Paul C. Whitford

Bio: Paul C. Whitford is an academic researcher from Northeastern University. The author has contributed to research in topics: Energy landscape & Transfer RNA. The author has an hindex of 31, co-authored 82 publications receiving 3524 citations. Previous affiliations of Paul C. Whitford include Los Alamos National Laboratory & Rice University.

An all‐atom structure‐based potential for proteins: Bridging minimal models with all‐atom empirical forcefields

Abstract: Protein dynamics take place on many time and length scales. Coarse-grained structure-based (Go) models utilize the funneled energy landscape theory of protein folding to provide an understanding of both long time and long length scale dynamics. All-atom empirical forcefields with explicit solvent can elucidate our understanding of short time dynamics with high energetic and structural resolution. Thus, structure-based models with atomic details included can be used to bridge our understanding between these two approaches. We report on the robustness of folding mechanisms in one such all-atom model. Results for the B domain of Protein A, the SH3 domain of C-Src Kinase, and Chymotrypsin Inhibitor 2 are reported. The interplay between side chain packing and backbone folding is explored. We also compare this model to a C(alpha) structure-based model and an all-atom empirical forcefield. Key findings include: (1) backbone collapse is accompanied by partial side chain packing in a cooperative transition and residual side chain packing occurs gradually with decreasing temperature, (2) folding mechanisms are robust to variations of the energetic parameters, (3) protein folding free-energy barriers can be manipulated through parametric modifications, (4) the global folding mechanisms in a C(alpha) model and the all-atom model agree, although differences can be attributed to energetic heterogeneity in the all-atom model, and (5) proline residues have significant effects on folding mechanisms, independent of isomerization effects. Because this structure-based model has atomic resolution, this work lays the foundation for future studies to probe the contributions of specific energetic factors on protein folding and function.

Head swivel on the ribosome facilitates translocation by means of intra-subunit tRNA hybrid sites

Abstract: During translation, transfer RNAs enter the ribosome and then move sequentially through three sites, known as A, P and E, as they transfer their attached amino acids onto the growing peptide chain. How the ribosome facilitates tRNA translocation between the sites remains largely unknown. Christian Spahn and colleagues have used multiparticle cryoelectron microscopy of a ribosome bound to the translation elongation factor, EF-G, to get information about tRNA movement. They identify two new sub-states and conclude that, following spontaneous inter-subunit ratcheting, translocation is the direct result of head swivelling and unratcheting of the 30S ribosomal subunit. During translation, tRNAs enter the ribosome and then move sequentially through three sites, known as A, P and E, as they transfer their attached amino acids onto the growing peptide chain. How the ribosome facilitates tRNA translocation between the sites remains largely unknown. Now a study uses multiparticle cryoelectron microscopy of a ribosome bound to the translation elongation factor, EF-G, to get information about tRNA movement. It identifies two new substates and sees that translocation is linked to unratcheting of the 30S ribosomal subunit. The elongation cycle of protein synthesis involves the delivery of aminoacyl-transfer RNAs to the aminoacyl-tRNA-binding site (A site) of the ribosome, followed by peptide-bond formation and translocation of the tRNAs through the ribosome to reopen the A site1,2. The translocation reaction is catalysed by elongation factor G (EF-G) in a GTP-dependent manner3. Despite the availability of structures of various EF-G–ribosome complexes, the precise mechanism by which tRNAs move through the ribosome still remains unclear. Here we use multiparticle cryoelectron microscopy analysis to resolve two previously unseen subpopulations within Thermus thermophilus EF-G–ribosome complexes at subnanometre resolution, one of them with a partly translocated tRNA. Comparison of these substates reveals that translocation of tRNA on the 30S subunit parallels the swivelling of the 30S head and is coupled to unratcheting of the 30S body. Because the tRNA maintains contact with the peptidyl-tRNA-binding site (P site) on the 30S head and simultaneously establishes interaction with the exit site (E site) on the 30S platform, a novel intra-subunit ‘pe/E’ hybrid state is formed. This state is stabilized by domain IV of EF-G, which interacts with the swivelled 30S-head conformation. These findings provide direct structural and mechanistic insight into the ‘missing link’ in terms of tRNA intermediates involved in the universally conserved translocation process.

