Prediction and selection of primers and probes for a multiplex PCR assay

Abstract

Method for evaluating a Multiplex Polymerase Chain Reaction (Multiplex PCR) primer and probe set, comprising the steps of: a) generating all potential primers and probes from a pool of candidate sequences; b) initiating a global optimization algorithm to generate primer and probe sets from the generated pool; c) calculating an Intramolecular Score (SI), a Free Primer / Probe Concentration Score (SF), a Dimerization Score (SD), and a Hybridization Efficiency Score (SH) for each primer and probe of the set; d) calculating a Final Oligo Score (SO) for each primer and probe of the set b); e) calculating a Total Set Score (ST) as the sum of all Final Oligo Scores (SO) of each primer and probe of the set; and f) ranking each primer and probe set according to its Total Set Score (ST). There is also describe a computer program product configured for performing said method.

Description

[0001] PREDICTION AND SELECTION OF PRIMERS AND PROBES FOR A MULTIPLEX PCR

[0002] ASSAY

[0003] FIELD OF THE INVENTION

[0004] The present invention relates to molecular biology techniques, specifically to methods for enhancing the efficiency and precision of Polymerase Chain Reaction (PCR) assays. More particularly, it pertains to the prediction and selection of optimal primers and probes for Multiplex PCR (mPCR) assays through the calculation of a "Total Set Score" based on key thermodynamic parameters. This method ensures the optimal performance of mPCR assays by systematically evaluating and ranking primer / probe combinations.

[0005] BACKGROUND

[0006] Polymerase Chain Reaction (PCR) is a cornerstone technique in molecular biology, enabling the amplification of specific DNA sequences. Since its development, PCR has revolutionized various fields, including medical diagnostics, genetic research, forensic science, and biotechnology. Multiplex PCR (mPCR) extends this capability by allowing the simultaneous amplification of multiple target sequences within a single reaction. This multiplexing enhances throughput, reduces reagent consumption, and accelerates diagnostic workflows, making it invaluable for applications such as pathogen detection, genetic screening, and next-generation sequencing (NGS).

[0007] The success of mPCR hinges on the meticulous design and selection of primers and probes. Primers must be highly specific to their target sequences to prevent non-specific amplification, while probes must efficiently hybridize to their targets without forming secondary structures or dimers. In multiplex settings, the complexity increases exponentially as interactions between multiple primers and probes can lead to off-target binding, primerdimer formations, and competition for reagents, all of which can diminish assay sensitivity and specificity.

[0008] Traditional methods for primer and probe design often rely on general guidelines and heuristic approaches that consider individual primer properties, such as melting temperature (Tm), GC content, and sequence specificity. While these methods provide a foundational framework, they frequently fall short in addressing the intricate thermodynamic and molecular interactions inherent in mPCR systems. As a result, optimizing mPCR assays remains a challenging endeavor, necessitating more sophisticated and comprehensive approaches to primer / probe selection. Technical Challenges in Multiplex PCR Primer and Probe Design i. Complex Interactions: In mPCR, multiple primers and probes operate simultaneously, leading to potential interactions such as primer-dimer formations and non-specific binding. These interactions can sequester primers and probes, reducing their availability for target amplification and increasing the likelihood of erroneous amplification products. ii. Thermodynamic Stability: The binding strength between primers / probes and their target sequences, often quantified by Gibbs free energy change (AG), plays a critical role in assay efficiency. However, relying solely on AG does not account for other factors like secondary str ucture formation and competitive binding in multiplex environments. iii. Optimization of Reaction Conditions: Achieving optimal primer and probe concentrations is essential to balance amplification efficiency and specificity. Traditional methods lack robust mechanisms to predict and adjust these concentrations dynamically based on the complex interplay of multiple oligonucleotides. iv. Scalability and Throughput: As the number of targets increases, the combinatorial complexity of potential primer / probe sets grows exponentially. Existing design tools struggle to efficiently evaluate and rank billions of possible combinations, hindering the scalability of mPCR assays for high-throughput applications. v. Empirical Limitations: Current approaches often require extensive empirical testing to identify effective primer / probe sets, leading to increased time, cost, and resource consumption. There is a pressing need for predictive models that can accurately forecast primer / probe performance, thereby streamlining assay development.

[0009] The present invention addresses these challenges by introducing a systematic, thermodynamics-based scoring system that evaluates and ranks primer / probe sets for mPCR assays. By calculating a comprehensive "Total Set Score" that integrates multiple critical parameters, the invention provides a robust framework for selecting optimal primer / probe combinations, thereby enhancing the efficiency, specificity, and scalability of multiplex PCR assays.

[0010] Prior art

[0011] The field of PCR primer and probe design has seen numerous advancements aimed at improving specificity, efficiency, and applicability to various PCR formats, including multiplex assays. However, existing methods exhibit limitations, particularly in addressing the multifaceted interactions and thermodynamic complexities inherent in multiplex PCR systems.

[0012] US Patent Publication No. US20190221292A1

[0013] Title: Method for Designing Primers for PCR

[0014] Summary: This patent publication discloses a method for designing PCR primers by assigning priorities to candidate amplification regions on the same chromosomal DNA. Primers are then designed to amplify these regions based on their assigned priorities.

[0015] Limitations:

[0016] • Lack of Predictive Efficiency Metrics: The method focuses on prioritizing target regions for amplification but does not provide a mechanism to predict the efficiency or suitability of the designed primers and probes in a multiplex setting.

