Substitution scoring matrices for proteins - An overview

Labeling of methyl groups: a streamlined protocol and guidance for the selection of 2H precursors based on molecular weight

A streamlined one-day protocol is described to produce isotopically methyl-labeled protein with high levels of deuterium for NMR studies. Using this protocol, the D2O and 2H-glucose content of the media and protonation level of ILV labeling precursors (ketobutyrate and ketovalerate) were varied. The relaxation rate of the multiple-quantum (MQ) state that is present during the HMQC-TROSY pulse sequence was measured for different labeling schemes and this rate was used to predict upper limits of molecular weights for various labeling schemes. The use of deuterated solvents (D2O) or deuterated glucose is not required to obtain 1H-13C correlated NMR spectra of a 50 kDa homodimeric protein that are suitable for assignment by mutagenesis. High quality spectra of 100-150 kDa proteins, suitable for most applications, can be obtained without the use of deuterated glucose. The proton on the β-position of ketovalerate appears to undergo partial exchange with deuterium under the growth conditions used in this study.

Creation of the HIV-1 antisense gene asp coincided with the emergence of the pandemic group M and is associated with faster disease progression

Despite being first identified more than three decades ago, the antisense gene asp of HIV-1 remains an enigma. asp is present uniquely in pandemic (group M) HIV-1 strains, and it is absent in all non-pandemic (out-of-M) HIV-1 strains and virtually all non-human primate lentiviruses. This suggests that the creation of asp may have contributed to HIV-1 fitness or worldwide spread. It also raises the question of which evolutionary processes were at play in the creation of asp. Here, we show that HIV-1 genomes containing an intact asp gene are associated with faster HIV-1 disease progression. Furthermore, we demonstrate that the creation of a full-length asp gene occurred via the evolution of codon usage in env overlapping asp on the opposite strand. This involved differential use of synonymous codons or conservative amino acid substitution in env that eliminated internal stop codons in asp, and redistribution of synonymous codons in env that minimized the likelihood of new premature stops arising in asp. Nevertheless, the creation of a full-length asp gene reduced the genetic diversity of env. The Luria-Delbruck fluctuation test suggests that the interrupted asp open reading frame (ORF) is the progenitor of the intact ORF, rather than a descendant under random genetic drift. Therefore, the existence of group-M isolates with a truncated asp ORF indicates an incomplete transition process. For the first time, our study links the presence of a full-length asp ORF to faster disease progression, thus warranting further investigation into the cellular processes and molecular mechanisms through which the ASP protein impacts HIV-1 replication, transmission, and pathogenesis.IMPORTANCEOverlapping genes engage in a tug-of-war, constraining each other's evolution. The creation of a new gene overlapping an existing one comes at an evolutionary cost. Thus, its conservation must be advantageous, or it will be lost, especially if the pre-existing gene is essential for the viability of the virus or cell. We found that the creation and conservation of the HIV-1 antisense gene asp occurred through differential use of synonymous codons or conservative amino acid substitutions within the overlapping gene, env. This process did not involve amino acid changes in ENV that benefited its function, but rather it constrained the evolution of ENV. Nonetheless, the creation of asp brought a net selective advantage to HIV-1 because asp is conserved especially among high-prevalence strains. The association between the presence of an intact asp gene and faster HIV-1 disease progression supports that conclusion and warrants further investigation.

Computational approaches for identifying disease-causing mutations in proteins

Advancements in genome sequencing have expanded the scope of investigating mutations in proteins across different diseases. Amino acid mutations in a protein alter its structure, stability and function and some of them lead to diseases. Identification of disease-causing mutations is a challenging task and it will be helpful for designing therapeutic strategies. Hence, mutation data available in the literature have been curated and stored in several databases, which have been effectively utilized for developing computational methods to identify deleterious mutations (drivers), using sequence and structure-based properties of proteins. In this chapter, we describe the contents of specific databases that have information on disease-causing and neutral mutations followed by sequence and structure-based properties. Further, characteristic features of disease-causing mutations will be discussed along with computational methods for identifying cancer hotspot residues and disease-causing mutations in proteins.

