Pmf-cpi: assessing drug selectivity with a pretrained multi-functional model for compound-protein interactions.

Compound-protein interactions (CPI) play significant roles in drug development. To avoid side effects, it is also crucial to evaluate drug selectivity when binding to different targets. However, most selectivity prediction models are constructed for specific targets with limited data. In this study, we present a pretrained multi-functional model for compound-protein interaction prediction (PMF-CPI) and fine-tune it to assess drug selectivity. This model uses recurrent neural networks to process the protein embedding based on the pretrained language model TAPE, extracts molecular information from a graph encoder, and produces the output from dense layers. PMF-CPI obtained the best performance compared to outstanding approaches on both the binding affinity regression and CPI classification tasks. Meanwhile, we apply the model to analyzing drug selectivity after fine-tuning it on three datasets related to specific targets, including human cytochrome P450s. The study shows that PMF-CPI can accurately predict different drug affinities or opposite interactions toward similar targets, recognizing selective drugs for precise therapeutics.Kindly confirm if corresponding authors affiliations are identified correctly and amend if any.Yes, it is correct.

Dynamic evolution of brain structural patterns in liver transplantation recipients: a longitudinal study based on 3D convolutional neuronal network model.

OBJECTIVES: To evaluate the dynamic evolution process of overall brain health in liver transplantation (LT) recipients, we employed a deep learning-based neuroanatomic biomarker to measure longitudinal changes of brain structural patterns before and 1, 3, and 6 months after surgery. METHODS: Because of the ability to capture patterns across all voxels from a brain scan, the brain age prediction method was adopted. We constructed a 3D-CNN model through T1-weighted MRI of 3609 healthy individuals from 8 public datasets and further applied it to a local dataset of 60 LT recipients and 134 controls. The predicted age difference (PAD) was calculated to estimate brain changes before and after LT, and the network occlusion sensitivity analysis was used to determine the importance of each network in age prediction. RESULTS: The PAD of patients with cirrhosis increased markedly at baseline (+ 5.74 years) and continued to increase within one month after LT (+ 9.18 years). After that, the brain age began to decrease gradually, but it was still higher than the chronological age. The PAD values of the OHE subgroup were higher than those of the no-OHE, and the discrepancy was more obvious at 1-month post-LT. High-level cognition-related networks were more important in predicting the brain age of patients with cirrhosis at baseline, while the importance of primary sensory networks increased temporarily within 6-month post-LT. CONCLUSIONS: The brain structural patterns of LT recipients showed inverted U-shaped dynamic change in the early stage after transplantation, and the change in primary sensory networks may be the main contributor. KEY POINTS: • The recipients' brain structural pattern showed an inverted U-shaped dynamic change after LT. • The patients' brain aging aggravated within 1 month after surgery, and the subset of patients with a history of OHE was particularly affected. • The change of primary sensory networks is the main contributor to the change in brain structural patterns.

Integrative interaction of emotional speech in audio-visual modality.

Emotional clues are always expressed in many ways in our daily life, and the emotional information we receive is often represented by multiple modalities. Successful social interactions require a combination of multisensory cues to accurately determine the emotion of others. The integration mechanism of multimodal emotional information has been widely investigated. Different brain activity measurement methods were used to determine the location of brain regions involved in the audio-visual integration of emotional information, mainly in the bilateral superior temporal regions. However, the methods adopted in these studies are relatively simple, and the materials of the study rarely contain speech information. The integration mechanism of emotional speech in the human brain still needs further examinations. In this paper, a functional magnetic resonance imaging (fMRI) study was conducted using event-related design to explore the audio-visual integration mechanism of emotional speech in the human brain by using dynamic facial expressions and emotional speech to express emotions of different valences. Representational similarity analysis (RSA) based on regions of interest (ROIs), whole brain searchlight analysis, modality conjunction analysis and supra-additive analysis were used to analyze and verify the role of relevant brain regions. Meanwhile, a weighted RSA method was used to evaluate the contributions of each candidate model in the best fitted model of ROIs. The results showed that only the left insula was detected by all methods, suggesting that the left insula played an important role in the audio-visual integration of emotional speech. Whole brain searchlight analysis, modality conjunction analysis and supra-additive analysis together revealed that the bilateral middle temporal gyrus (MTG), right inferior parietal lobule and bilateral precuneus might be involved in the audio-visual integration of emotional speech from other aspects.