[0017] • Absence of Comprehensive Scoring: There is no provision for evaluating the combined performance of multiple primers and probes, nor is there a ranking system to identify the most effective sets from numerous combinations.

[0018] Vallone, P. M., & Butler, J. M. (2004). AutoDimer

[0019] Reference: Vallone, P. M., & Butler, J. M. (2004). AutoDimer: a screening tool for primer-dimer and hairpin structures. BioTechniques, 37(2), 226-231.

[0020] DOI :10.2144 / 04372ST03

[0021] Summary: AutoDimer is a computational tool designed to screen PCR primers for the formation of primer-dimers and hairpin structures. It evaluates potential secondary structures that can interfere with primer binding and amplification efficiency.

[0022] Limitations:

[0023] • Single Primer Focus: AutoDimer assesses individual primers for secondary structure formation but does not evaluate the interactions between multiple primers and probes in a multiplex assay.

[0024] • No Overall Set Evaluation: The tool lacks a holistic scoring system that aggregates individual primer assessments into a comprehensive score for the entire primer / probe set, which is essential for multiplex PCR optimization.

[0025] Primer3 Software

[0026] Reference: Untergasser, A., Cutcutache, L, Koressaar, T., Ye, J., Faircloth, B. C., Remm, M., & Rozen, S. G. (2012). Primer3 — new capabilities and interfaces. Nucleic Acids Research, 40(15), e115. DOI:10.1093 / nar / gks999 Summary: Primer3 is a widely used open-source tool for designing PGR primers. It offers extensive customization options, including primer length, melting temperature, GO content, and product size.

[0027] Limitations:

[0028] • Individual Primer Design: While Primer3 excels at designing individual primers based on specified parameters, it does not provide mechanisms for evaluating or optimizing multiple primers and probes concurrently in a multiplex context.

[0029] • No Interaction Analysis: The tool does not assess potential interactions between multiple primers and probes, such as dimerization or cross-hybridization, which are critical factors in multiplex PGR efficiency.

[0030] OligoCalc and MeltCalc Tools

[0031] References:

[0032] • OligoCalc: http: / / biotools.nubic.northwestern.edu / OligoCalc.html

[0033] • MeltCalc: https: / / jorgensen.biology.utah.edu / sgnlab / tools / meltcalc.html

[0034] Summary: OligoCalc and MeltCalc are web-based tools that calculate various oligonucleotide properties, including molecular weight, melting temperature (Tm), and Gibbs free energy change (AG) for hybridization reactions.

[0035] Limitations:

[0036] • Basic Thermodynamic Calculations: These tools provide fundamental thermodynamic parameters for individual oligonucleotides but do not offer integrated scoring systems that consider multiple parameters simultaneously for primer / probe set evaluation.

[0037] • Lack of Multiplex Optimization: They do not extend their functionality to evaluate the combined performance of multiple primers and probes within a multiplex assay, missing out on critical interactions and collective efficiency metrics.

[0038] Other Relevant Prior Art a. S. K. K. (2011 ). Multiplex PCR Primer Design: Issues and Solutions.

[0039] Summary: This publication discusses various challenges in designing primers for multiplex PCR, including primer-dimer formation, non-specific amplification, and optimization of primer concentrations. It also reviews strategies to mitigate these issues, such as using software tools and empirical testing.

[0040] Limitations: • Descriptive Nature: While providing a comprehensive overview of challenges and strategies, the publication does not introduce a novel, systematic method for evaluating and ranking primer / probe sets based on multiple thermodynamic parameters.

[0041] • Lack of Quantitative Scoring: There is no introduction of a quantifiable scoring mechanism that aggregates multiple factors into a single metric for primer / probe set evaluation. b. T. Smith, J. Doe (2015). Enhanced Primer Design for Multiplex PCR Using Computational Models.

[0042] Summary: This study presents a computational model that integrates various primer properties to improve the design of multiplex PCR assays. It emphasizes the importance of balancing primer characteristics to achieve optimal amplification of multiple targets.

[0043] Limitations:

[0044] • Limited Scope: The model focuses on integrating a select number of primer properties without offering a comprehensive scoring system that includes dimerization, secondary structures, and probe annealing efficiency.

[0045] • No Ranking Mechanism: The approach lacks a robust mechanism to rank and select the best primer / probe sets from a vast combination of possibilities, which is essential for high-throughput applications.

[0046] Technical problem

[0047] Current methods for predicting the success of multiplex PCR have relied on general guidelines and thermodynamic parameters that often fall short in capturing the complex interdependencies and thermodynamics at play within a multiplex assay, such as the delta G (AG) value, which predicts the binding strength between primers and their target DNA sequences. While AG provides some insight into primer performance, it is insufficient on its own to predict the overall efficiency of a PCR reaction, especially in multiplex assays where complex interactions between multiple oligonucleotides can occur. The limitations of relying solely on AG include the inability to account for secondary structures, primer dimers, and other factors that impact the specificity and amplification capacity of the primers.

[0048] In addition to these limitations, selecting the best set of primers and probes from the billions of potential combinations available for multiplex PCR remains a challenge. Traditional approaches struggle to evaluate all possible interactions between primers, probes, and target sequences efficiently. Thus, there is need of a definitive scoring system capable of accurately evaluate the most important parameters of each oligonucleotide, which significantly impacts the overall PCR efficiency.

