Neutralising antibodies in Spike mediated SARS-CoV-2 adaptation.

SARS-CoV-2 Spike protein is critical for virus infection via engagement of ACE2, and amino acid variation in Spike is increasingly appreciated. Given both vaccines and therapeutics are designed around Wuhan-1 Spike, this raises the theoretical possibility of virus escape, particularly in immunocompromised individuals where prolonged viral replication occurs. Here we report chronic SARS-CoV-2 with reduced sensitivity to neutralising antibodies in an immune suppressed individual treated with convalescent plasma, generating whole genome ultradeep sequences by both short and long read technologies over 23 time points spanning 101 days. Although little change was observed in the overall viral population structure following two courses of remdesivir over the first 57 days, N501Y in Spike was transiently detected at day 55 and V157L in RdRp emerged. However, following convalescent plasma we observed large, dynamic virus population shifts, with the emergence of a dominant viral strain bearing D796H in S2 and ΔH69/ΔV70 in the S1 N-terminal domain NTD of the Spike protein. As passively transferred serum antibodies diminished, viruses with the escape genotype diminished in frequency, before returning during a final, unsuccessful course of convalescent plasma. In vitro, the Spike escape double mutant bearing ΔH69/ΔV70 and D796H conferred decreased sensitivity to convalescent plasma, whilst maintaining infectivity similar to wild type. D796H appeared to be the main contributor to decreased susceptibility, but incurred an infectivity defect. The ΔH69/ΔV70 single mutant had two-fold higher infectivity compared to wild type and appeared to compensate for the reduced infectivity of D796H. Consistent with the observed mutations being outside the RBD, monoclonal antibodies targeting the RBD were not impacted by either or both mutations, but a non RBD binding monoclonal antibody was less potent against ΔH69/ΔV70 and the double mutant. These data reveal strong selection on SARS-CoV-2 during convalescent plasma therapy associated with emergence of viral variants with reduced susceptibility to neutralising antibodies.

Ethical consideration of incidental findings on adult brain MRI in research.

OBJECTIVE: To characterize the frequency and severity of incidental findings in brain MRIs of young and older adult research volunteers, and to provide an evaluation of the ethical challenges posed by the detection of such findings. METHODS: The authors reviewed 151 research MRI scans obtained retrospectively from subjects recruited to studies as healthy volunteers. Incidental findings were classified into four categories: no referral, routine, urgent, or immediate referral. p Values for significance were computed from chi(2) tests of contingency. RESULTS: Of 151 studies, the authors found an overall occurrence of incidental findings having required referral of 6.6%. By age, there were more findings in the older cohort (aged >60 years) than in the younger cohort (p < 0.05) and in more men than women in the older cohort (p < 0.001). Three of four (75%) findings in the younger cohort were classified in the urgent referral category; 100% of the findings in the older cohort were classified as routine (p < 0.05). CONCLUSION: The significant presence but different characteristics of incidental findings in young and older subjects presumed to be neurologically healthy suggest that standards of practice are needed to guide investigators in managing and communicating their discovery.

Distribution of Indel lengths.

Protein sequence alignment has become a widely used method in the study of newly sequenced proteins. Most sequence alignment methods use an affine gap penalty to assign scores to insertions and deletions. Although affine gap penalties represent the relative ease of extending a gap compared with initializing a gap, it is still an obvious oversimplification of the real processes that occur during sequence evolution. To improve the efficiency of sequence alignment methods and to obtain a better understanding of the process of sequence evolution, we wanted to find a more accurate model of insertions and deletions in homologous proteins. In this work, we extract the probability of a gap occurrence and the resulting gap length distribution in distantly related proteins (sequence identity < 25%) using alignments based on their common structures. We observe a distribution of gaps that can be fitted with a multiexponential with four distinct components. The results suggest new approaches to modeling insertions and deletions in sequence alignments.

Evolution of functionality in lattice proteins.

We study the evolution of protein functionality using a two-dimensional lattice model. The characteristics particular to evolution, such as population dynamics and early evolutionary trajectories, have a large effect on the distribution of observed structures. Only subtle differences are observed between the distribution of structures evolved for function and those evolved for their ability to form compact structures.

