A5090421599- Protein Structure and Dynamics
- Enzyme Structure and Function
- Machine Learning in Bioinformatics
- Computational Drug Discovery Methods
- Microbial Metabolic Engineering and Bioproduction
- Machine Learning in Materials Science
- Fractal and DNA sequence analysis
- RNA and protein synthesis mechanisms
- Glycosylation and Glycoproteins Research
- Bioinformatics and Genomic Networks
- Genomics and Phylogenetic Studies
- Plant biochemistry and biosynthesis
- Algorithms and Data Compression
- Force Microscopy Techniques and Applications
- Biochemical and Structural Characterization
- Transportation Planning and Optimization
- Software Engineering Research
- vaccines and immunoinformatics approaches
- Traffic Prediction and Management Techniques
- Polyamine Metabolism and Applications
- Traffic control and management
University of Missouri
2020-2023
University of Liberal Arts Bangladesh
2014
Abstract We present the results for CAPRI Round 54, 5th joint CASP‐CAPRI protein assembly prediction challenge. The offered 37 targets, including 14 homodimers, 3 homo‐trimers, 13 heterodimers antibody–antigen complexes, and 7 large assemblies. On average ~70 CASP predictor groups, more than 20 automatics servers, submitted models each target. A total of 21 941 by these groups 15 scorer were evaluated using model quality measures DockQ score consolidating measures. performance was quantified...
Abstract We present the results for CAPRI Round 50, fourth joint CASP‐CAPRI protein assembly prediction challenge. The comprised a total of twelve targets, including six dimers, three trimers, and higher‐order oligomers. Four these were easy which good structural templates available either full assembly, or main interfaces (of oligomers). Eight difficult targets only distantly related found individual subunits. Twenty‐five groups eight automatic servers submitted ~1250 models per target....
Abstract To enhance the AlphaFold-Multimer-based protein complex structure prediction, we developed a quaternary prediction system (MULTICOM) to improve input fed AlphaFold-Multimer and evaluate refine its outputs. MULTICOM samples diverse multiple sequence alignments (MSAs) templates for generate structural predictions by using both traditional Foldseek-based alignments, ranks through complementary metrics, refines via Foldseek alignment-based refinement method. The with different...
Deep learning has revolutionized protein tertiary structure prediction recently. The cutting-edge deep methods such as AlphaFold can predict high-accuracy structures for most individual chains. However, the accuracy of predicting quaternary complexes consisting multiple chains is still relatively low due to lack advanced in field. Because interchain residue-residue contacts be used distance restraints guide modeling, here we develop a dilated convolutional residual network method (DRCon)...
AlphaFold-Multimer has emerged as the state-of-the-art tool for predicting quaternary structure of protein complexes (assemblies or multimers) since its release in 2021. To further enhance AlphaFold-Multimer-based complex prediction, we developed a new prediction system (MULTICOM) to improve input fed and evaluate refine outputs generated by AlphaFold2-Multimer. Specifically, MULTICOM samples diverse multiple sequence alignments (MSAs) templates generate structural models using both traditional
Abstract Deep learning methods that achieved great success in predicting intrachain residue-residue contacts have been applied to predict interchain between proteins. However, these require multiple sequence alignments (MSAs) of a pair interacting proteins (dimers) as input, which are often difficult obtain because there not many known protein complexes available generate MSAs sufficient depth for In recognizing monomer forms homomultimers contain the co-evolutionary signals both and residue...
Proteins interact to form complexes. Predicting the quaternary structure of protein complexes is useful for function analysis, engineering, and drug design. However, few user-friendly tools leveraging latest deep learning technology inter-chain contact prediction distance-based modelling predict structures are available. To address this gap, we develop DeepComplex, a web server predicting dimeric It uses contacts in homodimer or heterodimer. The predicted then used construct dimer by...
We present the results for CAPRI Round 54, 5th joint CASP-CAPRI protein assembly prediction challenge. The offered 37 targets, including 14 homo-dimers, 3 homo-trimers, 13 hetero-dimers antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP predictor groups, more than 20 automatics servers, submitted models each target. A total of 21941 by these groups 15 scorer were evaluated using model quality measures DockQ score consolidating measures. performance was quantified a...
Predicting the quaternary structure of protein complex is an important problem. Inter-chain residue-residue contact prediction can provide useful information to guide ab initio reconstruction structures. However, few methods have been developed build structures from predicted inter-chain contacts. Here, we develop first method based on gradient descent optimization (GD) dimers utilizing contacts as distance restraints. We evaluate GD several datasets homodimers and heterodimers using...
Computational biology is one of many scientific disciplines ripe for innovation and acceleration with the advent high-performance computing (HPC). In recent years, field machine learning has also seen significant benefits from adopting HPC practices. this work, we present a novel pipeline that incorporates various machine-learning approaches structure-based functional annotation proteins on scale whole genomes. Our makes extensive use deep provides computational insights into best practices...
