Determining Protein Folding Pathway and Associated Energetics through Partitioned Integrated-Tempering-Sampling Simulation

Replica exchange molecular dynamics (REMD) and integrated-tempering-sampling (ITS) are two representative enhanced sampling methods which utilize parallel and integrated tempering approaches, respectively. In this work, a partitioned integrated-tempering-sampling (P-ITS) method is proposed which takes advantage of the benefits of both parallel and integrated tempering approaches. Using P-ITS, the folding pathways of a series of proteins with diverse native structures are explored on multidimensional free-energy landscapes, and the associated thermodynamics are evaluated. In comparison to the original form of ITS, P-ITS improves the sampling efficiency and measures the folding/unfolding thermodynamic quantities more consistently with experimental data. In comparison to REMD, P-ITS significantly reduces the requirement of computational resources and meanwhile achieves similar simulation results. The observed structural characterizations of transition and intermediate states of the proteins under study are in good agreement with previous experimental and simulation studies on the same proteins and homologues. Therefore, the P-ITS method has great potential in simulating the structural dynamics of complex biomolecular systems

Building a Better Fragment Library for <i>De Novo</i> Protein Structure Prediction

<div><p>Fragment-based approaches are the current standard for <i>de novo</i> protein structure prediction. These approaches rely on accurate and reliable fragment libraries to generate good structural models. In this work, we describe a novel method for structure fragment library generation and its application in fragment-based <i>de novo</i> protein structure prediction. The importance of correct testing procedures in assessing the quality of fragment libraries is demonstrated. In particular, the exclusion of homologs to the target from the libraries to correctly simulate a <i>de novo</i> protein structure prediction scenario, something which surprisingly is not always done. We demonstrate that fragments presenting different predominant predicted secondary structures should be treated differently during the fragment library generation step and that exhaustive and random search strategies should both be used. This information was used to develop a novel method, Flib. On a validation set of 41 structurally diverse proteins, Flib libraries presents both a higher precision and coverage than two of the state-of-the-art methods, NNMake and HHFrag. Flib also achieves better precision and coverage on the set of 275 protein domains used in the two previous experiments of the the Critical Assessment of Structure Prediction (CASP9 and CASP10). We compared Flib libraries against NNMake libraries in a structure prediction context. Of the 13 cases in which a correct answer was generated, Flib models were more accurate than NNMake models for 10. “Flib is available for download at: <a href="http://www.stats.ox.ac.uk/research/proteins/resources" target="_blank">http://www.stats.ox.ac.uk/research/proteins/resources</a>”.</p></div

Comparison between HHFrag, NNMake and Flib.

<p>Precision of fragment libraries generated using NNMake (red), HHFrag (green), and Flib (blue) separated by SS Class. The precision of the fragment libraries were averaged on a set of 41 structurally diverse proteins. We varied the cutoff to define a good fragment (x axis) and evaluated the precision (proportion of good fragments in the libraries) for each method within four different SS classes: majority α-helical (top left), majority β-strand (top right), majority loop (bottom right) and other (bottom left).</p

Underestimated Halogen Bonds Forming with Protein Backbone in Protein Data Bank

Halogen bonds (XBs) are attracting increasing attention in biological systems. Protein Data Bank (PDB) archives experimentally determined XBs in biological macromolecules. However, no software for structure refinement in X-ray crystallography takes into account XBs, which might result in the weakening or even vanishing of experimentally determined XBs in PDB. In our previous study, we showed that side-chain XBs forming with protein side chains are underestimated in PDB on the basis of the phenomenon that the proportion of side-chain XBs to overall XBs decreases as structural resolution becomes lower and lower. However, whether the dominant backbone XBs forming with protein backbone are overlooked is still a mystery. Here, with the help of the ratio (<i>R</i>_<i>F</i>) of the observed XBs’ frequency of occurrence to their frequency expected at random, we demonstrated that backbone XBs are largely overlooked in PDB, too. Furthermore, three cases were discovered possessing backbone XBs in high resolution structures while losing the XBs in low resolution structures. In the last two cases, even at 1.80 Å resolution, the backbone XBs were lost, manifesting the urgent need to consider XBs in the refinement process during X-ray crystallography study

Relationship between secondary structure class (SS-Class) and fragment quality.