Analyzing site heterogeneity during protein evolution.

New computational models of the kinetics of natural site substitutions in proteins are described based on the underlying physical chemical properties of the amino acids. The corresponding reduction in the number of adjustable parameters allows us to analyze site-heterogeneity. Applying this evolutionary model to various data sets allows us to identify the important factors constraining molecular evolution, providing insight into the relationship between amino acid properties and protein structure.

Optimization of a new score function for the detection of remote homologs.

The growth in protein sequence data has placed a premium on ways to infer structure and function of the newly sequenced proteins. One of the most effective ways is to identify a homologous relationship with a protein about which more is known. While close evolutionary relationships can be confidently determined with standard methods, the difficulty increases as the relationships become more distant. All of these methods rely on some score function to measure sequence similarity. The choice of score function is especially critical for these distant relationships. We describe a new method of determining a score function, optimizing the ability to discriminate between homologs and non-homologs. We find that this new score function performs better than standard score functions for the identification of distant homologies.

How to generate improved potentials for protein tertiary structure prediction: a lattice model study.

Success in the protein structure prediction problem relies heavily on the choice of an appropriate potential function. One approach toward extracting these potentials from a database of known protein structures is to maximize the Z-score of the database proteins, which represents the ability of the potential to discriminate correct from random conformations. These optimization methods model the entire distribution of alternative structures, reducing their ability to concentrate on the lowest energy structures most competitive with the native state and resulting in an unfortunate tendency to underestimate the repulsive interactions. This leads to reduced accuracy and predictive ability. Using a lattice model, we demonstrate how we can weight the distribution to suppress the contributions of the high-energy conformations to the Z-score calculation. The result is a potential that is more accurate and more likely to yield correct predictions than other Z-score optimization methods as well as potentials of mean force.

Modeling evolution at the protein level using an adjustable amino acid fitness model.

An adjustable fitness model for amino acid site substitutions is investigated. This model, a generalization of previously developed evolutionary models, has several distinguishing characteristics: it separately accounts for the processes of mutation and substitution, allows for heterogeneity among substitution rates and among evolutionary constraints, and does not make any prior assumptions about which sites or characteristics of proteins are important to molecular evolution. While the model has fewer adjustable parameters than the general reversible mtREV model, when optimized it outperforms mtREV in likelihood analysis on protein-coding mitochondrial genes. In addition, the optimized fitness parameters of the model show correspondence to some biophysical characteristics of amino acids.

The distribution of structures in evolving protein populations.

Proteins exhibit a nonuniform distribution of structures. A number of models have been advanced to explain this observation by considering the distribution of designabilities, that is, the fraction of all sequences that could successfully fold into any particular structure. It has been postulated that more designable structures should be more common, although the exact nature of this relationship has not been addressed. We find that the nonuniform distribution of protein structures found in nature can be explained by the interplay of evolution and population dynamics with the designability distribution. The relative frequency of different structures has a greater-than-linear dependence on designability, making the distribution of observed protein structures more uneven than the distribution of designabilities. The distribution of structures is also affected by additional factors such as the topology of the sequence space and the similarity of other structures.

Disseminated aspergillosis involving the brain: distribution and imaging characteristics.

BACKGROUND AND PURPOSE: Systemic invasive aspergillosis involves the brain through hematogenous dissemination. A retrospective review of 18 patients with aspergillosis involving the brain was performed in order to present imaging findings and thereby broaden the understanding of the distribution and imaging characteristics of brain Aspergillus infection and to facilitate its early diagnosis. METHODS: The neuroimaging studies of 17 biopsy- or autopsy-proved cases and one clinically diagnosed case were examined retrospectively by two neuroradiologists. The studies were evaluated for anatomic distribution of lesions, signal characteristics of lesions, enhancement, hemorrhage, and progression on serial studies (when performed). Medical records, biopsy reports, and autopsy findings were reviewed. RESULTS: Thirteen of 18 patients had involvement of the basal nuclei and/or thalami. Nine of the 10 patients with lesions at the corticomedullary junction also had lesions in the basal nuclei or thalami. Callosal lesions were seen in seven patients. Progression of lesion number and size was seen in all 11 patients in whom serial studies had been performed. Enhancement was minimal or absent in most cases. There was gross hemorrhage in eight of the 18, and definite ring-enhancement in three. CONCLUSION: Among our cases, lesions in perforating artery territories were more common than those at the corticomedullary junction. Ring enhancement and gross hemorrhage may be present, but are not necessary for the prospective diagnosis.