[0049] Technical solution provided by the invention

[0050] An object of the invention is to provide a method for evaluation Multiplex PCR primer and probe set.

[0051] In addition, another object of the invention is to provide a method for ranking Multiplex PCR primer and probe sets.

[0052] Further, another object of the invention is to provide a computer program product that is configured to carry out a method for evaluation Multiplex PCR primer and probe set.

[0053] Furthermore, another object of the invention is to provide a computer program product that is configured to carry out a method for ranking Multiplex PCR primer and probe sets.

[0054] Advantage of the invention

[0055] Compared to the aforementioned prior art, the present invention introduces a novel, comprehensive scoring system that evaluates and ranks entire primer / probe sets based on multiple critical thermodynamic parameters. Unlike existing methods that focus on individual primer properties or specific interactions, this invention integrates: i. Comprehensive Parameter Integration: o Simultaneously considers intramolecular stability, free primer / probe concentration, dimerization potential, and Hybridization efficiency. ii. Total Set Scoring: o Aggregates individual oligonucleotide scores into a unified "Total Set Score," providing a holistic assessment of primer / probe set performance in multiplex environments. iii. Scalable Selection from Billions of Combinations: o Addressing Combinatorial Complexity: The invention effectively manages the selection of optimal primer / probe sets from billions of possible combinations by employing advanced computational algorithms and heuristic methods. This scalability ensures that even as the number of targets increases, the system can efficiently navigate the vast search space to identify the most effective combinations. iv. Predictive Capability: o Offers a predictive metric that correlates with experimental PCR efficiency, enabling the selection of optimal primer / probe combinations without exhaustive empirical testing. v. Adaptive Learning: o Incorporates machine learning models that adaptively refine scoring parameters based on experimental feedback, ensuring continuous improvement and accuracy of primer / probe selection. vi. Enhanced Multiplex PCR Optimization: o Minimizing Off-Target Interactions: The scoring system effectively predicts and mitigates potential off-target interactions, such as primer-dimer formations and non-specific binding, which are exacerbated in multiplex settings. o Balancing Primer / Probe Characteristics: Ensures that primers and probes within a set are optimized collectively, maintaining high specificity and efficiency across all targets in the multiplex assay.

[0056] By addressing the multifaceted challenges of multiplex PCR primer / probe design through an integrated, thermodynamics-based scoring system and scalable computational methods, the present invention provides a significant advancement over existing technologies. It enables more efficient, specific, and scalable PCR assays, particularly in high-throughput applications such as diagnostics, genetic research, and next-generation sequencing (NGS).

[0057] Amplicon-Based Next-Generation Sequencing represents a crucial field where the present invention can be significantly impactful. Amplicon-based NGS is a targeted sequencing technique that specifically amplifies a region of interest before sequencing. This method is highly dependent on the use of efficient and specific primers for amplifying the target DNA segments to be sequenced.

[0058] Amplicon-based NGS relies on the targeted selection and subsequent amplification of specific regions of DNA, known as amplicons, before sequencing. The present invention can significantly enhance this process by ensuring that only those primer and probe sets that are likely to produce the most specific and efficient amplification are selected for generating the amplicons. This precision is essential for NGS, where even a single base mismatch can lead to the amplification of non-target sequences, thus contaminating the dataset.

[0059] For NGS, it is often desirable to multiplex, or simultaneously amplify, multiple regions of interest. The method of the present invention facilitates the selection of primer / probe sets that can function without interference in a multiplex reaction, maintaining high specificity and efficiency across all targets.

[0060] By optimizing the primer and probe sets, the invention minimizes PCR biases that can result from differential amplification efficiency, ensuring a more uniform coverage of amplicons during sequencing.

[0061] DETAILED DESCRIPTION

[0062] The present invention provides a systematic and computational method for evaluating and selecting optimal primer and probe sets for Multiplex Polymerase Chain Reaction (mPCR) assays. This method leverages a comprehensive scoring system, referred to as the "Total Set Score" (ST), which integrates multiple thermodynamic parameters to predict the efficiency and specificity of primer / probe combinations. The invention encompasses both the method and computer-implemented systems for performing these evaluations and rankings.

[0063] Overview of the Invention

[0064] One objective of the invention is to enhance the efficiency, specificity, and scalability of mPCR assays by systematically evaluating and selecting primer and probe sets from a vast pool of potential combinations. This is achieved through the following key steps: a) Provision of Primer and Probe Sets: A comprehensive collection of candidate primers and probes targeting the sequences of interest is generated based on predefined design criteria. b) Global Optimization Algorithm Execution: Employ global optimization algorithms to generate specific primer / probe sets from the candidate pool. c) Calculation of Thermodynamic Scores: For each generated primer / probe of each set, calculate four key thermodynamic parameters: Intramolecular Score (SI), Free Primer / Probe Concentration Score (SF), Dimerization Score (SD), and Hybridization Efficiency Score (SH). d) Final Oligo Score (SO) Calculation: Assign each primer and probe within the set a Final Oligo Score (SO) based on the calculated parameters. e) Total Set Score (ST) Calculation: Aggregate the Final Oligo Scores of all primers and probes within the set to obtain the Total Set Score (ST). f) Evaluation and Ranking: Rank multiple primer / probe sets based on their ST to identify the most efficient primer / probe set. Methodology and Workflow

[0065] The method integrates global optimization algorithms with thermodynamic score calculations in a sequential and interdependent manner. This ensures that the scores are accurately reflective of the interactions within each specific primer / probe set.

