Structure-based conformational preferences of amino acids

Proteins can be very tolerant to amino acid substitution, even within their core. Understanding the factors responsible for this behavior is of critical importance for protein engineering and design. Mutations in proteins have been quantified in terms of the changes in stability they induce. For example, guest residues in specific secondary structures have been used as probes of conformational preferences of amino acids, yielding propensity scales. Predicting these amino acid propensities would be a good test of any new potential energy functions used to mimic protein stability. We have recently developed a protein design procedure that optimizes whole sequences for a given target conformation based on the knowledge of the template backbone and on a semiempirical potential energy function. This energy function is purely physical, including steric interactions based on a Lennard-Jones potential, electrostatics based on a Coulomb potential, and hydrophobicity in the form of an environment free energy based on accessible surface area and interatomic contact areas. Sequences designed by this procedure for 10 different proteins were analyzed to extract conformational preferences for amino acids. The resulting structure-based propensity scales show significant agreements with experimental propensity scale values, both for α-helices and β-sheets. These results indicate that amino acid conformational preferences are a natural consequence of the potential energy we use. This confirms the accuracy of our potential and indicates that such preferences should not be added as a design criterion.

Invariance between subjects of brain wave representations of language

In three experiments, electric brain waves of 19 subjects were recorded under several different experimental conditions for two purposes. One was to test how well we could recognize which sentence, from a set of 24 or 48 sentences, was being processed in the cortex. The other was to study the invariance of brain waves between subjects. As in our earlier work, the analysis consisted of averaging over trials to create prototypes and test samples, to both of which Fourier transforms were applied, followed by filtering and an inverse transformation to the time domain. A least-squares criterion of fit between prototypes and test samples was used for classification. In all three experiments, averaging over subjects improved the recognition rates. The most significant finding was the following. When brain waves were averaged separately for two nonoverlapping groups of subjects, one for prototypes and the other for test samples, we were able to recognize correctly 90% of the brain waves generated by 48 different sentences about European geography.

Invariance of brain-wave representations of simple visual images and their names

In two experiments, electric brain waves of 14 subjects were recorded under several different conditions to study the invariance of brain-wave representations of simple patches of colors and simple visual shapes and their names, the words blue, circle, etc. As in our earlier work, the analysis consisted of averaging over trials to create prototypes and test samples, to both of which Fourier transforms were applied, followed by filtering and an inverse transformation to the time domain. A least-squares criterion of fit between prototypes and test samples was used for classification. The most significant results were these. By averaging over different subjects, as well as trials, we created prototypes from brain waves evoked by simple visual images and test samples from brain waves evoked by auditory or visual words naming the visual images. We correctly recognized from 60% to 75% of the test-sample brain waves. The general conclusion is that simple shapes such as circles and single-color displays generate brain waves surprisingly similar to those generated by their verbal names. These results, taken together with extensive psychological studies of auditory and visual memory, strongly support the solution proposed for visual shapes, by Bishop Berkeley and David Hume in the 18th century, to the long-standing problem of how the mind represents simple abstract ideas.

Brain wave recognition of words

Electrical and magnetic brain waves of seven subjects under three experimental conditions were recorded for the purpose of recognizing which one of seven words was processed. The analysis consisted of averaging over trials to create prototypes and test samples, to both of which Fourier transforms were applied, followed by filtering and an inverse transformation to the time domain. The filters used were optimal predictive filters, selected for each subject and condition. Recognition rates, based on a least-squares criterion, varied widely, but all but one of 24 were significantly different from chance. The two best were above 90%. These results show that brain waves carry substantial information about the word being processed under experimental conditions of conscious awareness.

Brain-wave representation of words by superposition of a few sine waves

Data from three previous experiments were analyzed to test the hypothesis that brain waves of spoken or written words can be represented by the superposition of a few sine waves. First, we averaged the data over trials and a set of subjects, and, in one case, over experimental conditions as well. Next we applied a Fourier transform to the averaged data and selected those frequencies with high energy, in no case more than nine in number. The superpositions of these selected sine waves were taken as prototypes. The averaged unfiltered data were the test samples. The prototypes were used to classify the test samples according to a least-squares criterion of fit. The results were seven of seven correct classifications for the first experiment using only three frequencies, six of eight for the second experiment using nine frequencies, and eight of eight for the third experiment using five frequencies.

