Shaping the dynamics of a bidirectional neural interface

Progress in decoding neural signals has enabled the development of interfaces that translate cortical brain activities into commands for operating robotic arms and other devices. The electrical stimulation of sensory areas provides a means to create artificial sensory information about the state of a device. Taken together, neural activity recording and microstimulation techniques allow us to embed a portion of the central nervous system within a closed-loop system, whose behavior emerges from the combined dynamical properties of its neural and artificial components. In this study we asked if it is possible to concurrently regulate this bidirectional brain-machine interaction so as to shape a desired dynamical behavior of the combined system. To this end, we followed a well-known biological pathway. In vertebrates, the communications between brain and limb mechanics are mediated by the spinal cord, which combines brain instructions with sensory information and organizes coordinated patterns of muscle forces driving the limbs along dynamically stable trajectories. We report the creation and testing of the first neural interface that emulates this sensory-motor interaction. The interface organizes a bidirectional communication between sensory and motor areas of the brain of anaesthetized rats and an external dynamical object with programmable properties. The system includes (a) a motor interface decoding signals from a motor cortical area, and (b) a sensory interface encoding the state of the external object into electrical stimuli to a somatosensory area. The interactions between brain activities and the state of the external object generate a family of trajectories converging upon a selected equilibrium point from arbitrary starting locations. Thus, the bidirectional interface establishes the possibility to specify not only a particular movement trajectory but an entire family of motions, which includes the prescribed reactions to unexpected perturbations.     

Redefining the head-trunk interface for the neural crest

The head-trunk interface lies at the occipito-cervical boundary, which corresponds to the somite 5/6 level. Previous studies have demonstrated that neural crest cells also behave differently either side of this boundary and that this may be due to intrinsic differences between cranial and trunk crest. However, it is also possible that some of the observed differences between cranial and trunk crest are assigned by environmental cues. We have therefore scrutinised the behaviour of the neural crest cells generated either side of the occipito-cervical boundary in chick and, interestingly, find that both behave in a truncal fashion by traversing the anterior half of their adjacent somites. Furthermore, although not previously described, we find that transient DRGs form opposite somites 4 and 5. Crest cells produced anterior of the somite 3/4 boundary avoid the somites and behave in a non-truncal fashion; these cells populate the pharyngeal arches, and thus contribute to the developing head. We have further shown, via somite transplantations, that differential behaviour of the posterior versus anterior occipital crest is assigned by the somites. If somites 1 to 3 are replaced by trunk somites, then the anterior occipital crest will behave in a truncal fashion by invading the somites. Correspondingly, if these anterior occipital somites are transplanted in place of trunk somites, they perturb the migration of trunk crest. Thus, for the neural crest, the head-trunk interface does not lie at the occipito-cervical boundary, but rather lies at the somite 3/4 level and is defined by the somites. The fact that this boundary lies at the somite 3/4 level in chick is significant as it reflects the more ancient posterior occipital boundary; in fish, only the first three somites contribute to the occipital bone.     

Neural interface with a silicon neural probe in the advancement of microtechnology

In this paper we describe the status of a silicon-based microelectrode for neural recording and an advanced neural interface. We have developed a silicon neural probe, using a combination of plasma and wet etching techniques. This process enables the probe thickness to be controlled precisely. To enhance the CMOS compatibility in the fabrication process, we investigated the feasibility of the site material of the doped polycrystalline silicon with small grains of around 50 nm in size. This silicon electrode demonstrated a favorable performance with respect to impedance spectra, surface topography and acute neural recording. These results showed that the silicon neural probe can be used as an advanced microelectrode for neurological applications.     

Protein design at the interface of the pre-biotic and biotic worlds

"Proteogenesis" (the origin of proteins) is a likely key event in the unsolved problem of biogenesis (the origin of life). The raw material for the very first proteins comprised the available amino acids produced and accumulated upon the early earth via abiotic chemical and physical processes. A broad consensus is emerging that this pre-biotic set likely comprised Ala, Asp, Glu, Gly, Ile, Leu, Pro, Ser, Thr, and Val. A key question in proteogenesis is whether such abiotically-produced amino acids comprise a "foldable" set. Current knowledge of protein folding identifies properties of complexity, secondary structure propensity, hydrophobic-hydrophilic patterning, core-packing potential, among others, as necessary elements of foldability. None of these requirements excludes the pre-biotic set of amino acids from being a foldable set. Moreover, nucleophile and metal ion/mineral binding capabilities also appear present in the pre-biotic set. Properties of the pre-biotic set, however, likely restrict foldability to the acidophilic/halophilic environment.     

