Towards a structure-based exciton Hamiltonian for the CP29 antenna of photosystem II

The exciton Hamiltonian pertaining to the first excited states of chlorophyll (Chl) a and b pigments in the minor light-harvesting complex CP29 of plant photosystem II is determined based on the recent crystal structure at 2.8 Å resolution applying a combined quantum chemical/electrostatic approach as used earlier for the major light-harvesting complex LHCII. Two electrostatic methods for the calculation of the local transition energies (site energies), referred to as the Poisson–Boltzmann/quantum chemical (PBQC) and charge density coupling (CDC) method, which differ in the way the polarizable environment of the pigments is described, are compared and found to yield comparable results, when tested against fits of measured optical spectra (linear absorption, linear dichroism, circular dichroism, and fluorescence). The crystal structure shows a Chl a/b ratio of 2.25, whereas a ratio between 2.25 and 3.0 can be estimated from the simulation of experimental spectra. Thus, it is possible that up to one Chl b is lost in CP29 samples. The lowest site energy is found to be located at Chl a604 close to neoxanthin. This assignment is confirmed by the simulation of wild-type-minus-mutant difference spectra of reconstituted CP29, where a tyrosine residue next to Chl a604 is modified in the mutant. Nonetheless, the terminal emitter domain (TED), i.e. the pigments contributing mostly to the lowest exciton state, is found at the Chl a611–a612–a615 trimer due to strong excitonic coupling between these pigments, with the largest contributions from Chls a611 and a612. A major difference between CP29 and LHCII is that Chl a610 is not the energy sink in CP29, which is presumably to a large extent due to the replacement of a lysine residue with alanine close to the TED

Computational Protein Design: Validation and Possible Relevance as a Tool for Homology Searching and Fold Recognition

International audienceBACKGROUND: Protein fold recognition usually relies on a statistical model of each fold; each model is constructed from an ensemble of natural sequences belonging to that fold. A complementary strategy may be to employ sequence ensembles produced by computational protein design. Designed sequences can be more diverse than natural sequences, possibly avoiding some limitations of experimental databases. METHODOLOGY/PRINCIPAL FINDINGS: WE EXPLORE THIS STRATEGY FOR FOUR SCOP FAMILIES: Small Kunitz-type inhibitors (SKIs), Interleukin-8 chemokines, PDZ domains, and large Caspase catalytic subunits, represented by 43 structures. An automated procedure is used to redesign the 43 proteins. We use the experimental backbones as fixed templates in the folded state and a molecular mechanics model to compute the interaction energies between sidechain and backbone groups. Calculations are done with the Proteins@Home volunteer computing platform. A heuristic algorithm is used to scan the sequence and conformational space, yielding 200,000-300,000 sequences per backbone template. The results confirm and generalize our earlier study of SH2 and SH3 domains. The designed sequences ressemble moderately-distant, natural homologues of the initial templates; e.g., the SUPERFAMILY, profile Hidden-Markov Model library recognizes 85% of the low-energy sequences as native-like. Conversely, Position Specific Scoring Matrices derived from the sequences can be used to detect natural homologues within the SwissProt database: 60% of known PDZ domains are detected and around 90% of known SKIs and chemokines. Energy components and inter-residue correlations are analyzed and ways to improve the method are discussed. CONCLUSIONS/SIGNIFICANCE: For some families, designed sequences can be a useful complement to experimental ones for homologue searching. However, improved tools are needed to extract more information from the designed profiles before the method can be of general use

Testing the Coulomb/Accessible Surface Area solvent model for protein stability, ligand binding, and protein design

<p>Abstract</p> <p>Background</p> <p>Protein structure prediction and computational protein design require efficient yet sufficiently accurate descriptions of aqueous solvent. We continue to evaluate the performance of the Coulomb/Accessible Surface Area (CASA) implicit solvent model, in combination with the Charmm19 molecular mechanics force field. We test a set of model parameters optimized earlier, and we also carry out a new optimization in this work, using as a target a set of experimental stability changes for single point mutations of various proteins and peptides. The optimization procedure is general, and could be used with other force fields. The computation of stability changes requires a model for the unfolded state of the protein. In our approach, this state is represented by tripeptide structures of the sequence Ala-X-Ala for each amino acid type X. We followed an iterative optimization scheme which, at each cycle, optimizes the solvation parameters and a set of tripeptide structures for the unfolded state. This protocol uses a set of 140 experimental stability mutations and a large set of tripeptide conformations to find the best tripeptide structures and solvation parameters.</p> <p>Results</p> <p>Using the optimized parameters, we obtain a mean unsigned error of 2.28 kcal/mol for the stability mutations. The performance of the CASA model is assessed by two further applications: (i) calculation of protein-ligand binding affinities and (ii) computational protein design. For these two applications, the previous parameters and the ones optimized here give a similar performance. For ligand binding, we obtain reasonable agreement with a set of 55 experimental mutation data, with a mean unsigned error of 1.76 kcal/mol with the new parameters and 1.47 kcal/mol with the earlier ones. We show that the optimized CASA model is not inferior to the Generalized Born/Surface Area (GB/SA) model for the prediction of these binding affinities. Likewise, the new parameters perform well for the design of 8 SH3 domain proteins where an average of 32.8% sequence identity relative to the native sequences was achieved. Further, it was shown that the computed sequences have the character of naturally-occuring homologues of the native sequences.</p> <p>Conclusion</p> <p>Overall, the two CASA variants explored here perform very well for a wide variety of applications. Both variants provide an efficient solvent treatment for the computational engineering of ligands and proteins.</p

