Carlos Bustamante

Bio: Carlos Bustamante is an academic researcher from Stanford University. The author has contributed to research in topics: Population & Optical tweezers. The author has an hindex of 161, co-authored 770 publications receiving 106053 citations. Previous affiliations of Carlos Bustamante include Lawrence Berkeley National Laboratory & University of California.

The complete validated mitochondrial genome of the silver gemfish Rexea solandri (Cuvier, 1832) (Perciformes, Gempylidae).

Abstract: The silver gemfish Rexea solandri is an important economic resource but vulnerable to overfishing in Australian waters. The complete mitochondrial genome sequence is described from 1.6 million reads obtained via next generation sequencing. The total length of the mitogenome is 16,350 bp comprising 2 rRNA, 13 protein-coding genes, 22 tRNA and 2 non-coding regions. The mitogenome sequence was validated against sequences of PCR fragments and BLAST queries of Genbank. Gene order was equivalent to that found in marine fishes.

Identification of a Novel Somatic Mutation Leading to Allele Dropout for EGFR L858R Genotyping in Non-Small Cell Lung Cancer.

Abstract: While PCR-based genotyping methods abound in molecular testing for lung cancer therapy, these approaches may not provide the robust sensitivity to detect accurate genotypes in a variable cancer genomic background. Here, we describe a study of a clinical tumor specimen containing a novel somatic single nucleotide variant that caused allele drop-out in EGFR L858R genotyping, resulting in a false-negative interpretation and impacting patient clinical management. We demonstrate that a subsequent unbiased next-generation sequencing approach correctly identified the driver mutation, and therefore may be more reliable for somatic variant detection. These findings magnify the potential pitfalls of PCR amplification-based approaches and stress the importance of unbiased and sensitive molecular testing strategies for therapeutic marker detection as molecular testing becomes the standard for determining clinical management of cancer patients.

Molecular machines one molecule at a time.

Abstract: Machines have exerted a strange fascination for humans throughout history. Perhaps the realization of the “transformative” power of certain simple devices and tools (a wedge, an axe, an inclined plane, etc.) by means of which some action could be either amplified, converted into a different one, made to act in another direction, or somewhere else in space, is the origin of this enthrallment. The word machine derives from the Greek Ionian term mechane to designate an engine or contrivance; mechane, in turn, likely derives from the Hebrew mekhonot, a term used in the Torah to describe 4-wheel water carts built by Solomon for the Temple. The term in Latin is machina. In the third century BCE, Archimedes described and classified them as levers, pulleys, or screws, and discovered the mechanical advantage of the lever. Nearly 400 years later, in the first century CE, Heron of Alexandria (10–85 CE) described 5 mechanisms for “moving a given weight by a given force”; he included the lever, the windlass, the screw for power, the wedge, and the tackle block (pulley). The Greeks’ only provided a “static” description of machines as the concept of mechanical work and its equivalence to energy was still not understood. It was in the 16 century that this idea permitted the description of machines in dynamical terms. In 1600, Galileo in “Le Mechaniche” (“The Mechaninal Devices,” “The Machines”) provided the first complete dynamic theory of machines. Already in 1647, in “La description du Corps Humain,” Ren e Descartes proposes that the body works like a machine, through a number of automated functions. But it was the invention of the microscope, in the late 16 century, that permitted scientists to use this instrument to observe and describe for the first time the microscopic organization of living matter. This newly acquired capability brought about a profound revolution in the biological sciences. In 1666, in his treatise “De Viscerum Structura,” Marcello Malpighi—the “father” of microscopic anatomy—wrote “The operative industry of Nature is so prolific, that machines will be eventually found not only unknown to us but also unimaginable by our mind.” This revolution in Biology was paralleled by the one ongoing in Physics, where Galileo first, and then Newton, refined the concepts of inertia, force, torque, mass, acceleration, and work that were to become the appropriate terms to describe the operation of machines. The mechanical paradigm is recurrent in biology. During the industrial revolution, the need to understand the efficiency of vapor and combustion engines would eventually lead Carnot (1796–1832) to establish the foundations of thermodynamics. Meanwhile, during the 1780s Laviosier and Laplace, through carefully designed experiments on the utilization of oxygen and production of carbon dioxide and heat by the human body, concluded “Respiration is therefore a combustion, admittedly very slow, but otherwise exactly similar to that of charcoal.” In the last two decades, as a result of the great advances in structural biology and biophysics, a new understanding has emerged about the mechanical nature of the cell. We know now that this basic living unit has a modular architecture in which many of its central functions (replication, transcription, translation, splicing, protein degradation, energy generation, motility, etc.) are performed by interconnected and highly coordinated protein machines. These are assemblies of 5 or more polypeptide chains, contain various parts with specialized functions, and provide a localized environment where chemical species can interact and react in highly specific fashion. A protein machine is then a molecular “device” that, like its macroscopic counterpart, performs highly specialized functions requiring the conversion of chemical energy into mechanical work. This process—almost invariably—involves parts that DOI: 10.1002/pro.3205 Published online in Wiley Online Library (wileyonlinelibrary.com).

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Scalable molecular dynamics with NAMD

Abstract: NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high-end parallel platforms, as well as tens of processors on low-cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This article, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical molecular dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temperature and pressure controls used. Features for steering the simulation across barriers and for calculating both alchemical and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Finally, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomolecular system, highlighting particular features of NAMD, for example, the Tcl scripting language. The article also provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the molecular graphics/sequence analysis software VMD and the grid computing/collaboratory software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu.

A global reference for human genetic variation.

Abstract: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.