Microbe-bridged disease-metabolite associations identification by heterogeneous graph fusion.

MOTIVATION: Metabolomics has developed rapidly in recent years, and metabolism-related databases are also gradually constructed. Nowadays, more and more studies are being carried out on diverse microbes, metabolites and diseases. However, the logics of various associations among microbes, metabolites and diseases are limited understanding in the biomedicine of gut microbial system. The collection and analysis of relevant microbial bioinformation play an important role in the revelation of microbe-metabolite-disease associations. Therefore, the dataset that integrates multiple relationships and the method based on complex heterogeneous graphs need to be developed. RESULTS: In this study, we integrated some databases and extracted a variety of associations data among microbes, metabolites and diseases. After obtaining the three interconnected bilateral association data (microbe-metabolite, metabolite-disease and disease-microbe), we considered building a heterogeneous graph to describe the association data. In our model, microbes were used as a bridge between diseases and metabolites. In order to fuse the information of disease-microbe-metabolite graph, we used the bipartite graph attention network on the disease-microbe and metabolite-microbe bipartite graph. The experimental results show that our model has good performance in the prediction of various disease-metabolite associations. Through the case study of type 2 diabetes mellitus, Parkinson's disease, inflammatory bowel disease and liver cirrhosis, it is noted that our proposed methodology are valuable for the mining of other associations and the prediction of biomarkers for different human diseases.Availability and implementation: https://github.com/Selenefreeze/DiMiMe.git.

Functional brain abnormalities in major depressive disorder using a multiscale community detection approach.

Major depressive disorder (MDD) is a serious disease associated with abnormal brain regions, however, the interconnection between specific brain regions related to depression has not been fully explored. To solve this problem, the paper proposes a novel multiscale community detection method to compare the differences in brain regions between normal controls (NC) and MDD patients. This study adopted the Brainnetome Atlas to divide the brain into 246 regions and extract the time series of each region. The Pearson correlation was used to measure the similarity among different brain regions to conduct the brain functional network and to perform multiscale community detection. The optimal brain community structure of each group was further explored based on the modularized Qcut algorithm, normalized mutual information (NMI), and variation of information (VI). The Jaccard index was then applied to compare the abnormalities of each brain region from different community environments between the brain function networks of NC and MDD patients. The experiments revealed several abnormal brain regions between NC and MDD, including the superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, orbital gyrus, superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, posterior superior temporal sulcus, inferior parietal gyrus, precuneus, postcentral gyrus, insular gyrus, cingulate gyrus, hippocampus and basal ganglia. Finally, a new subnetwork related to cognitive function was discovered, which was composed of the island gyrus and inferior frontal gyrus. All experiments indicated that the proposed method is useful in detecting functional brain abnormalities in MDD, and it can provide valuable insights into the diagnosis and treatment of MDD.

Altered white matter structural connectivity in primary Sjögren's syndrome: a link-based analysis.

PURPOSE: Cognitive impairment has been revealed in primary Sjögren's syndrome (pSS). However, the underlying white matter structural connectivity (SC) changes have not been studied. This study aimed to investigate the altered white matter brain network in patients with pSS using diffusion tensor imaging (DTI). METHODS: Forty-one pSS patients and sixty matched healthy controls (HCs) underwent neuropsychological tests and the subsequent MRI examinations. The clinical data were gathered from the medical record. The structural brain network was established using DTI, and a link-based comparison was performed between patients with pSS and HCs (false discovery rate correction, P < 0.05). Furthermore, the mean fractional anisotropy (FA) of the altered SCs was correlated with the neuropsychological tests and clinical data in patients with pSS (Bonferroni correction, P < 0.05). RESULTS: Compared with HCs, patients with pSS mainly exhibited decreased SC in the frontal and parietal lobes and some parts of the temporal and occipital lobes. In addition, increased SC was found between the right caudate nucleus and right median cingulate/paracingulate gyri. Specifically, the reduced SC between the left middle temporal gyrus and left middle occipital gyrus was negatively correlated with white matter high signal intensity (WMH). CONCLUSIONS: Patients with pSS showed diffusely decreased SC mainly in the frontoparietal network and exhibited a negative correlation between the reduced SC and WMH. SC represents a potential biomarker for preclinical brain impairment in patients with pSS.