Ab initio Berechnung von pKa Werten und Redox Potentialen

0\. Title page and table of contents 1\. Introduction 2\. Methods 3\. Results 4\. Discussion and Conclusion 5\. Appendix 6\. BibliographyThis work investigates the ab initio computation of pKa values and redox potentials of medium size organic molecules in different solvents through a combination of quantum chemical and electrostatic methods. With the help of suitable thermodynamic cycles the overall reaction was divided into two phases. The gasphase was modeled by quantum chemical methods, whereas the condensed phase was described by means of electrostatic methods. Within this work the solvation energies were computed without the usage of so-called "selfconsistent reactionfield methods". The good agreement between experimental and computed data within this work proofs that this method is succesful.Die folgende Arbeit beschreibt die Berechnung von pKa Werten und Redox Potentialen organischer Molekuele durch Kombination von Quantenchemie und elektrostatischen Rechenmethoden.Die Aufteilung in zwei Phasen erfolgte durch den Einsatz geeigneter thermodynamischer Zyklen. Die Gasphase wurde mit Hilfe quantenchemischer und die kondensierte Phase mittels elektrostatischen Rechenmethoden beschrieben. Im Gegensatz zu vorherigen Arbeiten zu diesem Thema erfolgte die Berechnung der Solvatationsenergien ohne Anwendung sogenannte "selbstkonsistenter Reaktionsfelder". Sowohl fuer die Berechnung von pka Werten als auch von Redox Potentialen konnte fuer ein breites Spektrum eine gute Uebereinstimmung mit experimentellen Werten erzielt werden

Computational design of protein-ligand binding: modifying the specificity of asparaginyl-tRNA synthetase.

International audienceA method for computational design of protein-ligand interactions is implemented and tested on the asparaginyl- and aspartyl-tRNA synthetase enzymes (AsnRS, AspRS). The substrate specificity of these enzymes is crucial for the accurate translation of the genetic code. The method relies on a molecular mechanics energy function and a simple, continuum electrostatic, implicit solvent model. As test calculations, we first compute AspRS-substrate binding free energy changes due to nine point mutations, for which experimental data are available; we also perform large-scale redesign of the entire active site of each enzyme (40 amino acids) and compare to experimental sequences. We then apply the method to engineer an increased binding of aspartyl-adenylate (AspAMP) into AsnRS. Mutants are obtained using several directed evolution protocols, where four or five amino acid positions in the active site are randomized. Promising mutants are subjected to molecular dynamics simulations; Poisson-Boltzmann calculations provide an estimate of the corresponding, AspAMP, binding free energy changes, relative to the native AsnRS. Several of the mutants are predicted to have an inverted binding specificity, preferring to bind AspAMP rather than the natural substrate, AsnAMP. The computed binding affinities are significantly weaker than the native, AsnRS:AsnAMP affinity, and in most cases, the active site structure is significantly changed, compared to the native complex. This almost certainly precludes catalytic activity. One of the designed sequences has a higher affinity and more native-like structure and may represent a valid candidate for Asp activity

Computational protein design: software implementation, parameter optimization, and performance of a simple model.

International audienceComputational protein design will continue to improve as new implementations and parameterizations are explored. An automated protein design procedure is implemented and applied to the full redesign of 16 globular proteins. We combine established but simple ingredients: a molecular mechanics description of the protein where nonpolar hydrogens are implicit, a simple solvent model, a folded state where the backbone is fixed, and a tripeptide model of the unfolded state. Sequences are selected to optimize the folding free energy, using a simple heuristic algorithm to explore sequence and conformational space. We show that a balanced parametrization, obtained here and in our previous work, makes this procedure effective, despite the simplicity of the ingredients. Calculations were done using our Proteins @ Home distributed computing platform, with the help of several thousand volunteers. We describe the software implementation, the optimization of selected terms in the energy function, and the performance of the method. We allowed all amino acids to mutate except glycines, prolines, and cysteines. For 15 of the 16 test proteins, the scores of the computed sequences were comparable to those of natural homologues. Using the low energy computed sequences in a BLAST search of the SWISSPROT database, we could retrieve natural sequences for all protein families considered, with no high-ranking false-positives. The good stability of the designed sequences was supported by molecular dynamics simulations of selected sequences, which gave structures close to the experimental native structure

The inverse protein folding problem: protein design and structure prediction in the genomic era.

International audienceMillions of proteins are being identified every year by high throughput genome sequencing projects. Many others can potentially be created by protein engineering and design methods. Here, we review a method for computational protein design (CPD), which starts from a known protein and its 3D structure, and seeks to modify it by mutating some or all of the amino acid sidechains. The mutations are selected to provide stability, and possibly other properties, such as ligand binding. For each set of candidate mutations, the 3D structure is modeled, with an assumption of small, localized perturbations; in particular, we assume the backbone conformation does not change significantly. As in other CPD implementations, the structure is modeled using a classical, molecular mechanics approach along with a simple, implicit description of solvent. Some of the calculations have been distributed to volunteers on the Internet, through our Proteins@Home volunteer computing project. The method and selected results are described, which show that the designed sequences share important properties of natural proteins

Swissprot sequences retrieved using natural, designed, and random PSSMs.

<p>Number of false positives in parantheses.</p><p><i>^a</i>The sequences used to construct the PSSM are either natural sequences from the NR01 database, low-energy designed sequences, or random sequences.</p><p><i>^b</i>The designed sequences with the highest CDD scores (Chemokines) or with five SBPs reset to their native types (PDZ domains).</p