On the utility of ultrafast MS1-only proteomics in drug target discovery studies based on thermal proteome profiling method.

Advances in high-throughput high-resolution mass spectrometry and the development of thermal proteome profiling approach (TPP) have made it possible to accelerate a drug target search. Since its introduction in 2014, TPP quickly became a method of choice in chemical proteomics for identifying drug-to-protein interactions on a proteome-wide scale and mapping the pathways of these interactions, thus further elucidating the unknown mechanisms of action of a drug under study. However, the current TPP implementations based on tandem mass spectrometry (MS/MS), associated with employing lengthy peptide separation protocols and expensive labeling techniques for sample multiplexing, limit the scaling of this approach for the ever growing variety of drug-to-proteomes. A variety of ultrafast proteomics methods have been developed in the last couple of years. Among them, DirectMS1 provides MS/MS-free quantitative proteome-wide analysis in 5-min time scale, thus opening the way for sample-hungry applications, such as TPP. In this work, we demonstrate the first implementation of the TPP approach using the ultrafast proteome-wide analysis based on DirectMS1. Using a drug topotecan, which is a known topoisomerase I (TOP1) inhibitor, the feasibility of the method for identifying drug targets at the whole proteome level was demonstrated for an ovarian cancer cell line.

Ultrafast Proteomics.

Current stage of proteomic research in the field of biology, medicine, development of new drugs, population screening, or personalized approaches to therapy dictates the need to analyze large sets of samples within the reasonable experimental time. Until recently, mass spectrometry measurements in proteomics were characterized as unique in identifying and quantifying cellular protein composition, but low throughput, requiring many hours to analyze a single sample. This was in conflict with the dynamics of changes in biological systems at the whole cellular proteome level upon the influence of external and internal factors. Thus, low speed of the whole proteome analysis has become the main factor limiting developments in functional proteomics, where it is necessary to annotate intracellular processes not only in a wide range of conditions, but also over a long period of time. Enormous level of heterogeneity of tissue cells or tumors, even of the same type, dictates the need to analyze biological systems at the level of individual cells. These studies involve obtaining molecular characteristics for tens, if not hundreds of thousands of individual cells, including their whole proteome profiles. Development of mass spectrometry technologies providing high resolution and mass measurement accuracy, predictive chromatography, new methods for peptide separation by ion mobility and processing of proteomic data based on artificial intelligence algorithms have opened a way for significant, if not radical, increase in the throughput of whole proteome analysis and led to implementation of the novel concept of ultrafast proteomics. Work done just in the last few years has demonstrated the proteome-wide analysis throughput of several hundred samples per day at a depth of several thousand proteins, levels unimaginable three or four years ago. The review examines background of these developments, as well as modern methods and approaches that implement ultrafast analysis of the entire proteome.

Proteomics-based scoring of cellular response to stimuli for improved characterization of signaling pathway activity.

Omics technologies focus on uncovering the complex nature of molecular mechanisms in cells and organisms, including biomarkers and drug targets discovery. Aiming at these tasks, we see that information extracted from omics data is still underused. In particular, characteristics of differentially regulated molecules can be combined in a single score to quantify the signaling pathway activity. Such a metric can be useful for comprehensive data interpretation to follow: (1) developing molecular responses in time; (2) potency of a drug on a certain cell culture; (3) ranking the signaling pathway activity in stimulated cells; and (4) integration of the omics data and assay-based measurements of cell viability, cytotoxicity, and proliferation. With recent advances in ultrafast mass spectrometry for quantitative proteomics allowing data collection in a few minutes, proteomics score for cellular response to stimuli can become a fast, accurate, and informative complement to bioassays. Here, we utilized an interquartile-based selection of differentially regulated features and a variety of schemes for quantifying cellular responses to come up with the quantitative metric for total cellular response and pathway activity. Validation was performed using antiproliferative and virus assays and label-free proteomics data collected for cancer cells subjected to drug stimulation.

Massive Proteogenomic Reanalysis of Publicly Available Proteomic Datasets of Human Tissues in Search for Protein Recoding via Adenosine-to-Inosine RNA Editing.