Structure-activity relationship studies on O-alkylamino-tethered salicylamide derivatives with various amino acid linkers as potent anticancer agents.

In our continued SAR study efforts, a series of O-alkylamino-tethered salicylamide derivatives with various amino acid linkers has been designed, synthesized, and biologically evaluated as potent anticancer agents. Five selected compounds with different representative chemical structures were found to show broad anti-proliferative activities, effective against all tested ER-positive breast cancer (BC) and triple-negative breast cancer (TNBC) cell lines with low micromolar IC50 values. Among these compounds, compound 9a (JMX0293) maintained good potency against MDA-MB-231 cell line (IC50 = 3.38 ± 0.37 µM) while exhibiting very low toxicity against human non-tumorigenic breast epithelial cell line MCF-10A (IC50 > 60 µM). Further mechanistic studies showed that compound 9a could inhibit STAT3 phosphorylation and contribute to apoptosis in TNBC MDA-MB-231 cells. More importantly, compound 9a significantly suppressed MDA-MB-231 xenograft tumor growth in vivo without significant toxicity, indicating its great potential as a promising anticancer drug candidate for further clinical development.

Uncovering the genetic profiles underlying the intrinsic organization of the human cerebellum.

The functional diversity of the human cerebellum is largely believed to be derived more from its extensive connections rather than being limited to its mostly invariant architecture. However, whether and how the determination of cerebellar connections in its intrinsic organization interact with microscale gene expression is still unknown. Here we decode the genetic profiles of the cerebellar functional organization by investigating the genetic substrates simultaneously linking cerebellar functional heterogeneity and its drivers, i.e., the connections. We not only identified 443 network-specific genes but also discovered that their co-expression pattern correlated strongly with intra-cerebellar functional connectivity (FC). Ninety of these genes were also linked to the FC of cortico-cerebellar cognitive-limbic networks. To further discover the biological functions of these genes, we performed a "virtual gene knock-out" by observing the change in the coupling between gene co-expression and FC and divided the genes into two subsets, i.e., a positive gene contribution indicator (GCI+) involved in cerebellar neurodevelopment and a negative gene set (GCI-) related to neurotransmission. A more interesting finding is that GCI- is significantly linked with the cerebellar connectivity-behavior association and many recognized brain diseases that are closely linked with the cerebellar functional abnormalities. Our results could collectively help to rethink the genetic substrates underlying the cerebellar functional organization and offer possible micro-macro interacted mechanistic interpretations of the cerebellum-involved high order functions and dysfunctions in neuropsychiatric disorders.

Morphometry Difference of the Hippocampal Formation Between Blind and Sighted Individuals.

The detailed morphometry alterations of the human hippocampal formation (HF) for blind individuals are still understudied. 50 subjects were recruited from Yantai Affiliated Hospital of Binzhou Medical University, including 16 congenital blindness, 14 late blindness, and 20 sighted controls. Volume and shape analysis were conducted between the blind (congenital or late) and sighted groups to observe the (sub)regional alterations of the HF. No significant difference of the hippocampal volume was observed between the blind and sighted subjects. Rightward asymmetry of the hippocampal volume was found for both congenital and late blind individuals, while no significant hemispheric difference was observed for the sighted controls. Shape analysis showed that the superior and inferior parts of both the hippocampal head and tail expanded, while the medial and lateral parts constrained for the blind individuals as compared to the sighted controls. The morphometry alterations for the congenital blind and late blind individuals are nearly the same. Significant expansion of the superior part of the hippocampal tail for both congenital and late blind groups were observed for the left hippocampi after FDR correction. Current results suggest that the cross-model plastic may occur in both hemispheres of the HF to improve the navigation ability without the stimuli of visual cues, and the alteration is more prominent for the left hemisphere.

