ProtDCal: A program to compute general-purpose-numerical descriptors for sequences and 3D-structures of proteins

Ruiz‐Blanco, Yasser B.; Paz, Waldo; Green, James R.; Marrero-Ponce, Yovani

doi:10.1186/s12859-015-0586-0

Cited by 65 publications

(86 citation statements)

References 55 publications

(56 reference statements)

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“…These properties are usually selected to provide a quantitative basis for an intuitive picture of the physical chemistry of amino acids. Subsequent analysis is carried out using either a detailed description of the sequences of interest, which involves a consideration of local sequence characteristics, or a set of sequenceaveraged property values, which involves discussion of global sequence characteristics (1,2).…”

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confidence: 99%

Global informatics and physical property selection in protein sequences

Scheraga

Rackovsky

2016

Proc. Natl. Acad. Sci. U.S.A.

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The degree of informatic independence between the physical properties of amino acids as encoded in actual protein sequences is calculated. It is shown that no physical property can be identified that carries significantly less information than others and that the information overlap between different properties and different length scales along the sequence is essentially zero. These observations suggest that bioinformatic models based on arbitrarily selected sets of physical properties are inherently deficient.protein bioinformatics | physical properties | information theory | Fourier analysis P rotein bioinformatics originated with the use of alignmentbased methods to compare protein sequences. In recent years, there has been a great increase in the use of "knowledgebased" methods, in which sequences are characterized by assigning to their component amino acids numerical values of physical properties that are believed to be important. These properties are usually selected to provide a quantitative basis for an intuitive picture of the physical chemistry of amino acids. Subsequent analysis is carried out using either a detailed description of the sequences of interest, which involves a consideration of local sequence characteristics, or a set of sequenceaveraged property values, which involves discussion of global sequence characteristics (1, 2).In previous work, we have considered various informatic aspects of the sequence-structure relationship in proteins. It was shown that there is structural uncertainty encoded in both local sequence information (3) and the global properties of sequences (4). These uncertainties constitute intrinsic limitations of knowledge-based protein bioinformatics. In this work, we examine investigator-imposed limitations resulting from the selection of "representative" sets of physical properties to characterize the amino acids. We ask whether an arbitrarily preselected set of amino acid physical properties can act as proxies for others that are not included. We also ask whether physical properties can be identified that are significantly less important than others and therefore, candidates for exclusion from knowledge-based models of the sequence-structure relationship. The following results are shown.i) The information-theoretic relationship between different global sequence physical properties can be computed. ii) Sequence-averaged and -specific variables can be treated in a unified manner. iii) When physical properties are expressed using an exhaustive and nonredundant representation, their longitudinal sequence distributions are found to be unrelated informatically. This observation implies that the use of a limited set of preselected variables is guaranteed to result in information loss. iv) The informatic contributions of all variables are found to be of the same order of magnitude. This fact implies that there are no variables that are safely negligible. ResultsOur approach is based on two tools. The first tool is the physical description of amino acids using the Property Fac...

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confidence: 99%

Global informatics and physical property selection in protein sequences

Scheraga

Rackovsky

2016

Proc. Natl. Acad. Sci. U.S.A.

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“…ProtDCal is a computational package for encoding the sequences and structures of proteins into numerical descriptors. These descriptors are the input to machine‐learning techniques (artificial neural networks, support vector machine, and random forest, among others) used for the development of novel predictors of protein functions and properties.…”

Section: Resultsmentioning

confidence: 99%

“…In this context, ProtDCal is a software package that transforms protein sequences or 3D‐structures into general‐purpose numerical descriptors, accounting for both global and local information . Due to its complementary performance with respect to other well‐established tools in the field like PROFEAT and PseAcc (later extended to Pse‐in‐one), ProtDCal has been used in a number of studies .…”

