Hui Lu

Bio: Hui Lu is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Medicine & Protein structure prediction. The author has an hindex of 43, co-authored 144 publications receiving 9071 citations. Previous affiliations of Hui Lu include Indiana University & Donald Danforth Plant Science Center.

Template-based protein structure modeling using the RaptorX web server

Abstract: A key challenge of modern biology is to uncover the functional role of the protein entities that compose cellular proteomes. To this end, the availability of reliable three-dimensional atomic models of proteins is often crucial. This protocol presents a community-wide web-based method using RaptorX ( http://raptorx.uchicago.edu/ ) for protein secondary structure prediction, template-based tertiary structure modeling, alignment quality assessment and sophisticated probabilistic alignment sampling. RaptorX distinguishes itself from other servers by the quality of the alignment between a target sequence and one or multiple distantly related template proteins (especially those with sparse sequence profiles) and by a novel nonlinear scoring function and a probabilistic-consistency algorithm. Consequently, RaptorX delivers high-quality structural models for many targets with only remote templates. At present, it takes RaptorX ∼35 min to finish processing a sequence of 200 amino acids. Since its official release in August 2011, RaptorX has processed ∼6,000 sequences submitted by ∼1,600 users from around the world.

Mechanical unfolding intermediates in titin modules.

Abstract: The modular protein titin, which is responsible for the passive elasticity of muscle, is subjected to stretching forces. Previous work on the experimental elongation of single titin molecules has suggested that force causes consecutive unfolding of each domain in an all-or-none fashion1,2,3,4,5,6. To avoid problems associated with the heterogeneity of the modular, naturally occurring titin, we engineered single proteins to have multiple copies of single immunoglobulin domains of human cardiac titin7. Here we report the elongation of these molecules using the atomic force microscope. We find an abrupt extension of each domain by ∼7 A before the first unfolding event. This fast initial extension before a full unfolding event produces a reversible ‘unfolding intermediate’. Steered molecular dynamics8,9 simulations show that the rupture of a pair of hydrogen bonds near the amino terminus of the protein domain causes an extension of about 6 A, which is in good agreement with our observations. Disruption of these hydrogen bonds by site-directed mutagenesis eliminates the unfolding intermediate. The unfolding intermediate extends titin domains by ∼15% of their slack length, and is therefore likely to be an important previously unrecognized component of titin elasticity.

Reverse engineering of the giant muscle protein titin

Abstract: Through the study of single molecules it has become possible to explain the function of many of the complex molecular assemblies found in cells1,2,3,4,5. The protein titin provides muscle with its passive elasticity. Each titin molecule extends over half a sarcomere, and its extensibility has been studied both in situ6,7,8,9,10 and at the level of single molecules11,12,13,14. These studies suggested that titin is not a simple entropic spring but has a complex structure-dependent elasticity. Here we use protein engineering and single-molecule atomic force microscopy15 to examine the mechanical components that form the elastic region of human cardiac titin16,17. We show that when these mechanical elements are combined, they explain the macroscopic behaviour of titin in intact muscle6. Our studies show the functional reconstitution of a protein from the sum of its parts.

The mechanical stability of ubiquitin is linkage dependent.

Abstract: Ubiquitin chains are formed through the action of a set of enzymes that covalently link ubiquitin either through peptide bonds or through isopeptide bonds between their C terminus and any of four lysine residues. These naturally occurring polyproteins allow one to study the mechanical stability of a protein, when force is applied through different linkages. Here we used single-molecule force spectroscopy techniques to examine the mechanical stability of N-C–linked and Lys48-C–linked ubiquitin chains. We combined these experiments with steered molecular dynamics (SMD) simulations and found that the mechanical stability and unfolding pathway of ubiquitin strongly depend on the linkage through which the mechanical force is applied to the protein. Hence, a protein that is otherwise very stable may be easily unfolded by a relatively weak mechanical force applied through the right linkage. This may be a widespread mechanism in biological systems.

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.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

The Phyre2 web portal for protein modeling, prediction and analysis

Abstract: Phyre2 is a web-based tool for predicting and analyzing protein structure and function. Phyre2 uses advanced remote homology detection methods to build 3D models, predict ligand binding sites, and analyze amino acid variants in a protein sequence. Phyre2 is a suite of tools available on the web to predict and analyze protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a paper in Nature Protocols. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence. Users are guided through results by a simple interface at a level of detail they determine. This protocol will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins that are difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2 . A typical structure prediction will be returned between 30 min and 2 h after submission.