SMOG@ctbp: simplified deployment of structure-based models in GROMACS.

Abstract: Molecular dynamics simulations with coarsegrained and/or simplified Hamiltonians are an effective means of capturing the functionally important long-time and large-length scale motions of proteins and RNAs. Structure-based Hamiltonians, simplified models developed from the energy landscape theory of protein folding, have become a standard tool for investigating biomolecular dynamics. SMOG@ctbp is an effort to simplify the use of structure-based models. The purpose of the web server is two fold. First, the web tool simplifies the process of implementing a well-characterized structure-based model on a state-of-the-art, open source, molecular dynamics package, GROMACS. Second, the tutorial-like format helps speed the learning curve of those unfamiliar with molecular dynamics. A web tool user is able to upload any multi-chain biomolecular system consisting of standard RNA, DNA and amino acids in PDB format and receive as output all files necessary to implement the model in GROMACS. Both Ca and all-atom versions of the model are available. SMOG@ctbp resides at http://smog.ucsd.edu.

Nanopore-Based Measurements of Protein Size, Fluctuations, and Conformational Changes

Abstract: Proteins are structurally dynamic macromolecules, and it is challenging to quantify the conformational properties of their native state in solution. Nanopores can be efficient tools to study proteins in a solution environment. In this method, an electric field induces electrophoretic and/or electro-osmotic transport of protein molecules through a nanopore slightly larger than the protein molecule. High-bandwidth ion current measurement is used to detect the transit of each protein molecule. First, our measurements reveal a correlation between the mean current blockade amplitude and the radius of gyration for each protein. Next, we find a correlation between the shape of the current signal amplitude distributions and the protein fluctuation as obtained from molecular dynamics simulations. Further, the magnitude of the structural fluctuations, as probed by experiments and simulations, correlates with the ratio of α-helix to β-sheet content. We highlight the resolution of our measurements by resolving two st...

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

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。

Conflict of interest statement. None declared.

Abstract: Hypertension 66 (20.3%) 24 (24.2%) 30 (16.3%) NS Diabetes 20 (6.2%) 7 (7.1%) 10 (5.4%) NS Excess weight 78 (24%) 27 (27.3%) 44 (23.9%) NS Smokers 64 (19.7%) 17 (17.2%) 35 (19.0%) NS Age >50 years 137 (42.2%) 54 (54.5%) 67 (36.4%) <0.02 Kidney disease 7 (2.2%) 1 (1%) 5 (2.7%) NS Family history, DM 102 (31.4%) 28 (28.3%) 66 (35.9%) NS

Ribosome-targeting antibiotics and mechanisms of bacterial resistance

Abstract: The ribosome is one of the main antibiotic targets in the bacterial cell. Crystal structures of naturally produced antibiotics and their semi-synthetic derivatives bound to ribosomal particles have provided unparalleled insight into their mechanisms of action, and they are also facilitating the design of more effective antibiotics for targeting multidrug-resistant bacteria. In this Review, I discuss the recent structural insights into the mechanism of action of ribosome-targeting antibiotics and the molecular mechanisms of bacterial resistance, in addition to the approaches that are being pursued for the production of improved drugs that inhibit bacterial protein synthesis.

Perspective: Coarse-grained models for biomolecular systems.

Abstract: By focusing on essential features, while averaging over less important details, coarse-grained (CG) models provide significant computational and conceptual advantages with respect to more detailed models. Consequently, despite dramatic advances in computational methodologies and resources, CG models enjoy surging popularity and are becoming increasingly equal partners to atomically detailed models. This perspective surveys the rapidly developing landscape of CG models for biomolecular systems. In particular, this review seeks to provide a balanced, coherent, and unified presentation of several distinct approaches for developing CG models, including top-down, network-based, native-centric, knowledge-based, and bottom-up modeling strategies. The review summarizes their basic philosophies, theoretical foundations, typical applications, and recent developments. Additionally, the review identifies fundamental inter-relationships among the diverse approaches and discusses outstanding challenges in the field. When carefully applied and assessed, current CG models provide highly efficient means for investigating the biological consequences of basic physicochemical principles. Moreover, rigorous bottom-up approaches hold great promise for further improving the accuracy and scope of CG models for biomolecular systems.