Abstract AlphaFold-Multimer has emerged as the state-of-the-art tool for predicting quaternary structure of protein complexes (assemblies or multimers) since its release in 2021. To further enhance AlphaFold-Multimer-based complex prediction, we developed a new prediction system (MULTICOM) to improve input fed and evaluate refine outputs generated by AlphaFold2-Multimer. Specifically, MULTICOM samples diverse multiple sequence alignments (MSAs) templates generate structural models using both...
Abstract Motivation Sphagnum-dominated peatlands store a substantial amount of terrestrial carbon. The genus is undersampled and under-studied. No experimental crystal structure from any Sphagnum species exists in the Protein Data Bank fewer than 200 Sphagnum-related genes have structural models available AlphaFold Structure Database. Tools resources are needed to help bridge these gaps, enable analysis other proteomes now made possible by accurate prediction. Results We present predicted...
The information about the domain architecture of proteins is useful for studying protein structure and function. However, accurate prediction boundaries (i.e., sequence regions separating two domains) from remains a significant challenge. In this work, we develop deep learning method based on multi-head U-Nets (called DistDom) to predict utilizing 1D features predicted 2D inter-residue distance map as input. contain evolutionary physicochemical sequences, whereas includes structural that was...
Abstract Deep learning methods that achieved great success in predicting intrachain residue-residue contacts have been applied to predict interchain between proteins. However, these require multiple sequence alignments (MSAs) of a pair interacting proteins (dimers) as input, which are often difficult obtain because there not many known protein complexes available generate MSAs sufficient depth for In recognizing monomer forms homomultimers contain the co-evolutionary signals both and residue...
Abstract Motivation Deep learning has revolutionized protein tertiary structure prediction recently. The cutting-edge deep methods such as AlphaFold can predict high-accuracy structures for most individual chains. However, the accuracy of predicting quaternary complexes consisting multiple chains is still relatively low due to lack advanced in field. Because interchain residue-residue contacts be used distance restraints guide modeling, here we develop a dilated convolutional residual...
Traffic jams have become a major issue in Dhaka city which results delay of numerous trips resulting late arrivals, monetary loss, and tiredness. data was analyzed to model visualization the flow traffic road. The performs fast analysis large volumes efficiently. funneling road at horizon solved spread out showing top view reveals track movement different types traffic. vehicle congregation hotspots can be identify some features on like bus stops. Lane changing frequencies vehicles been...
Predicting the quaternary structure of protein complex is an important problem. Inter-chain residue-residue contact prediction can provide useful information to guide ab initio reconstruction structures. However, few methods have been developed build structures from predicted inter-chain contacts. Here, we introduce a gradient descent optimization algorithm (GD) dimers utilizing contacts as distance restraints. We evaluate GD on several datasets homodimers and heterodimers using true or...
Abstract Predicting the quaternary structure of a protein complex is an important and challenging problem. Inter-chain residue-residue contact prediction can provide useful information to guide ab initio reconstruction structures complexes. However, few methods have been developed build from predicted inter-chain contacts. Here, we introduce new gradient descent optimization algorithm (GD) dimers utilizing contacts as distance restraints. We evaluate GD on several datasets homodimers...
Abstract Deep learning methods that achieved great success in predicting intrachain residue-residue contacts have been applied to predict interchain between proteins. However, these require multiple sequence alignments (MSAs) of a pair interacting proteins (dimers) as input, which are often difficult obtain because there not many known protein complexes available generate MSAs sufficient depth for In recognizing monomer forms homomultimers contain the co-evolutionary signals both and residue...
ABSTRACT The information about the domain architecture of proteins is useful for studying protein structure and function. However, accurate prediction boundaries (i.e., sequence regions separating two domains) from remains a significant challenge. In this work, we develop deep learning method based on multi-head U-Nets (called DistDom) to predict utilizing 1D features predicted 2D inter-residue distance map as input. contain evolutionary physicochemical sequences, whereas includes structural...
Predicted inter-chain residue-residue contacts can be used to build the quaternary structure of protein complexes from scratch. However, only a small number methods have been developed reconstruct structures using predicted contacts. Here, we present an agent-based self-learning method based on deep reinforcement learning (DRLComplex) complex as distance constraints. We rigorously tested DRLComplex two standard datasets homodimeric and heterodimeric (i.e., CASP-CAPRI homodimer Std_32...
Abstract Predicted interchain residue-residue contacts can be used to build the quaternary structure of protein complexes from scratch. However, only a small number methods have been developed reconstruct structures using predicted contacts. Here, we present an agent-based self-learning method based on deep reinforcement learning (DRLComplex) complex as distance constraints. We rigorously tested DRLComplex two standard datasets homodimeric and heterodimeric dimers (the CASP-CAPRI homodimer...
The information about the domain architecture of proteins is useful for studying protein structure and function. However, accurate prediction boundaries (i.e., sequence regions separating two domains) from remains a significant challenge. In this work, we develop deep learning method based on multi-head U-Nets (called DistDom) to predict utilizing 1D features predicted 2D inter-residue distance map as input. contain evolutionary physicochemical sequences, whereas includes structural that was...