Association of Tau Pathology With Clinical Symptoms in the Subfields of Hippocampal Formation.

Objective: To delineate the relationship between clinical symptoms and tauopathy of the hippocampal subfields under different amyloid statuses. Methods: One hundred and forty-three subjects were obtained from the ADNI project, including 87 individuals with normal cognition, 46 with mild cognitive impairment, and 10 with Alzheimer's disease (AD). All subjects underwent the tau PET, amyloid PET, T1W, and high-resolution T2W scans. Clinical symptoms were assessed by the Neuropsychiatric Inventory (NPI) total score and Alzheimer's Disease Assessment Scale cognition 13 (ADAS-cog-13) total score, comprising memory and executive function scores. The hippocampal subfields including Cornu Ammonis (CA1-3), subiculum (Sub), and dentate gyrus (DG), as well as the adjacent para-hippocampus (PHC) and entorhinal cortex (ERC), were segmented automatically using the Automatic Segmentation of Hippocampal Subfields (ASHS) software. The relationship between tauopathy/volume of the hippocampal subfields and assessment scores was calculated using partial correlation analysis under different amyloid status, by controlling age, gender, education, apolipoprotein E (APOE) allele ɛ4 carrier status, and, time interval between the acquisition time of tau PET and amyloid PET scans. Results: Compared with amyloid negative (A-) group, individuals from amyloid positive (A+) group are more impaired based on the Mini-mental State Examination (MMSE; p = 3.82e-05), memory (p = 6.30e-04), executive function (p = 0.0016), and ADAS-cog-13 scores (p = 5.11e-04). Significant decrease of volume (CA1, DG, and Sub) and increase of tau deposition (CA1, Sub, ERC, and PHC) of the hippocampal subfields of both hemispheres were observed for the A+ group compared to the A- group. Tauopathy of ERC is significantly associated with memory score for the A- group, and the associated regions spread into Sub and PHC for the A+ group. The relationship between the impairment of behavior or executive function and tauopathy of the hippocampal subfield was discovered within the A+ group. Leftward asymmetry was observed with the association between assessment scores and tauopathy of the hippocampal subfield, which is more prominent for the NPI score for the A+ group. Conclusion: The associations of tauopathy/volume of the hippocampal subfields with clinical symptoms provide additional insight into the understanding of local changes of the human HF during the AD continuum and can be used as a reference for future studies.

Passive static stretching alters the characteristics of the force-velocity curvature differently for fast and slow muscle groups-A practical application of Hill's equation.

Studies showed fast muscle fibers have a greater constant b value of Hill's equation than that of slow muscle fibers, and the changing ratio of b/Vmax indicates the altered characteristics of muscles under certain conditions such as static stretching. This study was to investigate the effect of acute passive static stretching on the curvature of force-velocity curve in people with different muscle fiber types. A two-step work was conducted in current study through using Hill's equation: 1) calculated b values for each subject at different conditions (non-stretched and stretched) to determine muscle groups, and 2) examined the effect of static stretching on different muscle groups. Sixty-five college students performed isokinetic leg extensions at 5 speeds to test peak torque, following either a non-stretching or two passive static quadriceps stretching exercises. The peak torque and corresponding velocity were used to calculate the b constant. Data reduction consisted of calculating a Z score for each non-stretched and stretched b values. Individuals, whose non-stretched b constant was above or below one standard deviation of the Z score, were designated as the less curved (fast) and more curved (slow) groups, respectively. A paired t-test was used to analyze the pre and post intervention effect on b values for each group (p < 0.05). This study found passive static stretching significantly altered the b constant of the fast group, but no effect on slow group. Therefore, we suggest static stretching should be avoided immediately before fast or explosive activities in individuals using predominantly fast muscle fibers.

Neuromarkers from Whole-Brain Functional Connectivity Reveal the Cognitive Recovery Scheme for Overt Hepatic Encephalopathy after Liver Transplantation.