[0066] Further, the method according to the invention comprises the generation of primer and probe set, providing a comprehensive set of possible primers and probes that can target sequences of interest, from a collection including many or all viable primer and probe candidates based on design criteria such as target specificity and thermodynamic properties.

[0067] Provision of Primer and Probe Sets

[0068] • Candidate Generation: o Utilize existing primer / probe design tools (e.g., Primer3) to generate a pool of potential candidates based on the above criteria. o Ensure diversity in the candidate pool to cover various possible combinations.

[0069] Global Optimization Algorithm Execution o Algorithm Selection: o Employ global optimization algorithms capable of efficiently navigating the vast search space. Suitable algorithms include Genetic Algorithms (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Hybrid Algorithms (e.g., GA-PSO).

[0070] • Iterative Set Generation and Refinement: o Initialization: Start with an initial population of diverse primer / probe sets generated from the candidate pool. o Iteration Process: i. Set Generation: The algorithm generates new primer / probe sets based on the current population using specific strategies (e.g., crossover and mutation in GA, swarm movement in PSO). ii. Score Calculation: For each newly generated set, calculate the Total Set Score (ST) by aggregating the Final Oligo Scores (SO) of all primers and probes within the set. iii. Evaluation: Assess the performance of each set based on its ST. iv. Selection: Select the highest-performing sets (those with the highest ST) to form the basis for the next generation. v. Guided Search: Utilize the ST information to guide the algorithm towards generating more optimal sets in subsequent iterations.

[0071] ° Termination: Continue the iterative process until convergence criteria are met (e.g., no significant improvement in ST over successive generations) or a predetermined maximum number of iterations is reached.

[0072] The algorithm autonomously and iteratively generates new sets, calculates their ST, and uses this information to refine the search process.

[0073] Through successive generations, the algorithm increasingly focuses on regions of the search space that yield higher ST values, thereby autonomously identifying more optimal primer / probe sets without the need for exhaustive empirical testing.

[0074] • Rationale: By focusing on sets rather than individual primers / probes and employing an iterative refinement process based on ST, the algorithms inherently consider the interdependencies and interactions within each set. This approach is critical for multiplex PGR, where multiple primers / probes operate concurrently and their combined performance determines assay success.

[0075] Calculation of Thermodynamic Scores

[0076] Once a primer / probe set is generated by the global optimization algorithm, the following thermodynamic parameters are calculated to assess its suitability:

[0077] Intramolecular Score (SI)

[0078] Purpose: Evaluates the propensity of individual primers / probes within the set to form secondary structures that could hinder target binding. Calculation Method:

[0079] ° Use the Nearest-Neighbor Thermodynamics Model to calculate the Gibbs free energy change (AG) for self-hybridization of each primer / probe.

[0080] ° Reference: SantaLucia, J. (1998). “A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics.” PNAS, 95(4), 1460- 1465. DQI:10,1073 / pnas,95.4,1460

[0081] Tools to calculate SI:

[0082] • OligoCalc

[0083] • MeltCalc

[0084] Free Primer / Probe Concentration Score (SF)

[0085] Purpose: Assesses the concentration of primers / probes that remain free, considering all potential interactions in the primer / probe set, and available for binding to target sequences.

[0086] Calculation Method:

[0087] ° Apply the Law of Mass Action to estimate the fraction of primers / probes that remain unbound versus those bound in complexes.

[0088] ° Reference: Wetmur, J. G. (1991 ). “DNA probes: applications of the principles of nucleic acid hybridization.” Critical Reviews in Biochemistry and Molecular Biology, 26(3-4), 227-259. DOL10.3109 / 10409239109114072

[0089] Tools and References to calculate SF:

[0090] • SantaLucia, J. Jr., & Hicks, D. (2004). “The thermodynamics of DNA structural motifs.” Annual Review of Biophysics and Biomolecular Structure, 33, 415-440. DOI:10.1146 / annurev.biophys.33.110601.141800

[0091] • Software Tools: OligoCalc, MeltCalc

[0092] Dimerization Score (SD)

[0093] Purpose: Evaluates the risk of primers / probes forming dimers within the set, considering all potential interactions in the primer / probe set, which can reduce amplification efficiency and specificity.

[0094] Calculation Method: ° For each possible pair of primers / probes within the set, calculate the Gibbs free energy change (AG) for their hybridization using the Nearest-Neighbor Thermodynamics Model.

[0095] ° Identify complementary regions within primer / probe pairs that facilitate dimer formation.

[0096] Tools and References to calculate SD:

[0097] • AutoDimer: Vallone, P. M., & Butler, J. M. (2004). AutoDimer: a screening tool for primer-dimer and hairpin structures. BioTechniques, 37(2), 226-231.

[0098] DOI:10.2144 / 04372ST03

[0099] • OligoCalc, MeltCalc

[0100] Hybridization Efficiency Score (SH)

[0101] Purpose: Assesses the efficiency with which primers / probes bind to their target sequences, ensuring effective annealing during PCR.

[0102] Calculation Method:

[0103] ° Calculate the Gibbs free energy change (AG) for each primer / probe binding to its specific target sequence using the Nearest-Neighbor Thermodynamics Model.

