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Eliminating primer dimers and improving SNP detection using self-avoiding molecular recognition systems.

ABSTRACT: Despite its widespread value to molecular biology, the polymerase chain reaction (PCR) encounters modes that unproductively consume PCR resources and prevent clean signals, especially when high sensitivity, high SNP discrimination, and high multiplexing are sought. Here, we show how "self-avoiding molecular recognition systems" (SAMRS) manage such difficulties. SAMRS nucleobases pair with complementary nucleotides with strengths comparable to the A:T pair, but do not pair with other SAMRS nucleobases. This should allow primers holding SAMRS components to avoid primer-primer interactions, preventing primer dimers, allowing more sensitive SNP detection, and supporting higher levels of multiplex PCR. The experiments here examine the PCR performances of primers containing different numbers of SAMRS components placed strategically at different positions, and put these performances in the context of estimates of SAMRS:standard pairing strengths. The impact of these variables on primer dimer formation, the overall efficiency and sensitivity of SAMRS-based PCR, and the value of SAMRS primers when detecting single nucleotide polymorphisms (SNPs) are also evaluated. With appropriately chosen polymerases, SNP discrimination can be greater than the conventional allele-specific PCR, with the further benefit of avoiding primer dimer artifacts. General rules guiding the design of SAMRS-modified primers are offered to support medical research and clinical diagnostics products.

SUBMITTER: Yang Z

PROVIDER: S-EPMC7200914 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPolymerase chain reaction (PCR) is very useful in many areas of molecular biology research. It is commonly observed that PCR success is critically dependent on design of an effective primer pair. Current tools for primer design do not adequately address the problem of PCR failure due to mis-priming on target-related sequences and structural variations in the genome.MethodsWe have developed an integrated graphical web-based application for primer design, called RExPrimer, which was written in Python language. The software uses Primer3 as the primer designing core algorithm. Locally stored sequence information and genomic variant information were hosted on MySQLv5.0 and were incorporated into RExPrimer.ResultsRExPrimer provides many functionalities for improved PCR primer design. Several databases, namely annotated human SNP databases, insertion/deletion (indel) polymorphisms database, pseudogene database, and structural genomic variation databases were integrated into RExPrimer, enabling an effective without-leaving-the-website validation of the resulting primers. By incorporating these databases, the primers reported by RExPrimer avoid mis-priming to related sequences (e.g. pseudogene, segmental duplication) as well as possible PCR failure because of structural polymorphisms (SNP, indel, and copy number variation (CNV)). To prevent mismatching caused by unexpected SNPs in the designed primers, in particular the 3' end (SNP-in-Primer), several SNP databases covering the broad range of population-specific SNP information are utilized to report SNPs present in the primer sequences. Population-specific SNP information also helps customize primer design for a specific population. Furthermore, RExPrimer offers a graphical user-friendly interface through the use of scalable vector graphic image that intuitively presents resulting primers along with the corresponding gene structure. In this study, we demonstrated the program effectiveness in successfully generating primers for strong homologous sequences.ConclusionThe improvements for primer design incorporated into RExPrimer were demonstrated to be effective in designing primers for challenging PCR experiments. Integration of SNP and structural variation databases allows for robust primer design for a variety of PCR applications, irrespective of the sequence complexity in the region of interest. This software is freely available at http://www4a.biotec.or.th/rexprimer.

Project description:BackgroundCommon structural biology methods (i.e., NMR and molecular dynamics) often produce ensembles of molecular structures. Consequently, averaging of 3D coordinates of molecular structures (proteins and RNA) is a frequent approach to obtain a consensus structure that is representative of the ensemble. However, when the structures are averaged, artifacts can result in unrealistic local geometries, including unphysical bond lengths and angles.ResultsHerein, we describe a method to derive representative structures while limiting the number of artifacts. Our approach is based on a Monte Carlo simulation technique that drives a starting structure (an extended or a 'close-by' structure) towards the 'averaged structure' using a harmonic pseudo energy function. To assess the performance of the algorithm, we applied our approach to C alpha models of 1364 proteins generated by the TASSER structure prediction algorithm. The average RMSD of the refined model from the native structure for the set becomes worse by a mere 0.08 A compared to the average RMSD of the averaged structures from the native structure (3.28 A for refined structures and 3.36 A for the averaged structures). However, the percentage of atoms involved in clashes is greatly reduced (from 63% to 1%); in fact, the majority of the refined proteins had zero clashes. Moreover, a small number (38) of refined structures resulted in lower RMSD to the native protein versus the averaged structure. Finally, compared to PULCHRA 1, our approach produces representative structure of similar RMSD quality, but with much fewer clashes.ConclusionThe benchmarking results demonstrate that our approach for removing averaging artifacts can be very beneficial for the structural biology community. Furthermore, the same approach can be applied to almost any problem where averaging of 3D coordinates is performed. Namely, structure averaging is also commonly performed in RNA secondary prediction 2, which could also benefit from our approach.

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Eliminating primer dimers and improving SNP detection using self-avoiding molecular recognition systems.

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