A statistical-physics approach for codon usage optimisation.

The concept of "codon optimisation" involves adjusting the coding sequence of a target protein to account for the inherent codon preferences of a host species and maximise protein expression in that species. However, there is still a lack of consensus on the most effective approach to achieve optimal results. Existing methods typically depend on heuristic combinations of different variables, leaving the user with the final choice of the sequence hit. In this study, we propose a new statistical-physics model for codon optimisation. This model, called the Nearest-Neighbour interaction (NN) model, links the probability of any given codon sequence to the "interactions" between neighbouring codons. We used the model to design codon sequences for different proteins of interest, and we compared our sequences with the predictions of some commercial tools. In order to assess the importance of the pair interactions, we additionally compared the NN model with a simpler method (Ind) that disregards interactions. It was observed that the NN method yielded similar Codon Adaptation Index (CAI) values to those obtained by other commercial algorithms, despite the fact that CAI was not explicitly considered in the algorithm. By utilising both the NN and Ind methods to optimise the reporter protein luciferase, and then analysing the translation performance in human cell lines and in a mouse model, we found that the NN approach yielded the highest protein expression in vivo. Consequently, we propose that the NN model may prove advantageous in biotechnological applications, such as heterologous protein expression or mRNA-based therapies.

A new humanized antibody is effective against pathogenic fungi in vitro.

Invasive fungal infections mainly affect patients undergoing transplantation, surgery, neoplastic disease, immunocompromised subjects and premature infants, and cause over 1.5 million deaths every year. The most common fungi isolated in invasive diseases are Candida spp., Cryptococcus spp., and Aspergillus spp. and even if four classes of antifungals are available (Azoles, Echinocandins, Polyenes and Pyrimidine analogues), the side effects of drugs and fungal acquired and innate resistance represent the major hurdles to be overcome. Monoclonal antibodies are powerful tools currently used as diagnostic and therapeutic agents in different clinical contexts but not yet developed for the treatment of invasive fungal infections. In this paper we report the development of the first humanized monoclonal antibody specific for ß-1,3 glucans, a vital component of several pathogenic fungi. H5K1 has been tested on C. auris, one of the most urgent threats and resulted efficient both alone and in combination with Caspofungin and Amphotericin B showing an enhancement effect. Our results support further preclinical and clinical developments for the use of H5K1 in the treatment of patients in need.

Humanization of Antibodies using a Statistical Inference Approach.

Antibody humanization is a key step in the preclinical phase of the development of therapeutic antibodies, originally developed and tested in non-human models (most typically, in mouse). The standard technique of Complementarity-Determining Regions (CDR) grafting into human Framework Regions of germline sequences has some important drawbacks, in that the resulting sequences often need further back-mutations to ensure functionality and/or stability. Here we propose a new method to characterize the statistical distribution of the sequences of the variable regions of human antibodies, that takes into account phenotypical correlations between pairs of residues, both within and between chains. We define a "humanness score" of a sequence, comparing its performance in distinguishing human from murine sequences, with that of some alternative scores in the literature. We also compare the score with the experimental immunogenicity of clinically used antibodies. Finally, we use the humanness score as an optimization function and perform a search in the sequence space, starting from different murine sequences and keeping the CDR regions unchanged. Our results show that our humanness score outperforms other methods in sequence classification, and the optimization protocol is able to generate humanized sequences that are recognized as human by standard homology modelling tools.

The closed conformation of the LDL receptor is destabilized by the low Ca(++) concentration but favored by the high Mg(++) concentration in the endosome.

The LDL receptor (LDLR) internalizes LDL and VLDL particles. In the endosomes, it adopts a closed conformation important for recycling, by interaction of two modules of the ligand binding domain (LR4-5) and a ß-propeller motif. Here, we investigate by SPR the interactions between those two modules and the ß-propeller. Our results indicate that the two modules cooperate to bind the ß-propeller. The binding is favored by low pH and by high [Ca(++)]. Our data show that Mg(++), at high concentration in the endosome, favors the formation of the closed conformation by replacing the structuring effect of Ca(++) in LR5. We propose a sequential model of LDL release where formation of the close conformation follows LDL release.

Mapping the Topography of a Protein Energy Landscape.

Protein energy landscapes are highly complex, yet the vast majority of states within them tend to be invisible to experimentalists. Here, using site-directed mutagenesis and exploiting the simplicity of tandem-repeat protein structures, we delineate a network of these states and the routes between them. We show that our target, gankyrin, a 226-residue 7-ankyrin-repeat protein, can access two alternative (un)folding pathways. We resolve intermediates as well as transition states, constituting a comprehensive series of snapshots that map early and late stages of the two pathways and show both to be polarized such that the repeat array progressively unravels from one end of the molecule or the other. Strikingly, we find that the protein folds via one pathway but unfolds via a different one. The origins of this behavior can be rationalized using the numerical results of a simple statistical mechanics model that allows us to visualize the equilibrium behavior as well as single-molecule folding/unfolding trajectories, thereby filling in the gaps that are not accessible to direct experimental observation. Our study highlights the complexity of repeat-protein folding arising from their symmetrical structures; at the same time, however, this structural simplicity enables us to dissect the complexity and thereby map the precise topography of the energy landscape in full breadth and remarkable detail. That we can recapitulate the key features of the folding mechanism by computational analysis of the native structure alone will help toward the ultimate goal of designed amino-acid sequences with made-to-measure folding mechanisms-the Holy Grail of protein folding.

