Chris Sander

Bio: Chris Sander is an academic researcher from Harvard University. The author has contributed to research in topics: Large Hadron Collider & Protein structure. The author has an hindex of 178, co-authored 713 publications receiving 233287 citations. Previous affiliations of Chris Sander include Purdue University & University of Leeds.

Fast and simple Monte Carlo algorithm for side chain optimization in proteins: application to model building by homology.

Abstract: An unknown protein structure can be predicted with fair accuracy once an evolutionary connection at the sequence level has been made to a protein of known 3-D structure. In model building by homology, one typically starts with a backbone framework, rebuilds new loop regions, and replaces nonconserved side chains. Here, we use an extremely efficient Monte Carlo algorithm in rotamer space with simulated annealing and simple potential energy functions to optimize the packing of side chains on given backbone models. Optimized models are generated within minutes on a workstation, with reasonable accuracy (average of 81% side chain chi 1 dihedral angles correct in the cores of proteins determined at better than 2.5 A resolution). As expected, the quality of the models decreases with decreasing accuracy of backbone coordinates. If the back-bone was taken from a homologous rather than the same protein, about 70% side chain chi 1 angles were modeled correctly in the core in a case of strong homology and about 60% in a case of medium homology. The algorithm can be used in automated, fast, and reproducible model building by homology.

Search for charged Higgs bosons decaying via H ± → τ ± ντ in the τ+jets and τ+lepton final states with 36 fb−1 of pp collision data recorded at √s=13 TeV with the ATLAS experiment

Abstract: Charged Higgs bosons produced either in top-quark decays or in association with a top-quark, subsequently decaying via H$^{±}$ → τ$^{±}$ν$_{τ}$, are searched for in 36.1 fb$^{−1}$ of proton-proton collision data at $ \sqrt{s}=13 $ TeV recorded with the ATLAS detector. Depending on whether the top-quark produced together with H$^{±}$ decays hadronically or leptonically, the search targets τ+jets and τ+lepton final states, in both cases with a hadronically decaying τ-lepton. No evidence of a charged Higgs boson is found. For the mass range of $ {m}_{H^{\pm }} $ = 90–2000 GeV, upper limits at the 95% confidence level are set on the production cross-section of the charged Higgs boson times the branching fraction $ \mathrm{\mathcal{B}}\left({H}^{\pm}\to {\tau}^{\pm }{ u}_{\tau}\right) $ in the range 4.2–0.0025 pb. In the mass range 90–160 GeV, assuming the Standard Model cross-section for $ t\overline{t} $ production, this corresponds to upper limits between 0.25% and 0.031% for the branching fraction $ \mathrm{\mathcal{B}}\left(t\to b{H}^{\pm}\right)\times \mathrm{\mathcal{B}}\left({H}^{\pm}\to {\tau}^{\pm }{ u}_{\tau}\right) $ .

Bridging the Protein Sequence-Structure Gap by Structure Predictions

Abstract: The problem of accurately predicting protein three-dimensional structure from sequence has yet to be solved. Recently, several new and promising methods that work in one, two, or three dimensions have invigorated the field. Modeling by homology can yield fairly accurate three-dimensional structures for approximately 25% of the currently known protein sequences. Techniques for cooperatively fitting sequences into known three-dimensional folds, called threading methods, can increase this rate by detecting very remote homologies in favorable cases. Prediction of protein structure in two dimensions, i.e. prediction of interresidue contacts, is in its infancy. Prediction tools that work in one dimension are both mature and generally applicable; they predict secondary structure, residue solvent accessibility, and the location of transmembrane helices with reasonable accuracy. These and other prediction methods have gained immensely from the rapid increase of information in publicly accessible databases. Growing databases will lead to further improvements of prediction methods and, thus, to narrowing the gap between the number of known protein sequences and known protein structures.

Applications of targeted proteomics in systems biology and translational medicine

Abstract: Biological systems are composed of numerous components of which proteins are of particularly high functional significance. Network models are useful abstractions for studying these components in context. Network representations display molecules as nodes and their interactions as edges. Because they are difficult to directly measure, functional edges are frequently inferred from suitably structured datasets consisting of the accurate and consistent quantification of network nodes under a multitude of perturbed conditions. For the precise quantification of a finite list of proteins across a wide range of samples, targeted proteomics exemplified by selected/multiple reaction monitoring (SRM, MRM) mass spectrometry has proven useful and has been applied to a variety of questions in systems biology and clinical studies. Here, we survey the literature of studies using SRM-MS in systems biology and clinical proteomics. Systems biology studies frequently examine fundamental questions in network biology, whereas clinical studies frequently focus on biomarker discovery and validation in a variety of diseases including cardiovascular disease and cancer. Targeted proteomics promises to advance our understanding of biological networks and the phenotypic significance of specific network states and to advance biomarkers into clinical use.

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

Abstract: The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSIBLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.

Clustal w: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice

Abstract: The sensitivity of the commonly used progressive multiple sequence alignment method has been greatly improved for the alignment of divergent protein sequences. Firstly, individual weights are assigned to each sequence in a partial alignment in order to down-weight near-duplicate sequences and up-weight the most divergent ones. Secondly, amino acid substitution matrices are varied at different alignment stages according to the divergence of the sequences to be aligned. Thirdly, residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Fourthly, positions in early alignments where gaps have been opened receive locally reduced gap penalties to encourage the opening up of new gaps at these positions. These modifications are incorporated into a new program, CLUSTAL W which is freely available.

The CLUSTAL_X windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools.

Abstract: CLUSTAL X is a new windows interface for the widely-used progressive multiple sequence alignment program CLUSTAL W. The new system is easy to use, providing an integrated system for performing multiple sequence and profile alignments and analysing the results. CLUSTAL X displays the sequence alignment in a window on the screen. A versatile sequence colouring scheme allows the user to highlight conserved features in the alignment. Pull-down menus provide all the options required for traditional multiple sequence and profile alignment. New features include: the ability to cut-and-paste sequences to change the order of the alignment, selection of a subset of the sequences to be realigned, and selection of a sub-range of the alignment to be realigned and inserted back into the original alignment. Alignment quality analysis can be performed and low-scoring segments or exceptional residues can be highlighted. Quality analysis and realignment of selected residue ranges provide the user with a powerful tool to improve and refine difficult alignments and to trap errors in input sequences. CLUSTAL X has been compiled on SUN Solaris, IRIX5.3 on Silicon Graphics, Digital UNIX on DECstations, Microsoft Windows (32 bit) for PCs, Linux ELF for x86 PCs, and Macintosh PowerMac.

MUSCLE: multiple sequence alignment with high accuracy and high throughput

Abstract: We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the logexpectation score, and refinement using treedependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.

Gene Ontology: tool for the unification of biology

Abstract: Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.