Nucleic Acid Sequence Database

Chen, H.R.; Dayhoff, M. O.; Barker, W.C.; Hunt, Lois T.; Yeh, Lai-Su L.; George, D. G.; Orcutt, Bruce C.

doi:10.1089/dna.1.1981.1.51

Cited by 100 publications

(80 citation statements)

References 3 publications

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“…Therefore, the best predictors from those attempts are probably the ones that do not do particularly well (nor particularly poorly) in either the polymorphism or fixation phase. This compromise applies to other measures of evolutionary dynamics such as percent accepted mutation (PAM) (24) and blocks substitution matrix (Blosum) (25). For similar reasons, physicochemical distance measures of amino acids have not been very successful in predicting EI (5), because most of these measures rely on some evolutionary indices as well.…”

Section: Contrasting Divergence and Polymorphism For The 75 Elementarymentioning

confidence: 99%

Adaptive evolution in humans revealed by the negative correlation between the polymorphism and fixation phases of evolution

Gojobori

Tang

Akey

et al. 2007

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

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The selective forces acting on amino acid substitutions may be different in the two phases of molecular evolution: polymorphism and fixation. Negative selection and genetic drift may dominate the first phase, whereas positive selection may become much more significant in the second phase. However, the conventional dichotomy of synonymous vs. nonsynonymous changes does not offer the resolution needed to study the dynamics of these two phases. Following previously published methods, we separated amino acid changes into 75 elementary types (1-bp substitution between their respective codons). The likelihood of each type of amino acid change becoming polymorphic (PI, which stands for ''polymorphic index''), relative to synonymous changes, can then be calculated. Similarly, the likelihood of fixation (FI, for "fixation index"), conditional on common polymorphisms, is also calculated. Using Perlegen and HapMap data on human polymorphisms and the chimpanzee sequences as the outgroup, we compared the evolutionary dynamics of the 75 elementary changes in the two phases. We found a strong ''L-shaped'' negative correlation (P < 0.001) between FI and PI. Only those changes with low PIs show FI > 1, which is often a signature of adaptive evolution. These patterns suggest that negative and positive selection operate more effectively on the same set of amino acid changes and that Ϸ10 -13% of amino acid substitutions between humans and chimpanzee may be adaptive. 2). Many studies have attempted to address this issue by considering levels of within-species polymorphism or between-species divergence (3, 4). One may gain further resolution by dividing the process of mutation substitution into two separate phases, polymorphism and fixation. When a new mutation arises in a population, it exists as a low-frequency polymorphism. In this first phase of evolution, the dominant forces are genetic drift and negative selection. If this mutation can reach a substantial frequency (say, 20% in humans), then it is unlikely to be strongly deleterious. Hence, the second phase of evolution, the process of fixation, is governed mainly by drift and positive selection. Whether these two phases are positively or negatively correlated will inform us about how natural selection operates. In this report, we shall address these questions by using human data, but the approach should be general.To investigate the evolutionary dynamics and correlation between these two phases, we need to be able to classify mutations in coding regions into more categories than the traditional nonsynonymous and synonymous dichotomy. We follow the procedure developed by Tang et al. (refs. 5 and 6, see also ref. 7), who classified amino acid changes into 75 elementary types. An elementary amino acid change is one that can be reached by 1-bp substitution in the codons (Phe to Leu, for example). The rest of the 115 types are all composites of two or three elementary changes (Phe to Pro, for example). One can then ask how these 75 elementary changes differ in their likelihoods of ...

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Section: Contrasting Divergence and Polymorphism For The 75 Elementarymentioning

confidence: 99%

Adaptive evolution in humans revealed by the negative correlation between the polymorphism and fixation phases of evolution

Gojobori

Tang

Akey

et al. 2007

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

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“…It deals with the assignment of sequences to known categories based on homology detection properties (sequence similarity). In several studies, protein classification has been examined at various levels, according to a top-down hierarchy in molecular taxonomy, consisting of superfamilies, families, and subfamilies (Dayhoff et al, 1978). Throughout this paper, we will use the terms family (or subfamily) and class interchangeably to denote any collection of sequences that are presumed to share common characteristics and belong to the same category.…”

Section: Introduction Pmentioning

confidence: 99%

Motif-Based Protein Sequence Classification Using Neural Networks

Blekas

Fotiadis²,

Likas³

2005

Journal of Computational Biology

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We present a system for multi-class protein classification based on neural networks. The basic issue concerning the construction of neural network systems for protein classification is the sequence encoding scheme that must be used in order to feed the neural network. To deal with this problem we propose a method that maps a protein sequence into a numerical feature space using the matching scores of the sequence to groups of conserved patterns (called motifs) into protein families. We consider two alternative ways for identifying the motifs to be used for feature generation and provide a comparative evaluation of the two schemes. We also evaluate the impact of the incorporation of background features (2-grams) on the performance of the neural system. Experimental results on real datasets indicate that the proposed method is highly efficient and is superior to other well-known methods for protein classification.