Dynamic interplay between catalytic and lectin domains of GalNAc-transferases modulates protein O-glycosylation.

Protein O-glycosylation is controlled by polypeptide GalNAc-transferases (GalNAc-Ts) that uniquely feature both a catalytic and lectin domain. The underlying molecular basis of how the lectin domains of GalNAc-Ts contribute to glycopeptide specificity and catalysis remains unclear. Here we present the first crystal structures of complexes of GalNAc-T2 with glycopeptides that together with enhanced sampling molecular dynamics simulations demonstrate a cooperative mechanism by which the lectin domain enables free acceptor sites binding of glycopeptides into the catalytic domain. Atomic force microscopy and small-angle X-ray scattering experiments further reveal a dynamic conformational landscape of GalNAc-T2 and a prominent role of compact structures that are both required for efficient catalysis. Our model indicates that the activity profile of GalNAc-T2 is dictated by conformational heterogeneity and relies on a flexible linker located between the catalytic and the lectin domains. Our results also shed light on how GalNAc-Ts generate dense decoration of proteins with O-glycans.

MS/MS spectra interpretation as a statistical-mechanics problem.

We describe a new method for peptide sequencing based on the mapping of the interpretation of tandem mass spectra onto the analysis of the equilibrium distribution of a suitably defined physical model, whose variables describe the positions of the fragmentation sites along a discrete mass index. The model is governed by a potential energy function that, at present, we derive ad hoc from the distribution of peaks in a data set of experimental spectra. The statistical-physics perspective prompts for a consistent and unified approach to de novo and database-search methods, which is a distinctive feature of this approach over alternative ones: the characterization of the ground state of the model allows the de novo identification of the precursor peptide; the study of the thermodynamic variables as a function of the (fictitious) temperature gives insight on the quality of the prediction, while the probability profiles at nonzero temperature reveal, on one hand, which fragments are more reliably predicted. On the other hand, they can be used as a spectrum-adapted, a posteriori score for database search. Results obtained with two different test data sets reveal a performance similar to that of other de novo and database-search methods, which is reasonable, given the lack of an aggressive optimization of the energy function at this stage. An important feature of the method is that it is quite general and can be applied with different choices of the energy function: we discuss its possible improvements and generalizations.

Sequence determinants of protein folding rates: positive correlation between contact energy and contact range indicates selection for fast folding.

In comparison with intense investigation of the structural determinants of protein folding rates, the sequence features favoring fast folding have received little attention. Here, we investigate this subject using simple models of protein folding and a statistical analysis of the Protein Data Bank (PDB). The mean-field model by Plotkin and coworkers predicts that the folding rate is accelerated by stronger-than-average interactions at short distance along the sequence. We confirmed this prediction using the Finkelstein model of protein folding, which accounts for realistic features of polymer entropy. We then tested this prediction on the PDB. We found that native interactions are strongest at contact range l = 8. However, since short range contacts tend to be exposed and they are frequently formed in misfolded structures, selection for folding stability tends to make them less attractive, that is, stability and kinetics may have contrasting requirements. Using a recently proposed model, we predicted the relationship between contact range and contact energy based on buriedness and contact frequency. Deviations from this prediction induce a positive correlation between contact range and contact energy, that is, short range contacts are stronger than expected, for 2/3 of the proteins. This correlation increases with the absolute contact order (ACO), as expected if proteins that tend to fold slowly due to large ACO are subject to stronger selection for sequence features favoring fast folding. Our results suggest that the selective pressure for fast folding is detectable only for one third of the proteins in the PDB, in particular those with large contact order.

Quantitative prediction of protein folding behaviors from a simple statistical model.

The statistical nature of the protein folding process requires the use of equally detailed yet simple models that lend themselves to characterize experiments. One such model is the Wako-Saitô-Muñoz-Eaton model, that we extend here to include solvation effects (WSME-S), introduced via empirical terms. We employ the novel version to analyze the folding of two proteins, gpW and SH3, that have similar size and thermodynamic stability but with the former folding 3 orders of magnitude faster than SH3. A quantitative analysis reveals that gpW presents at most marginal barriers, in contrast to SH3 that folds following a simple two-state approximation. We reproduce the observed experimental differences in melting temperature in gpW as seen by different experimental spectroscopic probes and the shape of the rate-temperature plot. In parallel, we predict the folding complexity expected in gpW from the analysis of both the residue-level thermodynamics and kinetics. SH3 serves as a stringent control with neither folding complexity nor dispersion in melting temperatures being observed. The extended model presented here serves as an ideal tool not only to characterize folding data but also to make experimentally testable predictions.

Analysis of the equilibrium and kinetics of the ankyrin repeat protein myotrophin.