A knowledge-based forcefield for protein-protein interface design

A distance-dependent knowledge-based potential for protein-protein interactions is derived and tested for application in protein design. Information on residue type specific C(alpha) and C(beta) pair distances is extracted from complex crystal structures in the Protein Data Bank and used in the form of radial distribution functions. The use of only backbone and C(beta) position information allows generation of relative protein-protein orientation poses with minimal sidechain information. Further coarse-graining can be done simply in the same theoretical framework to give potentials for residues of known type interacting with unknown type, as in a one-sided interface design problem. Both interface design via pose generation followed by sidechain repacking and localized protein-protein docking tests are performed on 39 nonredundant antibody-antigen complexes for which crystal structures are available. As reference, Lennard-Jones potentials, unspecific for residue type and biasing toward varying degrees of residue pair separation are used as controls. For interface design, the knowledge-based potentials give the best combination of consistently designable poses, low RMSD to the known structure, and more tightly bound interfaces with no added computational cost. 77% of the poses could be designed to give complexes with negative free energies of binding. Generally, larger interface separation promotes designability, but weakens the binding of the resulting designs. A localized docking test shows that the knowledge-based nature of the potentials improves performance and compares respectably with more sophisticated all-atoms potentials.     

神经环路设计的普遍原则

该文介绍一种可以不断升级神经环路的规模的设计原则。作者将单个视网膜神经节细胞的树突区域定义为类似于数码相机CCD上的一个“像素”。决定这个像素的大小（即树突区域所占的大小）存在两种相互竞争的因素：为得到最高的分辨率,像素应越小越好;另一方面,像素越大越利于平均化输入信号,以得到信噪比精确度最高的光强度值递呈给大脑。作者列举了三种可能的策略来阐释如何设计一个视网膜像素大小,并且为进化中实际选择的策略提供了证据。     

Application of competitive Hopfield neural network to brain-computer interface systems

We propose an unsupervised recognition system for single-trial classification of motor imagery (MI) electroencephalogram (EEG) data in this study. Competitive Hopfield neural network (CHNN) clustering is used for the discrimination of left and right MI EEG data posterior to selecting active segment and extracting fractal features in multi-scale. First, we use continuous wavelet transform (CWT) and Student's two-sample t-statistics to select the active segment in the time-frequency domain. The multiresolution fractal features are then extracted from wavelet data by means of modified fractal dimension. At last, CHNN clustering is adopted to recognize extracted features. Due to the characteristic of non-supervision, it is proper for CHNN to classify non-stationary EEG signals. The results indicate that CHNN achieves 81.9% in average classification accuracy in comparison with self-organizing map (SOM) and several popular supervised classifiers on six subjects from two data sets.     

Bridging the brain to the world: a perspective on neural interface systems

Neural interface (NI) systems hold the potential to return lost functions to persons with paralysis. Impressive progress has been made, including evaluation of neural control signals, sensor testing in humans, signal decoding advances, and proof-of-concept validation. Most importantly, the field has demonstrated that persons with paralysis can use prototype systems for spelling, "point and click," and robot control. Human and animal NI research is advancing knowledge about neural information processing and plasticity in healthy, diseased, and injured nervous systems. This emerging field promises a range of neurotechnologies able to return communication, independence, and control to people with movement limitations.     

A new model for simple neural nets and its application in the design of a neural oscillator

The results of a previous theoretical study of a class of systems are applied for the design of neural nets which try to simulate biological behavior. Besides the models for single aperiodic and periodic neurons, a “neural oscillator” is developed which consists of two cross-excited neurons. Its response is similar to the firing pattern of certain biological neural oscillators, like the flying system of the locust. Also, by proper change of its parameters, it can be made highly irregular, providing a deterministic model for the spontaneous neural activity.     