The proteogenomic search pipeline developed in this work has been applied for reanalysis of 40 publicly available shotgun proteomic datasets from various human tissues comprising more than 8000 individual LC-MS/MS runs, of which 5442 .raw data files were processed in total. This reanalysis was focused on searching for ADAR-mediated RNA editing events, their clustering across samples of different origins, and classification. In total, 33 recoded protein sites were identified in 21 datasets. Of those, 18 sites were detected in at least two datasets, representing the core human protein editome. In agreement with prior artworks, neural and cancer tissues were found to be enriched with recoded proteins. Quantitative analysis indicated that recoding the rate of specific sites did not directly depend on the levels of ADAR enzymes or targeted proteins themselves, rather it was governed by differential and yet undescribed regulation of interaction of enzymes with mRNA. Nine recoding sites conservative between humans and rodents were validated by targeted proteomics using stable isotope standards in the murine brain cortex and cerebellum, and an additional one was validated in human cerebrospinal fluid. In addition to previous data of the same type from cancer proteomes, we provide a comprehensive catalog of recoding events caused by ADAR RNA editing in the human proteome.

Ultra-Fast Mass Spectrometry in Plant Biochemistry: Response of Winter Wheat Proteomics to Pre-Sowing Treatment with Iron Compounds.

In recent years, ultrafast liquid chromatography/mass spectrometry methods have been extensively developed for the use in proteome profiling in biochemical studies. These methods are intended for express monitoring of cell response to biotic stimuli and elucidation of correlation of molecular changes with biological processes and phenotypical changes. New technologies, including the use of nanomaterials, are actively introduced to increase agricultural production. However, this requires complex approbation of new fertilizers and investigation of mechanisms underlying the biotic effects on the germination, growth, and development of plants. The aim of this work was to adapt the method of ultrafast chromatography/mass spectrometry for rapid quantitative profiling of molecular changes in 7-day-old wheat seedlings in response to pre-sowing seed treatment with iron compounds. The used method allows to analyze up to 200 samples per day; its practical value lies in the possibility of express proteomic diagnostics of the biotic action of new treatments, including those intended for agricultural needs. Changes in the regulation of photosynthesis, biosynthesis of chlorophyll and porphyrin- and tetrapyrrole-containing compounds, glycolysis (in shoot tissues), and polysaccharide metabolism (in root tissues) were shown after seed treatment with suspensions containing film-forming polymers (PEG 400, Na-CMC, Na2-EDTA), iron (II, III) nanoparticles, or iron (II) sulfate. Observations at the protein levels were consistent with the results of morphometry, superoxide dismutase activity assay, and microelement analysis of 3-day-old germinated seeds and shoots and roots of 7-day-old seedlings. A characteristic molecular signature involving proteins participating in the regulation of photosynthesis and glycolytic process was suggested as a potential marker of the biotic effects of seed treatment with iron compounds, which will be confirmed in further studies.

DirectMS1Quant: Ultrafast Quantitative Proteomics with MS/MS-Free Mass Spectrometry.

Recently, we presented the DirectMS1 method of ultrafast proteome-wide analysis based on minute-long LC gradients and MS1-only mass spectra acquisition. Currently, the method provides the depth of human cell proteome coverage of 2500 proteins at a 1% false discovery rate (FDR) when using 5 min LC gradients and 7.3 min runtime in total. While the standard MS/MS approaches provide 4000-5000 protein identifications within a couple of hours of instrumentation time, we advocate here that the higher number of identified proteins does not always translate into better quantitation quality of the proteome analysis. To further elaborate on this issue, we performed a one-on-one comparison of quantitation results obtained using DirectMS1 with three popular MS/MS-based quantitation methods: label-free (LFQ) and tandem mass tag quantitation (TMT), both based on data-dependent acquisition (DDA) and data-independent acquisition (DIA). For comparison, we performed a series of proteome-wide analyses of well-characterized (ground truth) and biologically relevant samples, including a mix of UPS1 proteins spiked at different concentrations into an Echerichia coli digest used as a background and a set of glioblastoma cell lines. MS1-only data was analyzed using a novel quantitation workflow called DirectMS1Quant developed in this work. The results obtained in this study demonstrated comparable quantitation efficiency of 5 min DirectMS1 with both TMT and DIA methods, yet the latter two utilized a 10-20-fold longer instrumentation time.

Mass Spectrometry-Based Chemical Proteomics for Drug Target Discoveries.

