization chemistries to generate libraries of macrocyclic peptides. Meanwhile, the split-intein circular ligation of peptides and proteins (SICLOPPS) approach relies on the in vivo production of macrocyclic peptides. In vitro and in vivo peptide library screening is discussed as an advanced strategy for cyclic peptide selection. Specifically, biosynthetic bicyclic peptides are highlighted as versatile and attractive modalities. Bicyclic peptides represent another type of promising therapeutics that allow for building blocks with a heterotrimeric conjugate to address intractable challenges and enable multimer complexes via linkers. Additionally, we discuss the cell-free chemoenzymatic synthesis of macrocyclic peptides with a non-ribosomal catalase known as the non-ribosomal synthetase (NRPS) and chemo-enzymatic approach, with recombinant thioesterase (TE) domains. Novel insights into the use of peptide library tools, activity-based two-hybrid screening, structure diversification, inclusion of NAAs, combinatorial libraries, expanding the toolbox for macrocyclic peptides, bicyclic peptides, chemoenzymatic strategies, and future perspectives are presented. This review highlights the broad spectrum of strategy classes, novel platforms, structure diversity, chemical space, and functionalities of macrocyclic peptides enabled by emerging biosynthetic platforms to achieve bioactivity and for therapeutic purposes.     

ω-芋螺毒素的定量构效关系与虚拟筛选

ω-芋螺毒素属于海洋生物活性多肽, 由24-31 个氨基酸残基组成. 特异性作用于电压敏感的钙离子通道(VGCCs), 能够直接开发成药物或作为先导化合物进行新药开发. 本文应用新型氨基酸残基结构描述符cscales和遗传偏最小二乘算法, 对ω-芋螺毒素进行定量构效关系(QSAR)研究, 并设计、构建了容量为2244 个化合物的N-型和P/Q-型VGCC拮抗剂虚拟组合多肽库, 然后分别采用QSAR模型预测和相似性搜索方法对组合多肽库进行了虚拟筛选. 研究结果表明, 建立的N-型和P/Q-型VGCC拮抗剂QSAR模型均具有较好的预测能力, 交叉验证相关系数(CV-r²)均大于0.89. 主成分分析和聚类分析结果表明, 虚拟组合多肽库中化合物具有较好的结构多样性和差异性. 通过虚拟筛选, 得到了具有高预测活性的6 个N-型和19 个P/Q-型钙离子通道拮抗剂, 为进一步的合成和活性评价奠定了理论基础. 同时, 本文建立的多肽QSAR预测模型和虚拟筛选策略, 为其它多肽类化合物的定量构效关系研究和虚拟筛选提供了参考.     

The use of proteotypic peptide libraries for protein identification

This paper describes an algorithm to apply proteotypic peptide sequence libraries to protein identifications performed using tandem mass spectrometry (MS/MS). Proteotypic peptides are those peptides in a protein sequence that are most likely to be confidently observed by current MS-based proteomics methods. Libraries of proteotypic peptide sequences were compiled from the Global Proteome Machine Database for Homo sapiens and Saccharomyces cerevisiae model species proteomes. These libraries were used to scan through collections of tandem mass spectra to discover which proteins were represented by the data sets, followed by detailed analysis of the spectra with the full protein sequences corresponding to the discovered proteotypic peptides. This algorithm (Proteotypic Peptide Profiling, or P3) resulted in sequence-to-spectrum matches comparable to those obtained by conventional protein identification algorithms using only full protein sequences, with a 20-fold reduction in the time required to perform the identification calculations. The proteotypic peptide libraries, the open source code for the implementation of the search algorithm and a website for using the software have been made freely available. Approximately 4% of the residues in the H. sapiens proteome were required in the proteotypic peptide library to successfully identify proteins.     