Neurocognitive impairment is present in cirrhosis and may be more severe in cirrhosis with overt hepatic encephalopathy (OHE). Liver transplantation (LT) can restore liver function, but how it reverses the impaired brain function is still unclear. MRI of resting-state functional connectivity can help reveal the underlying mechanisms that lead to these cognitive deficits and cognitive recovery. In this study, 64 patients with cirrhosis (28 with OHE; 36 without OHE) and 32 healthy control subjects were recruited for resting-state fMRI. The patients were scanned before and after LT. We evaluated presurgical and postsurgical neurocognitive performance in cirrhosis patients using psychomotor tests. Network-based statistics found significant disrupted connectivity in both groups of cirrhotic patients, with OHE and without OHE, compared with control subjects. However, the presurgical connectivity disruption in patients with OHE affected a greater number of connections than those without OHE. The decrease in functional connectivity for both OHE and non-OHE patient groups was reversed after LT to the level of control subjects. An additional hyperconnected network (i.e., higher connected than control subjects) was observed in OHE patients after LT. Regarding the neural-behavior relationship, the functional network that predicted cognitive performance in healthy individuals showed no correlation in presurgical cirrhotic patients. The impaired neural-behavior relationship was re-established after LT for non-OHE patients, but not for OHE patients. OHE patients displayed abnormal hyperconnectivity and a persistently impaired neural-behavior relationship after LT. Our results suggest that patients with OHE may undergo a different trajectory of postsurgical neurofunctional recovery compared with those without, which needs further clarification in future studies.

Weighted RSA: An Improved Framework on the Perception of Audio-visual Affective Speech in Left Insula and Superior Temporal Gyrus.

Being able to accurately perceive the emotion expressed by the facial or verbal expression from others is critical to successful social interaction. However, only few studies examined the multimodal interactions on speech emotion, and there is no consistence in studies on the speech emotion perception. It remains unclear, how the speech emotion of different valence is perceived on the multimodal stimuli by our human brain. In this paper, we conducted a functional magnetic resonance imaging (fMRI) study with an event-related design, using dynamic facial expressions and emotional speech stimuli to express different emotions, in order to explore the perception mechanism of speech emotion in audio-visual modality. The representational similarity analysis (RSA), whole-brain searchlight analysis, and conjunction analysis of emotion were used to interpret the representation of speech emotion in different aspects. Significantly, a weighted RSA approach was creatively proposed to evaluate the contribution of each candidate model to the best fitted model and provided a supplement to RSA. The results of weighted RSA indicated that the fitted models were superior to all candidate models and the weights could be used to explain the representation of ROIs. The bilateral amygdala has been shown to be associated with the processing of both positive and negative emotions except neutral emotion. It is indicated that the left posterior insula and the left anterior superior temporal gyrus (STG) play important roles in the perception of multimodal speech emotion.

Efficient learning with augmented spikes: A case study with image classification.

Efficient learning of spikes plays a valuable role in training spiking neural networks (SNNs) to have desired responses to input stimuli. However, current learning rules are limited to a binary form of spikes. The seemingly ubiquitous phenomenon of burst in nervous systems suggests a new way to carry more information with spike bursts in addition to times. Based on this, we introduce an advanced form, the augmented spikes, where spike coefficients are used to carry additional information. How could neurons learn and benefit from augmented spikes remains unclear. In this paper, we propose two new efficient learning rules to process spatiotemporal patterns composed of augmented spikes. Moreover, we examine the learning abilities of our methods with a synthetic recognition task of augmented spike patterns and two practical ones for image classification. Experimental results demonstrate that our rules are capable of extracting information carried by both the timing and coefficient of spikes. Our proposed approaches achieve remarkable performance and good robustness under various noise conditions, as compared to benchmarks. The improved performance indicates the merits of augmented spikes and our learning rules, which could be beneficial and generalized to a broad range of spike-based platforms.

Multi-Scale Time-Series Kernel-Based Learning Method for Brain Disease Diagnosis.