Using the antibody-antigen binding interface to train image-based deep neural networks for antibody-epitope classification

High-throughput B-cell sequencing has opened up new avenues for investigating complex mechanisms underlying our adaptive immune response. These technological advances drive data generation and the need to mine and analyze the information contained in these large datasets, in particular the identification of therapeutic antibodies (Abs) or those associated with disease exposure and protection. Here, we describe our efforts to use artificial intelligence (AI)-based image-analyses for prospective classification of Abs based solely on sequence information. We hypothesized that Abs recognizing the same part of an antigen share a limited set of features at the binding interface, and that the binding site regions of these Abs share share common structure and physicochemical property patterns that can serve as a “fingerprint” to recognize uncharacterized Abs. We combined large-scale sequence-based protein-structure predictions to generate ensembles of 3-D Ab models, reduced the Ab binding interface to a 2-D image (fingerprint), used pre-trained convolutional neural networks to extract features, and trained deep neural networks (DNNs) to classify Abs. We evaluated this approach using Ab sequences derived from human HIV and Ebola viral infections to differentiate between two Abs, Abs belonging to specific B-cell family lineages, and Abs with different epitope preferences. In addition, we explored a different type of DNN method to detect one class of Abs from a larger pool of Abs. Testing on Ab sets that had been kept aside during model training, we achieved average prediction accuracies ranging from 71–96% depending on the complexity of the classification task. The high level of accuracies reached during these classification tests suggests that the DNN models were able to learn a series of structural patterns shared by Abs belonging to the same class. The developed methodology provides a means to apply AI-based image recognition techniques to analyze high-throughput B-cell sequencing datasets (repertoires) for Ab classification.     

The Binding Database: data management and interface design

MOTIVATION: The large and growing body of experimental data on biomolecular binding is of enormous value in developing a deeper understanding of molecular biology, in developing new therapeutics, and in various molecular design applications. However, most of these data are found only in the published literature and are therefore difficult to access and use. No existing public database has focused on measured binding affinities and has provided query capabilities that include chemical structure and sequence homology searches. METHODS & RESULTS: We have created Binding DataBase (BindingDB), a public, web-accessible database of measured binding affinities. BindingDB is based upon a relational data specification for describing binding measurements via Isothermal Titration Calorimetry (ITC) and enzyme inhibition. A corresponding XML Document Type Definition (DTD) is used to create and parse intermediate files during the on-line deposition process and will also be used for data interchange, including collection of data from other sources. The on-line query interface, which is constructed with Java Servlet technology, supports standard SQL queries as well as searches for molecules by chemical structure and sequence homology. The on-line deposition interface uses Java Server Pages and JavaBean objects to generate dynamic HTML and to store intermediate results. The resulting data resource provides a range of functionality with brisk response-times, and lends itself well to continued development and enhancement.     

Nonlinear system modelling via optimal design of neural trees

This paper introduces a flexible neural tree model. The model is computed as a flexible multi-layer feed-forward neural network. A hybrid learning/evolutionary approach to automatically optimize the neural tree model is also proposed. The approach includes a modified probabilistic incremental program evolution algorithm (MPIPE) to evolve and determine a optimal structure of the neural tree and a parameter learning algorithm to optimize the free parameters embedded in the neural tree. The performance and effectiveness of the proposed method are evaluated using function approximation, time series prediction and system identification problems and compared with the related methods.     

The design of Jemboss: a graphical user interface to EMBOSS

DESIGN: Jemboss is a graphical user interface (GUI) for the European Molecular Biology Open Software Suite (EMBOSS). It is being developed at the MRC UK HGMP-RC as part of the EMBOSS project. This paper explains the technical aspects of the Jemboss client-server design. The client-server model optionally allows that a Jemboss user have an account on the remote server. The Jemboss client is written in Java and is downloaded automatically to a user's workstation via Java Web Start using the HTML protocol. The client then communicates with the remote server using SOAP (Simple Object Access Protocol). A Tomcat server listens on the remote machine and communicates the SOAP requests to a Jemboss server, again written in Java. This Java server interprets the client requests and executes them through Java Native Interface (JNI) code written in the C language. Another C program having setuid privilege, jembossctl, is called by the JNI code to perform the client requests under the user's account on the server. The commands include execution of EMBOSS applications, file management and project management tasks. Jemboss allows the use of JSSE for encryption of communication between the client and server. The GUI parses the EMBOSS Ajax Command Definition language for form generation and maximum input flexibility. Jemboss interacts directly with the EMBOSS libraries to allow dynamic generation of application default settings. RESULTS: This interface is part of the EMBOSS distribution and has attracted much interest. It has been set up at many other sites globally as well as being used at the HGMP-RC for registered users. AVAILABILITY: The software, EMBOSS and Jemboss, is freely available to academics and commercial users under the GPL licence. It can be downloaded from the EMBOSS ftp server: http://www.uk.embnet.org/Software/EMBOSS/, ftp://ftp.uk.embnet.org/pub/EMBOSS/. Registered HGMP-RC users can access an installed server from: http://www.uk.embnet.org/Software/EMBOSS/Jemboss/     

