49 research outputs found
Ab initio modeling of small proteins by iterative TASSER simulations
Background: Predicting 3-dimensional protein structures from amino-acid sequences is an important unsolved problem in computational structural biology. The problem becomes relatively easier if close homologous proteins have been solved, as high-resolution models can be built by aligning target sequences to the solved homologous structures. However, for sequences without similar folds in the Protein Data Bank (PDB) library, the models have to be predicted from scratch. Progress in the ab initio structure modeling is slow. The aim of this study was to extend the TASSER (threading/assembly/refinement) method for the ab initio modeling and examine systemically its ability to fold small single-domain proteins.
Results: We developed I-TASSER by iteratively implementing the TASSER method, which is used in the folding test of three benchmarks of small proteins. First, data on 16 small proteins (< 90 residues) were used to generate I-TASSER models, which had an average Cα-root mean square deviation (RMSD) of 3.8Å, with 6 of them having a Cα-RMSD < 2.5Å. The overall result was comparable with the all-atomic ROSETTA simulation, but the central processing unit (CPU) time by I-TASSER was much shorter (150 CPU days vs. 5 CPU hours). Second, data on 20 small proteins (< 120 residues) were used. I-TASSER folded four of them with a Cα-RMSD < 2.5Å. The average Cα-RMSD of the I-TASSER models was 3.9Å, whereas it was 5.9Å using TOUCHSTONE-II software. Finally, 20 non-homologous small proteins (< 120 residues) were taken from the PDB library. An average Cα-RMSD of 3.9Å was obtained for the third benchmark, with seven cases having a Cα-RMSD < 2.5Å.
Conclusion: Our simulation results show that I-TASSER can consistently predict the correct folds and sometimes high-resolution models for small single-domain proteins. Compared with other ab initio modeling methods such as ROSETTA and TOUCHSTONE II, the average performance of I-TASSER is either much better or is similar within a lower computational time. These data, together with the significant performance of automated I-TASSER server (the Zhang-Server) in the 'free modeling' section of the recent Critical Assessment of Structure Prediction (CASP)7 experiment, demonstrate new progresses in automated ab initio model generation. The I-TASSER server is freely available for academic users http://zhang.bioinformatics.ku.edu/I-TASSER webcite
Learning Emotion Representations from Verbal and Nonverbal Communication
Emotion understanding is an essential but highly challenging component of
artificial general intelligence. The absence of extensively annotated datasets
has significantly impeded advancements in this field. We present EmotionCLIP,
the first pre-training paradigm to extract visual emotion representations from
verbal and nonverbal communication using only uncurated data. Compared to
numerical labels or descriptions used in previous methods, communication
naturally contains emotion information. Furthermore, acquiring emotion
representations from communication is more congruent with the human learning
process. We guide EmotionCLIP to attend to nonverbal emotion cues through
subject-aware context encoding and verbal emotion cues using sentiment-guided
contrastive learning. Extensive experiments validate the effectiveness and
transferability of EmotionCLIP. Using merely linear-probe evaluation protocol,
EmotionCLIP outperforms the state-of-the-art supervised visual emotion
recognition methods and rivals many multimodal approaches across various
benchmarks. We anticipate that the advent of EmotionCLIP will address the
prevailing issue of data scarcity in emotion understanding, thereby fostering
progress in related domains. The code and pre-trained models are available at
https://github.com/Xeaver/EmotionCLIP.Comment: CVPR 202
CARMA: Context-Aware Runtime Reconfiguration for Energy-Efficient Sensor Fusion
Autonomous systems (AS) are systems that can adapt and change their behavior
in response to unanticipated events and include systems such as aerial drones,
autonomous vehicles, and ground/aquatic robots. AS require a wide array of
sensors, deep-learning models, and powerful hardware platforms to perceive and
safely operate in real-time. However, in many contexts, some sensing modalities
negatively impact perception while increasing the system's overall energy
consumption. Since AS are often energy-constrained edge devices,
energy-efficient sensor fusion methods have been proposed. However, existing
methods either fail to adapt to changing scenario conditions or to optimize
energy efficiency system-wide. We propose CARMA: a context-aware sensor fusion
approach that uses context to dynamically reconfigure the computation flow on a
Field-Programmable Gate Array (FPGA) at runtime. By clock-gating unused sensors
and model sub-components, CARMA significantly reduces the energy used by a
multi-sensory object detector without compromising performance. We use a
Deep-learning Processor Unit (DPU) based reconfiguration approach to minimize
the latency of model reconfiguration. We evaluate multiple
context-identification strategies, propose a novel system-wide