Estimating the total number of protein folds.

Many seemingly unrelated protein families share common folds. Theoretical models based on structure designability have suggested that a few folds should be very common while many others have low probability. In agreement with the predictions of these models, we show that the distribution of observed protein families over different folds can be modeled with a highly-stretched exponential. Our results suggest that there are approximately 4,000 possible folds, some so unlikely that only approximately 2,000 folds existing among naturally-occurring proteins. Due to the large number of extremely rare folds, constructing a comprehensive database of all existent folds would be difficult. Constructing a database of the most-likely folds representing the vast majority of protein families would be considerably easier.

Effect of alphabet size and foldability requirements on protein structure designability.

A number of investigators have addressed the issue of why certain protein structures are especially common by considering structure designability, defined as the number of sequences that would successfully fold into any particular native structure. One such approach, based on foldability, suggested that structures could be classified according to their maximum possible foldability and that this optimal foldability would be highly correlated with structure designability. Other approaches have focused on computing the designability of lattice proteins written with reduced two-letter amino acid alphabets. These different approaches suggested contrasting characteristics of the most designable structures. This report compares the designability of lattice proteins over a wide range of amino acid alphabets and foldability requirements. While all alphabets have a wide distribution of protein designabilities, the form of the distribution depends on how protein "viability" is defined. Furthermore, under increasing foldability requirements, the change in designabilities for all alphabets are in good agreement with the previous conclusions of the foldability approach. Most importantly, it was noticed that those structures that were highly designable for the two-letter amino acid alphabets are not especially designable with higher-letter alphabets.

Using physical-chemistry-based substitution models in phylogenetic analyses of HIV-1 subtypes.

HIV-1 subtype phylogeny is investigated using a previously developed computational model of natural amino acid site substitutions. This model, based on Boltzmann statistics and Metropolis kinetics, involves an order of magnitude fewer adjustable parameters than traditional substitution matrices and deals more effectively with the issue of protein site heterogeneity. When optimized for sequences of HIV-1 envelope (env) proteins from a few specific subtypes, our model is more likely to describe the evolutionary record for other subtypes than are methods using a single substitution matrix, even a matrix optimized over the same data. Pairwise distances are calculated between various probabilistic ancestral subtype sequences, and a distance matrix approach is used to find the optimal phylogenetic tree. Our results indicate that the relationships between subtypes B, C, and D and those between subtypes A and H may be closer than previously thought.

Optimizing potentials for the inverse protein folding problem.

Inverse protein folding, which seeks to identify sequences that fold into a given structure, has been approached by threading candidate sequences onto the structure and scoring them with database-derived potentials. The sequences with the lowest energies are predicted to fold into that structure. It has been argued that the limited success of this type of approach is not due to the discrepancy between the scoring potential and the true potential but is rather due to the fact that sequences choose their lowest-energy structure rather than structures choosing the lowest-energy sequences. Here we develop a non-physical potential scheme optimized for the inverse folding problem. We maximize the average probability of success for a set of lattice proteins to obtain the optimal potential energy function, and show that the potential obtained by our method is more likely to produce successful predictions than the true potential.

Models of natural mutations including site heterogeneity.

New computational models of natural site mutations are developed that account for the different selective pressures acting on different locations in the protein. The number of adjustable parameters is greatly reduced by basing the models on the underlying physical-chemical properties of the amino acids. This allows us to use our method on small data sets built of specific protein types. We demonstrate that with this approach we can represent the evolutionary patterns in HIV envelope proteins far better than with more traditional methods.

Optimizing energy potentials for success in protein tertiary structure prediction.

