MOTIVATION: Protein structure prediction has been revolutionized and generalized with the advent of cutting-edge AI methods such as AlphaFold, but reliance on computationally intensive multiple sequence alignments (MSA) remains a major limitation. RESULTS: We introduce DeepFold-PLM, a novel framework that integrates advanced protein language models with vector embedding databases to enhance ultra-fast MSA construction, remote homology detection, and protein structure prediction. DeepFold-PLM utilizes high-dimensional embeddings and contrastive learning, significantly accelerate MSA generation, achieving 47 times faster than standard methods, while maintaining prediction accuracy comparable to AlphaFold. In addition, it enhances structure prediction by extending modeling capabilities to multimeric protein complexes, provides a scalable PyTorch-based implementation for efficient large-scale prediction. Our method also effectively increases sequence diversity (Neff = 8.65 versus 4.83 with JackHMMER) enriching coevolutionary information critical for accurate structure prediction. DeepFold-PLM thus represents a versatile and practical resource that enables high-throughput applications in computational structural biology. AVAILABILITY AND IMPLEMENTATION: Source codes and user-friendly Python API of all modules of DeepFold-PLM publicly available at https://github.com/DeepFoldProtein/DeepFold-PLM.

MOTIVATION: Predicting protein structures with high accuracy is a critical challenge for the broad community of life sciences and industry. Despite progress made by deep neural networks like AlphaFold2, there is a need for further improvements in the quality of detailed structures, such as side-chains, along with protein backbone structures. RESULTS: Building upon the successes of AlphaFold2, the modifications we made include changing the losses of side-chain torsion angles and frame aligned point error, adding loss functions for side chain confidence and secondary structure prediction, and replacing template feature generation with a new alignment method based on conditional random fields. We also performed re-optimization by conformational space annealing using a molecular mechanics energy function which integrates the potential energies obtained from distogram and side-chain prediction. In the CASP15 blind test for single protein and domain modeling (109 domains), DeepFold ranked fourth among 132 groups with improvements in the details of the structure in terms of backbone, side-chain, and Molprobity. In terms of protein backbone accuracy, DeepFold achieved a median GDT-TS score of 88.64 compared with 85.88 of AlphaFold2. For TBM-easy/hard targets, DeepFold ranked at the top based on Z-scores for GDT-TS. This shows its practical value to the structural biology community, which demands highly accurate structures. In addition, a thorough analysis of 55 domains from 39 targets with publicly available structures indicates that DeepFold shows superior side-chain accuracy and Molprobity scores among the top-performing groups. AVAILABILITY AND IMPLEMENTATION: DeepFold tools are open-source software available at https://github.com/newtonjoo/deepfold.

Software quality evaluation based on ISO/IEC 9126 and ISO/IEC 14598 has been widely accepted in various areas. However, these standards for software quality do not provide practical guidelines to apply the quality model and the evaluation process of software products. And the quality management in most software projects has been conducted by managing defects without applying the standard quality model. Thus, we propose a software product evaluation process (SPEP) to support the component based development (CBD). The SPEP adopts a tailored process from the standard evaluation process with applying weights of quality characteristics and checklists for the CBD artifacts. We also show a trial evaluation case through applying the SPEP to a small-scale software project. As a result, we believe that the SPEP will contribute to acquire high quality software.

Recently, many researchers have become interested in mobile ad hoc networks (MANET) to construct a self-configurable network without existing communication infrastructure. In this paper we focus on the performance of on-demand routing protocols such as DSR, AODV, and TORA in MANET. We have observed the performance change of each protocol through simulations with varying the application data such as sensor, text, voice, and video data. And then, we have evaluated the performance of three protocols in a predesigned ad hoc network, which has 20 mobile nodes. We have also assumed the link layer as the IEEE 802.11 wireless LAN. We have analyzed the simulation results with respect to packet delivery fraction, average end-to-end delay, routing load for application data. Then, we present the possible alternative to routing protocols and wireless characteristics for the types of application data. Moreover, we believe that research result can be helpful to the community with similar interest.

Recently, software quality evaluation based on ISO/IEC 9126 and ISO/IEC 14598 has been used widely. However, these standards for software quality don't provide practical guidelines to apply the quality model and the evaluation process to real projects. Hence, this paper presents a quantitative software quality evaluation model for the artifacts of the component based development (CBD) methodology which is developed by the Ministry of National Defense of the Republic of Korea, Particularly, our model adopts the weights of quality characteristics which are obtained by carefully selected questionnaires for the stakeholders and analytic hierarchical process (AHP) technique. We also present the evaluation process using checklists and the result of a trial evaluation for validation of our model. As a result, we believe that the proposed model helps to acquire high quality software.