DeepAFP: An effective computational framework for identifying antifungal peptides based on deep learning

Fungal infections have become a significant global health issue, affecting millions worldwide. Antifungal peptides (AFPs) have emerged as a promising alternative to conventional antifungal drugs due to their low toxicity and low propensity for inducing resistance. In this study, we developed a deep learning-based framework called DeepAFP to efficiently identify AFPs. DeepAFP fully leverages and mines composition information, evolutionary information, and physicochemical properties of peptides by employing combined kernels from multiple branches of convolutional neural network with bi-directional long short-term memory layers. In addition, DeepAFP integrates a transfer learning strategy to obtain efficient representations of peptides for improving model performance. DeepAFP demonstrates strong predictive ability on carefully curated datasets, yielding an accuracy of 93.29% and an F1-score of 93.45% on the DeepAFP-Main dataset. The experimental results show that DeepAFP outperforms existing AFP prediction tools, achieving state-of-the-art performance. Finally, we provide a downloadable AFP prediction tool to meet the demands of large-scale prediction and facilitate the usage of our framework by the public or other researchers. Our framework can accurately identify AFPs in a short time without requiring significant human and material resources, and hence can accelerate the development of AFPs as well as contribute to the treatment of fungal infections. Furthermore, our method can provide new perspectives for other biological sequence analysis tasks.

Proteome-Wide Structural Computations Provide Insights into Empirical Amino Acid Substitution Matrices

The relative contribution of mutation and selection to the amino acid substitution rates observed in empirical matrices is unclear. Herein, we present a neutral continuous fitness-stability model, inspired by the Arrhenius law (qij=aije-ΔΔGij). The model postulates that the rate of amino acid substitution (i→j) is determined by the product of a pre-exponential factor, which is influenced by the genetic code structure, and an exponential term reflecting the relative fitness of the amino acid substitutions. To assess the validity of our model, we computed changes in stability of 14,094 proteins, for which 137,073,638 in silico mutants were analyzed. These site-specific data were summarized into a 20 square matrix, whose entries, ΔΔGij, were obtained after averaging through all the sites in all the proteins. We found a significant positive correlation between these energy values and the disease-causing potential of each substitution, suggesting that the exponential term accurately summarizes the fitness effect. A remarkable observation was that amino acids that were highly destabilizing when acting as the source, tended to have little effect when acting as the destination, and vice versa (source → destination). The Arrhenius model accurately reproduced the pattern of substitution rates collected in the empirical matrices, suggesting a relevant role for the genetic code structure and a tuning role for purifying selection exerted via protein stability.

Intrinsically Disordered Proteins: An Overview

Many proteins and protein segments cannot attain a single stable three-dimensional structure under physiological conditions; instead, they adopt multiple interconverting conformational states. Such intrinsically disordered proteins or protein segments are highly abundant across proteomes, and are involved in various effector functions. This review focuses on different aspects of disordered proteins and disordered protein regions, which form the basis of the so-called "Disorder-function paradigm" of proteins. Additionally, various experimental approaches and computational tools used for characterizing disordered regions in proteins are discussed. Finally, the role of disordered proteins in diseases and their utility as potential drug targets are explored.

Insights from analyses of low complexity regions with canonical methods for protein sequence comparison

Low complexity regions are fragments of protein sequences composed of only a few types of amino acids. These regions frequently occur in proteins and can play an important role in their functions. However, scientists are mainly focused on regions characterized by high diversity of amino acid composition. Similarity between regions of protein sequences frequently reflect functional similarity between them. In this article, we discuss strengths and weaknesses of the similarity analysis of low complexity regions using BLAST, HHblits and CD-HIT. These methods are considered to be the gold standard in protein similarity analysis and were designed for comparison of high complexity regions. However, we lack specialized methods that could be used to compare the similarity of low complexity regions. Therefore, we investigated the existing methods in order to understand how they can be applied to compare such regions. Our results are supported by exploratory study, discussion of amino acid composition and biological roles of selected examples. We show that existing methods need improvements to efficiently search for similar low complexity regions. We suggest features that have to be re-designed specifically for comparing low complexity regions: scoring matrix, multiple sequence alignment, e-value, local alignment and clustering based on a set of representative sequences. Results of this analysis can either be used to improve existing methods or to create new methods for the similarity analysis of low complexity regions.