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“…2). The amino acid sequence predicted by the nucleotide sequence differs from the published amino acid sequence of bGH (5,24) (24,25); the individual mRNA molecule leading to pBP348 coded for leucine at this position. The amino acid sequence of the signal peptide was deduced from the nucleotide sequence preceding the codon for amino acid 1.…”

Section: Preparation and Danslationmentioning

confidence: 85%

Molecular cloning of DNA complementary to bovine growth hormone mRNA

1985

Cell Differentiation

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We have cloned DNA complementary to mRNA coding for bovine growth hormone (bGH). Doublestranded DNA complementary t~ bovine pituitary mRNA was inserted into the Pst I site of plasmid pBR322 by the dC-dG tailing technique and amplified in E. coli .1776. A recombinant plasmid containing bGH cDNA was identified by hybridization to cloned rat growth hormone cDNA. It contains the entire coding and 3'-untranslated regions and 31 bases in the 6'-untranslated region. Nucleotide sequence analysis determined the sequence of the %-amino acid signal peptide and confirmed the published amino acid sequence of the secreted hormone at all but 2 residues. Codon usage is nonrandom, with 81.7% of the codons ending in G or C. The nucleotide sequence of bGH mRNA is 83.9% homologous with rat GH mRNA and 76.6% homologous with human GH mRNA, while the respective amino acid sequence homologies are 83.6% and 66.8%.Growth hormone, a protein of about 22,000 daltons synthesized in anterior pituitaries, is required for pre-adult growth. GH' is structurally, evolutionarily, and functionally related to two other protein hormones, prolactin, also made in the anterior pituitary, and chorionic somatomammotropin, made in the placenta (1-4). One of the best studied GHs is bovine GH. The complete sequence of its 191 amino acids has been determined (for review, see Ref. 5), but little is known about its precursor form or the mRNA which codes for it. For the purpose of studying structural and evolutionary relationships among mammalian GHs, and also for possible application in the production of GH useful in animal husbandry, we have studied DNA complementary to mRNA coding for GH. We now report the cloning and sequencing of cDNA coding for full length bovine growth pre-hormone. To whom reprint requests should be addressex 'The abbreviations used are: (b,h,r)GH, (bovine, human, rat) growth hormone; (h)CS, (human) chorionic somatomammotropin; Prl, prolactin; NaDodS04, sodium dodecyl sulfate. MATERIALS AND METHODS Preparation ofshortly after killing at a local abattoir arld were frozen immediately in liquid NZ. Total RNA was prepared by extraction with guanidine thiocyanate, centrifugation through 5.7 M CsCl (6, 71, phenol extraction, and ethanol precipitation. Polyadenylated RNA (poly(A)+ RNA) was prepared by oligo(dT)-cellulose chromatography using a m o acation (8) of the method of Aviv and Leder (9). Integrity of the poly@)+ RNA was seseased by cell-free translation using a rabbit reticulocyte system (8, 10). Bovine growth pre-hormone was immunoprecipitated using an heterologous anti-ovine GH antiserum and prepared by adsorption to formalin-fixed S. aureus Cowan strain I, as described previously (10, 11). Cell-free translation products and immunoprecipitates were dieplayed on NaDodSOd-acrylamide gels (12).Preparation of Double-stranded cDNA for Cloning-Polyadenylated RNA was reverse-transcribed into single-stranded complementary DNA using reverse transcriptase by a modification (8) of the method of Monahan et al. (13). After alkali digestion of the RN...

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Nucleic Acid Sequence Database

Cited by 100 publications

References 3 publications

Adaptive evolution in humans revealed by the negative correlation between the polymorphism and fixation phases of evolution

Adaptive evolution in humans revealed by the negative correlation between the polymorphism and fixation phases of evolution

Motif-Based Protein Sequence Classification Using Neural Networks

Molecular cloning of DNA complementary to bovine growth hormone mRNA

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