Studying protein-protein interactions using peptide arrays

Screening of arrays and libraries of compounds is well-established as a high-throughput method for detecting and analyzing interactions in both biological and chemical systems. Arrays and libraries can be composed from various types of molecules, ranging from small organic compounds to DNA, proteins and peptides. The applications of libraries for detecting and characterizing biological interactions are wide and diverse, including for example epitope mapping, carbohydrate arrays, enzyme binding and protein-protein interactions. Here, we will focus on the use of peptide arrays to study protein-protein interactions. Characterization of protein-protein interactions is crucial for understanding cell functionality. Using peptides, it is possible to map the precise binding sites in such complexes. Peptide array libraries usually contain partly overlapping peptides derived from the sequence of one protein from the complex of interest. The peptides are attached to a solid support using various techniques such as SPOT-synthesis and photolithography. Then, the array is incubated with the partner protein from the complex of interest. Finally, the detection of the protein-bound peptides is carried out by using immunodetection assays. Peptide array screening is semi-quantitative, and quantitative studies with selected peptides in solution are required to validate and complement the screening results. These studies can improve our fundamental understanding of cellular processes by characterizing amino acid patterns of protein-protein interactions, which may even develop into prediction algorithms. The binding peptides can then serve as a basis for the design of drugs that inhibit or activate the target protein-protein interactions. In the current review, we will introduce the recent work on this subject performed in our and in other laboratories. We will discuss the applications, advantages and disadvantages of using peptide arrays as a tool to study protein-protein interactions.     

Structure of peptides on metal oxide surfaces probed by NMR

Peptides that bind inorganic surfaces and template the formation of nanometer-sized inorganic particles are of great interest for the self- or directed assembly of nanomaterials for sensors and diagnostic applications. These surface-recognizing peptides can be identified from combinatorial phage-display peptide libraries, but little experimental information is available for understanding the relationship between the peptide sequence, structure at the nanoparticle surface, and function. We have developed NMR methods to determine the structures of peptides bound to inorganic nanoparticles and report on the structure of three peptides bound to silica and titania surfaces. Samples were prepared under conditions leading to rapid peptide exchange at the surface such that solution-based nuclear Overhauser experiments can be used to determine the three-dimensional structure of the bound peptide. The binding motif is defined by a compact "C"-shaped structure for the first six amino acids in the 12-mer. The orientation of the peptide on the nanoparticle surface was determined by magnetization transfer from the nanoparticle surface to the nearby peptide protons. These methods can be applied to a wide variety of abiotic interfaces to provide an insight into the relationship between the primary sequence of peptides and their functionality at the interface.     

Past and future perspectives of synthetic peptide libraries

Combinatorial preparation and HTS of arrays of compounds have increased the speed of drug discovery. A strong impulse in this field has come by the introduction of the solid phase synthesis method that, through automation and miniaturization, has paved the way to the preparation of large collections of compounds in compact and trackable formats. Due to the well established synthetic procedures, peptides have been largely used to develop the basic concepts of combinatorial chemistry and peptide libraries are still successfully employed in screening programs. However, peptides generally do not fulfil the requirements of low conformational flexibility, stability and bioavailability needed for good drug candidates and peptide leads with high potency and selectivity are often made "druggable" by conversion to more stable structures with improved pharmacological profiles. Such an approach makes the screening of peptide libraries still a valuable tool for drug discovery. We propose here a panoramic review of the most common methods for the preparation and screening of peptide libraries and the most interesting findings of the last decade. We also report on a new approach we follow in our laboratory that is based on the use of "simplified" libraries composed by a minimum number of non-redundant amino acids for the assembly of short peptides. The choice of amino acids is dictated by diversity in lipophilicity, MW, charge and polarity. Newly identified active sequences are then modified by preparing new variants containing analogous amino acids, so that the chemical space occupied by the excluded residues can be explored. This approach offers the advantage of simplifying the synthesis and deconvolution of libraries and provides new active compounds with a molecular size similar to that of small molecules, to which they can be easily converted.     