Chemical proteomics, emerging rapidly in recent years, has become a main approach to identifying interactions between the small molecules and proteins in the cells on a proteome scale and mapping the signaling and/or metabolic pathways activated and regulated by these interactions. The methods of chemical proteomics allow not only identifying proteins targeted by drugs, characterizing their toxicity and discovering possible off-target proteins, but also elucidation of the fundamental mechanisms of cell functioning under conditions of drug exposure or due to the changes in physiological state of the organism itself. Solving these problems is essential for both basic research in biology and clinical practice, including approaches to early diagnosis of various forms of serious diseases or prediction of the effectiveness of therapeutic treatment. At the same time, recent developments in high-resolution mass spectrometry have provided the technology for searching the drug targets across the whole cell proteomes. This review provides a concise description of the main objectives and problems of mass spectrometry-based chemical proteomics, the methods and approaches to their solution, and examples of implementation of these methods in biomedical research.

On the Feasibility of Using an Ultra-Fast DirectMS1 Method of Proteome-Wide Analysis for Searching Drug Targets in Chemical Proteomics.

Protein quantitation in tissue cells or physiological fluids based on liquid chromatography/mass spectrometry is one of the key sources of information on the mechanisms of cell functioning during chemotherapeutic treatment. Information on significant changes in protein expression upon treatment can be obtained by chemical proteomics and requires analysis of the cellular proteomes, as well as development of experimental and bioinformatic methods for identification of the drug targets. Low throughput of whole proteome analysis based on liquid chromatography and tandem mass spectrometry is one of the main factors limiting the scale of these studies. The method of direct mass spectrometric identification of proteins, DirectMS1, is one of the approaches developed in recent years allowing ultrafast proteome-wide analyses employing minute-scale gradients for separation of proteolytic mixtures. Aim of this work was evaluation of both possibilities and limitations of the method for identification of drug targets at the level of whole proteome and for revealing cellular processes activated by the treatment. Particularly, the available literature data on chemical proteomics obtained earlier for a large set of onco-pharmaceuticals using multiplex quantitative proteome profiling were analyzed. The results obtained were further compared with the proteome-wide data acquired by the DirectMS1 method using ultrashort separation gradients to evaluate efficiency of the method in identifying known drug targets. Using ovarian cancer cell line A2780 as an example, a whole-proteome comparison of two cell lysis techniques was performed, including the freeze-thaw lysis commonly employed in chemical proteomics and the one based on ultrasonication for cell disruption, which is the widely accepted as a standard in proteomic studies. Also, the proteome-wide profiling was performed using ultrafast DirectMS1 method for A2780 cell line treated with lonidamine, followed by gene ontology analyses to evaluate capabilities of the method in revealing regulation of proteins in the cellular processes associated with drug treatment.

Multiomic Profiling Identified EGF Receptor Signaling as a Potential Inhibitor of Type I Interferon Response in Models of Oncolytic Therapy by Vesicular Stomatitis Virus.

Cancer cell lines responded differentially to type I interferon treatment in models of oncolytic therapy using vesicular stomatitis virus (VSV). Two opposite cases were considered in this study, glioblastoma DBTRG-05MG and osteosarcoma HOS cell lines exhibiting resistance and sensitivity to VSV after the treatment, respectively. Type I interferon responses were compared for these cell lines by integrative analysis of the transcriptome, proteome, and RNA editome to identify molecular factors determining differential effects observed. Adenosine-to-inosine RNA editing was equally induced in both cell lines. However, transcriptome analysis showed that the number of differentially expressed genes was much higher in DBTRG-05MG with a specific enrichment in inflammatory proteins. Further, it was found that two genes, EGFR and HER2, were overexpressed in HOS cells compared with DBTRG-05MG, supporting recent reports that EGF receptor signaling attenuates interferon responses via HER2 co-receptor activity. Accordingly, combined treatment of cells with EGF receptor inhibitors such as gefitinib and type I interferon increases the resistance of sensitive cell lines to VSV. Moreover, sensitive cell lines had increased levels of HER2 protein compared with non-sensitive DBTRG-05MG. Presumably, the level of this protein expression in tumor cells might be a predictive biomarker of their resistance to oncolytic viral therapy.

Proteomics of Cellular Response to Stress: Taking Control of False Positive Results.