Section: Introductionmentioning

confidence: 99%

ProtDCal‐Suite: A web server for the numerical codification and functional analysis of proteins

et al. 2019

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Computational tools for the analysis of protein data and the prediction of biological properties are essential in life sciences and biomedical research. Here, we introduce ProtDCal‐Suite, a web server comprising a set of machine learning‐based methods for studying proteins. The main module of ProtDCal‐Suite is the ProtDCal software. ProtDCal translates the structural information of proteins into numerical descriptors that serve as input to machine‐learning techniques. The ProtDCal‐Suite server also incorporates a post‐processing optional stage that allows ranking and filtering the obtained descriptors by computing their Shannon entropy values across the input set of proteins. ProtDCal's codification was used in the development of models for the prediction of specific protein properties. Thus, the other modules of ProtDCal‐Suite are protein analysis tools implemented using ProtDCal's descriptors. Among them are PPI‐Detect, for predicting the interaction likelihood of protein–protein and protein–peptide pairs, Enzyme Identifier, for identifying enzymes from amino acid sequences or 3D structures, and Pred‐NGlyco, for predicting N‐glycosylation sites. ProtDCal‐Suite is freely accessible at https://protdcal.zmb.uni‐due.de.

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“…However, there is high degree of sequence similarity, especially in the Fc region, and this would mean that appropriate techniques such as benchmarking would have to be incorporated to select relevant descriptor sets (van Westen et al 2013a, b). Descriptor for proteins molecules can be generated by different software such as PseAAC, Protein Recon, PROFEAT and ProtDCal, of which ProtDCal, a freely available tool with a friendly graphical user interface, has the capacity to generate a higher number of non-redundant of molecular descriptors for proteins from FASTA or PDB files (Ruiz-Blanco et al 2015). Another possible concern is that primary sequence-based descriptors do not take into account neither interactions between amino acid residues nor the antibody-antigen and antibody-receptor interaction space.…”

Section: Discussion: Status Quo and Scope For Mab-based Applicationmentioning

confidence: 99%

Applicability of predictive toxicology methods for monoclonal antibody therapeutics: status Quo and scope

2016

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Biopharmaceuticals, monoclonal antibody (mAb)-based therapeutics in particular, have positively impacted millions of lives. MAbs and related therapeutics are highly desirable from a biopharmaceutical perspective as they are highly target specific and well tolerated within the human system. Nevertheless, several mAbs have been discontinued or withdrawn based either on their inability to demonstrate efficacy and/or due to adverse effects. Approved monoclonal antibodies and derived therapeutics have been associated with adverse effects such as immunogenicity, cytokine release syndrome, progressive multifocal leukoencephalopathy, intravascular haemolysis, cardiac arrhythmias, abnormal liver function, gastrointestinal perforation, bronchospasm, intraocular inflammation, urticaria, nephritis, neuropathy, birth defects, fever and cough to name a few. The advances made in this field are also impeded by a lack of progress in bioprocess development strategies as well as increasing costs owing to attrition, wherein the lack of efficacy and safety accounts for nearly 60 % of all factors contributing to attrition. This reiterates the need for smarter preclinical development using quality by design-based approaches encompassing carefully designed predictive models during early stages of drug development. Different in vitro and in silico methods are extensively used for predicting biological activity as well as toxicity during small molecule drug development; however, their full potential has not been utilized for biological drug development. The scope of in vitro and in silico tools in early developmental stages of monoclonal antibody-based therapeutics production and how it contributes to lower attrition rates leading to faster development of potential drug candidates has been evaluated. The applicability of computational toxicology approaches in this context as well as the pitfalls and promises of extending such techniques to biopharmaceutical development has been highlighted.Electronic supplementary materialThe online version of this article (doi:10.1007/s00204-016-1876-7) contains supplementary material, which is available to authorized users.

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ProtDCal: A program to compute general-purpose-numerical descriptors for sequences and 3D-structures of proteins

Cited by 65 publications

References 55 publications

Global informatics and physical property selection in protein sequences

Global informatics and physical property selection in protein sequences

ProtDCal‐Suite: A web server for the numerical codification and functional analysis of proteins

Applicability of predictive toxicology methods for monoclonal antibody therapeutics: status Quo and scope

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