Performance optimization in DNA short-read alignment

SUMMARY

Over the past decade, short-read sequence alignment has become a mature technology. Optimized algorithms, careful software engineering and high-speed hardware have contributed to greatly increased throughput and accuracy. With these improvements, many opportunities for performance optimization have emerged. In this review, we examine three general-purpose short-read alignment tools-BWA-MEM, Bowtie 2 and Arioc-with a focus on performance optimization. We analyze the performance-related behavior of the algorithms and heuristics each tool implements, with the goal of arriving at practical methods of improving processing speed and accuracy. We indicate where an aligner's default behavior may result in suboptimal performance, explore the effects of computational constraints such as end-to-end mapping and alignment scoring threshold, and discuss sources of imprecision in the computation of alignment scores and mapping quality. With this perspective, we describe an approach to tuning short-read aligner performance to meet specific data-analysis and throughput requirements while avoiding potential inaccuracies in subsequent analysis of alignment results. Finally, we illustrate how this approach avoids easily overlooked pitfalls and leads to verifiable improvements in alignment speed and accuracy.

CONTACT

richard.wilton@jhu.edu.

SUPPLEMENTARY INFORMATION

Appendices referenced in this article are available at Bioinformatics online.

Quantifying prediction of pathogenicity for within-codon concordance (PM5) using 7541 functional classifications of BRCA1 and MSH2 missense variants

PURPOSE

Conditions and thresholds applied for evidence weighting of within-codon concordance (PM5) for pathogenicity vary widely between laboratories and expert groups. Because of the sparseness of available clinical classifications, there is little evidence for variation in practice.

METHODS

We used as a truthset 7541 dichotomous functional classifications of BRCA1 and MSH2, spanning 311 codons of BRCA1 and 918 codons of MSH2, generated from large-scale functional assays that have been shown to correlate excellently with clinical classifications. We assessed PM5 at 5 stringencies with incorporation of 8 in silico tools. For each analysis, we quantified a positive likelihood ratio (pLR, true positive rate/false positive rate), the predictive value of PM5-lookup in ClinVar compared with the functional truthset.

RESULTS

pLR was 16.3 (10.6-24.9) for variants for which there was exactly 1 additional colocated deleterious variant on ClinVar, and the variant under examination was equally or more damaging when analyzed using BLOSUM62. pLR was 71.5 (37.8-135.3) for variants for which there were 2 or more colocated deleterious ClinVar variants, and the variant under examination was equally or more damaging than at least 1 colocated variant when analyzed using BLOSUM62.

CONCLUSION

These analyses support the graded use of PM5, with potential to use it at higher evidence weighting where more stringent criteria are met.

Substitution scoring matrices for proteins - An overview

Sequence analysis is the primary and simplest approach to discover structural, functional and evolutionary details of related proteins. All the alignment based approaches of sequence analysis make use of amino acid substitution matrices, and the accuracy of the results largely depends on the type of scoring matrices used to perform alignment tasks. An amino acid substitution matrix is a 20 × 20 matrix in which the individual elements encapsulate the rates at which each of the 20 amino acid residues in proteins are substituted by other amino acid residues over time. In contrast to most globular/ordered proteins whose amino acids composition is considered as standard, there are several classes of proteins (e.g., transmembrane proteins) in which certain types of amino acid (e.g., hydrophobic residues) are enriched. These compositional differences among various classes of proteins are manifested in their underlying residue substitution frequencies. Therefore, each of the compositionally distinct class of proteins or protein segments should be studied using specific scoring matrices that reflect their distinct residue substitution pattern. In this review, we describe the development and application of various substitution scoring matrices peculiar to proteins with standard and biased compositions. Along with most commonly used standard matrices (PAM, BLOSUM, MD and VTML) that act as default parameters in various homologs search and alignment tools, different substitution scoring matrices specific to compositionally distinct class of proteins are discussed in detail.