One of the main goals of quantitative proteomics is molecular profiling of cellular response to stress at the protein level. To perform this profiling, statistical analysis of experimental data involves multiple testing of a hypothesis about the equality of protein concentrations between the cells under normal and stress conditions. This analysis is then associated with the multiple testing problem dealing with the increased chance of obtaining false positive results. A number of solutions to this problem are known, yet, they may lead to the loss of potentially important biological information when applied with commonly accepted thresholds of statistical significance. Using the proteomic data obtained earlier for the yeast samples containing proteins at known concentrations and the biological models of early and late cellular responses to stress, we analyzed dependences of distributions of false positive and false negative rates on the protein fold changes and thresholds of statistical significance. Based on the analysis of the density of data points in the volcano plots, Benjamini-Hochberg method, and gene ontology analysis, visual approach for optimization of the statistical threshold and selection of the differentially regulated proteins has been suggested, which could be useful for researchers working in the field of quantitative proteomics.

DirectMS1: MS/MS-Free Identification of 1000 Proteins of Cellular Proteomes in 5 Minutes.

Proteome characterization relies heavily on tandem mass spectrometry (MS/MS) and is thus associated with instrumentation complexity, lengthy analysis time, and limited duty cycle. It was always tempting to implement approaches that do not require MS/MS, yet they were constantly failing to achieve a meaningful depth of quantitative proteome coverage within short experimental times, which is particularly important for clinical or biomarker-discovery applications. Here, we report on the first successful attempt to develop a truly MS/MS-free method, DirectMS1, for bottom-up proteomics. The method is compared with the standard MS/MS-based data-dependent acquisition approach for proteome-wide analysis using 5 min LC gradients. Specifically, we demonstrate identification of 1 000 protein groups for a standard HeLa cell line digest. The amount of loaded sample was varied in a range from 1 to 500 ng, and the method demonstrated 10-fold higher sensitivity. Combined with the recently introduced Diffacto approach for relative protein quantification, DirectMS1 outperforms most popular MS/MS-based label-free quantitation approaches because of significantly higher protein sequence coverage.

Rpn4 and proteasome-mediated yeast resistance to ethanol includes regulation of autophagy.

Distilled spirits production using Saccharomyces cerevisiae requires understanding of the mechanisms of yeast cell response to alcohol stress. Reportedly, specific mutations in genes of the ubiquitin-proteasome system, e.g., RPN4, may result in strains exhibiting hyper-resistance to different alcohols. To study the Rpn4-dependent yeast response to short-term ethanol exposure, we performed a comparative analysis of the wild-type (WT) strain, strain with RPN4 gene deletion (rpn4-Δ), and a mutant strain with decreased proteasome activity and consequent Rpn4 accumulation due to PRE1 deregulation (YPL). The stress resistance tests demonstrated an increased sensitivity of mutant strains to ethanol compared with WT. Comparative proteomics analysis revealed significant differences in molecular responses to ethanol between these strains. GO analysis of proteins upregulated in WT showed enrichments represented by oxidative and heat responses, protein folding/unfolding, and protein degradation. Enrichment of at least one of these responses was not observed in the mutant strains. Moreover, activity of autophagy was not increased in the RPN4 deletion strain upon ethanol stress which agrees with changes in mRNA levels of ATG7 and PRB1 genes of the autophagy system. Activity of the autophagic system was clearly induced and accompanied with PRB1 overexpression in the YPL strain upon ethanol stress. We demonstrated that Rpn4 stabilization contributes to the PRB1 upregulation. CRISPR-Cas9-mediated repression of PACE-core Rpn4 binding sites in the PRB1 promoter inhibits PRB1 induction in the YPL strain upon ethanol treatment and results in YPL hypersensitivity to ethanol. Our data suggest that Rpn4 affects the autophagic system activity upon ethanol stress through the PRB1 regulation. These findings can be a basis for creating genetically modified yeast strains resistant to high levels of alcohol, being further used for fermentation in ethanol production.

C-reactive protein as marker of post-operative analgesic quality after primary total knee arthroplasty.