Rationalization and design of the complementarity determining region sequences in an antibody-antigen recognition interface

Protein-protein interactions are critical determinants in biological systems. Engineered proteins binding to specific areas on protein surfaces could lead to therapeutics or diagnostics for treating diseases in humans. But designing epitope-specific protein-protein interactions with computational atomistic interaction free energy remains a difficult challenge. Here we show that, with the antibody-VEGF (vascular endothelial growth factor) interaction as a model system, the experimentally observed amino acid preferences in the antibody-antigen interface can be rationalized with 3-dimensional distributions of interacting atoms derived from the database of protein structures. Machine learning models established on the rationalization can be generalized to design amino acid preferences in antibody-antigen interfaces, for which the experimental validations are tractable with current high throughput synthetic antibody display technologies. Leave-one-out cross validation on the benchmark system yielded the accuracy, precision, recall (sensitivity) and specificity of the overall binary predictions to be 0.69, 0.45, 0.63, and 0.71 respectively, and the overall Matthews correlation coefficient of the 20 amino acid types in the 24 interface CDR positions was 0.312. The structure-based computational antibody design methodology was further tested with other antibodies binding to VEGF. The results indicate that the methodology could provide alternatives to the current antibody technologies based on animal immune systems in engineering therapeutic and diagnostic antibodies against predetermined antigen epitopes.     

Combining different design strategies for rational affinity maturation of the MICA-NKG2D interface

We redesigned residues on the surface of MICA, a protein that binds the homodimeric immunoreceptor NKG2D, to increase binding affinity with a series of rational, incremental changes. A fixed-backbone RosettaDesign protocol scored a set of initial mutations, which we tested by surface plasmon resonance for thermodynamics and kinetics of NKG2D binding, both singly and in combination. We combined the best four mutations at the surface with three affinity-enhancing mutations below the binding interface found with a previous design strategy. After curating design scores with three cross-validated tests, we found a linear relationship between free energy of binding and design score, and to a lesser extent, enthalpy and design score. Multiple mutants bound with substantial subadditivity, but in at least one case full additivity was observed when combining distant mutations. Altogether, combining the best mutations from the two strategies into a septuple mutant enhanced affinity by 50-fold, to 50 nM, demonstrating a simple, effective protocol for affinity enhancement.     

Improved modeling of side-chain--base interactions and plasticity in protein--DNA interface design

Combinatorial sequence optimization for protein design requires libraries of discrete side-chain conformations. The discreteness of these libraries is problematic, particularly for long, polar side chains, since favorable interactions can be missed. Previously, an approach to loop remodeling where protein backbone movement is directed by side-chain rotamers predicted to form interactions previously observed in native complexes (termed "motifs") was described. Here, we show how such motif libraries can be incorporated into combinatorial sequence optimization protocols and improve native complex recapitulation. Guided by the motif rotamer searches, we made improvements to the underlying energy function, increasing recapitulation of native interactions. To further test the methods, we carried out a comprehensive experimental scan of amino acid preferences in the I-AniI protein-DNA interface and found that many positions tolerated multiple amino acids. This sequence plasticity is not observed in the computational results because of the fixed-backbone approximation of the model. We improved modeling of this diversity by introducing DNA flexibility and reducing the convergence of the simulated annealing algorithm that drives the design process. In addition to serving as a benchmark, this extensive experimental data set provides insight into the types of interactions essential to maintain the function of this potential gene therapy reagent.