The functional magnetic resonance imaging (fMRI) is a noninvasive technique for studying brain activity, such as brain network analysis, neural disease automated diagnosis and so on. However, many existing methods have some drawbacks, such as limitations of graph theory, lack of global topology characteristic, local sensitivity of functional connectivity, and absence of temporal or context information. In addition to many numerical features, fMRI time series data also cover specific contextual knowledge and global fluctuation information. Here, we propose multi-scale time-series kernel-based learning model for brain disease diagnosis, based on Jensen-Shannon divergence. First, we calculate correlation value within and between brain regions over time. In addition, we extract multi-scale synergy expression probability distribution (interactional relation) between brain regions. Also, we produce state transition probability distribution (sequential relation) on single brain regions. Then, we build time-series kernel-based learning model based on Jensen-Shannon divergence to measure similarity of brain functional connectivity. Finally, we provide an efficient system to deal with brain network analysis and neural disease automated diagnosis. On Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, our proposed method achieves accuracy of 0.8994 and AUC of 0.8623. On Major Depressive Disorder (MDD) dataset, our proposed method achieves accuracy of 0.9166 and AUC of 0.9263. Experiments show that our proposed method outperforms other existing excellent neural disease automated diagnosis approaches. It shows that our novel prediction method performs great accurate for identification of brain diseases as well as existing outstanding prediction tools.

KNIndex: a comprehensive database of physicochemical properties for k-tuple nucleotides.

With the development of high-throughput sequencing technology, the genomic sequences increased exponentially over the last decade. In order to decode these new genomic data, machine learning methods were introduced for genome annotation and analysis. Due to the requirement of most machines learning methods, the biological sequences must be represented as fixed-length digital vectors. In this representation procedure, the physicochemical properties of k-tuple nucleotides are important information. However, the values of the physicochemical properties of k-tuple nucleotides are scattered in different resources. To facilitate the studies on genomic sequences, we developed the first comprehensive database, namely KNIndex (https://knindex.pufengdu.org), for depositing and visualizing physicochemical properties of k-tuple nucleotides. Currently, the KNIndex database contains 182 properties including one for mononucleotide (DNA), 169 for dinucleotide (147 for DNA and 22 for RNA) and 12 for trinucleotide (DNA). KNIndex database also provides a user-friendly web-based interface for the users to browse, query, visualize and download the physicochemical properties of k-tuple nucleotides. With the built-in conversion and visualization functions, users are allowed to display DNA/RNA sequences as curves of multiple physicochemical properties. We wish that the KNIndex will facilitate the related studies in computational biology.

Muscle Sentry® has No Effect on Total Work Performed and Estimated MVO 2 after High Intensity Short Duration Resistance Training.

Supplements are widely used in recreational and professional participants; however, their claimed benefits are hardly to test. This study compared the total lifted numbers and post-exercise estimated MVO2 while subjects were treated with either Muscle Sentry® (MS) or placebo (PL), in a 7-day washout period. Participants (11 women, 10 men, 20-24 years) performed 3 sets to failure chest and leg press exercises at 8 RM with 2 min rest between sets. Each exercise was performed four times (2 × MS, 2 × PL) at the same time of the day separated by 48 h. The supplementation was ingested 40 min prior to perform the exercise. Prior to the exercise and immediately after each set, both HR and BP were obtained. The rate pressure product (RPP) was then calculated to determine estimated MVO2. Daily RPP and total weight lifted (chest + leg) for each supplementation were averaged. Normalized RPP was the ratio of averaged RPP and averaged total weight lifted. No treatment effect on chest, leg and total lift numbers, normalized post RPP (NPRPP), normalized RPPdiff (NRPPdiff) (p=0.94, 0.86, 0.87, 0.87, 0.43 respectively); No treatment effect on total lift numbers, NPRPP, NRPPdiff for gender (p=0.87, 0.95, 0.96 respectively). Ingestion of Muscle Sentry® 40 min prior to do 3 sets to failure of both chest and leg presses had no effect upon either total lift numbers or estimated MVO2. This suggests that, in some instances, the benefits of Muscle Sentry® are less than those claimed by the manufacturer.