A general method for designing combinatorial peptide libraries decodable by amino acid analysis

Herein we describe an algorithm for designing combinatorial peptide libraries for split-and-mix synthesis on solid support that are decodable by amino acid analysis (AAA) of the beads. AAA is a standard service analysis available in most biochemical laboratories, and it allows one to control the quality of the peptide on each bead, an important feature that is missing from most library decoding protocols. In the algorithm, each AA is assigned to two variable positions in the sequence grouped in a "unique pair". This arrangement limits sequence design because both the number of unique pairs U (setting the maximum number of variable AA) and the maximum number S of different AA per variable position depend on the peptide length N (U=N(N-1)/2), S=N-1). The method is therefore only suitable for focused libraries. An application example is shown for the selection of peptides with N-terminal proline or hydroxyproline catalyzing an aldol reaction from a combinatorial library of 65536 octapeptides. A simple enumeration program is available to help design combinatorial libraries decodable by amino acid analysis. The method applies to linear and cyclic peptides, can be used for nonnatural building blocks, including beta-amino acids, and should help to explore the vast chemistry of linear and cyclic peptide for catalysis and bioactivity.     

Automated mass spectrometric sequence determination of cyclic peptide library members

Cyclic peptides have come under scrutiny as potential antimicrobial therapeutic agents. Combinatorial split-and-pool synthesis of cyclic peptides can afford single compound per well libraries for antimicrobial screening, new lead identification, and construction of quantitative structure-activity relationships (QSAR). Here, we report a new sequencing protocol for rapid identification of the members of a cyclic peptide library based on automated computer analysis of mass spectra, obviating the need for library encoding/decoding strategies. Furthermore, the software readily integrates with common spreadsheet and database packages to facilitate data visualization and archiving. The utility of the new MS-sequencing approach is demonstrated using sonic spray ionization ion trap MS and MS/MS spectrometry on a single compound per bead cyclic peptide library and validated with individually synthesized pure cyclic D,L-alpha-peptides.     

3D-Epitope-Explorer (3DEX): localization of conformational epitopes within three-dimensional structures of proteins

Neutralizing antibodies often recognize conformational, discontinuous epitopes. Linear peptides mimicking such conformational epitopes can be selected from phage display peptide libraries by screening with the respective antibodies. However, it is difficult to localize these "mimotopes" within the three-dimensional (3D) structures of the target proteins. Knowledge of conformational epitopes of neutralizing antibodies would help to design antigens able to elicit protective immune responses. Therefore, we provide here a software that allows to localize linear peptide sequences within 3D structures of proteins. The 3D-Epitope-Explorer (3DEX) software allows to map conformational epitopes in 3D protein structures based on an algorithm that takes into account the physicochemical neighborhood of C(alpha)- or C(beta)-atoms of individual amino acids. A given amino acid of a peptide sequence is localized within the protein and the software searches within predefined distances for the amino acids neighboring that amino acid in the peptide. Surface exposure of the amino acids can also be taken into consideration. The procedure is then repeated for the remaining amino acids of the peptide. The introduction of a joker function allows to map peptide mimotopes, which do not necessarily have 100% sequence homology to the protein. Using this software we were able to localize mimotopes selected from phage displayed peptide libraries with polyclonal antibodies from HIV-positive patient plasma within the 3D structure of gp120, the exterior glycoprotein of HIV-1. We also analyzed two recently published peptide sequences corresponding to known conformational epitopes to further confirm the integrity of 3DEX.     

Homology-free prediction of functional class of proteins and peptides by support vector machines

Protein and peptide sequences contain clues for functional prediction. A challenge is to predict sequences that show low or no homology to proteins or peptides of known function. A machine learning method, support vector machines (SVM), has recently been explored for predicting functional class of proteins and peptides from sequence-derived properties irrespective of sequence similarity, which has shown impressive performance for predicting a wide range of protein and peptide classes including certain low- and non- homologous sequences. This method serves as a new and valuable addition to complement the extensively-used alignment-based, clustering-based, and structure-based functional prediction methods. This article evaluates the strategies, current progresses, reported prediction performances, available software tools, and underlying difficulties in using SVM for predicting the functional class of proteins and peptides.