OBJECTIVE: To study the correlation between the level of C-reactive protein (CRP) and the severity of pain in the post-operative period with primary total knee arthroplasty and to conduct a comparative assessment of these indicators with various methods of pain relief. The primary hypothesis of the investigation was that post-operative CRP level is likely to be correlated with the severity of post-operative pain after total knee arthroplasty. The secondary points were the evaluation of CRP and pain syndrome in the groups, as well as the identification of the correlation between the level of CRP and the method of analgesia. MATERIALS AND METHODS: Peri-operative levels of CRP and pain syndrome (10-point visual analogue scale) studied 160 patients with grade III gonarthrosis who have underwent primary total knee arthroplasty under conditions of subarachnoid anaesthesia in the period from years 2017 to 2019. Depending on the method of post-operative analgesia, patients were divided into five groups: group 1 had only systemic multimodal analgesia (SMA, n = 56), group 2 were treated with the epidural analgesia (EDA, n = 20), group 3 had local high-volume infiltration anaesthesia (LHVIA, n = 20), group 4 were getting LHVIA with a wound catheter (LHVIAc, n = 48), and group 5 had a single blockade of the femoral nerve (FNB, n = 16). RESULTS: A direct strong correlation was obtained between the level of CRP and the severity of pain syndrome in the knee joint during movement in four to six hours after surgery (n = 160, Kendall coefficient τ = 0.230, p = 0,000) and on the first post-operative day (n = 160, τ = 0.21, p = 0.001). The increase in CRP (the difference between pre-operative and post-operative CRP levels) also was positively correlated with the severity of pain in the post-operative period (n = 160, τ = 0.257, p = 0.000 and τ = 0.187, p = 0.001, respectively). CRP level significantly has increased in the post-operative period (p = 0,000). The lowest CRP indicators in the first post-operative day were recorded during the infiltrative anaesthesia (3rd and 4th groups);, the highest were during the administration of SMA group (1st group). CONCLUSION: The results confirm the correlation between the level of CRP and the severity of pain syndrome in the early post-operative period after total knee arthroplasty, its dependence on the method of analgesia, and allow to use it as a criterion for evaluating of the effectiveness of analgesia.

IdentiPy: An Extensible Search Engine for Protein Identification in Shotgun Proteomics.

We present an open-source, extensible search engine for shotgun proteomics. Implemented in Python programming language, IdentiPy shows competitive processing speed and sensitivity compared with the state-of-the-art search engines. It is equipped with a user-friendly web interface, IdentiPy Server, enabling the use of a single server installation accessed from multiple workstations. Using a simplified version of X!Tandem scoring algorithm and its novel "autotune" feature, IdentiPy outperforms the popular alternatives on high-resolution data sets. Autotune adjusts the search parameters for the particular data set, resulting in improved search efficiency and simplifying the user experience. IdentiPy with the autotune feature shows higher sensitivity compared with the evaluated search engines. IdentiPy Server has built-in postprocessing and protein inference procedures and provides graphic visualization of the statistical properties of the data set and the search results. It is open-source and can be freely extended to use third-party scoring functions or processing algorithms and allows customization of the search workflow for specialized applications.

MS/MS-Free Protein Identification in Complex Mixtures Using Multiple Enzymes with Complementary Specificity.

In this work, we present the results of evaluation of a workflow that employs a multienzyme digestion strategy for MS1-based protein identification in "shotgun" proteomic applications. In the proposed strategy, several cleavage reagents of different specificity were used for parallel digestion of the protein sample followed by MS1 and retention time (RT) based search. Proof of principle for the proposed strategy was performed using experimental data obtained for the annotated 48-protein standard. By using the developed approach, up to 90% of proteins from the standard were unambiguously identified. The approach was further applied to HeLa proteome data. For the sample of this complexity, the proposed MS1-only strategy determined correctly up to 34% of all proteins identified using standard MS/MS-based database search. It was also found that the results of MS1-only search were independent of the chromatographic gradient time in a wide range of gradients from 15-120 min. Potentially, rapid MS1-only proteome characterization can be an alternative or complementary to the MS/MS-based "shotgun" analyses in the studies, in which the experimental time is more important than the depth of the proteome coverage.

Comparative evaluation of label-free quantification methods for shotgun proteomics.

RATIONALE: Label-free quantification (LFQ) is a popular strategy for shotgun proteomics. A variety of LFQ algorithms have been developed recently. However, a comprehensive comparison of the most commonly used LFQ methods is still rare, in part due to a lack of clear metrics for their evaluation and an annotated and quantitatively well-characterized data set. METHODS: Five LFQ methods were compared: spectral counting based algorithms SIN , emPAI, and NSAF, and approaches relying on the extracted ion chromatogram (XIC) intensities, MaxLFQ and Quanti. We used three criteria for performance evaluation: coefficient of variation (CV) of protein abundances between replicates; analysis of variance (ANOVA); and the root-mean-square error of logarithmized calculated concentration ratios, referred to as standard quantification error (SQE). Comparison was performed using a quantitatively annotated publicly available data set. RESULTS: The best results in terms of inter-replicate reproducibility were observed for MaxLFQ and NSAF, although they exhibited larger standard quantification errors. Using NSAF, all quantitatively annotated proteins were correctly identified in the Bonferronni-corrected results of the ANOVA test. SIN was found to be the most accurate in terms of SQE. Finally, the current implementations of XIC-based LFQ methods did not outperform the methods based on spectral counting for the data set used in this study. CONCLUSIONS: Surprisingly, the performances of XIC-based approaches measured using three independent metrics were found to be comparable with more straightforward and simple MS/MS-based spectral counting approaches. The study revealed no clear leader among the latter. Copyright © 2017 John Wiley & Sons, Ltd.