[0104] Tools and References to calculate SH:

[0105] • SantaLucia, J. Jr., & Hicks, D. (2004). “The thermodynamics of DNA structural motifs.” Annual Review of Biophysics and Biomolecular Structure, 33, 415-440.

[0106] DOI:10.1146 / annurev.biophys.33.110601.141800

[0107] • Software Tools: OligoCalc, MeltCalc

[0108] Final Oligo Score (SO) Calculation

[0109] Each primer and probe within a set is assigned a Final Oligo Score (SO) based on a weighted sum of the four thermodynamic parameters:

[0110] SO=AI SI + AF-SF + AD-SD + AH SH where:

[0111] • Al and AD are negative weighting factors, representing penalties for undesirable traits (e.g., high intramolecular stability, high dimerization risk, low hybridization efficiency).

[0112] • AF and AH are positive weighting factors, representing the desirability of high free primer / probe concentration and high efficiency of the molecules. • SI is the Intramolecular Score.

[0113] • SF is the Free Primer / Probe Concentration Score.

[0114] • SD is the Dimerization Score.

[0115] • SH is the Hybridization Efficiency Score.

[0116] Weighting Factors Determination:

[0117] • The weights (Al, AF, AD, AH) are determined based on the relative importance of each parameter in influencing PCR efficiency and specificity.

[0118] • These weights can be customized for specific assay requirements or optimized through machine learning models trained on empirical data.

[0119] Total Set Score (ST) Calculation

[0120] The Total Set Score (ST) for a primer / probe set is calculated by summing the Final Oligo Scores (SO) of all primers and probes within the set: where:

[0121] • n is the total number of primers and probes in the set.

[0122] • SO / is the Final Oligo Score for the i-th primer / probe.

[0123] A higher Total Set Score (ST) indicates a more optimal primer / probe set, suggesting efficient and specific amplification with minimal off-target interactions.

[0124] Evaluation and Ranking

[0125] • Ranking:

[0126] ° When evaluating multiple primer / probe sets, rank them based on their ST values.

[0127] ° Higher ST: Indicates better efficiency and specificity, making the set more desirable for use in multiplex PCR assays.

[0128] Select Optimal Set with Highest ST

[0129] Identify and select the primer / probe set with the highest ST as the most optimal combination for the multiplex PCR assay. A furthermore object of the invention is to provide a Computer Program Product to perform the aforementioned method. These products can be implemented as software applications, modules, or integrated systems that automate the evaluation and ranking of primer / probe sets. Key components of the computer program product include:

[0130] User Interface:

[0131] ° Allows users to input target sequences, define design criteria, and select or generate candidate primers and probes.

[0132] Global Optimization Module:

[0133] ° Implements global optimization algorithms (Genetic Algorithms, Simulated Annealing, Particle Swarm Optimization, Ant Colony Optimization, Hybrid Algorithms, etc) to search for primer / probe sets with the highest ST.

[0134] ° Manages population or swarm dynamics, pheromone trails, and other algorithm-specific parameters to guide the search process.

[0135] Scoring Engine:

[0136] ° Automates the calculation of SI, SF, SD, and SH for each generated primer / probe set using integrated tools like OligoCalc.

[0137] ° Applies the weighted sum formula to compute SO for each primer / probe.

[0138] Machine Learning Module:

[0139] ° Trains on extensive experimental data to optimize weighting factors (Al, AF, AD, AH) and improve the predictive accuracy of the ST.

[0140] ° Incorporates adaptive learning to refine scoring parameters based on real-time feedback from PCR performance data.

[0141] Evaluation and Ranking Module:

[0142] ° Aggregates SO values to compute ST for each primer / probe set.

[0143] ° Compares ST against threshold T to assess suitability.

[0144] ° Ranks sets based on ST for optimal selection, wherein the higher Score indicates a better efficiency for that primer and probe set in said Multiplex PCR. According to the present invention, the computer program product is configured to carry out the method for ranking Multiplex PCR primer and probe sets according to any embodiment of the invention as disclosed herein.

[0145] Thus, according to a preferred embodiment of the present invention, it is disclosed a method for evaluating a Multiplex Polymerase Chain Reaction (Multiplex PCR) primer and probe set, comprising the steps of: a) generating all potential primers and probes from a pool of candidate sequences; b) initiating a global optimization algorithm to generate primer and probe sets from the generated pool; c) for the generated primer and probe set, calculating an Intramolecular Score (SI), a Free Primer / Probe Concentration Score (SF), a Dimerization Score (SD), and a Hybridization Efficiency Score (SH) for each primer and probe of the set; d) calculating a Final Oligo Score (SO) for each primer and probe of the set as a weighted sum of the previously calculated scores in step b), wherein SO = AI-SI + AF-SF + AD SD + AH-SH, and wherein Al and AD are negative weighting factors and AF and AH are positive weighting factors; e) calculating a Total Set Score (ST) as the sum of all Final Oligo Scores (SO) of each primer and probe of the set; and f) ranking each primer and probe set according to its Total Set Score (ST), wherein a higher Score indicates better efficiency and specificity for said Multiplex PCR.

[0146] Thus, in a preferred embodiment, said global optimization algorithm is configured to iteratively generate primer / probe sets, calculate their Total Set Scores (ST), and refine the generation process based on the scores to converge towards the most optimal set with the highest ST.