Brain-wave recognition of sentences

Electrical and magnetic brain waves of two subjects were recorded for the purpose of recognizing which one of 12 sentences or seven words auditorily presented was processed. The analysis consisted of averaging over trials to create prototypes and test samples, to each of which a Fourier transform was applied, followed by filtering and an inverse transformation to the time domain. The filters used were optimal predictive filters, selected for each subject. A still further improvement was obtained by taking differences between recordings of two electrodes to obtain bipolar pairs that then were used for the same analysis. Recognition rates, based on a least-squares criterion, varied, but the best were above 90%. The first words of prototypes of sentences also were cut and pasted to test, at least partially, the invariance of a word’s brain wave in different sentence contexts. The best result was above 80% correct recognition. Test samples made up only of individual trials also were analyzed. The best result was 134 correct of 288 (47%), which is promising, given that the expected recognition number by chance is just 24 (or 8.3%). The work reported in this paper extends our earlier work on brain-wave recognition of words only. The recognition rates reported here further strengthen the case that recordings of electric brain waves of words or sentences, together with extensive mathematical and statistical analysis, can be the basis of new developments in our understanding of brain processing of language.

Identification of protein oligomerization states by analysis of interface conservation

The discrimination of true oligomeric protein–protein contacts from nonspecific crystal contacts remains problematic. Criteria that have been used previously base the assignment of oligomeric state on consideration of the area of the interface and/or the results of scoring functions based on statistical potentials. Both techniques have a high success rate but fail in more than 10% of cases. More importantly, the oligomeric states of several proteins are incorrectly assigned by both methods. Here we test the hypothesis that true oligomeric contacts should be identifiable on the basis of an increased degree of conservation of the residues involved in the interface. By quantifying the degree of conservation of the interface and comparing it with that of the remainder of the protein surface, we develop a new criterion that provides a highly effective complement to existing methods.

Active barnase variants with completely random hydrophobic cores.

The central structural feature of natural proteins is a tightly packed and highly ordered hydrophobic core. If some measure of exquisite, native-like core packing is necessary for enzymatic function, this would constitute a significant obstacle to the development of novel enzymes, either by design or by natural or experimental evolution. To test the minimum requirements for a core to provide sufficient structural integrity for enzymatic activity, we have produced mutants of the ribonuclease barnase in which 12 of the 13 core residues have together been randomly replaced by hydrophobic alternatives. Using a sensitive biological screen, we find that a strikingly high proportion of these mutants (23%) retain enzymatic activity in vivo. Further substitution at the 13th core position shows that a similar proportion of completely random hydrophobic cores supports enzyme function. Of the active mutants produced, several have no wild-type core residues. These results imply that hydrophobicity is nearly a sufficient criterion for the construction of a functional core and, in conjunction with previous studies, that refinement of a crudely functional core entails more stringent sequence constraints than does the initial attainment of crude core function. Since attainment of crude function is the critical initial step in evolutionary innovation, the relatively scant requirements contributed by the hydrophobic core would greatly reduce the initial hurdle on the evolutionary pathway to novel enzymes. Similarly, experimental development of novel functional proteins might be simplified by limiting core design to mere specification of hydrophobicity and using iterative mutation-selection to optimize core structure.

Functional anatomy of human eyeblink conditioning determined with regional cerebral glucose metabolism and positron-emission tomography.

Relative cerebral glucose metabolism was examined with positron-emission tomography (PET) as a measure of neuronal activation during performance of the classically conditioned eyeblink response in 12 young adult subjects. Each subject received three sessions: (i) a control session with PET scan in which unpaired presentations of the tone conditioned stimulus and corneal airpuff unconditioned stimulus were administered, (ii) a paired training session to allow associative learning to occur, and (iii) a paired test session with PET scan. Brain regions exhibiting learning-related activation were identified as those areas that showed significant differences in glucose metabolism between the unpaired control condition and well-trained state in the 9 subjects who met the learning criterion. Areas showing significant activation included bilateral sites in the inferior cerebellar cortex/deep nuclei, anterior cerebellar vermis, contralateral cerebellar cortex and pontine tegmentum, ipsilateral inferior thalamus/red nucleus, ipsilateral hippocampal formation, ipsilateral lateral temporal cortex, and bilateral ventral striatum. Among all subjects, including those who did not meet the learning criterion, metabolic changes in ipsilateral cerebellar nuclei, bilateral cerebellar cortex, anterior vermis, contralateral pontine tegmentum, ipsilateral hippocampal formation, and bilateral striatum correlated with degree of learning. The localization to cerebellum and its associated brainstem circuitry is consistent with neurobiological studies in the rabbit model of eyeblink classical conditioning and neuropsychological studies in brain-damaged humans. In addition, these data support a role for the hippocampus in conditioning and suggest that the ventral striatum may also be involved.

Image processing via level set curvature flow.

We present a controlled image smoothing and enhancement method based on a curvature flow interpretation of the geometric heat equation. Compared to existing techniques, the model has several distinct advantages. (i) It contains just one enhancement parameter. (ii) The scheme naturally inherits a stopping criterion from the image; continued application of the scheme produces no further change. (iii) The method is one of the fastest possible schemes based on a curvature-controlled approach.