Correction: Predictive chromatography of peptides and proteins as a complementary tool for proteomics.

Correction for 'Predictive chromatography of peptides and proteins as a complementary tool for proteomics' by Irina A. Tarasova et al., Analyst, 2016, 141, 4816-4832.

Depletion of human serum albumin in embryo culture media for in vitro fertilization using monolithic columns with immobilized antibodies.

Affinity depletion of abundant proteins such as HSA is an important stage in routine sample preparation prior to MS/MS analysis of biological samples with high range of concentrations. Due to the charge competition effects in electrospray ion source that results in discrimination of the low-abundance species, as well as limited dynamic range of MS/MS, restricted typically by three orders of magnitude, the identification of low-abundance proteins becomes a challenge unless the sample is depleted from high-concentration compounds. This dictates a need for developing efficient separation technologies allowing fast and automated protein depletion. In this study, we performed evaluation of a novel immunoaffinity-based Convective Interaction Media analytical columns (CIMac) depletion column with specificity to HSA (CIMac-αHSA). Because of the convective flow-through channels, the polymethacrylate CIMac monoliths afford flow rate independent binding capacity and resolution that results in relatively short analysis time compared with traditional chromatographic supports. Seppro IgY14 depletion kit was used as a benchmark to control the results of depletion. Bottom-up proteomic approach followed by label-free quantitation using normalized spectral indexes were employed for protein quantification in G1/G2 and cleavage/blastocyst in vitro fertilization culture media widely utilized in clinics for embryo growth in vitro. The results revealed approximately equal HSA level of 100 ± 25% in albumin-enriched fractions relative to the nondepleted samples for both CIMac-αHSA column and Seppro kit. In the albumin-free fractions concentrated 5.5-fold by volume, serum albumin was identified at the levels of 5-30% and 20-30% for the CIMac-αHSA and Seppro IgY14 spin columns, respectively.

Predictive chromatography of peptides and proteins as a complementary tool for proteomics.

In the last couple of decades, considerable effort has been focused on developing methods for quantitative and qualitative proteome characterization. The method of choice in this characterization is mass spectrometry used in combination with sample separation. One of the most widely used separation techniques at the front end of a mass spectrometer is high performance liquid chromatography (HPLC). A unique feature of HPLC is its specificity to the amino acid sequence of separated peptides and proteins. This specificity may provide additional information about the peptides or proteins under study which is complementary to the mass spectrometry data. The value of this information for proteomics has been recognized in the past few decades, which has stimulated significant effort in the development and implementation of computational and theoretical models for the prediction of peptide retention time for a given sequence. Here we review the advances in this area and the utility of predicted retention times for proteomic applications.

Application of Statistical Thermodynamics To Predict the Adsorption Properties of Polypeptides in Reversed-Phase HPLC.

The theory of critical chromatography for biomacromolecules (BioLCCC) describes polypeptide retention in reversed-phase HPLC using the basic principles of statistical thermodynamics. However, whether this theory correctly depicts a variety of empirical observations and laws introduced for peptide chromatography over the last decades remains to be determined. In this study, by comparing theoretical results with experimental data, we demonstrate that the BioLCCC: (1) fits the empirical dependence of the polypeptide retention on the amino acid sequence length with R(2) > 0.99 and allows in silico determination of the linear regression coefficients of the log-length correction in the additive model for arbitrary sequences and lengths and (2) predicts the distribution coefficients of polypeptides with an accuracy from 0.98 to 0.99 R(2). The latter enables direct calculation of the retention factors for given solvent compositions and modeling of the migration dynamics of polypeptides separated under isocratic or gradient conditions. The obtained results demonstrate that the suggested theory correctly relates the main aspects of polypeptide separation in reversed-phase HPLC.