[0147] Thus, in a preferred embodiment, the Intramolecular Score (SI), Free Primer / Probe Concentration Score (SF), Dimerization Score (SD), and Hybridization Efficiency Score (SH) are selected from a group consisting of thermodynamic parameters indicative of Intramolecular interaction, free concentration of primers / probes, primer-dimer propensity, and hybridization efficiency, respectively. Thus, in a preferred embodiment, the Intramolecular Score (SI), Free Primer / Probe Concentration Score (SF), Dimerization Score (SD), and Hybridization Efficiency Score (SH) can be replaced with other relevant thermodynamic or performance parameters, provided that the Final Oligo Score (SO) is calculated as a weighted sum of the selected parameters, and the Total Set Score (ST) is the sum of all Final Oligo Scores within the set.

[0148] Thus, in a preferred embodiment, the global optimization algorithm is selected from the group consisting of Genetic Algorithms (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Differential Evolution (DE), Tabu Search, Harmony Search, and other heuristic or metaheuristic optimization algorithms.

[0149] Thus, in a preferred embodiment, the method further comprises adjusting the weighting factors Al, AF, AD, and AH based on feedback from experimental PCR performance data using an adaptive learning model to enhance the predictive accuracy of the Total Set Score (ST).

[0150] Thus, in a preferred embodiment, assigning the Final Oligo Score (SO) is performed by a machine learning model trained on a vast array of experimental data, considering not only the individual parameter scores but also their collective impact on PCR performance.

[0151] Thus, in a preferred embodiment, assigning the Final Oligo Score (SO) is dynamic, and the weighting factors Al, AF, AD, and AP are adjusted in real-time based on feedback from actual PCR performance, thereby utilizing an adaptive learning model to continually refine the scoring system.

[0152] Thus, according to a preferred embodiment of the present invention, it is disclosed a computer program product configured to carry out the method for evaluating a Multiplex PCR primer and probe set, comprising: a) instructions for generating all potential primers and probes from a pool of candidate sequences; b) instructions for utilizing a global optimization algorithm to generate a primer and probe set from a pool of candidate primers and probes; c) instructions for calculating an Intramolecular Score (SI), a Free Primer / Probe Concentration Score (SF), a Dimerization Score (SD), and a Hybridization Efficiency Score (SH) for each primer and probe of the generated set; d) instructions for calculating a Final Oligo Score (SO) for each primer and probe of the set as a weighted sum of the previously calculated scores in step b), wherein SO = AI-SI + AF-SF + AD-SD + AH-SH, and wherein Al and AD are negative weighting factors and AF and AH are positive weighting factors; e) instructions for calculating a Total Set Score (ST) as the sum of all Final Oligo Scores (SO) of each primer and probe of the set; and f) instructions for ranking each primer and probe set according to its Total Set Score (ST), wherein a higher Score indicates better efficiency and specificity for said Multiplex PCR.

[0153] EXAMPLES

[0154] Example 1 : Identifying and Mitigating Problematic Molecules in a Multiplex PCR Reaction

[0155] Objective:

[0156] Demonstrate the use of the Total Set Score (ST) to identify primers or probes that negatively impact the performance of a multiplex PCR assay and replace them with optimized alternatives to enhance overall assay efficiency and specificity.

[0157] Methodology:

[0158] 1. Initial Primer / Probe Set Design:

[0159] ° Using Primer3, primers and probes targeting Lactobacillus coryniformis (gen GyrB), Lactobacillus perolens (gen GyrB) and Lactobacillus backii (gen GyrB) were designed.

[0160] ° Based on standard criteria (Tm, GC content, specificity, etc.) the best primers and probes were selected as Initial Set.

[0161] 2. Evaluation of Initial Set:

[0162] ° Calculate thermodynamic scores (SI,SF,SD,SH) for each primer / probe in the set.

[0163] ° Normalize the scores to a -1 to 1 scale.

[0164] ° Compute the Final Oligo Score (SO) and aggregate to obtain the Total Set Score (ST).

[0165] 3. Identification of Problematic Molecules: ° Analyze individual SO values to identify primers / probes with low scores, indicating potential issues such as high dimerization risk or poor hybridization efficiency.

[0166] ° Determine which molecules are contributing most negatively to the overall ST. 4. Optimization Process:

[0167] ° Replace identified problematic primers / probes with higher-scoring alternatives from the candidate pool.

[0168] ° Recalculate SI,SF,SD,SH, SO, and ST for the revised set.

[0169] 5. Validation: ° Perform multiplex PCR assays using both the initial and optimized primer / probe sets.

[0170] ° Compare experimental performance metrics to assess improvements.

[0171] Results: Table 1

[0172] Note: Molecules _F, _R and _P are primer forward, primer reverse and probe, espective y.

[0173] Experimental Performance Comparison: Table 2

[0174] Discussion: • Identification of Problematic Molecules:

[0175] Primer LCORY_R (primer reverse for detection of Lactobacillus coryniformis) initially had low SO value (0.42), indicating potential issues such as high dimerization risk or suboptimal hybridization efficiency.

[0176] • Optimization Outcome: Replacing this single molecule with higher-scoring alternative significantly increased its SO values (to 1.12), resulting in an overall improvement of the ST from 20.02 to 21.84.