Multiscale Community Detection in Functional Brain Networks Constructed Using Dynamic Time Warping.

Previous studies have focused on the detection of community structures of brain networks constructed with resting-state functional magnetic resonance imaging (fMRI) data. Pearson correlation is often used to describe the connections between nodes in the construction of functional brain networks, which typically ignores the inherent timing and validity of fMRI time series. To solve this problem, this study applied the Dynamic Time Warp (DTW) algorithm to determine the correlation between two brain regions by comparing the synchronization and asynchrony of the time series. In addition, to determine the best community structure for each subject, we further divided the brain network into different scales, and then detected the different communities in these brain networks by using Modularity, Variation of Information (VI) and Normalized Mutual Information (NMI) as structural monitoring variables. Finally, we affirmed each subject's best community structure based on them. The experiments showed that through the method proposed in this paper, we not only accurately discovered important components of seven basic functional subnetworks, but also found that the putamen and Heschl's gyrus have a relationship with the inferior parietal network. Most importantly, this method can also determine each subject's functional brain network density, thus confirming the findings of studies testing real brain networks.

Identifying protein-protein interface via a novel multi-scale local sequence and structural representation.

BACKGROUND: Protein-protein interaction plays a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. Gaining insights of various binding abilities can deepen our understanding of the interaction. It is of great interest to understand how proteins in a complex interact with each other. Many efficient methods have been developed for identifying protein-protein interface. RESULTS: In this paper, we obtain the local information on protein-protein interface, through multi-scale local average block and hexagon structure construction. Given a pair of proteins, we use a trained support vector regression (SVR) model to select best configurations. On Benchmark v4.0, our method achieves average Irmsd value of 3.28Å and overall Fnat value of 63%, which improves upon Irmsd of 3.89Å and Fnat of 49% for ZRANK, and Irmsd of 3.99Å and Fnat of 46% for ClusPro. On CAPRI targets, our method achieves average Irmsd value of 3.45Å and overall Fnat value of 46%, which improves upon Irmsd of 4.18Å and Fnat of 40% for ZRANK, and Irmsd of 5.12Å and Fnat of 32% for ClusPro. The success rates by our method, FRODOCK 2.0, InterEvDock and SnapDock on Benchmark v4.0 are 41.5%, 29.0%, 29.4% and 37.0%, respectively. CONCLUSION: Experiments show that our method performs better than some state-of-the-art methods, based on the prediction quality improved in terms of CAPRI evaluation criteria. All these results demonstrate that our method is a valuable technological tool for identifying protein-protein interface.

Reduced Dynamic Interactions Within Intrinsic Functional Brain Networks in Early Blind Patients.

Neuroimaging studies in early blind (EB) patients have shown altered connections or brain networks. However, it remains unclear how the causal relationships are disrupted within intrinsic brain networks. In our study, we used spectral dynamic causal modeling (DCM) to estimate the causal interactions using resting-state data in a group of 20 EB patients and 20 healthy controls (HC). Coupling parameters in specific regions were estimated, including the medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), and inferior parietal lobule (IPC) in the default mode network (DMN); dorsal anterior cingulate cortex (dACC) and bilateral anterior insulae (AI) in the salience network (SN), and bilateral frontal eye fields (FEF) and superior parietal lobes (SPL) within the dorsal attention network (DAN). Statistical analyses found that all endogenous connections and the connections from the mPFC to bilateral IPCs in EB patients were significantly reduced within the DMN, and the effective connectivity from the PCC and lIPC to the mPFC, and from the mPFC to the PCC were enhanced. For the SN, all significant connections in EB patients were significantly decreased, except the intrinsic right AI connections. Within the DAN, more significant effective connections were observed to be reduced between the EB and HC groups, while only the connections from the right SPL to the left SPL and the intrinsic connection in the left SPL were significantly enhanced. Furthermore, discovery of more decreased effective connections in the EB subjects suggested that the disrupted causal interactions between specific regions are responsive to the compensatory brain plasticity in early deprivation.