energy-performance joint optimization, and evaluate scenario-specific
perception performance. Across challenging real-world sensing contexts, CARMA
outperforms state-of-the-art methods with up to 1.3x speedup and 73% lower
energy consumption.Comment: Accepted to be published in the 2023 ACM/IEEE International Symposium
on Low Power Electronics and Design (ISLPED 2023
LOMETS: A local meta-threading-server for protein structure prediction
We developed LOMETS, a local threading meta-server, for quick and automated predictions of protein tertiary structures and spatial constraints. Nine state-of-the-art threading programs are installed and run in a local computer cluster, which ensure the quick generation of initial threading alignments compared with traditional remote-server-based meta-servers. Consensus models are generated from the top predictions of the component-threading servers, which are at least 7% more accurate than the best individual servers based on TM-score at a t-test significance level of 0.1%. Moreover, side-chain and C-alpha (Cα) contacts of 42 and 61% accuracy respectively, as well as long- and short-range distant maps, are automatically constructed from the threading alignments. These data can be easily used as constraints to guide the ab initio procedures such as TASSER for further protein tertiary structure modeling. The LOMETS server is freely available to the academic community at http://zhang.bioinformatics.ku.edu/LOMETS
ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction
We developed a composite machine-learning based algorithm, called ANGLOR, to predict real-value protein backbone torsion angles from amino acid sequences. The input features of ANGLOR include sequence profiles, predicted secondary structure and solvent accessibility. In a large-scale benchmarking test, the mean absolute error (MAE) of the phi/psi prediction is 28°/46°, which is ∼10% lower than that generated by software in literature. The prediction is statistically different from a random predictor (or a purely secondary-structure-based predictor) with p-value <1.0×10−300 (or <1.0×10−148) by Wilcoxon signed rank test. For some residues (ILE, LEU, PRO and VAL) and especially the residues in helix and buried regions, the MAE of phi angles is much smaller (10–20°) than that in other environments. Thus, although the average accuracy of the ANGLOR prediction is still low, the portion of the accurately predicted dihedral angles may be useful in assisting protein fold recognition and ab initio 3D structure modeling
Alignment behaviors of short peptides provide a roadmap for functional profiling of metagenomic data
Ab initio modeling of small proteins by iterative TASSER simulations
© 2007 Wu et al; licensee BioMed Central Ltd.The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1741-7007/5/17doi:10.1186/1741-7007-5-17Background: Predicting 3-dimensional protein structures from amino-acid sequences is an
important unsolved problem in computational structural biology. The problem becomes relatively
easier if close homologous proteins have been solved, as high-resolution models can be built by
aligning target sequences to the solved homologous structures. However, for sequences without
similar folds in the Protein Data Bank (PDB) library, the models have to be predicted from scratch.
Progress in the ab initio structure modeling is slow. The aim of this study was to extend the TASSER
(threading/assembly/refinement) method for the ab initio modeling and examine systemically its
ability to fold small single-domain proteins.
Results: We developed I-TASSER by iteratively implementing the TASSER method, which is used
in the folding test of three benchmarks of small proteins. First, data on 16 small proteins (< 90
residues) were used to generate I-TASSER models, which had an average Cα-root mean square
deviation (RMSD) of 3.8Å, with 6 of them having a Cα-RMSD < 2.5Å. The overall result was
comparable with the all-atomic ROSETTA simulation, but the central processing unit (CPU) time
by I-TASSER was much shorter (150 CPU days vs. 5 CPU hours). Second, data on 20 small proteins
(< 120 residues) were used. I-TASSER folded four of them with a Cα-RMSD < 2.5Å. The average
Cα-RMSD of the I-TASSER models was 3.9Å, whereas it was 5.9Å using TOUCHSTONE-II
software. Finally, 20 non-homologous small proteins (< 120 residues) were taken from the PDB
library. An average Cα-RMSD of 3.9Å was obtained for the third benchmark, with seven cases
having a Cα-RMSD < 2.5Å.
Conclusion: Our simulation results show that I-TASSER can consistently predict the correct folds
and sometimes high-resolution models for small single-domain proteins. Compared with other ab
initio modeling methods such as ROSETTA and TOUCHSTONE II, the average performance of ITASSER
is either much better or is similar within a lower computational time. These data, together
with the significant performance of automated I-TASSER server (the Zhang-Server) in the 'free
modeling' section of the recent Critical Assessment of Structure Prediction (CASP)7 experiment,
demonstrate new progresses in automated ab initio model generation. The I-TASSER server is
freely available for academic users http://zhang.bioinformatics.ku.edu/I-TASSER