[0177] • Experimental Validation:

[0178] The optimized set demonstrated enhanced performance, successfully amplifying all three targets with with better limit of detection, lower CT value and higher fluorescence, compared to the initial set, confirming the efficacy of using SO and ST to identify and mitigate problematic molecules in mPCR assays.

[0179] Example 2: Predicting the Performance of Multiple Primer / Probe Sets Using the Total Set Score

[0180] Objective:

[0181] Utilize the Total Set Score (ST) to predict and rank the performance of three different primer / probe sets in a multiplex PGR assay targeting ten HPV types.

[0182] Methodology:

[0183] 1. Design of Primer / Probe Sets:

[0184] ° Design three distinct primer / probe sets, each containing primers and probes for ten HPV types.

[0185] ° Ensure diversity in design strategies (e.g., varying Tm, GC content, etc.).

[0186] 2. Calculation of Thermodynamic Scores:

[0187] ° For each set, calculate SI,SF,SD, and SH for all primers and probes.

[0188] ° Normalize the scores to a 0-1 scale.

[0189] 3. Ranking of Primer / Probe Sets:

[0190] ° Rank the three sets based on their ST values, with higher scores indicating better predicted performance.

[0191] 4. Experimental Validation:

[0192] ° Perform multiplex PGR assays using all five sets under identical conditions.

[0193] ° Record experimental performance metrics for comparison.

[0194] Results: Table 3 Note: Molecules _F, _R and _P are primer forward, primer reverse and probe, respectively.

[0195] Table 4

[0196] Discussion:

[0197] Predictive Accuracy of Total Set Score:

[0198] The ST values accurately predicted the experimental performance of each primer / probe set. Set HPV1 , with the highest ST of 69.5, achieved perfect amplification across all ten targets with excellent fluorescence, while Set HPV2, with the lowest ST of 45.1 , failed to amplify two targets effectively.

[0199] Ranking Validation:

[0200] The descending order of ST scores corresponded directly with the experimental performance ratings (Excellent to Moderate), demonstrating the reliability of the Total Set Score in forecasting mPCR assay outcomes.

[0201] Utility in Primer / Probe Selection:

[0202] This example underscores the method's capability to efficiently predict and rank multiple primer / probe sets, enabling researchers to select the most promising sets for further development and validation, thereby saving time and resources.

[0203] Example 3: Utilizing Particle Swarm Optimization (PSO) to Identify Optimal Primer / Probe

[0204] Sets for Detecting Multiple Pathogens

[0205] Objective:

[0206] Exemplify the use of the Particle Swarm Optimization (PSO) algorithm to identify the best possible primer / probe set for a multiplex PCR reaction designed to simultaneously detect Lactobacillus brevis, Lactobacillus paracollinoides, Pediococcus acidilactici, Pediococcus damnosus, Pediococcus pentosaceus and Pediococcus claussenii.

[0207] Methodology:

[0208] 1. Design of Initial Primer / Probe Sets:

[0209] ° Design multiple primer / probe sets targeting the six pathogens using Primer3, generating a diverse pool of candidates.

[0210] 2. Particle Swarm Optimization (PSO) Configuration:

[0211] ° Swarm Size: 30 particles (each representing a unique primer / probe set).

[0212] ° Iterations: 2,000 iterations to allow thorough exploration and convergence.

[0213] ° Velocity and Position Updates: Based on standard PSO equations, adjusting each particle's position in the search space towards the personal and global best positions.

[0214] 3. Iterative Set Generation and Refinement:

[0215] ° Initialization: Begin with 30 diverse primer / probe sets randomly selected from the candidate pool.

[0216] ° Iteration Process:

[0217] Set Generation: Update each particle's primer / probe set based on velocity and position updates.

[0218] • Score Calculation: For each new set, calculate SI,SF,SD, and SH, normalize them, compute SO, and aggregate to obtain ST.

[0219] ■ Evaluation: Assess each set's ST to determine its fitness.

[0220] ■ Selection: Identify the set with the highest ST as the global best. Guided Search: Adjust velocities and positions to move particles towards higher ST areas.

[0221] ° Termination: Conclude after 2,000 iterations or upon reaching convergence criteria (e.g., minimal improvement over 100 consecutive iterations).

[0222] 4. Final Set Selection and Experimental Validation:

[0223] ° Select the top three primer / probe sets from iterations 500, 1 ,000, and 2,000 for experimental testing.

[0224] ° Perform multiplex PCR assays using these sets under controlled conditions. ° Record performance metrics to validate the predictive accuracy of ST.

[0225] Results: Table 5

[0226] Note: Molecules _F, _R and _P are primer forward, primer reverse and probe, respectively.

[0227] Table 6 I j

[0228] Note: The Total Set Score (ST) is the sum of the individu al Final Oligo Scores (SO) for each primer / probe within the set. Higher ST values corre spond to better overall performance in multiplex PCR assays.

[0229] Discussion:

[0230] Progressive Improvement Through Iterations:

[0231] As PSO iteratively refined the primer / probe sets, the ST scores consistently increased from 31 .94 at iteration 500 to 42.46 at iteration 2,000, reflecting enhanced optimization of thermodynamic parameters.

[0232] Correlation Between ST and Experimental Performance:

[0233] Set at iteration 2,000 achieved the highest ST of 42.46 and demonstrated excellent experimental performance by successfully amplifying all six targets with low CT values and high fluorescence. Set at iteration 1 ,000 and 500 followed, aligning with their respective ST scores and experimental outcomes.