We apply the Wako-Saito-Muñoz-Eaton model to the study of myotrophin, a small ankyrin repeat protein, whose folding equilibrium and kinetics have been recently characterized experimentally. The model, which is a native-centric with binary variables, provides a finer microscopic detail than the Ising model that has been recently applied to some different repeat proteins, while being still amenable for an exact solution. In partial agreement with the experiments, our results reveal a weakly three-state equilibrium and a two-state-like kinetics of the wild-type protein despite the presence of a nontrivial free-energy profile. These features appear to be related to a careful "design" of the free-energy landscape, so that mutations can alter this picture, stabilizing some intermediates and changing the position of the rate-limiting step. Also, the experimental findings of two alternative pathways, an N-terminal and a C-terminal one, are qualitatively confirmed, even if the variations in the rates upon the experimental mutations cannot be quantitatively reproduced. Interestingly, the folding and unfolding pathways appear to be different, even if closely related: a property that is not generally considered in the phenomenological interpretation of the experimental data.

A simple simulation model can reproduce the thermodynamic folding intermediate of apoflavodoxin.

Flavodoxins are single domain proteins with an alpha/beta structure, whose function and folding have been well studied. Detailed experiments have shown that several members of this protein family present a stable intermediate, which accumulates along the folding process. In this work, we use a coarse-grained model for protein folding, whose interactions are based on the topology of the native state, to analyze the thermodynamic characteristics of the folding of Anabaena apoflavodoxin. Our model shows evidence for the existence of a thermodynamic folding intermediate, which reaches a significant population along the thermal transition. According to our simulation results, the intermediate is compact, well packed, and involves distortions of the native structure similar to those experimentally found. These mainly affect the long loop in the protein surface comprising residues 120-139. Although the agreement between simulation and experiment is not perfect, something impossible for a crude model, our results show that the topology of the native state is able to dictate a folding process which includes the presence of an intermediate for this protein.

Computational protein design with side-chain conformational entropy.

Recent advances in modeling protein structures at the atomic level have made it possible to tackle "de novo" computational protein design. Most procedures are based on combinatorial optimization using a scoring function that estimates the folding free energy of a protein sequence on a given main-chain structure. However, the computation of the conformational entropy in the folded state is generally an intractable problem, and its contribution to the free energy is not properly evaluated. In this article, we propose a new automated protein design methodology that incorporates such conformational entropy based on statistical mechanics principles. We define the free energy of a protein sequence by the corresponding partition function over rotamer states. The free energy is written in variational form in a pairwise approximation and minimized using the Belief Propagation algorithm. In this way, a free energy is associated to each amino acid sequence: we use this insight to rescore the results obtained with a standard minimization method, with the energy as the cost function. Then, we set up a design method that directly uses the free energy as a cost function in combination with a stochastic search in the sequence space. We validate the methods on the design of three superficial sites of a small SH3 domain, and then apply them to the complete redesign of 27 proteins. Our results indicate that accounting for entropic contribution in the score function affects the outcome in a highly nontrivial way, and might improve current computational design techniques based on protein stability.

Rate determining factors in protein model structures.

Previous research has shown a strong correlation of protein folding rates to the native state geometry, yet a complete explanation for this dependence is still lacking. Here we study the rate-geometry relationship with a simple statistical physics model, and focus on two classes of model geometries, representing ideal parallel and antiparallel structures. We find that the logarithm of the rate shows an almost perfect linear correlation with the "absolute contact order", but the slope depends on the particular class considered. We discuss these findings in the light of experimental results.

Downhill versus two-state protein folding in a statistical mechanical model.

The authors address the problem of downhill protein folding in the framework of a simple statistical mechanical model, which allows an exact solution for the equilibrium and a semianalytical treatment of the kinetics. Focusing on protein 1BBL, a candidate for downhill folding behavior, and comparing it to the WW domain of protein PIN1, a two-state folder of comparable size, the authors show that there are qualitative differences in both the equilibrium and kinetic properties of the two molecules. However, the barrierless scenario which would be expected if 1BBL were a true downhill folder is observed only at low enough temperature.

Analysis of PIN1 WW domain through a simple statistical mechanics model.

We have applied a simple statistical mechanics Go-like model to the analysis of the PIN1 WW domain, resorting to mean field and Monte Carlo techniques to characterize its thermodynamics, and comparing the results with the wealth of available experimental data. PIN1 WW domain is a 39-residue protein fragment which folds on an antiparallel beta-sheet, thus representing an interesting model system to study the behavior of these secondary structure elements. Results show that the model correctly reproduces the two-state behavior of the protein, and also the trends of the experimental phi(T) values. Moreover, there is a good agreement between Monte Carlo results and the mean field ones, which can be obtained with a substantially smaller computational effort.

Exact solution of the Muñoz-Eaton model for protein folding.

A transfer-matrix formalism is introduced to evaluate exactly the partition function of the Muñoz-Eaton model, relating the folding kinetics of proteins of known structure to their thermodynamics and topology. This technique can be used for a generic protein, for any choice of the energy and entropy parameters, and in principle allows the model to be used as a first tool to characterize the dynamics of a protein of known native state and equilibrium population. Applications to a beta-hairpin and to protein CI-2, with comparisons to previous results, are also shown.