<p>Boxplot of the RMSD to native structure (y-axis) of 200 fragments per target position (x-axis) for the protein 1E6K. The top-200 scoring fragments from its LIB3000 were selected and are displayed. This subset of LIB3000 was chosen to increase performance of data visualization. Four Different SS Classes are defined: <i>majority α-helical</i> (green), <i>majority β-strand</i> (red), <i>majority loop</i> (blue) and <i>other</i> (black). Positions for which fragments are <i>majority α-helical</i> or <i>majority β-strand</i> present significantly lower RMSDs to the native structure and a smaller spread compared to <i>majority loop</i> and <i>other</i> positions.</p

Effect of Homologs on fragment library quality.

<p>Precision (left) and coverage (right) of fragment libraries generated using three different methods: Rosetta’s NNMake (crosses), our method Flib (circles), and HHFrag (triangles). We varied the cutoff to define a good fragment (x axis) and evaluated the precision (proportion of good fragments in the libraries) and coverage (proportion of protein residues represented by a good fragment) for each of the methods when: homologs are included (red and orange) and when homologs are excluded (light and dark green). Homologs are always excluded from Flib (blue).</p

Effect of protein threading hits on fragment library quality.

<p>Analysis of the impact of fragments extracted from protein threading hits. Precision and coverage are shown for the fragment libraries generated by LIB20, Protein Threading Hits and Flib (a combination of the other two approaches). We varied the RMSD to native structure cutoff to define a good fragment from 0.1 to 2.0 Angstroms (x-axis). The average precision and coverage on the 43 proteins in the test data set is shown for each approach. The precision indicates the proportion of good fragments in the generated libraries (y-axis). The coverage indicate the proportion of residues of the target represented by at least one good fragment.</p

Schematics of Flib.

<p>Starting from a target sequence, we predict secondary structure (SS) and torsion angles for the target (green). We extract fragments from a template database using a combination of random and exhaustive approaches. Fragments are extracted for each target position. A library containing the top-3000 fragments per position is compiled using the SS score and the Ramachandran-specific sequence score (LIB3000). LIB3000 is then sorted according to the torsion angle score and the top-20 fragments per position are selected to comprise the final library. The final library (FLIB) is complemented by fragments that originate by an enrichment routine (in yellow) and fragments that originate from protein threading hits (orange).</p

Examining Variable Domain Orientations in Antigen Receptors Gives Insight into TCR-Like Antibody Design

<div><p>The variable domains of antibodies and T-Cell receptors (TCRs) share similar structures. Both molecules act as sensors for the immune system but recognise their respective antigens in different ways. Antibodies bind to a diverse set of antigenic shapes whilst TCRs only recognise linear peptides presented by a major histocompatibility complex (MHC). The antigen specificity and affinity of both receptors is determined primarily by the sequence and structure of their complementarity determining regions (CDRs). In antibodies the binding site is also known to be affected by the relative orientation of the variable domains, VH and VL. Here, the corresponding property for TCRs, the Vβ-Vα orientation, is investigated and compared with that of antibodies. We find that TCR and antibody orientations are distinct. General antibody orientations are found to be incompatible with binding to the MHC in a canonical TCR-like mode. Finally, factors that cause the orientation of TCRs and antibodies to be different are investigated. Packing of the long Vα CDR3 in the domain-domain interface is found to be influential. In antibodies, a similar packing affect can be achieved using a bulky residue at IMGT position 50 on the VH domain. Along with IMGT VH 50, other positions are identified that may help to promote a TCR-like orientation in antibodies. These positions should provide useful considerations in the engineering of therapeutic TCR-like antibodies.</p></div

The docking angle for each TCR-like antibody/MHC complex.

<p>The docking angle for each TCR-like antibody/MHC complex.</p