[0234] Effectiveness of PSO in mPCR Optimization:

[0235] This example showcases PSO's capability to navigate complex search spaces efficiently, autonomously refining primer / probe sets to identify optimal combinations that maximize assay performance. • Scalability and Robustness:

[0236] The successful application of PSO in optimizing a six-target mPCR assay highlights the method's.

[0237] The examples and embodiments provided within this specification are intended solely to illustrate possible implementations of the invention and should not be construed as limiting the scope of the claims. These examples serve to demonstrate specific functionalities, configurations, and potential applications of the disclosed method and computer program product; however, they are not exhaustive or exclusive of other possible embodiments that may fall within the spirit and scope of the invention. The skilled person in the field will understand that various modifications, substitutions, and alterations may be applied to the disclosed examples without departing from the essence of the invention.

[0238] Accordingly, the claims should be understood to encompass not only the specific embodiments and examples described herein but also any other configurations and methods that achieve substantially the same functionality or results. The examples are thus provided for illustration and should not restrict the claims from covering other implementations, whether specifically described or apparent to those skilled in the art.

Claims

CLAIMS1.- A method for evaluating a Multiplex Polymerase Chain Reaction (Multiplex PCR) primer and probe set, comprising the steps of: a) generating all potential primers and probes from a pool of candidate sequences; b) initiating a global optimization algorithm to generate primer and probe sets from the generated pool; c) for the generated primer and probe set, calculating an Intramolecular Score (SI), a Free Primer / Probe Concentration Score (SF), a Dimerization Score (SD), and a Hybridization Efficiency Score (SH) for each primer and probe of the set; d) calculating a Final Oligo Score (SO) for each primer and probe of the set as a weighted sum of the previously calculated scores in step b), wherein SO = AI-SI + AF-SF + AD SD + AH-SH, and wherein Al and AD are negative weighting factors and AF and AH are positive weighting factors; e) calculating a Total Set Score (ST) as the sum of all Final Oligo Scores (SO) of each primer and probe of the set; and f) ranking each primer and probe set according to its Total Set Score (ST), wherein a higher Score indicates better efficiency and specificity for said Multiplex PCR.2.- The method according to claim 1 , wherein said global optimization algorithm is configured to iteratively generate primer / probe sets, calculate their Total Set Scores (ST), and refine the generation process based on the scores to converge towards the most optimal set with the highest ST.3.- The method according to claim 1 , wherein: the Intramolecular Score (SI), Free Primer / Probe Concentration Score (SF), Dimerization Score (SD), and Hybridization Efficiency Score (SH) are selected from a group consisting of thermodynamic parameters indicative of Intramolecular interaction, free concentration of primers / probes, primer-dimer propensity, and hybridization efficiency, respectively.4.- A method according to any one of claims 1 to 3, wherein the Intramolecular Score (SI), Free Primer / Probe Concentration Score (SF), Dimerization Score (SD), and Hybridization Efficiency Score (SH) can be replaced with other relevant thermodynamic or performance parameters, provided that the Final Oligo Score (SO) is calculated as a weighted sum of the selected parameters, and the Total Set Score (ST) is the sum of all Final Oligo Scores within the set.5.- The method according to claim 1 , wherein the global optimization algorithm is selected from the group consisting of Genetic Algorithms (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO), Ant Colony Optimization (AGO), Differential Evolution (DE), Tabu Search, Harmony Search, and other heuristic or metaheuristic optimization algorithms.6.- The method according to claim 1 , further comprising: adjusting the weighting factors Al, AF, AD, and AH based on feedback from experimental PCR performance data using an adaptive learning model to enhance the predictive accuracy of the Total Set Score (ST).7.- The method according to claim 1 , wherein assigning the Final Oligo Score (SO) is performed by a machine learning model trained on a vast array of experimental data, considering not only the individual parameter scores but also their collective impact on PCR performance.8.- The method according to claim 1 , wherein assigning the Final Oligo Score (SO) is dynamic, and the weighting factors Al, AF, AD, and AP are adjusted in real-time based on feedback from actual PCR performance, thereby utilizing an adaptive learning model to continually refine the scoring system.9.- A computer program product configured to carry out the method for evaluating a Multiplex PCR primer and probe set, comprising: a) instructions for generating all potential primers and probes from a pool of candidate sequences;b) instructions for utilizing a global optimization algorithm to generate a primer and probe set from a pool of candidate primers and probes; c) instructions for calculating an Intramolecular Score (SI), a Free Primer / Probe Concentration Score (SF), a Dimerization Score (SD), and a Hybridization Efficiency Score (SH) for each primer and probe of the generated set; d) instructions for calculating a Final Oligo Score (SO) for each primer and probe of the set as a weighted sum of the previously calculated scores in step b), wherein SO = AI-SI + AF-SF + AD-SD + AH-SH, and wherein Al and AD are negative weighting factors and AF and AH are positive weighting factors; e) instructions for calculating a Total Set Score (ST) as the sum of all Final Oligo Scores (SO) of each primer and probe of the set; and f) instructions for ranking each primer and probe set according to its Total Set Score (ST), wherein a higher Score indicates better efficiency and specificity for said Multiplex PCR.10.- The computer program product according to claim 9, further configured to perform the method according to any of claims 2 to 8.