BACKGROUND: Success in solving the protein structure prediction problem relies on the choice of an accurate potential energy function. for a single protein sequence, it has been shown that the potential energy function can be optimized for predictive success by maximizing the energy gap between the correct structure and the ensemble of random structures relative to the distribution of the energies of these random structures (the Z-score). Different methods have been described for implementing this procedure for an ensemble of database proteins. Here, we demonstrate a new approach. RESULTS: For a single protein sequence, the probability of success (i.e the probability that the folded state is the lowest energy state) is derived. We then maximize the average probability of success for a set of proteins to obtain the optimal potential energy function. This results in maximum attention being focused on the proteins whose structures are difficult but not impossible to predict. CONCLUSIONS: Using a lattice model of proteins, we show that the optimal interaction potentials obtained by our method are both more accurate and more likely to produce successful predictions than those obtained by other averaging procedures.

On the thermodynamic hypothesis of protein folding.

The validity of the thermodynamic hypothesis of protein folding was explored by simulating the evolution of protein sequences. Simple models of lattice proteins were allowed to evolve by random point mutations subject to the constraint that they fold into a predetermined native structure with a Monte Carlo folding algorithm. We employed a simple analytical approach to compute the probability of violation of the thermodynamic hypothesis as a function of the size of the protein, the fraction of the total number of possible conformations which are kinetically accessible, and the roughness of the free-energy landscape. It was found that even if the folding is under kinetic control, the sequence will evolve so that the native state is most often the state of minimum free energy.

Evolution of model proteins on a foldability landscape.

We model the evolution of simple lattice proteins as a random walk in a fitness landscape, where the fitness represents the ability of the protein to fold. At higher selective pressure, the evolutionary trajectories are confined to neutral networks where the native structure is conserved and the dynamics are non self-averaging and nonexponential. The optimizability of the corresponding native structure has a strong effect on the size of these neutral networks and thus on the nature of the evolutionary process.

The foldability landscape of model proteins.

Molecular evolution may be considered as a walk in a multidimensional fitness landscape, where the fitness at each point is associated with features such as the function, stability, and survivability of these molecules. We present a simple model for the evolution of protein sequences on a landscape with a precisely defined fitness function. We use simple lattice models to represent protein structures, with the ability of a protein sequence to fold into the structure with lowest energy, quantified as the foldability, representing the fitness of the sequence. The foldability of the sequence is characterized based on the spin glass model of protein folding. We consider evolution as a walk in this foldability landscape and study the nature of the landscape and the resulting dynamics. Selective pressure is explicitly included in this model in the form of a minimum foldability requirement. We find that different native structures are not evenly distributed in interaction space, with similar structures and structures with similar optimal foldabilities clustered together. Evolving proteins marginally fulfill the selective criteria of foldability. As the selective pressure is increased, evolutionary trajectories become increasingly confined to "neutral networks," where the sequence and the interactions can be significantly changed while a constant structure is maintained.

Predicting protein secondary structure with probabilistic schemata of evolutionarily derived information.

We demonstrate the applicability of our previously developed Bayesian probabilistic approach for predicting residue solvent accessibility to the problem of predicting secondary structure. Using only single-sequence data, this method achieves a three-state accuracy of 67% over a database of 473 non-homologous proteins. This approach is more amenable to inspection and less likely to overlearn specifics of a dataset than "black box" methods such as neural networks. It is also conceptually simpler and less computationally costly. We also introduce a novel method for representing and incorporating multiple-sequence alignment information within the prediction algorithm, achieving 72% accuracy over a dataset of 304 non-homologous proteins. This is accomplished by creating a statistical model of the evolutionarily derived correlations between patterns of amino acid substitution and local protein structure. This model consists of parameter vectors, termed "substitution schemata," which probabilistically encode the structure-based heterogeneity in the distributions of amino acid substitutions found in alignments of homologous proteins. The model is optimized for structure prediction by maximizing the mutual information between the set of schemata and the database of secondary structures. Unlike "expert heuristic" methods, this approach has been demonstrated to work well over large datasets. Unlike the opaque neural network algorithms, this approach is physicochemically intelligible. Moreover, the model optimization procedure, the formalism for predicting one-dimensional structural features and our previously developed method for tertiary structure recognition all share a common Bayesian probabilistic basis. This consistency starkly contrasts with the hybrid and ad hoc nature of methods that have dominated this field in recent years.